Sample records for observing classifying collecting
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Shells. Modified Primary. Revised. Anchorage School District Elementary Science Program.
ERIC Educational Resources Information Center
Defendorf, Jean, Ed.
This publication provides information and activities for teaching about seashells and process skills including observing, classifying, collecting and interpreting data, inferring, measuring, and predicting. There are 10 lessons. Lessons 1 through 5 deal with an introduction to shells, why animals have shells, observing and classifying shells, the…
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Event-shape fluctuations and flow correlations in ultra-relativistic heavy-ion collisions
Jia, Jiangyong
2014-12-01
I review recent measurements of a large set of flow observables associated with event-shape fluctuations and collective expansion in heavy ion collisions. First, these flow observables are classified and experiment methods are introduced. The experimental results for each type of observables are then presented and compared to theoretical calculations. A coherent picture of initial condition and collective flow based on linear and non-linear hydrodynamic responses is derived, which qualitatively describe most experimental results. I discuss new types of fluctuation measurements that can further our understanding of the event-shape fluctuations and collective expansion dynamics.
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On Burst Detection and Prediction in Retweeting Sequence
2015-05-22
We conduct a comprehensive empirical analysis of a large microblogging dataset collected from the Sina Weibo and report our observations of burst...whether and how accurate we can predict bursts using classifiers based on the extracted features. Our empirical study of the Sina Weibo data shows the...feasibility of burst prediction using appropriately extracted features and classic classifiers. 1 Introduction Microblogging, such as Twitter and Sina
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Ebener, Mark P.; Bence, James R.; Bergstedt, Roger A.; Mullet, Katherine M.
2003-01-01
In 1997 and 1998 two workshops were held to evaluate how consistent observers were at classifying sea lamprey (Petromyzon marinus) marks on Great Lakes lake trout (Salvelinus namaycush) as described in the King classification system. Two trials were held at each workshop, with group discussion between trials. Variation in counting and classifying marks was considerable, such that reporting rates for A1–A3 marks varied two to three-fold among observers of the same lake trout. Observer variation was greater for classification of healing or healed marks than for fresh marks. The workshops highlighted, as causes for inconsistent mark classification, both departures from the accepted protocol for classifying marks by some agencies, and differences in how sliding and multiple marks were interpreted. Group discussions led to greater agreement in classifying marks. We recommend ways to improve the reliability of marking statistics, including the use of a dichotomous key to classify marks. Laboratory data show that healing times of marks on lake trout were much longer at 4°C and 1°C than at 10°C and varied greatly among individuals. Reported A1–A3 and B1–B3 marks observed in late summer and fall collections likely result from a mixture of attacks by two year classes of sea lamprey. It is likely that a substantial but highly uncertain proportion of attacks that occur in late summer and fall lead to marks that are classified as A1–A3 the next spring. We recommend additional research on mark stage duration.
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NASA Astrophysics Data System (ADS)
Luo, Y.; Wang, H.; Ma, R.; Zipser, E. J.; Liu, C.
2017-12-01
This study examines the vertical structure of precipitation echoes in central Tibetan Plateau using observations collected at Naqu during the Third Tibetan Plateau Atmospheric Scientific Experiment in July-August 2014. Precipitation reaching the surface is classified into stratiform, convective, and other by analyzing the vertical profiles of reflectivity (Ze) at 30-m spacing and 3-s temporal resolution made with the vertical pointing C-band frequency-modulated continuous-wave (C-FMCW) radar. Radar echoes with non-zero surface rainfall rate are observed during 17.96% of the entire observing period. About 52.03% of the precipitation reaching the surface includes a bright band and lacks a thick layer (≥1 km) of large Ze (> 35 dBZ); these are classified as stratiform; non-stratiform echoes with Ze > 35 dBZ are classified as convective (4.99%); the remainder (42.98%) as other. Based on concurrent measurements made with a collocated disdrometer, the classified stratiform, convective, and other precipitation echoes contribute 53.84%, 23.08%, and 23.08%, respectively, to the surface rainfall amount. Distinct internal structural features of each echo type are revealed by collectively analyzing the vertical profiles of Ze, radial velocity (Vr), and spectral width (SW) observed by the C-FMCW radar. The stratiform precipitation contains a melting-layer centered at 0.97 km above ground with an average depth of 415 m. The median Ze at 0°C -15°C levels in convective regions at Naqu is weaker than those in some midlatitude continental convection and stronger than those in some tropical continents, suggesting that convective intensity measured by mixed-phase microphysical processes at Naqu is intermediate.
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ERIC Educational Resources Information Center
Alexander, David
1994-01-01
Presents an activity of students hunting for dirt in the classroom to launch a study of the environment and to provide children with an exercise in which they learn and use the skills of collecting, labeling, organizing, observational drawing, classifying, hypothesizing, and summarizing. (PR)
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SUVI Thematic Maps: A new tool for space weather forecasting
NASA Astrophysics Data System (ADS)
Hughes, J. M.; Seaton, D. B.; Darnel, J.
2017-12-01
The new Solar Ultraviolet Imager (SUVI) instruments aboard NOAA's GOES-R series satellites collect continuous, high-quality imagery of the Sun in six wavelengths. SUVI imagers produce at least one image every 10 seconds, or 8,640 images per day, considerably more data than observers can digest in real time. Over the projected 20-year lifetime of the four GOES-R series spacecraft, SUVI will provide critical imagery for space weather forecasters and produce an extensive but unwieldy archive. In order to condense the database into a dynamic and searchable form we have developed solar thematic maps, maps of the Sun with key features, such as coronal holes, flares, bright regions, quiet corona, and filaments, identified. Thematic maps will be used in NOAA's Space Weather Prediction Center to improve forecaster response time to solar events and generate several derivative products. Likewise, scientists use thematic maps to find observations of interest more easily. Using an expert-trained, naive Bayesian classifier to label each pixel, we create thematic maps in real-time. We created software to collect expert classifications of solar features based on SUVI images. Using this software, we compiled a database of expert classifications, from which we could characterize the distribution of pixels associated with each theme. Given new images, the classifier assigns each pixel the most appropriate label according to the trained distribution. Here we describe the software to collect expert training and the successes and limitations of the classifier. The algorithm excellently identifies coronal holes but fails to consistently detect filaments and prominences. We compare the Bayesian classifier to an artificial neural network, one of our attempts to overcome the aforementioned limitations. These results are very promising and encourage future research into an ensemble classification approach.
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Automated computer-based detection of encounter behaviours in groups of honeybees.
Blut, Christina; Crespi, Alessandro; Mersch, Danielle; Keller, Laurent; Zhao, Linlin; Kollmann, Markus; Schellscheidt, Benjamin; Fülber, Carsten; Beye, Martin
2017-12-15
Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers' activities because we lack methods that enable collection of simultaneous and continuous behavioural information for each worker bee. In this study, we introduce the Bee Behavioral Annotation System (BBAS), which enables the automated detection of bees' behaviours in small observation hives. Continuous information on position and orientation were obtained by marking worker bees with 2D barcodes in a small observation hive. We computed behavioural and social features from the tracking information to train a behaviour classifier for encounter behaviours (interaction of workers via antennation) using a machine learning-based system. The classifier correctly detected 93% of the encounter behaviours in a group of bees, whereas 13% of the falsely classified behaviours were unrelated to encounter behaviours. The possibility of building accurate classifiers for automatically annotating behaviours may allow for the examination of individual behaviours of worker bees in the social environments of small observation hives. We envisage that BBAS will be a powerful tool for detecting the effects of experimental manipulation of social attributes and sub-lethal effects of pesticides on behaviour.
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Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks
NASA Technical Reports Server (NTRS)
Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran
2012-01-01
Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.
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Fostering Argumentation Skills: Doing What Real Scientists Really Do
ERIC Educational Resources Information Center
Llewellyn, Douglas; Rajesh, Hema
2011-01-01
Elementary and middle school teachers often provide students with hands-on activities or even inquiry-based investigations that emphasize science process skills such as observing, classifying, identifying and controlling variables, hypothesizing, experimenting, and collecting and analyzing data. These activities and investigations are frequently…
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Real-time Automatic Search for Multi-wavelength Counterparts of DWF Transients
NASA Astrophysics Data System (ADS)
Murphy, Christopher; Cucchiara, Antonino; Andreoni, Igor; Cooke, Jeff; Hegarty, Sarah
2018-01-01
The Deeper Wider Faster (DWF) survey aims to find and classify the fastest transients in the Universe. DWF utilizes the Dark Energy Camera (DECam), collecting a continuous sequence of 20s images over a 3 square degree field of view.Once an interesting transient is detected during DWF observations, the DWF collaboration has access to several facilities for rapid follow-up in multiple wavelengths (from gamma to radio).An online web tool has been designed to help with real-time visual classification of possible astrophysical transients in data collected by the DWF observing program. The goal of this project is to create a python-based code to improve the classification process by querying several existing archive databases. Given the DWF transient location and search radius, the developed code will extract a list of possible counterparts and all available information (e.g. magnitude, radio fluxes, distance separation).Thanks to this tool, the human classifier can make a quicker decision in order to trigger the collaboration rapid-response resources.
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Menezes, Ana M; Wehrmeister, Fernando C; Perez-Padilla, Rogelio; Viana, Karynna P; Soares, Claudia; Müllerova, Hana; Valdivia, Gonzalo; Jardim, José R; Montes de Oca, Maria
2017-01-01
The Global Initiative for Chronic Obstructive Lung Disease (GOLD) report provides a framework for classifying COPD reflecting the impacts of disease on patients and for targeting treatment recommendations. The GOLD 2017 introduced a new classification with 16 subgroups based on a composite of spirometry and symptoms/exacerbations. Data from the population-based PLATINO study, collected at baseline and at follow-up, in three sites in Latin America were analyzed to compare the following: 1) the distribution of COPD patients according to GOLD 2007, 2013, and 2017; 2) the stability of the 2007 and 2013 classifications; and 3) the mortality rate over time stratified by GOLD 2007, 2013, and 2017. Of the 524 COPD patients evaluated, most of them were classified as Grade I or II (GOLD 2007) and Group A or B (GOLD 2013), with ≈70% of those classified as Group A in GOLD 2013 also classified as Grade I in GOLD 2007 and the highest percentage (41%) in Group D (2013) classified as Grade III (2007). According to GOLD 2017, among patients with Grade I airflow limitation, 69% of them were categorized into Group A, whereas Grade IV patients were more evenly distributed among Groups A-D. Most of the patients classified by GOLD 2007 remained in the same airflow limitation group at the follow-up; a greater temporal variability was observed with GOLD 2013 classification. Incidence-mortality rate in patients classified by GOLD 2007 was positively associated with increasing severity of airflow obstruction; for GOLD 2013 and GOLD 2017 (Groups A-D), highest mortality rates were observed in Groups C and D. No clear pattern was observed for mortality across the GOLD 2017 subgroups. The PLATINO study data suggest that GOLD 2007 classification shows more stability over time compared with GOLD 2013. No clear patterns with respect to the distribution of patients or incidence-mortality rates were observed according to GOLD 2013/2017 classification.
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The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance
Aslam, Anoshé A; Spitzberg, Brian H; An, Li; Gawron, J Mark; Gupta, Dipak K; Peddecord, K Michael; Nagel, Anna C; Allen, Christopher; Yang, Jiue-An; Lindsay, Suzanne
2014-01-01
Background Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. Objective There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. Methods Tweets containing the keyword “flu” were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was “valid”, or from a user who was likely ill with the flu. Results Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. Conclusions Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data. PMID:25406040
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Minibeasts and Butterflies. First Grade. Anchorage School District Elementary Science Program.
ERIC Educational Resources Information Center
Defendorf, Jean, Ed.
This publication provides information and activities for teaching about insects and process skills including observing, classifying, collecting and interpreting data, inferring, measuring, and predicting. There are 13 lessons. Lessons 1 through 3 deal with insects, in general, and with moths and butterflies. Lessons 4 through 7 consist of…
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Change in physiological signals during mindfulness meditation
Ahani, Asieh; Wahbeh, Helane; Miller, Meghan; Nezamfar, Hooman; Erdogmus, Deniz; Oken, Barry
2014-01-01
Mindfulness meditation (MM) is an inward mental practice, in which a resting but alert state of mind is maintained. MM intervention was performed for a population of older people with high stress levels. This study assessed signal processing methodologies of electroencephalographic (EEG) and respiration signals during meditation and control condition to aid in quantification of the meditative state. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis and support vector machine classification to evaluate an objective marker for meditation. We observed meditation and control condition differences in the alpha, beta and theta frequency bands. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy at discriminating between meditation and control conditions than one using the EEG signal only. EEG and respiration based classifier is a viable objective marker for meditation ability. Future studies should quantify different levels of meditation depth and meditation experience using this classifier. Development of an objective physiological meditation marker will allow the mind-body medicine field to advance by strengthening rigor of methods. PMID:24748422
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Morton, Robert A.; Montgomery, Marilyn C.
2010-01-01
The primary mapping procedures were supervised functions within a Geographic Information System (GIS) that were applied to delineate and classify depositional subenvironments and features, collectively referred to as map units. The delineated boundaries of the map units were exported to create one shapefile, and are differentiated by the field "Type" in the associated attribute table. Map units were delineated and classified based on differences in tonal patterns of features in contrast to adjacent features observed on orthophotography. Land elevations from recent lidar surveys served as supplementary data to assist in delineating the map unit boundaries.
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Welvaert, Marijke; Caley, Peter
2016-01-01
Citizen science and crowdsourcing have been emerging as methods to collect data for surveillance and/or monitoring activities. They could be gathered under the overarching term citizen surveillance . The discipline, however, still struggles to be widely accepted in the scientific community, mainly because these activities are not embedded in a quantitative framework. This results in an ongoing discussion on how to analyze and make useful inference from these data. When considering the data collection process, we illustrate how citizen surveillance can be classified according to the nature of the underlying observation process measured in two dimensions-the degree of observer reporting intention and the control in observer detection effort. By classifying the observation process in these dimensions we distinguish between crowdsourcing, unstructured citizen science and structured citizen science. This classification helps the determine data processing and statistical treatment of these data for making inference. Using our framework, it is apparent that published studies are overwhelmingly associated with structured citizen science, and there are well developed statistical methods for the resulting data. In contrast, methods for making useful inference from purely crowd-sourced data remain under development, with the challenges of accounting for the unknown observation process considerable. Our quantitative framework for citizen surveillance calls for an integration of citizen science and crowdsourcing and provides a way forward to solve the statistical challenges inherent to citizen-sourced data.
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NASA Astrophysics Data System (ADS)
Subasinghe, Dilini; Campbell-Brown, Margaret D.; Stokan, Edward
2016-04-01
Optical observations of faint meteors (10-7 < mass < 10-4 kg) were collected by the Canadian Automated Meteor Observatory between 2010 April and 2014 May. These high-resolution (metre scale) observations were combined with two-station light-curve observations and the meteoroid orbit to classify meteors and attempt to answer questions related to meteoroid fragmentation, strength, and light-curve shape. The F parameter was used to classify the meteor light-curve shape; the observed morphology was used to classify the fragmentation mode; and the Tisserand parameter described the origin of the meteoroid. We find that most meteor light curves are symmetric (mean F parameter 0.49), show long distinct trails (continuous fragmentation), and are cometary in origin. Meteors that show no obvious fragmentation (presumably single body objects) show mostly symmetric light curves, surprisingly, and this indicates that light-curve shape is not an indication of fragility or fragmentation behaviour. Approximately 90 per cent of meteors observed with high-resolution video cameras show some form of fragmentation. Our results also show, unexpectedly, that meteors which show negligible fragmentation are more often on high-inclination orbits (I > 60°) than low-inclination ones. We also find that dynamically asteroidal meteors fragment as often as dynamically cometary meteors, which may suggest mixing in the early Solar system, or contamination between the dynamic groups.
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Automated time activity classification based on global positioning system (GPS) tracking data
2011-01-01
Background Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. Methods We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Results Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Conclusions Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns. PMID:22082316
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Automated time activity classification based on global positioning system (GPS) tracking data.
Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph
2011-11-14
Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.
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Sink or Float. Modified Primary. Revised. Anchorage School District Elementary Science Program.
ERIC Educational Resources Information Center
Defendorf, Jean, Ed.
This publication provides information and activities for teaching about water, whether certain objects will sink or float, and process skills including observing, classifying, inferring, measuring, predicting, and collecting and interpreting data. There are 14 lessons in the unit. The first four lessons deal with the classification of objects and…
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Mystery Powders. [Modified Primary]. Revised. Anchorage School District Elementary Science Program.
ERIC Educational Resources Information Center
Anchorage School District, AK.
This publication provides information and activities for identifying objects using the five senses and process skills including observing, classifying, collecting and interpreting data, inferring, and predicting. Lessons 1 through 3 deal with the identification of an unknown substance and the physical properties of powders. Lessons 4 through 6 are…
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Federal Register 2010, 2011, 2012, 2013, 2014
2012-10-30
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A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA
NASA Astrophysics Data System (ADS)
Wagstaff, Kiri L.; Tang, Benyang; Thompson, David R.; Khudikyan, Shakeh; Wyngaard, Jane; Deller, Adam T.; Palaniswamy, Divya; Tingay, Steven J.; Wayth, Randall B.
2016-08-01
Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%-90% of the candidates, with an accuracy greater than 98%, leaving only the 10%-20% most promising candidates to be reviewed by humans.
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50 Years of Silent Service: Inside the CIA Library.
ERIC Educational Resources Information Center
Wright, Susan L.
1997-01-01
Explains some of the collections and operations of the library at the Central Intelligence Agency (CIA). Highlights include disseminating classified information from the classified collection, subject matter requirements, the unclassified Historical Intelligence Collection which deals with the intelligence profession, client confidentiality, and…
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Shi, De-Zhi; Wu, Wei-Xiang; Lu, Sheng-Yong; Chen, Tong; Huang, Hui-Liang; Chen, Ying-Xu; Yan, Jian-Hua
2008-05-01
Municipal solid waste (MSW) source-classified collection represents a change in MSW management in China and other developing countries. Comparative experiments were performed to evaluate the effect of a newly established MSW source-classified collection system on the emission of PCDDs/Fs (polychlorinated dibenzo-p-dioxins and dibenzofurans) and heavy metals (HMs) from a full-scale incinerator in China. As a result of presorting and dewatering, the chlorine level, heavy metal and water content were lower, but heat value was higher in the source-classified MSW (classified MSW) as compared with the conventionally mixed collected MSW (mixed MSW). The generation of PCDDs/Fs in flue gas from the classified MSW incineration was 9.28 ng I-TEQ/Nm(3), only 69.4% of that from the mixed MSW incineration, and the final emission of PCDDs/Fs was only 0.12 ng I-TEQ/Nm(3), although activated carbon injection was reduced by 20%. The level of PCDDs/Fs in fly ash from the bag filter was 0.27 ng I-TEQ/g. These results indicated that the source-classified collection with pretreatment could improve the characteristics of MSW for incineration, and significantly decrease formation of PCDDs/Fs in MSW incineration. Furthermore, distributions of HMs such as Cd, Pb, Cu, Zn, Cr, As, Ni, Hg in bottom ash and fly ash were investigated to assess the need for treatment of residual ash.
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2012-10-11
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2012-05-16
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Classifying Urban Space Types of Seoul using Time-series Heat Island map
NASA Astrophysics Data System (ADS)
Jung, S.; KIM, H.; JE, M.
2017-12-01
In August 2016, the hottest heat occurred in Korea since the weather observation started in Korea. Due to climate changes, this heat phenomenon is expected to be severe more in the future. Thus, this study analyzed the heatwave occurred in 2016 with regard to Seoul from various angles to identify the characteristics of urban regions where the heat island phenomenon occurred. To do this, first, temperature data for two days on August 6 and 12 in 2016 when the hottest heatwave occurred were collected from 287 places of automatic weather stations (AWS) installed in Seoul and adjacent suburbs. The temperature distribution of Seoul was mapped using interpolation in every hour using the collected temperature data. Second, regions in Seoul were classified using statistical methods based on spatial characteristics such as land coverage, density, use type, and traffic volume in Seoul. Third, a daily pattern of change in temperature in the classified regions was depicted with a graph, and regions were re-classified based on the daily pattern of change in temperature. Finally, the characteristics of the classified regions were re-reviewed and then, heat island occurrence, continuation, and reduction measure by region type were discussed. The analysis results showed that a pattern of heatwave occurrence was exhibited differently by the classified region type. The results also showed that not only physical characteristics such as land coverage but also socioeconomic index such as population density and floating population that induced a traffic volume influenced the pattern of heatwave occurrence despite of the same land usage regions. This study not only classified urban climate regions by existing mean temperature and specific time-point temperature but also proposed a methodology that analyzed heat island phenomenon inside cities by using time-series temperature data in a day. Furthermore, this study enabled regional classification based on heat island characteristics to contribute to establishment of measure for each regional classification.
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2013-01-28
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75 FR 65670 - Notice of Information Collection
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2010-10-26
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75 FR 54189 - Notice of Information Collection
Federal Register 2010, 2011, 2012, 2013, 2014
2010-09-03
... INFORMATION: I. Abstract The Marshall Star is a Center newsletter available in print and PDF formats. Marshall employees and retirees may place classified ads to appear in the Marshall Star. II. Method of Collection Phone. III. Data Title: Marshall Star Classified Ads. OMB Number: 2700-XXXX. Type of Review: New...
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Virtual Brain Bank a public collection of classified head MRI
NASA Astrophysics Data System (ADS)
Barrios, Fernando A.
2000-10-01
In this work I present the effort at the Neurobiology Center for creating a digital Brain Bank, a collection of well classified human brains that are used for teaching and research, this bank will be based in a collection of high resolution three dimensional head MRI. For this reason this bank is being named "virtual" and eventually will be of public access though a WEB page in the INTERNET.
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Image Change Detection via Ensemble Learning
DOE Office of Scientific and Technical Information (OSTI.GOV)
Martin, Benjamin W; Vatsavai, Raju
2013-01-01
The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work,more » we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.« less
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Analyzing Body Movements within the Laban Effort Framework Using a Single Accelerometer
Kikhia, Basel; Gomez, Miguel; Jiménez, Lara Lorna; Hallberg, Josef; Karvonen, Niklas; Synnes, Kåre
2014-01-01
This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong—Light, Free—Bound and Sudden—Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting Strong—Light body movements using the Random Forest classifier. The wrist placement was also the best location for classifying Bound—Free body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting Sudden—Sustained body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement. PMID:24662408
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Development of Criteria and Procedures for Management of Classified Document Collections.
ERIC Educational Resources Information Center
Rea, Jack C.
The report describes work done in development of criteria and procedures for management of collections of classified documents. Material is presented on philosophy of operation, concept of user service, accession and retention. Much of the discussion is based upon the concept of conversion to a microfiche-oriented library; however, hard copy…
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Staging Liver Fibrosis with Statistical Observers
NASA Astrophysics Data System (ADS)
Brand, Jonathan Frieman
Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically on order of 1mm, which close to the resolution limit of in vivo Gd-enhanced MRI. In this work the methods to collect training and testing images for a Hotelling observer are covered. An observer based on local texture analysis is trained and tested using wet-tissue phantoms. The technique is used to optimize the MRI sequence based on task performance. The final method developed is a two stage model observer to classify fibrotic and healthy tissue in both phantoms and in vivo MRI images. The first stage observer tests for the presence of local texture. Test statistics from the first observer are used to train the second stage observer to globally sample the local observer results. A decision of the disease class is made for an entire MRI image slice using test statistics collected from the second observer. The techniques are tested on wet-tissue phantoms and in vivo clinical patient data.
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Grünewälder, Steffen; Broekhuis, Femke; Macdonald, David Whyte; Wilson, Alan Martin; McNutt, John Weldon; Shawe-Taylor, John; Hailes, Stephen
2012-01-01
We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be 83%-94%, but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.
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Grünewälder, Steffen; Broekhuis, Femke; Macdonald, David Whyte; Wilson, Alan Martin; McNutt, John Weldon; Shawe-Taylor, John; Hailes, Stephen
2012-01-01
We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be , but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail. PMID:23185301
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Federal Register 2010, 2011, 2012, 2013, 2014
2010-10-25
... Classify Orphan as an Immediate Relative and Application for Advance Processing of Orphan Petition; OMB... encouraged and will be accepted until November 24, 2010. This process is conducted in accordance with 5 CFR...) Title of the Form/Collection: Petition to Classify Orphan as an Immediate Relative and Application for...
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Kalkwarf, D.R.
1980-05-01
Airborne uranium products were collected at the perimeter of the uranium-conversion plant operated by the Allied Chemical Corporation at Metropolis, Illinois, and the dissolution rates of these products were classified in terms of the ICRP Task Group Lung Model. Assignments were based on measurements of the dissolution half-times exhibited by uranium components of the dust samples as they dissolved in simulated lung fluid at 37/sup 0/C. Based on three trials, the dissolution behavior of dust with aerodynamic equivalent diameter (AED) less than 5.5 ..mu..m and collected nearest the closest residence to the plant was classified 0.40 D, 0.60 Y. Basedmore » on two trials, the dissolution behavior of dust with AED greater than 5.5 ..mu..m and collected at this location was classified 0.37 D, 0.63 Y. Based on one trial, the dissolution behavior of dust with AED less than 5.5 ..mu..m and collected at a location on the opposite side of the plant was classified 0.68 D, 0.32 Y. There was some evidence for adsorption of dissolved uranium onto other dust components during dissolution, and preliminary dissolution trials are recommended for future samples in order to optimize the fluid replacement schedule.« less
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Agreement between 24-hour salt ingestion and sodium excretion in a controlled environment.
Lerchl, Kathrin; Rakova, Natalia; Dahlmann, Anke; Rauh, Manfred; Goller, Ulrike; Basner, Mathias; Dinges, David F; Beck, Luis; Agureev, Alexander; Larina, Irina; Baranov, Victor; Morukov, Boris; Eckardt, Kai-Uwe; Vassilieva, Galina; Wabel, Peter; Vienken, Jörg; Kirsch, Karl; Johannes, Bernd; Krannich, Alexander; Luft, Friedrich C; Titze, Jens
2015-10-01
Accurately collected 24-hour urine collections are presumed to be valid for estimating salt intake in individuals. We performed 2 independent ultralong-term salt balance studies lasting 105 (4 men) and 205 (6 men) days in 10 men simulating a flight to Mars. We controlled dietary intake of all constituents for months at salt intakes of 12, 9, and 6 g/d and collected all urine. The subjects' daily menus consisted of 27 279 individual servings, of which 83.0% were completely consumed, 16.5% completely rejected, and 0.5% incompletely consumed. Urinary recovery of dietary salt was 92% of recorded intake, indicating long-term steady-state sodium balance in both studies. Even at fixed salt intake, 24-hour urine collection for sodium excretion (UNaV) showed infradian rhythmicity. We defined a ±25 mmol deviation from the average difference between recorded sodium intake and UNaV as the prediction interval to accurately classify a 3-g difference in salt intake. Because of the biological variability in UNaV, only every other daily urine sample correctly classified a 3-g difference in salt intake (49%). By increasing the observations to 3 consecutive 24-hour collections and sodium intakes, classification accuracy improved to 75%. Collecting seven 24-hour urines and sodium intake samples improved classification accuracy to 92%. We conclude that single 24-hour urine collections at intakes ranging from 6 to 12 g salt per day were not suitable to detect a 3-g difference in individual salt intake. Repeated measurements of 24-hour UNaV improve precision. This knowledge could be relevant to patient care and the conduct of intervention trials. © 2015 American Heart Association, Inc.
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NASA Astrophysics Data System (ADS)
Renkoski, Timothy E.; Hatch, Kenneth D.; Utzinger, Urs
2012-03-01
With no sufficient screening test for ovarian cancer, a method to evaluate the ovarian disease state quickly and nondestructively is needed. The authors have applied a wide-field spectral imager to freshly resected ovaries of 30 human patients in a study believed to be the first of its magnitude. Endogenous fluorescence was excited with 365-nm light and imaged in eight emission bands collectively covering the 400- to 640-nm range. Linear discriminant analysis was used to classify all image pixels and generate diagnostic maps of the ovaries. Training the classifier with previously collected single-point autofluorescence measurements of a spectroscopic probe enabled this novel classification. The process by which probe-collected spectra were transformed for comparison with imager spectra is described. Sensitivity of 100% and specificity of 51% were obtained in classifying normal and cancerous ovaries using autofluorescence data alone. Specificity increased to 69% when autofluorescence data were divided by green reflectance data to correct for spatial variation in tissue absorption properties. Benign neoplasm ovaries were also found to classify as nonmalignant using the same algorithm. Although applied ex vivo, the method described here appears useful for quick assessment of cancer presence in the human ovary.
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Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.
Chen, Peng-Jen; Lin, Meng-Chiung; Lai, Mei-Ju; Lin, Jung-Chun; Lu, Henry Horng-Shing; Tseng, Vincent S
2018-02-01
Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 ± 0.07 seconds-shorter than the time required by experts (1.54 ± 1.30 seconds) and nonexperts (1.77 ± 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists. We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.
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NASA Technical Reports Server (NTRS)
Score, Roberta; Lindstrom, Marilyn M.
1990-01-01
The state of the collection of Antarctic Meteorites is summarized. This guide is intended to assist investigators plan their meteorite research and select and request samples. Useful information is presented for all classified meteorites from 1976 to 1988 collections, as of Sept. 1989. The meteorite collection has grown over 13 years to include 4264 samples of which 2754 have been classified. Most of the unclassified meteorites are ordinary chondrites because the collections have been culled for specimens of special petrologic type. The guide consists of two large classification tables. They are preceded by a list of sample locations and important notes to make the tables understandable.
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Acoustic classification of zooplankton
NASA Astrophysics Data System (ADS)
Martin Traykovski, Linda V.
1998-11-01
Work on the forward problem in zooplankton bioacoustics has resulted in the identification of three categories of acoustic scatterers: elastic-shelled (e.g. pteropods), fluid-like (e.g. euphausiids), and gas-bearing (e.g. siphonophores). The relationship between backscattered energy and animal biomass has been shown to vary by a factor of ~19,000 across these categories, so that to make accurate estimates of zooplankton biomass from acoustic backscatter measurements of the ocean, the acoustic characteristics of the species of interest must be well-understood. This thesis describes the development of both feature based and model based classification techniques to invert broadband acoustic echoes from individual zooplankton for scatterer type, as well as for particular parameters such as animal orientation. The feature based Empirical Orthogonal Function Classifier (EOFC) discriminates scatterer types by identifying characteristic modes of variability in the echo spectra, exploiting only the inherent characteristic structure of the acoustic signatures. The model based Model Parameterisation Classifier (MPC) classifies based on correlation of observed echo spectra with simplified parameterisations of theoretical scattering models for the three classes. The Covariance Mean Variance Classifiers (CMVC) are a set of advanced model based techniques which exploit the full complexity of the theoretical models by searching the entire physical model parameter space without employing simplifying parameterisations. Three different CMVC algorithms were developed: the Integrated Score Classifier (ISC), the Pairwise Score Classifier (PSC) and the Bayesian Probability Classifier (BPC); these classifiers assign observations to a class based on similarities in covariance, mean, and variance, while accounting for model ambiguity and validity. These feature based and model based inversion techniques were successfully applied to several thousand echoes acquired from broadband (~350 kHz-750 kHz) insonifications of live zooplankton collected on Georges Bank and the Gulf of Maine to determine scatterer class. CMVC techniques were also applied to echoes from fluid-like zooplankton (Antarctic krill) to invert for angle of orientation using generic and animal-specific theoretical and empirical models. Application of these inversion techniques in situ will allow correct apportionment of backscattered energy to animal biomass, significantly improving estimates of zooplankton biomass based on acoustic surveys. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
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Agreement between twenty-four hour salt ingestion and sodium excretion in a controlled environment
Lerchl, Kathrin; Rakova, Natalia; Dahlmann, Anke; Rauh, Manfred; Goller, Ulrike; Basner, Mathias; Dinges, David F.; Beck, Luis; Agureev, Alexander; Larina, Irina; Baranov, Victor; Morukov, Boris; Eckardt, Kai-Uwe; Vassilieva, Galina; Wabel, Peter; Vienken, Jörg; Kirsch, Karl; Johannes, Bernd; Krannich, Alexander; Luft, Friedrich C.; Titze, Jens
2015-01-01
Accurately collected 24-hour urine collections are presumed to be valid for estimating salt intake in individuals. We performed two independent ultra-long-term salt balance studies lasting 105 (4 men) and 205 (6 men) days in 10 men simulating a flight to Mars. We controlled dietary intake of all constituents for months at salt intakes of 12, 9, and 6 grams per day and collected all urine. The subjects’ daily menus consisted of 27,279 individual servings, out of which 83.0% were completely consumed, 16.5% completely rejected, and 0.5% incompletely consumed. Urinary recovery of dietary salt was 92% of recorded intake, indicating long-term steady state sodium balance in both studies. Even at fixed salt intake, 24-hour sodium excretion (UNaV) showed infradian rhythmicity. We defined a ±25 mmol deviation from the average difference between recorded sodium intake and UNaV as the prediction interval to accurately classify a 3-gram difference in salt intake. Due to the biological variability in UNaV, only every-other daily urine sample correctly classified a 3-gram difference in salt intake (49%). By increasing the observations to three consecutive 24-hour collections and sodium intakes, classification accuracy improved to 75%. Collecting seven 24-hour urines and sodium intake samples improved classification accuracy to 92%. We conclude that single 24-hour urine collections at intakes ranging from 6–12 grams salt per day were not suitable to detect a 3-gram difference in individual salt intake. Repeated measurements of 24-hour UNaV improve precision. This knowledge could be relevant to patient care and the conduct of intervention trials. PMID:26259596
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Wan, Boyong; Small, Gary W
2011-01-21
A novel synthetic data generation methodology is described for use in the development of pattern recognition classifiers that are employed for the automated detection of volatile organic compounds (VOCs) during infrared remote sensing measurements. The approach used is passive Fourier transform infrared spectrometry implemented in a downward-looking mode on an aircraft platform. A key issue in developing this methodology in practice is the need for example data that can be used to train the classifiers. To replace the time-consuming and costly collection of training data in the field, this work implements a strategy for taking laboratory analyte spectra and superimposing them on background spectra collected from the air. The resulting synthetic spectra can be used to train the classifiers. This methodology is tested by developing classifiers for ethanol and methanol, two prevalent VOCs in wide industrial use. The classifiers are successfully tested with data collected from the aircraft during controlled releases of ethanol and during a methanol release from an industrial facility. For both ethanol and methanol, missed detections in the aircraft data are in the range of 4 to 5%, with false positive detections ranging from 0.1 to 0.3%.
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Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain
Vildjiounaite, Elena; Gimel'farb, Georgy; Kyllönen, Vesa; Peltola, Johannes
2015-01-01
Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design. PMID:26473165
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Hatch, Kenneth D.
2012-01-01
Abstract. With no sufficient screening test for ovarian cancer, a method to evaluate the ovarian disease state quickly and nondestructively is needed. The authors have applied a wide-field spectral imager to freshly resected ovaries of 30 human patients in a study believed to be the first of its magnitude. Endogenous fluorescence was excited with 365-nm light and imaged in eight emission bands collectively covering the 400- to 640-nm range. Linear discriminant analysis was used to classify all image pixels and generate diagnostic maps of the ovaries. Training the classifier with previously collected single-point autofluorescence measurements of a spectroscopic probe enabled this novel classification. The process by which probe-collected spectra were transformed for comparison with imager spectra is described. Sensitivity of 100% and specificity of 51% were obtained in classifying normal and cancerous ovaries using autofluorescence data alone. Specificity increased to 69% when autofluorescence data were divided by green reflectance data to correct for spatial variation in tissue absorption properties. Benign neoplasm ovaries were also found to classify as nonmalignant using the same algorithm. Although applied ex vivo, the method described here appears useful for quick assessment of cancer presence in the human ovary. PMID:22502561
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NASA Astrophysics Data System (ADS)
Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.
2017-10-01
Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.
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MacNeil, Adam; Lee, Chung-Won; Dietz, Vance
2014-09-03
Accurate estimates of vaccination coverage are crucial for assessing routine immunization program performance. Community based household surveys are frequently used to assess coverage within a country. In household surveys to assess routine immunization coverage, a child's vaccination history is classified on the basis of observation of the immunization card, parental recall of receipt of vaccination, or both; each of these methods has been shown to commonly be inaccurate. The use of serologic data as a biomarker of vaccination history is a potential additional approach to improve accuracy in classifying vaccination history. However, potential challenges, including the accuracy of serologic methods in classifying vaccination history, varying vaccine types and dosing schedules, and logistical and financial implications must be considered. We provide historic and scientific context for the potential use of serologic data to assess vaccination history and discuss in detail key areas of importance for consideration in the context of using serologic data for classifying vaccination history in household surveys. Further studies are needed to directly evaluate the performance of serologic data compared with use of immunization cards or parental recall for classification of vaccination history in household surveys, as well assess the impact of age at the time of sample collection on serologic titers, the predictive value of serology to identify a fully vaccinated child for multi-dose vaccines, and the cost impact and logistical issues on outcomes associated with different types of biological samples for serologic testing. Published by Elsevier Ltd.
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Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?
Kendra, Rachel Lynn; Karki, Suman; Eickholt, Jesse Lee; Gandy, Lisa
2015-06-19
User content posted through Twitter has been used for biosurveillance, to characterize public perception of health-related topics, and as a means of distributing information to the general public. Most of the existing work surrounding Twitter and health care has shown Twitter to be an effective medium for these problems but more could be done to provide finer and more efficient access to all pertinent data. Given the diversity of user-generated content, small samples or summary presentations of the data arguably omit a large part of the virtual discussion taking place in the Twittersphere. Still, managing, processing, and querying large amounts of Twitter data is not a trivial task. This work describes tools and techniques capable of handling larger sets of Twitter data and demonstrates their use with the issue of antibiotics. This work has two principle objectives: (1) to provide an open-source means to efficiently explore all collected tweets and query health-related topics on Twitter, specifically, questions such as what users are saying and how messages are spread, and (2) to characterize the larger discourse taking place on Twitter with respect to antibiotics. Open-source software suites Hadoop, Flume, and Hive were used to collect and query a large number of Twitter posts. To classify tweets by topic, a deep network classifier was trained using a limited number of manually classified tweets. The particular machine learning approach used also allowed the use of a large number of unclassified tweets to increase performance. Query-based analysis of the collected tweets revealed that a large number of users contributed to the online discussion and that a frequent topic mentioned was resistance. A number of prominent events related to antibiotics led to a number of spikes in activity but these were short in duration. The category-based classifier developed was able to correctly classify 70% of manually labeled tweets (using a 10-fold cross validation procedure and 9 classes). The classifier also performed well when evaluated on a per category basis. Using existing tools such as Hive, Flume, Hadoop, and machine learning techniques, it is possible to construct tools and workflows to collect and query large amounts of Twitter data to characterize the larger discussion taking place on Twitter with respect to a particular health-related topic. Furthermore, using newer machine learning techniques and a limited number of manually labeled tweets, an entire body of collected tweets can be classified to indicate what topics are driving the virtual, online discussion. The resulting classifier can also be used to efficiently explore collected tweets by category and search for messages of interest or exemplary content.
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Reconstructing fish movements between coastal wetland and ...
The use of resources from multiple habitats has been shown to be important to the production of aquatic consumers. To quantify the support of Great Lakes coastal wetland (WL) and nearshore (NS) habitats to yellow perch, we used otolith microchemistry to trace movements between the habitats. WL and NS water and fish samples were collected from lakes Huron and Michigan for water and otolith trace element analysis. Recently deposited otolith-edge Sr:Ca and Ba:Ca from otoliths were strongly correlated with the chemistry of the water in which fish were caught. In general, Sr:Ca and Ba:Ca in otoliths were significantly greater for individuals collected from WL areas. Because of these observed chemical differences between WL and NS habitats, quadratic discriminant function analysis (QDFA) was used to classify individuals with high accuracy to the habitat from which they were collected. We then combined the predictive abilities of QDFA with the otolith chemistry transect data that represents an individuals’ entire life, to classify habitat use through each fish’s life. Our results suggest larval use of WL habitats as well as three life histories for adult yellow perch. These strategies include (1) fish utilizing WL once annually (2) WL residents (3) WL residence as juveniles followed by movement to nearshore as adults. This application represents a novel use of transect otolith microchemistry to reconstruct fish movements between freshwater environments acro
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Goldstein, Benjamin A; Chang, Tara I; Winkelmayer, Wolfgang C
2015-10-01
Electronic Health Records (EHRs) present the opportunity to observe serial measurements on patients. While potentially informative, analyzing these data can be challenging. In this work we present a means to classify individuals based on a series of measurements collected by an EHR. Using patients undergoing hemodialysis, we categorized people based on their intradialytic blood pressure. Our primary criteria were that the classifications were time dependent and independent of other subjects. We fit a curve of intradialytic blood pressure using regression splines and then calculated first and second derivatives to come up with four mutually exclusive classifications at different time points. We show that these classifications relate to near term risk of cardiac events and are moderately stable over a succeeding two-week period. This work has general application for analyzing dense EHR data. Copyright © 2015 Elsevier Inc. All rights reserved.
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The ASAS-SN Catalog of Variable Stars I: The Serendipitous Survey
NASA Astrophysics Data System (ADS)
Jayasinghe, T.; Kochanek, C. S.; Stanek, K. Z.; Shappee, B. J.; Holoien, T. W.-S.; Thompson, Todd A.; Prieto, J. L.; Dong, Subo; Pawlak, M.; Shields, J. V.; Pojmanski, G.; Otero, S.; Britt, C. A.; Will, D.
2018-04-01
The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to routinely monitor the whole sky with a cadence of ˜2 - 3 days down to V≲ 17 mag. ASAS-SN has monitored the whole sky since 2014, collecting ˜100 - 500 epochs of observations per field. The V-band light curves for candidate variables identified during the search for supernovae are classified using a random forest classifier and visually verified. We present a catalog of 66,533 bright, new variable stars discovered during our search for supernovae, including 27,753 periodic variables and 38,780 irregular variables. V-band light curves for the ASAS-SN variables are available through the ASAS-SN variable stars database (https://asas-sn.osu.edu/variables). The database will begin to include the light curves of known variable stars in the near future along with the results for a systematic, all-sky variability survey.
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NASA Astrophysics Data System (ADS)
Michard, R.
1998-06-01
From the consideration of a sample of color distributions in 67 E classified objects of the Local Supercluster, it is found that local dust features are much more frequent and important in disky E's than boxy E's. The subclass of undeterminate objects, those which cannot be assigned to the diE or boE groups, is intermediate. Subsets of objects of common properties are considered from the point of view of local dust features occurrence: giant boxy E's; minor boxy E's with rotational support; compact dwarfs; SB0-like E's. It is noted that the detection of dust features is more than twice less frequent in Virgo cluster ellipticals than in the full sample, but the significance of this result is not clear. Based on observations collected at the Canada-France-Hawaii Telescope and at the Observatoire du Pic du Midi
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NASA Astrophysics Data System (ADS)
Iijima, A.; Sato, K.; Fujitani, Y.; Fujimori, E.; Tanabe, K.; Ohara, T.; Shimoda, M.; Kozawa, K.; Furuta, N.
2008-12-01
The results of the long-term monitoring of airborne particulate matter (APM) in Tokyo indicated that APM have been extremely enriched with antimony (Sb) compared to crustal composition. This observation suggests that the airborne Sb is distinctly derived from human activities. According to the material flow analysis, automotive brake abrasion dust and fly ash from waste incinerator were suspected as the significant Sb sources. To clarify the emission sources of the airborne Sb, elemental composition, particle size distribution, and morphological profiles of dust particles collected from two possible emission sources were characterized and compared to the field observation data. Brake abrasion dust samples were generated by using a brake dynamometer. During the abrasion test, particle size distribution was measured by an aerodynamic particle sizer spectrometer. Concurrently, size- classified dust particles were collected by an Andersen type air sampler. Fly ash samples were collected from several municipal waste incinerators, and the bulk ash samples were re-dispersed into an enclosed chamber. The measurement of particle size distribution and the collection of size-classified ash particles were conducted by the same methodologies as described previously. Field observations of APM were performed at a roadside site and a residential site by using an Andersen type air sampler. Chemical analyses of metallic elements were performed by an inductively coupled plasma atomic emission spectrometry and an inductively coupled plasma mass spectrometr. Morphological profiling of the individual particle was conducted by a scanning electron microscope equipped with an energy dispersive X-ray spectrometer. High concentration of Sb was detected from both of two possible sources. Particularly, Sb concentrations in a brake abrasion dust were extremely high compared to that in an ambient APM, suggesting that airborne Sb observed at the roadside might have been largely derived from mechanical abrasion of automotive brake pads. The peak of the mass-based particle size distribution of brake abrasion dust was found in a diameter of 2-3 μm. From the morphological viewpoints, shape of brake abrasion dust particle was typically edge- shaped, and high concentrated Sb and sulfur were simultaneously detected in a brake abrasion dust particle because Sb2S3 is used as a solid lubricant for automotive brake pad. Indeed, at the roadside site, total concentration of airborne Sb was twice as much as that observed at residential site. Moreover, the most concentrated Sb was found in a diameter of 2.1-3.6 μm for the roadside APM. Furthermore, in the collected particles with this size range, we found a number of particles of which morphological profiles were similar to those of the brake abrasion dust. Consequently, an automotive brake abrasion dust is expected as the predominant source of airborne Sb in the roadside atmosphere.
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Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.
Duong, Bach Phi; Kim, Jong-Myon
2018-04-07
The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.
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Genetic Structure and Selection of a Core Collection for Long Term Conservation of Avocado in Mexico
Guzmán, Luis F.; Machida-Hirano, Ryoko; Borrayo, Ernesto; Cortés-Cruz, Moisés; Espíndola-Barquera, María del Carmen; Heredia García, Elena
2017-01-01
Mexico, as the center of origin of avocado (Persea americama Mill.), harbors a wide genetic diversity of this species, whose identification may provide the grounds to not only understand its unique population structure and domestication history, but also inform the efforts aimed at its conservation. Although molecular characterization of cultivated avocado germplasm has been studied by several research groups, this had not been the case in Mexico. In order to elucidate the genetic structure of avocado in Mexico and the sustainable use of its genetic resources, 318 avocado accessions conserved in the germplasm collection in the National Avocado Genebank were analyzed using 28 markers [9 expressed sequence tag-Simple Sequence Repeats (SSRs) and 19 genomic SSRs]. Deviation from Hardy Weinberg Equilibrium and high inter-locus linkage disequilibrium were observed especially in drymifolia, and guatemalensis. Total averages of the observed and expected heterozygosity were 0.59 and 0.75, respectively. Although clear genetic differentiation was not observed among 3 botanical races: americana, drymifolia, and guatemalensis, the analyzed Mexican population can be classified into two groups that correspond to two different ecological regions. We developed a core-collection by K-means clustering method. The selected 36 individuals as core-collection successfully represented more than 80% of total alleles and showed heterozygosity values equal to or higher than those of the original collection, despite its constituting slightly more than 10% of the latter. Accessions selected as members of the core collection have now become candidates to be introduced in cryopreservation implying a minimum loss of genetic diversity and a back-up for existing field collections of such important genetic resources. PMID:28286510
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Lenticular fibroxanthomatous nodule.
Lee, Seok J; Ling, Jun X; Aaberg, Thomas M; Grossniklaus, Hans E
2003-02-01
To describe two patients with unique lenticular nodular proliferations. Observational case reports. The clinical histories and pathologic findings of two patients with lenticular nodular proliferations were reviewed. One patient with persistent hyperplastic primary vitreous and another patient with trauma developed lenticular nodular proliferations. The nodules were vascularized collections of foamy histiocytes, multinucleated cells, lens capsule, and lens epithelium that had undergone fibrous metaplasia. The lesions were classified as lenticular fibroxanthomatous nodules. A lenticular fibroxanthomatous nodule is a unique clinicopathologic entity that should be differentiated from Soemmerring ring, Elschnig pearl, and other simulating entities such as juvenile xanthogranuloma.
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Online and unsupervised face recognition for continuous video stream
NASA Astrophysics Data System (ADS)
Huo, Hongwen; Feng, Jufu
2009-10-01
We present a novel online face recognition approach for video stream in this paper. Our method includes two stages: pre-training and online training. In the pre-training phase, our method observes interactions, collects batches of input data, and attempts to estimate their distributions (Box-Cox transformation is adopted here to normalize rough estimates). In the online training phase, our method incrementally improves classifiers' knowledge of the face space and updates it continuously with incremental eigenspace analysis. The performance achieved by our method shows its great potential in video stream processing.
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Microbiological analysis of debris from STS-42 IML-1 by direct plating of rinse waters
NASA Technical Reports Server (NTRS)
Smithers, G. A.
1992-01-01
Microbial analysis of air filter debris from the Spacelab International Microgravity Laboratory-1 (IML-1) mission was performed via direct plating of rinse waters on a battery of selective and nonselective nutrient agars. Microbial isolates were identified using Minitek and Biolog technologies. Twenty-four types of bacteria were recovered and classified; a similar number of fungal types was observed, but these were not identified. This procedure can provide information about the proportions of organism types present at the time of debris collection.
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Mechanism underlying the diverse collective behavior in the swarm oscillator model
NASA Astrophysics Data System (ADS)
Iwasa, Masatomo; Tanaka, Dan
2017-09-01
The swarm oscillator model describes the long-time behavior of interacting chemotactic particles, and it shows numerous types of macroscopic patterns. However, the reason why so many kinds of patterns emerge is not clear. In this study, we elucidate the mechanism underlying the diversity of the pattens by analyzing the model for two particles. Focusing on the behavior when the two particles are spatially close, we find that the dynamics is classified into eight types, which explain most of the observed 13 types of patterns.
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Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft
NASA Technical Reports Server (NTRS)
Castano, Rebecca; Mazzoni, Dominic; Tang, Nghia; Greeley, Ron; Doggett, Thomas; Cichy, Ben; Chien, Steve; Davies, Ashley
2006-01-01
Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.
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Incorporation of operator knowledge for improved HMDS GPR classification
NASA Astrophysics Data System (ADS)
Kennedy, Levi; McClelland, Jessee R.; Walters, Joshua R.
2012-06-01
The Husky Mine Detection System (HMDS) detects and alerts operators to potential threats observed in groundpenetrating RADAR (GPR) data. In the current system architecture, the classifiers have been trained using available data from multiple training sites. Changes in target types, clutter types, and operational conditions may result in statistical differences between the training data and the testing data for the underlying features used by the classifier, potentially resulting in an increased false alarm rate or a lower probability of detection for the system. In the current mode of operation, the automated detection system alerts the human operator when a target-like object is detected. The operator then uses data visualization software, contextual information, and human intuition to decide whether the alarm presented is an actual target or a false alarm. When the statistics of the training data and the testing data are mismatched, the automated detection system can overwhelm the analyst with an excessive number of false alarms. This is evident in the performance of and the data collected from deployed systems. This work demonstrates that analyst feedback can be successfully used to re-train a classifier to account for variable testing data statistics not originally captured in the initial training data.
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Helweg, D A; Au, W W; Roitblat, H L; Nachtigall, P E
1996-04-01
The relationships between acoustic features of target echoes and the cognitive representations of the target formed by an echolocating dolphin will influence the ease with which the dolphin can recognize a target. A blindfolded Atlantic bottlenose dolphin (Tursiops truncatus) learned to match aspect-dependent three-dimensional targets (such as a cube) at haphazard orientations, although with some difficulty. This task may have been difficult because aspect-dependent targets produce different echoes at different orientations, which required the dolphin to have some capability for object constancy across changes in echo characteristics. Significant target-related differences in echo amplitude, rms bandwidth, and distributions of interhighlight intervals were observed among echoes collected when the dolphin was performing the task. Targets could be classified using a combination of energy flux density and rms bandwidth by a linear discriminant analysis and a nearest centroid classifier. Neither statistical model could classify targets without amplitude information, but the highest accuracy required spectral information as well. This suggests that the dolphin recognized the targets using a multidimensional representation containing amplitude and spectral information and that dolphins can form stable representations of targets regardless of orientation based on varying sensory properties.
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A connectionist-geostatistical approach for classification of deformation types in ice surfaces
NASA Astrophysics Data System (ADS)
Goetz-Weiss, L. R.; Herzfeld, U. C.; Hale, R. G.; Hunke, E. C.; Bobeck, J.
2014-12-01
Deformation is a class of highly non-linear geophysical processes from which one can infer other geophysical variables in a dynamical system. For example, in an ice-dynamic model, deformation is related to velocity, basal sliding, surface elevation changes, and the stress field at the surface as well as internal to a glacier. While many of these variables cannot be observed, deformation state can be an observable variable, because deformation in glaciers (once a viscosity threshold is exceeded) manifests itself in crevasses.Given the amount of information that can be inferred from observing surface deformation, an automated method for classifying surface imagery becomes increasingly desirable. In this paper a Neural Network is used to recognize classes of crevasse types over the Bering Bagley Glacier System (BBGS) during a surge (2011-2013-?). A surge is a spatially and temporally highly variable and rapid acceleration of the glacier. Therefore, many different crevasse types occur in a short time frame and in close proximity, and these crevasse fields hold information on the geophysical processes of the surge.The connectionist-geostatistical approach uses directional experimental (discrete) variograms to parameterize images into a form that the Neural Network can recognize. Recognizing that each surge wave results in different crevasse types and that environmental conditions affect the appearance in imagery, we have developed a semi-automated pre-training software to adapt the Neural Net to chaining conditions.The method is applied to airborne and satellite imagery to classify surge crevasses from the BBGS surge. This method works well for classifying spatially repetitive images such as the crevasses over Bering Glacier. We expand the network for less repetitive images in order to analyze imagery collected over the Arctic sea ice, to assess the percentage of deformed ice for model calibration.
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A semi-automated image analysis procedure for in situ plankton imaging systems.
Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M
2015-01-01
Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.
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A Semi-Automated Image Analysis Procedure for In Situ Plankton Imaging Systems
Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C.; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M.
2015-01-01
Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups. PMID:26010260
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K-mean clustering algorithm for processing signals from compound semiconductor detectors
NASA Astrophysics Data System (ADS)
Tada, Tsutomu; Hitomi, Keitaro; Wu, Yan; Kim, Seong-Yun; Yamazaki, Hiromichi; Ishii, Keizo
2011-12-01
The K-mean clustering algorithm was employed for processing signal waveforms from TlBr detectors. The signal waveforms were classified based on its shape reflecting the charge collection process in the detector. The classified signal waveforms were processed individually to suppress the pulse height variation of signals due to the charge collection loss. The obtained energy resolution of a 137Cs spectrum measured with a 0.5 mm thick TlBr detector was 1.3% FWHM by employing 500 clusters.
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ERIC Educational Resources Information Center
Western New York Regional Office for Educational Planning, Cheektowaga.
This survey is designed to provide comparative information for classified personnel in the six counties of western New York State. Data collection was more difficult this year than in previous years because of the increasing length of the negotiation process. This year 74 of 89 districts supplied data. This study includes two features that were…
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Towards SSVEP-based, portable, responsive Brain-Computer Interface.
Kaczmarek, Piotr; Salomon, Pawel
2015-08-01
A Brain-Computer Interface in motion control application requires high system responsiveness and accuracy. SSVEP interface consisted of 2-8 stimuli and 2 channel EEG amplifier was presented in this paper. The observed stimulus is recognized based on a canonical correlation calculated in 1 second window, ensuring high interface responsiveness. A threshold classifier with hysteresis (T-H) was proposed for recognition purposes. Obtained results suggest that T-H classifier enables to significantly increase classifier performance (resulting in accuracy of 76%, while maintaining average false positive detection rate of stimulus different then observed one between 2-13%, depending on stimulus frequency). It was shown that the parameters of T-H classifier, maximizing true positive rate, can be estimated by gradient-based search since the single maximum was observed. Moreover the preliminary results, performed on a test group (N=4), suggest that for T-H classifier exists a certain set of parameters for which the system accuracy is similar to accuracy obtained for user-trained classifier.
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Infrared Telescope Facility's Spectrograph Observations of Human-Made Space Objects
NASA Technical Reports Server (NTRS)
Abercromby, K.; Buckalew, B.; Abell, P.; Cowardin, H.
2015-01-01
Presented here are the results of the Infrared Telescope Facility (IRTF) spectral observations of human-made space objects taken from 2006 to 2008. The data collected using the SpeX infrared spectrograph cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 20 different orbiting objects at or near the geosynchronous (GEO) regime. Four of the objects were controlled spacecraft, seven were non-controlled spacecraft, five were rocket bodies, and the final four were cataloged as debris pieces. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials, thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons, silicon, and thermal emission. The spacecraft, both the controlled and non-controlled, show distinct features due to the presence of solar panels, whereas the rocket bodies do not. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give excellent indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. However, based on the current state of the comparison between the observations and the laboratory data, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.
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United Kingdom Infrared Telescope's Spectrograph Observations of Human-Made Space Objects
NASA Technical Reports Server (NTRS)
Buckalew, Brent; Abercromby, Kira; Lederer, Susan; Frith, James; Cowardin, Heather
2017-01-01
Presented here are the results of the United Kingdom Infrared Telescope (UKIRT) spectral observations of human-made space objects taken from 2014 to 2015. The data collected using the UIST infrared spectrograph cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 18 different orbiting objects at or near the geosynchronous (GEO) regime. Thirteen of the objects are spacecraft, one is a rocket body, and four are cataloged as debris pieces. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials; thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons and silicon. The spacecraft show distinct features due to the presence of solar panels. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give excellent indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include more materials, noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. A comparison conducted between objects observed previously with the NASA Infrared Telescope Facility (IRTF) shows similar materials and trends from the two telescopes and from the two distinct data sets. However, based on the current state of the model, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.
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Spectral Trends of Solar Bursts at Sub-THz Frequencies
NASA Astrophysics Data System (ADS)
Fernandes, L. O. T.; Kaufmann, P.; Correia, E.; Giménez de Castro, C. G.; Kudaka, A. S.; Marun, A.; Pereyra, P.; Raulin, J.-P.; Valio, A. B. M.
2017-01-01
Previous sub-THz studies were derived from single-event observations. We here analyze for the first time spectral trends for a larger collection of sub-THz bursts. The collection consists of a set of 16 moderate to small impulsive solar radio bursts observed at 0.2 and 0.4 THz by the Solar Submillimeter-wave Telescope (SST) in 2012 - 2014 at El Leoncito, in the Argentinean Andes. The peak burst spectra included data from new solar patrol radio telescopes (45 and 90 GHz), and were completed with microwave data obtained by the Radio Solar Telescope Network, when available. We critically evaluate errors and uncertainties in sub-THz flux estimates caused by calibration techniques and the corrections for atmospheric transmission, and introduce a new method to obtain a uniform flux scale criterion for all events. The sub-THz bursts were searched during reported GOES soft X-ray events of class C or larger, for periods common to SST observations. Seven out of 16 events exhibit spectral maxima in the range 5 - 40 GHz with fluxes decaying at sub-THz frequencies (three of them associated to GOES class X, and four to class M). Nine out of 16 events exhibited the sub-THz spectral component. In five of these events, the sub-THz emission fluxes increased with a separate frequency from that of the microwave spectral component (two classified as X and three as M), and four events have only been detected at sub-THz frequencies (three classified as M and one as C). The results suggest that the THz component might be present throughout, with the minimum turnover frequency increasing as a function of the energy of the emitting electrons. The peculiar nature of many sub-THz burst events requires further investigations of bursts that are examined from SST observations alone to better understand these phenomena.
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New data from cold war treasure trove
NASA Astrophysics Data System (ADS)
Carlowicz, Michael
For half a century, the Russian and United States navies competed for tactical advantage in the Arctic Ocean, mapping seafloor and floating ice sheets, measuring temperatures and reckoning chemistry. But with old enemies becoming new friends, data once collected for the sake of war now are being shared in the name of scientific cooperation.In mid-January, the U.S. and Russian governments announced the release of the first of four volumes of a new atlas of the Arctic Ocean. The previously classified data it contains will effectively double the amount of Arctic data that is available to the scientific community. The set includes more than 1.3 million temperature and salinity observations collected from 1948 to 1993 by drifting ice camps and stations, icebreaking ships, land—and airborne expeditions, and buoys. Approximately 70% of the observations for the Arctic Ocean and shelf seas were derived from Russian archives of formerly restricted data, with the other 30% coming from comparable sources in the U.S., Canada, and other Western nations.
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Jackowski, Konrad; Krawczyk, Bartosz; Woźniak, Michał
2014-05-01
Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues and propose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide range of computer experiments that show that AdaSS+ can outperform the original method and several reference classifiers.
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Combination of dynamic Bayesian network classifiers for the recognition of degraded characters
NASA Astrophysics Data System (ADS)
Likforman-Sulem, Laurence; Sigelle, Marc
2009-01-01
We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.
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Westbrook, Johanna I; Ampt, Amanda
2009-04-01
Evidence regarding how health information technologies influence clinicians' patterns of work and support efficient practices is limited. Traditional paper-based data collection methods are unable to capture clinical work complexity and communication patterns. The use of electronic data collection tools for such studies is emerging yet is rarely assessed for reliability or validity. Our aim was to design, apply and test an observational method which incorporated the use of an electronic data collection tool for work measurement studies which would allow efficient, accurate and reliable data collection, and capture greater degrees of work complexity than current approaches. We developed an observational method and software for personal digital assistants (PDAs) which captures multiple dimensions of clinicians' work tasks, namely what task, with whom, and with what; tasks conducted in parallel (multi-tasking); interruptions and task duration. During field-testing over 7 months across four hospital wards, fifty-two nurses were observed for 250 h. Inter-rater reliability was tested and validity was measured by (i) assessing whether observational data reflected known differences in clinical role work tasks and (ii) by comparing observational data with participants' estimates of their task time distribution. Observers took 15-20 h of training to master the method and data collection process. Only 1% of tasks observed did not match the classification developed and were classified as 'other'. Inter-rater reliability scores of observers were maintained at over 85%. The results discriminated between the work patterns of enrolled and registered nurses consistent with differences in their roles. Survey data (n=27) revealed consistent ratings of tasks by nurses, and their rankings of most to least time-consuming tasks were significantly correlated with those derived from the observational data. Over 40% of nurses' time was spent in direct care or professional communication, with 11.8% of time spent multi-tasking. Nurses were interrupted approximately every 49 min. One quarter of interruptions occurred while nurses were preparing or administering medications. This method efficiently produces reliable and valid data. The multi-dimensional nature of the data collected provides greater insights into patterns of clinicians' work and communication than has previously been possible using other methods.
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Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model
Wang, Guofeng; Yang, Yinwei; Li, Zhimeng
2014-01-01
Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514
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Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.
Wang, Guofeng; Yang, Yinwei; Li, Zhimeng
2014-11-14
Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.
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Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis
Kim, Jong-Myon
2018-01-01
The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466
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USDA-ARS?s Scientific Manuscript database
Optical detection of foodborne bacteria such as Salmonella classifies bacteria by analyzing spectral data, and has potential for rapid detection. In this experiment hyperspectral microscopy is explored as a means for classifying five Salmonella serotypes. Initially, the microscope collects 89 spect...
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Vegetation communities at Big Muddy National Fish and Wildlife Refuge, Missouri
Struckhoff, Matthew A.; Grabner, Keith W.; Stroh, Esther D.
2011-01-01
New and existing data were used to describe and map vegetation communities at Big Muddy National Fish and Wildlife Refuge. Existing data had been gathered during the growing seasons of 2002, 2003, and 2004. New data were collected in 2007 to describe previously unsampled communities and communities within which insufficient data had been collected. Plot data and field observations were used to describe 17 natural and semi-natural communities at the Association level of the National Vegetation Classification System (NVCS). Four ruderal communities not included in the NVCS are also described. Data were used to inform delineation of communities using aerial photos from 2000, 2002, 2003, 2005, 2006, and 2007. During this process, eleven additional land cover classes including cultural features, managed vegetation communities, and water features were identified. These features were mapped, some were described, but no vegetation data were collected. In 2009, nearly all community polygons were field visited and classified to the Association level. When necessary, polygon boundaries were adjusted based on field observations. The final map includes 482 polygons of 27 land cover classes encompassing 3,174 hectares on 5 units of the refuge. Data and information will inform the development of the refuge Comprehensive Conservation Plan.
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Quantitative analysis of bloggers' collective behavior powered by emotions
NASA Astrophysics Data System (ADS)
Mitrović, Marija; Paltoglou, Georgios; Tadić, Bosiljka
2011-02-01
Large-scale data resulting from users' online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study the emergence of emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite networks of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion classifier developed for this type of text. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore the robustness of these critical states, we design a network-automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states.
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Quantum ensembles of quantum classifiers.
Schuld, Maria; Petruccione, Francesco
2018-02-09
Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.
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Using Machine Learning to Enable Big Data Analysis within Human Review Time Budgets
NASA Astrophysics Data System (ADS)
Bue, B.; Rebbapragada, U.; Wagstaff, K.; Thompson, D. R.
2014-12-01
The quantity of astronomical observations collected by today's instruments far exceeds the capability of manual inspection by domain experts. Scientists often have a fixed time budget of a few hours spend to perform the monotonous task of scanning through a live stream or data dump of candidates that must be prioritized for follow-up analysis. Today's and next generation astronomical instruments produce millions of candidate detection per day, and necessitate the use of automated classifiers that serve as "data triage" in order to filter out spurious signals. Automated data triage enables increased science return by prioritizing interesting or anomalous observations for follow-up inspection, while also expediting analysis by filtering out noisy or redundant observations. We describe three specific astronomical investigations that are currently benefiting from data triage techniques in their respective processing pipelines.
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Software platform for managing the classification of error- related potentials of observers
NASA Astrophysics Data System (ADS)
Asvestas, P.; Ventouras, E.-C.; Kostopoulos, S.; Sidiropoulos, K.; Korfiatis, V.; Korda, A.; Uzunolglu, A.; Karanasiou, I.; Kalatzis, I.; Matsopoulos, G.
2015-09-01
Human learning is partly based on observation. Electroencephalographic recordings of subjects who perform acts (actors) or observe actors (observers), contain a negative waveform in the Evoked Potentials (EPs) of the actors that commit errors and of observers who observe the error-committing actors. This waveform is called the Error-Related Negativity (ERN). Its detection has applications in the context of Brain-Computer Interfaces. The present work describes a software system developed for managing EPs of observers, with the aim of classifying them into observations of either correct or incorrect actions. It consists of an integrated platform for the storage, management, processing and classification of EPs recorded during error-observation experiments. The system was developed using C# and the following development tools and frameworks: MySQL, .NET Framework, Entity Framework and Emgu CV, for interfacing with the machine learning library of OpenCV. Up to six features can be computed per EP recording per electrode. The user can select among various feature selection algorithms and then proceed to train one of three types of classifiers: Artificial Neural Networks, Support Vector Machines, k-nearest neighbour. Next the classifier can be used for classifying any EP curve that has been inputted to the database.
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DETAIL VIEW OF CLASSIFIER, TAILINGS LAUNDER TROUGH, LINE SHAFTS, AND ...
DETAIL VIEW OF CLASSIFIER, TAILINGS LAUNDER TROUGH, LINE SHAFTS, AND CONCENTRATION TABLES, LOOKING SOUTHWEST. SLURRY EXITING THE BALL MILL WAS COLLECTED IN AN AMALGAMATION BOX (MISSING) FROM THE END OF THE MILL, AND INTRODUCED INTO THE CLASSIFIER. THE TAILINGS LAUDER IS ON THE GROUND AT LOWER RIGHT. THE LINE SHAFTING ABOVE PROVIDED POWER TO THE CONCENTRATION TABLES BELOW AT CENTER RIGHT. - Gold Hill Mill, Warm Spring Canyon Road, Death Valley Junction, Inyo County, CA
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Activity recognition using a single accelerometer placed at the wrist or ankle.
Mannini, Andrea; Intille, Stephen S; Rosenberger, Mary; Sabatini, Angelo M; Haskell, William
2013-11-01
Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.
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Federal Register 2010, 2011, 2012, 2013, 2014
2013-01-23
...-0015] Agency Information Collection Activities: Immigrant Petition for Alien Workers, Form I-140... Approved Collection. (2) Title of the Form/Collection: Immigrant Petition for Alien Worker. (3) Agency form... other for-profit. The information furnished on Form I-140 will be used by USCIS to classify aliens under...
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Local classifier weighting by quadratic programming.
Cevikalp, Hakan; Polikar, Robi
2008-10-01
It has been widely accepted that the classification accuracy can be improved by combining outputs of multiple classifiers. However, how to combine multiple classifiers with various (potentially conflicting) decisions is still an open problem. A rich collection of classifier combination procedures -- many of which are heuristic in nature -- have been developed for this goal. In this brief, we describe a dynamic approach to combine classifiers that have expertise in different regions of the input space. To this end, we use local classifier accuracy estimates to weight classifier outputs. Specifically, we estimate local recognition accuracies of classifiers near a query sample by utilizing its nearest neighbors, and then use these estimates to find the best weights of classifiers to label the query. The problem is formulated as a convex quadratic optimization problem, which returns optimal nonnegative classifier weights with respect to the chosen objective function, and the weights ensure that locally most accurate classifiers are weighted more heavily for labeling the query sample. Experimental results on several data sets indicate that the proposed weighting scheme outperforms other popular classifier combination schemes, particularly on problems with complex decision boundaries. Hence, the results indicate that local classification-accuracy-based combination techniques are well suited for decision making when the classifiers are trained by focusing on different regions of the input space.
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Activity recognition using dynamic multiple sensor fusion in body sensor networks.
Gao, Lei; Bourke, Alan K; Nelson, John
2012-01-01
Multiple sensor fusion is a main research direction for activity recognition. However, there are two challenges in those systems: the energy consumption due to the wireless transmission and the classifier design because of the dynamic feature vector. This paper proposes a multi-sensor fusion framework, which consists of the sensor selection module and the hierarchical classifier. The sensor selection module adopts the convex optimization to select the sensor subset in real time. The hierarchical classifier combines the Decision Tree classifier with the Naïve Bayes classifier. The dataset collected from 8 subjects, who performed 8 scenario activities, was used to evaluate the proposed system. The results show that the proposed system can obviously reduce the energy consumption while guaranteeing the recognition accuracy.
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2017-01-01
The application of insect and arthropod information to medicolegal death investigations is one of the more exacting applications of entomology. Historically limited to homicide investigations, the integration of full time forensic entomology services to the medical examiner’s office in Harris County has opened up the opportunity to apply entomology to a wide variety of manner of death classifications and types of scenes to make observations on a number of different geographical and species-level trends in Harris County, Texas, USA. In this study, a retrospective analysis was made of 203 forensic entomology cases analyzed during the course of medicolegal death investigations performed by the Harris County Institute of Forensic Sciences in Houston, TX, USA from January 2013 through April 2016. These cases included all manner of death classifications, stages of decomposition and a variety of different scene types that were classified into decedents transported from the hospital (typically associated with myiasis or sting allergy; 3.0%), outdoor scenes (32.0%) or indoor scenes (65.0%). Ambient scene air temperature at the time scene investigation was the only significantly different factor observed between indoor and outdoor scenes with average indoor scene temperature being slightly cooler (25.2°C) than that observed outdoors (28.0°C). Relative humidity was not found to be significantly different between scene types. Most of the indoor scenes were classified as natural (43.3%) whereas most of the outdoor scenes were classified as homicides (12.3%). All other manner of death classifications came from both indoor and outdoor scenes. Several species were found to be significantly associated with indoor scenes as indicated by a binomial test, including Blaesoxipha plinthopyga (Wiedemann) (Diptera: Sarcophagidae), all Sarcophagidae (including B. plinthopyga), Megaselia scalaris Loew (Diptera: Phoridae), Synthesiomyia nudiseta Wulp (Diptera: Muscidae) and Lucilia cuprina (Wiedemann) (Diptera: Calliphoridae). The only species that was a significant indicator of an outdoor scene was Lucilia eximia (Wiedemann) (Diptera: Calliphoridae). All other insect species that were collected in five or more cases were collected from both indoor and outdoor scenes. A species list with month of collection and basic scene characteristics with the length of the estimated time of colonization is also presented. The data presented here provide valuable casework related species data for Harris County, TX and nearby areas on the Gulf Coast that can be used to compare to other climate regions with other species assemblages and to assist in identifying new species introductions to the area. This study also highlights the importance of potential sources of uncertainty in preparation and interpretation of forensic entomology reports from different scene types. PMID:28604832
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Sanford, Michelle R
2017-01-01
The application of insect and arthropod information to medicolegal death investigations is one of the more exacting applications of entomology. Historically limited to homicide investigations, the integration of full time forensic entomology services to the medical examiner's office in Harris County has opened up the opportunity to apply entomology to a wide variety of manner of death classifications and types of scenes to make observations on a number of different geographical and species-level trends in Harris County, Texas, USA. In this study, a retrospective analysis was made of 203 forensic entomology cases analyzed during the course of medicolegal death investigations performed by the Harris County Institute of Forensic Sciences in Houston, TX, USA from January 2013 through April 2016. These cases included all manner of death classifications, stages of decomposition and a variety of different scene types that were classified into decedents transported from the hospital (typically associated with myiasis or sting allergy; 3.0%), outdoor scenes (32.0%) or indoor scenes (65.0%). Ambient scene air temperature at the time scene investigation was the only significantly different factor observed between indoor and outdoor scenes with average indoor scene temperature being slightly cooler (25.2°C) than that observed outdoors (28.0°C). Relative humidity was not found to be significantly different between scene types. Most of the indoor scenes were classified as natural (43.3%) whereas most of the outdoor scenes were classified as homicides (12.3%). All other manner of death classifications came from both indoor and outdoor scenes. Several species were found to be significantly associated with indoor scenes as indicated by a binomial test, including Blaesoxipha plinthopyga (Wiedemann) (Diptera: Sarcophagidae), all Sarcophagidae (including B. plinthopyga), Megaselia scalaris Loew (Diptera: Phoridae), Synthesiomyia nudiseta Wulp (Diptera: Muscidae) and Lucilia cuprina (Wiedemann) (Diptera: Calliphoridae). The only species that was a significant indicator of an outdoor scene was Lucilia eximia (Wiedemann) (Diptera: Calliphoridae). All other insect species that were collected in five or more cases were collected from both indoor and outdoor scenes. A species list with month of collection and basic scene characteristics with the length of the estimated time of colonization is also presented. The data presented here provide valuable casework related species data for Harris County, TX and nearby areas on the Gulf Coast that can be used to compare to other climate regions with other species assemblages and to assist in identifying new species introductions to the area. This study also highlights the importance of potential sources of uncertainty in preparation and interpretation of forensic entomology reports from different scene types.
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The ASAS-SN catalogue of variable stars I: The Serendipitous Survey
NASA Astrophysics Data System (ADS)
Jayasinghe, T.; Kochanek, C. S.; Stanek, K. Z.; Shappee, B. J.; Holoien, T. W.-S.; Thompson, Toda A.; Prieto, J. L.; Dong, Subo; Pawlak, M.; Shields, J. V.; Pojmanski, G.; Otero, S.; Britt, C. A.; Will, D.
2018-07-01
The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to routinely monitor the whole sky with a cadence of ˜2-3 d down to V ≲ 17 mag. ASAS-SN has monitored the whole sky since 2014, collecting ˜100-500 epochs of observations per field. The V-band light curves for candidate variables identified during the search for supernovae are classified using a random forest classifier and visually verified. We present a catalogue of 66 179 bright, new variable stars discovered during our search for supernovae, including 27 479 periodic variables and 38 700 irregular variables. V-band light curves for the ASAS-SN variables are available through the ASAS-SN variable stars data base (https://asas-sn.osu.edu/variables). The data base will begin to include the light curves of known variable stars in the near future along with the results for a systematic, all-sky variability survey.
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Spectral classifying base on color of live corals and dead corals covered with algae
NASA Astrophysics Data System (ADS)
Nurdin, Nurjannah; Komatsu, Teruhisa; Barille, Laurent; Akbar, A. S. M.; Sawayama, Shuhei; Fitrah, Muh. Nur; Prasyad, Hermansyah
2016-05-01
Pigments in the host tissues of corals can make a significant contribution to their spectral signature and can affect their apparent color as perceived by a human observer. The aim of this study is classifying the spectral reflectance of corals base on different color. It is expected that they can be used as references in discriminating between live corals, dead coral covered with algae Spectral reflectance data was collected in three small islands, Spermonde Archipelago, Indonesia by using a hyperspectral radiometer underwater. First and second derivative analysis resolved the wavelength locations of dominant features contributing to reflectance in corals and support the distinct differences in spectra among colour existed. Spectral derivative analysis was used to determine the specific wavelength regions ideal for remote identification of substrate type. The analysis results shown that yellow, green, brown and violet live corals are spectrally separable from each other, but they are similar with dead coral covered with algae spectral.
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NASA Astrophysics Data System (ADS)
Crozier, Marisa
When learning is an adventure rather than an exercise in memorization, students can enjoy the process and be motivated to participate in classroom activities (Clem, Mennicke, & Beasley, 2014). Students classified as emotionally disturbed are prone to disruptive behaviors and struggle learning in a traditional science classroom consisting of lecture and demonstrations. They cannot maintain the necessary level of attention nor have the strong reading, writing or memory skills needed to succeed. Therefore, this study examined whether the use of experiential learning would increase on-task behavior and improve the motivation of emotionally disturbed, middle school students in science. Students completed four hands-on experiments aligned with the science curriculum. The data collection methods implemented were an observation checklist with corresponding journal entries, a summative assessment in the form of lab sheets, and student interviews. Through triangulation and analysis, data revealed that the students had more on-task behaviors, were engaged in the lessons, and improved grades in science.
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NASA Astrophysics Data System (ADS)
Canu I, Guseva; C, Ducros; S, Ducamp; L, Delabre; S, Audignon-Durand; C, Durand; Y, Iwatsubo; D, Jezewski-Serra; Bihan O, Le; S, Malard; A, Radauceanu; M, Reynier; M, Ricaud; O, Witschger
2015-05-01
The French national epidemiological surveillance program EpiNano aims at surveying mid- and long-term health effects possibly related with occupational exposure to either carbon nanotubes or titanium dioxide nanoparticles (TiO2). EpiNano is limited to workers potentially exposed to these nanomaterials including their aggregates and agglomerates. In order to identify those workers during the in-field industrial hygiene visits, a standardized non-instrumental method is necessary especially for epidemiologists and occupational physicians unfamiliar with nanoparticle and nanomaterial exposure metrology. A working group, Quintet ExpoNano, including national experts in nanomaterial metrology and occupational hygiene reviewed available methods, resources and their practice in order to develop a standardized tool for conducting company industrial hygiene visits and collecting necessary information. This tool, entitled “Onsite technical logbook”, includes 3 parts: company, workplace, and workstation allowing a detailed description of each task, process and exposure surrounding conditions. This logbook is intended to be completed during the company industrial hygiene visit. Each visit is conducted jointly by an industrial hygienist and an epidemiologist of the program and lasts one or two days depending on the company size. When all collected information is computerized using friendly-using software, it is possible to classify workstations with respect to their potential direct and/or indirect exposure. Workers appointed to workstations classified as concerned with exposure are considered as eligible for EpiNano program and invited to participate. Since January 2014, the Onsite technical logbook has been used in ten company visits. The companies visited were mostly involved in research and development. A total of 53 workstations with potential exposure to nanomaterials were pre-selected and observed: 5 with TiO2, 16 with single-walled carbon nanotubes, 27 multiwalled carbon nanotubes. Among the tasks observed there were: nanomaterial characterisation analysis (8), weighing (7), synthesis (6), functionalization (5), and transfer (5). The manipulated quantities were usually very small. After analysis of the data gathered in logbooks, 30 workstations have been classified as concerned with exposure to carbon nanotubes or TiO2. Additional tool validity as well as inter-and intra-evaluator reproducibility studies are ongoing. The first results are promising.
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Alahmadi, Hanin H; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B; Bentham, Peter; Kourtzi, Zoe; Tino, Peter
2016-01-01
Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.
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Comparisons of Young Children's Private Speech Profiles: Analogical Versus Nonanalogical Reasoners.
ERIC Educational Resources Information Center
Manning, Brenda H.; White, C. Stephen
The primary intention of this study was to compare private speech profiles of young children classified as analogical reasoners (AR) with young children classified as nonanalogical reasoners (NAR). The secondary purpose was to investigate Berk's (1986) research methodology and categorical scheme for the collection and coding of private speech…
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Quantitative change of EEG and respiration signals during mindfulness meditation.
Ahani, Asieh; Wahbeh, Helane; Nezamfar, Hooman; Miller, Meghan; Erdogmus, Deniz; Oken, Barry
2014-05-14
This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.
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Quantitative change of EEG and respiration signals during mindfulness meditation
2014-01-01
Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies. PMID:24939519
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NASA Technical Reports Server (NTRS)
Markert, Kel; Ashmall, William; Johnson, Gary; Saah, David; Mollicone, Danilo; Diaz, Alfonso Sanchez-Paus; Anderson, Eric; Flores, Africa; Griffin, Robert
2017-01-01
Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.
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NASA Astrophysics Data System (ADS)
Markert, K. N.; Ashmall, W.; Johnson, G.; Saah, D. S.; Anderson, E.; Flores Cordova, A. I.; Díaz, A. S. P.; Mollicone, D.; Griffin, R.
2017-12-01
Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.
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10 CFR 824.15 - Collection of civil penalties.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 10 Energy 4 2011-01-01 2011-01-01 false Collection of civil penalties. 824.15 Section 824.15 Energy DEPARTMENT OF ENERGY PROCEDURAL RULES FOR THE ASSESSMENT OF CIVIL PENALTIES FOR CLASSIFIED INFORMATION SECURITY VIOLATIONS § 824.15 Collection of civil penalties. If any person fails to pay an...
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10 CFR 824.15 - Collection of civil penalties.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 10 Energy 4 2010-01-01 2010-01-01 false Collection of civil penalties. 824.15 Section 824.15 Energy DEPARTMENT OF ENERGY PROCEDURAL RULES FOR THE ASSESSMENT OF CIVIL PENALTIES FOR CLASSIFIED INFORMATION SECURITY VIOLATIONS § 824.15 Collection of civil penalties. If any person fails to pay an...
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Kalyanam, Janani; Quezada, Mauricio; Poblete, Barbara; Lanckriet, Gert
2016-01-01
On-line social networks publish information on a high volume of real-world events almost instantly, becoming a primary source for breaking news. Some of these real-world events can end up having a very strong impact on on-line social networks. The effect of such events can be analyzed from several perspectives, one of them being the intensity and characteristics of the collective activity that it produces in the social platform. We research 5,234 real-world news events encompassing 43 million messages discussed on the Twitter microblogging service for approximately 1 year. We show empirically that exogenous news events naturally create collective patterns of bursty behavior in combination with long periods of inactivity in the network. This type of behavior agrees with other patterns previously observed in other types of natural collective phenomena, as well as in individual human communications. In addition, we propose a methodology to classify news events according to the different levels of intensity in activity that they produce. In particular, we analyze the most highly active events and observe a consistent and strikingly different collective reaction from users when they are exposed to such events. This reaction is independent of an event's reach and scope. We further observe that extremely high-activity events have characteristics that are quite distinguishable at the beginning stages of their outbreak. This allows us to predict with high precision, the top 8% of events that will have the most impact in the social network by just using the first 5% of the information of an event's lifetime evolution. This strongly implies that high-activity events are naturally prioritized collectively by the social network, engaging users early on, way before they are brought to the mainstream audience.
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Osorio-Guarín, Jaime A; Berdugo-Cely, Jhon; Coronado, Roberto Antonio; Zapata, Yeny Patricia; Quintero, Constanza; Gallego-Sánchez, Gerardo; Yockteng, Roxana
2017-01-01
Beans of the species Theobroma cacao L., also known as cacao, are the raw material to produce chocolate. Colombian cacao has been classified as a fine flavor cacao that represents the 5% of cacao world's production. Colombian genetic resources from this species are conserved in ex situ and in-field germplasm banks, since T. cacao has recalcitrant seeds to desication and long-term storage. Currently, the collection of T. cacao of the Colombian Corporation of Agricultural Research (CORPOICA) has approximately 700 germplasm accessions. We conducted a molecular analysis of Corpoica's cacao collection and a morphological characterization of some accessions with the goal to study its genetic diversity and population structure and, to select interesting accessions for the cacao's breeding program. Phenotypic evaluation was performed based on 18 morphological traits and 4 biochemical traits. PCA analysis of morphological traits explained 60.6% of the total variation in seven components and 100% of the total variation of biochemical traits in four components, grouping the collection in 4 clusters for both variables. We explored 565 accessions from Corpoica's germplasm and 252 accessions from reference populations using 96 single nucleotide polymorphism (SNP) molecular markers. Molecular patterns of cacao Corpoica's collection were obtained amplifying specific alleles in a Fluidigm platform that used integrated circuits of fluids. Corpoica's collection showed highest genetic diversity [Expected Heterozygosity ( H E = 0.314), Observed Heterozygosity ( H O = 0.353)] that is reduced when reference populations were included in the dataset ( H E = 0.294, H O = 0.261). The collection was divided into four clusters based on population structure analysis. Cacao accessions from distinct groups showed some taxonomic concordance and reflected their geographic origins. For instance, accessions classified as Criollo were clearly differentiated in one group and we identified two new Colombian genetic groups. Using a number of allelic variations based on 87 SNP markers and 22 different morphological/biochemical traits, a core collection with a total of 232 accessions was selected as a primary genetic resource for cacao breeders.
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Osorio-Guarín, Jaime A.; Berdugo-Cely, Jhon; Coronado, Roberto Antonio; Zapata, Yeny Patricia; Quintero, Constanza; Gallego-Sánchez, Gerardo; Yockteng, Roxana
2017-01-01
Beans of the species Theobroma cacao L., also known as cacao, are the raw material to produce chocolate. Colombian cacao has been classified as a fine flavor cacao that represents the 5% of cacao world’s production. Colombian genetic resources from this species are conserved in ex situ and in-field germplasm banks, since T. cacao has recalcitrant seeds to desication and long-term storage. Currently, the collection of T. cacao of the Colombian Corporation of Agricultural Research (CORPOICA) has approximately 700 germplasm accessions. We conducted a molecular analysis of Corpoica’s cacao collection and a morphological characterization of some accessions with the goal to study its genetic diversity and population structure and, to select interesting accessions for the cacao’s breeding program. Phenotypic evaluation was performed based on 18 morphological traits and 4 biochemical traits. PCA analysis of morphological traits explained 60.6% of the total variation in seven components and 100% of the total variation of biochemical traits in four components, grouping the collection in 4 clusters for both variables. We explored 565 accessions from Corpoica’s germplasm and 252 accessions from reference populations using 96 single nucleotide polymorphism (SNP) molecular markers. Molecular patterns of cacao Corpoica’s collection were obtained amplifying specific alleles in a Fluidigm platform that used integrated circuits of fluids. Corpoica’s collection showed highest genetic diversity [Expected Heterozygosity (HE = 0.314), Observed Heterozygosity (HO = 0.353)] that is reduced when reference populations were included in the dataset (HE = 0.294, HO = 0.261). The collection was divided into four clusters based on population structure analysis. Cacao accessions from distinct groups showed some taxonomic concordance and reflected their geographic origins. For instance, accessions classified as Criollo were clearly differentiated in one group and we identified two new Colombian genetic groups. Using a number of allelic variations based on 87 SNP markers and 22 different morphological/biochemical traits, a core collection with a total of 232 accessions was selected as a primary genetic resource for cacao breeders. PMID:29209353
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Muhlbaier, Michael D; Topalis, Apostolos; Polikar, Robi
2009-01-01
We have previously introduced an incremental learning algorithm Learn(++), which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn(++) suffers from an inherent "outvoting" problem when asked to learn a new class omega(new) introduced by a subsequent data set, as earlier classifiers not trained on this class are guaranteed to misclassify omega(new) instances. The collective votes of earlier classifiers, for an inevitably incorrect decision, then outweigh the votes of the new classifiers' correct decision on omega(new) instances--until there are enough new classifiers to counteract the unfair outvoting. This forces Learn(++) to generate an unnecessarily large number of classifiers. This paper describes Learn(++).NC, specifically designed for efficient incremental learning of multiple new classes using significantly fewer classifiers. To do so, Learn (++).NC introduces dynamically weighted consult and vote (DW-CAV), a novel voting mechanism for combining classifiers: individual classifiers consult with each other to determine which ones are most qualified to classify a given instance, and decide how much weight, if any, each classifier's decision should carry. Experiments on real-world problems indicate that the new algorithm performs remarkably well with substantially fewer classifiers, not only as compared to its predecessor Learn(++), but also as compared to several other algorithms recently proposed for similar problems.
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Zonta, Marco Antonio; Velame, Fernanda; Gema, Samara; Filassi, Jose Roberto; Longatto-Filho, Adhemar
2014-01-01
Background Breast cancer is the second cause of death in women worldwide. The spontaneous breast nipple discharge may contain cells that can be analyzed for malignancy. Halo® Mamo Cyto Test (HMCT) was recently developed as an automated system indicated to aspirate cells from the breast ducts. The objective of this study was to standardize the methodology of sampling and sample preparation of nipple discharge obtained by the automated method Halo breast test and perform cytological evaluation in samples preserved in liquid medium (SurePath™). Methods We analyzed 564 nipple fluid samples, from women between 20 and 85 years old, without history of breast disease and neoplasia, no pregnancy, and without gynecologic medical history, collected by HMCT method and preserved in two different vials with solutions for transport. Results From 306 nipple fluid samples from method 1, 199 (65%) were classified as unsatisfactory (class 0), 104 (34%) samples were classified as benign findings (class II), and three (1%) were classified as undetermined to neoplastic cells (class III). From 258 samples analyzed in method 2, 127 (49%) were classified as class 0, 124 (48%) were classified as class II, and seven (2%) were classified as class III. Conclusion Our study suggests an improvement in the quality and quantity of cellular samples when the association of the two methodologies is performed, Halo breast test and the method in liquid medium. PMID:29147397
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De Cesare, Alessandra; Parisi, Antonio; Mioni, Renzo; Comin, Damiano; Lucchi, Alex; Manfreda, Gerardo
2017-03-01
Rabbit meat has outstanding dietetic and nutritional properties. However, few data on microbiological hazards associated with rabbit productions are available. In this study, the presence of Listeria monocytogenes was determined in 430 rabbit carcasses, 256 rabbit meat cuts and products, and 599 environmental sponges collected from four Italian rabbit slaughterhouses over a period of 1 year. Prevalence of L. monocytogenes among the 1285 rabbit meat and environmental samples was 11%, with statistically significant differences between slaughterhouses. The highest prevalence (33.6%) was observed in rabbit meat cuts and products; the majority of positive environmental samples were collected from conveyor belts. Overall, 27.9% and 14.3% of rabbit cuts and carcasses, respectively, had L. monocytogenes counts higher than 1 colony-forming unit (CFU)/10 g. A selection of 123 isolates from positive samples was genotyped and serotyped to determine genetic profiles and diversity among L. monocytogenes isolates contaminating different slaughterhouses and classes of products investigated. Discriminatory power and concordance among the results obtained using multilocus variable-number tandem-repeat analysis (MLVA), multilocus sequence typing (MLST), pulsed-field gel electrophoresis (PFGE), automated EcoRI ribotyping, and serotyping were assessed. The isolates selected for typing were classified into serotypes 1/2a (52.8%), 1/2c (32.5%), and 1/2b (14.6%). The majority of the isolates were classified as ST14 (34.1%), ST9 (35.5%), ST121 (17.9%), and ST224 (14.6%). The greatest discriminatory power was observed with the MLVA typing, followed by MLST, PFGE, and ribotyping. The best bidirectional concordance was achieved between PFGE and MLST. There was 100% correlation between both MLST and MLVA with serotype. Moreover, a high unidirectional correspondence was observed between MLVA and both MLST and PFGE, as well as between PFGE and both MLST and serotyping. The results of this study show for the first time in Italy prevalence and genetic profiles of L. monocytogenes isolated in rabbit products and slaughterhouses.
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On the design of classifiers for crop inventories
NASA Technical Reports Server (NTRS)
Heydorn, R. P.; Takacs, H. C.
1986-01-01
Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.
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NASA Astrophysics Data System (ADS)
Salem, Talaat A.; Omar, Mohie El Din M.; El Gammal, H. A. A.
2017-11-01
Alternative clean water resources are needed in Egypt to face the current water shortage and water quality deterioration. Therefore, this research investigates the suitability of harvesting fog and rain water for irrigation using a pilot fog collector for water quantity, water quality, and economic aspects. A pilot fog collector was installed at one location at Delta Barrage, Egypt. Freeze liquid nitrogen was fixed at the back of the fiberglass sheet to increase the condensation rate. The experiment was conducted during the period from November 2015 to February 2016. In general, all physicochemical variables are observed with higher values in the majority of fog than rain water. The fog is assumed to contain higher concentrations of anthropogenic emissions. TDS in both waters collected are less than 700 mg/l at sodium content less than 60%, classifying these waters as good for various plants under most conditions. In addition, SAR calculated values are less than 3.0 in each of fog and rain water, which proves the water suitability for all irrigated agriculture. Al and Fe concentrations were found common in all samples with values less than the permissible limits of the guidelines. These metals originate from soil material, ash and metal surfaces. The sensitive heavy metals (Cd and Pb) were within the permissible limits of the guideline in fog water, indicating this water is suitable for irrigation. On the contrary, rain water that has heavy metals is not permitted in irrigation water as per the Egyptian law. As per WQI, the rain water is classified as good quality while fog is classified as medium quality. Regarding the water quantity, a significant increase in the harvested fog quantity was observed after cooling the collector surface with freeze liquid nitrogen. The current fog collector produced the lowest water quantity among different fog collectors worldwide. However, these comparative results confirmed that quantity is different from one location to another worldwide even in the same country. The cost of the unit water volume of harvested water by the current pilot collector is relatively low among different collectors worldwide. This study proves that fog harvesting in Egypt is feasible using the current pilot collector in terms of water quantity, water quality, and economy. But it recommends collection of fog at various locations and times, since both water quantity and water quality are variable in time and space. It is more or less viable solution to meet the shortage of water in Egypt.
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NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet; Kabiri, Keivan
2012-07-01
This paper describes an assessment of coral reef mapping using multi sensor satellite images such as Landsat ETM, SPOT and IKONOS images for Tioman Island, Malaysia. The study area is known to be one of the best Islands in South East Asia for its unique collection of diversified coral reefs and serves host to thousands of tourists every year. For the coral reef identification, classification and analysis, Landsat ETM, SPOT and IKONOS images were collected processed and classified using hierarchical classification schemes. At first, Decision tree classification method was implemented to separate three main land cover classes i.e. water, rural and vegetation and then maximum likelihood supervised classification method was used to classify these main classes. The accuracy of the classification result is evaluated by a separated test sample set, which is selected based on the fieldwork survey and view interpretation from IKONOS image. Few types of ancillary data in used are: (a) DGPS ground control points; (b) Water quality parameters measured by Hydrolab DS4a; (c) Sea-bed substrates spectrum measured by Unispec and; (d) Landcover observation photos along Tioman island coastal area. The overall accuracy of the final classification result obtained was 92.25% with the kappa coefficient is 0.8940. Key words: Coral reef, Multi-spectral Segmentation, Pixel-Based Classification, Decision Tree, Tioman Island
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Dess, Brian W; Cardarelli, John; Thomas, Mark J; Stapleton, Jeff; Kroutil, Robert T; Miller, David; Curry, Timothy; Small, Gary W
2018-03-08
A generalized methodology was developed for automating the detection of radioisotopes from gamma-ray spectra collected from an aircraft platform using sodium-iodide detectors. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, multivariate classification models based on nonparametric linear discriminant analysis were developed for application to spectra that were preprocessed through a combination of altitude-based scaling and digital filtering. Training sets of spectra for use in building classification models were assembled from a combination of background spectra collected in the field and synthesized spectra obtained by superimposing laboratory-collected spectra of target radioisotopes onto field backgrounds. This approach eliminated the need for field experimentation with radioactive sources for use in building classification models. Through a bi-Gaussian modeling procedure, the discriminant scores that served as the outputs from the classification models were related to associated confidence levels. This provided an easily interpreted result regarding the presence or absence of the signature of a specific radioisotope in each collected spectrum. Through the use of this approach, classifiers were built for cesium-137 ( 137 Cs) and cobalt-60 ( 60 Co), two radioisotopes that are of interest in airborne radiological monitoring applications. The optimized classifiers were tested with field data collected from a set of six geographically diverse sites, three of which contained either 137 Cs, 60 Co, or both. When the optimized classification models were applied, the overall percentages of correct classifications for spectra collected at these sites were 99.9 and 97.9% for the 60 Co and 137 Cs classifiers, respectively. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.
Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting
2014-01-01
This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.
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Estimation from incomplete multinomial data. Ph.D. Thesis - Harvard Univ.
NASA Technical Reports Server (NTRS)
Credeur, K. R.
1978-01-01
The vector of multinomial cell probabilities was estimated from incomplete data, incomplete in that it contains partially classified observations. Each such partially classified observation was observed to fall in one of two or more selected categories but was not classified further into a single category. The data were assumed to be incomplete at random. The estimation criterion was minimization of risk for quadratic loss. The estimators were the classical maximum likelihood estimate, the Bayesian posterior mode, and the posterior mean. An approximation was developed for the posterior mean. The Dirichlet, the conjugate prior for the multinomial distribution, was assumed for the prior distribution.
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Ensemble LUT classification for degraded document enhancement
NASA Astrophysics Data System (ADS)
Obafemi-Ajayi, Tayo; Agam, Gady; Frieder, Ophir
2008-01-01
The fast evolution of scanning and computing technologies have led to the creation of large collections of scanned paper documents. Examples of such collections include historical collections, legal depositories, medical archives, and business archives. Moreover, in many situations such as legal litigation and security investigations scanned collections are being used to facilitate systematic exploration of the data. It is almost always the case that scanned documents suffer from some form of degradation. Large degradations make documents hard to read and substantially deteriorate the performance of automated document processing systems. Enhancement of degraded document images is normally performed assuming global degradation models. When the degradation is large, global degradation models do not perform well. In contrast, we propose to estimate local degradation models and use them in enhancing degraded document images. Using a semi-automated enhancement system we have labeled a subset of the Frieder diaries collection.1 This labeled subset was then used to train an ensemble classifier. The component classifiers are based on lookup tables (LUT) in conjunction with the approximated nearest neighbor algorithm. The resulting algorithm is highly effcient. Experimental evaluation results are provided using the Frieder diaries collection.1
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United Kingdom Infrared Telescope's Spectrograph Observations of Human-Made Space Objects
NASA Technical Reports Server (NTRS)
Buckalew, Brent; Abercromby, Kira; Lederer, Susan; Cowardin, Heather; Frith, James
2017-01-01
Presented here are the results of the United Kingdom Infrared Telescope (UKIRT) spectral observations of human-made space objects taken from 2014 to 2015. The data collected using the UKIRT 1-5 micron Imager Spectrometer (UIST) cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 18 different orbiting objects at or near geosynchronous orbit (GEO). Two of the objects are controlled spacecraft, twelve are non-controlled spacecraft, one is a rocket body, and three are cataloged as debris. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials; thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons and silicon. The spacecraft, both the controlled and non-controlled, show distinct features due to the presence of solar panels whereas the rocket bodies do not. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give well-correlated indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. A comparison conducted between objects observed previously with the NASA Infrared Telescope Facility (IRTF) shows similar materials and trends from the two telescopes and different times. However, based on the current state of the model, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.
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International perception of lung sounds: a comparison of classification across some European borders
Aviles-Solis, Juan Carlos; Vanbelle, Sophie; Halvorsen, Peder A; Francis, Nick; Cals, Jochen W L; Andreeva, Elena A; Marques, Alda; Piirilä, Päivi; Pasterkamp, Hans; Melbye, Hasse
2017-01-01
Introduction Lung auscultation is helpful in the diagnosis of lung and heart diseases; however, the diagnostic value of lung sounds may be questioned due to interobserver variation. This situation may also impair clinical research in this area to generate evidence-based knowledge about the role that chest auscultation has in a modern clinical setting. The recording and visual display of lung sounds is a method that is both repeatable and feasible to use in large samples, and the aim of this study was to evaluate interobserver agreement using this method. Methods With a microphone in a stethoscope tube, we collected digital recordings of lung sounds from six sites on the chest surface in 20 subjects aged 40 years or older with and without lung and heart diseases. A total of 120 recordings and their spectrograms were independently classified by 28 observers from seven different countries. We employed absolute agreement and kappa coefficients to explore interobserver agreement in classifying crackles and wheezes within and between subgroups of four observers. Results When evaluating agreement on crackles (inspiratory or expiratory) in each subgroup, observers agreed on between 65% and 87% of the cases. Conger’s kappa ranged from 0.20 to 0.58 and four out of seven groups reached a kappa of ≥0.49. In the classification of wheezes, we observed a probability of agreement between 69% and 99.6% and kappa values from 0.09 to 0.97. Four out of seven groups reached a kappa ≥0.62. Conclusions The kappa values we observed in our study ranged widely but, when addressing its limitations, we find the method of recording and presenting lung sounds with spectrograms sufficient for both clinic and research. Standardisation of terminology across countries would improve international communication on lung auscultation findings. PMID:29435344
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Aviles-Solis, Juan Carlos; Vanbelle, Sophie; Halvorsen, Peder A; Francis, Nick; Cals, Jochen W L; Andreeva, Elena A; Marques, Alda; Piirilä, Päivi; Pasterkamp, Hans; Melbye, Hasse
2017-01-01
Lung auscultation is helpful in the diagnosis of lung and heart diseases; however, the diagnostic value of lung sounds may be questioned due to interobserver variation. This situation may also impair clinical research in this area to generate evidence-based knowledge about the role that chest auscultation has in a modern clinical setting. The recording and visual display of lung sounds is a method that is both repeatable and feasible to use in large samples, and the aim of this study was to evaluate interobserver agreement using this method. With a microphone in a stethoscope tube, we collected digital recordings of lung sounds from six sites on the chest surface in 20 subjects aged 40 years or older with and without lung and heart diseases. A total of 120 recordings and their spectrograms were independently classified by 28 observers from seven different countries. We employed absolute agreement and kappa coefficients to explore interobserver agreement in classifying crackles and wheezes within and between subgroups of four observers. When evaluating agreement on crackles (inspiratory or expiratory) in each subgroup, observers agreed on between 65% and 87% of the cases. Conger's kappa ranged from 0.20 to 0.58 and four out of seven groups reached a kappa of ≥0.49. In the classification of wheezes, we observed a probability of agreement between 69% and 99.6% and kappa values from 0.09 to 0.97. Four out of seven groups reached a kappa ≥0.62. The kappa values we observed in our study ranged widely but, when addressing its limitations, we find the method of recording and presenting lung sounds with spectrograms sufficient for both clinic and research. Standardisation of terminology across countries would improve international communication on lung auscultation findings.
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Rodriguez, Edward K; Kwon, John Y; Herder, Lindsay M; Appleton, Paul T
2013-11-01
Our aim was to assess whether the Lauge-Hansen (LH) and the Muller AO classification systems for ankle fractures radiographically correlate with in vivo injuries based on observed mechanism of injury. Videos of potential study candidates were reviewed on YouTube.com. Individuals were recruited for participation if the video could be classified by injury mechanism with a high likelihood of sustaining an ankle fracture. Corresponding injury radiographs were obtained. Injury mechanism was classified using the LH system as supination/external rotation (SER), supination/adduction (SAD), pronation/external rotation (PER), or pronation/abduction (PAB). Corresponding radiographs were classified by the LH system and the AO system. Thirty injury videos with their corresponding radiographs were collected. Of the video clips reviewed, 16 had SAD mechanisms and 14 had PER mechanisms. There were 26 ankle fractures, 3 nonfractures, and 1 subtalar dislocation. Twelve fractures with SAD mechanisms had corresponding SAD fracture patterns. Five PER mechanisms had PER fracture patterns. Eight PER mechanisms had SER fracture patterns and 1 had SAD fracture pattern. When the AO classification was used, all 12 SAD type injuries had a 44A type fracture, whereas the 14 PER injuries resulted in nine 44B fractures, two 44C fractures, and three 43A fractures. When injury video clips of ankle fractures were matched to their corresponding radiographs, the LH system was 65% (17/26) consistent in predicting fracture patterns from the deforming injury mechanism. When the AO classification system was used, consistency was 81% (21/26). The AO classification, despite its development as a purely radiographic system, correlated with in vivo injuries, as based on observed mechanism of injury, more closely than did the LH system. Level IV, case series.
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Influence of season and type of restaurants on sashimi microbiota.
Miguéis, S; Moura, A T; Saraiva, C; Esteves, A
2016-10-01
In recent years, an increase in the consumption of Japanese food in European countries has been verified, including in Portugal. These specialities made with raw fish, typical Japanese meals, have been prepared in typical and on non-typical restaurants, and represent a challenge to risk analysis on HACCP plans. The aim of this study was to evaluate the influence of the type of restaurant, season and type of fish used on sashimi microbiota. Sashimi samples (n = 114) were directly collected from 23 sushi restaurants and were classified as Winter and Summer Samples. They were also categorized according to the type of restaurant where they were obtained: as typical or non-typical. The samples were processed using international standards procedures. A middling seasonality influence was observed in microbiota using mesophilic aerobic bacteria, psychrotrophic microorganisms, Lactic acid bacteria, Pseudomonas spp., H 2 S positive bacteria, mould and Bacillus cereus counts parameters. During the Summer Season, samples classified as unacceptable or potentially Hazardous were observed. Non-typical restaurants had the most cases of Unacceptable/potentially hazardous samples 83.33%. These unacceptable results were obtained as a result of high values of pathogenic bacteria like Listeria monocytogenes and Staphylococcus aureus No significant differences were observed on microbiota counts from different fish species. The need to implement more accurate food safety systems was quite evident, especially in the warmer season, as well as in restaurants where other kinds of food, apart from Japanese meals, was prepared. © Crown copyright 2016.
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NASA Technical Reports Server (NTRS)
Calvin, M.
1975-01-01
The insoluble organic materials present in the algal mats at Laguna Mormona, Baja California were studied. A series of six identical sediments collected from Mono lake which were stored under different conditions was investigated to see if any changes are observed in the lipid distribution patterns as a result of differences in sample storage conditions. Bacteria strains from Mono Lake sediments were cultured in bulk quantities and the sterol fractions from them were isolated and analyzed. Results add further support to the utility of the sterols as a chemotaxonomical tool in distinguishing and classifying these bacteria.
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NASA Technical Reports Server (NTRS)
Dillman, R. D.; Eav, B. B.; Baldwin, R. R.
1984-01-01
The Office of Space and Terrestrial Applications-3 payload, scheduled for flight on STS Mission 17, consists of four earth-observation experiments. The Feature Identification and Location Experiment-1 will spectrally sense and numerically classify the earth's surface into water, vegetation, bare earth, and ice/snow/cloud-cover, by means of spectra ratio techniques. The Measurement of Atmospheric Pollution from Satellite experiment will measure CO distribution in the middle and upper troposphere. The Imaging Camera-B uses side-looking SAR to create two-dimensional images of the earth's surface. The Large Format Camera/Attitude Reference System will collect metric quality color, color-IR, and black-and-white photographs for topographic mapping.
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Kiranyaz, Serkan; Mäkinen, Toni; Gabbouj, Moncef
2012-10-01
In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a "Divide and Conquer" approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Albert, Mark V; Azeze, Yohannes; Courtois, Michael; Jayaraman, Arun
2017-02-06
Although commercially available activity trackers can aid in tracking therapy and recovery of patients, most devices perform poorly for patients with irregular movement patterns. Standard machine learning techniques can be applied on recorded accelerometer signals in order to classify the activities of ambulatory subjects with incomplete spinal cord injury in a way that is specific to this population and the location of the recording-at home or in the clinic. Subjects were instructed to perform a standardized set of movements while wearing a waist-worn accelerometer in the clinic and at-home. Activities included lying, sitting, standing, walking, wheeling, and stair climbing. Multiple classifiers and validation methods were used to quantify the ability of the machine learning techniques to distinguish the activities recorded in-lab or at-home. In the lab, classifiers trained and tested using within-subject cross-validation provided an accuracy of 91.6%. When the classifier was trained on data collected in the lab but tested on at home data, the accuracy fell to 54.6% indicating distinct movement patterns between locations. However, the accuracy of the at-home classifications, when training the classifier with at-home data, improved to 85.9%. Individuals with unique movement patterns can benefit from using tailored activity recognition algorithms easily implemented using modern machine learning methods on collected movement data.
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Comparison of US Antarctic Meteorite Collection to Other Cold and Hot Deserts and Modern Falls
NASA Technical Reports Server (NTRS)
McBride, K. M.; Righter, K.
2010-01-01
The US Antarctic meteorite collection has grown close to 18,000 specimens, over 16,000 of which have been classified. Because of this growth, the parallel growth of Antarctic meteorite collections by Japan and China, and also the hot desert collections (from Africa and Australia), we will update the statistical overview of the US collection (last done in 1990 [1]), and make comparisons to other collections and modern falls.
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78 FR 43181 - Proposed collection; comment request
Federal Register 2010, 2011, 2012, 2013, 2014
2013-07-19
... and guidance, and to assist with investigations into possible violations of NGA rules and regulations, including the possible loss or compromise of classified or protected NGA information. Affected Public... Information Collection Respondents are NGA employees, military and contractor personnel who provide personal...
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Kumar, Keshav; Cava, Felipe
2018-04-10
In the present work, Principal coordinate analysis (PCoA) is introduced to develop a robust model to classify the chromatographic data sets of peptidoglycan sample. PcoA captures the heterogeneity present in the data sets by using the dissimilarity matrix as input. Thus, in principle, it can even capture the subtle differences in the bacterial peptidoglycan composition and can provide a more robust and fast approach for classifying the bacterial collection and identifying the novel cell wall targets for further biological and clinical studies. The utility of the proposed approach is successfully demonstrated by analysing the two different kind of bacterial collections. The first set comprised of peptidoglycan sample belonging to different subclasses of Alphaproteobacteria. Whereas, the second set that is relatively more intricate for the chemometric analysis consist of different wild type Vibrio Cholerae and its mutants having subtle differences in their peptidoglycan composition. The present work clearly proposes a useful approach that can classify the chromatographic data sets of chromatographic peptidoglycan samples having subtle differences. Furthermore, present work clearly suggest that PCoA can be a method of choice in any data analysis workflow. Copyright © 2018 Elsevier Inc. All rights reserved.
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MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging
NASA Astrophysics Data System (ADS)
Chen, Lei; Kamel, Mohamed S.
2016-01-01
In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.
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Classification of deadlift biomechanics with wearable inertial measurement units.
O'Reilly, Martin A; Whelan, Darragh F; Ward, Tomas E; Delahunt, Eamonn; Caulfield, Brian M
2017-06-14
The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Federal Register 2010, 2011, 2012, 2013, 2014
2012-10-30
...-0015] Agency Information Collection Activities: Immigrant Petition for Alien Worker, Form I-140... Form/Collection: Immigrant Petition for Alien Worker. (3) Agency form number, if any, and the... information furnished on Form I-140 will be used by USCIS to classify aliens under sections 203(b)(1), 203(b...
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Automated Assessment of Dynamic Knee Valgus and Risk of Knee Injury During the Single Leg Squat
Lee, Alexander; Raina, Sachin; Kulić, Dana
2017-01-01
Many clinical assessment protocols of the lower limb rely on the evaluation of functional movement tests such as the single leg squat (SLS), which are often assessed visually. Visual assessment is subjective and depends on the experience of the clinician. In this paper, an inertial measurement unit (IMU)-based method for automated assessment of squat quality is proposed to provide clinicians with a quantitative measure of SLS performance. A set of three IMUs was used to estimate the joint angles, velocities, and accelerations of the squatting leg. Statistical time domain features were generated from these measurements. The most informative features were used for classifier training. A data set of SLS performed by healthy participants was collected and labeled by three expert clinical raters using two different labeling criteria: “observed amount of knee valgus” and “overall risk of injury”. The results showed that both flexion at the hip and knee, as well as hip and ankle internal rotation are discriminative features, and that participants with “poor” squats bend the hip and knee less than those with better squat performance. Furthermore, improved classification performance is achieved for females by training separate classifiers stratified by gender. Classification results showed excellent accuracy, 95.7 % for classifying squat quality as “poor” or “good” and 94.6% for differentiating between high and no risk of injury. PMID:29204327
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NASA Astrophysics Data System (ADS)
Navratil, Peter; Wilps, Hans
2013-01-01
Three different object-based image classification techniques are applied to high-resolution satellite data for the mapping of the habitats of Asian migratory locust (Locusta migratoria migratoria) in the southern Aral Sea basin, Uzbekistan. A set of panchromatic and multispectral Système Pour l'Observation de la Terre-5 satellite images was spectrally enhanced by normalized difference vegetation index and tasseled cap transformation and segmented into image objects, which were then classified by three different classification approaches: a rule-based hierarchical fuzzy threshold (HFT) classification method was compared to a supervised nearest neighbor classifier and classification tree analysis by the quick, unbiased, efficient statistical trees algorithm. Special emphasis was laid on the discrimination of locust feeding and breeding habitats due to the significance of this discrimination for practical locust control. Field data on vegetation and land cover, collected at the time of satellite image acquisition, was used to evaluate classification accuracy. The results show that a robust HFT classifier outperformed the two automated procedures by 13% overall accuracy. The classification method allowed a reliable discrimination of locust feeding and breeding habitats, which is of significant importance for the application of the resulting data for an economically and environmentally sound control of locust pests because exact spatial knowledge on the habitat types allows a more effective surveying and use of pesticides.
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Textual and visual content-based anti-phishing: a Bayesian approach.
Zhang, Haijun; Liu, Gang; Chow, Tommy W S; Liu, Wenyin
2011-10-01
A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases. © 2011 IEEE
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Portell, Mariona; Anguera, M Teresa; Hernández-Mendo, Antonio; Jonsson, Gudberg K
2015-01-01
Contextual factors are crucial for evaluative research in psychology, as they provide insights into what works, for whom, in what circumstances, in what respects, and why. Studying behavior in context, however, poses numerous methodological challenges. Although a comprehensive framework for classifying methods seeking to quantify biopsychosocial aspects in everyday contexts was recently proposed, this framework does not contemplate contributions from observational methodology. The aim of this paper is to justify and propose a more general framework that includes observational methodology approaches. Our analysis is rooted in two general concepts: ecological validity and methodological complementarity. We performed a narrative review of the literature on research methods and techniques for studying daily life and describe their shared properties and requirements (collection of data in real time, on repeated occasions, and in natural settings) and classification criteria (eg, variables of interest and level of participant involvement in the data collection process). We provide several examples that illustrate why, despite their higher costs, studies of behavior and experience in everyday contexts offer insights that complement findings provided by other methodological approaches. We urge that observational methodology be included in classifications of research methods and techniques for studying everyday behavior and advocate a renewed commitment to prioritizing ecological validity in behavioral research seeking to quantify biopsychosocial aspects. PMID:26089708
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Dante, V; Del Giudice, P; Mattia, M
2001-01-01
We review a series of implementations of electronic devices aiming at imitating to some extent structure and function of simple neural systems, with particular emphasis on communication issues. We first provide a short overview of general features of such "neuromorphic" devices and the implications of setting up "tests" for them. We then review the developments directly related to our work at the Istituto Superiore di Sanità (ISS): a pilot electronic neural network implementing a simple classifier, autonomously developing internal representations of incoming stimuli; an output network, collecting information from the previous classifier and extracting the relevant part to be forwarded to the observer; an analog, VLSI (very large scale integration) neural chip implementing a recurrent network of spiking neurons and plastic synapses, and the test setup for it; a board designed to interface the standard PCI (peripheral component interconnect) bus of a PC with a special purpose, asynchronous bus for communication among neuromorphic chips; a short and preliminary account of an application-oriented device, taking advantage of the above communication infrastructure.
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Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method
NASA Astrophysics Data System (ADS)
Nurdiansyah, Yanuar; Bukhori, Saiful; Hidayat, Rahmad
2018-04-01
There are many ways of implementing the use of sentiments often found in documents; one of which is the sentiments found on the product or service reviews. It is so important to be able to process and extract textual data from the documents. Therefore, we propose a system that is able to classify sentiments from review documents into two classes: positive sentiment and negative sentiment. We use Naive Bayes Classifier method in this document classification system that we build. We choose Movienthusiast, a movie reviews in Bahasa Indonesia website as the source of our review documents. From there, we were able to collect 1201 movie reviews: 783 positive reviews and 418 negative reviews that we use as the dataset for this machine learning classifier. The classifying accuracy yields an average of 88.37% from five times of accuracy measuring attempts using aforementioned dataset.
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A survey of decision tree classifier methodology
NASA Technical Reports Server (NTRS)
Safavian, S. R.; Landgrebe, David
1991-01-01
Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.
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A survey of decision tree classifier methodology
NASA Technical Reports Server (NTRS)
Safavian, S. Rasoul; Landgrebe, David
1990-01-01
Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.
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Reconstructing the Prostate Cancer Transcriptional Regulatory Network
2010-07-01
the Medical Scientist Training Program. The funders had no role in study design , data collection and analysis , decision to publish, or preparation of...reverse analysis , building a cell line subtype classifier to classify 86 breast tumors (from the original Stanford/Norway study defining the five tumor...Army position, policy or decision unless so designated by other documentation. REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public
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Over 5,600 Japanese collection of Antarctic meteorites: Recoveries, curation and distribution
NASA Technical Reports Server (NTRS)
Yanai, K.; Kojima, H.
1986-01-01
The history of recovery of meteorite fragments in the Yamato Mountains, Allan Hills, and Victoria Land, Antarctica is reviewed. The Japanese collection of Antarctic meteorites were numbered, weighed, photographed, identified, and classified. Sample distribution of the Japanese Antarctic meteorites is described.
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Use of visible and near-infrared spectroscopy to predict pork longissimus lean color stability.
King, D A; Shackelford, S D; Wheeler, T L
2011-12-01
This study evaluated the use of visible and near-infrared (VISNIR) spectroscopy to predict lean color stability in pork loin chops. Spectra were collected immediately after and approximately 1 h after rib removal on 1,208 loins. Loins were aged for 14 d before a 2.54-cm chop was placed in simulated retail display. Spectra were collected on aged loins immediately after removal from the vacuum package and on chops 10 min after cutting. Instrumental color measurements [L*, a*, b*, hue angle, chroma, and E (overall color change)] were determined on d 0, 1, 7, 11, and 14 of display. Principal components analysis of display d 0 and 14 values of these traits identified a factor (first principal component; PC1) explaining 67% of the variance that was related to color change. Partial least squares regression was used to develop 3 models to predict PC1 values by using VISNIR spectra collected in the plant, on aged loins, and on chops. Loins with predicted PC1 values less than 0 were classified as having a stable color, whereas values greater than 0 were classified as having a labile lean color. Loins classified as stable by the in-plant model had smaller (P < 0.05) L* values than those classified as labile. Hue angle and ΔE values were less (P < 0.05) and a* and chroma values were greater (P < 0.05) after d 7 of display in loins predicted to have a stable color than in loins predicted to have a labile lean color. Similarly, chops from loins classified as stable using the aged loin model had smaller (P < 0.05) L* values than those from loins classified as labile. Furthermore, loins predicted to be stable had smaller (P < 0.05) hue angle and ΔE values and greater (P < 0.05) a* and chroma values after d 7 of display than did loins predicted to be labile. Results for the chop model were similar to those from the 2 loin models. Chops predicted to have a stable lean color had smaller (P < 0.05) L* values than did those predicted to have a labile lean color. Chops classified as stable had smaller (P < 0.05) hue angle and ΔE values and greater (P < 0.05) a* and chroma values after d 7 of display compared with chops classified as labile. All 3 models effectively segregated chops based on color stability, particularly with regard to redness. Regardless of the model being used, d 14 display values for a*, hue angle, and ΔE in loins classified as stable were similar to the d 7 values of loins classified as labile. Thus, these results suggest that VISNIR spectroscopy would be an effective technology for sorting pork loins with regard to lean color stability.
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A procedure for classifying textural facies in gravel-bed rivers
John M. Buffington; David R. Montgomery
1999-01-01
Textural patches (i.e., grain-size facies) are commonly observed in gravel-bed channels and are of significance for both physical and biological processes at subreach scales. We present a general framework for classifying textural patches that allows modification for particular study goals, while maintaining a basic degree of standardization. Textures are classified...
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The behavior of a macroscopic granular material in vortex flow
NASA Astrophysics Data System (ADS)
Nishikawa, Asami
A granular material is defined as a collection of discrete particles such as powder and grain. Granular materials display a large number of complex behaviors. In this project, the behavior of macroscopic granular materials under tornado-like vortex airflow, with varying airflow velocity, was observed and studied. The experimental system was composed of a 9.20-cm inner diameter acrylic pipe with a metal mesh bottom holding the particles, a PVC duct, and an airflow source controlled by a variable auto-transformer, and a power-meter. A fixed fan blade was attached to the duct's inner wall to create a tornado-like vortex airflow from straight flow. As the airflow velocity was increased gradually, the behavior of a set of same-diameter granular materials was observed. The observed behaviors were classified into six phases based on the macroscopic mechanical dynamics. Through this project, we gained insights on the significant parameters for a computer simulation of a similar system by Heath Rice [5]. Comparing computationally and experimentally observed phase diagrams, we can see similar structure. The experimental observations showed the effect of initial arrangement of particles on the phase transitions.
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Travel Mode Detection with Varying Smartphone Data Collection Frequencies
Shafique, Muhammad Awais; Hato, Eiji
2016-01-01
Smartphones are becoming increasingly popular day-by-day. Modern smartphones are more than just calling devices. They incorporate a number of high-end sensors that provide many new dimensions to smartphone experience. The use of smartphones, however, can be extended from the usual telecommunication field to applications in other specialized fields including transportation. Sensors embedded in the smartphones like GPS, accelerometer and gyroscope can collect data passively, which in turn can be processed to infer the travel mode of the smartphone user. This will solve most of the shortcomings associated with conventional travel survey methods including biased response, no response, erroneous time recording, etc. The current study uses the sensors’ data collected by smartphones to extract nine features for classification. Variables including data frequency, moving window size and proportion of data to be used for training, are dealt with to achieve better results. Random forest is used to classify the smartphone data among six modes. An overall accuracy of 99.96% is achieved, with no mode less than 99.8% for data collected at 10 Hz frequency. The accuracy is observed to decrease with decrease in data frequency, but at the same time the computation time also decreases. PMID:27213380
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Shishkin, Sergei L.; Nuzhdin, Yuri O.; Svirin, Evgeny P.; Trofimov, Alexander G.; Fedorova, Anastasia A.; Kozyrskiy, Bogdan L.; Velichkovsky, Boris M.
2016-01-01
We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for paralyzed and healthy users, the EBCI “Wish Mouse.” PMID:27917105
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Classifying with confidence from incomplete information.
Parrish, Nathan; Anderson, Hyrum S.; Gupta, Maya R.; ...
2013-12-01
For this paper, we consider the problem of classifying a test sample given incomplete information. This problem arises naturally when data about a test sample is collected over time, or when costs must be incurred to compute the classification features. For example, in a distributed sensor network only a fraction of the sensors may have reported measurements at a certain time, and additional time, power, and bandwidth is needed to collect the complete data to classify. A practical goal is to assign a class label as soon as enough data is available to make a good decision. We formalize thismore » goal through the notion of reliability—the probability that a label assigned given incomplete data would be the same as the label assigned given the complete data, and we propose a method to classify incomplete data only if some reliability threshold is met. Our approach models the complete data as a random variable whose distribution is dependent on the current incomplete data and the (complete) training data. The method differs from standard imputation strategies in that our focus is on determining the reliability of the classification decision, rather than just the class label. We show that the method provides useful reliability estimates of the correctness of the imputed class labels on a set of experiments on time-series data sets, where the goal is to classify the time-series as early as possible while still guaranteeing that the reliability threshold is met.« less
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Effects of Fertility on Gene Expression and Function of the Bovine Endometrium
Minten, Megan A.; Bilby, Todd R.; Bruno, Ralph G. S.; Allen, Carolyn C.; Madsen, Crystal A.; Wang, Zeping; Sawyer, Jason E.; Tibary, Ahmed; Neibergs, Holly L.; Geary, Thomas W.; Bauersachs, Stefan; Spencer, Thomas E.
2013-01-01
Infertility and subfertility are important and pervasive reproductive problems in both domestic animals and humans. The majority of embryonic loss occurs during the first three weeks of pregnancy in cattle and women due, in part, to inadequate endometrial receptivity for support of embryo implantation. To identify heifers of contrasting fertility, serial rounds of artificial insemination (AI) were conducted in 201 synchronized crossbred beef heifers. The heifers were then fertility classified based on number of pregnancies detected on day 35 in four AI opportunities. Heifers, classified as having high fertility, subfertility or infertility, were selected for further study. The fertility-classified heifers were superovulated and flushed, and the recovered embryos were graded and then transferred to synchronized recipients. Quantity of embryos recovered per flush, embryo quality, and subsequent recipient pregnancy rates did not differ by fertility classification. Two in vivo-produced bovine embryos (stage 4 or 5, grade 1 or 2) were then transferred into each heifer on day 7 post-estrus. Pregnancy rates were greater in high fertility than lower fertility heifers when heifers were used as embryo recipients. The reproductive tracts of the classified heifers were obtained on day 14 of the estrous cycle. No obvious morphological differences in reproductive tract structures and histology of the uterus were observed in the heifers. Microarray analysis revealed differences in the endometrial transcriptome based on fertility classification. A genome-wide association study, based on SNP genotyping, detected 7 moderate associations with fertility across 6 different chromosomes. Collectively, these studies support the idea that innate differences in uterine function underlie fertility and early pregnancy loss in ruminants. Cattle with defined early pregnancy success or loss is useful to elucidate the complex biological and genetic mechanisms governing endometrial receptivity and uterine competency for pregnancy. PMID:23940519
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Classifying smoking urges via machine learning
Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin
2016-01-01
Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. PMID:28110725
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Classifying smoking urges via machine learning.
Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin
2016-12-01
Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Exploring diversity in ensemble classification: Applications in large area land cover mapping
NASA Astrophysics Data System (ADS)
Mellor, Andrew; Boukir, Samia
2017-07-01
Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area remote sensing applications, for which training data is costly and resource intensive to collect.
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Chen, Yen-Kuang; Li, Kuo-Bin
2013-02-07
The type information of un-annotated membrane proteins provides an important hint for their biological functions. The experimental determination of membrane protein types, despite being more accurate and reliable, is not always feasible due to the costly laboratory procedures, thereby creating a need for the development of bioinformatics methods. This article describes a novel computational classifier for the prediction of membrane protein types using proteins' sequences. The classifier, comprising a collection of one-versus-one support vector machines, makes use of the following sequence attributes: (1) the cationic patch sizes, the orientation, and the topology of transmembrane segments; (2) the amino acid physicochemical properties; (3) the presence of signal peptides or anchors; and (4) the specific protein motifs. A new voting scheme was implemented to cope with the multi-class prediction. Both the training and the testing sequences were collected from SwissProt. Homologous proteins were removed such that there is no pair of sequences left in the datasets with a sequence identity higher than 40%. The performance of the classifier was evaluated by a Jackknife cross-validation and an independent testing experiments. Results show that the proposed classifier outperforms earlier predictors in prediction accuracy in seven of the eight membrane protein types. The overall accuracy was increased from 78.3% to 88.2%. Unlike earlier approaches which largely depend on position-specific substitution matrices and amino acid compositions, most of the sequence attributes implemented in the proposed classifier have supported literature evidences. The classifier has been deployed as a web server and can be accessed at http://bsaltools.ym.edu.tw/predmpt. Copyright © 2012 Elsevier Ltd. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Islam, Md. Shafiqul, E-mail: shafique@eng.ukm.my; Hannan, M.A., E-mail: hannan@eng.ukm.my; Basri, Hassan
Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensormore » intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.« less
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NASA Astrophysics Data System (ADS)
Lee, Dong Hyuk; Kim, JongHyo; Kim, Hee C.; Lee, Yong W.; Min, Byong Goo
1997-04-01
There have been a number of studies on the quantitative evaluation of diffuse liver disease by using texture analysis technique. However, the previous studies have been focused on the classification between only normal and abnormal pattern based on textural properties, resulting in lack of clinically useful information about the progressive status of liver disease. Considering our collaborative research experience with clinical experts, we judged that not only texture information but also several shape properties are necessary in order to successfully classify between various states of disease with liver ultrasonogram. Nine image parameters were selected experimentally. One of these was texture parameter and others were shape parameters measured as length, area and curvature. We have developed a neural-net algorithm that classifies liver ultrasonogram into 9 categories of liver disease: 3 main category and 3 sub-steps for each. Nine parameters were collected semi- automatically from the user by using graphical user interface tool, and then processed to give a grade for each parameter. Classifying algorithm consists of two steps. At the first step, each parameter was graded into pre-defined levels using neural network. in the next step, neural network classifier determined disease status using graded nine parameters. We implemented a PC based computer-assist diagnosis workstation and installed it in radiology department of Seoul National University Hospital. Using this workstation we collected 662 cases during 6 months. Some of these were used for training and others were used for evaluating accuracy of the developed algorithm. As a conclusion, a liver ultrasonogram classifying algorithm was developed using both texture and shape parameters and neural network classifier. Preliminary results indicate that the proposed algorithm is useful for evaluation of diffuse liver disease.
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Release of (and lessons learned from mining) a pioneering large toxicogenomics database.
Sandhu, Komal S; Veeramachaneni, Vamsi; Yao, Xiang; Nie, Alex; Lord, Peter; Amaratunga, Dhammika; McMillian, Michael K; Verheyen, Geert R
2015-07-01
We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds. The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays. The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches. Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.
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A translational platform for prototyping closed-loop neuromodulation systems
Afshar, Pedram; Khambhati, Ankit; Stanslaski, Scott; Carlson, David; Jensen, Randy; Linde, Dave; Dani, Siddharth; Lazarewicz, Maciej; Cong, Peng; Giftakis, Jon; Stypulkowski, Paul; Denison, Tim
2013-01-01
While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders. PMID:23346048
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A translational platform for prototyping closed-loop neuromodulation systems.
Afshar, Pedram; Khambhati, Ankit; Stanslaski, Scott; Carlson, David; Jensen, Randy; Linde, Dave; Dani, Siddharth; Lazarewicz, Maciej; Cong, Peng; Giftakis, Jon; Stypulkowski, Paul; Denison, Tim
2012-01-01
While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders.
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Fiber optic FTIR instrument for in vivo detection of colonic neoplasia
NASA Astrophysics Data System (ADS)
Van Nortwick, Matthew; Hargrove, John; Wolters, Rolf; Crawford, James M.; Arroyo, May; Mackanos, Mark; Contag, Christopher H.; Wang, Thomas D.
2009-02-01
We demonstrate the proof of concept for use of a fiber optic FTIR instrument to perform in vivo detection of colonic neoplasia as an adjunct to medical endoscopy. FTIR is sensitive to the molecular composition of tissue, and can be used as a guide for biopsy by identifying pre-malignant tissue (dysplasia). First, we demonstrate the use of a silver halide optical fiber to collect mid-infrared absorption spectra in the 950 to 1800 cm-1 regime with high signal-to-noise from biopsy specimens of colonic mucosa tissue ex vivo. We observed subtle differences in wavenumber and magnitude of the absorbance peaks over this regime. We then show that optimal sub-ranges can be defined within this spectral regime and that spectral pre-processing can be performed to classify the tissue as normal, hyperplasia, or dysplasia with high levels of performance. We used a partial least squares discriminant analysis and a leave-one-subject-out crossvalidation strategy to classify the spectra. The results were compared with histology, and the optimal thresholds resulted in an overall sensitivity, specificity, accuracy, and positive predictive value of 96%, 92%, 93%, and 82%, respectively for this technique. We demonstrate that mid-infrared absorption spectra can be collected remotely with an optical fiber and used to identify colonic dysplasia with high accuracy. We are now developing an endoscope compatible optical fiber to use this technique clinically for the early detection of cancer.
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Williams, M L; Mac Parthaláin, N; Brewer, P; James, W P J; Rose, M T
2016-03-01
A better understanding of the behavior of individual grazing dairy cattle will assist in improving productivity and welfare. Global positioning systems (GPS) applied to cows could provide a means of monitoring grazing herds while overcoming the substantial efforts required for manual observation. Any model of behavioral prediction using GPS needs to be accurate and robust by accounting for inter-cow variation as well as atmospheric effects. We evaluated the performance using a series of machine learning algorithms on GPS data collected from 40 pasture-based dairy cows over 4 mo. A feature extraction step was performed on the collected raw GPS data, which resulted in 43 different attributes. The evaluated behaviors were grazing, resting, and walking. Classifier learners were built using 10 times 10-fold cross validation and tested on an independent test set. Results were evaluated using a variety of statistical significance tests across all parameters. We found that final model selection depended upon level of performance and model complexity. The classifier learner deemed most suitable for this particular problem was JRip, a rule-based learner (classification accuracy=0.85; false positive rate=0.10; F-measure=0.76; area under the receiver operating curve=0.87). This model will be used in further studies to assess the behavior and welfare of pasture-based dairy cows. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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Classifying Degraded Modern Polymeric Museum Artefacts by Their Smell.
Curran, Katherine; Underhill, Mark; Grau-Bové, Josep; Fearn, Tom; Gibson, Lorraine T; Strlič, Matija
2018-02-05
The use of VOC analysis to diagnose degradation in modern polymeric museum artefacts is reported. Volatile organic compound (VOC) analysis is a successful method for diagnosing medical conditions but to date has found little application in museums. Modern polymers are increasingly found in museum collections but pose serious conservation difficulties owing to unstable and widely varying formulations. Solid-phase microextraction gas chromatography/mass spectrometry and linear discriminant analysis were used to classify samples according to the length of time they had been artificially degraded. Accuracies in classification of 50-83 % were obtained after validation with separate test sets. The method was applied to three artefacts from collections at Tate to detect evidence of degradation. This approach could be used for any material in heritage collections and more widely in the field of polymer degradation. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
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2013-11-15
features and designed a classifier that achieves up to 95% classification accuracy on classifying the occupancy with indoor footstep data. MDL-based...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other
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Classification of collected trot, passage and piaffe based on temporal variables.
Clayton, H M
1997-05-01
The objective was to determine whether collected trot, passage and piaffe could be distinguished as separate gaits on the basis of temporal variables. Sagittal plane, 60 Hz videotapes of 10 finalists in the dressage competitions at the 1992 Olympic Games were analysed to measure the temporal variables in absolute terms and as percentages of stride duration. Classification was based on analysis of variance, a graphical method and discriminant analysis. Stride duration was sufficient to distinguish collected trot from passage and piaffe in all horses. The analysis of variance showed that the mean values of most variables differed significantly between passage and piaffe. When hindlimb stance percentage was plotted against diagonal advanced placement percentage, some overlap was found between all 3 movements indicating that individual horses could not be classified reliably in this manner. Using hindlimb stance percentage and diagonal advanced placement percentage as input in a discriminant analysis, 80% of the cases were classified correctly, but at least one horse was misclassified in each movement. When the absolute, rather than percentage, values of the 2 variables were used as input in the discriminant analysis, 90% of the cases were correctly classified and the only misclassifications were between passage and piaffe. However, the 2 horses in which piaffe was misclassified as passage were the gold and silver medallists. In general, higher placed horses tended toward longer diagonal advanced placements, especially in collected trot and passage, and shorter hindlimb stance percentages in passage and piaffe.
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Ensembles of novelty detection classifiers for structural health monitoring using guided waves
NASA Astrophysics Data System (ADS)
Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias; Khomenko, Anton; Haq, Mahmoodul; Udpa, Lalita
2018-01-01
Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.
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Anthropometric survey of the astronaut applicants and astronauts from 1985 to 1991
NASA Technical Reports Server (NTRS)
Rajulu, Sudhakar L.; Klute, Glenn K.
1993-01-01
The Anthropometry and Biomechanics Laboratory at the Johnson Space Center has been collecting anthropometric data from astronaut applicants since 1977. These anthropometric measurements had been taken from 473 applicants. Based on the position they applied for, these applicants were classified as either mission specialists, payload specialists, pilots, or observers. The main objective was to document the variations among these applicants and tabulate the percentile data for each anthropometric dimension. The percentile and the descriptive statistics data were tabulated and graphed for the whole astronaut candidate population; for the male and female groups; for each subject classification such as pilot, mission specialist, and payload specialist; and finally, for those who were selected as astronauts.
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A Computerized Architecture Slide Classification for a Small University Collection.
ERIC Educational Resources Information Center
Powell, Richard K.
This paper briefly outlines the process used to organize, classify, and make accessible a collection of architecture slides in the Architecture Resource Center at Andrews University in Michigan. The classification system includes the use of Art and Architecture Thesaurus subject headings, the ERIC (Educational Resources Information Center) concept…
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78 FR 23785 - Agency Information Collection Activities: Submission for OMB Review; Comment Request
Federal Register 2010, 2011, 2012, 2013, 2014
2013-04-22
...) whether small businesses are affected by this collection. In this notice, NARA is soliciting comments..., Business or other for- profit, Federal government. Estimated number of respondents: 1,500. Estimated time... Government intelligence agencies for storage of classified information and serves to comply with E.O. 12958...
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A Big-Data-based platform of workers' behavior: Observations from the field.
Guo, S Y; Ding, L Y; Luo, H B; Jiang, X Y
2016-08-01
Behavior-Based Safety (BBS) has been used in construction to observe, analyze and modify workers' behavior. However, studies have identified that BBS has several limitations, which have hindered its effective implementation. To mitigate the negative impact of BBS, this paper uses a case study approach to develop a Big-Data-based platform to classify, collect and store data about workers' unsafe behavior that is derived from a metro construction project. In developing the platform, three processes were undertaken: (1) a behavioral risk knowledge base was established; (2) images reflecting workers' unsafe behavior were collected from intelligent video surveillance and mobile application; and (3) images with semantic information were stored via a Hadoop Distributed File System (HDFS). The platform was implemented during the construction of the metro-system and it is demonstrated that it can effectively analyze semantic information contained in images, automatically extract workers' unsafe behavior and quickly retrieve on HDFS as well. The research presented in this paper can enable construction organizations with the ability to visualize unsafe acts in real-time and further identify patterns of behavior that can jeopardize safety outcomes. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Novel classification system of rib fractures observed in infants.
Love, Jennifer C; Derrick, Sharon M; Wiersema, Jason M; Pinto, Deborrah C; Greeley, Christopher; Donaruma-Kwoh, Marcella; Bista, Bibek
2013-03-01
Rib fractures are considered highly suspicious for nonaccidental injury in the pediatric clinical literature; however, a rib fracture classification system has not been developed. As an aid and impetus for rib fracture research, we developed a concise schema for classifying rib fracture types and fracture location that is applicable to infants. The system defined four fracture types (sternal end, buckle, transverse, and oblique) and four regions of the rib (posterior, posterolateral, anterolateral, and anterior). It was applied to all rib fractures observed during 85 consecutive infant autopsies. Rib fractures were found in 24 (28%) of the cases. A total of 158 rib fractures were identified. The proposed schema was adequate to classify 153 (97%) of the observed fractures. The results indicate that the classification system is sufficiently robust to classify rib fractures typically observed in infants and should be used by researchers investigating infant rib fractures. © 2013 American Academy of Forensic Sciences.
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Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P
2010-07-15
Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and relating neural activity to behavior. Copyright 2010 Elsevier Inc. All rights reserved.
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Probing the use of spectroscopy to determine the meteoritic analogues of meteors
NASA Astrophysics Data System (ADS)
Drouard, A.; Vernazza, P.; Loehle, S.; Gattacceca, J.; Vaubaillon, J.; Zanda, B.; Birlan, M.; Bouley, S.; Colas, F.; Eberhart, M.; Hermann, T.; Jorda, L.; Marmo, C.; Meindl, A.; Oefele, R.; Zamkotsian, F.; Zander, F.
2018-05-01
Context. Determining the source regions of meteorites is one of the major goals of current research in planetary science. Whereas asteroid observations are currently unable to pinpoint the source regions of most meteorite classes, observations of meteors with camera networks and the subsequent recovery of the meteorite may help make progress on this question. The main caveat of such an approach, however, is that the recovery rate of meteorite falls is low (<20%), implying that the meteoritic analogues of at least 80% of the observed falls remain unknown. Aims: Spectroscopic observations of incoming bolides may have the potential to mitigate this problem by classifying the incoming meteoritic material. Methods: To probe the use of spectroscopy to determine the meteoritic analogues of incoming bolides, we collected emission spectra in the visible range (320-880 nm) of five meteorite types (H, L, LL, CM, and eucrite) acquired in atmospheric entry-like conditions in a plasma wind tunnel at the Institute of Space Systems (IRS) at the University of Stuttgart (Germany). A detailed spectral analysis including a systematic line identification and mass ratio determinations (Mg/Fe, Na/Fe) was subsequently performed on all spectra. Results: It appears that spectroscopy, via a simple line identification, allows us to distinguish the three main meteorite classes (chondrites, achondrites and irons) but it does not have the potential to distinguish for example an H chondrite from a CM chondrite. Conclusions: The source location within the main belt of the different meteorite classes (H, L, LL, CM, CI, etc.) should continue to be investigated via fireball observation networks. Spectroscopy of incoming bolides only marginally helps precisely classify the incoming material (iron meteorites only). To reach a statistically significant sample of recovered meteorites along with accurate orbits (>100) within a reasonable time frame (10-20 years), the optimal solution may be the spatial extension of existing fireball observation networks. The movie associated to this article is available at http://www.aanda.org
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Molecular Characterization of Hypoderma SPP. in Domestic Ruminants from Turkey and Pakistan.
Ahmed, Haroon; Simsek, Sami; Saki, Cem Ecmel; Kesik, Harun Kaya; Kilinc, Seyma Gunyakti
2017-08-01
The aim of this study was to determine the morphological and molecular characterization of Hypoderma spp. in cattle and yak from provinces in Turkey and Pakistan. In total, 78 Hypoderma larvae were collected from slaughtered animals in Turkey and Pakistan from October 2015 to January 2016. Thirty-eight of these 78 Hypoderma larvae were morphologically classified as third instar larvae (L3s) of Hypoderma bovis, 37 were classified as Hypoderma lineatum, and 3 were classified as suspected or unidentified. The restriction enzyme TaqI was used to differentiate the Hypoderma spp. by polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP). According to the sequences and the PCR-RFLP results, all larval samples from cattle from Turkey were classified as H. bovis, except for 1 sample classified as H. lineatum. All Hypoderma larvae from Pakistan were classified as H. lineatum from cattle and as Hypoderma sinense from yak. This study provides the first molecular characterization of H. lineatum (cattle) and H. sinense (yak) in Pakistan based on PCR-RFLP and sequencing results.
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[Family functioning of elderly with depressive symptoms].
Souza, Rosely Almeida; Desani da Costa, Gislaine; Yamashita, Cintia Hitomi; Amendola, Fernanda; Gaspar, Jaqueline Correa; Alvarenga, Márcia Regina Martins; Faccenda, Odival; Oliveira, Maria Amélia de Campos
2014-06-01
To classify families of elderly with depressive symptoms regarding their functioning and to ascertain the presence of an association between these symptoms, family functioning and the characteristics of the elderly. This was an observational, analytical, cross-sectional study performed with 33 teams of the Family Health Strategy in Dourados, MS. The sample consisted of 374 elderly divided into two groups (with and without depressive symptoms). The instruments for data collection were a sociodemographic instrument, the GeriatricDepression Scale (15 items) and the Family Apgar. An association was observed between depressive symptoms and family dysfunction, female gender, four or more people living together, and physical inactivity. The functional family may represent effective support for the elderly with depressive symptoms, because it offers a comfortable environment that ensures the well-being of its members. The dysfunctional family can barely provide necessary care for the elderly, which can exacerbate depressive symptoms.
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A Hybrid Template-Based Composite Classification System
2009-02-01
Hybrid Classifier: Forced Decision . . . . 116 5.3.2 Forced Decision Experimental Results . . . . . 119 5.3.3 Test for Statistical Significance ...Results . . . . . . . . . . 127 5.4.2 Test for Statistical Significance : NDEC Option 129 5.5 Implementing the Hyrid Classifier with OOL Targets . 130...comple- mentary in nature . Complementary classifiers are observed by finding an optimal method for partitioning the problem space. For example, the
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Uncertain Classification of Variable Stars: Handling Observational GAPS and Noise
NASA Astrophysics Data System (ADS)
Castro, Nicolás; Protopapas, Pavlos; Pichara, Karim
2018-01-01
Automatic classification methods applied to sky surveys have revolutionized the astronomical target selection process. Most surveys generate a vast amount of time series, or “lightcurves,” that represent the brightness variability of stellar objects in time. Unfortunately, lightcurves’ observations take several years to be completed, producing truncated time series that generally remain without the application of automatic classifiers until they are finished. This happens because state-of-the-art methods rely on a variety of statistical descriptors or features that present an increasing degree of dispersion when the number of observations decreases, which reduces their precision. In this paper, we propose a novel method that increases the performance of automatic classifiers of variable stars by incorporating the deviations that scarcity of observations produces. Our method uses Gaussian process regression to form a probabilistic model of each lightcurve’s observations. Then, based on this model, bootstrapped samples of the time series features are generated. Finally, a bagging approach is used to improve the overall performance of the classification. We perform tests on the MAssive Compact Halo Object (MACHO) and Optical Gravitational Lensing Experiment (OGLE) catalogs, results show that our method effectively classifies some variability classes using a small fraction of the original observations. For example, we found that RR Lyrae stars can be classified with ~80% accuracy just by observing the first 5% of the whole lightcurves’ observations in the MACHO and OGLE catalogs. We believe these results prove that, when studying lightcurves, it is important to consider the features’ error and how the measurement process impacts it.
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NASA Astrophysics Data System (ADS)
Ojeda, G. Y.; Gayes, P. T.; van Dolah, R. F.; Schwab, W. C.
2002-12-01
Assessment of the extent and variability of benthic habitats is an important mission of biologists and marine scientists, and has supreme relevance in monitoring and maintaining the offshore resources of coastal nations. Mapping `hard bottoms', in particular, is of critical importance because these are the areas that support sessile benthic habitats and associated fisheries. To quantify the extent and distribution of habitats offshore northern South Carolina, we used a spatially quantitative approach that involved textural analysis of side scan sonar images and training of an artificial neural network classifier. This approach was applied to a 2 m-pixel image mosaic of sonar data collected by the USGS in 1999 and 2000. The entire mosaic covered some 686 km2 and extended between the ~6 m and ~10+ m isobaths off the Grand Strand region of South Carolina. Bottom video transects across selected sites provided 2,119 point observations which were used for image-to-ground control as well as training of the neural network classifier. A sensitivity study of 52 space-domain textural features indicated that 12 of them provided reasonable discriminating power between two end-member bottom types: hard bottom and sand. The selected features were calculated over 5 by 5 pixel windows of the image where video point observations existed. These feature vectors were then fed to a 3-layer neural network classifier, trained with a Levenberg-Marquardt backpropagation algorithm. Registration and display of the output habitat map were performed in GIS. Results of our classification indicate that outcropping Tertiary and Cretaceous strata are exposed over a significant portion of northern South Carolina's inner shelf, consistent with a sediment-starved margin type. The combined surface extent classified as hard bottom was 405 km2 -or 59 % of the imaged area-, while only 281 km2 -or 41 % of the area were classified as sand. In addition, our results provided constraints on the spatial continuity of nearshore benthic habitats. The median surface area of the regions classified as hard bottom (n= 190,521) and sand (n= 234,946) were both equal to the output cell size (100 m2), confirming the `patchy' nature of these habitats and suggesting that these medians probably represent upper bounds rather than estimates of the typical extent of individual patches. Furthermore, comparison of the interpretive habitat map with available swath bathymetry data suggests positive correlation between bathymetry `highs' and the major sandy-bottom areas interpreted with our routine. In contrast, the location of hard bottom areas does not appear to be significantly correlated with major bathymetric features. Our findings are in agreement with published qualitative estimates of hard bottom areas on neighboring North Carolina's inner shelf.
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NASA Technical Reports Server (NTRS)
Kettig, R. L.
1975-01-01
A method of classification of digitized multispectral images is developed and experimentally evaluated on actual earth resources data collected by aircraft and satellite. The method is designed to exploit the characteristic dependence between adjacent states of nature that is neglected by the more conventional simple-symmetric decision rule. Thus contextual information is incorporated into the classification scheme. The principle reason for doing this is to improve the accuracy of the classification. For general types of dependence this would generally require more computation per resolution element than the simple-symmetric classifier. But when the dependence occurs in the form of redundance, the elements can be classified collectively, in groups, therby reducing the number of classifications required.
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Classifying Noisy Protein Sequence Data: A Case Study of Immunoglobulin Light Chains
2005-01-01
collected from patients with and without amyloidosis , and indicates that the proposed modified classifi- ers are more robust to sequence variability than...piled from patients with and without amyloidosis provides unique features to serve as a model system, not only for conformational disease studies but...produced by patients with amyloidosis . SVMs have been used recently in a wide variety of applica- tions in computational biology (Noble, 2004). Most
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Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers
NASA Astrophysics Data System (ADS)
Assaleh, Khaled; Al-Rousan, M.
2005-12-01
Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.
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Calamari, L; Gobbi, L; Russo, F; Cappelli, F Piccioli
2015-08-01
The main objective of this experiment was to study the γ-glutamyl transferase (GGT) activity in milk during lactation and its relationship with metabolic status of dairy cows, milk yield, milk composition, and cheesemaking properties. The study was performed in a tied stall barn and involved 20 lactations from 12 healthy multiparous Italian Friesian dairy cows. During lactation starting at d 10, milk samples were collected weekly and analyzed for composition, somatic cells count, titratable acidity, and milk coagulation properties. The GGT activity was measured in defatted samples. Blood samples were collected weekly to assess biochemical indicators related to energy, protein, and mineral metabolism, markers of inflammation and some enzyme activities. The lactations of each cow were retrospectively categorized into 2 groups according to their milk GGT activity value through lactation. A median value of GGT activity in the milk of all lactations was calculated (3,045 U/L), and 10 lactations with lower GGT activity were classified as low while 10 lactations with greater GGT activity were classified as high. The average value of milk GGT activity during lactation was 3,863 and 3,024 U/L for high and low, respectively. The GGT activity decreased in early lactation and reached minimum values in the second month (3,289 and 2,355 U/L for high and low, respectively). Thereafter GGT activity increased progressively, reaching values in late lactation of 4,511 and 3,540 U/L in high and low, respectively. On average, milk yield was 40.81 and 42.76 kg/d in high and low, respectively, and a negative partial correlation with milk GGT activity was observed. A greater milk protein concentration was observed in high (3.39%) compared with low (3.18%), and a positive partial correlation with milk GGT activity was observed. Greater titratable acidity in high than that in low (3.75 vs. 3.45 degrees Soxhlet-Henkel/50 mL, respectively) was also observed. Plasma glucose was greater in cows of high than in low group, while plasma urea was lower in the high than in the low group. No relationship between plasma GGT and milk GGT activity was observed. Our results show an important effect of lactation stage on milk GGT activity. The individual effect observed from consecutive lactations and the relationship between milk GGT activity and milk protein concentration in healthy cows could open prospects for GGT as a future tool in improving milk protein content.
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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%
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Study on environmental indices and heat tolerance tests in hair sheep.
Seixas, L; de Melo, C B; Menezes, A M; Ramos, A F; Paludo, G R; Peripolli, V; Tanure, C B; Costa Junior, J B G; McManus, C
2017-06-01
The ability to predict the effects of climatic factors on animals and their adaptability is important for livestock production. The aim of the present study was to analyze whether existing indices are suitable for evaluating heat stress in Santa Ines and Morada Nova sheep, which are locally adapted hair sheep breeds from northeastern Brazil, and if the limits used to classify thermal stress are suitable for these breeds. Therefore, climatic, physiological, and physical parameters, as well as thermographic images, were collected in 26 sheep, 1 1/2 years old, from two genetic groups (Santa Ines 12 males and 4 females; Morada Nov. 7 males and 3 females) for 3 days in both morning (4:00 a.m.) and afternoon (2:00 p.m.) with six repetitions, totalizing 156 repetitions. Statistical analysis included correlations and broken-line regressions. Iberia and Benezra indices were the tolerance tests that best correlated with the assessed parameters. High correlations between environmental indices and rectal or skin surface temperatures was observed, which indicates that these indices can be used for Santa Ines and Morada Nova sheep raised in central Brazil. However, some indicative values of thermal discomfort are different from the existing classification. Therefore, in order to classify appropriately, the model used needs to be carefully studied, because these classifying values can vary according to the species and model. Further research is necessary to establish indicators of thermal stress for sheep breeds raised in the region.
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Classification of Ancient Mammal Individuals Using Dental Pulp MALDI-TOF MS Peptide Profiling
Tran, Thi-Nguyen-Ny; Aboudharam, Gérard; Gardeisen, Armelle; Davoust, Bernard; Bocquet-Appel, Jean-Pierre; Flaudrops, Christophe; Belghazi, Maya; Raoult, Didier; Drancourt, Michel
2011-01-01
Background The classification of ancient animal corpses at the species level remains a challenging task for forensic scientists and anthropologists. Severe damage and mixed, tiny pieces originating from several skeletons may render morphological classification virtually impossible. Standard approaches are based on sequencing mitochondrial and nuclear targets. Methodology/Principal Findings We present a method that can accurately classify mammalian species using dental pulp and mass spectrometry peptide profiling. Our work was organized into three successive steps. First, after extracting proteins from the dental pulp collected from 37 modern individuals representing 13 mammalian species, trypsin-digested peptides were used for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis. The resulting peptide profiles accurately classified every individual at the species level in agreement with parallel cytochrome b gene sequencing gold standard. Second, using a 279–modern spectrum database, we blindly classified 33 of 37 teeth collected in 37 modern individuals (89.1%). Third, we classified 10 of 18 teeth (56%) collected in 15 ancient individuals representing five mammal species including human, from five burial sites dating back 8,500 years. Further comparison with an upgraded database comprising ancient specimen profiles yielded 100% classification in ancient teeth. Peptide sequencing yield 4 and 16 different non-keratin proteins including collagen (alpha-1 type I and alpha-2 type I) in human ancient and modern dental pulp, respectively. Conclusions/Significance Mass spectrometry peptide profiling of the dental pulp is a new approach that can be added to the arsenal of species classification tools for forensics and anthropology as a complementary method to DNA sequencing. The dental pulp is a new source for collagen and other proteins for the species classification of modern and ancient mammal individuals. PMID:21364886
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Contextual classification on the massively parallel processor
NASA Technical Reports Server (NTRS)
Tilton, James C.
1987-01-01
Classifiers are often used to produce land cover maps from multispectral Earth observation imagery. Conventionally, these classifiers have been designed to exploit the spectral information contained in the imagery. Very few classifiers exploit the spatial information content of the imagery, and the few that do rarely exploit spatial information content in conjunction with spectral and/or temporal information. A contextual classifier that exploits spatial and spectral information in combination through a general statistical approach was studied. Early test results obtained from an implementation of the classifier on a VAX-11/780 minicomputer were encouraging, but they are of limited meaning because they were produced from small data sets. An implementation of the contextual classifier is presented on the Massively Parallel Processor (MPP) at Goddard that for the first time makes feasible the testing of the classifier on large data sets.
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Classifying seismic waveforms from scratch: a case study in the alpine environment
NASA Astrophysics Data System (ADS)
Hammer, C.; Ohrnberger, M.; Fäh, D.
2013-01-01
Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.
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77 FR 7567 - Proposed Collection; Comment Request
Federal Register 2010, 2011, 2012, 2013, 2014
2012-02-13
... authoritative source for clearance information resulting in accesses determinations to sensitive/classified... Personnel Security System and is the authoritative source for clearance information resulting in accesses...
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12 CFR 792.68 - Use and collection of Social Security numbers.
Code of Federal Regulations, 2014 CFR
2014-01-01
... INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED INFORMATION The Privacy... 12 Banks and Banking 7 2014-01-01 2014-01-01 false Use and collection of Social Security numbers. 792.68 Section 792.68 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING THE...
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12 CFR 792.68 - Use and collection of Social Security numbers.
Code of Federal Regulations, 2012 CFR
2012-01-01
... INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED INFORMATION The Privacy... 12 Banks and Banking 7 2012-01-01 2012-01-01 false Use and collection of Social Security numbers. 792.68 Section 792.68 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING THE...
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12 CFR 792.68 - Use and collection of Social Security numbers.
Code of Federal Regulations, 2013 CFR
2013-01-01
... INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED INFORMATION The Privacy... 12 Banks and Banking 7 2013-01-01 2013-01-01 false Use and collection of Social Security numbers. 792.68 Section 792.68 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING THE...
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Federal Register 2010, 2011, 2012, 2013, 2014
2012-09-26
...: Class I Motor Carriers of Passengers. Estimated Number of Respondents: 2 (per year). Estimated Time per Response: 18 minutes per response. Expiration Date: September 30, 2012. Frequency of Response: Annually and..., passenger carriers are classified into two groups: (1) Class I carriers are those having average annual...
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Persian Speakers' Use of Refusal Strategies across Politeness Systems
ERIC Educational Resources Information Center
Salmani Nodoushan, Mohammad Ali
2016-01-01
This study aimed at investigating the preferred refusal strategies in Persian. 3047 refusals collected by 108 field workers as well as 376 refusals collected through face to face interviews were analyzed and classified according to the descriptions proposed by Liao (1994) and Liao and Bresnahan (1996). The frequencies of the resulting direct and…
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Hand Gesture Data Collection Procedure Using a Myo Armband for Machine Learning
2015-09-01
instructions, searching existing data sources , gathering and maintaining the data needed, and completing and reviewing the collection information...data using a Myo armband. The source code for this work is included as an Appendix. 15. SUBJECT TERMS Myo, Machine Learning, Classifier, Data...development in multiple platfonns (e.g., Windows, iOS, Android , etc.) and many languages (e.g. , Java, C++, C#, Lua, etc.). For the data collection
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Development of a noninvasive system for monitoring dairy cattle sleep.
Klefot, J M; Murphy, J L; Donohue, K D; O'Hara, B F; Lhamon, M E; Bewley, J M
2016-10-01
Limited research has been conducted to assess sleep in production livestock primarily because of limitations with monitoring capabilities. Consequently, biological understanding of production circumstances and facility options that affect sleep is limited. The objective of this study was to assess if data collected from a proof-of-concept, noninvasive 3-axis accelerometer device are correlated with sleep and wake-like behaviors in dairy cattle. Four Holstein dairy cows housed at the University of Kentucky Coldstream Dairy in September 2013 were visually observed for 2 consecutive 24-h periods. The accelerometer device was attached to a harness positioned on the right side of each cow's neck. Times of classified behaviors of wake (standing, head up, alert, eyes open) or sleep-like behaviors (lying, still, head resting on ground, eyes closed) were recorded continuously by 2 observers who each watched 2 cows at a time. The radial signal was extracted from 3 different axes of the accelerometer to obtain a motion signal independent of direction of movement. Radial signal features were examined for maximizing the performance of detecting sleep-like behaviors using a Fisher's linear discriminant analysis classifier. The study included 652min of high-activity wake behaviors and 107min of sleep-like behavior among 4 cows. Results from a bootstrapping analysis showed an agreement between human observation and the linear discriminant analysis classifier, with an accuracy of 93.7±0.7% for wake behavior and 92.2±0.8% for sleep-like behavior (±95% confidence interval).This prototype shows promise in measuring sleep-like behaviors. Improvements to both hardware and software should allow more accurate determinations of subtle head movements and respiratory movements that will further improve the assessment of these sleep-like behaviors, including estimates of deep, light, and rapid eye movement sleep. These future studies will require simultaneous electroencephalography and electromyography measures and perhaps additional measures of arousal thresholds to validate this system for measuring true sleep. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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Ensembles of novelty detection classifiers for structural health monitoring using guided waves
DOE Office of Scientific and Technical Information (OSTI.GOV)
Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias
Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions. To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations.We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a differentmore » segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using monte-carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate.We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all environmental and operating conditions, while the latter does not and leverages the fact that environmental and operating conditions vary slowly over time and can be modeled as a Gaussian process.« less
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Acute necrotizing pancreatitis: a multicenter study.
Fernández-Cruz, L; Navarro, S; Valderrama, R; Sáenz, A; Guarner, L; Aparisi, L; Espi, A; Jaurietta, E; Marruecos, L; Gener, J
1994-04-01
A multicenter study of acute necrotizing pancreatitis (ANP) classified in accordance with the Balthazar criteria (grades D and E), has been performed in 12 teaching hospitals. A total of 233 patients were reviewed, and the mortality rate was 26.6%. The most common etiology was biliary pancreatitis (45.5%). Among the complications, shock, renal insufficiency, pulmonary insufficiency and hemorrhagic gastritis were associated with a mortality rate of 51-66%. Diffuse fluid collections were associated with a higher mortality rate (26.8%) than localized fluid collections (14.5%). In 106 patients with gallstone pancreatitis, early surgery was performed in 17, and 5 patients (29.4%) died. No mortality was observed in 32 patients with delayed surgery. Sphincterotomy was performed in 13 patients, and 4 (30.7%) died. Early surgery (necrosectomy and closed peritoneal lavage) was undertaken in 75 patients, with a mortality rate of 39%. In conclusion, the morbidity and mortality rates of ANP can be improved with proper monitoring, adequate supportive care and the judicious use of surgery based on clinical and morphological findings.
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Tsatsarelis, Thomas; Antonopoulos, Ioannis; Karagiannidis, Avraam; Perkoulidis, George
2007-10-01
This study presents an assessment of the current status of open dumps in Laconia prefecture of Peloponnese in southern Greece, where all open dumps are targeted for closure by 2008. An extensive field survey was conducted in 2005 to register existing sites in the prefecture. The data collected included the site area and age, waste depth, type of disposed waste, distance from nearest populated area, local geographical features and observed practices of open burning and soil coverage. On the basis of the collected data, a GIS database was developed, and the above parameters were statistically analysed. Subsequently, a decision tool for the restoration of open dumps was implemented, which led to the prioritization of site restorations and specific decisions about appropriate restoration steps for each site. The sites requiring restoration were then further classified using Principal Component Analysis, in order to categorize them into groups suitable for similar restoration work, thus facilitating fund allocation and subsequent restoration project management.
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Cosmic-ray discrimination capabilities of /ΔE-/E silicon nuclear telescopes using neural networks
NASA Astrophysics Data System (ADS)
Ambriola, M.; Bellotti, R.; Cafagna, F.; Castellano, M.; Ciacio, F.; Circella, M.; Marzo, C. N. D.; Montaruli, T.
2000-02-01
An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the ``mixture of experts'' type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a ``classical'' classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable - within /1% - with that of the classical one, with efficiency of the order of /98%. A possible hardware implementation of such a kind of neural architecture in future space missions is considered.
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Tripathi, S K; Farman, M; Nandi, S; Mondal, S; Gupta, Psp; Kumar, V Girish
2016-07-01
The present study was undertaken to investigate the oocyte morphology, its fertilizing capacity and granulosa cell functions in ewes (obese, normal, metabolic stressed and emaciated). Ewes (Ovis aries) of approximately 3 years of age (Bellary breed) from a local village were screened, chosen and categorized into a) normal b) obese but not metabolically stressed, c) Emaciated but not metabolically stressed d) Metabolically stressed based on body condition scoring and blood markers. Oocytes and granulosa cells were collected from ovaries of the ewes of all categories after slaughter and were classified into good (oocytes with more than three layers of cumulus cells and homogenous ooplasm), fair (oocytes one or two layers of cumulus cells and homogenous ooplasm) and poor (denuded oocytes or with dark ooplasm). The good and fair quality oocytes were in vitro matured and cultured with fresh semen present and the fertilization, cleavage and blastocyst development were observed. The granulosa cells were cultured for evaluation of metabolic activity by use of the MTT assay, and cell viability, cell number as well as estrogen and progesterone production were assessed. It was observed that the good and fair quality oocytes had greater metabolic activity when collected from normal and obese ewes compared with those from emaciated and metabolically stressed ewes. No significant difference was observed in oocyte quality and maturation amongst the oocytes collected from normal and obese ewes. The cleavage and blastocyst production rates were different for the various body condition classifications and when ranked were: normal>obese>metabolically stressed>emaciated. Lesser metabolic activity was observed in granulosa cells obtained from ovaries of emaciated ewes. However, no changes were observed in viability and cell number of granulosa cells obtained from ewes with the different body condition categories. Estrogen and progesterone production from cultured granulosa cells were not different in normal and obese ewes. Estrogen and progesterone secretions were less from granulosa cells recovered from metabolically stressed and emaciated ewes. The results suggested that oocyte morphology, fertilizing capacity and granulosa cell growth were dependent on body condition and feeding status of the animals. Copyright © 2016. Published by Elsevier B.V.
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An Investigation to Improve Classifier Accuracy for Myo Collected Data
2017-02-01
distribution is unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT A naïve Bayes classifier trained with 1,360 samples from 17 volunteers performs at...movement data from 17 volunteers . Each volunteer performed 8 gestures (Freeze, Rally Point, Hurry Up, Down, Come, Stop, Line Abreast Formation, and Vehicle...line chart was plotted for each gesture’s feature (e.g., Pitch, xAcc) per user. All 10 recorded samples of a particular gesture for a single volunteer
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Annual Report to Congress on Foreign Economic Collection and Industrial Espionage, FY 2008
2009-07-23
Warcraft —could offer access to information that would be valuable to economic collectors or industrial espionage in the future. The virtual world ...Industrial Espionage, 2007. Economic espionage cases went up slightly and nearly every day brought reports—in the press and in the classified world —of...reports—in the press and in the classified world —of new cyber attacks against US Government and business entities. Additionally, the increasing use of
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Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models
2013-05-01
distribution p by minimizing the Kullback – Leibler (KL) distance D(p‖p̂) = Ep[log(p/p̂)] using first- and second-order statistics, via a maximum-weight...Obtain sparse representations αl, l = 1, . . . , T , in RN from test image. 6: Inference: Classify based on the output of the resulting classifier using ...The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing
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Spectral analysis for automated exploration and sample acquisition
NASA Technical Reports Server (NTRS)
Eberlein, Susan; Yates, Gigi
1992-01-01
Future space exploration missions will rely heavily on the use of complex instrument data for determining the geologic, chemical, and elemental character of planetary surfaces. One important instrument is the imaging spectrometer, which collects complete images in multiple discrete wavelengths in the visible and infrared regions of the spectrum. Extensive computational effort is required to extract information from such high-dimensional data. A hierarchical classification scheme allows multispectral data to be analyzed for purposes of mineral classification while limiting the overall computational requirements. The hierarchical classifier exploits the tunability of a new type of imaging spectrometer which is based on an acousto-optic tunable filter. This spectrometer collects a complete image in each wavelength passband without spatial scanning. It may be programmed to scan through a range of wavelengths or to collect only specific bands for data analysis. Spectral classification activities employ artificial neural networks, trained to recognize a number of mineral classes. Analysis of the trained networks has proven useful in determining which subsets of spectral bands should be employed at each step of the hierarchical classifier. The network classifiers are capable of recognizing all mineral types which were included in the training set. In addition, the major components of many mineral mixtures can also be recognized. This capability may prove useful for a system designed to evaluate data in a strange environment where details of the mineral composition are not known in advance.
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Pervasive Sound Sensing: A Weakly Supervised Training Approach.
Kelly, Daniel; Caulfield, Brian
2016-01-01
Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smartphone sensors in the context of human behavior sensing. We postulate that, in order for microphones to be useful in behavior sensing applications, the analysis techniques must be flexible and allow easy modification of the types of sounds to be sensed. A simplification of the training data collection process could allow a more flexible sound classification framework. We hypothesize that detailed training, a prerequisite for the majority of sound sensing techniques, is not necessary and that a significantly less detailed and time consuming data collection process can be carried out, allowing even a nonexpert to conduct the collection, labeling, and training process. To test this hypothesis, we implement a diverse density-based multiple instance learning framework, to identify a target sound, and a bag trimming algorithm, which, using the target sound, automatically segments weakly labeled sound clips to construct an accurate training set. Experiments reveal that our hypothesis is a valid one and results show that classifiers, trained using the automatically segmented training sets, were able to accurately classify unseen sound samples with accuracies comparable to supervised classifiers, achieving an average F -measure of 0.969 and 0.87 for two weakly supervised datasets.
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Effects of target typicality on categorical search.
Maxfield, Justin T; Stalder, Westri D; Zelinsky, Gregory J
2014-10-01
The role of target typicality in a categorical visual search task was investigated by cueing observers with a target name, followed by a five-item target present/absent search array in which the target images were rated in a pretest to be high, medium, or low in typicality with respect to the basic-level target cue. Contrary to previous work, we found that search guidance was better for high-typicality targets compared to low-typicality targets, as measured by both the proportion of immediate target fixations and the time to fixate the target. Consistent with previous work, we also found an effect of typicality on target verification times, the time between target fixation and the search judgment; as target typicality decreased, verification times increased. To model these typicality effects, we trained Support Vector Machine (SVM) classifiers on the target categories, and tested these on the corresponding specific targets used in the search task. This analysis revealed significant differences in classifier confidence between the high-, medium-, and low-typicality groups, paralleling the behavioral results. Collectively, these findings suggest that target typicality broadly affects both search guidance and verification, and that differences in typicality can be predicted by distance from an SVM classification boundary. © 2014 ARVO.
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NASA Astrophysics Data System (ADS)
Chiaro, G.; Salvetti, D.; La Mura, G.; Giroletti, M.; Thompson, D. J.; Bastieri, D.
2016-11-01
The Fermi-Large Area Telescope (LAT) is currently the most important facility for investigating the GeV γ-ray sky. With Fermi-LAT, more than three thousand γ-ray sources have been discovered so far. 1144 (˜40 per cent) of the sources are active galaxies of the blazar class, and 573 (˜20 per cent) are listed as blazar candidate of uncertain type (BCU), or sources without a conclusive classification. We use the empirical cumulative distribution functions and the artificial neural networks for a fast method of screening and classification for BCUs based on data collected at γ-ray energies only, when rigorous multiwavelength analysis is not available. Based on our method, we classify 342 BCUs as BL Lacs and 154 as flat-spectrum radio quasars, while 77 objects remain uncertain. Moreover, radio analysis and direct observations in ground-based optical observatories are used as counterparts to the statistical classifications to validate the method. This approach is of interest because of the increasing number of unclassified sources in Fermi catalogues and because blazars and in particular their subclass high synchrotron peak objects are the main targets of atmospheric Cherenkov telescopes.
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Peng, Yang; Ou, Qianting; Lin, Dongxin; Xu, Ping; Li, Ying; Ye, Xiaohua; Zhou, Junli; Yao, Zhenjiang
2015-10-29
Staphylococci are common causes of healthcare-associated and community-associated infections. However, limited data are available on the prevalence, phenotypes and molecular characteristics of Staphylococci in metro system around the world. 320 surface samples were collected from the Guangzhou metro system to isolate and characterize Staphylococci strains. Of the samples, 75.6% (242/320) were contaminated with Staphylococci. The Staphylococci isolates, especially the methicillin resistant isolates, were resistance to most of the antibiotics, with 79.8% (193/242) classified as multidrug resistant (MDR) strains. 8 strains of methicillin-resistant Staphylococcus aureus (MRSA) carried a range of staphylococcal cassette chromosome mec (SCCmec) types [I (1), II (3), III (2) and NT (2)]. Staphylococcus aureus isolates were classified into several ST types and showed possible cross transmissions of strains from various sources. All MRSA strains were positive for the qac gene, and only one methicillin-susceptible Staphylococci aureus (MSSA) strain was positive for the Panton-Valentine leukocidin (PVL) genes. This study demonstrated that environmental surfaces in the Guangzhou metro system may be a hazardous reservoir for transmission of Staphylococci to passengers. The resistance to antibiotics and disinfectants observed among isolates was also noteworthy.
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A kinematic method for footstrike pattern detection in barefoot and shod runners.
Altman, Allison R; Davis, Irene S
2012-02-01
Footstrike patterns during running can be classified discretely into a rearfoot strike, midfoot strike and forefoot strike by visual observation. However, the footstrike pattern can also be classified on a continuum, ranging from 0% to 100% (extreme rearfoot to extreme forefoot) using the strike index, a measure requiring force plate data. When force data are not available, an alternative method to quantify the strike pattern must be used. The purpose of this paper was to quantify the continuum of foot strike patterns using an easily attainable kinematic measure, and compare it to the strike index measure. Force and kinematic data from twenty subjects were collected as they ran across an embedded force plate. Strike index and the footstrike angle were identified for the four running conditions of rearfoot strike, midfoot strike and forefoot strike, as well as barefoot. The footstrike angle was calculated as the angle of the foot with respect to the ground in the sagittal plane. Results indicated that the footstrike angle was significantly correlated with strike index. The linear regression model suggested that strike index can be accurately estimated, in both barefoot and shod conditions, in the absence of force data. Copyright © 2011 Elsevier B.V. All rights reserved.
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The application of cat swarm optimisation algorithm in classifying small loan performance
NASA Astrophysics Data System (ADS)
Kencana, Eka N.; Kiswanti, Nyoman; Sari, Kartika
2017-10-01
It is common for banking system to analyse the feasibility of credit application before its approval. Although this process has been carefully done, there is no warranty that all credits will be repaid smoothly. This study aimed to know the accuracy of Cat Swarm Optimisation (CSO) algorithm in classifying small loans’ performance that is approved by Bank Rakyat Indonesia (BRI), one of several public banks in Indonesia. Data collected from 200 lenders were used in this work. The data matrix consists of 9 independent variables that represent profile of the credit, and one categorical dependent variable reflects credit’s performance. Prior to the analyses, data was divided into two data subset with equal size. Ordinal logistic regression (OLR) procedure is applied for the first subset and gave 3 out of 9 independent variables i.e. the amount of credit, credit’s period, and income per month of lender proved significantly affect credit performance. By using significantly estimated parameters from OLR procedure as the initial values for observations at the second subset, CSO procedure started. This procedure gave 76 percent of classification accuracy of credit performance, slightly better compared to 64 percent resulted from OLR procedure.
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Izumi, Takato; Yanagi, Kensuke; Fujita, Toshihiko
2016-08-01
In the present study, we report the identification of a sea anemone, Antennapeachia setouchi, collected in the Seto Inland Sea, which represents a new genus and new species. This new species has unusual tentacle and mesenterial arrangements that have not been observed in other species of Haloclavidae. There are 12 regular marginal tentacles and two 'antenna tentacles,' with the latter always rising upward and located on the oral disk near the mouth; the species is also characterized by its peculiar mesenterial pairs, consisting of a macrocneme and a microcneme. Furthermore, this species shows an interesting behavior: it can inflate its body like a balloon, lift above the seafloor, and drift with the sea current. The presence of a single, strong siphonoglyph, physa-like aboral end, and the lack of sphincter muscle classify this sea anemone within Haloclavidae. It resembles Peachia species, but cannot be classified in this genus as the new species has two pairs of mesenteries, consisting of a macrocneme and a microcneme, and irregular antenna tentacles. Therefore, we propose a new genus Antennapeachia to accommodate this species.
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PhenoTips: patient phenotyping software for clinical and research use.
Girdea, Marta; Dumitriu, Sergiu; Fiume, Marc; Bowdin, Sarah; Boycott, Kym M; Chénier, Sébastien; Chitayat, David; Faghfoury, Hanna; Meyn, M Stephen; Ray, Peter N; So, Joyce; Stavropoulos, Dimitri J; Brudno, Michael
2013-08-01
We have developed PhenoTips: open source software for collecting and analyzing phenotypic information for patients with genetic disorders. Our software combines an easy-to-use interface, compatible with any device that runs a Web browser, with a standardized database back end. The PhenoTips' user interface closely mirrors clinician workflows so as to facilitate the recording of observations made during the patient encounter. Collected data include demographics, medical history, family history, physical and laboratory measurements, physical findings, and additional notes. Phenotypic information is represented using the Human Phenotype Ontology; however, the complexity of the ontology is hidden behind a user interface, which combines simple selection of common phenotypes with error-tolerant, predictive search of the entire ontology. PhenoTips supports accurate diagnosis by analyzing the entered data, then suggesting additional clinical investigations and providing Online Mendelian Inheritance in Man (OMIM) links to likely disorders. By collecting, classifying, and analyzing phenotypic information during the patient encounter, PhenoTips allows for streamlining of clinic workflow, efficient data entry, improved diagnosis, standardization of collected patient phenotypes, and sharing of anonymized patient phenotype data for the study of rare disorders. Our source code and a demo version of PhenoTips are available at http://phenotips.org. © 2013 WILEY PERIODICALS, INC.
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ERIC Educational Resources Information Center
Baker, William P.; Leyva, Kathryn J.; Lang, Michael; Goodmanis, Ben
2002-01-01
Focuses on an activity in which students sample air at school and generate ideas about how to classify the microorganisms they observe. The results are used to compare air quality among schools via the Internet. Supports the development of scientific inquiry and technology skills. (DDR)
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Lili, Yang; Debiao, Du; Ruoyu, Ning; Deying, Chen; Junling, Wu
2017-08-01
Objective In this study, we aimed to evaluate the clinical effect of single-retainer all-ceramic resin-bonded fixed partial denture (RBFPD) on the single anterior tooth loss patients. Methods A total of 20 single-retainer all-ceramic RBFPD were fabricated and evaluated in a two-year follow-up observation. The restorations were examined on the basis of the American Public Health Association (APHA) criteria. Results A total of 20 single-retainer all-ceramic RBFPD achieved class A evaluation after a six-month follow-up observation. One single-retainer all-ceramic RBFPD was classified as class B for secondary caries after a one-year follow-up observation. After a two-year follow-up observation, one single-retainer all-ceramic RBFPD was classified as class B because of secondary caries, and one single-retainer all-ceramic RBFPD was classified as class B because of fracture. Conclusion Single-retainer all-ceramic RBFPD is a promising and optional method in replacing single anterior tooth.
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Pärkkä, Juha; Cluitmans, Luc; Ermes, Miikka
2010-09-01
Inactive and sedentary lifestyle is a major problem in many industrialized countries today. Automatic recognition of type of physical activity can be used to show the user the distribution of his daily activities and to motivate him into more active lifestyle. In this study, an automatic activity-recognition system consisting of wireless motion bands and a PDA is evaluated. The system classifies raw sensor data into activity types online. It uses a decision tree classifier, which has low computational cost and low battery consumption. The classifier parameters can be personalized online by performing a short bout of an activity and by telling the system which activity is being performed. Data were collected with seven volunteers during five everyday activities: lying, sitting/standing, walking, running, and cycling. The online system can detect these activities with overall 86.6% accuracy and with 94.0% accuracy after classifier personalization.
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Documentation of procedures for textural/spatial pattern recognition techniques
NASA Technical Reports Server (NTRS)
Haralick, R. M.; Bryant, W. F.
1976-01-01
A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.
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Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.
Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong
2018-05-24
This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.
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An Analysis of Measures Used to Evaluate the Air Force Critical Item Program
1991-09-01
example of a histogram. Cause & Effect Diagram. The cause and effect diagram was introduced in 1953 by Dr. Kaoru Ishikawa in summarizing the opinions of...Personal Interview. Air Force Institute of Technology, School of Engineering, Wright-Patterson AFB OH, 24 April 1991. 31. Ishikawa , Dr. Kaoru . Guide to...collected. How the data are collected will determine which measurement techniques are appropriate. Ishikawa classifies data collection into five categories
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Feather conditions and clinical scores as indicators of broilers welfare at the slaughterhouse.
Saraiva, S; Saraiva, C; Stilwell, G
2016-08-01
The objective of this study was to evaluate the welfare of 64 different broiler farms on the basis of feather conditions and clinical scores measures collected at the slaughterhouse. A 3-point scale (0, 1 or 2) was used to classify dirty feathers, footpad dermatitis and hock burns measures, and a 2-point scale (present or absent) was used to classify breast burns, breast blisters and breast ulcer measures. Flocks were allocated into three body weight (BW) classes (A, B, C): class A (light) ≥1.43 and ≤1.68kg, class B (medium) ≥1.69 and ≤1.93kg; class C (heavy) ≥1.94 and ≤2.41kg. The absence of hock burns was more common in class A, while mild hock burns was more common in class B flocks. Breast ulcer was observed in class C flocks. The association observed for mild hock burns, breast burns and severe footpad dermatitis can indicate a simultaneous occurrence of these painful lesions. Very dirty feathers and severe footpad dermatitis relationship suggest litter humidity to be the common underlying cause. In conclusion, it was shown that clinical indicators can be used at the slaughterhouse to identify welfare problems. In the studied flocks, footpad dermatitis, feather conditions and hock burns were the main restrictions for good welfare and should be considered significant welfare indicators of the on-farm rearing conditions. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Carrying capacity of Peucang Island for ecotourism management in Ujung Kulon National Park
NASA Astrophysics Data System (ADS)
Wiyono, K. H.; Muntasib, E. K. S. H.; Yulianda, F.
2018-05-01
Peucang Island is one of island in Ujung Kulon National Park (UKNP), appointed as priority area and welcome area for tourism. This research aimed to calculate the carrying capacity of Peucang Island for ecotourism development (Study sites of this research are Karang Copong jungle trail and 8 sites of Peucangs beach). This research used observation method (wildlife exploration, measure the lenght of jungle track, and measure 10 parameters of beach), literature study and and interview method to collect data. The data of jungle track analyzed use Cifuentes’s formula. The result showed that Karang Copong jungle trekking had 20,000 visitors/day for Physical Carrying Capacity (PCC), 4 838 visitors/day for Real Carrying Capacity (RCC), and 6 visitors/day for Efective Carrying Capacity (ECC). Observation of biological aspect showed that there were some damages of vegetation along the track, and the changes in animal behavior. The data of beach carrying capacity analyzed use Yulianda’s formula that measured with the suitability map approach. Based on the suitability map, two beaches were classified in suitable category, while six beaches) were classified in highly suitable category for tourism activities. All of the beaches had different number of carrying capacity, specifically there are 70 visitors/day in highly suitable beach and 27 visitors/day in suitable beach. The number of visitor nowadays still not exceed from carrying capacity number of PCC, RCC of jungle trails and carrying capacity of the beach area, but the number has exceeded from the ECC numbers.
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Collaborative Supervised Learning for Sensor Networks
NASA Technical Reports Server (NTRS)
Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran
2011-01-01
Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.
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Simões-Araújo, Jean Luiz; Rumjanek, Norma Gouvêa; Xavier, Gustavo Ribeiro; Zilli, Jerri Édson
The strain BR 3351 T (Bradyrhizobium manausense) was obtained from nodules of cowpea (Vigna unguiculata L. Walp) growing in soil collected from Amazon rainforest. Furthermore, it was observed that the strain has high capacity to fix nitrogen symbiotically in symbioses with cowpea. We report here the draft genome sequence of strain BR 3351 T . The information presented will be important for comparative analysis of nodulation and nitrogen fixation for diazotrophic bacteria. A draft genome with 9,145,311bp and 62.9% of GC content was assembled in 127 scaffolds using 100bp pair-end Illumina MiSeq system. The RAST annotation identified 8603 coding sequences, 51 RNAs genes, classified in 504 subsystems. Published by Elsevier Editora Ltda.
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Macrolides in Chronic Inflammatory Skin Disorders
Alzolibani, Abdullateef A.; Zedan, Khaled
2012-01-01
Long-term therapy with the macrolide antibiotic erythromycin was shown to alter the clinical course of diffuse panbronchiolitis in the late 1980s. Since that time, macrolides have been found to have a large number of anti-inflammatory properties in addition to being antimicrobials. These observations provided the rationale for many studies performed to assess the usefulness of macrolides in other inflammatory diseases including skin and hair disorders, such as rosacea, psoriasis, pityriasis rosea, alopecia areata, bullous pemphigoid, and pityriasis lichenoides. This paper summarizes a collection of clinical studies and case reports dealing with the potential benefits of macrolides antibiotics in the treatment of selected dermatoses which have primarily been classified as noninfectious and demonstrating their potential for being disease-modifying agents. PMID:22685371
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Marques, Gabriela Franco; Martins, Ana Luiza Grizzo Peres; Sousa, Juliana Martins Prazeres; Brandão, Letícia Stella Gardini; Wachholz, Patrick Alexander; Masuda, Paula Yoshiko
2015-01-01
We conducted a transversal retrospective study with secondary data collection from 25 cases of sporotrichosis, treated at a teaching unit in inner São Paulo (Brazil), between the years 2003-2013. We found that the prevalence was higher in men (72%), rural workers (44%) and those living in rural areas (60%), with an average age of 42.48 years. The median between the onset of lesions and diagnosis was six weeks. Lesions predominated in the upper limbs (92%), and were classified as lymphocutaneous (80%) and fixed cutaneous (20%) forms. Clinical cure was observed in 62.5% of the cases treated with potassium iodide and 100% of cases treated with itraconazole.
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Marques, Gabriela Franco; Martins, Ana Luiza Grizzo Peres; Sousa, Juliana Martins Prazeres; Brandão, Letícia Stella Gardini; Wachholz, Patrick Alexander; Masuda, Paula Yoshiko
2015-01-01
We conducted a transversal retrospective study with secondary data collection from 25 cases of sporotrichosis, treated at a teaching unit in inner São Paulo (Brazil), between the years 2003-2013. We found that the prevalence was higher in men (72%), rural workers (44%) and those living in rural areas (60%), with an average age of 42.48 years. The median between the onset of lesions and diagnosis was six weeks. Lesions predominated in the upper limbs (92%), and were classified as lymphocutaneous (80%) and fixed cutaneous (20%) forms. Clinical cure was observed in 62.5% of the cases treated with potassium iodide and 100% of cases treated with itraconazole. PMID:25831006
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USDA-ARS?s Scientific Manuscript database
Do near infrared spectra from lab-reared mosquitoes differ from spectra from wild mosquitoes? Near infrared spectroscopy (NIRS) can classify the age of lab-reared mosquitoes as younger or older than seven days with accuracy greater than 80%. Hence, it has been proposed in several studies as a comple...
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Highly Interactive WWW Services: A New Type of Information Sources.
ERIC Educational Resources Information Center
Vanouplines, Patrick; Nieuwenhuysen, P.
The World Wide Web is evolving from a collection of texts linked by hypertext and hypermedia toward services that operate interactively with the information user, and which offer results through use of a broad spectrum of tools. This paper presents a collection of interactive WWW services. The services are classified on the basis of the client…
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2012-01-01
Background Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble? Results The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity. Conclusion Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway. PMID:23216969
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Günther, Oliver P; Chen, Virginia; Freue, Gabriela Cohen; Balshaw, Robert F; Tebbutt, Scott J; Hollander, Zsuzsanna; Takhar, Mandeep; McMaster, W Robert; McManus, Bruce M; Keown, Paul A; Ng, Raymond T
2012-12-08
Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble? The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity. Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.
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NASA Astrophysics Data System (ADS)
Orenstein, E. C.; Morgado, P. M.; Peacock, E.; Sosik, H. M.; Jaffe, J. S.
2016-02-01
Technological advances in instrumentation and computing have allowed oceanographers to develop imaging systems capable of collecting extremely large data sets. With the advent of in situ plankton imaging systems, scientists must now commonly deal with "big data" sets containing tens of millions of samples spanning hundreds of classes, making manual classification untenable. Automated annotation methods are now considered to be the bottleneck between collection and interpretation. Typically, such classifiers learn to approximate a function that predicts a predefined set of classes for which a considerable amount of labeled training data is available. The requirement that the training data span all the classes of concern is problematic for plankton imaging systems since they sample such diverse, rapidly changing populations. These data sets may contain relatively rare, sparsely distributed, taxa that will not have associated training data; a classifier trained on a limited set of classes will miss these samples. The computer vision community, leveraging advances in Convolutional Neural Networks (CNNs), has recently attempted to tackle such problems using "zero-shot" object categorization methods. Under a zero-shot framework, a classifier is trained to map samples onto a set of attributes rather than a class label. These attributes can include visual and non-visual information such as what an organism is made out of, where it is distributed globally, or how it reproduces. A second stage classifier is then used to extrapolate a class. In this work, we demonstrate a zero-shot classifier, implemented with a CNN, to retrieve out-of-training-set labels from images. This method is applied to data from two continuously imaging, moored instruments: the Scripps Plankton Camera System (SPCS) and the Imaging FlowCytobot (IFCB). Results from simulated deployment scenarios indicate zero-shot classifiers could be successful at recovering samples of rare taxa in image sets. This capability will allow ecologists to identify trends in the distribution of difficult to sample organisms in their data.
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Elizabeth Brown and the Classification of Sunspots in the 19th Century
NASA Astrophysics Data System (ADS)
Larsen, Kristine
2014-06-01
British amateur astronomers collected solar observation data as members of organizations such as the British Astronomical Association (BAA) and Liverpool Astronomical Society (LAS) in the late 1800s. Amateur astronomer Elizabeth Brown (1830-99) served as Solar Section Director of both groups, and not only aggregated solar observations (including hand-drawn illustrations) from observers from around the globe, but worked closely with solar astronomer Edward Maunder and other professionals in an attempt to garner specific types of observations from BAA members in order to answer a number of astronomical questions of the day. For example, she encouraged the monitoring of the growth and decay of sunspot groups and published a number of her own observations of particular groups, urging observers to note whether faculae were seen before the birth of sunspots in a given region, a topic of controversy at that time. She also developed a system for classifying sunspots and sunspot groups based on their appearance, dividing then into 11 types: normal, compound, pairs, clusters, trains, streams, zigzags, elliptical, vertical, nebulous, and dots. This poster will summarize Brown’s important contributions to solar observing in the late 19th century and situate her classification scheme relative to those of A.L. Cortie (1901), M. Waldmeier (1938; 1947) and the modified Zurich system of McIntosh (1966; 1969; 1989).
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Applying machine-learning techniques to Twitter data for automatic hazard-event classification.
NASA Astrophysics Data System (ADS)
Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.
2017-12-01
The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy is lower (83%) than the SVM-LR, since the algorithm needs a bigger training dataset to increase its accuracy. We used TensorFlow framework for applying CNN classifier to the same collection of tweets.In future we will modify both classifiers to work with other geo-hazards, use larger training datasets and apply them in real-time.
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Gambini, A; Andrés, G; Jarazo, J; Javier, J; Karlanian, F; Florencia, K; De Stéfano, A; Salamone, D F
2014-02-01
The current limitations for obtaining ovaries from slaughterhouses and the low efficiency of in vivo follicular aspiration necessitate a complete understanding of the variables that affect oocyte developmental competence in the equine. For this reason, we assessed the effect on equine oocyte meiotic competence and the subsequent in vitro cloned embryo development of 1) the time interval between ovary collection and the onset of oocyte in vitro maturation (collection-maturation interval time) and 2) the pregnancy status of the donor mares. To define the collection-maturation interval time, collected oocytes were classified according to the slaughtering time and the pregnancy status of the mare. Maturation rate was recorded and some matured oocytes of each group were used to reconstruct zona free cloned embryos. Nuclear maturation rates were lower when the collection-maturation interval time exceeded 10 h as compared to 4 h (32/83 vs. 76/136, respectively; P = 0.0128) and when the donor mare was pregnant as compared to nonpregnant (53/146 vs. 177/329, respectively; P = 0.0004). Low rates of cleaved embryos were observed when the collection-maturation interval time exceeded 10 h as compared to 6 to 10 h (11/27 vs. 33/44, respectively; P = 0.0056), but the pregnancy status of donor mares did not affect cloned equine blastocyst development (3/49 vs. 1/27 for blastocyst rates of nonpregnant and pregnant groups, respectively; P = 1.00). These results indicate that, to apply assisted reproductive technologies in horses, oocytes should be harvested within approximately 10 h after ovary collection. Also, even though ovaries from pregnant mares are a potential source of oocytes, they should be processed at the end of the collection routine due to the lower collection and maturation rate in this group.
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12 CFR 792.63 - Collection of information from individuals; information forms.
Code of Federal Regulations, 2010 CFR
2010-01-01
... FREEDOM OF INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED... information concerning religion, political beliefs or activities, association memberships (other than those...
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Separation of Powers in Classifying International Agreements
1996-01-01
SEPARATION OF POWERS IN CLASSIFYING INTERNATIONAL AGREEMENTS CORE COURSE III ESSAY CDR James F Duffy, JAGC, USN, Class of 96 The National Secmty Policy Process SemmrH Faculty Semmar Instructor Dr John Rexhart Faculty Adviser CAPT J Kelso, USN Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing
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Graphical classification of DNA sequences of HLA alleles by deep learning.
Miyake, Jun; Kaneshita, Yuhei; Asatani, Satoshi; Tagawa, Seiichi; Niioka, Hirohiko; Hirano, Takashi
2018-04-01
Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence "Deep Learning (Stacked autoencoder)". Nucleotide sequence data corresponding to the length of 822 bp, collected from the Immuno Polymorphism Database, were compressed to 2-dimensional representation and were plotted. Profiles of the two-dimensional plots indicate that the alleles can be classified as clusters are formed. The two-dimensional plot of HLA-A DNAs gives a clear outlook for characterizing the various alleles.
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Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie
2015-01-01
Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.
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2015-01-01
Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations. PMID:25885272
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Brand, John; Johnson, Aaron P
2014-01-01
In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks.
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Brand, John; Johnson, Aaron P.
2014-01-01
In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks. PMID:25520675
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Meeuwig, M.H.; Bayer, J.M.; Seelye, J.G.; Reiche, R.A.
2003-01-01
Two fundamental aspects of lamprey biology were examined to provide tools for population assessment and determination of critical habitat needs of Columbia River Basin (CRB) lampreys (the Pacific lamprey, Lampetra tridentata, and the western brook lamprey, L. richardsoni). We evaluated the usefulness of current diagnostic characteristics for identification of larval lampreys (i.e., pigment patterns) and collected material for development of meristic and morphometric descriptions of early life stage CRB lampreys, and we determined the effects of temperature on survival and development of early life stage CRB lampreys. Thirty-one larval lampreys were collected from locations throughout the CRB and transported to the Columbia River Research Laboratory. Lampreys were sampled at six-week intervals at which time they were identified to the species level based on current diagnostic characteristics. Sampling was repeated until lampreys metamorphosed, at which time species identification was validated based on dentition, or until they died, at which time they were preserved for genetic examination. These lampreys were sampled 30 times with two individuals metamorphosing, both of which were consistently identified, and subsequently validated, as Pacific lampreys. Of the remaining lampreys, only one was inconsistently identified (Pacific lamprey in 83% of the sampling events and western brook lamprey in 17% of the sampling events). These data suggest that pigmentation patterns do not change appreciably through time. In 2001 and 2002 we artificially spawned Pacific and western brook lampreys in the laboratory to provide material for meristic and morphometric descriptions. We collected, digitized, preserved, and measured the mean chorion diameter of Pacific and western brook lamprey embryos. Embryos ranged in development from 1 d post fertilization to just prior to hatch, and were incubated at 14 C. Mean chorion diameter was greater and more variable for Pacific lampreys (mean {+-} SD; 1.468 {+-} 0.107 mm, N = 320) than for western brook lampreys (1.237 {+-} 0.064 mm, N = 280). An unpaired t-test showed that the difference in mean chorion diameter between species was highly significant (t = 32.788, df = 528.62, P < 0.0001). For larvae, we collected, digitized, and preserved 156 individuals from each species. Eight homologous landmarks defining a two-cell truss network with two appended triangles were selected for morphometric analyses and species discrimination. A full model discriminant analysis correctly classified 92% of the Pacific lampreys and 93% of the western brook lampreys in a classification data set. When applied to a test data set, the classification functions correctly classified 91% of the Pacific lampreys and 85% of the western brook lampreys. A backward elimination discriminant analysis removed four variables from the full model, and the reduced model correctly classified 91% of the Pacific lampreys and 93% of the western brook lampreys in a classification data set. The reduced model classification functions correctly classified 91% of the Pacific lampreys and 85% of the western brook lampreys in a test data set. In 2001 and 2002 Pacific and western brook lampreys were artificially spawned and resulting progeny were reared in the laboratory at 10 C, 14 C, 18 C, and 22 C. The estimated temperature for zero development was 4.85 C for Pacific and 4.97 C for western brook lampreys. Survival was greatest at 18 C followed by 14 C, 10 C, and 22 C, with significant differences observed between 22 C and other temperatures. Overall survival was significantly greater for western brook than for Pacific lampreys, although the difference in proportion of individuals surviving was only 0.02. Survival to hatch was significantly greater than survival to the larval stage with a difference of only 0.03. The proportion of individuals exhibiting abnormalities at the larval stage was greatest at 22 C followed by 18 C, 10 C, and 14 C, with significant differences observed between 22 C and other temperatures.
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A bench-top hyperspectral imaging system to classify beef from Nellore cattle based on tenderness
NASA Astrophysics Data System (ADS)
Nubiato, Keni Eduardo Zanoni; Mazon, Madeline Rezende; Antonelo, Daniel Silva; Calkins, Chris R.; Naganathan, Govindarajan Konda; Subbiah, Jeyamkondan; da Luz e Silva, Saulo
2018-03-01
The aim of this study was to evaluate the accuracy of classification of Nellore beef aged for 0, 7, 14, or 21 days and classification based on tenderness and aging period using a bench-top hyperspectral imaging system. A hyperspectral imaging system (λ = 928-2524 nm) was used to collect hyperspectral images of the Longissimus thoracis et lumborum (aging n = 376 and tenderness n = 345) of Nellore cattle. The image processing steps included selection of region of interest, extraction of spectra, and indentification and evalution of selected wavelengths for classification. Six linear discriminant models were developed to classify samples based on tenderness and aging period. The model using the first derivative of partial absorbance spectra (give wavelength range spectra) was able to classify steaks based on the tenderness with an overall accuracy of 89.8%. The model using the first derivative of full absorbance spectra was able to classify steaks based on aging period with an overall accuracy of 84.8%. The results demonstrate that the HIS may be a viable technology for classifying beef based on tenderness and aging period.
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Xu, Yan; Wang, Yining; Sun, Jian-Tao; Zhang, Jianwen; Tsujii, Junichi; Chang, Eric
2013-01-01
To build large collections of medical terms from semi-structured information sources (e.g. tables, lists, etc.) and encyclopedia sites on the web. The terms are classified into the three semantic categories, Medical Problems, Medications, and Medical Tests, which were used in i2b2 challenge tasks. We developed two systems, one for Chinese and another for English terms. The two systems share the same methodology and use the same software with minimum language dependent parts. We produced large collections of terms by exploiting billions of semi-structured information sources and encyclopedia sites on the Web. The standard performance metric of recall (R) is extended to three different types of Recall to take the surface variability of terms into consideration. They are Surface Recall (), Object Recall (), and Surface Head recall (). We use two test sets for Chinese. For English, we use a collection of terms in the 2010 i2b2 text. Two collections of terms, one for English and the other for Chinese, have been created. The terms in these collections are classified as either of Medical Problems, Medications, or Medical Tests in the i2b2 challenge tasks. The English collection contains 49,249 (Problems), 89,591 (Medications) and 25,107 (Tests) terms, while the Chinese one contains 66,780 (Problems), 101,025 (Medications), and 15,032 (Tests) terms. The proposed method of constructing a large collection of medical terms is both efficient and effective, and, most of all, independent of language. The collections will be made publicly available. PMID:23874426
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Xu, Yan; Wang, Yining; Sun, Jian-Tao; Zhang, Jianwen; Tsujii, Junichi; Chang, Eric
2013-01-01
To build large collections of medical terms from semi-structured information sources (e.g. tables, lists, etc.) and encyclopedia sites on the web. The terms are classified into the three semantic categories, Medical Problems, Medications, and Medical Tests, which were used in i2b2 challenge tasks. We developed two systems, one for Chinese and another for English terms. The two systems share the same methodology and use the same software with minimum language dependent parts. We produced large collections of terms by exploiting billions of semi-structured information sources and encyclopedia sites on the Web. The standard performance metric of recall (R) is extended to three different types of Recall to take the surface variability of terms into consideration. They are Surface Recall (R(S)), Object Recall (R(O)), and Surface Head recall (R(H)). We use two test sets for Chinese. For English, we use a collection of terms in the 2010 i2b2 text. Two collections of terms, one for English and the other for Chinese, have been created. The terms in these collections are classified as either of Medical Problems, Medications, or Medical Tests in the i2b2 challenge tasks. The English collection contains 49,249 (Problems), 89,591 (Medications) and 25,107 (Tests) terms, while the Chinese one contains 66,780 (Problems), 101,025 (Medications), and 15,032 (Tests) terms. The proposed method of constructing a large collection of medical terms is both efficient and effective, and, most of all, independent of language. The collections will be made publicly available.
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Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven
2017-01-01
Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive (> 50 cm -3 ) change for C6-derived CDNC relative to C5.1 for the 1.6 µm and 2.1 µm channel retrievals, corresponding to a neutral to -2 µm difference in droplet effective radius. For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning -25 to +50 cm -3 related to a +2.5 to -1 µm transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC.
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Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René; Mosimann, Urs
2012-01-01
Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario. PMID:26007727
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Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René; Mosimann, Urs
2015-05-21
Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.
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Collective behaviour across animal species.
DeLellis, Pietro; Polverino, Giovanni; Ustuner, Gozde; Abaid, Nicole; Macrì, Simone; Bollt, Erik M; Porfiri, Maurizio
2014-01-16
We posit a new geometric perspective to define, detect, and classify inherent patterns of collective behaviour across a variety of animal species. We show that machine learning techniques, and specifically the isometric mapping algorithm, allow the identification and interpretation of different types of collective behaviour in five social animal species. These results offer a first glimpse at the transformative potential of machine learning for ethology, similar to its impact on robotics, where it enabled robots to recognize objects and navigate the environment.
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Moreno, Mabel; Ferro, Cristina; Rosales-Chilama, Mariana; Rubiano, Luisa; Delgado, Marcela; Cossio, Alexandra; Gómez, Maria Adelaida; Ocampo, Clara; Saravia, Nancy Gore
2015-01-01
The expansion of transmission of cutaneous leishmaniasis from sylvatic ecosystems into peri-urban and domestic settings has occurred as sand flies have adapted to anthropogenic environmental modifications. Assessment of the intradomiciliary presence of sand flies in households of the settlement “La Cabaña”, in the Department of Risaralda, Colombia, revealed an abundance of Warileya rotundipennis. This unexpected observation motivated further analyses to evaluate the participation of this species in the transmission of cutaneous leishmaniasis. Collections using CDC light traps were conducted during two consecutive nights in May and August 2011. The total of 667 sand flies collected were classified into five species: W. rotundipennis (n = 654; 98.05%), Nyssomyia trapidoi (n = 7; 1.04%); Lutzomyia (Helcocyrtomyia) hartmanni (n = 3; 0.44%); Lutzomyia lichyi (n = 2; 0.29%) and Psychodopygus panamensis (n = 1; 0.14%). The striking predominance of W. rotundipennis within households during both wet (May) and dry (August) seasons, anthropophilic behavior demonstrated by human blood in 95.23% (60/63) evaluable blood-engorged specimens, and natural infection (5/168–3%) with genetically similar parasites of the Leishmania (Viannia) subgenus observed in a patient in this community, support the involvement of W. rotundipennis in the domestic transmission of cutaneous leishmaniasis in “La Cabaña”. PMID:25917717
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Sjetne, Ingeborg S; Iversen, Hilde H
2017-01-18
A national survey was conducted to measure and benchmark women's experiences with pregnancy, birth and postnatal care in Norway. The purpose of this secondary analysis is to explore potential variation in these experiences with regard to the survey respondents' geographic origin. Data were collected in a national observational cross-sectional study, by a self-administered questionnaire and from registries. The questionnaire collects patient reported experience measures (PREMS) of mainly nontechnical aspects of the health-care services. While taking the clustered characteristics of the respondents into consideration, we compared the mean scores on 16 indexes between women of four different geographic origins using linear regression models. The origin of the 4904 respondents were classified as Norway (n = 4028, 82%), Western Europe, North-America, Oceania (n = 233, 5%), Eastern Europe (n = 290, 6%), and Asia, Turkey, Africa, and South-America) (n = 353, 7%). The observed differences were moderate, and no consistency was present in the results in respect of direction or magnitude of the differences between the groups. With some important cautions, we conclude that this study did not detect systematic differences between groups of different geographic origin, in their experiences with pregnancy and maternity care in Norway.
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Decoding ecosystem services in the neighborhood through ...
Remediation to Restoration to Revitalization (R2R2R) is a place-based practice that requires ongoing communication amongst agencies, local governments, and citizens. One of the challenges is that each of these entities have different relationships with and responsibilities to sites where R2R2R unfolds. Sediment remediation and habitat restoration project goals, community planning, and lived experiences diverge in scale, focus, and interaction depending on the agency or individual. In order to address this disconnect, we developed a framework to sort and classify data and identify ecosystem services collected through inductive methods like participant observation and document analysis. Data were collected between June 2015 and December 2016 and analyzed through content analysis as a first step. Participant observation was conducted in relation to the City of Duluth St. Louis River Corridor planning process at park planning public meetings, community group meetings, and City of Duluth technical advisory meetings. Document analysis was conducted on a variety of City of Duluth plans. The framework that emerged from the analysis includes neighborhood components that individuals, organizations, and local governments may discuss in the context of their community. The characteristics are a mix of built environment types, structural dimensions, personal experiences, and human-environment relationships and include: parks/open spaces, trails or connections, housing, schoo
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NASA Technical Reports Server (NTRS)
Chapman, Bruce; Celi, Jorge; Hamilton, Steve; McDonald, Kyle
2013-01-01
UAVSAR, NASA's airborne Synthetic Aperture Radar (SAR), conducted an extended observational campaign in Central and South America in March 2013, primarily related to volcanic deformations along the Andean Mountain Range but also including a large number of flights studying other scientific phenomena. During this campaign, the L-Band SAR collected data over the Napo River in Ecuador. The objectives of this experiment were to acquire polarimetric and interferometric L-Band SAR data over an inundated tropical forest in Ecuador simultaneously with on-the-ground field work ascertaining the extent of inundation, and to then derive from this data a quantitative estimate for the error in the SAR-derived inundation extent. In this paper, we will first describe the processing and preliminary analysis of the SAR data. The polarimetric SAR data will be classified by land cover and inundation state. The interferometric SAR data will be used to identify those areas where change in inundation extent occurred, and to measure the change in water level between two observations separated by a week. Second, we will describe the collection of the field estimates of inundation, and have preliminary comparisons of inundation extent measured in the field field versus that estimated from the SAR data.
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Quantification and characterization of greywater from schools.
Alsulaili, Abdalrahman D; Hamoda, Mohamed F
2015-01-01
Survey of schools of different education levels (primary, intermediate and secondary) in Kuwait showed an average greywater generation rate of 7.3 L/p/d and varied in the range of 2.9-16 l/p/d, reflecting the school level of education (i.e. student age). The highest rates were observed for primary schools while the lowest rates were observed in secondary schools where students are more mature and use the water more wisely. The greywater characteristics indicated waste with low chemical oxygen demand (COD) and 5-day biochemical oxygen demand (BOD5) values but relatively high solids, conductivity, and sodium content due to excessive use of hand soap. Total coliform values ranged between 89 and 352 most probable number (MPN)/mL with an average of 196 MPN/mL while no fecal coliform values were detected. Greywater collected from schools is classified as light greywater and contains much lower levels of organic matter and nutrients compared to residential greywater and domestic wastewater. It is suitable for non-potable reuse after minimal treatment since microbial contamination may pose a serious threat to health if greywater comes into contact with humans. It also provides a good opportunity for reuse in toilet flushing since it can be easily collected from wash sinks and fountains, as major sources, and recycled.
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Date palm sap linked to Nipah virus outbreak in Bangladesh, 2008.
Rahman, Muhammad Aziz; Hossain, Mohammad Jahangir; Sultana, Sharmin; Homaira, Nusrat; Khan, Salah Uddin; Rahman, Mahmudur; Gurley, Emily S; Rollin, Pierre E; Lo, Michael K; Comer, James A; Lowe, Luis; Rota, Paul A; Ksiazek, Thomas G; Kenah, Eben; Sharker, Yushuf; Luby, Stephen P
2012-01-01
We investigated a cluster of patients with encephalitis in the Manikgonj and Rajbari Districts of Bangladesh in February 2008 to determine the etiology and risk factors for disease. We classified persons as confirmed Nipah cases by the presence of immunoglobulin M antibodies against Nipah virus (NiV), or by the presence of NiV RNA or by isolation of NiV from cerebrospinal fluid or throat swabs who had onset of symptoms between February 6 and March 10, 2008. We classified persons as probable cases if they reported fever with convulsions or altered mental status, who resided in the outbreak areas during that period, and who died before serum samples were collected. For the case-control study, we compared both confirmed and probable Nipah case-patients to controls, who were free from illness during the reference period. We used motion-sensor-infrared cameras to observe bat's contact of date palm sap. We identified four confirmed and six probable case-patients, nine (90%) of whom died. The median age of the cases was 10 years; eight were males. The outbreak occurred simultaneously in two communities that were 44 km apart and separated by a river. Drinking raw date palm sap 2-12 days before illness onset was the only risk factor most strongly associated with the illness (adjusted odds ratio 25, 95% confidence intervals 3.3-∞, p<0.001). Case-patients reported no history of physical contact with bats, though community members often reported seeing bats. Infrared camera photographs showed that Pteropus bats frequently visited date palm trees in those communities where sap was collected for human consumption. This is the second Nipah outbreak in Bangladesh where date palm sap has been implicated as the vehicle of transmission. Fresh date palm sap should not be drunk, unless effective steps have been taken to prevent bat access to the sap during collection.
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A Random Forest-based ensemble method for activity recognition.
Feng, Zengtao; Mo, Lingfei; Li, Meng
2015-01-01
This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.
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Geophysical and Geochemical Analysis of the 8°20' N Seamount Chain: Studies of Off-Axis Volcanism
NASA Astrophysics Data System (ADS)
McCully, E.; Fornari, D. J.; Gregg, P. M.; Perfit, M. R.; Wanless, V. D.; Anderson, M.; Lubetkin, M.
2017-12-01
The 8°20' N Seamount Chain is an off-axis lineament of volcanoes located west of the East Pacific Rise (EPR) and 15 km north of the Siqueiros Fracture Zone. The volcanoes are located 11 km west of the EPR axis and extend 160 km to the west. The OASIS (Off-Axis Seamount Investigations at Siqueiros) expedition in November 2016 collected ship-based EM122 bathymetry aboard the R/V Atlantis over the entire seamount chain at a 50 m resolution, and AUV Sentry bathymetric and sidescan sonar data were collected over 11 selected areas on some of the seamount summits and flanks at 1-2 m resolution. 90,000 high-resolution digital images were acquired using DSV Alvin and analyzed and classified according to morphology, structure, sediment and manganese presence, and biology. These data are used to create geologic facies maps to correlate seafloor morphology and type with acoustic reflectivity. Major and trace element data of samples collected by Alvin and dredging are also correlated to geological parameters of the seafloor features on each studied seamount. Initial estimates for the volumes of individual constructional features (e.g., mounds, cones) that comprise the seamounts were derived from the high-resolution EM122 multibeam and Sentry AUV bathymetric data and calculated using IVS Fledermaus and plotted as a function of distance from the EPR. These individually constructed volcanic features, dependent on geochemical diversity, may ultimately be grouped into larger eruptive volumes. Thus far, Sentry-derived volumes range from 0.0011-2.96 km3, while EM122-derived volumes range from 0.13-123 km3. The seamounts were classified into 3 shapes; circular, volcanic lineaments aligning parallel to the ridge-axis, and ridge-like constructions, trending perpendicular to the EPR axis. The central 60 km of the chain (60-120 km off-axis) is dominated by ridges and circular seamounts, which exhibit the largest volumes observed along the 8°20' N chain. The seamounts with the lowest volumes are observed in the eastern-most 50 km of the lineament, nearest to the ridge axis. Future work includes distinguishing monogenetic and polygenetic cones and better quantifying how many eruptive periods occurred to form the present seamount morphology.
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Assessing the work of medical audit advisory groups in promoting audit in general practice.
Baker, R; Hearnshaw, H; Cooper, A; Cheater, F; Robertson, N
1995-12-01
Objectives--To determine the role of medical audit advisory groups in audit activities in general practice. Design--Postal questionnaire survey. Subjects--All 104 advisory groups in England and Wales in 1994. Main measures--Monitoring audit: the methods used to classify audits, the methods used by the advisory group to collect data on audits from general practices, the proportion of practices undertaking audit. Directing and coordinating audits: topics and number of practices participating in multipractice audits. Results--The response rate was 86-5%. In 1993-4, 54% of the advisory groups used the Oxfordshire or Kirklees methods for classifying audits, or modifications of them. 99% of the advisory groups collected data on audit activities at least once between 1991-2 and 1993-4. Visits, questionnaires, and other methods were used to collect information from all or samples of practices in each of the advisory group's areas. Some advisory groups used different methods in different years. In 1991-2, 57% of all practices participated in some audit, in 1992-3, 78%, and in 1993-4, 86%. 428 multipractice audits were identified. The most popular topic was diabetes. Conclusions--Advisory groups have been active in monitoring audit in general practice. However, the methods used to classify and collect information about audits in general practices varied widely. The number of practices undertaking audit increased between 1991-2 and 1993 1. The large number of multipractice audits supports the view that the advisory groups have directed and coordinated audit activities. This example of a national audit programme for general practice may be helpful in other countries in which the introduction of quality assurance is being considered.
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A new algorithm for reducing the workload of experts in performing systematic reviews.
Matwin, Stan; Kouznetsov, Alexandre; Inkpen, Diana; Frunza, Oana; O'Blenis, Peter
2010-01-01
To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment. The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifier's parameters and performance. Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance. The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system. The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machine-learning-based automation of systematic reviews of drug class efficacy for disease treatment.
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NASA Astrophysics Data System (ADS)
Molenda-Żakowicz, Joanna; Frasca, Antonio; De Cat, Peter; Catanzaro, Giovanni
2017-09-01
We summarize the results of the completed first round of the LAMOST-Kepler project, and describe the status of its on-going second round. As a result of the first round of this project, the atmospheric parameters (Teff, log g, and [Fe/H]), the spectral classification (spectral type and luminosity class), and the radial velocities (RV) have been measured for 51,385 stars. For 4031 stars, we were able to measure the projected rotational velocity, while the minimum detectable v sin i was 120 km s-1. For 8821 stars with more than one observation, we computed the χ-square probability that the detected RV variations have a random occurrence. Finally, we classified 442 stars as chromospherically active on the basis of the analysis of their Hα and Ca II-IRT fluxes. All our results have been obtained from the low-resolution (R ˜ 1800) spectroscopic observations acquired with the LAMOST instrument. Based on observations collected with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) located at the Xinglong Observatory, China.
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Climatically driven loss of calcium in steppe soil as a sink for atmospheric carbon
A.G. Lapenis; G.B. Lawrence; S.W. Bailey; B.F. Aparin; A.I. Shiklomanov; N.A. Speranskaya; M.S. Torn; M. Calef
2008-01-01
During the last several thousand years the semi-arid, cold climate of the Russian steppe formed highly fertile soils rich in organic carbon and calcium (classified as Chernozems in the Russian system). Analysis of archived soil samples collected in Kemannaya Steppe Preserve in 1920, 1947, 1970, and fresh samples collected in 1998 indicated that the native steppe...
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Classification of yeast cells from image features to evaluate pathogen conditions
NASA Astrophysics Data System (ADS)
van der Putten, Peter; Bertens, Laura; Liu, Jinshuo; Hagen, Ferry; Boekhout, Teun; Verbeek, Fons J.
2007-01-01
Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images. We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building and analyzing image collections from the yeast under different environmental or genetic conditions may help to diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating feature extraction from the images. We compare various expertise based and fully automated methods of feature selection and benchmark a range of classification algorithms and illustrate successful application to this particular domain.
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Tamura, Masato; Sugiura, Shinji; Takagi, Toshiyuki; Satoh, Taku; Sumaru, Kimio; Kanamori, Toshiyuki; Okada, Tomoko; Matsui, Hirofumi
2017-01-01
Understanding tumor heterogeneity is an urgent and unmet need in cancer research. In this study, we used a morphology-based optical cell separation process to classify a heterogeneous cancer cell population into characteristic subpopulations. To classify the cell subpopulations, we assessed their morphology in hydrogel, a three-dimensional culture environment that induces morphological changes according to the characteristics of the cells (i.e., growth, migration, and invasion). We encapsulated the murine breast cancer cell line 4T1E, as a heterogeneous population that includes highly metastatic cells, in click-crosslinkable and photodegradable gelatin hydrogels, which we developed previously. We observed morphological changes within 3 days of encapsulating the cells in the hydrogel. We separated the 4T1E cell population into colony- and granular-type cells by optical separation, in which local UV-induced degradation of the photodegradable hydrogel around the target cells enabled us to collect those cells. The obtained colony- and granular-type cells were evaluated in vitro by using a spheroid assay and in vivo by means of a tumor growth and metastasis assay. The spheroid assay showed that the colony-type cells formed compact spheroids in 2 days, whereas the granular-type cells did not form spheroids. The tumor growth assay in mice revealed that the granular-type cells exhibited lower tumor growth and a different metastasis behavior compared with the colony-type cells. These results suggest that morphology-based optical cell separation is a useful technique to classify a heterogeneous cancer cell population according to its cellular characteristics.
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Canine ocular gliomas: a retrospective study.
Naranjo, Carolina; Schobert, Charles; Dubielzig, Richard
2008-01-01
The purpose of this paper is to classify glial tumors observed in the canine retina and optic nerve, describe the histopathological features and provide prognostic information on these neoplasms. The database of the Comparative Ocular Pathology Laboratory of Wisconsin (COPLOW) was searched to collect canine glioma cases. Clinical and follow-up information was gathered from submission forms and an extensive follow-up survey. Slides were reviewed to describe the histopathological characteristics of the neoplasm and classify them. Immunohistochemistry for Glial Fibrillary Acidic Protein (GFAP) was performed in all cases. 18 canine glioma cases were found in the COPLOW database. There was no breed or gender predilection. The mean age was 9.33 +/- 3.67 years. Follow-up information was available for 12 dogs, 8 of which were dead at the time of most recent contact, with a survival time ranging from 0 days (globes received after euthanasia) up to 20 months post-enucleation. In 6 of the 8 dogs that had died during this stud), tumor extended to the margin where the optic nerve had been sectioned. Light microscopic examination of the optic nerve of the affected eyes of four dogs that were still alive during this study revealed no tumor at this surgical margin. One neoplasm was classified as low-grade astrocytoma, 5 tumors as medium-grade astrocytoma, 11 tumors as high grade-astrocytoma and 1 tumor as oligodendroglioma. GFAP was positive in all but two tumors. Retinal and optic nerve gliomas may be considered as differential diagnoses of intraocular and orbital masses. The metastatic potential appears to be low, but ascending invasion into the ventral aspect of the brain is possible.
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Activity classification using realistic data from wearable sensors.
Pärkkä, Juha; Ermes, Miikka; Korpipää, Panu; Mäntyjärvi, Jani; Peltola, Johannes; Korhonen, Ilkka
2006-01-01
Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.
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Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)
Mad Saad, Shaharil; Melvin Andrew, Allan; Md Shakaff, Ali Yeon; Mohd Saad, Abdul Rahman; Muhamad Yusof @ Kamarudin, Azman; Zakaria, Ammar
2015-01-01
Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity. PMID:26007724
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Thenkabail, P.S.; Mariotto, I.; Gumma, M.K.; Middleton, E.M.; Landis, D.R.; Huemmrich, K.F.
2013-01-01
The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy ~70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using ~20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was ~ 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysi- al characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI.
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NASA Technical Reports Server (NTRS)
Thenkabail, Prasad S.; Mariotto, Isabella; Gumma, Murali Krishna; Middleton, Elizabeth M.; Landis, David R.; Huemmrich, K. Fred
2013-01-01
The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.
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NASA Astrophysics Data System (ADS)
Lazri, Mourad; Ameur, Soltane
2018-05-01
A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.
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Study of deaths by suicide of homosexual prisoners in Nazi Sachsenhausen concentration camp
Cuerda-Galindo, Esther; Krischel, Matthis; Ley, Astrid
2017-01-01
Living conditions in Nazi concentration camps were harsh and inhumane, leading many prisoners to commit suicide. Sachsenhausen (Oranienburg, Germany) was a concentration camp that operated from 1936 to 1945. More than 200,000 people were detained there under Nazi rule. This study analyzes deaths classified as suicides by inmates in this camp, classified as homosexuals, both according to the surviving Nazi files. This collective was especially repressed by the Nazi authorities. Data was collected from the archives of Sachsenhausen Memorial and the International Tracing Service in Bad Arolsen. Original death certificates and autopsy reports were reviewed. Until the end of World War II, there are 14 death certificates which state “suicide” as cause of death of prisoners classified as homosexuals, all of them men aged between 23 and 59 years and of various religions and social strata. Based on a population of 1,200 prisoners classified as homosexuals, this allows us to calculate a suicide rate of 1,167/100,000 (over the period of eight years) for this population, a rate 10 times higher than for global inmates (111/100,000). However, our study has several limitations: not all suicides are registered; some murders were covered-up as suicides; most documents were lost during the war or destroyed by the Nazis when leaving the camps and not much data is available from other camps to compare. We conclude that committing suicides in Sachsenhausen was a common practice, although accurate data may be impossible to obtain. PMID:28426734
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Study of deaths by suicide of homosexual prisoners in Nazi Sachsenhausen concentration camp.
Cuerda-Galindo, Esther; López-Muñoz, Francisco; Krischel, Matthis; Ley, Astrid
2017-01-01
Living conditions in Nazi concentration camps were harsh and inhumane, leading many prisoners to commit suicide. Sachsenhausen (Oranienburg, Germany) was a concentration camp that operated from 1936 to 1945. More than 200,000 people were detained there under Nazi rule. This study analyzes deaths classified as suicides by inmates in this camp, classified as homosexuals, both according to the surviving Nazi files. This collective was especially repressed by the Nazi authorities. Data was collected from the archives of Sachsenhausen Memorial and the International Tracing Service in Bad Arolsen. Original death certificates and autopsy reports were reviewed. Until the end of World War II, there are 14 death certificates which state "suicide" as cause of death of prisoners classified as homosexuals, all of them men aged between 23 and 59 years and of various religions and social strata. Based on a population of 1,200 prisoners classified as homosexuals, this allows us to calculate a suicide rate of 1,167/100,000 (over the period of eight years) for this population, a rate 10 times higher than for global inmates (111/100,000). However, our study has several limitations: not all suicides are registered; some murders were covered-up as suicides; most documents were lost during the war or destroyed by the Nazis when leaving the camps and not much data is available from other camps to compare. We conclude that committing suicides in Sachsenhausen was a common practice, although accurate data may be impossible to obtain.
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The appearance of facial foundation cosmetics applied after metronidazole gel 1%.
Draelos, Zoe D; Colón, Luz E; Preston, Norman; Johnson, Lori A; Gottschalk, Ronald W
2011-05-01
The purpose of this study was to assess the cosmetic appearance of commonly marketed facial cosmetics when used after the application of metronidazole gel 1%. An observational. open-label, single-site study was conducted with women (N=30) aged 20 to 75 years and diagnosed with moderate papulopustular rosacea (investigator global severity score of 3). After cleansing the face with a gentle skin cleanser, participants applied metronidazole gel 1% once daily before applying their usual facial foundation. Two surveys were conducted: (1) investigator assessment of cosmetic appearance; and (2) participant assessment of cosmetic appearance. The investigator also evaluated erythema, disease severity, and tolerability at baseline and week 2. Adverse events were collected. The 28 per-protocol (PP) participants had a mean age (standard deviation [SD]) of 54.0 (10.3) years and a mean duration (SD) of rosacea of 15.4 (13.2) years. The median response score for both the investigator and participant assessments of cosmetic appearance was 10 (best) for each survey question. Signs and symptoms of rosacea did not increase with use of metronidazole gel 1% and the participants' selected cosmetic regimen. At baseline all 28 participants were classified as having moderate erythema. At week 2, 18 (64%) participants were classified as having moderate erythema and 10 (36%) mild. At baseline all 28 (100%) participants were classified as having moderate rosacea according to the investigator global severity score. At week 2, 10 (36%) participants were classified as mild and 18 (64%) moderate. In addition, few participants reported cutaneous irritation during the study. At week 2, 10 participants had dryness, 2 had itching, 8 had scaling, and 2 had stinging/burning. According to surveys completed by the investigator and the participants themselves, most participants had a good cosmetic appearance with their facial foundation cosmetics that were applied after metronidazole gel 1%. The use of various cosmetic regimens after application of metronidazole gel 1% did not cause rosacea symptoms to worsen and treatment was well-tolerated.
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A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping
Kzar, Ahmed Asal; Mat Jafri, Mohd Zubir; Mutter, Kussay N.; Syahreza, Saumi
2015-01-01
Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images). PMID:26729148
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Classification of Normal and Pathological Gait in Young Children Based on Foot Pressure Data.
Guo, Guodong; Guffey, Keegan; Chen, Wenbin; Pergami, Paola
2017-01-01
Human gait recognition, an active research topic in computer vision, is generally based on data obtained from images/videos. We applied computer vision technology to classify pathology-related changes in gait in young children using a foot-pressure database collected using the GAITRite walkway system. As foot positioning changes with children's development, we also investigated the possibility of age estimation based on this data. Our results demonstrate that the data collected by the GAITRite system can be used for normal/pathological gait classification. Combining age information and normal/pathological gait classification increases the accuracy of the classifier. This novel approach could support the development of an accurate, real-time, and economic measure of gait abnormalities in children, able to provide important feedback to clinicians regarding the effect of rehabilitation interventions, and to support targeted treatment modifications.
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A method for detecting fungal contaminants in wall cavities.
Spurgeon, Joe C
2003-01-01
This article describes a practical method for detecting the presence of both fungal spores and culturable fungi in wall cavities. Culturable fungi were collected in 25 mm cassettes containing 0.8 microm mixed cellulose ester filters using aggressive sampling conditions. Both culturable fungi and fungal spores were collected in modified slotted-disk cassettes. The sample volume was 4 L. The filters were examined microscopically and dilution plated onto multiple culture media. Collecting airborne samples in filter cassettes was an effective method for assessing wall cavities for fungal contaminants, especially because this method allowed the sample to be analyzed by both microscopy and culture media. Assessment criteria were developed that allowed the sample results to be used to classify wall cavities as either uncontaminated or contaminated. As a criterion, wall cavities with concentrations of culturable fungi below the limit of detection (LOD) were classified as uncontaminated, whereas those cavities with detectable concentrations of culturable fungi were classified as contaminated. A total of 150 wall cavities was sampled as part of a field project. The concentrations of culturable fungi were below the LOD in 34% of the samples, whereas Aspergillus and/or Penicillium were the only fungal genera detected in 69% of the samples in which culturable fungi were detected. Spore counting resulted in the detection of Stachybotrys-like spores in 25% of the samples that were analyzed, whereas Stachybotrys chartarum colonies were only detected on 2% of malt extract agar plates and on 6% of corn meal agar plates.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Álvarez Crespo, N.; Massaro, F.; Masetti, N.
The extragalactic γ-ray sky is dominated by emission from blazars, a peculiar class of active galactic nuclei. Many of the γ-ray sources included in the Fermi-Large Area Telescope Third Source catalog (3FGL) are classified as blazar candidates of uncertain type (BCUs) because there are no optical spectra available in the literature to confirm their nature. In 2013, we started a spectroscopic campaign to look for the optical counterparts of the BCUs and of the unidentified γ-ray sources to confirm their blazar nature. Whenever possible we also determine their redshifts. Here, we present the results of the observations carried out inmore » the northern hemisphere in 2013 and 2014 at the Telescopio Nazionale Galileo, Kitt Peak National Observatory, and Observatorio Astronómico Nacional in San Pedro Mártir. In this paper, we describe the optical spectra of 25 sources. We confirmed that all of the 15 BCUs observed in our campaign and included in our sample are blazars and we estimated the redshifts for three of them. In addition, we present the spectra for three sources classified as BL Lacs in the literature but with no optical spectra available to date. We found that one of them is a quasar (QSO) at a redshift of z = 0.208 and the other two are BL Lacs. Moreover, we also present seven new spectra for known blazars listed in the Roma-BZCAT that have an uncertain redshift or are classified as BL Lac candidates. We found that one of them, 5BZB J0724+2621, is a “changing look” blazar. According to the spectrum available in the literature, it was classified as a BL Lac, but in our observation we clearly detected a broad emission line that led us to classify this source as a QSO at z = 1.17.« less
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A Campus-Wide Study of STEM Courses: New Perspectives on Teaching Practices and Perceptions
Vinson, Erin L.; Smith, Jeremy A.; Lewin, Justin D.; Stetzer, MacKenzie R.
2014-01-01
At the University of Maine, middle and high school science, technology, engineering, and mathematics (STEM) teachers observed 51 STEM courses across 13 different departments and collected information on the active-engagement nature of instruction. The results of these observations show that faculty members teaching STEM courses cannot simply be classified into two groups, traditional lecturers or instructors who teach in a highly interactive manner, but instead exhibit a continuum of instructional behaviors between these two classifications. In addition, the observation data reveal that student behavior differs greatly in classes with varied levels of lecture. Although faculty members who teach large-enrollment courses are more likely to lecture, we also identified instructors of several large courses using interactive teaching methods. Observed faculty members were also asked to complete a survey about how often they use specific teaching practices, and we find that faculty members are generally self-aware of their own practices. Taken together, these findings provide comprehensive information about the range of STEM teaching practices at a campus-wide level and how such information can be used to design targeted professional development for faculty. PMID:25452485
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Assessment of sensor performance
NASA Astrophysics Data System (ADS)
Waldmann, C.; Tamburri, M.; Prien, R. D.; Fietzek, P.
2010-02-01
There is an international commitment to develop a comprehensive, coordinated and sustained ocean observation system. However, a foundation for any observing, monitoring or research effort is effective and reliable in situ sensor technologies that accurately measure key environmental parameters. Ultimately, the data used for modelling efforts, management decisions and rapid responses to ocean hazards are only as good as the instruments that collect them. There is also a compelling need to develop and incorporate new or novel technologies to improve all aspects of existing observing systems and meet various emerging challenges. Assessment of Sensor Performance was a cross-cutting issues session at the international OceanSensors08 workshop in Warnemünde, Germany, which also has penetrated some of the papers published as a result of the workshop (Denuault, 2009; Kröger et al., 2009; Zielinski et al., 2009). The discussions were focused on how best to classify and validate the instruments required for effective and reliable ocean observations and research. The following is a summary of the discussions and conclusions drawn from this workshop, which specifically addresses the characterisation of sensor systems, technology readiness levels, verification of sensor performance and quality management of sensor systems.
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Rotational Study of Ambiguous Taxonomic Classified Asteroids
NASA Astrophysics Data System (ADS)
Linder, Tyler R.; Sanchez, Rick; Wuerker, Wolfgang; Clayson, Timothy; Giles, Tucker
2017-01-01
The Sloan Digital Sky Survey (SDSS) moving object catalog (MOC4) provided the largest ever catalog of asteroid spectrophotometry observations. Carvano et al. (2010), while analyzing MOC4, discovered that individual observations of asteroids which were observed multiple times did not classify into the same photometric-based taxonomic class. A small subset of those asteroids were classified as having both the presence and absence of a 1um silicate absorption feature. If these variations are linked to differences in surface mineralogy, the prevailing assumption that an asteroid’s surface composition is predominantly homogenous would need to be reexamined. Furthermore, our understanding of the evolution of the asteroid belt, as well as the linkage between certain asteroids and meteorite types may need to be modified.This research is an investigation to determine the rotational rates of these taxonomically ambiguous asteroids. Initial questions to be answered:Do these asteroids have unique or nonstandard rotational rates?Is there any evidence in their light curve to suggest an abnormality?Observations were taken using PROMPT6 a 0.41-m telescope apart of the SKYNET network at Cerro Tololo Inter-American Observatory (CTIO). Observations were calibrated and analyzed using Canopus software. Initial results will be presented at AAS.
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Cerruela García, G; García-Pedrajas, N; Luque Ruiz, I; Gómez-Nieto, M Á
2018-03-01
This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
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Collective behaviour across animal species
DeLellis, Pietro; Polverino, Giovanni; Ustuner, Gozde; Abaid, Nicole; Macrì, Simone; Bollt, Erik M.; Porfiri, Maurizio
2014-01-01
We posit a new geometric perspective to define, detect, and classify inherent patterns of collective behaviour across a variety of animal species. We show that machine learning techniques, and specifically the isometric mapping algorithm, allow the identification and interpretation of different types of collective behaviour in five social animal species. These results offer a first glimpse at the transformative potential of machine learning for ethology, similar to its impact on robotics, where it enabled robots to recognize objects and navigate the environment. PMID:24430561
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Liébanas, G.; Peña-Santiago, R.; Real, R.; Márquez, A. L.
2002-01-01
The spatial distribution of 138 Dorylaimid and Mononchid species collected in a natural area from the Southeast Iberian Peninsula was studied. A chorological classification was used to examine distribution patterns shared by groups of species. Eighty species were classified into 14 collective and 16 individual chorotypes. The geographical projections of several collective chorotypes are illustrated along with their corresponding distribution maps. The importance of this analysis to nematological study is briefly discussed. PMID:19265962
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Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps
Cheu, Ruey Long; Guo, Xiucheng; Romo, Alicia
2014-01-01
A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower's velocity, relative velocity, and gap) while the output signals represented the response (the follower's acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity. PMID:25538767
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Villumsen, Morten; Jorgensen, Martin Gronbech; Andreasen, Jane; Rathleff, Michael Skovdal; Mølgaard, Carsten Møller
2015-10-01
Lack of activity during hospitalization may contribute to functional decline. The purpose of this study was to investigate (1) the time spent walking during hospitalization by geriatric patients referred to physical and/or occupational therapy and (2) the development in time spent walking during hospitalization. In this observational study, 24-hr accelerometer data (ActivPal) were collected from inclusion to discharge in 124 patients at an acute geriatric ward. The median time spent walking was 7 min per day. During the first quartile of hospitalization, the patients spent 4 (IQR:1;11) min per day walking, increasing to 10 (IQR:1;29) min during the last quartile. Improvement in time spent walking was primarily observed in the group able to perform the Timed Up & Go task at admission. When walking only 7 min per day, patients could be classified as inactive and at risk for functional decline; nonetheless, the physical activity level increased significantly during hospitalization.
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ANTIRADICAL AND ANTIMICROBIAL ACTIVITY OF PHENOLIC FRACTIONS OBTAINED FROM HONEYS.
Mazol, Irena; Sroka, Zbigniew; Sowa, Alina; Ostrowska, Anna; Dryś, Andrzej; Gamian, Andrzej
2016-01-01
Honey is a natural product consisting of multiple components which determine its dietary and medicinal properties. In this work there were studied methanol fractions obtained from seven honeys from Lower Silesia (Poland) collected in different seasons of three successive years. Melissopalynologic studies revealed that two of them were polyfloral, and five were classified as monofloral (two buckwheat and three rapes). The amount of phenolic compounds in honeys varied from 0.09 to 0.38 mg per g of honey. Honeys harvested in 2010 were the richest in phenolic compounds and especially rich was buckwheat honey, comparing to 2011- 2012. Determination of antioxidant potential with the DPPH radical revealed that the strongest antiradical activity was exhibited by extracts obtained from polyfloral (1.22 TAU(515/mg)) and buckwheat (1.06 TAU(515lmg)) honeys, while the highest number of antiradical units was observed for rape honey (3.64 TAU(515/g)). Polyphenolic fractions exhibited various bactericidal activities against Klebsiella pneumoniae and Staphylococcus aureus and weak or no activity was observed against Pseudomonas aeruginosa.
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Work ability in nursing: relationship with psychological demands and control over the work.
Prochnow, Andrea; Magnago, Tânia Solange Bosi de Souza; Urbanetto, Janete de Souza; Beck, Carmem Lúcia Colomé; Lima, Suzinara Beatriz Soares de; Greco, Patrícia Bitencourt Toscani
2013-01-01
to evaluate the association between psychological demands, control over the work and the reduction of work ability of nursing professionals. this cross-sectional study involved 498 nursing professionals of a university hospital in the State of Rio Grande do Sul, Brazil. Data collection was carried out in 2009 using the Brazilian versions of the Work Ability Index and Job Stress Scale, with logistic regression models used for the data analysis. the prevalence of 43.3% for reduced work ability and 29.7% for high-strain in the job (high psychological demand and low control) were observed. The chances for professionals presenting reduced work ability under high-strain were higher and significant when compared to those classified as being under low-strain, even after adjusting for potential confounders, except for age and gender. a high prevalence of reduced work ability was observed. This evidence indicates the need for investigation and detailed analysis of the psychosocial aspects of the professionals with regard to the health/disease process of nursing professionals.
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Urine cell-based DNA methylation classifier for monitoring bladder cancer.
van der Heijden, Antoine G; Mengual, Lourdes; Ingelmo-Torres, Mercedes; Lozano, Juan J; van Rijt-van de Westerlo, Cindy C M; Baixauli, Montserrat; Geavlete, Bogdan; Moldoveanud, Cristian; Ene, Cosmin; Dinney, Colin P; Czerniak, Bogdan; Schalken, Jack A; Kiemeney, Lambertus A L M; Ribal, Maria J; Witjes, J Alfred; Alcaraz, Antonio
2018-01-01
Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). Voided urine samples ( N = 725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients ( N = 399). In the discovery phase, seven selected genes from the literature ( CDH13 , CFTR , NID2 , SALL3 , TMEFF2 , TWIST1 , and VIM2 ) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). A three-gene methylation classifier containing CFTR , SALL3 , and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented.
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Front-Line Educators: The Impact of Classified Staff Interactions on the Student Experience
ERIC Educational Resources Information Center
Schmitt, Mary Ann; Duggan, Molly H.; Williams, Mitchell R.; McMillan, Judy B.
2015-01-01
This multiple case study explored classified staff interactions with students as a strategy for increasing success. Interviews, observations, and focus groups examined interactions from the staff perspective. Findings indicate staff members enhance the educational process by providing a human connection, offering practical strategies for success,…
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Exposure control strategies in the carbonaceous nanomaterial industry.
Dahm, Matthew M; Yencken, Marianne S; Schubauer-Berigan, Mary K
2011-06-01
Little is known about exposure control strategies currently being implemented to minimize exposures during the production or use of nanomaterials in the United States. Our goal was to estimate types and quantities of materials used and factors related to workplace exposure reductions among companies manufacturing or using engineered carbonaceous nanomaterials (ECNs). Information was collected through phone surveys on work practices and exposure control strategies from 30 participating producers and users of ECN. The participants were classified into three groups for further examination. We report here the use of exposure control strategies. Observed patterns suggest that large-scale manufacturers report greater use of nanospecific exposure control strategies particularly for respiratory protection. Workplaces producing or using ECN generally report using engineering and administrative controls as well as personal protective equipment to control workplace employee exposure.
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Turbulent chimeras in large semiconductor laser arrays
Shena, J.; Hizanidis, J.; Kovanis, V.; Tsironis, G. P.
2017-01-01
Semiconductor laser arrays have been investigated experimentally and theoretically from the viewpoint of temporal and spatial coherence for the past forty years. In this work, we are focusing on a rather novel complex collective behavior, namely chimera states, where synchronized clusters of emitters coexist with unsynchronized ones. For the first time, we find such states exist in large diode arrays based on quantum well gain media with nearest-neighbor interactions. The crucial parameters are the evanescent coupling strength and the relative optical frequency detuning between the emitters of the array. By employing a recently proposed figure of merit for classifying chimera states, we provide quantitative and qualitative evidence for the observed dynamics. The corresponding chimeras are identified as turbulent according to the irregular temporal behavior of the classification measure. PMID:28165053
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Turbulent chimeras in large semiconductor laser arrays
NASA Astrophysics Data System (ADS)
Shena, J.; Hizanidis, J.; Kovanis, V.; Tsironis, G. P.
2017-02-01
Semiconductor laser arrays have been investigated experimentally and theoretically from the viewpoint of temporal and spatial coherence for the past forty years. In this work, we are focusing on a rather novel complex collective behavior, namely chimera states, where synchronized clusters of emitters coexist with unsynchronized ones. For the first time, we find such states exist in large diode arrays based on quantum well gain media with nearest-neighbor interactions. The crucial parameters are the evanescent coupling strength and the relative optical frequency detuning between the emitters of the array. By employing a recently proposed figure of merit for classifying chimera states, we provide quantitative and qualitative evidence for the observed dynamics. The corresponding chimeras are identified as turbulent according to the irregular temporal behavior of the classification measure.
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Search for top-quark production via flavor-changing neutral currents in W+1 jet events at CDF.
Aaltonen, T; Adelman, J; Akimoto, T; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burke, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Chwalek, T; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Cordelli, M; Cortiana, G; Cox, C A; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Frank, M J; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hays, C; Heck, M; Heijboer, A; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Hussein, M; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jang, D; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, H W; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, E; Lee, H S; Lee, S W; Leone, S; Lewis, J D; Lin, C-S; Linacre, J; Lindgren, M; Lipeles, E; Liss, T M; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lucchesi, D; Luci, C; Lueck, J; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mathis, M; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlock, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Nett, J; Neu, C; Neubauer, M S; Neubauer, S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Peiffer, T; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Renton, P; Renz, M; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Rutherford, B; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sforza, F; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Strycker, G L; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Ttito-Guzmán, P; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Trovato, M; Tsai, S-Y; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Wagner, P; Wagner, R G; Wagner, R L; Wagner, W; Wagner-Kuhr, J; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Weinelt, J; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Wilbur, S; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Würthwein, F; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zhang, X; Zheng, Y; Zucchelli, S
2009-04-17
We report on a search for the non-standard-model process u(c) + g --> t using pp[over ] collision data collected by the Collider Detector at Fermilab II detector corresponding to 2.2 fb;{-1}. The candidate events are classified as signal-like or backgroundlike by an artificial neural network. The observed discriminant distribution yields no evidence for flavor-changing neutral current top-quark production, resulting in an upper limit on the production cross section sigma(u(c) + g --> t) < 1.8 pb at the 95% C.L. Using theoretical predictions we convert the cross section limit to upper limits on flavor-changing neutral current branching ratios: B(t --> u + g) < 3.9 x 10;{-4} and B(t --> c + g) < 5.7 x 10;{-3}.
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ERIC Educational Resources Information Center
Education Commission of the States, Denver, CO.
This is a collection of consumer skills items for state and local education agencies to draw upon in composing consumer skills instruments. It provides items to assess seventeen-year-olds' consumer skills. The booklet contains items classified under eight major topics: behavior, contracts, economics, energy, finances, mathematics, projection, and…
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Strain Gage Signal Interpretation.
1986-02-01
blades and vanes in many engines have been collected, played back and examined. The engine types encompass GE’s stable of turbine engines from the small...aeromechanical engineer . 1.3 SUMMARY OF RESULTS Strain gage signals from vibrating rotor blades and vanes were collected, examined, classified, and generalized...turboprops, to turbojets and to the large high bypass turbofan engines . Test conditions include all the phases that are investigated
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Madigan, Sheri; Goldberg, Susan; Moran, Greg; Pederson, David R
2004-09-01
Previous research has succeeded in distinguishing among drawings made by children with histories of organized attachment relationships (secure, avoidant, and resistant); however, drawings of children with histories of disorganized attachment have yet to be systematically investigated. The purpose of this study was to determine whether naïve observers would respond differentially to family drawings of 7-year-olds who were classified in infancy as disorganized vs. organized. Seventy-three undergraduate students from one university and 78 from a second viewed 50 family drawings of 7-year-olds (25 by children with organized infant attachment and 25 by children with disorganized infant attachment). Participants were asked to (1) circle the emotion that best described their reaction to the drawings and (2) rate the drawings on 6 bipolar scales. Drawings from children classified as disorganized in infancy evoked positive emotion labels less often and negative emotion labels more often than those children classified as organized. Furthermore, drawings from children classified as disorganized in infancy received higher ratings on scales for disorganization, carelessness, family chaos, bizarreness, uneasiness, and dysfunction. These data indicate that naive observers are relatively successful in distinguishing selected features of drawings by children with histories of disorganized vs. organized attachment.
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AO Distal Radius Fracture Classification: Global Perspective on Observer Agreement.
Jayakumar, Prakash; Teunis, Teun; Giménez, Beatriz Bravo; Verstreken, Frederik; Di Mascio, Livio; Jupiter, Jesse B
2017-02-01
Background The primary objective of this study was to test interobserver reliability when classifying fractures by consensus by AO types and groups among a large international group of surgeons. Secondarily, we assessed the difference in inter- and intraobserver agreement of the AO classification in relation to geographical location, level of training, and subspecialty. Methods A randomized set of radiographic and computed tomographic images from a consecutive series of 96 distal radius fractures (DRFs), treated between October 2010 and April 2013, was classified using an electronic web-based portal by an invited group of participants on two occasions. Results Interobserver reliability was substantial when classifying AO type A fractures but fair and moderate for type B and C fractures, respectively. No difference was observed by location, except for an apparent difference between participants from India and Australia classifying type B fractures. No statistically significant associations were observed comparing interobserver agreement by level of training and no differences were shown comparing subspecialties. Intra-rater reproducibility was "substantial" for fracture types and "fair" for fracture groups with no difference accounting for location, training level, or specialty. Conclusion Improved definition of reliability and reproducibility of this classification may be achieved using large international groups of raters, empowering decision making on which system to utilize. Level of Evidence Level III.
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AO Distal Radius Fracture Classification: Global Perspective on Observer Agreement
Jayakumar, Prakash; Teunis, Teun; Giménez, Beatriz Bravo; Verstreken, Frederik; Di Mascio, Livio; Jupiter, Jesse B.
2016-01-01
Background The primary objective of this study was to test interobserver reliability when classifying fractures by consensus by AO types and groups among a large international group of surgeons. Secondarily, we assessed the difference in inter- and intraobserver agreement of the AO classification in relation to geographical location, level of training, and subspecialty. Methods A randomized set of radiographic and computed tomographic images from a consecutive series of 96 distal radius fractures (DRFs), treated between October 2010 and April 2013, was classified using an electronic web-based portal by an invited group of participants on two occasions. Results Interobserver reliability was substantial when classifying AO type A fractures but fair and moderate for type B and C fractures, respectively. No difference was observed by location, except for an apparent difference between participants from India and Australia classifying type B fractures. No statistically significant associations were observed comparing interobserver agreement by level of training and no differences were shown comparing subspecialties. Intra-rater reproducibility was “substantial” for fracture types and “fair” for fracture groups with no difference accounting for location, training level, or specialty. Conclusion Improved definition of reliability and reproducibility of this classification may be achieved using large international groups of raters, empowering decision making on which system to utilize. Level of Evidence Level III PMID:28119795
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Classification of Odours for Mobile Robots Using an Ensemble of Linear Classifiers
NASA Astrophysics Data System (ADS)
Trincavelli, Marco; Coradeschi, Silvia; Loutfi, Amy
2009-05-01
This paper investigates the classification of odours using an electronic nose mounted on a mobile robot. The samples are collected as the robot explores the environment. Under such conditions, the sensor response differs from typical three phase sampling processes. In this paper, we focus particularly on the classification problem and how it is influenced by the movement of the robot. To cope with these influences, an algorithm consisting of an ensemble of classifiers is presented. Experimental results show that this algorithm increases classification performance compared to other traditional classification methods.
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Hoffheins, B.S.; Lauf, R.J.
1997-08-05
A gas detecting system is described for classifying the type of liquid fuel in a container or tank. The system includes a plurality of semiconductor gas sensors, each of which differs from the other in its response to various organic vapors. The system includes a means of processing the responses of the plurality of sensors such that the responses to any particular organic substance or mixture is sufficiently distinctive to constitute a recognizable ``signature``. The signature of known substances are collected and divided into two classes based on some other known characteristic of the substances. A pattern recognition system classifies the signature of an unknown substance with reference to the two user-defined classes, thereby classifying the unknown substance with regard to the characteristic of interest, such as its suitability for a particular use. 14 figs.
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Hoffheins, Barbara S.; Lauf, Robert J.
1997-01-01
A gas detecting system for classifying the type of liquid fuel in a container or tank. The system includes a plurality of semiconductor gas sensors, each of which differs from the other in its response to various organic vapors. The system includes a means of processing the responses of the plurality of sensors such that the responses to any particular organic substance or mixture is sufficiently distinctive to constitute a recognizable "signature". The signature of known substances are collected and divided into two classes based on some other known characteristic of the substances. A pattern recognition system classifies the signature of an unknown substance with reference to the two user-defined classes, thereby classifying the unknown substance with regard to the characteristic of interest, such as its suitability for a particular use.
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Using Trained Pixel Classifiers to Select Images of Interest
NASA Technical Reports Server (NTRS)
Mazzoni, D.; Wagstaff, K.; Castano, R.
2004-01-01
We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.
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Evaluation of the U.S. Geological Survey Ground-Water Data-Collection Program in Hawaii, 1992
Anthony, Stephen S.
1997-01-01
In 1992, the U.S. Geological Survey ground-water data-collection program in the State of Hawaii consisted of 188 wells distributed among the islands of Oahu, Kauai, Maui, Molokai, and Hawaii. Water-level and water-quality (temperature, specific conductance, and chloride concentration) data were collected from observation wells, deep monitoring wells that penetrate the zone of transition between freshwater and saltwater, free-flowing wells, and pumped wells. The objective of the program was to collect sufficient spatial and temporal data to define seasonal and long-term changes in ground-water levels and chloride concentrations induced by natural and human-made stresses for different climatic and hydrogeologic settings. Wells needed to meet this objective can be divided into two types of networks: (1) a water-management network to determine the response of ground-water flow systems to human-induced stresses, such as pumpage, and (2) a baseline network to determine the response of ground-water flow systems to natural stresses for different climatic and hydrogeologic settings. Maps showing the distribution and magnitude of pumpage and the distribution of proposed pumped wells are presented to identify areas in need of water-management networks. Wells in the 1992 U.S. Geological Survey ground-water data-collection program were classified as either water-management or baseline network wells. In addition, locations where additional water-management network wells are needed for water-level and water-quality data were identified.
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On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor.
Kim, Woosuk; Kim, Myunggyu
2018-03-19
In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.
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NASA Technical Reports Server (NTRS)
Sheridan, Patrick J.
1999-01-01
Herein is reported activities to support the characterization of the aerosol in the upper troposphere (UT) and lower stratosphere (LS) collected during the Airborne Southern Hemisphere Ozone Experiment/Measurements for Assessing the Effects of Stratospheric Aircraft (ASHOE/MAESA) missions in 1994. Through a companion proposal, another group was to measure the size distribution of aerosols in the 0.008 to 2 micrometer diameter range and to collect for us impactor samples of particles larger than about 0.02 gm. In the first year, we conducted laboratory studies related to particulate deposition patterns on our collection substrates, and have performed the analysis of many ASHOE/MAESA aerosol samples from 1994 using analytical electron microscopy (AEM). We have been building an "aerosol climatology" with these data that documents the types and relative abundances of particles observed at different latitudes and altitudes. The second year (and non-funded extension periods) saw continued analyses of impactor aerosol samples, including more ASHOE/MAESA samples, some northern hemisphere samples from the NASA Stratospheric Photochemistry Aerosols and Dynamics Expedition (SPADE) program for comparison, and a few aerosol samples from the NASA Stratospheric TRacers of Atmospheric Transport (STRAT) program. A high-resolution field emission microscope was used for the analysis and re-analysis of a number of samples to determine if this instrument was superior in performance to our conventional electron microscope. In addition, some basic laboratory studies were conducted to determine the minimum detectable and analyzable particle size for different types of aerosols. In all, 61 aerosol samples were analyzed, with a total of over 30,000 individual particle analyses. In all analyzed samples, sulfate particles comprised the major aerosol number fraction. It must be stressed that particles composed of more than one species, for example sulfate and organic carbon, were classified according to the major fraction. Thus, many of the particles classified as sulfate may have contained significant mass fractions of carbonaceous or other material. These particles for the most part did not show two physical phases, however. Nonsulfate particles were classified according to the physical and chemical characteristics of each particle, and were grouped into the major nonsulfate particle classes, including C-rich, crustal, metallic, and salts. Our UT and LS sample analyses indicate a maximum for crustal and C-rich particle abundance in the Northern Hemisphere upper troposphere, and a salt particle maximum in the Southern Hemisphere upper troposphere. Metallic particles are clearly more prevalent in the troposphere than in the stratosphere, but interhemispheric differences appear small.
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Ro, Chul-Un; Kim, HyeKyeong; Oh, Keun-Young; Yea, Sun Kyung; Lee, Chong Bum; Jang, Meongdo; Van Grieken, René
2002-11-15
A recently developed single-particle analytical technique, called low-Z electron probe X-ray microanalysis (low-Z EPMA), was applied to characterize urban aerosol particles collected in three cities of Korea (Seoul, CheongJu, and ChunCheon) on single days in the winter of 1999. In this study, it is clearly demonstrated that the low-Z EPMA technique can provide detailed and quantitative information on the chemical composition of particles in the urban atmosphere. The collected aerosol particles were analyzed and classified on the basis of their chemical species. Various types of particles were identified, such as soil-derived, carbonaceous, marine-originated, and anthropogenic particles. In the sample collected in Seoul, carbonaceous, aluminosilicates, silicon dioxide, and calcium carbonate aerosol particles were abundantly encountered. In the CheongJu and ChunCheon samples, carbonaceous, aluminosilicates, reacted sea salts, and ammonium sulfate aerosol particles were often seen. However, in the CheongJu sample, ammonium sulfate particles were the most abundant in the fine fraction. Also, calcium sulfate and nitrate particles were significantly observed. In the ChunCheon sample, organic particles were the most abundant in the fine fraction. Also, sodium nitrate particles were seen at high levels. The ChunCheon sample seemed to be strongly influenced by sea-salt aerosols originating from the Yellow Sea, which is located about 115 km away from the city.
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Halley, Meghan; Gillespie, Katherine; Rendle, Katharine; Luft, Harold
2014-01-01
Background/Aims Since 1973, the National Ambulatory Medical Care Survey (NAMCS), administered by the National Center for Health Statistics (NCHS) has been widely used in studies of ambulatory care. With the growth in large multispecialty practices – including many members of the HMORN – there is a need to understand how NAMCS data are collected and whether current processes yield accurate and reliable data. NAMCS collects data from physicians about their practices and abstracts a sample of patient visit records. This study reports on the physician component. Methods In collaboration with NCHS, nine physicians were randomly sampled from a multispecialty clinic using standard NAMCS recruitment procedures; eight physicians were eligible and agreed to participate. Using their standard protocols, three Field Representatives (FRs) conducted NAMCS physician interviews while a trained ethnographer (MH, KR) observed and audio-recorded each interview. Transcripts and field notes were analyzed using a grounded theory approach to identify key themes. Results Data have been collected and analyzed. They are currently undergoing standard confidentiality review by NCHS. However, this process has been delayed due to the government shutdown. We fully anticipate that results will be released in time for presentation at the HMORN conference. Conclusions Though we are precluded from disseminating results at this time, we will provide a full report of our results in our HMORN conference presentation.
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Small Vocabulary Recognition Using Surface Electromyography in an Acoustically Harsh Environment
NASA Technical Reports Server (NTRS)
Betts, Bradley J.; Jorgensen, Charles
2005-01-01
This paper presents results of electromyographic-based (EMG-based) speech recognition on a small vocabulary of 15 English words. The work was motivated in part by a desire to mitigate the effects of high acoustic noise on speech intelligibility in communication systems used by first responders. Both an off-line and a real-time system were constructed. Data were collected from a single male subject wearing a fireghter's self-contained breathing apparatus. A single channel of EMG data was used, collected via surface sensors at a rate of 104 samples/s. The signal processing core consisted of an activity detector, a feature extractor, and a neural network classifier. In the off-line phase, 150 examples of each word were collected from the subject. Generalization testing, conducted using bootstrapping, produced an overall average correct classification rate on the 15 words of 74%, with a 95% confidence interval of [71%, 77%]. Once the classifier was trained, the subject used the real-time system to communicate and to control a robotic device. The real-time system was tested with the subject exposed to an ambient noise level of approximately 95 decibels.
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Identification of linearised RMS-voltage dip patterns based on clustering in renewable plants
DOE Office of Scientific and Technical Information (OSTI.GOV)
García-Sánchez, Tania; Gómez-Lázaro, Emilio; Muljadi, Edward
Generation units connected to the grid are currently required to meet low-voltage ride-through (LVRT) requirements. In most developed countries, these requirements also apply to renewable sources, mainly wind power plants and photovoltaic installations connected to the grid. This study proposes an alternative characterisation solution to classify and visualise a large number of collected events in light of current limits and requirements. The authors' approach is based on linearised root-mean-square-(RMS)-voltage trajectories, taking into account LRVT requirements, and a clustering process to identify the most likely pattern trajectories. The proposed solution gives extensive information on an event's severity by providing a simplemore » but complete visualisation of the linearised RMS-voltage patterns. In addition, these patterns are compared to current LVRT requirements to determine similarities or discrepancies. A large number of collected events can then be automatically classified and visualised for comparative purposes. Real disturbances collected from renewable sources in Spain are used to assess the proposed solution. Extensive results and discussions are also included in this study.« less
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NASA Astrophysics Data System (ADS)
Gabányi, K. É.; Frey, S.; An, T.
2018-05-01
Context. The Fermi Large Area Telescope revealed that the extragalactic γ-ray sky is dominated by blazars, active galactic nuclei (AGN) whose jet is seen at very small angle to the line of sight. To associate and then classify the γ-ray sources, data have been collected from lower frequency surveys and observations. Since those have superior angular resolution and positional accuracy compared to the γ-ray observations, some associations are not straightforward. Aims: The γ-ray source 3FGL J1323.0+2942 is associated with the radio source 4C+29.48 and classified as a blazar of unknown type, lacking optical spectrum and redshift. The higher-resolution radio data showed that 4C+29.48 comprises three bright radio-emitting features located within a 1'-diameter area. We aim to reveal their nature and pinpoint the origin of the γ-ray emission. Methods: We (re-)analyzed archival Very Large Array (VLA) and unpublished very long baseline interferometry (VLBI) observations conducted by the Very Long Baseline Array (VLBA) and the European VLBI Network of 4C+29.48. We also collected data form optical, infrared and X-ray surveys. Results: According to the VLBI data, the northernmost complex of 4C+29.48 contains a blazar with a high brightness temperature compact core and a steep-spectrum jet feature. The blazar is positionally coincident with an optical source at a redshift of 1.142. Its mid-infrared colors also support its association with a γ-ray emitting blazar. The two other radio complexes have steep radio spectra similar to AGN-related lobes and do not have optical or infrared counterparts in currently available surveys. Based on the radio morphology, they are unlikely to be related to the blazar. There is an optical source between the two radio features, also detected in infrared wavebands. We discuss the possibilities whether the two radio features are lobes of a radio galaxy, or gravitationally lensed images of a background source. Conclusions: We propose to associate the γ-ray source 3FGL J1323.0+2942 in subsequent versions of the Fermi catalog with the blazar residing in northernmost complex. We suggest naming this radio source J1323+2941A to avoid misinterpretation arising from the fact that the coordinates of the currently listed radio counterpart 4C+29.48 is closer to a most probably unrelated radio source.
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NASA Astrophysics Data System (ADS)
Pastorello, G.; Agarwal, D.; Poindexter, C.; Papale, D.; Trotta, C.; Ribeca, A.; Canfora, E.; Faybishenko, B.; Gunter, D.; Chu, H.
2015-12-01
The fluxes-measuring sites that are part of AmeriFlux are operated and maintained in a fairly independent fashion, both in terms of scientific goals and operational practices. This is also the case for most sites from other networks in FLUXNET. This independence leads to a degree of heterogeneity in the data sets collected at the sites, which is also reflected in data quality levels. The generation of derived data products and data synthesis efforts, two of the main goals of these networks, are directly affected by the heterogeneity in data quality. In a collaborative effort between AmeriFlux and ICOS, a series of quality checks are being conducted for the data sets before any network-level data processing and product generation take place. From these checks, a set of common data issues were identified, and are being cataloged and classified into data quality patterns. These patterns are now being used as a basis for implementing automation for certain data quality checks, speeding up the process of applying the checks and evaluating the data. Currently, most data checks are performed individually in each data set, requiring visual inspection and inputs from a data curator. This manual process makes it difficult to scale the quality checks, creating a bottleneck for the data processing. One goal of the automated checks is to free up time of data curators so they can focus on new or less common issues. As new issues are identified, they can also be cataloged and classified, extending the coverage of existing patterns or potentially generating new patterns, helping both improve existing automated checks and create new ones. This approach is helping make data quality evaluation faster, more systematic, and reproducible. Furthermore, these patterns are also helping with documenting common causes and solutions for data problems. This can help tower teams with diagnosing problems in data collection and processing, and also in correcting historical data sets. In this presentation, using AmeriFlux fluxes and micrometeorological data, we discuss our approach to creating observational data patterns, and how we are using them to implement new automated checks. We also detail examples of these observational data patterns, illustrating how they are being used.
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Meneguzzo, Dacia M; Liknes, Greg C; Nelson, Mark D
2013-08-01
Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis-[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.
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Khuluza, Felix; Kigera, Stephen; Heide, Lutz
2017-01-01
Substandard and falsified antimalarial and antibiotic medicines represent a serious problem for public health, especially in low- and middle-income countries. However, information on the prevalence of poor-quality medicines is limited. In the present study, samples of six antimalarial and six antibiotic medicines were collected from 31 health facilities and drug outlets in southern Malawi. Random sampling was used in the selection of health facilities. For sample collection, an overt approach was used in licensed facilities, and a mystery shopper approach in nonlicensed outlets. One hundred and fifty-five samples were analyzed by visual and physical examination and by rapid prescreening tests, that is, disintegration testing and thin-layer chromatography using the GPHF-Minilab. Fifty-six of the samples were analyzed according to pharmacopeial monographs in a World Health Organization-prequalified quality control laboratory. Seven out-of-specification medicines were identified. One sample was classified as falsified, lacking the declared active ingredients, and containing other active ingredients instead. Three samples were classified as substandard with extreme deviations from the pharmacopeial standards, and three further samples as substandard with nonextreme deviations. Of the substandard medicines, three failed in dissolution testing, two in the assay for the content of the active pharmaceutical ingredient, and one failed in both dissolution testing and assay. Six of the seven out-of-specification medicines were from private facilities. Only one out-of-specification medicine was found within the samples from public and faith-based health facilities. Although the observed presence of substandard and falsified medicines in Malawi requires action, their low prevalence in public and faith-based health facilities is encouraging. PMID:28219993
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Gourmelon, Michèle; Caprais, Marie Paule; Ségura, Raphaël; Le Mennec, Cécile; Lozach, Solen; Piriou, Jean Yves; Rincé, Alain
2007-01-01
In order to identify the origin of the fecal contamination observed in French estuaries, two library-independent microbial source tracking (MST) methods were selected: (i) Bacteroidales host-specific 16S rRNA gene markers and (ii) F-specific RNA bacteriophage genotyping. The specificity of the Bacteroidales markers was evaluated on human and animal (bovine, pig, sheep, and bird) feces. Two human-specific markers (HF183 and HF134), one ruminant-specific marker (CF193′), and one pig-specific marker (PF163) showed a high level of specificity (>90%). However, the data suggest that the proposed ruminant-specific CF128 marker would be better described as an animal marker, as it was observed in all bovine and sheep feces and 96% of pig feces. F RNA bacteriophages were detected in only 21% of individual fecal samples tested, in 60% of pig slurries, but in all sewage samples. Most detected F RNA bacteriophages were from genotypes II and III in sewage samples and from genotypes I and IV in bovine, pig, and bird feces and from pig slurries. Both MST methods were applied to 28 water samples collected from three watersheds at different times. Classification of water samples as subject to human, animal, or mixed fecal contamination was more frequent when using Bacteroidales markers (82.1% of water samples) than by bacteriophage genotyping (50%). The ability to classify a water sample increased with increasing Escherichia coli or enterococcus concentration. For the samples that could be classified by bacteriophage genotyping, 78% agreed with the classification obtained from Bacteroidales markers. PMID:17557850
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Schmidt, Holger; Eisenmann, Yvonne; Golla, Heidrun; Voltz, Raymond; Perrar, Klaus Maria
2018-03-01
People with advanced dementia present an important target group for palliative care. They suffer a range of symptoms, and their verbal communication abilities are highly restricted. At present, little is known about their needs in the final phase of life. To identify the needs of people with advanced dementia in their final phase of life and to explore the aspects relevant to first recognize and then meet these needs. Multi-perspective qualitative study using grounded theory methodology conducting group discussions, individual interviews, and participant observation. The study encompassed nursing homes and involved health professionals, relatives, and residents with advanced dementia. Data were collected in six nursing homes. Nine group discussions and three individual interviews were conducted comprising 42 health professionals and 14 relatives. Participant observations aided in giving the perspective of 30 residents with advanced dementia. Data analysis generated a total of 25 physical, psychosocial, and spiritual needs divided into 10 categories. Physical needs were classified as follows: "food intake," "physical well-being," and "physical activity and recovery." Categories of psychosocial needs were classified as follows: "adaptation of stimuli," "communication," "personal attention," "participation," "familiarity and safety," as well as "self-determination." Spiritual needs addressed "religion." The results revealed a multitude of key aspects for recognizing and meeting these needs, stressing the importance of personhood. People with advanced dementia in their final phase of life have a multitude of individual and complex needs. This evidence contributes to narrowing the current research gap, offering an orientation framework for research and practice.
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Zhang, Jingjing; Dennis, Todd E.
2015-01-01
We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known ‘artificial behaviours’ comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified. PMID:25922935
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Zhang, Jingjing; O'Reilly, Kathleen M; Perry, George L W; Taylor, Graeme A; Dennis, Todd E
2015-01-01
We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known 'artificial behaviours' comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.
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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.
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SCOPE - Stellar Classification Online Public Exploration
NASA Astrophysics Data System (ADS)
Harenberg, Steven
2010-01-01
The Astronomical Photographic Data Archive (APDA) has been established to be the primary North American archive for the collections of astronomical photographic plates. Located at the Pisgah Astronomical Research Institute (PARI) in Rosman, NC, the archive contains hundreds of thousands stellar spectra, many of which have never before been classified. To help classify the vast number of stars, the public is invited to participate in a distributed computing online environment called Stellar Classification Online - Public Exploration (SCOPE). Through a website, the participants will have a tutorial on stellar spectra and practice classifying. After practice, the participants classify spectra on photographic plates uploaded online from APDA. These classifications will be recorded in a database where the results from many users will be statistically analyzed. Stars with known spectral types will be included to test the reliability of classifications. The process of building the database of stars from APDA, which the citizen scientist will be able to classify, includes: scanning the photographic plates, orienting the plate to correct for the change in right ascension/declination using Aladin, stellar HD catalog identification using Simbad, marking the boundaries for each spectrum, and setting up the image for use on the website. We will describe the details of this process.
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NASA Astrophysics Data System (ADS)
Zaryab, Mohammad; Singh-Moon, Rajinder P.; Hendon, Christine P.
2017-02-01
Using light-based catheters for radiofrequency ablation (RFA) therapies grants the ability to accurately derive tissue properties such as lesion depth and overtreatment from spectroscopic information. However, this information is heavily reliant on contact quality with the treatment area and the orientation of the catheter. Thus to improve assessments of tissue properties, this work utilizes Bayesian modelling to classify whether the catheter is indeed in proper contact with the tissue. Initially in-laboratory experiments were conducted with ten fresh swine hearts submerged in blood. A total of 1555 unique near infrared spectra were collected from a spectrometer using a light-based catheter and manually tagged as "full perpendicular contact," "angled contact," and "no contact," between the catheter and heart tissue. Three features were prominent in all spectra for distinguishing purposes: area underneath the spectra, an intensity "valley" between 730 nm and 800 nm, along with the slope between 850 nm and 1150 nm. A classifier featuring bootstrapping, adaboost, and k-means techniques was thus created and achieved a 96.05% accuracy in classifying full contact, 98.33% accuracy in classifying angled contact, and 100% accuracy in classifying no contact.
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Mahoney, Gerald; Wheeden, C Abigail; Perales, Frida
2004-01-01
Developmental outcomes attained by children receiving preschool special education services in relationship to both the general instructional approach used by their teachers and their parents' style of interaction were examined. The sample included 70 children from 41 Early Childhood Special Education (ECSE) classrooms. The type of instructional model children received was determined by dividing the sample into three clusters based upon six global ratings of children's classroom environment: Choice; Cognitive Problem-Solving; Child-Initiated Learning; Developmental Match; Child-Centered Routines; and Rewards and Discipline Strategies. Based on this analysis, 27 children were classified as receiving developmental instruction; 15 didactic instruction; and 28 naturalistic instruction. Observations of parent-child interaction collected at the beginning and end of the year were classified along four dimensions using the Maternal Behavior Rating Scale: Responsiveness, Affect, Achievement Orientation and Directiveness. Results indicated that the kinds of experiences that children received varied significantly across the three instructional models. However, there were no significant differences in the impact of these instructional models on children's rate of development. Regression analyses indicated that children's rate of development at the end of intervention was significantly related to their parents' style of interaction but was unrelated to the type of instructional model they received.
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Monteiro, Miguel; Reino, Luís; Beja, Pedro; Mills, Michael Stuart Lyne; Bastos-Silveira, Cristiane; Ramos, Manuela; Rodrigues, Diana; Neves, Isabel Queirós; Consciência, Susana; Figueira, Rui
2014-01-01
Abstract The bird collection of the Instituto de Investigação Cientítica Tropical (Lisbon, Portugal) holds 5598 preserved specimens (skins), mainly from Angola, Mozambique, Guinea-Bissau, São Tomé and Principe, and Cape Verde. The subset collection from Angola includes 1560 specimens, which were taxonomically revised and georeferenced for the publication of this data paper. The collection contains a total of 522 taxa, including 161 species and 361 subspecies. Two species are classified by the IUCN Red List as Endangered - the wattled crane (Grus carunculata) and the Gabela bush-shrike (Laniarius amboimensis) - and two are classified as vulnerable - African penguin (Spheniscus demersus) and the white-headed vulture (Trigonoceps occipitalis). The temporal span of the database ranges between 1943 and 1979, but 32% are from years 1958–1959, and 25% from years 1968–1969. The spatial coverage of the collection is uneven, with 2/3 of the records representing only four of the eighteen provinces of the country, namely Huíla, Moxico, Namibe and Cuanza Sul. It adds, however, valuable information for the Huíla area of the Angolan Scarp, which is probably a biodiversity hotspot of global conservation priority. Furthermore, this georeferenced database adds invaluable bird information to the GBIF network, for one of the countries with highest but less known biodiversity in Africa. PMID:24693221
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Automated classification of self-grooming in mice using open-source software.
van den Boom, Bastijn J G; Pavlidi, Pavlina; Wolf, Casper J H; Mooij, Adriana H; Willuhn, Ingo
2017-09-01
Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. We trained a freely available, automated classifier, Janelia Automatic Animal Behavior Annotator (JAABA), to quantify self-grooming duration and number of bouts based on video recordings of SAPAP3 knockout mice (a mouse line that self-grooms excessively) and wild-type animals. We compared the JAABA classifier with human expert observers to test its ability to measure self-grooming in three scenarios: mice in an open field, mice on an elevated plus-maze, and tethered mice in an open field. In each scenario, the classifier identified both grooming and non-grooming with great accuracy and correlated highly with results obtained by human observers. Consistently, the JAABA classifier confirmed previous reports of excessive grooming in SAPAP3 knockout mice. Thus far, manual analysis was regarded as the only valid quantification method for self-grooming. We demonstrate that the JAABA classifier is a valid and reliable scoring tool, more cost-efficient than manual scoring, easy to use, requires minimal effort, provides high throughput, and prevents inter-rater variability. We introduce the JAABA classifier as an efficient analysis tool for the assessment of rodent self-grooming with expert quality. In our "how-to" instructions, we provide all information necessary to implement behavioral classification with JAABA. Copyright © 2017 Elsevier B.V. All rights reserved.
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Roseman, E.F.; Stott, W.; O'Brien, T. P.; Riley, S.C.; Schaeffer, J.S.
2009-01-01
Restoration of lake trout Salvelinus namaycush stocks in Lake Huron is a fish community objective developed to promote sustainable fish communities in the lake. Between 1985 and 2004, 12.65 million lake trout were stocked into Lake Huron representing eight different genetic strains. Collections of bona fide wild fish in USGS surveys have increased in recent years and this study examined the ancestry and diet of fish collected between 2004 and 2006 to explore the ecological role they occupy in Lake Huron. Analysis of microsatellite DNA revealed that both pure strain and inter-strain hybrids were observed, and the majority of fish were classified as Seneca Lake strain or Seneca Lake hybrids. Diets of 50 wild age-0 lake trout were examined. Mysis, chironomids, and zooplankton were common prey items of wild age-0 lake trout. These results indicate that stocked fish are successfully reproducing in Lake Huron indicating a level of restoration success. However, continued changes to the benthic macroinvertebrate community, particularly declines of Mysis, may limit growth and survival of wild fish and hinder restoration efforts.
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Detection of explosives by differential hyperspectral imaging
NASA Astrophysics Data System (ADS)
Dubroca, Thierry; Brown, Gregory; Hummel, Rolf E.
2014-02-01
Our team has pioneered an explosives detection technique based on hyperspectral imaging of surfaces. Briefly, differential reflectometry (DR) shines ultraviolet (UV) and blue light on two close-by areas on a surface (for example, a piece of luggage on a moving conveyer belt). Upon reflection, the light is collected with a spectrometer combined with a charge coupled device (CCD) camera. A computer processes the data and produces in turn differential reflection spectra taken from these two adjacent areas on the surface. This differential technique is highly sensitive and provides spectroscopic data of materials, particularly of explosives. As an example, 2,4,6-trinitrotoluene displays strong and distinct features in differential reflectograms near 420 and 250 nm, that is, in the near-UV region. Similar, but distinctly different features are observed for other explosives. Finally, a custom algorithm classifies the collected spectral data and outputs an acoustic signal if a threat is detected. This paper presents the complete DR hyperspectral imager which we have designed and built from the hardware to the software, complete with an analysis of the device specifications.
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Foo, Brian; van der Schaar, Mihaela
2010-11-01
In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.
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Pairwise diversity ranking of polychotomous features for ensemble physiological signal classifiers.
Gupta, Lalit; Kota, Srinivas; Molfese, Dennis L; Vaidyanathan, Ravi
2013-06-01
It is well known that fusion classifiers for physiological signal classification with diverse components (classifiers or data sets) outperform those with less diverse components. Determining component diversity, therefore, is of the utmost importance in the design of fusion classifiers that are often employed in clinical diagnostic and numerous other pattern recognition problems. In this article, a new pairwise diversity-based ranking strategy is introduced to select a subset of ensemble components, which when combined will be more diverse than any other component subset of the same size. The strategy is unified in the sense that the components can be classifiers or data sets. Moreover, the classifiers and data sets can be polychotomous. Classifier-fusion and data-fusion systems are formulated based on the diversity-based selection strategy, and the application of the two fusion strategies are demonstrated through the classification of multichannel event-related potentials. It is observed that for both classifier and data fusion, the classification accuracy tends to increase/decrease when the diversity of the component ensemble increases/decreases. For the four sets of 14-channel event-related potentials considered, it is shown that data fusion outperforms classifier fusion. Furthermore, it is demonstrated that the combination of data components that yield the best performance, in a relative sense, can be determined through the diversity-based selection strategy.
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Egg embryo development detection with hyperspectral imaging
NASA Astrophysics Data System (ADS)
Lawrence, Kurt C.; Smith, Douglas P.; Windham, William R.; Heitschmidt, Gerald W.; Park, Bosoon
2006-10-01
In the U. S. egg industry, anywhere from 130 million to over one billion infertile eggs are incubated each year. Some of these infertile eggs explode in the hatching cabinet and can potentially spread molds or bacteria to all the eggs in the cabinet. A method to detect the embryo development of incubated eggs was developed. Twelve brown-shell hatching eggs from two replicates (n=24) were incubated and imaged to identify embryo development. A hyperspectral imaging system was used to collect transmission images from 420 to 840 nm of brown-shell eggs positioned with the air cell vertical and normal to the camera lens. Raw transmission images from about 400 to 900 nm were collected for every egg on days 0, 1, 2, and 3 of incubation. A total of 96 images were collected and eggs were broken out on day 6 to determine fertility. After breakout, all eggs were found to be fertile. Therefore, this paper presents results for egg embryo development, not fertility. The original hyperspectral data and spectral means for each egg were both used to create embryo development models. With the hyperspectral data range reduced to about 500 to 700 nm, a minimum noise fraction transformation was used, along with a Mahalanobis Distance classification model, to predict development. Days 2 and 3 were all correctly classified (100%), while day 0 and day 1 were classified at 95.8% and 91.7%, respectively. Alternatively, the mean spectra from each egg were used to develop a partial least squares regression (PLSR) model. First, a PLSR model was developed with all eggs and all days. The data were multiplicative scatter corrected, spectrally smoothed, and the wavelength range was reduced to 539 - 770 nm. With a one-out cross validation, all eggs for all days were correctly classified (100%). Second, a PLSR model was developed with data from day 0 and day 3, and the model was validated with data from day 1 and 2. For day 1, 22 of 24 eggs were correctly classified (91.7%) and for day 2, all eggs were correctly classified (100%). Although the results are based on relatively small sample sizes, they are encouraging. However, larger sample sizes, from multiple flocks, will be needed to fully validate and verify these models. Additionally, future experiments must also include non-fertile eggs so the fertile / non-fertile effect can be determined.
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NASA-SETI microwave observing project: Targeted Search Element (TSE)
NASA Technical Reports Server (NTRS)
Webster, L. D.
1991-01-01
The Targeted Search Element (TSE) performs one of two complimentary search strategies of the NASA-SETI Microwave Observing Project (MOP): the targeted search. The principle objective of the targeted search strategy is to scan the microwave window between the frequencies of one and three gigahertz for narrowband microwave emissions eminating from the direction of 773 specifically targeted stars. The scanning process is accomplished at a minimum resolution of one or two Hertz at very high sensitivity. Detectable signals will be of a continuous wave or pulsed form and may also drift in frequency. The TSE will possess extensive radio frequency interference (RFI) mitigation and verification capability as the majority of signals detected by the TSE will be of local origin. Any signal passing through RFI classification and classifiable as an extraterrestrial intelligence (ETI) candidate will be further validated at non-MOP observatories using established protocol. The targeted search will be conducted using the capability provided by the TSE. The TSE provides six Targeted Search Systems (TSS) which independently or cooperatively perform automated collection, analysis, storage, and archive of signal data. Data is collected in 10 megahertz chunks and signal processing is performed at a rate of 160 megabits per second. Signal data is obtained utilizing the largest radio telescopes available for the Targeted Search such as those at Arecibo and Nancay or at the dedicated NASA-SETI facility. This latter facility will allow continuous collection of data. The TSE also provides for TSS utilization planning, logistics, remote operation, and for off-line data analysis and permanent archive of both the Targeted Search and Sky Survey data.
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Fault detection and multiclassifier fusion for unmanned aerial vehicles (UAVs)
NASA Astrophysics Data System (ADS)
Yan, Weizhong
2001-03-01
UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a pilot aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naieve Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers) show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.
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Solving a Higgs optimization problem with quantum annealing for machine learning.
Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria
2017-10-18
The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.
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Solving a Higgs optimization problem with quantum annealing for machine learning
NASA Astrophysics Data System (ADS)
Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria
2017-10-01
The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.
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"JCE" Classroom Activity #105. A Sticky Situation: Chewing Gum and Solubility
ERIC Educational Resources Information Center
Montes-Gonzalez, Ingrid; Cintron-Maldonado, Jose A.; Perez-Medina, Ilia E.; Montes-Berrios, Veronica; Roman-Lopez, Saurie N.
2010-01-01
In this Activity, students perform several solubility tests using common food items such as chocolate, chewing gum, water, sugar, and oil. From their observations during the Activity, students will initially classify the substances tested as soluble or insoluble. They will then use their understanding of the chemistry of solubility to classify the…
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The Preservation of Two Infant Temperaments into Adolescence
ERIC Educational Resources Information Center
Kagan, Jerome; Snidman, Nancy; Kahn, Vali; Towsley, Sara
2007-01-01
This "Monograph" reports theoretically relevant behavioral, biological, and self-report assessments of a sample of 14-17-year-olds who had been classified into one of four temperamental groups at 4 months of age. The infant temperamental categories were based on observed behavior to a battery of unfamiliar stimuli. The infants classified as high…
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NASA Astrophysics Data System (ADS)
Wu, J.; Yao, W.; Zhang, J.; Li, Y.
2018-04-01
Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling); another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city). Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.
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Automatic Feature Selection and Improved Classification in SICADA Counterfeit Electronics Detection
2017-03-20
The SICADA methodology was developed to detect such counterfeit microelectronics by collecting power side channel data and applying machine learning...to identify counterfeits. This methodology has been extended to include a two-step automated feature selection process and now uses a one-class SVM...classifier. We describe this methodology and show results for empirical data collected from several types of Microchip dsPIC33F microcontrollers
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Mizuno, Koh; Matsumoto, Akiko; Aiba, Tatsuya; Abe, Takashi; Ohshima, Hiroshi; Takahashi, Masaya; Inoue, Yuichi
2016-09-01
Flight controllers of the International Space Station (ISS) are engaged in shift work to provide 24-h coverage to support ISS systems. The purpose of this study was to investigate the prevalence and associated factors of shift work sleep disorder (SWSD) among Japanese ISS flight controllers. A questionnaire study was conducted using the Standard Shiftwork Index to evaluate sleep-related problems and possible associated variables. Among 52 respondents out of 73 flight controllers, 30 subjects were identified as night shift workers who worked 3 or more night shifts per month. Those night shift workers who answered "almost always" to questions about experiencing insomnia or excessive sleepiness in any case of work shifts and days off were classified as having SWSD. Additionally, 7 night shift workers participated in supplemental wrist actigraphy data collection for 7 to 8 days including 3 to 4 days of consecutive night shifts. Fourteen of 30 night shift workers were classified as having SWSD. Significant group differences were observed where the SWSD group felt that night shift work was harder and reported more frequent insomniac symptoms after a night shift. However, no other variables demonstrated remarkable differences between groups. Actigraphy results characterized 5 subjects reporting better perceived adaptation as having regular daytime sleep, for 6 to 9 h in total, between consecutive night shifts. On the other hand, 2 subjects reporting perceived maladaptation revealed different sleep patterns, with longer daytime sleep and large day-to-day variation in daytime sleep between consecutive night shifts, respectively. As the tasks for flight control require high levels of alertness and cognitive function, several characteristics, namely shift-working schedule (2 to 4 consecutive night shifts), very short break time (5 to 10 min/h) during work shifts, and cooperative work with onboard astronauts during the evening/night shift, accounted for increasing workloads especially in the case of night shifts, resulting in higher or equal prevalence of SWSD to that among other shift-working populations. Further studies are required to collect more actigraphy data and examine the possibility of interventions to improve SWSD.
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Using Data From the Microsoft Kinect 2 to Quantify Upper Limb Behavior: A Feasibility Study.
Dehbandi, Behdad; Barachant, Alexandre; Harary, David; Long, John Davis; Tsagaris, K Zoe; Bumanlag, Silverio Joseph; He, Victor; Putrino, David
2017-09-01
The objective of this study was to assess whether the novel application of a machine learning approach to data collected from the Microsoft Kinect 2 (MK2) could be used to classify differing levels of upper limb impairment. Twenty-four healthy subjects completed items of the Wolf Motor Function Test (WMFT), which is a clinically validated metric of upper limb function for stroke survivors. Subjects completed the WMFT three times: 1) as a healthy individual; 2) emulating mild impairment; and 3) emulating moderate impairment. A MK2 was positioned in front of participants, and collected kinematic data as they completed the WMFT. A classification framework, based on Riemannian geometry and the use of covariance matrices as feature representation of the MK2 data, was developed for these data, and its ability to successfully classify subjects as either "healthy," "mildly impaired," or "moderately impaired" was assessed. Mean accuracy for our classifier was 91.7%, with a specific accuracy breakdown of 100%, 83.3%, and 91.7% for the "healthy," "mildly impaired," and "moderately impaired" conditions, respectively. We conclude that data from the MK2 is of sufficient quality to perform objective motor behavior classification in individuals with upper limb impairment. The data collection and analysis framework that we have developed has the potential to disrupt the field of clinical assessment. Future studies will focus on validating this protocol on large populations of individuals with actual upper limb impairments in order to create a toolkit that is clinically validated and available to the clinical community.
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NASA Astrophysics Data System (ADS)
Cubillas, J. E.; Japitana, M.
2016-06-01
This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Karaca, F.; Alagha, O.; Erturk, F.
Daily samples of fine (PM2.5) and coarse (PM2.5-10) particles were collected from July 2002 to July 2003 to provide a better understanding of the elemental concentration and source contribution to both PM fractions. Sampling location represents suburban part of Istanbul metropolitan city. Samples were collected on Teflon filters using a 'Dichotomous Sampler.' Concentrations of Al, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, and Zn were measured by GFAAS, FAAS, and FAES techniques. Elemental variations of heating and nonheating seasons were discussed. Fossil fuel-related atmospheric metals dramatically increased during the heating season, while natural originatedmore » atmospheric metals increased during the nonheating season. Seasonal variations of source contributions were evaluated using factor analysis, which was separately applied to the collected fine and coarse particles data sets during heating and nonheating seasons (four data sets: PM2.5 heating, PM2.5 nonheating, PM2.5-10 heating, and PM2.5-10 nonheating). Significant seasonal differences in source contributions were observed. Four factor groups were extracted for PM2.5 dataset during the nonheating season, while five factor groups were extracted for all the other cases. Mineral dust transportation, traffic, and industry-related activities were classified as different factor groups in all the cases.« less
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Information Theoretic Extraction of EEG Features for Monitoring Subject Attention
NASA Technical Reports Server (NTRS)
Principe, Jose C.
2000-01-01
The goal of this project was to test the applicability of information theoretic learning (feasibility study) to develop new brain computer interfaces (BCI). The difficulty to BCI comes from several aspects: (1) the effective data collection of signals related to cognition; (2) the preprocessing of these signals to extract the relevant information; (3) the pattern recognition methodology to detect reliably the signals related to cognitive states. We only addressed the two last aspects in this research. We started by evaluating an information theoretic measure of distance (Bhattacharyya distance) for BCI performance with good predictive results. We also compared several features to detect the presence of event related desynchronization (ERD) and synchronization (ERS), and concluded that at least for now the bandpass filtering is the best compromise between simplicity and performance. Finally, we implemented several classifiers for temporal - pattern recognition. We found out that the performance of temporal classifiers is superior to static classifiers but not by much. We conclude by stating that the future of BCI should be found in alternate approaches to sense, collect and process the signals created by populations of neurons. Towards this goal, cross-disciplinary teams of neuroscientists and engineers should be funded to approach BCIs from a much more principled view point.
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Movement Processes as Observable Behavior.
ERIC Educational Resources Information Center
Harrington, Wilma M.
The operations for achieving skill in motor performance are perceiving, patterning, adapting, refining, varying, improvising, and composing. These operations are readily observable in physical education classes. An observation record containing the seven catagories was used to classify teacher feedback to students. The teachers observed were…
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Cultural Differences in Color/Form Preference and in Classificatory Behavior
ERIC Educational Resources Information Center
Schmidt, W. H. O.; Nzimande, A.
1970-01-01
Cross-cultural data was collected on color/form preference and the ability to classify among Zulu children and adults. Significant differences were found attributable to factors of Western-type schooling, literacy, and urban or rural life. (NH)
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Procedures for waste management from street sweeping and stormwater systems.
DOT National Transportation Integrated Search
2016-05-01
Street sweeping and storm water system cleaning activities are conducted regularly by ODOT to comply with NPDES permit requirements and to ensure roadway safety. Once collected, these materials are classified as solid waste and require cost-effective...
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NASA Astrophysics Data System (ADS)
Duffy, James P.; Pratt, Laura; Anderson, Karen; Land, Peter E.; Shutler, Jamie D.
2018-01-01
Seagrass ecosystems are highly sensitive to environmental change. They are also in global decline and under threat from a variety of anthropogenic factors. There is now an urgency to establish robust monitoring methodologies so that changes in seagrass abundance and distribution in these sensitive coastal environments can be understood. Typical monitoring approaches have included remote sensing from satellites and airborne platforms, ground based ecological surveys and snorkel/scuba surveys. These techniques can suffer from temporal and spatial inconsistency, or are very localised making it hard to assess seagrass meadows in a structured manner. Here we present a novel technique using a lightweight (sub 7 kg) drone and consumer grade cameras to produce very high spatial resolution (∼4 mm pixel-1) mosaics of two intertidal sites in Wales, UK. We present a full data collection methodology followed by a selection of classification techniques to produce coverage estimates at each site. We trialled three classification approaches of varying complexity to investigate and illustrate the differing performance and capabilities of each. Our results show that unsupervised classifications perform better than object-based methods in classifying seagrass cover. We also found that the more sparsely vegetated of the two meadows studied was more accurately classified - it had lower root mean squared deviation (RMSD) between observed and classified coverage (9-9.5%) compared to a more densely vegetated meadow (RMSD 16-22%). Furthermore, we examine the potential to detect other biotic features, finding that lugworm mounds can be detected visually at coarser resolutions such as 43 mm pixel-1, whereas smaller features such as cockle shells within seagrass require finer grained data (<17 mm pixel-1).
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Socioeconomic status and obesity in Abia State, South East Nigeria.
Chukwuonye, Innocent Ijezie; Chuku, Abali; Okpechi, Ikechi Gareth; Onyeonoro, Ugochukwu Uchenna; Madukwe, Okechukwu Ojoemelam; Okafor, Godwin Oguejiofor Chukwuebuka; Ogah, Okechukwu Samuel
2013-01-01
Obesity is a major risk factor for cardiovascular disease in developed and emerging economies. There is a paucity of data from Nigeria on the association between socioeconomic status and obesity. The aim of this study is to highlight that association in Abia State, South East Nigeria. This was a cross-sectional survey in South East Nigeria. Participating subjects were recruited from the three senatorial zones of Abia state. A total of 2,487 adults took part in the study. The subjects were classified based on their monthly income and level of educational attainment (determinants of obesity). Monthly income was classified into three groups: low, middle, and upper income, while educational level was classified into four groups: no formal education, primary, secondary, and tertiary education. Body mass index of subjects was determined and used for defining obesity. Data on blood pressure and other anthropometric measurements were also collected using a questionnaire, modified from the World Health Organization STEPwise Approach to Chronic Disease Risk Factor Surveillance. Overall, the prevalence of obesity in low, middle, and upper income groups was 12.2%, 16%, and 20%, respectively. The overall prevalence of obesity in individuals with no formal education, primary, secondary, and tertiary education was 6.3%, 14.9%, 10.5%, and 17.7%, respectively. Educational status was found to be significantly associated with obesity in women, but not in men, or in the combined group. However, level of income was observed to be significantly associated with obesity in men, women, and in the combined group. Sociodemographic and socioeconomic factors are important determinants of obesity in our study population, and therefore may be indirectly linked to the prevalence and the outcomes of cardiovascular disease in Nigeria.
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Socioeconomic status and obesity in Abia State, South East Nigeria
Chukwuonye, Innocent Ijezie; Chuku, Abali; Okpechi, Ikechi Gareth; Onyeonoro, Ugochukwu Uchenna; Madukwe, Okechukwu Ojoemelam; Okafor, Godwin Oguejiofor Chukwuebuka; Ogah, Okechukwu Samuel
2013-01-01
Background and objectives Obesity is a major risk factor for cardiovascular disease in developed and emerging economies. There is a paucity of data from Nigeria on the association between socioeconomic status and obesity. The aim of this study is to highlight that association in Abia State, South East Nigeria. Material and methods This was a cross-sectional survey in South East Nigeria. Participating subjects were recruited from the three senatorial zones of Abia state. A total of 2,487 adults took part in the study. The subjects were classified based on their monthly income and level of educational attainment (determinants of obesity). Monthly income was classified into three groups: low, middle, and upper income, while educational level was classified into four groups: no formal education, primary, secondary, and tertiary education. Body mass index of subjects was determined and used for defining obesity. Data on blood pressure and other anthropometric measurements were also collected using a questionnaire, modified from the World Health Organization STEPwise Approach to Chronic Disease Risk Factor Surveillance. Results Overall, the prevalence of obesity in low, middle, and upper income groups was 12.2%, 16%, and 20%, respectively. The overall prevalence of obesity in individuals with no formal education, primary, secondary, and tertiary education was 6.3%, 14.9%, 10.5%, and 17.7%, respectively. Educational status was found to be significantly associated with obesity in women, but not in men, or in the combined group. However, level of income was observed to be significantly associated with obesity in men, women, and in the combined group. Conclusion Sociodemographic and socioeconomic factors are important determinants of obesity in our study population, and therefore may be indirectly linked to the prevalence and the outcomes of cardiovascular disease in Nigeria. PMID:24204167
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Langbeen, A; Jorssen, E P A; Fransen, E; Rodriguez, A P A; García, M Chong; Leroy, J L M R; Bols, P E J
2015-10-01
Due to the increased interest in preantral follicular physiology, non-invasive retrieval and morphological classification are crucial. Therefore, this study aimed: (1) to standardize a minimally invasive isolation protocol, applicable to three ruminant species; (2) to morphologically classify preantral follicles upon retrieval; and (3) to describe morphological features of freshly retrieved follicles compared with follicle characteristics using invasive methods. Bovine, caprine and ovine ovarian cortex strips were retrieved from slaughterhouse ovaries and dispersed. This suspension was filtered, centrifuged, re-suspended and transferred to a Petri dish, to which 0.025 mg/ml neutral red (NR) was added to assess the viability of the isolated follicles. Between 59 and 191 follicles per follicle class and per species were collected and classified by light microscopy, based on follicular cell morphology. Subsequently, follicle diameters were measured. The proposed isolation protocol was applicable to all three species and showed a significant, expected increase in diameter with developmental stage. With an average diameter of 37 ± 5 μm for primordial follicles, 47 ± 6.3 μm for primary follicles and 67.1 ± 13.1 μm for secondary follicles, no significant difference in diameter among the three species was observed. Bovine, caprine and ovine follicles (63, 59 and 50% respectively) were graded as viable upon retrieval. Using the same morphological characteristics as determined by invasive techniques [e.g. haematoxylin-eosin (HE) sections], cumulus cell morphology and follicle diameter could be used routinely to classify freshly retrieved follicles. Finally, we applied a mechanical, minimally invasive, follicle isolation protocol and extended it to three ruminant species, yielding viable preantral follicles without compromising further in vitro processing and allowing routine follicle characterization upon retrieval.
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Assessing Immunity to Rubella Virus: a Plea for Standardization of IgG (Immuno)assays
Bouthry, Elise; Huzly, Daniela; Ogee-Nwankwo, Adaeze; Hao, LiJuan; Adebayo, Adebola; Icenogle, Joseph; Sarasini, Antonella; Revello, Maria Grazia; Grangeot-Keros, Liliane
2016-01-01
Immunity to rubella virus (RV) is commonly determined by measuring specific immunoglobulin G (RV IgG). However, RV IgG results and their interpretation may vary, depending on the immunoassay, even though most commercial immunoassays (CIAs) have been calibrated against an international standard and results are reported in international units per milliliter. A panel of 322 sera collected from pregnant women that tested negative or equivocal for RV IgG in a prior test (routine screening) was selected. This panel was tested with two reference tests, immunoblotting (IB) and neutralization (Nt), and with 8 CIAs widely used in Europe. IB and Nt gave concordant results on 267/322 (82.9%) sera. Of these, 85 (26.4%) sera were negative and 182 (56.5%) sera were positive for both tests. All 85 IB/Nt-negative samples were classified as negative with all CIAs. Of the 182 IB/Nt-positive samples, 25.3 to 61.5% were classified as equivocal and 6 to 64.8% were classified as positive with the CIAs. Wide variations in titers in international units per milliliter were observed. In our series, more than half of the women considered susceptible to RV based on CIA results tested positive for RV antibodies by IB/Nt. Our data suggest that (i) sensitivity of CIAs could be increased by considering equivocal results as positive and (ii) the definition of immunity to RV as the 10-IU/ml usual cutoff as well as the use of quantitative results for clinical decisions may warrant reconsideration. A better standardization of CIAs for RV IgG determination is needed. PMID:27147722
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Assessing Immunity to Rubella Virus: a Plea for Standardization of IgG (Immuno)assays.
Bouthry, Elise; Furione, Milena; Huzly, Daniela; Ogee-Nwankwo, Adaeze; Hao, LiJuan; Adebayo, Adebola; Icenogle, Joseph; Sarasini, Antonella; Revello, Maria Grazia; Grangeot-Keros, Liliane; Vauloup-Fellous, Christelle
2016-07-01
Immunity to rubella virus (RV) is commonly determined by measuring specific immunoglobulin G (RV IgG). However, RV IgG results and their interpretation may vary, depending on the immunoassay, even though most commercial immunoassays (CIAs) have been calibrated against an international standard and results are reported in international units per milliliter. A panel of 322 sera collected from pregnant women that tested negative or equivocal for RV IgG in a prior test (routine screening) was selected. This panel was tested with two reference tests, immunoblotting (IB) and neutralization (Nt), and with 8 CIAs widely used in Europe. IB and Nt gave concordant results on 267/322 (82.9%) sera. Of these, 85 (26.4%) sera were negative and 182 (56.5%) sera were positive for both tests. All 85 IB/Nt-negative samples were classified as negative with all CIAs. Of the 182 IB/Nt-positive samples, 25.3 to 61.5% were classified as equivocal and 6 to 64.8% were classified as positive with the CIAs. Wide variations in titers in international units per milliliter were observed. In our series, more than half of the women considered susceptible to RV based on CIA results tested positive for RV antibodies by IB/Nt. Our data suggest that (i) sensitivity of CIAs could be increased by considering equivocal results as positive and (ii) the definition of immunity to RV as the 10-IU/ml usual cutoff as well as the use of quantitative results for clinical decisions may warrant reconsideration. A better standardization of CIAs for RV IgG determination is needed. Copyright © 2016, American Society for Microbiology. All Rights Reserved.
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Consensus Classification Using Non-Optimized Classifiers.
Brownfield, Brett; Lemos, Tony; Kalivas, John H
2018-04-03
Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.
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Self-reports of induced abortion: an empathetic setting can improve the quality of data.
Rasch, V; Muhammad, H; Urassa, E; Bergström, S
2000-01-01
OBJECTIVES: This study estimated the proportion of incomplete abortions that are induced in hospital-based settings in Tanzania. METHODS: A cross-sectional questionnaire study was conducted in 2 phases at 3 hospitals in Tanzania. Phase 1 included 302 patients with a diagnosis of incomplete abortion, and phase 2 included 823 such patients. RESULTS: In phase 1, in which cases were classified by clinical criteria and information from the patient, 3.9% to 16.1% of the cases were classified as induced abortion. In phase 2, in which the structured interview was changed to an empathetic dialogue and previously used clinical criteria were omitted, 30.9% to 60.0% of the cases were classified as induced abortion. CONCLUSIONS: An empathetic dialogue improves the quality of data collected among women with induced abortion. PMID:10897196
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The problem is education not ``special education''
NASA Astrophysics Data System (ADS)
Fellner, Gene
2015-12-01
In his article, Urban special education policy and the lived experience of stigma in a high school science classroom, Chris Hale persuasively argues that the Individuals with Disabilities Education Act and subsequent special education policies have largely failed to serve special education students who are stigmatized by their deficit classification. Though classified students may be doubly stigmatized, research suggests that students of color who live in economically stressed communities are also subject to systemic educational policies that produce stigma; special education should be understood within the larger context of educational policy in the inner city. Though we cannot immediately dismantle the macro level structures that nurture stigma, I suggest pedagogies based on facilitating phenomenological awareness enacted through individual-collectively based methodologies to challenge the stigma that classified as well as non-classified students in the inner city often carry with them.
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Classification Studies in an Advanced Air Classifier
NASA Astrophysics Data System (ADS)
Routray, Sunita; Bhima Rao, R.
2016-10-01
In the present paper, experiments are carried out using VSK separator which is an advanced air classifier to recover heavy minerals from beach sand. In classification experiments the cage wheel speed and the feed rate are set and the material is fed to the air cyclone and split into fine and coarse particles which are collected in separate bags. The size distribution of each fraction was measured by sieve analysis. A model is developed to predict the performance of the air classifier. The objective of the present model is to predict the grade efficiency curve for a given set of operating parameters such as cage wheel speed and feed rate. The overall experimental data with all variables studied in this investigation is fitted to several models. It is found that the present model is fitting good to the logistic model.
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Moreno, Mabel; Ferro, Cristina; Rosales-Chilama, Mariana; Rubiano, Luisa; Delgado, Marcela; Cossio, Alexandra; Gómez, Maria Adelaida; Ocampo, Clara; Saravia, Nancy Gore
2015-08-01
The expansion of transmission of cutaneous leishmaniasis from sylvatic ecosystems into peri-urban and domestic settings has occurred as sand flies have adapted to anthropogenic environmental modifications. Assessment of the intradomiciliary presence of sand flies in households of the settlement "La Cabaña", in the Department of Risaralda, Colombia, revealed an abundance of Warileya rotundipennis. This unexpected observation motivated further analyses to evaluate the participation of this species in the transmission of cutaneous leishmaniasis. Collections using CDC light traps were conducted during two consecutive nights in May and August 2011.The total of 667 sand flies collected were classified into five species: W. rotundipennis (n=654; 98.05%), Nyssomyia trapidoi (n=7; 1.04%); Lutzomyia (Helcocyrtomyia) hartmanni (n=3; 0.44%); Lutzomyia lichyi (n=2; 0.29%) and Psychodopygus panamensis (n=1; 0.14%). The striking predominance of W. rotundipennis within households during both wet (May) and dry (August) seasons, anthropophilic behavior demonstrated by human blood in 95.23% (60/63) evaluable blood-engorged specimens, and natural infection (5/168-3%) with genetically similar parasites of the Leishmania (Viannia) subgenus observed in a patient in this community, support the involvement of W. rotundipennis in the domestic transmission of cutaneous leishmaniasis in "La Cabaña". Copyright © 2015 Elsevier B.V. All rights reserved.
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Dilli, Dilek; Suna Oğuz, S; Erol, Reyhan; Ozkan-Ulu, Hülya; Dumanlı, Hüseyin; Dilmen, Uğur
2011-03-01
To explore whether addition of abdominal sonography (AUS) to plain radiography is helpful in the management of premature newborns with necrotizing enterocolitis (NEC). This study is a prospective analysis of 93 premature neonates with NEC who were followed-up in our neonatal intensive care unit between October 2007 and April 2009. Patients were classified into two groups; group I with suspected NEC (stage I) (n = 54) and group II with definite NEC (stage ≥II) (n = 39). Pneumatosis intestinalis (PI) (n = 29), free air (n = 9), and portal venous gas (PVG) (n = 1) were observed in group II on plain radiography. In the same group, echoic free fluid (EFF) (n = 9), PVG (n = 6), PI (n = 5), and focal fluid collection (n = 3) were the most prominent sonographic findings. In patients with intestinal perforation, whereas EFF and bowel wall thinning were observed on AUS, free air was not detected on plain radiography as a sign of intestinal perforation. Our results suggest AUS to be superior to plain radiography on early detection of intestinal perforation by demonstrating PVG and EFF collection. Therefore, it may be life-saving by directing the surgeon to perform surgical intervention in the case of clinical deterioration in the course of NEC.
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Exploring students' patterns of reasoning
NASA Astrophysics Data System (ADS)
Matloob Haghanikar, Mojgan
As part of a collaborative study of the science preparation of elementary school teachers, we investigated the quality of students' reasoning and explored the relationship between sophistication of reasoning and the degree to which the courses were considered inquiry oriented. To probe students' reasoning, we developed open-ended written content questions with the distinguishing feature of applying recently learned concepts in a new context. We devised a protocol for developing written content questions that provided a common structure for probing and classifying students' sophistication level of reasoning. In designing our protocol, we considered several distinct criteria, and classified students' responses based on their performance for each criterion. First, we classified concepts into three types: Descriptive, Hypothetical, and Theoretical and categorized the abstraction levels of the responses in terms of the types of concepts and the inter-relationship between the concepts. Second, we devised a rubric based on Bloom's revised taxonomy with seven traits (both knowledge types and cognitive processes) and a defined set of criteria to evaluate each trait. Along with analyzing students' reasoning, we visited universities and observed the courses in which the students were enrolled. We used the Reformed Teaching Observation Protocol (RTOP) to rank the courses with respect to characteristics that are valued for the inquiry courses. We conducted logistic regression for a sample of 18courses with about 900 students and reported the results for performing logistic regression to estimate the relationship between traits of reasoning and RTOP score. In addition, we analyzed conceptual structure of students' responses, based on conceptual classification schemes, and clustered students' responses into six categories. We derived regression model, to estimate the relationship between the sophistication of the categories of conceptual structure and RTOP scores. However, the outcome variable with six categories required a more complicated regression model, known as multinomial logistic regression, generalized from binary logistic regression. With the large amount of collected data, we found that the likelihood of the higher cognitive processes were in favor of classes with higher measures on inquiry. However, the usage of more abstract concepts with higher order conceptual structures was less prevalent in higher RTOP courses.
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The natural history of cystic echinococcosis in untreated and albendazole-treated patients.
Solomon, N; Kachani, M; Zeyhle, E; Macpherson, C N L
2017-07-01
The World Health Organization (WHO) treatment protocols for cystic echinococcosis (CE) are based on the standardized ultrasound (US) classification. This study examined whether the classification reflected the natural history of CE in untreated and albendazole-treated patients. Data were collected during mass US screenings in CE endemic regions among transhumant populations, the Turkana and Berber peoples of Kenya and Morocco. Cysts were classified using the WHO classification. Patient records occurring prior to treatment, and after albendazole administration, were selected. 852 paired before/after observations of 360 cysts from 257 patients were analyzed. A McNemar-Bowker χ 2 test for symmetry was significant (p<0.0001). 744 observations (87.3%) maintained the same class, and 101 (11.9%) progressed, consistent with the classification. Regression to CE3B occurred in seven of 116 CE4 cyst observations (6.0%). A McNemar-Bowker χ 2 test of 1414 paired before/after observations of 288 cysts from 157 albendazole-treated patients was significant (p<0.0001). 1236 observations (87.4%) maintained the same class, and 149 (10.5%) progressed, consistent with the classification. Regression to CE3B occurred in 29 of 206 CE4 observations (14.1%). Significant asymmetry confirms the WHO classification's applicability to the natural history of CE and albendazole-induced changes. Regressions may reflect the stability of CE3B cysts. Copyright © 2017. Published by Elsevier B.V.
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NASA Technical Reports Server (NTRS)
Thomson, F.
1975-01-01
Two tasks of machine processing of S-192 multispectral scanner data are reviewed. In the first task, the effects of changing atmospheric and base altitude on the ability to machine-classify agricultural crops were investigated. A classifier and atmospheric effects simulation model was devised and its accuracy verified by comparison of its predicted results with S-192 processed results. In the second task, land resource maps of a mountainous area near Cripple Creek, Colorado were prepared from S-192 data collected on 4 August 1973.
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Classification of cardiac arrhythmias using competitive networks.
Leite, Cicilia R M; Martin, Daniel L; Sizilio, Glaucia R A; Dos Santos, Keylly E A; de Araujo, Bruno G; Valentim, Ricardo A M; Neto, Adriao D D; de Melo, Jorge D; Guerreiro, Ana M G
2010-01-01
Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.
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Increasing accuracy of vehicle speed measurement in congested traffic over dual-loop sensors.
DOT National Transportation Integrated Search
2014-09-01
Classified vehicle counts are a critical measure for forecasting the health of the roadway infrastructure : and for planning future improvements to the transportation network. Balancing the cost of data : collection with the fidelity of the measureme...
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Mergers and Anti-trust Issues in Recent CAB Cases
NASA Technical Reports Server (NTRS)
Andrews, A. M.
1972-01-01
The airline industry is surveyed-particularly domestic trunklines-in relation to collective approaches to industry concerns. These actions are classified by the apparent degree of anti-trust issue present. Recent route merger cases are considered from the CAB staff viewpoint.
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Wang, Lei; Pedersen, Peder C; Agu, Emmanuel; Strong, Diane M; Tulu, Bengisu
2017-09-01
The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.
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Rantalainen, Timo; Chivers, Paola; Beck, Belinda R; Robertson, Sam; Hart, Nicolas H; Nimphius, Sophia; Weeks, Benjamin K; McIntyre, Fleur; Hands, Beth; Siafarikas, Aris
Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual inspection) and objective assessments of the scans. However, previously reported objective methods either cannot be applied on the reconstructed image or have not been tested for distal bone sites. Therefore, the purpose of the present study was to develop and validate motion artifact classifiers to quantify motion artifact in pQCT scans. Whether textural features could provide adequate motion artifact classification performance in 2 adolescent datasets with pQCT scans from tibial and radial diaphyses and epiphyses was tested. The first dataset was split into training (66% of sample) and validation (33% of sample) datasets. Visual classification was used as the ground truth. Moderate to substantial classification performance (J48 classifier, kappa coefficients from 0.57 to 0.80) was observed in the validation dataset with the novel texture-based classifier. In applying the same classifier to the second cross-sectional dataset, a slight-to-fair (κ = 0.01-0.39) classification performance was observed. Overall, this novel textural analysis-based classifier provided a moderate-to-substantial classification of motion artifact when the classifier was specifically trained for the measurement device and population. Classification based on textural features may be used to prescreen obviously acceptable and unacceptable scans, with a subsequent human-operated visual classification of any remaining scans. Copyright © 2017 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.
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Leboeuf-Yde, Charlotte; Lemeunier, Nadège; Wedderkopp, Niels; Kjaer, Per
2013-01-01
It was previously assumed that low back pain (LBP) is a disorder that can be classified as acute, subacute and chronic. Lately, the opinion seems to have veered towards a concept of it being a more recurrent or cyclic condition. Interestingly, a recent review of the literature indicated that LBP in the general population is a rather stable condition, characterized as either being present or absent. However, only one of the reviewed studies had used frequent data collection, which would be necessary when studying detailed course patterns over time. It was the purpose of this study to see, if it was possible to identify whether LBP, when present, is rather episodic or chronic/persistent. Further, we wanted to see if it was possible to describe any specific course profiles of LBP in the general population. In all, 293 49/50-yr old Danes, who previously participated in a population-based study on LBP were invited to respond to 26 fortnightly text-messages over one year, each time asking them the number of days they had been bothered by LBP in the past two weeks. The course patterns for these individuals were identified through manual analysis, by observing the interplay between non-episodes and episodes of LBP. A non-episode of LBP was defined as a period of at least one month without LBP as proposed by de Vet et al. A fortnight with at least one day of pain was defined as a pain fortnight (FN). At least one pain FN surrounded by a non-episode on each side was defined as an episode of LBP. After some preliminary observations of the spread of data, episodes were further classified as brief (consisting of only one pain FN) or longer (if there were at least 2 pain FNs in a row). An episode of at least 6 pain FNs in a row (i.e. 3 months) was defined as a long-lasting episode. In all, 261 study subjects were included in the analyses, for which 7 distinct LBP subsets could be identified. These could be grouped into three major clusters; those mainly without LBP (35%), those with episodic LBP (30%) and those with persistent LBP (35%). There was a positive association between number of episodes and their duration. In this study population, consisting of 50-yr old persons from the general population, LBP, when present, could be classified as either 'episodic' or 'mainly persistent'. About one third was mainly LBP-free throughout the year of study. More information is needed in relation to their relative proportions in various populations and the clinical relevance of these subgroups.
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Ingersoll, Christopher G.; Haverland, Pamela S.; Brunson, Eric L.; Canfield, Timothy J.; Dwyer, F. James; Henke, Chris; Kemble, Nile E.; Mount, David R.; Fox, Richard G.
1996-01-01
Procedures are described for calculating and evaluating sediment effect concentrations (SECs) using laboratory data on the toxicity of contaminants associated with field-collected sediment to the amphipod Hyalella azteca and the midge Chironomus riparius. SECs are defined as the concentrations of individual contaminants in sediment below which toxicity is rarely observed and above which toxicity is frequently observed. The objective of the present study was to develop SECs to classify toxicity data for Great Lake sediment samples tested with Hyalella azteca and Chironomus riparius. This SEC database included samples from additional sites across the United States in order to make the database as robust as possible. Three types of SECs were calculated from these data: (1) Effect Range Low (ERL) and Effect Range Median (ERM), (2) Threshold Effect Level (TEL) and Probable Effect Level (PEL), and (3) No Effect Concentration (NEC). We were able to calculate SECs primarily for total metals, simultaneously extracted metals, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). The ranges of concentrations in sediment were too narrow in our database to adequately evaluate SECs for butyltins, methyl mercury, polychlorinated dioxins and furans, or chlorinated pesticides. About 60 to 80% of the sediment samples in the database are correctly classified as toxic or not toxic depending on type of SEC evaluated. ERMs and ERLs are generally as reliable as paired PELs and TELs at classifying both toxic and non-toxic samples in our database. Reliability of the SECs in terms of correctly classifying sediment samples is similar between ERMs and NECs; however, ERMs minimize Type I error (false positives) relative to ERLs and minimize Type II error (false negatives) relative to NECs. Correct classification of samples can be improved by using only the most reliable individual SECs for chemicals (i.e., those with a higher percentage of correct classification). SECs calculated using sediment concentrations normalized to total organic carbon (TOC) concentrations did not improve the reliability compared to SECs calculated using dry-weight concentrations. The range of TOC concentrations in our database was relatively narrow compared to the ranges of contaminant concentrations. Therefore, normalizing dry-weight concentrations to a relatively narrow range of TOC concentrations had little influence on relative concentra of contaminants among samples. When SECs are used to conduct a preliminary screening to predict the potential for toxicity in the absence of actual toxicity testing, a low number of SEC exceedances should be used to minimize the potential for false negatives; however, the risk of accepting higher false positives is increased.
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Poker as a skill game: rational versus irrational behaviors
NASA Astrophysics Data System (ADS)
Javarone, Marco Alberto
2015-03-01
In many countries poker is one of the most popular card games. Although each variant of poker has its own rules, all involve the use of money to make the challenge meaningful. Nowadays, in the collective consciousness, some variants of poker are referred to as games of skill, others as gambling. A poker table can be viewed as a psychology lab, where human behavior can be observed and quantified. This work provides a preliminary analysis of the role of rationality in poker games, using a stylized version of Texas Hold'em. In particular, we compare the performance of two different kinds of players, i.e. rational versus irrational players, during a poker tournament. Results show that these behaviors (i.e. rationality and irrationality) affect both the outcomes of challenges and the way poker should be classified.
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NASA Astrophysics Data System (ADS)
Mayasari, F.; Raharjo; Supardi, Z. A. I.
2018-01-01
This research aims to develop the material eligibility to complete the inquiry learning of student in the material organization system of junior high school students. Learning materials developed include syllabi, lesson plans, students’ textbook, worksheets, and learning achievement test. This research is the developmental research which employ Dick and Carey model to develop learning material. The experiment was done in Junior High School 4 Lamongan regency using One Group Pretest-Posttest Design. The data collection used validation, observation, achievement test, questionnaire administration, and documentation. Data analysis techniques used quantitative and qualitative descriptive.The results showed that the developed learning material was valid and can be used. Learning activity accomplished with good category, where student activities were observed. The aspects of attitudes were observed during the learning process are honest, responsible, and confident. Student learning achievement gained an average of 81, 85 in complete category, with N-Gain 0, 75 for a high category. The activities and student response to learning was very well categorized. Based on the results, this researcher concluded that the device classified as feasible of inquiry-based learning (valid, practical, and effective) system used on the material organization of junior high school students.
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Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Aben, R; Abolins, M; AbouZeid, O S; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Alconada Verzini, M J; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Alimonti, G; Alio, L; Alison, J; Alkire, S P; Allbrooke, B M M; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Altheimer, A; Alvarez Gonzalez, B; Álvarez Piqueras, D; Alviggi, M G; Amadio, B T; Amako, K; Amaral Coutinho, Y; Amelung, C; Amidei, D; Amor Dos Santos, S P; Amorim, A; Amoroso, S; Amram, N; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoki, M; Aperio Bella, L; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Arnaez, O; Arnal, V; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Aurousseau, M; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bacci, C; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Bain, T; Baines, J T; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Banas, E; Banerjee, Sw; Bannoura, A A E; Bansil, H S; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barisonzi, M; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska, Z; Baroncelli, A; Barone, G; Barr, A J; Barreiro, F; Barreiro Guimarães da Costa, J; Bartoldus, R; Barton, A E; Bartos, P; Basalaev, A; Bassalat, A; Basye, A; Bates, R L; Batista, S J; Batley, J R; Battaglia, M; Bauce, M; Bauer, F; Bawa, H S; Beacham, J B; Beattie, M D; Beau, T; Beauchemin, P H; Beccherle, R; Bechtle, P; Beck, H P; Becker, K; Becker, M; Beckingham, M; Becot, C; Beddall, A J; Beddall, A; Bednyakov, V A; Bee, C P; Beemster, L J; Beermann, T A; Begel, M; Behr, J K; Belanger-Champagne, C; Bell, W H; Bella, G; Bellagamba, L; Bellerive, A; Bellomo, M; Belotskiy, K; Beltramello, O; Benary, O; Benchekroun, D; Bender, M; Bendtz, K; Benekos, N; Benhammou, Y; Benhar Noccioli, E; Benitez Garcia, J A; Benjamin, D P; Bensinger, J R; Bentvelsen, S; Beresford, L; Beretta, M; Berge, D; Bergeaas Kuutmann, E; Berger, N; Berghaus, F; Beringer, J; Bernard, C; Bernard, N R; Bernius, C; Bernlochner, F U; Berry, T; Berta, P; Bertella, C; Bertoli, G; Bertolucci, F; Bertsche, C; Bertsche, D; Besana, M I; Besjes, G J; Bessidskaia Bylund, O; Bessner, M; Besson, N; Betancourt, C; Bethke, S; Bevan, A J; Bhimji, W; Bianchi, R M; Bianchini, L; Bianco, M; Biebel, O; Biedermann, D; Bieniek, S P; Biglietti, M; Bilbao De Mendizabal, J; Bilokon, H; Bindi, M; Binet, S; Bingul, A; Bini, C; Biondi, S; Black, C W; Black, J E; Black, K M; Blackburn, D; Blair, R E; Blanchard, J-B; Blanco, J E; Blazek, T; Bloch, I; Blocker, C; Blum, W; Blumenschein, U; Bobbink, G J; Bobrovnikov, V S; Bocchetta, S S; Bocci, A; Bock, C; Boehler, M; Bogaerts, J A; Bogavac, D; Bogdanchikov, A G; Bohm, C; Boisvert, V; Bold, T; Boldea, V; Boldyrev, A S; Bomben, M; Bona, M; Boonekamp, M; Borisov, A; Borissov, G; Borroni, S; Bortfeldt, J; Bortolotto, V; Bos, K; Boscherini, D; Bosman, M; Boudreau, J; Bouffard, J; Bouhova-Thacker, E V; Boumediene, D; Bourdarios, C; Bousson, N; Boveia, A; Boyd, J; Boyko, I R; Bozic, I; Bracinik, J; Brandt, A; Brandt, G; Brandt, O; Bratzler, U; Brau, B; Brau, J E; Braun, H M; Brazzale, S F; Breaden Madden, W D; Brendlinger, K; Brennan, A J; Brenner, L; Brenner, R; Bressler, S; Bristow, K; Bristow, T M; Britton, D; Britzger, D; Brochu, F M; Brock, I; Brock, R; Bronner, J; Brooijmans, G; Brooks, T; Brooks, W K; Brosamer, J; Brost, E; Brown, J; Bruckman de Renstrom, P A; Bruncko, D; Bruneliere, R; Bruni, A; Bruni, G; Bruschi, M; Bruscino, N; Bryngemark, L; Buanes, T; Buat, Q; Buchholz, P; Buckley, A G; Buda, S I; Budagov, I A; Buehrer, F; Bugge, L; Bugge, M K; Bulekov, O; Bullock, D; Burckhart, H; Burdin, S; Burgard, C D; Burghgrave, B; Burke, S; Burmeister, I; Busato, E; Büscher, D; Büscher, V; Bussey, P; Butler, J M; Butt, A I; Buttar, C M; Butterworth, J M; Butti, P; Buttinger, W; Buzatu, A; Buzykaev, A R; Cabrera Urbán, S; Caforio, D; Cairo, V M; Cakir, O; Calace, N; Calafiura, P; Calandri, A; Calderini, G; Calfayan, P; Caloba, L P; Calvet, D; Calvet, S; Camacho Toro, R; Camarda, S; Camarri, P; Cameron, D; Caminal Armadans, R; Campana, S; Campanelli, M; Campoverde, A; Canale, V; Canepa, A; Cano Bret, M; Cantero, J; Cantrill, R; Cao, T; Capeans Garrido, M D M; Caprini, I; Caprini, M; Capua, M; Caputo, R; Cardarelli, R; Cardillo, F; Carli, T; Carlino, G; Carminati, L; Caron, S; Carquin, E; Carrillo-Montoya, G D; Carter, J R; Carvalho, J; Casadei, D; Casado, M P; Casolino, M; Castaneda-Miranda, E; Castelli, A; Castillo Gimenez, V; Castro, N F; Catastini, P; Catinaccio, A; Catmore, J R; Cattai, A; Caudron, J; Cavaliere, V; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Ceradini, F; Cerio, B C; Cerny, K; Cerqueira, A S; Cerri, A; Cerrito, L; Cerutti, F; Cerv, M; Cervelli, A; Cetin, S A; Chafaq, A; Chakraborty, D; Chalupkova, I; Chang, P; Chapman, J D; Charlton, D G; Chau, C C; Chavez Barajas, C A; Cheatham, S; Chegwidden, A; Chekanov, S; Chekulaev, S V; Chelkov, G A; Chelstowska, M A; Chen, C; Chen, H; Chen, K; Chen, L; Chen, S; Chen, X; Chen, Y; Cheng, H C; Cheng, Y; Cheplakov, A; Cheremushkina, E; Cherkaoui El Moursli, R; Chernyatin, V; Cheu, E; Chevalier, L; Chiarella, V; Chiarelli, G; Chiodini, G; Chisholm, A S; Chislett, R T; Chitan, A; Chizhov, M V; Choi, K; Chouridou, S; Chow, B K B; Christodoulou, V; Chromek-Burckhart, D; Chudoba, J; Chuinard, A J; Chwastowski, J J; Chytka, L; Ciapetti, G; Ciftci, A K; Cinca, D; Cindro, V; Cioara, I A; Ciocio, A; Cirotto, F; Citron, Z H; Ciubancan, M; Clark, A; Clark, B L; Clark, P J; Clarke, R N; Cleland, W; Clement, C; Coadou, Y; Cobal, M; Coccaro, A; Cochran, J; Coffey, L; Cogan, J G; Colasurdo, L; Cole, B; Cole, S; Colijn, A P; Collot, J; Colombo, T; Compostella, G; Conde Muiño, P; Coniavitis, E; Connell, S H; Connelly, I A; Consorti, V; Constantinescu, S; Conta, C; Conti, G; Conventi, F; Cooke, M; Cooper, B D; Cooper-Sarkar, A M; Cornelissen, T; Corradi, M; Corriveau, F; Corso-Radu, A; Cortes-Gonzalez, A; Cortiana, G; Costa, G; Costa, M J; Costanzo, D; Côté, D; Cottin, G; Cowan, G; Cox, B E; Cranmer, K; Cree, G; Crépé-Renaudin, S; Crescioli, F; Cribbs, W A; Crispin Ortuzar, M; Cristinziani, M; Croft, V; Crosetti, G; Cuhadar Donszelmann, T; Cummings, J; Curatolo, M; Cuthbert, C; Czirr, H; Czodrowski, P; D'Auria, S; 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A search for single top-quark production via flavour-changing neutral current processes from gluon plus up- or charm-quark initial states in proton-proton collisions at the LHC is presented. Data collected with the ATLAS detector in 2012 at a centre-of-mass energy of 8 TeV and corresponding to an integrated luminosity of 20.3 fb[Formula: see text] are used. Candidate events for a top quark decaying into a lepton, a neutrino and a jet are selected and classified into signal- and background-like candidates using a neural network. No signal is observed and an upper limit on the production cross-section multiplied by the [Formula: see text] branching fraction is set. The observed 95 % CL limit is [Formula: see text] and the expected 95 % CL limit is [Formula: see text]. The observed limit can be interpreted as upper limits on the coupling constants of the flavour-changing neutral current interactions divided by the scale of new physics [Formula: see text] and [Formula: see text] and on the branching fractions [Formula: see text] and [Formula: see text].
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Kabiri, Keivan; Rezai, Hamid; Moradi, Masoud
2018-04-01
High spatial resolution WorldView-2 (WV2) satellite imagery coupled with field observations have been utilized for mapping the coral reefs around Hendorabi Island in the northern Persian Gulf. In doing so, three standard multispectral bands (red, green, and blue) were selected to produce a classified map for benthic habitats. The in-situ observations were included photo-transects taken by snorkeling in water surface and manta tow technique. The satellite image has been classified using support vector machine (SVM) classifier by considering the information obtained from field measurements as both training and control points data. The results obtained from manta tow demonstrated that the mean total live hard coral coverage was 29.04% ± 2.44% around the island. Massive corals poritiids (20.70%) and branching corals acroporiids (20.33%) showed higher live coral coverage compared to other corals. Moreover, the map produced from satellite image illustrated the distribution of habitats with 78.1% of overall accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Classification of cardiac patient states using artificial neural networks
Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM
2003-01-01
Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222
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The large-scale environment from cosmological simulations - I. The baryonic cosmic web
NASA Astrophysics Data System (ADS)
Cui, Weiguang; Knebe, Alexander; Yepes, Gustavo; Yang, Xiaohu; Borgani, Stefano; Kang, Xi; Power, Chris; Staveley-Smith, Lister
2018-01-01
Using a series of cosmological simulations that includes one dark-matter-only (DM-only) run, one gas cooling-star formation-supernova feedback (CSF) run and one that additionally includes feedback from active galactic nuclei (AGNs), we classify the large-scale structures with both a velocity-shear-tensor code (VWEB) and a tidal-tensor code (PWEB). We find that the baryonic processes have almost no impact on large-scale structures - at least not when classified using aforementioned techniques. More importantly, our results confirm that the gas component alone can be used to infer the filamentary structure of the universe practically un-biased, which could be applied to cosmology constraints. In addition, the gas filaments are classified with its velocity (VWEB) and density (PWEB) fields, which can theoretically connect to the radio observations, such as H I surveys. This will help us to bias-freely link the radio observations with dark matter distributions at large scale.
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Proposed hybrid-classifier ensemble algorithm to map snow cover area
NASA Astrophysics Data System (ADS)
Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir
2018-01-01
Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.
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A semi-automated method for bone age assessment using cervical vertebral maturation.
Baptista, Roberto S; Quaglio, Camila L; Mourad, Laila M E H; Hummel, Anderson D; Caetano, Cesar Augusto C; Ortolani, Cristina Lúcia F; Pisa, Ivan T
2012-07-01
To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al. A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naïve Bayes algorithm were built and assessed using a software program. The classifier with the greatest accuracy according to the weighted kappa test was considered best. The classifier showed a weighted kappa coefficient of 0.861 ± 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 ± 0.019. Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice.
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Characterisation of iron-rich atmospheric submicrometre particles in the roadside environment
NASA Astrophysics Data System (ADS)
Sanderson, P.; Su, S. S.; Chang, I. T. H.; Delgado Saborit, J. M.; Kepaptsoglou, D. M.; Weber, R. J. M.; Harrison, Roy M.
2016-09-01
Human exposure to ambient metallic nanoparticles is an area of great interest owing to their potential health impacts. Ambient metallic nanoparticles found in the roadside environment are contributed by combustion engines and wear of brakes, tyres and road surfaces. Submicrometre atmospheric particles collected at two UK urban sites have been subject to detailed characterisation. It is found that many metallic nanoparticles collected from roadside sampling sites are rich in iron. The Fe-rich nanoparticles can be classified into (1) high Fe content (ca 90 wt%) with each alloying element less than 1 wt%; and (2) moderate Fe content (<75 wt%) with high manganese and silicon content. Both clusters contain a variable mix of minor constituents, Mn, S and Si being most important in the high-Fe group. The moderate Fe group also contains Zn, Cu, Ba, Al and Ca. The Fe-rich nanoparticles exhibit primary particle sizes ranging between 20 and 30 nm, although some much larger particles up to around 100 nm can also be observed, along with some very small particles of 10 nm or less. These tend to agglomerate forming clusters ranging from ∼200 nm to 1 μm in diameter. The iron-rich particles observed are oxides, taking the form of spheres or multifaceted regular polyhedra. Analysis by EELS shows that both high- and moderate-Fe groups include particles of FeO, Fe3O4, α-Fe2O3 and γ-Fe2O3 of which γ-Fe2O3 is the most prominent. Internal mixing of different Fe-oxides is not observed.
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Fan, Jianping; Gao, Yuli; Luo, Hangzai
2008-03-01
In this paper, we have developed a new scheme for achieving multilevel annotations of large-scale images automatically. To achieve more sufficient representation of various visual properties of the images, both the global visual features and the local visual features are extracted for image content representation. To tackle the problem of huge intraconcept visual diversity, multiple types of kernels are integrated to characterize the diverse visual similarity relationships between the images more precisely, and a multiple kernel learning algorithm is developed for SVM image classifier training. To address the problem of huge interconcept visual similarity, a novel multitask learning algorithm is developed to learn the correlated classifiers for the sibling image concepts under the same parent concept and enhance their discrimination and adaptation power significantly. To tackle the problem of huge intraconcept visual diversity for the image concepts at the higher levels of the concept ontology, a novel hierarchical boosting algorithm is developed to learn their ensemble classifiers hierarchically. In order to assist users on selecting more effective hypotheses for image classifier training, we have developed a novel hyperbolic framework for large-scale image visualization and interactive hypotheses assessment. Our experiments on large-scale image collections have also obtained very positive results.
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Wang, Zhengfang; Chen, Pei; Yu, Liangli; Harrington, Peter de B.
2013-01-01
Basil plants cultivated by organic and conventional farming practices were accurately classified by pattern recognition of gas chromatography/mass spectrometry (GC/MS) data. A novel extraction procedure was devised to extract characteristic compounds from ground basil powders. Two in-house fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM) for the first time, were used to build classification models. Two crisp classifiers, i.e., soft independent modeling by class analogy (SIMCA) and the partial least-squares discriminant analysis (PLS-DA), were used as control methods. Prior to data processing, baseline correction and retention time alignment were performed. Classifiers were built with the two-way data sets, the total ion chromatogram representation of data sets, and the total mass spectrum representation of data sets, separately. Bootstrapped Latin partition (BLP) was used as an unbiased evaluation of the classifiers. By using two-way data sets, average classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0%, respectively, for 100 independent evaluations. The established classifiers were used to classify a new validation set collected 2.5 months later with no parametric changes except that the training set and validation set were individually mean-centered. For the new two-way validation set, classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100%, 83%, 97%, and 100%, respectively. Thereby, the GC/MS analysis was demonstrated as a viable approach for organic basil authentication. It is the first time that a FOAM has been applied to classification. A novel baseline correction method was used also for the first time. The FuRES and the FOAM are demonstrated as powerful tools for modeling and classifying GC/MS data of complex samples and the data pretreatments are demonstrated to be useful to improve the performance of classifiers. PMID:23398171
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Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven
2017-01-01
Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive (> 50 cm−3) change for C6-derived CDNC relative to C5.1 for the 1.6 µm and 2.1 µm channel retrievals, corresponding to a neutral to −2 µm difference in droplet effective radius. For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning −25 to +50 cm−3 related to a +2.5 to −1 µm transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC. PMID:29098040
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NASA Technical Reports Server (NTRS)
Rausch, John; Meyer, Kerry; Bennartz, Ralf; Platnick, Steven
2017-01-01
Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1 degree x 1 degree and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive ( greater than 50cm(exp. -3) change for C6-derived CDNC relative to C5.1 for the 1.6 micrometers and 2.1 micrometers channel retrievals, corresponding to a neutral to -2 micrometers difference in droplet effective radius. For 3.7 micrometer retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning -25 to +50 cm(exp. -3) related to a +2.5 to -1 micrometers transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC.
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Song, Wenjing; Li, Yonghui; Wang, Jianguo; Li, Zeyou; Zhang, Junqing
2014-03-01
The fruit of Alpinia oxyphylla, known as Yizhi, Yakuchi and Ikji in Chinese, Japanese, and Korean, respectively, has been utilized as an important drug for the treatment of diarrhea, dyspepsia, spermatorrhea, kidney asthenia and abdominal pain in East Asian traditional medicine for thousands of years. Since the therapeutic effects of A. oxyphylla are attributed to multiple components and nucleobases and nucleosides exhibit various bioactivities, it is necessary to explore the chemical characterization of nucleobases and nucleosides in this herb. Herein, 10 common nucleobases and nucleosides, including cytidine, adenosine, thymidine, inosine, guanosine, 2'-deoxyinosine, guanine, adenine, cytosine, and hypoxanthine, were quantified simultaneously in the fruit of A. oxyphylla collected from different geographical regions. Changes in their contents were discussed, and hierarchical cluster analysis (HCA) was performed to classify all samples on the basis of the contents of the investigated analytes. The results indicated that there was a large variation in the contents of nucleobases and nucleosides among the herbs from different regions, and the samples collected from the same cultivation region were mostly classified in one cluster. The method can be used for comprehensive quality evaluation of A. oxyphylla. Copyright © 2013 John Wiley & Sons, Ltd.
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Gymnastics Safety and The Law.
ERIC Educational Resources Information Center
Dailey, Bob
Data collected from the National Electronic Injury Surveillance System (NEISS) and 26 tort liability cases are examined as a basis for recommendations for gymnastics instructors, supervisors, and administrators. Tables supply supportive statistics for a discussion of gymnastics injuries classified by sex, body part injured, severity, and…
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Pongutta, Suladda; Chongwatpol, Pitipa; Tantayapirak, Parwin; Vandevijvere, Stefanie
2018-06-01
The present study assessed the nutrition information displayed on ready-to-eat packaged foods and the nutritional quality of those food products in Thailand. In March 2015, the nutrition information panels and nutrition and health claims on ready-to-eat packaged foods were collected from the biggest store of each of the twelve major retailers, using protocols developed by the International Network for Food and Obesity/Non-communicable Diseases Research, Monitoring and Action Support (INFORMAS). The Thai Nutrient Profile Model was used to classify food products according to their nutritional quality as 'healthier' or 'less healthy'. In total, information from 7205 food products was collected across five broad food categories. Out of those products, 5707 (79·2 %), 2536 (35·2 %) and 1487 (20·6 %) carried a nutrition facts panel, a Guideline Daily Amount (GDA) label and health-related claims, respectively. Only 4691 (65·1 %) and 2484 (34·5 %) of the products that displayed the nutrition facts or a GDA label, respectively, followed the guidelines of the Thai Food and Drug Administration. In total, 4689 products (65·1 %) could be classified according to the Thai Nutrient Profile Model, of which 432 products (9·2 %) were classified as healthier. Moreover, among the 1487 products carrying health-related claims, 1219 (82·0 %) were classified as less healthy. Allowing less healthy food products to carry claims could mislead consumers and result in overconsumption of ready-to-eat food products. The findings suggest effective policies should be implemented to increase the relative availability of healthier ready-to-eat packaged foods, as well as to improve the provision of nutrition information on labels in Thailand.
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Hoff, Michael H.
2004-01-01
The lake herring (Coregonus artedi) was one of the most commercially and ecologically valuable Lake Superior fishes, but declined in the second half of the 20th century as the result of overharvest of putatively discrete stocks. No tools were previously available that described lake herring stock structure and accurately classified lake herring to their spawning stocks. The accuracy of discriminating among spawning aggregations was evaluated using whole-body morphometrics based on a truss network. Lake herring were collected from 11 spawning aggregations in Lake Superior and two inland Wisconsin lakes to evaluate morphometrics as a stock discrimination tool. Discriminant function analysis correctly classified 53% of all fish from all spawning aggregations, and fish from all but one aggregation were classified at greater rates than were possible by chance. Discriminant analysis also correctly classified 66% of fish to nearest neighbor groups, which were groups that accounted for the possibility of mixing among the aggregations. Stepwise discriminant analysis showed that posterior body length and depth measurements were among the best discriminators of spawning aggregations. These findings support other evidence that discrete stocks of lake herring exist in Lake Superior, and fishery managers should consider all but one of the spawning aggregations as discrete stocks. Abundance, annual harvest, total annual mortality rate, and exploitation data should be collected from each stock, and surplus production of each stock should be estimated. Prudent management of stock surplus production and exploitation rates will aid in restoration of stocks and will prevent a repeat of the stock collapses that occurred in the middle of the 20th century, when the species was nearly extirpated from the lake.
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Mata-Cases, Manel; Mauricio, Dídac; Real, Jordi; Bolíbar, Bonaventura; Franch-Nadal, Josep
2016-11-01
To assess the prevalence of miscoding, misclassification, misdiagnosis and under-registration of diabetes mellitus (DM) in primary health care in Catalonia (Spain), and to explore use of automated algorithms to identify them. In this cross-sectional, retrospective study using an anonymized electronic general practice database, data were collected from patients or users with a diabetes-related code or from patients with no DM or prediabetes code but treated with antidiabetic drugs (unregistered DM). Decision algorithms were designed to classify the true diagnosis of type 1 DM (T1DM), type 2 DM (T2DM), and undetermined DM (UDM), and to classify unregistered DM patients treated with antidiabetic drugs. Data were collected from a total of 376,278 subjects with a DM ICD-10 code, and from 8707 patients with no DM or prediabetes code but treated with antidiabetic drugs. After application of the algorithms, 13.9% of patients with T1DM were identified as misclassified, and were probably T2DM; 80.9% of patients with UDM were reclassified as T2DM, and 19.1% of them were misdiagnosed as DM when they probably had prediabetes. The overall prevalence of miscoding (multiple codes or UDM) was 2.2%. Finally, 55.2% of subjects with unregistered DM were classified as prediabetes, 35.7% as T2DM, 8.5% as UDM treated with insulin, and 0.6% as T1DM. The prevalence of inappropriate codification or classification and under-registration of DM is relevant in primary care. Implementation of algorithms could automatically flag cases that need review and would substantially decrease the risk of inappropriate registration or coding. Copyright © 2016 SEEN. Publicado por Elsevier España, S.L.U. All rights reserved.
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NASA Astrophysics Data System (ADS)
Mikhailov, Eugene F.; Mironova, Svetlana; Mironov, Gregory; Vlasenko, Sergey; Panov, Alexey; Chi, Xuguang; Walter, David; Carbone, Samara; Artaxo, Paulo; Heimann, Martin; Lavric, Jost; Pöschl, Ulrich; Andreae, Meinrat O.
2017-12-01
We present long-term (5-year) measurements of particulate matter with an upper diameter limit of ˜ 10 µm (PM10), elemental carbon (EC), organic carbon (OC), and water-soluble organic carbon (WSOC) in aerosol filter samples collected at the Zotino Tall Tower Observatory in the middle-taiga subzone (Siberia). The data are complemented with carbon monoxide (CO) measurements. Air mass back trajectory analysis and satellite image analysis were used to characterise potential source regions and the transport pathway of haze plumes. Polluted and background periods were selected using a non-parametric statistical approach and analysed separately. In addition, near-pristine air masses were selected based on their EC concentrations being below the detection limit of our thermal-optical instrument. Over the entire sampling campaign, 75 and 48 % of air masses in winter and in summer, respectively, and 42 % in spring and fall are classified as polluted. The observed background concentrations of CO and EC showed a sine-like behaviour with a period of 365 ± 4 days, mostly due to different degrees of dilution and the removal of polluted air masses arriving at the Zotino Tall Tower Observatory (ZOTTO) from remote sources. Our analysis of the near-pristine conditions shows that the longest periods with clean air masses were observed in summer, with a frequency of 17 %, while in wintertime only 1 % can be classified as a clean. Against a background of low concentrations of CO, EC, and OC in the near-pristine summertime, it was possible to identify pollution plumes that most likely came from crude-oil production sites located in the oil-rich regions of Western Siberia. Overall, our analysis indicates that most of the time the Siberian region is impacted by atmospheric pollution arising from biomass burning and anthropogenic emissions. A relatively clean atmosphere can be observed mainly in summer, when polluted species are removed by precipitation and the aerosol burden returns to near-pristine conditions.
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NASA Astrophysics Data System (ADS)
Çakırlı, Ö.; Ibanoǧlu, C.; Southworth, J.; Frasca, A.; Hernandez, J.
2008-09-01
We present differential V-band photometric observations and the first radial velocities of NSV24512, which is embedded in the Serpens star-forming region. This double-lined system has an eccentric orbit with an eccentricity of 0.193. The system is a member of visual double star ADS11410AB with a separation of about 0.3 arcsec and an apparent visual magnitude difference of 0.125 mag; we find that the fainter component (component B) is responsible for the periodic light variation. Therefore, we subtracted the light contribution of component A from the total light. The V-band photometric data and radial velocities were then analysed simultaneously using the Wilson-Devinney program. From the blue-wavelength spectroscopic observations and radial velocities, we classify the primary and secondary components as B8V and B9V stars, respectively. The masses and radii of the component stars have been derived as 3.68 +/- 0.05 and 3.36 +/- 0.04Msolar and 3.21 +/- 0.05 and 2.93 +/- 0.05Rsolar, respectively. Comparison with theoretical evolutionary models indicates that both components are pre-main-sequence stars with an age of about 2.1 Myr. The projected rotational velocities of the components measured by us are much smaller than the synchronous rotational velocities. The high asynchronism is further evidence of the very young age of the system. Using the radiative properties of the stars, we have redetermined the distance to NSV24512 as 247 +/- 5 pc, which is in good agreement with, and more precise than, previous determinations. Adopting the same interstellar extinction and distance, we classify component A to be of spectral type B7, if it is a single star. Based on observations collected at Catania Astrophysical Observatory (Italy) and Ege University Observatory (Turkey). E-mail: omur.cakirli@gmail.com
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Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy
Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less
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Least Squares Neural Network-Based Wireless E-Nose System Using an SnO₂ Sensor Array.
Shahid, Areej; Choi, Jong-Hyeok; Rana, Abu Ul Hassan Sarwar; Kim, Hyun-Seok
2018-05-06
Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH₄) and carbon monoxide (CO), using an array of SnO₂ gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH₄ and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.
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Martin, Bryan D.; Wolfson, Julian; Adomavicius, Gediminas; Fan, Yingling
2017-01-01
We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimension reduction and classification algorithms and compare them with a metric that balances accuracy and dimensionality. In doing so, we develop a classification algorithm that accurately classifies five different modes of transportation (i.e., walking, biking, car, bus and rail) while being computationally simple enough to run on a typical smartphone. Further, we use data that required no behavioral changes from the smartphone users to collect. Our best classification model uses the random forest algorithm to achieve 96.8% accuracy. PMID:28885550
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Martin, Bryan D; Addona, Vittorio; Wolfson, Julian; Adomavicius, Gediminas; Fan, Yingling
2017-09-08
We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimension reduction and classification algorithms and compare them with a metric that balances accuracy and dimensionality. In doing so, we develop a classification algorithm that accurately classifies five different modes of transportation (i.e., walking, biking, car, bus and rail) while being computationally simple enough to run on a typical smartphone. Further, we use data that required no behavioral changes from the smartphone users to collect. Our best classification model uses the random forest algorithm to achieve 96.8% accuracy.
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2010 NCCA oligochaete trophic index results to inform benthic ...
Over 400 sites were sampled in the nearshore of the U.S. Great Lakes during the National Coastal Condition Assessment (NCCA) field survey in summer 2010. To assess benthic ecological condition, 393 PONARs were attempted, and collected macroinvertebrates were identified and enumerated. Biological condition at each site was classified as good, fair or poor using the Oligochaete Trophic Index (OTI). The Great Lakes coasts were then classified by calculating percent area within a condition class: good (20.3%), fair (11.6%), and poor (18.0%). Due to unsuccessful PONARs, unclassified oligochaetes or no oligochaetes captured, 50.1% of the sampled area was classified as missing. In order to help focus future discussion and development of a Great Lakes benthic index, OTI results were compared to other traditional biotic integrity indices. In addition, unclassified sites were examined to determine possible methods or metrics that could prevent missing data in a newly developed index. not applicable
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Single-accelerometer-based daily physical activity classification.
Long, Xi; Yin, Bin; Aarts, Ronald M
2009-01-01
In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of approximately 80%, which was comparable with that obtained by a Decision Tree classifier.
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Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists
Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy; ...
2018-01-24
Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less
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Random forests for classification in ecology
Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.
2007-01-01
Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.
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NASA Technical Reports Server (NTRS)
Rignot, E.; Chellappa, R.
1993-01-01
We present a maximum a posteriori (MAP) classifier for classifying multifrequency, multilook, single polarization SAR intensity data into regions or ensembles of pixels of homogeneous and similar radar backscatter characteristics. A model for the prior joint distribution of the multifrequency SAR intensity data is combined with a Markov random field for representing the interactions between region labels to obtain an expression for the posterior distribution of the region labels given the multifrequency SAR observations. The maximization of the posterior distribution yields Bayes's optimum region labeling or classification of the SAR data or its MAP estimate. The performance of the MAP classifier is evaluated by using computer-simulated multilook SAR intensity data as a function of the parameters in the classification process. Multilook SAR intensity data are shown to yield higher classification accuracies than one-look SAR complex amplitude data. The MAP classifier is extended to the case in which the radar backscatter from the remotely sensed surface varies within the SAR image because of incidence angle effects. The results obtained illustrate the practicality of the method for combining SAR intensity observations acquired at two different frequencies and for improving classification accuracy of SAR data.
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NASA Astrophysics Data System (ADS)
Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Tseng, Derek; Benien, Parul; Ozcan, Aydogan
2017-03-01
Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.
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Cabré, Mateu; Serra-Prat, Mateu; Force, Ll; Almirall, Jordi; Palomera, Elisabet; Clavé, Pere
2014-03-01
To determine whether oropharyngeal dysphagia is a risk factor for readmission for pneumonia in elderly persons discharged from an acute geriatric unit. Observational prospective cohort study with data collection based on clinical databases and electronic clinical notes. All elderly individuals discharged from an acute geriatric unit from June 2002 to December 2009 were recruited and followed until death or December 31, 2010. All individuals were initially classified according to the presence of oropharyngeal dysphagia assessed by bedside clinical examination. Main outcome measure was readmission for pneumonia. Clinical notes were reviewed by an expert clinician to verify diagnosis and classify pneumonia as aspiration or nonaspiration pneumonia. A total of 2,359 patients (61.9% women, mean age 84.9 y) were recruited and followed for a mean of 24 months. Dysphagia was diagnosed in 47.5% of cases. Overall, 7.9% of individuals were readmitted for pneumonia during follow-up, 24.2% of these had aspiration pneumonia. The incidence rate of hospital readmission for pneumonia was 3.67 readmissions per 100 person-years (95% CI 3.0-4.4) in individuals without dysphagia and 6.7 (5.5-7.8) in those with dysphagia, with an attributable risk of 3.02 readmissions per 100 person-years (1.66-4.38) and a rate ratio of 1.82 (1.41-2.36). Multivariate Cox regression showed an independent effect of oropharyngeal dysphagia, with a hazard ratio of 1.6 (1.15-2.2) for hospitalization for pneumonia, 4.48 (2.01-10.0) for aspiration pneumonia, and 1.44 (1.02-2.03) for nonaspiration pneumonia. Oropharyngeal dysphagia is a very prevalent and relevant risk factor associated with hospital readmission for both aspiration and nonaspiration pneumonia in the very elderly persons.
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Wang, Yiwei; Nickel, Barry; Rutishauser, Matthew; Bryce, Caleb M; Williams, Terrie M; Elkaim, Gabriel; Wilmers, Christopher C
2015-01-01
Accelerometers are useful tools for biologists seeking to gain a deeper understanding of the daily behavior of cryptic species. We describe how we used GPS and tri-axial accelerometer (sampling at 64 Hz) collars to monitor behaviors of free-ranging pumas (Puma concolor), which are difficult or impossible to observe in the wild. We attached collars to twelve pumas in the Santa Cruz Mountains, CA from 2010-2012. By implementing Random Forest models, we classified behaviors in wild pumas based on training data from observations and measurements of captive puma behavior. We applied these models to accelerometer data collected from wild pumas and identified mobile and non-mobile behaviors in captive animals with an accuracy rate greater than 96%. Accuracy remained above 95% even after downsampling our accelerometer data to 16 Hz. We were further able to predict low-acceleration movement behavior (e.g. walking) and high-acceleration movement behavior (e.g. running) with 93.8% and 92% accuracy, respectively. We had difficulty predicting non-movement behaviors such as feeding and grooming due to the small size of our training dataset. Lastly, we used model-predicted and field-verified predation events to quantify acceleration characteristics of puma attacks on large prey. These results demonstrate that accelerometers are useful tools for classifying the behaviors of cryptic medium and large-sized terrestrial mammals in their natural habitats and can help scientists gain deeper insight into their fine-scale behavioral patterns. We also show how accelerometer measurements can provide novel insights on the energetics and predation behavior of wild animals. Lastly we discuss the conservation implications of identifying these behavioral patterns in free-ranging species as natural and anthropogenic landscape features influence animal energy allocation and habitat use.
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Watanabe, Dai; Yamamoto, Yudai; Suzuki, Sachiko; Ashida, Misa; Matsumoto, Erina; Yukawa, Satomi; Hirota, Kazuyuki; Ikuma, Motoko; Ueji, Takashi; Kasai, Daisuke; Nishida, Yasuharu; Uehira, Tomoko; Shirasaka, Takuma
2017-04-01
High human herpesvirus 8 (HHV-8) seroprevalence has been reported in men who have sex with men (MSM) and are infected with HIV-1. However, it is unclear when they become infected with HHV-8. Thus, we conducted cross-sectional and longitudinal investigations of HHV-8 seroprevalence in HIV-1-infected individuals in Osaka, Japan. Plasma was collected from 121 individuals infected with HIV-1 and the anti-HHV-8 antibody titer was measured using an enzyme-linked immunosorbent assay with whole virus lysate. Subjects were classified into those with and without a past medical history of HHV-8-associated disease; the latter group was then classified into 3 subgroups based on the assumed route of HIV-1 infection: blood products, homosexual contact, and other routes. HHV-8 seroprevalence was compared among the groups and measured again approximately 3 years after the baseline measurement. The relationship between HHV-8 seropositivity and possible associated factors was also investigated. All 15 subjects with HHV-8-associated disease were seropositive, and all 11 subjects in the blood product group were seronegative. In the MSM group, 25 (30%) of 79 subjects were HHV-8 seropositive and, in the non-MSM group, 1 (6%) of 16 subjects was (p < 0.0001). In the longitudinal investigation, seroconversion was observed in 10 (19%) of 52 subjects in the MSM group who were seronegative at baseline. A correlation was observed between seroconversion and symptomatic syphilis (p = 0.0432). HHV-8 seropositivity and seroconversion rates were high in HIV-1-infected MSM, suggesting that, currently, HHV-8 is an epidemic pathogen in this population. Copyright © 2016 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
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Franchelli, Simonetta; Pesce, Marianna; Savaia, Serena; Marchese, Anna; Barbieri, Ramona; Baldelli, Ilaria; De Maria, Andrea
2015-10-01
Implant infections represent a relevant problem after immediate breast cancer reconstruction. In addition to difficulties in distinguishing early infections from other post-surgical complications (such as hematoma, seroma, and liponecrosis) late breast implant infections still represent a grey area of our knowledge with regards to heir definition and management. To address this issue, we prospectively monitored breast cancer patients at their center. Between February 1, 2009, and May 31, 2013, we enrolled all patients undergoing breast implant reconstruction or expander-to-prosthesis substitution. Patients without at least 6 mo of post-operative observation were excluded. We collected data from patient records including age, days from surgery (DFS), chemotherapy/radiotherapy, infecting microorganism, type of implant, antibiotic management and eventual implant removal. Sixty days from surgery were defined as the clinical threshold between early and late infection. Infections were further classified according to a graded scale into possible, probable and microbiologically proved. Seventy-eight infections were recorded out of 766 surgical procedures (10.2%). Fifty-three (67%) cases occurred early ≤60 DFS, and 25 (33%) occurred late (i.e., beyond 60 d). By defining infection types as possible, probable or proved, the majority of late infections were classified as proved (84%) compared with 56% of early infections (p=0.0014). Microbiological isolate distribution was similar in proved early infections compared with proved late infections. Among late infections, a delayed occurrence was observed after prosthesis placement compared with expander insertion. Late infections were fraught with lower treatment success rates (12% vs. 41%, p=0.009). Late infection represents a consistent proportion of infections after immediate breast implant reconstruction or prosthesis placement and bear lower chance of salvage after treatment. An increased attention is warranted to improve prevention and treatment strategies.
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NASA Astrophysics Data System (ADS)
Sato, K.; Iijima, A.; Furuta, N.
2008-12-01
In our long-term monitoring of size-classified Airborne Particulate Matter (APM) in Tokyo since 1995, it had been demonstrated that toxic elements such as As, Se, Cd, Sb and Pb were extremely enriched in fine APM (PM2.5). However, in that study, total sampled APM on a filter was digested with acids, and thus only averaged elemental composition in fine APM could be obtained. One of the effective methods to determine the origin of APM is single particle measurement by using SEM-EDX. By using characteristic shapes observed by SEM and marker elements contained in APM measured by EDX, detailed information for source identification can be obtained. In this study, fine APM (PM2.5) was collected at various locations such as roadside, diesel vehicle exhaust, a heavy oil combustion plant and a waste incineration plant as well as ambient atmosphere in Tokyo, and characteristics of fine particles that will be utilized for identification of emission sources are elucidated. Fine particles can be classified into 3 main characteristic shape groups; edge-shaped, cotton-like and spherical. Shape of particles collected in a heavy oil combustion plant and a waste incineration plant was mostly spherical, and these particles may be associated with thermal process. Diesel exhaust particles were predominantly cotton-like which may consist of coagulated nano-sized particles. Most of brake abrasion dusts were edge-shaped, which may be associated with mechanical abrasion of brake pads. In the elemental analysis of fine particles, high concentrations of Sb, Cu, Ti and Ba were detected in brake abrasion dusts. Since these elements are major constituents of brake pads, these can be used for marker elements of brake abrasion dusts. High concentration of C was detected in diesel exhaust particles and oil combustion particles, and thus C can be used for marker elements of their origin. Furthermore, high concentrations of C, Ca and K were detected in fly ash from a waste incineration plant, which may be associated with emission from biomass combustion.
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A novel approach for diagnosing isohydric and anisohydric plant water use during drought
NASA Astrophysics Data System (ADS)
Novick, K. A.; Roman, D. T.; Brzostek, E. R.; Dragoni, D.; Phillips, R.
2014-12-01
Recent years have seen the emergence of a new framework for describing plant water use, whereby species-specific water use strategies during periods of hydrologic stress are classified as falling on a spectrum of isohydric to anisohydric behavior. Trees that regulate water potential to within a relatively narrow range, and thereby reduce the risk of damaging xylem cavitation, are categorized as isohydric. In contrast, anisohydric trees allow their leaf water potential to decrease during drought, which may improve gas exchange rates, but at the cost of a greater risk of cavitation in the xylem. To date, most of the approaches to diagnose and characterize isohydric as compared to anisohydric behavior rely on observations of stem or leaf water potential measurements, which are difficult to collect at a high temporal and spatial frequency and rely on destructive techniques. Here, we use cohesion-tension theory to develop a novel approach for diagnosing isohydric/anisohydric behavior in observations of leaf- or canopy-scale stomatal conductance, which are data that may be collected in situ and with relative ease. The approach is particularly focused on exploring how the relationship between stomatal conductance and vapor pressure deficit changes during dry-down periods. The theoretical predictions suggest that the sensitivity of stomatal conductance to vapor pressure deficit may decrease over the course of the drought event for more anisohydric trees, and increase in the case of more isohydric trees. Species-specific, leaf-level observations of the relevant variables collected during the course of a severe drought event affecting the Morgan-Monroe State Forest in 2012 are shown to confirm the theoretical predictions. Finally, the diagnostic approach is evaluated in the context of other emerging approaches for describing stomatal behavior, including the growing recognition of the role of hydraulic capacitance during drought, and recent advances in stomatal optimization theory. Ultimately, placing species along the isohydric-anisohydric contiuum may advance our understanding of the magnitude of drought-related declines in productivity and other physiological processes in forest ecosystems.
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Wang, Ning; Ingersoll, Christopher G.; Kunz, James L.; Brumbaugh, William G.; Kane, Cindy M.; Evans, R. Brian; Alexander, Steven; Walker, Craig; Bakaletz, Steve
2013-01-01
Sediment toxicity tests were conducted to assess potential effects of contaminants associated with coal mining or natural gas extraction activities in the upper Tennessee River basin and eastern Cumberland River basin in the United States. Test species included two unionid mussels (rainbow mussel, Villosa iris, and wavy-rayed lampmussel, Lampsilis fasciola, 28-d exposures), and the commonly tested amphipod, Hyalella azteca (28-d exposure) and midge, Chironomus dilutus (10-d exposure). Sediments were collected from seven test sites with mussel communities classified as impacted and in proximity to coal mining or gas extraction activities, and from five reference sites with mussel communities classified as not impacted and no or limited coal mining or gas extraction activities. Additional samples were collected from six test sites potentially with high concentrations of polycyclic aromatic hydrocarbons (PAHs) and from a test site contaminated by a coal ash spill. Mean survival, length, or biomass of one or more test species was reduced in 10 of 14 test samples (71%) from impacted areas relative to the response of organisms in the five reference samples. A higher proportion of samples was classified as toxic to mussels (63% for rainbow mussels, 50% for wavy-rayed lampmussels) compared with amphipods (38%) or midge (38%). Concentrations of total recoverable metals and total PAHs in sediments did not exceed effects-based probable effect concentrations (PECs). However, the survival, length, or biomasses of the mussels were reduced significantly with increasing PEC quotients for metals and for total PAHs, or with increasing sum equilibrium-partitioning sediment benchmark toxic units for PAHs. The growth of the rainbow mussel also significantly decreased with increasing concentrations of a major anion (chloride) and major cations (calcium and magnesium) in sediment pore water. Results of the present study indicated that (1) the findings from laboratory tests were generally consistent with the field observations of impacts on mussel populations; (2) total recoverable metals, PAHs, or major ions, or all three in sediments might have contributed to the sediment toxicity; (3) the mussels were more sensitive to the contaminants in sediments than the commonly tested amphipod and midge; and (4) a sediment toxicity benchmark of 1.0 based on PECs may not be protective of mussels.
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Passive and Active Analysis in DSR-Based Ad Hoc Networks
NASA Astrophysics Data System (ADS)
Dempsey, Tae; Sahin, Gokhan; Morton, Y. T. (Jade)
Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments. Furthermore, we investigate active analysis, which is the combination of a classifier and intelligent jammer to invoke specific responses from a victim network.
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Multimodality approach to classifying hand utilization for the clinical breast examination.
Laufer, Shlomi; Cohen, Elaine R; Maag, Anne-Lise D; Kwan, Calvin; Vanveen, Barry; Pugh, Carla M
2014-01-01
The clinical breast examination (CBE) is performed to detect breast pathology. However, little is known regarding clinical technique and how it relates to diagnostic accuracy. We sought to quantify breast examination search patterns and hand utilization with a new data collection and analysis system. Participants performed the CBE while the sensor mapping and video camera system collected performance data. From this data, algorithms were developed that measured the number of hands used during the exam and active examination time. This system is a feasible and reliable method to collect new information on CBE techniques.
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Land use classification utilizing remote multispectral scanner data and computer analysis techniques
NASA Technical Reports Server (NTRS)
Leblanc, P. N.; Johannsen, C. J.; Yanner, J. E.
1973-01-01
An airborne multispectral scanner was used to collect the visible and reflective infrared data. A small subdivision near Lafayette, Indiana was selected as the test site for the urban land use study. Multispectral scanner data were collected over the subdivision on May 1, 1970 from an altitude of 915 meters. The data were collected in twelve wavelength bands from 0.40 to 1.00 micrometers by the scanner. The results indicated that computer analysis of multispectral data can be very accurate in classifying and estimating the natural and man-made materials that characterize land uses in an urban scene.
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Cartwright, S L; Malchiodi, F; Thompson-Crispi, K; Miglior, F; Mallard, B A
2017-10-01
Lameness is a major animal welfare issue affecting Canadian dairy producers, and it can lead to production, reproduction, and health problems in dairy cattle herds. Although several different lesions affect dairy cattle hooves, studies show that digital dermatitis is the most common lesion identified in Canadian dairy herds. It has also been shown that dairy cattle classified as having high immune response (IR) have lower incidence of disease compared with those animals with average and low IR; therefore, it has been hypothesized that IR plays a role in preventing infectious hoof lesions. The objective of this study was to compare the prevalence of digital dermatitis in Canadian dairy cattle that were classified for antibody-mediated (AMIR) and cell-mediated (CMIR) immune response. Cattle (n = 329) from 5 commercial dairy farms in Ontario were evaluated for IR using a patented test protocol that captures both AMIR and CMIR. Individuals were classified as high, average, or low responders based on standardized residuals for AMIR and CMIR. Residuals were calculated using a general linear model that included the effects of herd, parity, stage of lactation, and stage of pregnancy. Hoof health data were collected from 2011 to 2013 by the farm's hoof trimmer using Hoof Supervisor software (KS Dairy Consulting Inc., Dresser, WI). All trim events were included for each animal, and lesions were assessed as a binary trait at each trim event. Hoof health data were analyzed using a mixed model that included the effects of herd, stage of lactation (at trim date), parity (at trim date), IR category (high, average, and low), and the random effect of animal. All data were presented as prevalence within IR category. Results showed that cows with high AMIR had significantly lower prevalence of digital dermatitis than cattle with average and low AMIR. No significant difference in prevalence of digital dermatitis was observed between high, average, and low CMIR cows. These results indicate that having more robust AMIR is associated with lower prevalence of digital dermatitis hoof lesions. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B; Dodd, Keith C; Young, Brittany M; Jacobson, Tyler; McMillan, Matthew; Thoma, Jaclyn; Advani, Hemali; Nair, Veena A; Kang, Theresa J; Caldera, Kristin; Edwards, Dorothy F; Williams, Justin C; Prabhakaran, Vivek
2018-01-01
Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.
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Dougherty, Donald M.; Karns, Tara E.; Mullen, Jillian; Liang, Yuanyuan; Lake, Sarah L.; Roache, John D.; Hill-Kapturczak, Nathalie
2017-01-01
Background Recently, we demonstrated that transdermal alcohol monitors could be used in a contingency management procedure to reduce problematic drinking; the frequency of self-reported heavy/moderate drinking days decreased and days of no to low drinking increased. These effects persisted for three months after intervention. In the current report, we used the transdermal alcohol concentration (TAC) data collected prior to and during the contingency management procedure to provide a detailed characterization of objectively measured alcohol use. Methods Drinkers (n = 80) who frequently engaged in risky drinking behaviors were recruited and participated in three study phases: a 4-week Observation phase where participants drank as usual; a 12-week Contingency Management phase where participants received $50 each week when TAC did not exceed 0.03 g/dl; and a 3-month Follow-up phase where self-reported alcohol consumption was monitored. Transdermal monitors were worn during the first two phases, where each week they recived $105 for visiting the clinic and wearing the monitor. Outcomes focused on using TAC data to objectively characterize drinking and were used to classify drinking levels as either no, low, moderate, or heavy drinking as a function of weeks and day of week. Results Compared to the Observation phase, TAC data indicated that episodes of heavy drinking days during the Contingency Management phase were reduced and episodes of no drinking and low to moderate drinking increased. Conclusions These results lend further support for linking transdermal alcohol monitoring with contingency management interventions. Collectively, studies to date indicate that interventions like these may be useful for both abstinence and moderation-based programs. PMID:25582388
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Dougherty, Donald M; Karns, Tara E; Mullen, Jillian; Liang, Yuanyuan; Lake, Sarah L; Roache, John D; Hill-Kapturczak, Nathalie
2015-03-01
Recently, we demonstrated that transdermal alcohol monitors could be used in a contingency management procedure to reduce problematic drinking; the frequency of self-reported heavy/moderate drinking days decreased and days of no to low drinking increased. These effects persisted for three months after intervention. In the current report, we used the transdermal alcohol concentration (TAC) data collected prior to and during the contingency management procedure to provide a detailed characterization of objectively measured alcohol use. Drinkers (n=80) who frequently engaged in risky drinking behaviors were recruited and participated in three study phases: a 4-week Observation phase where participants drank as usual; a 12-week Contingency Management phase where participants received $50 each week when TAC did not exceed 0.03g/dl; and a 3-month Follow-up phase where self-reported alcohol consumption was monitored. Transdermal monitors were worn during the first two phases, where each week they recived $105 for visiting the clinic and wearing the monitor. Outcomes focused on using TAC data to objectively characterize drinking and were used to classify drinking levels as either no, low, moderate, or heavy drinking as a function of weeks and day of week. Compared to the Observation phase, TAC data indicated that episodes of heavy drinking days during the Contingency Management phase were reduced and episodes of no drinking and low to moderate drinking increased. These results lend further support for linking transdermal alcohol monitoring with contingency management interventions. Collectively, studies to date indicate that interventions like these may be useful for both abstinence and moderation-based programs. Copyright © 2015. Published by Elsevier Ireland Ltd.
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HBV and HIV co-infection: Prevalence and clinical outcomes in tertiary care hospital Malaysia.
Akhtar, Ali; Khan, Amer Hayat; Sulaiman, Syed Azhar Syed; Soo, Chow Ting; Khan, Kashifullah
2016-03-01
According to WHO, Malaysia has been classified as a concentrated epidemic country due to progression of HIV infection in the population of injecting drug users. The main objectives of current study are to determine the prevalence of HBV among HIV-positive individuals in a tertiary care hospital of Malaysia and to assess the predictors involved in the outcomes of HIV-HBV co-infected patients. A retrospective, cross-sectional study is conducted at Hospital Palau Pinang, Malaysia. The collection of socio-demographic data as well as clinical data is done with the help of data collection form. Data were analyzed after putting the collected values of required data by using statistical software SPSS version 20.0 and P > 0.05 is considered as significant. Results show that the overall prevalence of HBV was 86 (13%) including 495 (74.5%) males and 169 (25.5%) females among a total of 664 HIV-infected patients. It was observed that there is a high prevalence of HIV-HBV co-infection in males 76 (11.4%) as compared to females 10 (1.5%) (P = 0.002). The median age of the study population was 39 years. The statistical significant risk factors involved in the outcomes of HIV-HBV co-infected patients were observed in the variables of gender, age groups, and injecting drug users. The findings of the present study shows that the prevalence of HBV infection among HIV-positive patients was 13% and the risk factors involved in the outcomes of HIV-HBV co-infected patients were gender, age, and intravenous drug users. © 2015 Wiley Periodicals, Inc.
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Ryan, D.F.; Huntington, T.G.; Wayne, Martin C.
1992-01-01
To investigate whether mechanical mixing during harvesting could account for losses observed from forest floor, we measured surface disturbance on a 22 ha watershed that was whole-tree harvested. Surface soil on each 10 cm interval along 81, randomly placed transects was classified immediately after harvesting as mineral or organic, and as undisturbed, depressed, rutted, mounded, scarified, or scalped (forest floor scraped away). We quantitatively sampled these surface categories to collect soil in which preharvest forest floor might reside after harvest. Mechanically mixed mineral and organic soil horizons were readily identified. Buried forest floor under mixed mineral soil occurred in 57% of mounds with mineral surface soil. Harvesting disturbed 65% of the watershed surface and removed forest floor from 25% of the area. Mechanically mixed soil under ruts with organic or mineral surface soil, and mounds with mineral surface soil contained organic carbon and nitrogen pools significantly greater than undisturbed forest floor. Mechanical mixing into underlying mineral soil could account for the loss of forest floor observed between the preharvest condition and the second growing season after whole-tree harvesting. ?? 1992.
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ERIC Educational Resources Information Center
Staples, Greg; And Others
This publication is a collection of science activities designed to enrich elementary or junior high science curricula. These activities encourage students to investigate facets of life sciences, physical sciences, and earth sciences either with a teacher or independently. The 70 activities have been classified into 10 subareas under these three…
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Collaborative Information Filtering in Cooperative Communities.
ERIC Educational Resources Information Center
Okamoto, T.; Miyahara, K.
1998-01-01
The purpose of this study was to develop an information filtering system which collects, classifies, selects, and stores various kinds of information found through the Internet. A collaborative form of information gathering was examined and a model was built and implemented in the Internet information space. (AEF)
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Making Choices in Functional Vision Evaluations: "Noodles, Needles, and Haystacks."
ERIC Educational Resources Information Center
Bishop, V. E.
1988-01-01
An approach to functional vision evaluations clarifies the types of data collection and suggests protocols for three broad categories of visually handicapped children: "normal" school-age students, "normal" preschoolers, and multiply handicapped pupils. Visually impaired infants are classified with multiply handicapped pupils…
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Validation of Metabolomic, Diagnostic, and Prognostic Classifiers of Lung Cancer
2016-10-01
information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this...Additionally, global metabolomic profiling will allow us to interrogate whether the military have unique exposures that may be related to lung cancer
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Crowd-sourcing relative preferences for ecosystem services in the St. Louis River AOC
Analysis of ecosystem service tradeoffs among project scenarios is more reliable when valuation data are available. Empirical valuation data are expensive and difficult to collect. As a possible alternative or supplement to empirical data, we downloaded and classified images from...
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Maps of seagrass beds are useful for monitoring estuarine condition, managing habitats, and modeling estuarine processes. We recently developed inexpensive methods for collecting and classifying sidescan sonar (SSS) imagery for seagrass presence in turbid waters as shallow as 1-...
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Rothenberg, Lesliebeth; Kronick, David A.; Rees, Alan M.
1971-01-01
An investigation of the manpower requirements of health sciences libraries and of educational programs appropriate to these manpower needs was begun in March 1968. To date, 4,727 libraries have been identified as being used by 14,000 health sciences institutions and programs. Of this total, 2,628 are hospital libraries; 1,328 are health sciences libraries and collections located outside of hospitals; and 771 are academic or public libraries. Within these libraries some 14,938 persons are directly involved, either full- or part-time, in the delivery of health sciences library services. Of the total work force, 5,861 persons are employed in hospital libraries and 9,077 are employed in health sciences libraries and collections. The ratio between professional and nonprofessional employees is 1:2; professional and nonprofessional status was assigned by the chief librarian. Survey data indicate a 7 percent manpower shortage in positions classified as professional, and a 3 percent shortage in positions classified as nonprofessional. PMID:5542913
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Rabilloud, M; Ecochard, R; Myard, A F; Delahaye, F; Colin, C; Matillon, Y
1998-06-01
The aim of the PMSI (Programme de Médicalisation du Système d'Information) is to describe the activity of hospitals for budget allocation. To control the quality of this information, the authors carried out a study comparing the classification in homogenous disease groups (HDG) obtained from the PMSI with that obtained from the epidemiological data base of the PRIMA trial for patients admitted to the Civil Hospitals of Lyon for myocardial infarction between September 1st 1993 and January 31st 1995. Six hundred and fifty standardised hospital summaries were reconstituted form PRIMA data and grouped using the GENRSA 3 software. Five hundred and forty-one of these hospital stays were found in the PMSI data base and grouped. The concordance not due to chance between the two classifications was then assessed by the global kappa coefficient. It was less than the 40% threshold under which concordance not due to chance is considered to be unlikely. The discordances were essentially due to the presence of an associated diagnosis classifying the hospital stay in the HDG corresponding to complicated myocardial infarction. The presence of a classifying associated diagnosis was observed significantly more often in the PRIMA than in the PMSI data base. This results in an underestimation of the hospital activity and could have important repercussions in terms of budget allocation.
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Shen, Si; Shen, Junwei; Zhu, Liang; Wu, Chaoqun; Li, Dongliang; Yu, Hongyu; Qiu, Yuanyuan; Zhou, Yi
2015-11-01
To establish and manage of multicentral collection bio-sample banks of malignant tumors from digestive system, the paper designed a multicentral management system, established the standard operation procedures (SOPs) and leaded ten hospitals nationwide to collect tumor samples. The biobank has been established for half a year, and has collected 695 samples from patients with digestive system malignant tumor. The clinical data is full and complete, labeled in a unified way and classified to be managed. The clinical and molecular biology researches were based on the biobank, and obtained achievements. The biobank provides a research platform for malignant tumor of digestive system from different regions and of different types.
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Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C
2014-08-01
The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.
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Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.
Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R; Nguyen, Tuan N; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T
2017-01-01
This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.
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Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks
Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R.; Nguyen, Tuan N.; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T.
2017-01-01
This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively. PMID:28326009
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NASA Astrophysics Data System (ADS)
Lee, Youngjoo; Seo, Joon Beom; Kang, Bokyoung; Kim, Dongil; Lee, June Goo; Kim, Song Soo; Kim, Namkug; Kang, Suk Ho
2007-03-01
The performance of classification algorithms for differentiating among obstructive lung diseases based on features from texture analysis using HRCT (High Resolution Computerized Tomography) images was compared. HRCT can provide accurate information for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobular emphysema and bronchiolitis obliterans. Features on HRCT images can be subtle, however, particularly in the early stages of disease, and image-based diagnosis is subject to inter-observer variation. To automate the diagnosis and improve the accuracy, we compared three types of automated classification systems, naÃve Bayesian classifier, ANN (Artificial Neural Net) and SVM (Support Vector Machine), based on their ability to differentiate among normal lung and three types of obstructive lung diseases. To assess the performance and cross-validation of these three classifiers, 5 folding methods with 5 randomly chosen groups were used. For a more robust result, each validation was repeated 100 times. SVM showed the best performance, with 86.5% overall sensitivity, significantly different from the other classifiers (one way ANOVA, p<0.01). We address the characteristics of each classifier affecting performance and the issue of which classifier is the most suitable for clinical applications, and propose an appropriate method to choose the best classifier and determine its optimal parameters for optimal disease discrimination. These results can be applied to classifiers for differentiation of other diseases.
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Acoustic target detection and classification using neural networks
NASA Technical Reports Server (NTRS)
Robertson, James A.; Conlon, Mark
1993-01-01
A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.
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Apostolova, Liana G.; Hwang, Kristy S.; Kohannim, Omid; Avila, David; Elashoff, David; Jack, Clifford R.; Shaw, Leslie; Trojanowski, John Q.; Weiner, Michael W.; Thompson, Paul M.
2014-01-01
Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity. PMID:24634832
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Classification of ligand molecules in PDB with fast heuristic graph match algorithm COMPLIG.
Saito, Mihoko; Takemura, Naomi; Shirai, Tsuyoshi
2012-12-14
A fast heuristic graph-matching algorithm, COMPLIG, was devised to classify the small-molecule ligands in the Protein Data Bank (PDB), which are currently not properly classified on structure basis. By concurrently classifying proteins and ligands, we determined the most appropriate parameter for categorizing ligands to be more than 60% identity of atoms and bonds between molecules, and we classified 11,585 types of ligands into 1946 clusters. Although the large clusters were composed of nucleotides or amino acids, a significant presence of drug compounds was also observed. Application of the system to classify the natural ligand status of human proteins in the current database suggested that, at most, 37% of the experimental structures of human proteins were in complex with natural ligands. However, protein homology- and/or ligand similarity-based modeling was implied to provide models of natural interactions for an additional 28% of the total, which might be used to increase the knowledge of intrinsic protein-metabolite interactions. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Learning to classify wakes from local sensory information
NASA Astrophysics Data System (ADS)
Alsalman, Mohamad; Colvert, Brendan; Kanso, Eva; Kanso Team
2017-11-01
Aquatic organisms exhibit remarkable abilities to sense local flow signals contained in their fluid environment and to surmise the origins of these flows. For example, fish can discern the information contained in various flow structures and utilize this information for obstacle avoidance and prey tracking. Flow structures created by flapping and swimming bodies are well characterized in the fluid dynamics literature; however, such characterization relies on classical methods that use an external observer to reconstruct global flow fields. The reconstructed flows, or wakes, are then classified according to the unsteady vortex patterns. Here, we propose a new approach for wake identification: we classify the wakes resulting from a flapping airfoil by applying machine learning algorithms to local flow information. In particular, we simulate the wakes of an oscillating airfoil in an incoming flow, extract the downstream vorticity information, and train a classifier to learn the different flow structures and classify new ones. This data-driven approach provides a promising framework for underwater navigation and detection in application to autonomous bio-inspired vehicles.
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Searchfield, Grant D; Linford, Tania; Kobayashi, Kei; Crowhen, David; Latzel, Matthias
2018-03-01
To compare preference for and performance of manually selected programmes to an automatic sound classifier, the Phonak AutoSense OS. A single blind repeated measures study. Participants were fit with Phonak Virto V90 ITE aids; preferences for different listening programmes were compared across four different sound scenarios (speech in: quiet, noise, loud noise and a car). Following a 4-week trial preferences were reassessed and the users preferred programme was compared to the automatic classifier for sound quality and hearing in noise (HINT test) using a 12 loudspeaker array. Twenty-five participants with symmetrical moderate-severe sensorineural hearing loss. Participant preferences of manual programme for scenarios varied considerably between and within sessions. A HINT Speech Reception Threshold (SRT) advantage was observed for the automatic classifier over participant's manual selection for speech in quiet, loud noise and car noise. Sound quality ratings were similar for both manual and automatic selections. The use of a sound classifier is a viable alternative to manual programme selection.
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NASA Astrophysics Data System (ADS)
Cox, S. J.
2013-12-01
Observations provide the fundamental constraint on natural science interpretations. Earth science observations originate in many contexts, including in-situ field observations and monitoring, various modes of remote sensing and geophysics, sampling for ex-situ (laboratory) analysis, as well as numerical modelling and simulation which also provide estimates of parameter values. Most investigations require a combination of these, often sourced from multiple initiatives and archives, so data discovery and re-organization can be a significant project burden. The Observations and Measurements (O&M) information model was developed to provide a common vocabulary that can be applied to all these cases, and thus provide a basis for cross-initiative and cross-domain interoperability. O&M was designed in the context of the standards for geographic information from OGC and ISO. It provides a complementary viewpoint to the well-known feature (object oriented) and coverage (property field) views, but prioritizes the property determination process. Nevertheless, use of O&M implies the existence of well defined feature types. In disciplines such as geology and ecosystem sciences the primary complexity is in their model of the world, for which the description of each item requires access to diverse observation sets. On the other hand, geophysics and earth observations work with simpler underlying information items, but in larger quantities over multiple spatio-temporal dimensions, acquired using complex sensor systems. Multiple transformations between the three viewpoints are involved in the data flows in most investigations, from collection through analysis to information and story. The O&M model classifies observations: - from a provider viewpoint: in terms of the sensor or procedure involved; - from a consumer viewpoint: in terms of the property being reported, and the feature with which it is associated. These concerns carry different weights in different applications. Communities generating data using ships, satellites and aircraft habitually classify observations by the source platform and mission, as this implies a rich set of metadata to the cognoscenti. However, integrators are more likely to focus on the phenomenon being observed, together with the location of the features carrying it. In this context sensor information informs quality evaluation, as a secondary consideration following after data discovery. The observation model is specialized by constraining facets, such as observed property, sensor or procedure, to be taken from a specific set or vocabulary. Such vocabularies are typically developed on a project or community basis, but data fusion depends on them being widely accessible, and comparable with related vocabularies. Better still if they are transparently governed, trusted and stable enough to encourage re-use. Semantic web technologies support distribution of rigorously constructed vocabularies through standard interfaces, with standard mechanisms for asserting or inferring of proximity and other relationships. Interoperability of observation data in future is likely to depend on the development of a viable ecosystem of these secondary resources.
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A cardiorespiratory classifier of voluntary and involuntary electrodermal activity
2010-01-01
Background Electrodermal reactions (EDRs) can be attributed to many origins, including spontaneous fluctuations of electrodermal activity (EDA) and stimuli such as deep inspirations, voluntary mental activity and startling events. In fields that use EDA as a measure of psychophysiological state, the fact that EDRs may be elicited from many different stimuli is often ignored. This study attempts to classify observed EDRs as voluntary (i.e., generated from intentional respiratory or mental activity) or involuntary (i.e., generated from startling events or spontaneous electrodermal fluctuations). Methods Eight able-bodied participants were subjected to conditions that would cause a change in EDA: music imagery, startling noises, and deep inspirations. A user-centered cardiorespiratory classifier consisting of 1) an EDR detector, 2) a respiratory filter and 3) a cardiorespiratory filter was developed to automatically detect a participant's EDRs and to classify the origin of their stimulation as voluntary or involuntary. Results Detected EDRs were classified with a positive predictive value of 78%, a negative predictive value of 81% and an overall accuracy of 78%. Without the classifier, EDRs could only be correctly attributed as voluntary or involuntary with an accuracy of 50%. Conclusions The proposed classifier may enable investigators to form more accurate interpretations of electrodermal activity as a measure of an individual's psychophysiological state. PMID:20184746
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NASA Astrophysics Data System (ADS)
Silva, Jeff A. K.; Chock, Taylor
2016-06-01
An evaluation of the current condition of sea-disposed military munitions observed during the 2009 Hawaii Undersea Military Munitions Assessment Project investigation is presented. The 69 km2 study area is located south of Pearl Harbor, Oahu, Hawaii, and is positioned within a former deep-sea disposal area designated as Hawaii-05 or HI-05 by the United States Department of Defense. HI-05 is known to contain both conventional and chemical munitions that were sea-disposed between 1920 and 1951. Digital images and video reconnaissance logs collected during six remotely operated vehicle and 16 human-occupied vehicle surveys were used to classify the integrity and state of corrosion of the 1842 discarded military munitions (DMM) objects encountered. Of these, 5% (or 90 individual DMM objects) were found to exhibit a mild-moderate degree of corrosion. The majority (66% or 1222 DMM objects) were observed to be significantly corroded, but visually intact on the seafloor. The remaining 29% of DMM encountered were found to be severely corroded and breached, with their contents exposed. Chemical munitions were not identified during the 2009 investigation. In general, identified munitions known to have been constructed with thicker casings were better preserved. Unusual corrosion features were also observed, including what are termed here as 'corrosion skirts' that resembled the flow and cementation of corrosion products at and away from the base of many munitions, and 'corrosion pedestal' features resembling a combination of cemented corrosion products and seafloor sediments that were observed to be supporting munitions above the surface of the seafloor. The origin of these corrosion features could not be determined due to the lack of physical samples collected. However, a microbial-mediated formation hypothesis is presented, based on visual analysis, which can serve as a testable model for future field programs.
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Histopathological changes in the pancreas of cattle with abdominal fat necrosis.
Tani, Chikako; Pratakpiriya, Watanyoo; Tani, Mineto; Yamauchi, Takenori; Hirai, Takuya; Yamaguchi, Ryoji; Ano, Hitoshi; Katamoto, Hiromu
2017-01-20
The association between pancreatic disorder and abdominal fat necrosis in cattle remains unclear. The pancreases of 29 slaughtered cattle with or without fat necrosis were collected to investigate pathological changes. Japanese Black (JB) cattle were classified into the FN group (with abdominal fat necrosis; n=9) and N group (without fat necrosis; n=5). The pancreases were also collected from 15 Holstein Friesian (HF) cows. All JB cattle showed high body condition scores. Regarding the pathological findings, fatty pancreas which involves adipocyte infiltration into the pancreas and fat necrosis (saponification) were observed in 25 and 27 cases, respectively. Immunohistochemical staining with anti-Iba-1 antibody showed large numbers of macrophages surrounding the saponified fat in the pancreas. CD3-positive T cells were significantly more common in the pancreas of both the FN and N groups compared with the HF group (P<0.05). Furthermore, fibrosis in the pancreas exhibited a correlative tendency with the formation of necrotic fat mass in the peritoneal cavity (P<0.1). These results indicate that obesity leads to increased severity of pancreatic disorder, including fatty pancreas and pancreatitis. The pathological lesions in the pancreas may play a key role in abdominal fat necrosis through the inflammatory process.
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NASA Astrophysics Data System (ADS)
Fritz, Steffen; Dias, Eduardo; Zeug, Guenther; Vescovi, Fabio; See, Linda; Sturn, Tobias; McCallum, Ian; Stammes, Piet; Snik, Frans; Hendriks, Elise
2015-04-01
The ESA funded EducEO project is aimed at demonstrating the potential of citizen science and crowdsourcing for Earth Observation (EO), where citizen science and crowdsourcing refer to the involvement of citizens in tasks such as data collection. The potential for using citizens in the calibration and validation of satellite imagery through in-situ measurements and image recognition is largely untapped. The EducEO project will aim to achieve good integration with networks such as GLOBE (primary and secondary education) and COST (higher education) to involve students in four different applications that will be piloted as part of the EducEO project. The presentation will provide a brief overview and initial results of these applications, which include: the iSpex tool for measuring air pollution using an iPhone; a game to classify cropland and deforested areas from high resolution satellite imagery; an application to monitor areas of forest change using radar data from Sentinel-1; and the collection of in-situ yield and production data from both farmers (using high-tech farming equipment) and students. In particular initial results and future potential of the serious game on land cover and forest change monitoring will be discussed.
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Fall spawning of Atlantic sturgeon in the Roanoke River, North Carolina
Smith, Joseph A.; Hightower, Joseph E.; Flowers, H. Jared
2015-01-01
In 2012, the National Oceanic and Atmospheric Administration (NOAA) declared Atlantic Sturgeon Acipenser oxyrinchus oxyrinchus to be threatened or endangered throughout its range in U.S. waters. Restoration of the subspecies will require much new information, particularly on the location and timing of spawning. We used a combination of acoustic telemetry and sampling with anchored artificial substrates (spawning pads) to detect fall (September–November) spawning in the Roanoke River in North Carolina. This population is included in the Carolina Distinct Population Segment, which was classified by NOAA as endangered. Sampling was done immediately below the first shoals encountered by anadromous fishes, near Weldon. Our collection of 38 eggs during the 21 d that spawning pads were deployed appears to be the first such collection (spring or fall) for wild-spawned Atlantic Sturgeon eggs. Based on egg development stages, estimated spawning dates were September 17–18 and 18–19 at water temperatures from 25.3°C to 24.3°C and river discharge from 55 to 297 m3/s. These observations about fall spawning and habitat use should aid in protecting critical habitats and planning research on Atlantic Sturgeon spawning in other rivers.
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12 CFR 792.63 - Collection of information from individuals; information forms.
Code of Federal Regulations, 2011 CFR
2011-01-01
...; information forms. 792.63 Section 792.63 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING THE OPERATIONS OF THE NATIONAL CREDIT UNION ADMINISTRATION REQUESTS FOR INFORMATION UNDER THE FREEDOM OF INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED...
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12 CFR 792.63 - Collection of information from individuals; information forms.
Code of Federal Regulations, 2012 CFR
2012-01-01
...; information forms. 792.63 Section 792.63 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING THE OPERATIONS OF THE NATIONAL CREDIT UNION ADMINISTRATION REQUESTS FOR INFORMATION UNDER THE FREEDOM OF INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED...
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12 CFR 792.63 - Collection of information from individuals; information forms.
Code of Federal Regulations, 2013 CFR
2013-01-01
...; information forms. 792.63 Section 792.63 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING THE OPERATIONS OF THE NATIONAL CREDIT UNION ADMINISTRATION REQUESTS FOR INFORMATION UNDER THE FREEDOM OF INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED...
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12 CFR 792.63 - Collection of information from individuals; information forms.
Code of Federal Regulations, 2014 CFR
2014-01-01
...; information forms. 792.63 Section 792.63 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING THE OPERATIONS OF THE NATIONAL CREDIT UNION ADMINISTRATION REQUESTS FOR INFORMATION UNDER THE FREEDOM OF INFORMATION ACT AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED...
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Federal Register 2010, 2011, 2012, 2013, 2014
2013-10-23
... of air conditioning systems and recovery/recycling equipment. Form Numbers: None. Respondents... automotive repair shops, automotive repair shops not elsewhere classified, including air conditioning and... Request Submitted to OMB for Review and Approval; Comment Request; Mobile Air Conditioner Retrofitting...
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ELECTROFISHING IN BOATABLE RIVERS: DOES SAMPLING DESIGN AFFECT BIOASSESSMENT METRICS?
Data were collected from 60 boatable sites using an electrofishing design that permitted comparisons of the effects of designs and distances on fish assemblage metrics. Sites were classified a priori as Run-of-the-River (ROR) or Restricted Flow (RF). Data representing four diff...
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Digital Libraries on the Internet.
ERIC Educational Resources Information Center
Sharon, Taly; Frank, Ariel J.
This paper discusses digital libraries on the Internet. The resource repository hierarchy, consisting of two major paradigms, search engines (SEs) and digital libraries, is presented. SEs are classified into three categories: basic-SE, directory, and meta-SE. The following six major characteristics of a library are summarized: collection of data…
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Faculty Members' Instructional Priorities for Adopting OER
ERIC Educational Resources Information Center
Jung, Insung; Hong, Seongyoun
2016-01-01
This study aimed to investigate and classify faculty members' instructional priorities for adopting OER. In-depth interview data were collected from 10 faculty members from different regions and analyzed with NVivo 10. The original supposition was that the well-established instructional priorities, effectiveness, efficiency, and appeal would…
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Coordination and collaboration to document the global cotton germplasm resources
USDA-ARS?s Scientific Manuscript database
Coordinated efforts to collect and maintain cotton genetic resources have increased over the last 100 years to insure the worldwide economic value of cotton fiber and cotton byproducts. The classified genetic resources of cotton are extensive and include five tetraploid species in the primary gene ...
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Militancy and Accommodativeness in Teachers' Negotiations: Two Ontario Surveys
ERIC Educational Resources Information Center
Fris, J.
1976-01-01
Reports findings of two surveys of Ontario elementary and secondary teachers that measured teachers' attitudes regarding collective bargaining tactics and classified teachers' responses according to their militancy or accomodativeness. Available from Department of Educational Administration, The University of Alberta, Edmonton, Alberta, Canada T6G…
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NASA Astrophysics Data System (ADS)
Chakravarty, T.; Chowdhury, A.; Ghose, A.; Bhaumik, C.; Balamuralidhar, P.
2014-03-01
Telematics form an important technology enabler for intelligent transportation systems. By deploying on-board diagnostic devices, the signatures of vehicle vibration along with its location and time are recorded. Detailed analyses of the collected signatures offer deep insights into the state of the objects under study. Towards that objective, we carried out experiments by deploying telematics device in one of the office bus that ferries employees to office and back. Data is being collected from 3-axis accelerometer, GPS, speed and the time for all the journeys. In this paper, we present initial results of the above exercise by applying statistical methods to derive information through systematic analysis of the data collected over four months. It is demonstrated that the higher order derivative of the measured Z axis acceleration samples display the properties Weibull distribution when the time axis is replaced by the amplitude of such processed acceleration data. Such an observation offers us a method to predict future behaviour where deviations from prediction are classified as context-based aberrations or progressive degradation of the system. In addition we capture the relationship between speed of the vehicle and median of the jerk energy samples using regression analysis. Such results offer an opportunity to develop a robust method to model road-vehicle interaction thereby enabling us to predict such like driving behaviour and condition based maintenance etc.
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Boyd, Robert A.
2001-01-01
Water samples collected from the alluvium indicated ground water can be classified as a calcium-magnesium-bicarbonate type. Reducing conditions likely occur in some localized areas of the alluvium, as suggested by relatively large concentrations of dissolved iron (4,390 micrograms per liter) and manganese (2, 430 micrograms per liter) in some ground-water samples. Nitrite plus nitrate was detected at concentrations greater than or equal to 8 milligrams per liter in three samples collected from observation wells completed in close proximity to cropland; the nitrite plus nitrate concentration in one groundwater sample exceeded the U.S. Environmental Protection Agency Maximum Contaminant Level for nitrate in drinking water (10 milligrams per liter as N). Triazine herbicides (atrazine, cyanazine, propazine, simazine, and selected degradation products) and chloroacetanilide herbicides (acetochlor, alachlor, and metolachlor) were detected in some water samples. A greater number of herbicide compounds were detected in surface-water samples than in ground-water samples. Herbicide concentrations typically were at least an order of magnitude greater in surfacewater samples than in ground-water samples. The Maximum Contaminant Level for alachlor (2 micrograms per liter) was exceeded in a sample from Dry Branch Creek at Tama Road and for atrazine (3 micrograms per liter) was exceeded in samples collected from Dry Branch Creek at Tama Road and the county drainage ditch at Tama Road.
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Ramirez, P G; Stein, M; Etchepare, E G; Almiron, W R
2016-12-01
In order to extend the knowledge of anopheline diversity and their habitats in three environments with different degrees of anthropic intervention in Puerto Iguazú, Misiones, anopheline larvae were collected and classified on the basis of similarities of their habitats. Spatio-temporal abundance was determined and larval diversity and complementarity index were calculated. Rank-abundance curves were performed to compare the composition, abundance, and species evenness among environments. A total of 783 larvae, belonging to six species: Anopheles argyritarsis, An. fluminensis, An. mediopunctatus, An. punctimacula, An. strodei s.l., and An. triannulatus s.l., were collected. A cluster analysis and a principal component analysis detected two groups; exposure to sunlight and type of habitat were the characteristics that explained the grouping of species. Higher abundances of anopheline larvae were observed during autumn and spring. The greatest richness was recorded in wild and peri-urban environments and the effective number of species was greater in the wild. Anopheles punctimacula and An. triannulatus s.l. are secondary vectors of malaria in other South American countries and both species were found in the three environments, so that deforestation poses a potential risk for malaria transmission as it contributes to the proliferation of larval habitats for these mosquitoes. © 2016 The Society for Vector Ecology.
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Traits and types of health data repositories.
Wade, Ted D
2014-01-01
We review traits of reusable clinical data and offer a typology of clinical repositories with a range of known examples. Sources of clinical data suitable for research can be classified into types reflecting the data's institutional origin, original purpose, level of integration and governance. Primary data nearly always come from research studies and electronic medical records. Registries collect data on focused populations primarily to track outcomes, often using observational research methods. Warehouses are institutional information utilities repackaging clinical care data. Collections organize data from more organizations than a data warehouse, and more original data sources than a registry. Therefore even if they are heavily curated, their level of internal integration, and thus ease of use, can be less than other types. Federations are like collections except that physical control over data is distributed among donor organizations. Federations sometimes federate, giving a second level of organization. While the size, in number of patients, varies widely within each type of data source, populations over 10 K are relatively numerous, and much larger populations can be seen in warehouses and federations. One imagined ideal structure for research progress has been called an "Information Commons". It would have longitudinal, multi-leveled (environmental through molecular) data on a large population of identified, consenting individuals. These are qualities whose achievement would require long term commitment on the part of many data donors, including a willingness to make their data public.
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Asthma Outcomes: Healthcare Utilization and Costs
Akinbami, Lara J.; Sullivan, Sean D.; Campbell, Jonathan D.; Grundmeier, Robert W.; Hartert, Tina V.; Lee, Todd A.; Smith, Robert A.
2014-01-01
Background Measures of healthcare utilization and indirect impact of asthma morbidity are used to assess clinical interventions and estimate cost. Objective National Institutes of Health (NIH) institutes and other federal agencies convened an expert group to propose standardized measurement, collection, analysis, and reporting of healthcare utilization and cost outcomes in future asthma studies. Methods We used comprehensive literature reviews and expert opinion to compile a list of asthma healthcare utilization outcomes that we classified as core (required in future studies), supplemental (used according to study aims and standardized) and emerging (requiring validation and standardization). We also have identified methodology to assign cost to these outcomes. This work was discussed at an NIH-organized workshop in March 2010 and finalized in September 2011. Results We identified 3 ways to promote comparability across clinical trials for measures of healthcare utilization, resource use, and cost: (1) specify the study perspective (patient, clinician, payer, society), (2) standardize the measurement period (ideally, 12 months), and (3) use standard units to measure healthcare utilization and other asthma-related events. Conclusions Large clinical trials and observational studies should collect and report detailed information on healthcare utilization, intervention resources, and indirect impact of asthma, so that costs can be calculated and cost-effectiveness analyses can be conducted across several studies. Additional research is needed to develop standard, validated survey instruments for collection of provider-reported and participant-reported data regarding asthma-related health care. PMID:22386509
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Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers
García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta
2016-01-01
The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653
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Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.
García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta
2016-06-29
The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.
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Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter Je; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong
2017-11-01
Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.
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Buick, Julie K.; Moffat, Ivy; Williams, Andrew; Swartz, Carol D.; Recio, Leslie; Hyduke, Daniel R.; Li, Heng‐Hong; Fornace, Albert J.; Aubrecht, Jiri
2015-01-01
The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid‐ and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells. Environ. Mol. Mutagen. 56:520–534, 2015. © 2015 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc. PMID:25733247
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Vegetation types in coastal Louisiana in 2013
Sasser, Charles E.; Visser, Jenneke M.; Mouton, Edmond; Linscombe, Jeb; Hartley, Steve B.
2014-01-01
During the summer of 2013, the U.S. Geological Survey, Louisiana State University, University of Louisiana at Lafayette, and the Louisiana Department of Wildlife and Fisheries Coastal and Nongame Resources Division jointly completed an aerial survey to collect data on 2013 vegetation types in coastal Louisiana. Plant species were listed and their abundance classified. On the basis of species composition and abundance, each marsh sampling station was assigned a marsh type: fresh, intermediate, brackish, or saline (saltwater) marsh. The current map presents the data collected in this effort.
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Vegetation database for land-cover mapping, Clark and Lincoln Counties, Nevada
Charlet, David A.; Damar, Nancy A.; Leary, Patrick J.
2014-01-01
Floristic and other vegetation data were collected at 3,175 sample sites to support land-cover mapping projects in Clark and Lincoln Counties, Nevada, from 2007 to 2013. Data were collected at sample sites that were selected to fulfill mapping priorities by one of two different plot sampling approaches. Samples were described at the stand level and classified into the National Vegetation Classification hierarchy at the alliance level and above. The vegetation database is presented in geospatial and tabular formats.
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Combining multiple decisions: applications to bioinformatics
NASA Astrophysics Data System (ADS)
Yukinawa, N.; Takenouchi, T.; Oba, S.; Ishii, S.
2008-01-01
Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct a multi-class classifier in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. In the second approach, misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model by making an analogy to the context of information transmission theory. Experimental studies using various real-world datasets including cancer classification problems reveal that both of the new methods are superior or comparable to other multi-class classification methods.
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Ali, Abdulbaset; Hu, Bing; Ramahi, Omar
2015-05-15
This work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impact in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.
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Optimal trajectories of brain state transitions
Gu, Shi; Betzel, Richard F.; Mattar, Marcelo G.; Cieslak, Matthew; Delio, Philip R.; Grafton, Scott T.; Pasqualetti, Fabio; Bassett, Danielle S.
2017-01-01
The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury. PMID:28088484
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Crozat, G.; Domergue, J.L.; Bogui, V.
Atmospheric aerosols were sampled on filters to the air near ground level over the Ivory Coast and the Gulf of Guinea. Several elements were measured of filters by neutron activation and gamma spectrometry. Correlation thats applied to these elements allow them to be classified in groups of common origin. Study of the sampling collected over the Ivory Coast, along a''North- South'' axis, showed an increase of the concentrations of the terrestrial elements, as one passes from the coast to the north of the country. However, no particular increase of the concentrations was observed, to ground level air, when passing frommore » one side of the intertropical front to the other. In the air above the land, concentrations of marine aerosols decrease from the coast forth, especially near it. Daily variations may be noticed for all the elements The experiments performed in marthe atmosphere, over the Guif of Guinea, show that a high number of the elements measured are of terrestrial origin. (UK)« less
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Micaceous Soil Strength And Permeability Improvement Induced By Microbacteria From Vegetable Waste
NASA Astrophysics Data System (ADS)
Omar, R. C.; Roslan, R.; Baharuddin, I. N. Z.; Hanafiah, M. I. M.
2016-11-01
Green technology method using vegetable waste are introduced in this paper for improvement of phyllite residual soil from UNITEN, Campus. Residual soil from phyllite are known as micaceous soils and it give problem in managing the stability of the slope especially in wet and extensively dry seasons. Micaceous soil are collected using tube sampler technique and mixed with liquid contain microorganism from fermented vegetable waste name as vege-grout to form remolded sample. The remolded sample are classify as 15.0%, 17.5%, 20.00% and 22.5% based on different incremental percentages of vege-grout. The curing time for the sample are 7, 14, 21, 28, and 35 days before the tests were conducted. Observation of the effect of treatment shows 20.0% of liquid contain Bacillus pasteurii and Bacillus Subtilis with 21 days curing time is the optimum value in strengthening the soil and improve the permeability.
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Qualitative ergonomics/human factors research in health care: Current state and future directions.
Valdez, Rupa Sheth; McGuire, Kerry Margaret; Rivera, A Joy
2017-07-01
The objective of this systematic review was to understand the current state of Ergonomics/Human Factors (E/HF) qualitative research in health care and to draw implications for future efforts. This systematic review identified 98 qualitative research papers published between January 2005 and August 2015 in the seven journals endorsed by the International Ergonomics Association with an impact factor over 1.0. The majority of the studies were conducted in hospitals and outpatient clinics, were focused on the work of formal health care professionals, and were classified as cognitive or organizational ergonomics. Interviews, focus groups, and observations were the most prevalent forms of data collection. Triangulation and data archiving were the dominant approaches to ensuring rigor. Few studies employed a formal approach to qualitative inquiry. Significant opportunities remain to enhance the use of qualitative research to advance systems thinking within health care. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Global and regional dissemination and evolution of Burkholderia pseudomallei
Chewapreecha, Claire; Holden, Matthew T. G.; Vehkala, Minna; Välimäki, Niko; Yang, Zhirong; Harris, Simon R; Mather, Alison E.; Tuanyok, Apichai; De Smet, Birgit; Le Hello, Simon; Bizet, Chantal; Mayo, Mark; Wuthiekanun, Vanaporn; Limmathurotsakul, Direk; Phetsouvanh, Rattanaphone; Spratt, Brian G; Corander, Jukka; Keim, Paul; Dougan, Gordon; Dance, David A. B.; Currie, Bart J; Parkhill, Julian; Peacock, Sharon J.
2017-01-01
The environmental bacterium Burkholderia pseudomallei causes an estimated 165,000 cases of human melioidosis per year worldwide, and is also classified as a biothreat agent. We used whole genome sequences of 469 B. pseudomallei isolates from 30 countries collected over 79 years to explore its geographic transmission. Our data point to Australia as an early reservoir, with transmission to Southeast Asia followed by onward transmission to South Asia, and East Asia. Repeated reintroduction was observed within the Malay Peninsula, and between countries bordered by the Mekong river. Our data support an African origin of the Central and South American isolates with introduction of B. pseudomallei into the Americas between 1650 and 1850, providing a temporal link with the slave trade. We also identified geographically distinct genes/variants in Australasian or Southeast Asian isolates alone, with virulence-associated genes being among those overrepresented. This provides a potential explanation for clinical manifestations of melioidosis that are geographically restricted. PMID:28112723
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NASA Technical Reports Server (NTRS)
Wu, S.-T.
1985-01-01
Seasonally compatible data collected by SIR-A and by Landsat 4 TM over the lower coastal plain in Alabama were coregistered, forming a SIR-A/TM multichannel data set with 30 m x 30 m pixel size. Spectral signature plots and histogram analysis of the data were used to observe data characteristics. Radar returns from pine forest classes correlated highly with the tree ages, suggesting the potential utility of microwave remote sensing for forest biomass estimation. As compared with the TM-only data set, the use of SIR-A/TM data set improved classification accuracy of the seven land cover types studied. In addition, the SIR-A/TM classified data support previous finding by Engheta and Elachi (1982) that microwave data appear to be correlated with differing bottomland hardwood forest vegetation as associated with varying water regimens (i.e., wet versus dry).
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Niagolova, Nedialka; McElmurry, Shawn P; Voice, Thomas C; Long, David T; Petropoulos, Evangelos A; Havezov, Ivan; Chou, Karen; Ganev, Varban
2005-03-01
This study explored two hypotheses relating elevated concentrations of nitrogen species in drinking water and the disease Balkan Endemic Nephropathy (BEN). Drinking water samples were collected from a variety of water supplies in both endemic and non-endemic villages in the Vratza and Montana districts of Bulgaria. The majority of well water samples exceeded US drinking water standards for nitrate + nitrite. No statistically significant difference was observed for any of the nitrogen species between villages classified as endemic and non-endemic. Other constituents (sodium, potassium and chloride) known to be indicators of anthropogenic pollution were also found at elevated concentrations and all followed the order wells > springs > taps. This ordering coincides with the proximity of human influences to the water sources. Our results clearly establish an exposure pathway between anthropogenic activity and drinking water supplies, suggesting that the causative agent for BEN could result from surface contamination.
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Mirabelli, Maria C.; London, Stephanie J.; Charles, Luenda E.; Pompeii, Lisa A.; Wagenknecht, Lynne E.
2011-01-01
Objectives To examine associations between occupation and respiratory health in a large, population-based cohort of adults in the United States. Methods Data from 15,273 participants, aged 45-64 years, in the Atherosclerosis Risk in Communities (ARIC) study were used to examine associations of current or most recent job held with the prevalence of self-reported chronic cough, chronic bronchitis, wheeze, asthma, and measures of lung function collected by spirometry. Results Eleven percent of participants reported wheeze and 9% were classified as having airway obstruction. Compared to individuals in managerial and administrative jobs, increased prevalences of respiratory outcomes were observed among participants in selected occupations, including construction and extractive trades (wheeze: prevalence ratio [PR]: 1.92, 95% confidence interval [CI]: 1.35, 2.73; airway obstruction: PR: 1.31, 95% CI: 1.05, 1.65). Conclusions Specific occupations are associated with adverse respiratory health. PMID:22157701
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Mirabelli, Maria C; London, Stephanie J; Charles, Luenda E; Pompeii, Lisa A; Wagenknecht, Lynne E
2012-02-01
To examine associations between occupation and respiratory health in a large, population-based cohort of adults in the United States. Data from 15,273 participants, aged 45 to 64 years, in the Atherosclerosis Risk in Communities study were used to examine associations of current or most recent job held with the prevalence of self-reported chronic cough, chronic bronchitis, wheezing, asthma, and measures of lung function collected by spirometry. Eleven percent of participants reported wheezing and 9% were classified as having airway obstruction. Compared with individuals in managerial and administrative jobs, increased prevalences of respiratory outcomes were observed among participants in selected occupations, including construction and extractive trades (wheezing, prevalence ratio = 1.92, 95% confidence interval = 1.35, 2.73; airway obstruction, prevalence ratio = 1.31, 95% confidence interval = 1.05, 1.65). Specific occupations are associated with adverse respiratory health.
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Quantum Hall ferromagnets and transport properties of buckled Dirac materials
NASA Astrophysics Data System (ADS)
Luo, Wenchen; Chakraborty, Tapash
2015-10-01
We study the ground states and low-energy excitations of a generic Dirac material with spin-orbit coupling and a buckling structure in the presence of a magnetic field. The ground states can be classified into three types under different conditions: SU(2), easy-plane, and Ising quantum Hall ferromagnets. For the SU(2) and the easy-plane quantum Hall ferromagnets there are goldstone modes in the collective excitations, while all the modes are gapped in an Ising-type ground state. We compare the Ising quantum Hall ferromagnet with that of bilayer graphene and present the domain-wall solution at finite temperatures. We then specify the phase transitions and transport gaps in silicene in Landau levels 0 and 1. The phase diagram depends strongly on the magnetic field and the dielectric constant. We note that there exist triple points in the phase diagrams in Landau level N =1 that could be observed in experiments.
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Quantifying functional mobility progress for chronic disease management.
Boyle, Justin; Karunanithi, Mohan; Wark, Tim; Chan, Wilbur; Colavitti, Christine
2006-01-01
A method for quantifying improvements in functional mobility is presented based on patient-worn accelerometer devices. For patients with cardiovascular, respiratory, or other chronic disease, increasing the amount of functional mobility is a large component of rehabilitation programs. We have conducted an observational trial on the use of accelerometers for quantifying mobility improvements in a small group of chronic disease patients (n=15, 48 - 86 yrs). Cognitive impairments precluded complex instrumentation of patients, and movement data was obtained from a single 2-axis accelerometer device worn at the hip. In our trial, movement data collected from accelerometer devices was classified into Lying vs Sitting/Standing vs Walking/Activity movements. This classification enabled the amount of walking to be quantified and graphically presented to clinicians and carers for feedback on exercise efficacy. Presenting long term trends in this data to patients also provides valuable feedback for self managed care and assisting with compliance.
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Ali, Abdulbaset; Hu, Bing; Ramahi, Omar M.
2015-01-01
This work presents a real-life experiment implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impacts in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing the data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks, and the experimental results showed good crack classification accuracy rates. PMID:25988871
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Measurement of the single-top-quark production cross section at CDF.
Aaltonen, T; Adelman, J; Akimoto, T; Albrow, M G; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Bednar, P; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Copic, K; Cordelli, M; Cortiana, G; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Koay, S A; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kusakabe, Y; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, E; Lee, H S; Lee, S W; Leone, S; Lewis, J D; Lin, C S; Linacre, J; Lindgren, M; Lipeles, E; Liss, T M; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lu, R-S; Lucchesi, D; Lueck, J; Luci, C; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlok, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Peiffer, T; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Renz, M; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Schall, I; Scheidle, T; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner-Kuhr, J; Wagner, W; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zheng, Y; Zucchelli, S
2008-12-19
We report a measurement of the single-top-quark production cross section in 2.2 fb;{-1} of pp collision data collected by the Collider Detector at Fermilab at sqrt[s]=1.96 TeV. Candidate events are classified as signal-like by three parallel analyses which use likelihood, matrix element, and neural network discriminants. These results are combined in order to improve the sensitivity. We observe a signal consistent with the standard model prediction, but inconsistent with the background-only model by 3.7 standard deviations with a median expected sensitivity of 4.9 standard deviations. We measure a cross section of 2.2(-0.6)(+0.7)(stat+syst) pb, extract the Cabibbo-Kobayashi-Maskawa matrix-element value |V(tb)|=0.88(-0.12)(+0.13)(stat+syst)+/-0.07(theory), and set the limit |V(tb)|>0.66 at the 95% C.L.
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Exploring the CAESAR database using dimensionality reduction techniques
NASA Astrophysics Data System (ADS)
Mendoza-Schrock, Olga; Raymer, Michael L.
2012-06-01
The Civilian American and European Surface Anthropometry Resource (CAESAR) database containing over 40 anthropometric measurements on over 4000 humans has been extensively explored for pattern recognition and classification purposes using the raw, original data [1-4]. However, some of the anthropometric variables would be impossible to collect in an uncontrolled environment. Here, we explore the use of dimensionality reduction methods in concert with a variety of classification algorithms for gender classification using only those variables that are readily observable in an uncontrolled environment. Several dimensionality reduction techniques are employed to learn the underlining structure of the data. These techniques include linear projections such as the classical Principal Components Analysis (PCA) and non-linear (manifold learning) techniques, such as Diffusion Maps and the Isomap technique. This paper briefly describes all three techniques, and compares three different classifiers, Naïve Bayes, Adaboost, and Support Vector Machines (SVM), for gender classification in conjunction with each of these three dimensionality reduction approaches.
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Building of fuzzy decision trees using ID3 algorithm
NASA Astrophysics Data System (ADS)
Begenova, S. B.; Avdeenko, T. V.
2018-05-01
Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.
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NASA Astrophysics Data System (ADS)
Yi, Wei-song; Cui, Dian-sheng; Li, Zhi; Wu, Lan-lan; Shen, Ai-guo; Hu, Ji-ming
2013-01-01
The manuscript has investigated the application of near-infrared (NIR) spectroscopy for differentiation gastric cancer. The 90 spectra from cancerous and normal tissues were collected from a total of 30 surgical specimens using Fourier transform near-infrared spectroscopy (FT-NIR) equipped with a fiber-optic probe. Major spectral differences were observed in the CH-stretching second overtone (9000-7000 cm-1), CH-stretching first overtone (6000-5200 cm-1), and CH-stretching combination (4500-4000 cm-1) regions. By use of unsupervised pattern recognition, such as principal component analysis (PCA) and cluster analysis (CA), all spectra were classified into cancerous and normal tissue groups with accuracy up to 81.1%. The sensitivity and specificity was 100% and 68.2%, respectively. These present results indicate that CH-stretching first, combination band and second overtone regions can serve as diagnostic markers for gastric cancer.
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NASA Astrophysics Data System (ADS)
Liu, George S.; Kim, Jinkyung; Applegate, Brian E.; Oghalai, John S.
2017-07-01
Diseases that cause hearing loss and/or vertigo in humans such as Meniere's disease are often studied using animal models. The volume of endolymph within the inner ear varies with these diseases. Here, we used a mouse model of increased endolymph volume, endolymphatic hydrops, to develop a computer-aided objective approach to measure endolymph volume from images collected in vivo using optical coherence tomography. The displacement of Reissner's membrane from its normal position was measured in cochlear cross sections. We validated our computer-aided measurements with manual measurements and with trained observer labels. This approach allows for computer-aided detection of endolymphatic hydrops in mice, with test performance showing sensitivity of 91% and specificity of 87% using a running average of five measurements. These findings indicate that this approach is accurate and reliable for classifying endolymphatic hydrops and quantifying endolymph volume.
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Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier
NASA Astrophysics Data System (ADS)
Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar
2015-02-01
In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.
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Lilenbaum, W; Dos Santos, M R
1995-01-01
Four hundred and five serum samples were drawn from cows with reproductive problems which were not vaccinated against leptospirosis from 21 dairy farms. Three distinct geographic regions were determined and the farms were also classified considering the production system, based on technological, zootechnical and sanitary resources. A total of 277 positive reactions were observed, corresponding to 68.39% of the samples. The predominant serovar was hardjo, reactive on 85 samples (20.98%), predominant on nine farms and observed on 17 farms (80.95%). It was observed the predominance of hardjo in all studied regions and on properties classified as type "A" (22 samples) and type "B" (49 samples). The role of this serovar on bovine leptospirosis in Brazil compared with other countries is discussed.
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Racowsky, Catherine; Stern, Judy E; Gibbons, William E; Behr, Barry; Pomeroy, Kimball O; Biggers, John D
2011-05-01
To evaluate the validity of collecting day 3 embryo morphology variables into the Society for Assisted Reproductive Technology Clinic Outcomes Reporting System (SART CORS). Retrospective. National database-SART CORS. Fresh autologous assisted reproductive technology (ART) cycles from 2006-2007 in which embryos were transferred singly (n=1,020) or in pairs (n=6,508) and embryo morphology was collected. None. Relationship between live birth, maternal age, and morphology of transferred day 3 embryos as defined by cell number, fragmentation, and blastomere symmetry. Logistic multiple regressions and receiver operating characteristic curve analyses were applied to determine specificity and sensitivity for correctly classifying embryos as either failures or successes. Live birth rate was positively associated with increasing cell number up to eight cells (<6 cells: 2.9%; 6 cells: 9.6%; 7 cells: 15.5%; 8 cells: 24.3%; and >8 cells: 16.2%), but was negatively associated with maternal age, increasing fragmentation, and asymmetry scores. An area under the receiver operating curve of 0.753 (95% confidence interval 0.740-0.766) was derived, with a sensitivity of 45.0%, a specificity of 83.2%, and 76.4% of embryos being correctly classified with a cutoff probability of 0.3. This analysis provides support for the validity of collecting morphology fields for day 3 embryos into SART CORS. Standardization of morphology collections will assist in controlling for embryo quality in future database analyses. Copyright © 2011 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.
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Leg injuries and wound repair among cosmetid harvestmen (Arachnida, Opiliones, Laniatores).
Townsend, Victor R; Schaus, Maynard H; Zvonareva, Tatyana; Illinik, Jeffrey J; Evans, John T
2017-01-01
Previous studies of leg injuries in harvestmen have focused on the fitness consequences for individuals that use autospasy (voluntary detachment of the leg) as a secondary defense mechanism. Leg damage among non-autotomizing species of laniatorean harvestmen has not been investigated. Under laboratory conditions, we damaged femur IV of Cynorta marginalis and observed with scanning electron microscopy (SEM) the changes in these wounds over ten days. We also used SEM to examine leg damage from individuals of three species of cosmetid harvestmen that were collected in the field. On the basis of changes in the external surface of the hemolymph coagulum, we classified these wounds as fresh (coagulum forming), recent (coagulum with smooth surface), older (coagulum is scale-like with visible cell fragments), and fully healed (scale replaced by new cuticle growth on the terminal stump). Our observations indicate that wound healing in harvestmen occurs in a manner comparable to that of other chelicerates. Leg injuries exhibited interspecific variation with respect to the overall frequency of leg wounds and the specific legs that were most commonly damaged. In addition, we measured walking and climbing speeds of adult C. marginalis and found that individuals with fresh injuries (lab-induced) to femur IV walked at speeds significantly slower than uninjured adults or individuals collected from the field that had fully healed wounds to a single leg. J. Morphol. 278:73-88, 2017. ©© 2016 Wiley Periodicals,Inc. © 2016 Wiley Periodicals, Inc.
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Planer, Katarina; Hagel, Anja
2018-01-01
A validity test was conducted to determine how care level–based nurse-to-resident ratios compare with actual daily care times per resident in Germany. Stability across different long-term care facilities was tested. Care level–based nurse-to-resident ratios were compared with the standard minimum nurse-to-resident ratios. Levels of care are determined by classification authorities in long-term care insurance programs and are used to distribute resources. Care levels are a powerful tool for classifying authorities in long-term care insurance. We used observer-based measurement of assignable direct and indirect care time in 68 nursing units for 2028 residents across 2 working days. Organizational data were collected at the end of the quarter in which the observation was made. Data were collected from January to March, 2012. We used a null multilevel model with random intercepts and multilevel models with fixed and random slopes to analyze data at both the organization and resident levels. A total of 14% of the variance in total care time per day was explained by membership in nursing units. The impact of care levels on care time differed significantly between nursing units. Forty percent of residents at the lowest care level received less than the standard minimum registered nursing time per day. For facilities that have been significantly disadvantaged in the current staffing system, a higher minimum standard will function more effectively than a complex classification system without scientific controls. PMID:29442533
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Brühl, Albert; Planer, Katarina; Hagel, Anja
2018-01-01
A validity test was conducted to determine how care level-based nurse-to-resident ratios compare with actual daily care times per resident in Germany. Stability across different long-term care facilities was tested. Care level-based nurse-to-resident ratios were compared with the standard minimum nurse-to-resident ratios. Levels of care are determined by classification authorities in long-term care insurance programs and are used to distribute resources. Care levels are a powerful tool for classifying authorities in long-term care insurance. We used observer-based measurement of assignable direct and indirect care time in 68 nursing units for 2028 residents across 2 working days. Organizational data were collected at the end of the quarter in which the observation was made. Data were collected from January to March, 2012. We used a null multilevel model with random intercepts and multilevel models with fixed and random slopes to analyze data at both the organization and resident levels. A total of 14% of the variance in total care time per day was explained by membership in nursing units. The impact of care levels on care time differed significantly between nursing units. Forty percent of residents at the lowest care level received less than the standard minimum registered nursing time per day. For facilities that have been significantly disadvantaged in the current staffing system, a higher minimum standard will function more effectively than a complex classification system without scientific controls.
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Age and gender estimation using Region-SIFT and multi-layered SVM
NASA Astrophysics Data System (ADS)
Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Hwang, Byunghun
2018-04-01
In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST_C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST_C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately.
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Gap Shape Classification using Landscape Indices and Multivariate Statistics
Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung
2016-01-01
This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127
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Gap Shape Classification using Landscape Indices and Multivariate Statistics.
Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung
2016-11-30
This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.
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NASA Astrophysics Data System (ADS)
Kostopoulos, S.; Sidiropoulos, K.; Glotsos, D.; Dimitropoulos, N.; Kalatzis, I.; Asvestas, P.; Cavouras, D.
2014-03-01
The aim of this study was to design a pattern recognition system for assisting the diagnosis of breast lesions, using image information from Ultrasound (US) and Digital Mammography (DM) imaging modalities. State-of-art computer technology was employed based on commercial Graphics Processing Unit (GPU) cards and parallel programming. An experienced radiologist outlined breast lesions on both US and DM images from 59 patients employing a custom designed computer software application. Textural features were extracted from each lesion and were used to design the pattern recognition system. Several classifiers were tested for highest performance in discriminating benign from malignant lesions. Classifiers were also combined into ensemble schemes for further improvement of the system's classification accuracy. Following the pattern recognition system optimization, the final system was designed employing the Probabilistic Neural Network classifier (PNN) on the GPU card (GeForce 580GTX) using CUDA programming framework and C++ programming language. The use of such state-of-art technology renders the system capable of redesigning itself on site once additional verified US and DM data are collected. Mixture of US and DM features optimized performance with over 90% accuracy in correctly classifying the lesions.
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Detection of chewing from piezoelectric film sensor signals using ensemble classifiers.
Farooq, Muhammad; Sazonov, Edward
2016-08-01
Selection and use of pattern recognition algorithms is application dependent. In this work, we explored the use of several ensembles of weak classifiers to classify signals captured from a wearable sensor system to detect food intake based on chewing. Three sensor signals (Piezoelectric sensor, accelerometer, and hand to mouth gesture) were collected from 12 subjects in free-living conditions for 24 hrs. Sensor signals were divided into 10 seconds epochs and for each epoch combination of time and frequency domain features were computed. In this work, we present a comparison of three different ensemble techniques: boosting (AdaBoost), bootstrap aggregation (bagging) and stacking, each trained with 3 different weak classifiers (Decision Trees, Linear Discriminant Analysis (LDA) and Logistic Regression). Type of feature normalization used can also impact the classification results. For each ensemble method, three feature normalization techniques: (no-normalization, z-score normalization, and minmax normalization) were tested. A 12 fold cross-validation scheme was used to evaluate the performance of each model where the performance was evaluated in terms of precision, recall, and accuracy. Best results achieved here show an improvement of about 4% over our previous algorithms.
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Intratumor heterogeneity of DCE-MRI reveals Ki-67 proliferation status in breast cancer
NASA Astrophysics Data System (ADS)
Cheng, Hu; Fan, Ming; Zhang, Peng; Liu, Bin; Shao, Guoliang; Li, Lihua
2018-03-01
Breast cancer is a highly heterogeneous disease both biologically and clinically, and certain pathologic parameters, i.e., Ki67 expression, are useful in predicting the prognosis of patients. The aim of the study is to identify intratumor heterogeneity of breast cancer for predicting Ki-67 proliferation status in estrogen receptor (ER)-positive breast cancer patients. A dataset of 77 patients was collected who underwent dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) examination. Of these patients, 51 were high-Ki-67 expression and 26 were low-Ki-67 expression. We partitioned the breast tumor into subregions using two methods based on the values of time to peak (TTP) and peak enhancement rate (PER). Within each tumor subregion, image features were extracted including statistical and morphological features from DCE-MRI. The classification models were applied on each region separately to assess whether the classifiers based on features extracted from various subregions features could have different performance for prediction. An area under a receiver operating characteristic curve (AUC) was computed using leave-one-out cross-validation (LOOCV) method. The classifier using features related with moderate time to peak achieved best performance with AUC of 0.826 than that based on the other regions. While using multi-classifier fusion method, the AUC value was significantly (P=0.03) increased to 0.858+/-0.032 compare to classifier with AUC of 0.778 using features from the entire tumor. The results demonstrated that features reflect heterogeneity in intratumoral subregions can improve the classifier performance to predict the Ki-67 proliferation status than the classifier using features from entire tumor alone.
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NASA Astrophysics Data System (ADS)
García-Resúa, C.; Giráldez, M. J.; Barreira, N.; Penedo, M. G.; Yebra-Pimentel, E.
2011-05-01
Purpose: The lipid layer of the tear film limits evaporation during the inter-blink interval and also affects tear stability. This study was designed to validate a new software application designed to characterize the tear film lipid layer through texture and colour pattern recognition. Methods: Using the Tearscope-plus (slit lamp magnification 200X), the lipid layer was examined in 105 healthy young adults and interference photographs acquired with a Topcon DV-3 digital camera. The photographs were classified by the new software and by 2 further observers (observer 1 and observer 2) with experience in examining the eye surface. Results: Strong correlation was detected between the categories determined by the new application, observer 1 and observer 2 (Cramer's V, from 0.81 to 0.87, p<0.001). Best agreement (96.2%) was noted between the new method and observers 1 and 2 for recognizing meshwork patterns, whereas observers 1 and 2 showed greatest correspondence when classifying colour fringe patterns. Conclusions: The new application can objectively categorize LLPs using the Tearscope-plus.
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Heft Lemisphere: Exchanges Predominate in Segmental Speech Errors
ERIC Educational Resources Information Center
Nooteboom, Sieb G.; Quene, Hugo
2013-01-01
In most collections of segmental speech errors, exchanges are less frequent than anticipations and perseverations. However, it has been suggested that in inner speech exchanges might be more frequent than either anticipations or perseverations, because many half-way repaired errors (Yew...uhh...New York) are classified as repaired anticipations,…
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A method for estimating operability and location of the timber resource.
John S. Jr. Spencer; Mark H. Hansen; Pamela J. Jakes
1986-01-01
Operability is the relative ease or difficulty of managing or harvesting timber because of physical conditions in the stand or on the site. Using data collected during standard statewide forest inventories, we developed a method for classifying timber by operability class based on seven operability components.
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Federal Register 2010, 2011, 2012, 2013, 2014
2013-03-28
... RIN 0694-AF73. FOR FURTHER INFORMATION CONTACT: Elizabeth Sangine, Director, Chemical and Biological... detection, identification, warning or monitoring of biological agents that is subject to the licensing... approved collections: (1) The Simplified Network Application Processing + System (control number 0694-0088...
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Teacher Supply in Hong Kong: Educational Qualifications and Growth.
ERIC Educational Resources Information Center
Chung, Yue-ping; Hung, Fan-sing
This paper explores Hong Kong secondary teacher supply patterns related to teacher retention using data collected from 1991-99. Secondary teachers are classified into five categories by initial educational qualifications: registered Graduate Master (trained GM), who are university graduates with majors in a subject discipline; permitted Graduate…
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USDA-ARS?s Scientific Manuscript database
Strains from a collection of 3,639 diverse Bacillus thuringiensis isolates were classified based on phenotypic profiles resulting from six biochemical tests, including production of amylase (T), lecithinase (L), urease (U), acid from sucrose (S) and salicin (A), and the hydrolysis of esculin (E). St...
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Children's Family Drawings: A Study of Attachment, Personality, and Adjustment
ERIC Educational Resources Information Center
Goldner, Limor; Scharf, Miri
2011-01-01
This study examined the relationship between children's attachment security, as manifested in their family drawings, and their personality and adjustment. Family drawings were collected from 222 Israeli children, as well as data regarding their personality and adjustment. Each drawing was coded and classified into 1 of 4 attachment categories…
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A Systematic Review of Part C Early Identification Studies
ERIC Educational Resources Information Center
Barger, Brian; Rice, Catherine; Simmons, Christina Anne; Wolf, Rebecca
2018-01-01
Authors conducted a systematic literature review on early identification steps leading at-risk young children to connect with Part C services. Authors classified data collection settings as primary (settings for general population) or specialized (settings for children at risk of developmental delay) and according to the phases of early…
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Activities to Promote Critical Thinking. Classroom Practices in Teaching English, 1986.
ERIC Educational Resources Information Center
National Council of Teachers of English, Urbana, IL.
Intended to involve students in language and communication study in such a way that significant thinking occurs, this collection of teaching ideas outlines ways to teach literature and composition that engage the students in such thinking processes as inferring, sequencing, predicting, classifying, problem solving, and synthesizing. The activities…
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NASA Astrophysics Data System (ADS)
Zink, Frank Edward
The detection and classification of pulmonary nodules is of great interest in chest radiography. Nodules are often indicative of primary cancer, and their detection is particularly important in asymptomatic patients. The ability to classify nodules as calcified or non-calcified is important because calcification is a positive indicator that the nodule is benign. Dual-energy methods offer the potential to improve both the detection and classification of nodules by allowing the formation of material-selective images. Tissue-selective images can improve detection by virtue of the elimination of obscuring rib structure. Bone -selective images are essentially calcium images, allowing classification of the nodule. A dual-energy technique is introduced which uses a computed radiography system to acquire dual-energy chest radiographs in a single-exposure. All aspects of the dual-energy technique are described, with particular emphasis on scatter-correction, beam-hardening correction, and noise-reduction algorithms. The adaptive noise-reduction algorithm employed improves material-selective signal-to-noise ratio by up to a factor of seven with minimal sacrifice in selectivity. A clinical comparison study is described, undertaken to compare the dual-energy technique to conventional chest radiography for the tasks of nodule detection and classification. Observer performance data were collected using the Free Response Observer Characteristic (FROC) method and the bi-normal Alternative FROC (AFROC) performance model. Results of the comparison study, analyzed using two common multiple observer statistical models, showed that the dual-energy technique was superior to conventional chest radiography for detection of nodules at a statistically significant level (p < .05). Discussion of the comparison study emphasizes the unique combination of data collection and analysis techniques employed, as well as the limitations of comparison techniques in the larger context of technology assessment.
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Mineralogy and noble gas isotopes of micrometeorites collected from Antarctic snow
NASA Astrophysics Data System (ADS)
Okazaki, Ryuji; Noguchi, Takaaki; Tsujimoto, Shin-ichi; Tobimatsu, Yu; Nakamura, Tomoki; Ebihara, Mitsuru; Itoh, Shoichi; Nagahara, Hiroko; Tachibana, Shogo; Terada, Kentaro; Yabuta, Hikaru
2015-06-01
We have investigated seven micrometeorites (MMs) from Antarctic snow collected in 2003 and 2010 by means of electron microscopy, X-ray diffraction, micro-Raman spectroscopy, transmission electron microscopy (TEM) observation, and noble-gas isotope analysis. Isotopic ratios of He and Ne indicate that the noble gases in these MMs are mostly of solar wind (SW). Based on the release patterns of SW 4He, which should reflect the degree of heating during atmospheric entry, the seven MMs were classified into three types including two least heated, three moderately heated, and two severely heated MMs. The heating degrees are well correlated to their mineralogical features determined by TEM observation. One of the least heated MMs is composed of phyllosilicates, whereas the other consists of anhydrous minerals within which solar flare tracks were observed. The two severely heated MMs show clear evidence of atmospheric heating such as partial melt of the uppermost surface layer in one and abundant patches of dendritic magnetite and Si-rich glass within an olivine grain in the other. It is noteworthy that a moderately heated MM composed of a single crystal of olivine has a 3He/4He ratio of 8.44 × 10-4, which is higher than the SW value of 4.64 × 10-4, but does not show a cosmogenic 21Ne signature such as 20Ne/21Ne/22Ne = 12.83/0.0284/1. The isotopic compositions of He and Ne in this sample cannot be explained by mixing of a galactic cosmic ray (GCR)-produced component and SW gases. The high 3He/4He ratio without cosmogenic 21Ne signature likely indicates the presence of a 3He-enriched component derived from solar energetic particles.
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DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.
Liang, Zhaohui; Huang, Jimmy Xiangji; Zeng, Xing; Zhang, Gang
2016-08-10
Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions. A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model. There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80 %. In the comparison of reliability, the deep learning model shows the best stability among the three models. Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.