A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

NASA Astrophysics Data System (ADS)

Laso Bayas, Juan Carlos; Lesiv, Myroslava; Waldner, François; Schucknecht, Anne; Duerauer, Martina; See, Linda; Fritz, Steffen; Fraisl, Dilek; Moorthy, Inian; McCallum, Ian; Perger, Christoph; Danylo, Olha; Defourny, Pierre; Gallego, Javier; Gilliams, Sven; Akhtar, Ibrar Ul Hassan; Baishya, Swarup Jyoti; Baruah, Mrinal; Bungnamei, Khangsembou; Campos, Alfredo; Changkakati, Trishna; Cipriani, Anna; Das, Krishna; Das, Keemee; Das, Inamani; Davis, Kyle Frankel; Hazarika, Purabi; Johnson, Brian Alan; Malek, Ziga; Molinari, Monia Elisa; Panging, Kripal; Pawe, Chandra Kant; Pérez-Hoyos, Ana; Sahariah, Parag Kumar; Sahariah, Dhrubajyoti; Saikia, Anup; Saikia, Meghna; Schlesinger, Peter; Seidacaru, Elena; Singha, Kuleswar; Wilson, John W.

2017-09-01

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

Kohonen and counterpropagation neural networks applied for mapping and interpretation of IR spectra.

PubMed

Novic, Marjana

2008-01-01

The principles of learning strategy of Kohonen and counterpropagation neural networks are introduced. The advantages of unsupervised learning are discussed. The self-organizing maps produced in both methods are suitable for a wide range of applications. Here, we present an example of Kohonen and counterpropagation neural networks used for mapping, interpretation, and simulation of infrared (IR) spectra. The artificial neural network models were trained for prediction of structural fragments of an unknown compound from its infrared spectrum. The training set contained over 3,200 IR spectra of diverse compounds of known chemical structure. The structure-spectra relationship was encompassed by the counterpropagation neural network, which assigned structural fragments to individual compounds within certain probability limits, assessed from the predictions of test compounds. The counterpropagation neural network model for prediction of fragments of chemical structure is reversible, which means that, for a given structural domain, limited to the training data set in the study, it can be used to simulate the IR spectrum of a chemical defined with a set of structural fragments.

Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.

[Ecology suitability study of Ephedra intermedia].

PubMed

Ma, Xiao-Hui; Lu, You-Yuan; Huang, De-Dong; Zhu, Tian-Tian; Lv, Pei-Lin; Jin, Ling

2017-06-01

The study aims at predicting ecological suitability of Ephedra intermedia in China by using maximum entropy Maxent model combined with GIS, and finding the main ecological factors affecting the distribution of E. intermedia suitability in appropriate growth area. Thirty-eight collected samples of E. intermedia and E. intermedia and 116 distribution information from CVH information using ArcGIS technology were analyzed. MaxEnt model was applied to forecast the E. intermedia in our country's ecology. E. intermedia MaxEnt ROC curve model training data and testing data sets the AUC value was 0.986 and 0.958, respectively, which were greater than 0.9, tending to be 1.The calculated E. intermedia habitat suitability by the model showed a high accuracy and credibility, which indicated that MaxEnt model could well predict the potential distribution area of E. intermedia in China. Copyright© by the Chinese Pharmaceutical Association.

Feed-forward neural network model for hunger and satiety related VAS score prediction.

PubMed

Krishnan, Shaji; Hendriks, Henk F J; Hartvigsen, Merete L; de Graaf, Albert A

2016-07-07

An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding. A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions. The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms. From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.

Virtual reality as a tool for cross-cultural communication: an example from military team training

NASA Astrophysics Data System (ADS)

Downes-Martin, Stephen; Long, Mark; Alexander, Joanna R.

1992-06-01

A major problem with communication across cultures, whether professional or national, is that simple language translation if often insufficient to communicate the concepts. This is especially true when the communicators come from highly specialized fields of knowledge or from national cultures with long histories of divergence. This problem becomes critical when the goal of the communication is national negotiation dealing with such high risk items as arms negotiation or trade wars. Virtual Reality technology has considerable potential for facilitating communication across cultures, by immersing the communicators within multiple visual representations of the concepts, and providing control over those representations. Military distributed team training provides a model for virtual reality suitable for cross cultural communication such as negotiation. In both team training and negotiation, the participants must cooperate, agree on a set of goals, and achieve mastery over the concepts being negotiated. Team training technologies suitable for supporting cross cultural negotiation exist (branch wargaming, computer image generation and visualization, distributed simulation), and have developed along different lines than traditional virtual reality technology. Team training de-emphasizes the realism of physiological interfaces between the human and the virtual reality, and emphasizes the interaction of humans with each other and with intelligent simulated agents within the virtual reality. This approach to virtual reality is suggested as being more fruitful for future work.

Teaching and learning in the operating theatre: a framework for trainers and advanced trainees in obstetrics and gynaecology.

PubMed

Mukhopadhyay, S; China, S

2010-04-01

Surgical training of 'advanced trainees' in Obstetrics and Gynaecology currently occurs in a rather unstructured fashion. This is even more complicated by reduced training time of doctors necessitated by the European working time directive. Teaching and learning in theatre is a combination of art and science. This paper attempts to address the issues hampering effective theatre training and suggests ways to overcome them. The 'operating theatre' plan includes a needs assessment of trainees, goal setting and instructional methodologies. Various learning styles could potentially be adopted, although it might be difficult to choose a learning style suitable for a particular trainee. Additionally, team working skills and experiential learning need to be facilitated.

An Affordable Microsurgical Training System for a Beginning Neurosurgeon: How to Realize the Self-Training Laboratory.

PubMed

Chung, Sang-Bong; Ryu, Jiwook; Chung, Yeongu; Lee, Sung Ho; Choi, Seok Keun

2017-09-01

To provide detailed information about how to realize a self-training laboratory with cost-effective microsurgical instruments, especially pertinent for the novice trainee. Our training model is designed to allow the practice of the microsurgery skills in an efficient and cost-effective manner. A used stereoscopic microscope is prepared for microsurgical training. A sufficient working distance for microsurgical practice is obtained by attaching an auxiliary objective lens. The minimum instrument list includes 2 jeweler's forceps, iris scissors, and alligator clips. The iris scissors and alligator clip provide good alternatives to micro-scissors and microvascular clamp. The short time needed to set up the microscope and suture the gauze with micro-forceps makes the training model suitable for daily practice. It takes about 15 minutes to suture 10 neighboring fibers of the gauze with 10-0 nylon; thus, training can be completed more quickly. We have developed an inexpensive and efficient micro-anastomosis training system using a stereoscopic microscope and minimal micro-instruments. Especially useful for novice trainees, this system provides high accessibility for microsurgical training. Copyright © 2017 Elsevier Inc. All rights reserved.

Automated classification of single airborne particles from two-dimensional angle-resolved optical scattering (TAOS) patterns by non-linear filtering

NASA Astrophysics Data System (ADS)

Crosta, Giovanni Franco; Pan, Yong-Le; Aptowicz, Kevin B.; Casati, Caterina; Pinnick, Ronald G.; Chang, Richard K.; Videen, Gorden W.

2013-12-01

Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized airborne particles. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. By reformulating the problem in statistical learning terms, a solution is proposed herewith: rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified through a learning machine, where feature extraction interacts with multivariate statistical analysis. Feature extraction relies on spectrum enhancement, which includes the discrete cosine FOURIER transform and non-linear operations. Multivariate statistical analysis includes computation of the principal components and supervised training, based on the maximization of a suitable figure of merit. All algorithms have been combined together to analyze TAOS patterns, organize feature vectors, design classification experiments, carry out supervised training, assign unknown patterns to classes, and fuse information from different training and recognition experiments. The algorithms have been tested on a data set with more than 3000 TAOS patterns. The parameters that control the algorithms at different stages have been allowed to vary within suitable bounds and are optimized to some extent. Classification has been targeted at discriminating aerosolized Bacillus subtilis particles, a simulant of anthrax, from atmospheric aerosol particles and interfering particles, like diesel soot. By assuming that all training and recognition patterns come from the respective reference materials only, the most satisfactory classification result corresponds to 20% false negatives from B. subtilis particles and <11% false positives from all other aerosol particles. The most effective operations have consisted of thresholding TAOS patterns in order to reject defective ones, and forming training sets from three or four pattern classes. The presented automated classification method may be adapted into a real-time operation technique, capable of detecting and characterizing micron-sized airborne particles.

Identifying FGA peptides as nasopharyngeal carcinoma-associated biomarkers by magnetic beads.

PubMed

Tao, Ya-Lan; Li, Yan; Gao, Jin; Liu, Zhi-Gang; Tu, Zi-Wei; Li, Guo; Xu, Bing-Qing; Niu, Dao-Li; Jiang, Chang-Bin; Yi, Wei; Li, Zhi-Qiang; Li, Jing; Wang, Yi-Ming; Cheng, Zhi-Bin; Liu, Qiao-Dan; Bai, Li; Zhang, Chun; Zhang, Jing-Yu; Zeng, Mu-Sheng; Xia, Yun-Fei

2012-07-01

Early diagnosis and treatment is known to improve prognosis for nasopharyngeal carcinoma (NPC). The study determined the specific peptide profiles by comparing the serum differences between NPC patients and healthy controls, and provided the basis for the diagnostic model and identification of specific biomarkers of NPC. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) can be used to detect the molecular mass of peptides. Mass spectra of peptides were generated after extracting and purification of 40 NPC samples in the training set, 21 in the single center validation set and 99 in the multicenter validation set using weak cationic-exchanger magnetic beads. The spectra were analyzed statistically using FlexAnalysis™ and ClinProt™ bioinformatics software. The four most significant peaks were selected out to train a genetic algorithm model to diagnose NPC. The diagnostic sensitivity and specificity were 100% and 100% in the training set, 90.5% and 88.9% in the single center validation set, 91.9% and 83.3% in the multicenter validation set, and the false positive rate (FPR) and false negative rate (FNR) were obviously lower in the NPC group (FPR, 16.7%; FNR, 8.1%) than in the other cancer group (FPR, 39%; FNR, 61%), respectively. So, the diagnostic model including four peptides can be suitable for NPC but not for other cancers. FGA peptide fragments identified may serve as tumor-associated biomarkers for NPC. Copyright © 2012 Wiley Periodicals, Inc.

Designing and evaluating Brain Powered Games for cognitive training and rehabilitation in at-risk African children.

PubMed

Giordani, B; Novak, B; Sikorskii, A; Bangirana, P; Nakasujja, N; Winn, B M; Boivin, M J

2015-01-01

Valid, reliable, accessible, and cost-effective computer-training approaches can be important components in scaling up educational support across resource-poor settings, such as sub-Saharan Africa. The goal of the current study was to develop a computer-based training platform, the Michigan State University Games for Entertainment and Learning laboratory's Brain Powered Games (BPG) package that would be suitable for use with at-risk children within a rural Ugandan context and then complete an initial field trial of that package. After game development was completed with the use of local stimuli and sounds to match the context of the games as closely as possible to the rural Ugandan setting, an initial field study was completed with 33 children (mean age = 8.55 ± 2.29 years, range 6-12 years of age) with HIV in rural Uganda. The Test of Variables of Attention (TOVA), CogState computer battery, and the Non-Verbal Index from the Kaufman Assessment Battery for Children, 2nd edition (KABC-II) were chosen as the outcome measures for pre- and post-intervention testing. The children received approximately 45 min of BPG training several days per week for 2 months (24 sessions). Although some improvements in test scores were evident prior to BPG training, following training, children demonstrated clinically significant changes (significant repeated-measures outcomes with moderate to large effect sizes) on specific TOVA and CogState measures reflecting processing speed, attention, visual-motor coordination, maze learning, and problem solving. Results provide preliminary support for the acceptability, feasibility, and neurocognitive benefit of BPG and its utility as a model platform for computerized cognitive training in cross-cultural low-resource settings.

TIER competency-based training course for the first receivers of CBRN casualties: a European perspective.

PubMed

Djalali, Ahmadreza; Della Corte, Francesco; Segond, Frederique; Metzger, Marie-Helene; Gabilly, Laurent; Grieger, Fiene; Larrucea, Xabier; Violi, Christian; Lopez, Cédric; Arnod-Prin, Philippe; Ingrassia, Pier L

2017-10-01

Education and training are key elements of health system preparedness vis-à-vis chemical, biological, radiological and nuclear (CBRN) emergencies. Medical respondents need sufficient knowledge and skills to manage the human impact of CBRN events. The current study was designed to determine which competencies are needed by hospital staff when responding to CBRN emergencies, define educational needs to develop these competencies, and implement a suitable delivery method. This study was carried out from September 2014 to February 2015, using a three-step modified Delphi method. On the basis of international experiences, publications, and experts' consensus, core competencies for hospital staff - as CBRN casualty receivers - were determined, and training curricula and delivery methods were defined. The course consists of 10 domains. These are as follows: threat identification; health effects of CBRN agents; planning; hospital incident command system; information management; safety, personal protective equipment and decontamination; medical management; essential resources; psychological support; and ethical considerations. Expected competencies for each domain were defined. A blended approach was chosen. By identifying a set of core competencies, this study aimed to provide the specific knowledge and skills required by medical staff to respond to CRBN emergencies. A blended approach may be a suitable delivery method, allowing medical staff to attend the same training sessions despite different time zones and locations. The study output provides a CBRN training scheme that may be adapted and used at the European Union level.

A Low-Cost, Hands-on Module to Characterize Antimicrobial Compounds Using an Interdisciplinary, Biophysical Approach

PubMed Central

Kaushik, Karishma S.; Kessel, Ashley; Ratnayeke, Nalin; Gordon, Vernita D.

2015-01-01

We have developed a hands-on experimental module that combines biology experiments with a physics-based analytical model in order to characterize antimicrobial compounds. To understand antibiotic resistance, participants perform a disc diffusion assay to test the antimicrobial activity of different compounds and then apply a diffusion-based analytical model to gain insights into the behavior of the active antimicrobial component. In our experience, this module was robust, reproducible, and cost-effective, suggesting that it could be implemented in diverse settings such as undergraduate research, STEM (science, technology, engineering, and math) camps, school programs, and laboratory training workshops. By providing valuable interdisciplinary research experience in science outreach and education initiatives, this module addresses the paucity of structured training or education programs that integrate diverse scientific fields. Its low-cost requirements make it especially suitable for use in resource-limited settings. PMID:25602254

A new voluntary blood collection method for the Andean bear (Tremarctos ornatus) and Asiatic black bear (Ursus thibetanus).

PubMed

Otaki, Yusuke; Kido, Nobuhide; Omiya, Tomoko; Ono, Kaori; Ueda, Miya; Azumano, Akinori; Tanaka, Sohei

2015-01-01

Various training methods have been developed for animal husbandry and health care in zoos and one of these trainings is blood collection. One training method, recently widely used for blood collection in Ursidae, requires setting up a sleeve outside the cage and gives access to limited blood collection sites. A new voluntary blood collection method without a sleeve was applied to the Andean bear (Tremarctos ornatus) and Asiatic black bear (Ursus thibetanus) with access to various veins at the same time. The present study evaluated the effectiveness of this new method and suggests improvements. Two Andean and two Asiatic black bears in Yokohama and Nogeyama Zoological Gardens, respectively, were trained to hold a bamboo pipe outside their cages. We could, thereby, simultaneously access superficial dorsal veins, the dorsal venous network of the hand, the cephalic vein from the carpal joint, and an area approximately 10 cm proximal to the carpal joint. This allowed us to evaluate which vein was most suitable for blood collection. We found that the cephalic vein, approximately 10 cm proximal to the carpal joint, was the most suitable for blood collection. This new method requires little or no modification of zoo facilities and provides a useful alternative method for blood collection. It could be adapted for use in other clinical examinations such as ultrasound examination. © 2015 Wiley Periodicals, Inc.

Experimental investigation by laser ultrasonics for high speed train axle diagnostics.

PubMed

Cavuto, A; Martarelli, M; Pandarese, G; Revel, G M; Tomasini, E P

2015-01-01

The present paper demonstrates the applicability of a laser-ultrasonic procedure to improve the performances of train axle ultrasonic inspection. The method exploits an air-coupled ultrasonic probe that detects the ultrasonic waves generated by a high-power pulsed laser. As a result, the measurement chain is completely non-contact, from generation to detection, this making it possible to considerably speed up inspection time and make the set-up more flexible. The main advantage of the technique developed is that it works in thermo-elastic regime and it therefore can be considered as a non-destructive method. The laser-ultrasonic procedure investigated has been applied for the inspection of a real high speed train axle provided by the Italian railway company (Trenitalia), on which typical fatigue defects have been expressly created according to standard specifications. A dedicated test bench has been developed so as to rotate the axle with the angle control and to speed up the inspection of the axle surface. The laser-ultrasonic procedure proposed can be automated and is potentially suitable for regular inspection of train axles. The main achievements of the activity described in this paper are: – the study of the effective applicability of laser-ultrasonics for the diagnostic of train hollow axles with variable sections by means of a numerical FE model, – the carrying out of an automated experiment on a real train axle, – the analysis of the sensitivity to experimental parameters, like laser source – receiving probe distance and receiving probe angular position, – the demonstration that the technique is suitable for the detection of surface defects purposely created on the train axle. Copyright © 2014 Elsevier B.V. All rights reserved.

Image aesthetic quality evaluation using convolution neural network embedded learning

NASA Astrophysics Data System (ADS)

Li, Yu-xin; Pu, Yuan-yuan; Xu, Dan; Qian, Wen-hua; Wang, Li-peng

2017-11-01

A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

Towards harmonized seismic analysis across Europe using supervised machine learning approaches

NASA Astrophysics Data System (ADS)

Zaccarelli, Riccardo; Bindi, Dino; Cotton, Fabrice; Strollo, Angelo

2017-04-01

In the framework of the Thematic Core Services for Seismology of EPOS-IP (European Plate Observing System-Implementation Phase), a service for disseminating a regionalized logic-tree of ground motions models for Europe is under development. While for the Mediterranean area the large availability of strong motion data qualified and disseminated through the Engineering Strong Motion database (ESM-EPOS), supports the development of both selection criteria and ground motion models, for the low-to-moderate seismic regions of continental Europe the development of ad-hoc models using weak motion recordings of moderate earthquakes is unavoidable. Aim of this work is to present a platform for creating application-oriented earthquake databases by retrieving information from EIDA (European Integrated Data Archive) and applying supervised learning models for earthquake records selection and processing suitable for any specific application of interest. Supervised learning models, i.e. the task of inferring a function from labelled training data, have been extensively used in several fields such as spam detection, speech and image recognition and in general pattern recognition. Their suitability to detect anomalies and perform a semi- to fully- automated filtering on large waveform data set easing the effort of (or replacing) human expertise is therefore straightforward. Being supervised learning algorithms capable of learning from a relatively small training set to predict and categorize unseen data, its advantage when processing large amount of data is crucial. Moreover, their intrinsic ability to make data driven predictions makes them suitable (and preferable) in those cases where explicit algorithms for detection might be unfeasible or too heuristic. In this study, we consider relatively simple statistical classifiers (e.g., Naive Bayes, Logistic Regression, Random Forest, SVMs) where label are assigned to waveform data based on "recognized classes" needed for our use case. These classes might be a simply binary case (e.g., "good for analysis" vs "bad") or more complex one (e.g., "good for analysis" vs "low SNR", "multi-event", "bad coda envelope"). It is important to stress the fact that our approach can be generalized to any use case providing, as in any supervised approach, an adequate training set of labelled data, a feature-set, a statistical classifier, and finally model validation and evaluation. Examples of use cases considered to develop the system prototype are the characterization of the ground motion in low seismic areas; harmonized spectral analysis across Europe for source and attenuation studies; magnitude calibration; coda analysis for attenuation studies.

In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

PubMed

Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi

2009-11-01

Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.

Attribute-based classification for zero-shot visual object categorization.

PubMed

Lampert, Christoph H; Nickisch, Hannes; Harmeling, Stefan

2014-03-01

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

PubMed

Tarasova, Olga A; Urusova, Aleksandra F; Filimonov, Dmitry A; Nicklaus, Marc C; Zakharov, Alexey V; Poroikov, Vladimir V

2015-07-27

Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.

Exercise to rest ratios in RSA training in women's soccer.

PubMed

Ruscello, Bruno; Esposito, Mario; Partipilo, Filippo; DI Cicco, Dalila; Filetti, Cristoforo; Pantanella, Laura; D'Ottavio, Stefano

2017-10-27

To investigate the applicability of three different exercise to rest ratios in RSA training in women's soccer players, applying those ones already adopted in male adult and young players, when performing three different sprinting modes (straight, shuttle and sprinting with changing of direction). 15 trained female soccer players (height: 1.65 ± 0.06 m; weight: 59.3 ± 9.0 kg; BMI 21.6 ± 2.7 kg·m-2; age: 23.3±5.9 years) participated to the study. In order to compare the different values of the time recorded, an Index of Fatigue was used. Recovery times among trials in the sets were administered according to the 1:5, 1:3; 1:2 exercise to rest ratio, respectively. Blood lactate concentrations at the end of each set (3') were analyzed. Significant differences among trials within each set (Repeated Measures Anova; p<0.05) were found, as evidence of fatigue over time, with an average decay of performance of about 5% but no significant differences were found in IF%, among the three different sprinting modalities when applying the investigated exercise to rest ratios (Factorial Anova; between; p>0.05). Significant differences were found in blood lactate concentrations (p<0.05). The results of this study confirm that the exercise to rest ratios considered in this study might be suitable to design effective testing protocols and training sessions aimed at the development of the RSA in women's soccer players, keeping the performances in the speed domain (IF% < ⊕7-8%) but inducing the fatigue processes sought with this kind of training method.

Setting Priorities in the Age of Austerity: British, French, and German Experiences

DTIC Science & Technology

2013-01-01

the cuts had not gone far enough, but that the scale of the necessary reductions made it impossible to get away with simply trimming the existing...2020, House of Commons Library, Standard Note 06396, July 26, 2012, pp. 27–28. 9 scale . For example, the 2008 “Future Land Operational Concept...paper called for maintaining broad capabilities: Land forces will need to project suitably configured, scaled and trained forces at appropriate

Influence of gravity compensation training on synergistic movement patterns of the upper extremity after stroke, a pilot study

PubMed Central

2012-01-01

Background The majority of stroke patients have to cope with impaired arm function. Gravity compensation of the arm instantaneously affects abnormal synergistic movement patterns. The goal of the present study is to examine whether gravity compensated training improves unsupported arm function. Methods Seven chronic stroke patients received 18 half-hour sessions of gravity compensated reach training, in a period of six weeks. During training a motivating computer game was played. Before and after training arm function was assessed with the Fugl-Meyer assessment and a standardized, unsupported circle drawing task. Synergistic movement patterns were identified based on concurrent changes in shoulder elevation and elbow flexion/extension angles. Results Median increase of Fugl-Meyer scores was 3 points after training. The training led to significantly increased work area of the hemiparetic arm, as indicated by the normalized circle area. Roundness of the drawn circles and the occurrence of synergistic movement patterns remained similar after the training. Conclusions A decreased strength of involuntary coupling might contribute to the increased arm function after training. More research is needed to study working mechanisms involved in post stroke rehabilitation training. The used training setup is simple and affordable and is therefore suitable to use in clinical settings. PMID:22824488

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

NASA Astrophysics Data System (ADS)

Middleton, Maarit; Närhi, Paavo; Sutinen, Raimo

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine ( Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set's quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce ( Picea abies L. Karst) - downy birch ( Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.

SkData: data sets and algorithm evaluation protocols in Python

NASA Astrophysics Data System (ADS)

Bergstra, James; Pinto, Nicolas; Cox, David D.

2015-01-01

Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y. Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.

Criteria for the assessment of analyser practicability

PubMed Central

Biosca, C.; Galimany, R.

1993-01-01

This article lists the theoretical criteria that need to be considered to assess the practicability of an automatic analyser. Two essential sets of criteria should be taken into account when selecting an automatic analyser: ‘reliability’ and ‘practicability’. Practibility covers the features that provide information about the suitability of an analyser for specific working conditions. These practibility criteria are classsified in this article and include the environment; work organization; versatility and flexibility; safely controls; staff training; maintenance and operational costs. PMID:18924972

Fuzzy CMAC With incremental Bayesian Ying-Yang learning and dynamic rule construction.

PubMed

Nguyen, M N

2010-04-01

Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.

Evaluation on the Perception of the New Portable Set for Hajj Course (P.A.H.A.M) Based on Design Characteristics

NASA Astrophysics Data System (ADS)

Shahir Yahya, Mohd; Rahim, Abd Khalil Abd; Mohammad, Musli; Ibrahim, Mustaffa; Kadis, Ghazali

2017-08-01

This paper is about the perceptions of the pilgrims to the innovation of the portable training set for hajj course (P.A.H.A.M). The acronym P.A.H.A.M come from the Malay word “Praktikal Haji Mudahalih” as a tool in helping the hajj and umrah pilgrims to better understand the concept while doing the hajj and umrah practical. In Malaysia, one of the managing bodies of pilgrimage for Muslims in is Pilgrims Fund Board. It provides a series of courses every year to help in boosting the understanding of the pilgrims before leaving for the Holy Land. During the practical session, they will provide the replica model to help pilgrims to familiarise themselves with the actual situation in Mecca. However, the current replica model was built using an iron structure that is relatively heavy, not portable and embarks more cost of transportation and labour. Therefore, this paper discusses the perceptions of the pilgrims on the characteristics of Portable Training Set for Hajj Course (P.A.H.A.M) compared to the existing set in order to increase the understanding of pilgrims. A total of 53 samples of hajj and umrah pilgrims participated in this study consisted of the general public. From the data analysed using Statistical Software for Social Sciences (SPSS v19), the results of the survey showed that more than 90% of respondents agreed that this portable training set meets the characteristics such as easy to handle, more safety in crowd, portable, stability, light weight, suitable in-door and out-door activity, easy to clean, and more structured with average score 4.00 (agreed) and above. In conclusion, it is expected that this Portable Training Set for Hajj Course (P.A.H.A.M) can be used by Muslim’s communities in Malaysia particularly and Muslim countries in general to better understand the hajj and umrah activities before leaving to the Holy Land.

Neural net applied to anthropological material: a methodical study on the human nasal skeleton.

PubMed

Prescher, Andreas; Meyers, Anne; Gerf von Keyserlingk, Diedrich

2005-07-01

A new information processing method, an artificial neural net, was applied to characterise the variability of anthropological features of the human nasal skeleton. The aim was to find different types of nasal skeletons. A neural net with 15*15 nodes was trained by 17 standard anthropological parameters taken from 184 skulls of the Aachen collection. The trained neural net delivers its classification in a two-dimensional map. Different types of noses were locally separated within the map. Rare and frequent types may be distinguished after one passage of the complete collection through the net. Statistical descriptive analysis, hierarchical cluster analysis, and discriminant analysis were applied to the same data set. These parallel applications allowed comparison of the new approach to the more traditional ones. In general the classification by the neural net is in correspondence with cluster analysis and discriminant analysis. However, it goes beyond these classifications because of the possibility of differentiating the types in multi-dimensional dependencies. Furthermore, places in the map are kept blank for intermediate forms, which may be theoretically expected, but were not included in the training set. In conclusion, the application of a neural network is a suitable method for investigating large collections of biological material. The gained classification may be helpful in anatomy and anthropology as well as in forensic medicine. It may be used to characterise the peculiarity of a whole set as well as to find particular cases within the set.

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

NASA Astrophysics Data System (ADS)

Ahn, Chul Kyun; Heo, Changyong; Jin, Heongmin; Kim, Jong Hyo

2017-03-01

Mammographic breast density is a well-established marker for breast cancer risk. However, accurate measurement of dense tissue is a difficult task due to faint contrast and significant variations in background fatty tissue. This study presents a novel method for automated mammographic density estimation based on Convolutional Neural Network (CNN). A total of 397 full-field digital mammograms were selected from Seoul National University Hospital. Among them, 297 mammograms were randomly selected as a training set and the rest 100 mammograms were used for a test set. We designed a CNN architecture suitable to learn the imaging characteristic from a multitudes of sub-images and classify them into dense and fatty tissues. To train the CNN, not only local statistics but also global statistics extracted from an image set were used. The image set was composed of original mammogram and eigen-image which was able to capture the X-ray characteristics in despite of the fact that CNN is well known to effectively extract features on original image. The 100 test images which was not used in training the CNN was used to validate the performance. The correlation coefficient between the breast estimates by the CNN and those by the expert's manual measurement was 0.96. Our study demonstrated the feasibility of incorporating the deep learning technology into radiology practice, especially for breast density estimation. The proposed method has a potential to be used as an automated and quantitative assessment tool for mammographic breast density in routine practice.

The relationship between hospital managers' leadership style and effectiveness with passing managerial training courses.

PubMed

Saleh Ardestani, Abbas; Sarabi Asiabar, Ali; Ebadifard Azar, Farbod; Abtahi, Seyyed Ali

2016-01-01

Background: Effective leadership that rises from managerial training courses is highly constructive in managing hospitals more effectively. This study aims at investigating the relationship between leadership effectiveness with providing management training courses for hospital managers. Methods: This was a cross-sectional study carried out on top and middle managers of 16 hospitals of Iran University of Medical Sciences. As a sample, 96 participants were selected through census method. Data were collected using leadership effectiveness and style questionnaire, whose validity and reliability were certified in previous studies. Pearson correlation coefficient and linear regressions were used for data analysis. Results: Leadership effectiveness score was estimated to be 4.36, showing a suitable status for managers' leadership effectiveness compared to the set criteria. No significant difference was found between leadership effectiveness and styles among managers who had passed the training courses with those who had not (p>0.05). Conclusion: Passing managerial training courses may have no significant effect on managers' leadership effectiveness, but there may be some other variables which should be meticulously studied.

The relationship between hospital managers' leadership style and effectiveness with passing managerial training courses

PubMed Central

Saleh Ardestani, Abbas; Sarabi Asiabar, Ali; Ebadifard Azar, Farbod; Abtahi, Seyyed Ali

2016-01-01

Background: Effective leadership that rises from managerial training courses is highly constructive in managing hospitals more effectively. This study aims at investigating the relationship between leadership effectiveness with providing management training courses for hospital managers. Methods: This was a cross-sectional study carried out on top and middle managers of 16 hospitals of Iran University of Medical Sciences. As a sample, 96 participants were selected through census method. Data were collected using leadership effectiveness and style questionnaire, whose validity and reliability were certified in previous studies. Pearson correlation coefficient and linear regressions were used for data analysis. Results: Leadership effectiveness score was estimated to be 4.36, showing a suitable status for managers' leadership effectiveness compared to the set criteria. No significant difference was found between leadership effectiveness and styles among managers who had passed the training courses with those who had not (p>0.05). Conclusion: Passing managerial training courses may have no significant effect on managers' leadership effectiveness, but there may be some other variables which should be meticulously studied. PMID:28491840

[Interactive computer-assisted learning program for diagnosis and therapy of recurrent laryngeal nerve paralysis].

PubMed

Stehle, A; Gross, M

1998-12-01

With the increasing capacity of personal computers more and more multimedia training programs are becoming available which make use of these possibilities. Computer-based presentation is usually interesting because it is visually attractive. However, the extent to which computer-based training programs correspond to international standards of quality of software ergonomics has never been the subject of systematic research. Another question is how much these programs motivate learning and what increase in knowledge can be achieved by using them. Using a multimedia interactive training program developed in our facility, 100 medical students were asked to evaluate the program after they had been using it for about one hour. In a questionnaire they first rated suitability for the task, self-descriptiveness, controllability, conformity with user expectation, error tolerance, suitability for individualization, and suitability for learning on a bipolar scale from "---" to "+3" (in numbers 1, worst result, to 7, best result). The median values achieved were rated between 6.0 and 6.2--software ergonomic criteria of the program ranged from good to very good. The second part was a subjective evaluation of the program's ability to deliver "medical knowledge which is relevant for the exam" (median = 6.0), "knowledge about systematic procedure in medicine" (median = 5.5), "knowledge about sensible use of diagnostic methods" (median = 6.0), "knowledge about clinical methods", and "experience with selective learning" (median = 6.0). This part was also rated good to very good. The third part of the questionnaire involved a pretest-posttest comparison. Two groups of students were asked how much benefit they had achieved by using the program. It was shown that the students were able to answer the exam questions significantly better than the control questions after they had used the program. This study confirms that the interactive computer-based training program is very well suited for providing knowledge in on appealing manner in an instructional setting.

Real time flaw detection and characterization in tube through partial least squares and SVR: Application to eddy current testing

NASA Astrophysics Data System (ADS)

Ahmed, Shamim; Miorelli, Roberto; Calmon, Pierre; Anselmi, Nicola; Salucci, Marco

2018-04-01

This paper describes Learning-By-Examples (LBE) technique for performing quasi real time flaw localization and characterization within a conductive tube based on Eddy Current Testing (ECT) signals. Within the framework of LBE, the combination of full-factorial (i.e., GRID) sampling and Partial Least Squares (PLS) feature extraction (i.e., GRID-PLS) techniques are applied for generating a suitable training set in offine phase. Support Vector Regression (SVR) is utilized for model development and inversion during offine and online phases, respectively. The performance and robustness of the proposed GIRD-PLS/SVR strategy on noisy test set is evaluated and compared with standard GRID/SVR approach.

3D printed reproductions of orbital dissections: a novel mode of visualising anatomy for trainees in ophthalmology or optometry.

PubMed

Adams, Justin W; Paxton, Lisa; Dawes, Kathryn; Burlak, Kateryna; Quayle, Michelle; McMenamin, Paul G

2015-09-01

The teaching of human head, neck and orbital anatomy forms a critical part of undergraduate and postgraduate medical and allied health professional training, including optometry. While still largely grounded in cadaveric dissection, this method of instruction is constrained in some countries and regional areas by access to real human cadavers, costs of cadaver bequest programmes, health and safety of students and staff and the shortage of adequate time in modern curricula. Many candidates choosing a postgraduate pathway in ophthalmological training, such as those accepted into the Royal Colleges of Ophthalmology in the UK, Australia and New Zealand programmes and the American Academy of Ophthalmologists in the USA, are compelled as adult learners to revise or revisit human orbital anatomy, ocular anatomy and select areas of head and neck anatomy. These candidates are often then faced with the issue of accessing facilities with dissected human cadaveric material. In light of these difficulties, we developed a novel means of creating high-resolution reproductions of prosected human cadaver orbits suitable for education and training. 3D printed copies of cadaveric orbital dissections (superior, lateral and medial views) showing a range of anatomical features were created. These 3D prints offer many advantages over plastinated specimens as they are suitable for rapid reproduction and as they are not human tissue they avoid cultural and ethical issues associated with viewing cadaver specimens. In addition, they are suitable for use in the office, home, laboratory or clinical setting in any part of the world for patient and doctor education. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Pilot test of cooperative learning format for training mental health researchers and black community leaders in partnership skills.

PubMed

Laborde, Danielle J; Brannock, Kristen; Breland-Noble, Alfiee; Parrish, Theodore

2007-12-01

To support reduction of racial disparities in mental health diagnosis and treatment, mental health researchers and black community-based organization (CBO) leaders need training on how to engage in collaborative research partnerships. In this study, we pilot tested a series of partnership skills training modules for researchers and CBO leaders in a collaborative learning format. Two different sets of three modules, designed for separate training of researchers and CBO leaders, covered considering, establishing and managing mental health research partnerships and included instructions for self-directed activities and discussions. Eight CBO leaders participated in 10 sessions, and six researchers participated in eight sessions. The effectiveness of the training content and format was evaluated through standardized observations, focus group discussions, participant evaluation forms and retrospective pre-/posttests to measure perceived gains in knowledge. Participants generally were satisfied with the training experience and gained new partnership knowledge and skills. Although the CBO leaders were more engaged in the cooperative learning process, this training format appealed to both audiences. Pilot testing demonstrated that: 1) our modules can equip researchers and CBO leaders with new partnership knowledge and skills and 2) the cooperative learning format is a well-received and suitable option for mental health research partnership training.

Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets.

PubMed

Valenzuela, Loreto M; Knight, Doyle D; Kohn, Joachim

2016-01-01

Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R (2) > 0.78 for test set) but very low accuracy on cross-validation sets (less than 19% of experimental points within experimental error). Instead, using consolidated parameters like equilibrium water uptake a good model is obtained (R (2) = 0.78 for test set), with accurate predictions for 50% of tested polymers. The semiempirical model was applied to the 56-polymer library of L-tyrosine-derived polyarylates, identifying groups of polymers that are likely to satisfy design criteria for water uptake. This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria.

Training of Tonal Similarity Ratings in Non-Musicians: A “Rapid Learning” Approach

PubMed Central

Oechslin, Mathias S.; Läge, Damian; Vitouch, Oliver

2012-01-01

Although cognitive music psychology has a long tradition of expert–novice comparisons, experimental training studies are rare. Studies on the learning progress of trained novices in hearing harmonic relationships are still largely lacking. This paper presents a simple training concept using the example of tone/triad similarity ratings, demonstrating the gradual progress of non-musicians compared to musical experts: In a feedback-based “rapid learning” paradigm, participants had to decide for single tones and chords whether paired sounds matched each other well. Before and after the training sessions, they provided similarity judgments for a complete set of sound pairs. From these similarity matrices, individual relational sound maps, intended to display mental representations, were calculated by means of non-metric multidimensional scaling (NMDS), and were compared to an expert model through procrustean transformation. Approximately half of the novices showed substantial learning success, with some participants even reaching the level of professional musicians. Results speak for a fundamental ability to quickly train an understanding of harmony, show inter-individual differences in learning success, and demonstrate the suitability of the scaling method used for learning research in music and other domains. Results are discussed in the context of the “giftedness” debate. PMID:22629252

An automatic panoramic image reconstruction scheme from dental computed tomography images

PubMed Central

Papakosta, Thekla K; Savva, Antonis D; Economopoulos, Theodore L; Gröhndal, H G

2017-01-01

Objectives: Panoramic images of the jaws are extensively used for dental examinations and/or surgical planning because they provide a general overview of the patient's maxillary and mandibular regions. Panoramic images are two-dimensional projections of three-dimensional (3D) objects. Therefore, it should be possible to reconstruct them from 3D radiographic representations of the jaws, produced by CBCT scanning, obviating the need for additional exposure to X-rays, should there be a need of panoramic views. The aim of this article is to present an automated method for reconstructing panoramic dental images from CBCT data. Methods: The proposed methodology consists of a series of sequential processing stages for detecting a fitting dental arch which is used for projecting the 3D information of the CBCT data to the two-dimensional plane of the panoramic image. The detection is based on a template polynomial which is constructed from a training data set. Results: A total of 42 CBCT data sets of real clinical pre-operative and post-operative representations from 21 patients were used. Eight data sets were used for training the system and the rest for testing. Conclusions: The proposed methodology was successfully applied to CBCT data sets, producing corresponding panoramic images, suitable for examining pre-operatively and post-operatively the patients' maxillary and mandibular regions. PMID:28112548

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

PubMed

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

2014-08-01

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

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

NASA Astrophysics Data System (ADS)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

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

Hentschke, Clemens M., E-mail: clemens.hentschke@gmail.com; Tönnies, Klaus D.; Beuing, Oliver

Purpose: The early detection of cerebral aneurysms plays a major role in preventing subarachnoid hemorrhage. The authors present a system to automatically detect cerebral aneurysms in multimodal 3D angiographic data sets. The authors’ system is parametrizable for contrast-enhanced magnetic resonance angiography (CE-MRA), time-of-flight magnetic resonance angiography (TOF-MRA), and computed tomography angiography (CTA). Methods: Initial volumes of interest are found by applying a multiscale sphere-enhancing filter. Several features are combined in a linear discriminant function (LDF) to distinguish between true aneurysms and false positives. The features include shape information, spatial information, and probability information. The LDF can either be parametrized bymore » domain experts or automatically by training. Vessel segmentation is avoided as it could heavily influence the detection algorithm. Results: The authors tested their method with 151 clinical angiographic data sets containing 112 aneurysms. The authors reach a sensitivity of 95% with CE-MRA data sets at an average false positive rate per data set (FP{sub DS}) of 8.2. For TOF-MRA, we achieve 95% sensitivity at 11.3 FP{sub DS}. For CTA, we reach a sensitivity of 95% at 22.8 FP{sub DS}. For all modalities, the expert parametrization led to similar or better results than the trained parametrization eliminating the need for training. 93% of aneurysms that were smaller than 5 mm were found. The authors also showed that their algorithm is capable of detecting aneurysms that were previously overlooked by radiologists. Conclusions: The authors present an automatic system to detect cerebral aneurysms in multimodal angiographic data sets. The system proved as a suitable computer-aided detection tool to help radiologists find cerebral aneurysms.« less

Development of rehabilitation training support system for occupational therapy of upper limb motor function

NASA Astrophysics Data System (ADS)

Morita, Yoshifumi; Hirose, Akinori; Uno, Takashi; Uchid, Masaki; Ukai, Hiroyuki; Matsui, Nobuyuki

2007-12-01

In this paper we propose a new rehabilitation training support system for upper limbs. The proposed system enables therapists to quantitatively evaluate the therapeutic effect of upper limb motor function during training, to easily change the load of resistance of training and to easily develop a new training program suitable for the subjects. For this purpose we develop control algorithms of training programs in the 3D force display robot. The 3D force display robot has parallel link mechanism with three motors. The control algorithm simulating sanding training is developed for the 3D force display robot. Moreover the teaching/training function algorithm is developed. It enables the therapists to easily make training trajectory suitable for subject's condition. The effectiveness of the developed control algorithms is verified by experiments.

Supervised Detection of Anomalous Light Curves in Massive Astronomical Catalogs

NASA Astrophysics Data System (ADS)

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos; Kim, Dae-Won

2014-09-01

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them we identified three unknown variability types and a few individual outliers that will be followed up in order to perform a deeper analysis.

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

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each ofmore » the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them we identified three unknown variability types and a few individual outliers that will be followed up in order to perform a deeper analysis.« less

Learning semantic histopathological representation for basal cell carcinoma classification

NASA Astrophysics Data System (ADS)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

Projecting the Global Distribution of the Emerging Amphibian Fungal Pathogen, Batrachochytrium dendrobatidis, Based on IPCC Climate Futures.

PubMed

Xie, Gisselle Yang; Olson, Deanna H; Blaustein, Andrew R

2016-01-01

Projected changes in climate conditions are emerging as significant risk factors to numerous species, affecting habitat conditions and community interactions. Projections suggest species range shifts in response to climate change modifying environmental suitability and is supported by observational evidence. Both pathogens and their hosts can shift ranges with climate change. We consider how climate change may influence the distribution of the emerging infectious amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), a pathogen associated with worldwide amphibian population losses. Using an expanded global Bd database and a novel modeling approach, we examined a broad set of climate metrics to model the Bd-climate niche globally and regionally, then project how climate change may influence Bd distributions. Previous research showed that Bd distribution is dependent on climatic variables, in particular temperature. We trained a machine-learning model (random forest) with the most comprehensive global compilation of Bd sampling records (~5,000 site-level records, mid-2014 summary), including 13 climatic variables. We projected future Bd environmental suitability under IPCC scenarios. The learning model was trained with combined worldwide data (non-region specific) and also separately per region (region-specific). One goal of our study was to estimate of how Bd spatial risks may change under climate change based on the best available data. Our models supported differences in Bd-climate relationships among geographic regions. We projected that Bd ranges will shift into higher latitudes and altitudes due to increased environmental suitability in those regions under predicted climate change. Specifically, our model showed a broad expansion of areas environmentally suitable for establishment of Bd on amphibian hosts in the temperate zones of the Northern Hemisphere. Our projections are useful for the development of monitoring designs in these areas, especially for sensitive species and those vulnerable to multiple threats.

Muscle synergy space: learning model to create an optimal muscle synergy

PubMed Central

Alnajjar, Fady; Wojtara, Tytus; Kimura, Hidenori; Shimoda, Shingo

2013-01-01

Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment. PMID:24133444

Train Control and Operations

DOT National Transportation Integrated Search

1971-06-01

ATO (automatic train operation) and ATC (automatic train control) systems are evaluated relative to available technology and cost-benefit. The technological evaluation shows that suitable mathematical models of the dynamics of long trains are require...

Development of a Suitable Survey Instrument To Identify Causes Behind High Turnover Rates within the 0301 Series in the Communications Electronics Command

DTIC Science & Technology

2017-09-01

2008, the intern training program did not offer specialized training to interns hired under series 1101 (see Figure 1). The program provided...MMS interns (Logistics and Readiness Center [LRC], 2010-b, p. 5) (see Appendix B). This training offered 1101 interns suitable specialized LDS...level in place of the former Directorates of Logistics. ILSC customers should see seamless continuity of cutting edge logistics services . (Egolf

Training Zambian traditional birth attendants to reduce neonatal mortality in the Lufwanyama Neonatal Survival Project (LUNESP)

PubMed Central

Gill, Christopher J.; Guerina, Nicholas G.; Mulenga, Charity; Knapp, Anna B.; Mazala, Grace; Hamer, Davidson H.

2012-01-01

Objective To provide relevant details on how interventions in the Lufwanyama Neonatal Survival Project (LUNESP) were developed and how Zambian traditional birth attendants (TBAs) were trained to perform them. Methods The study tested 2 interventions: a simplified version of the American Academy of Pediatrics’ neonatal resuscitation protocol (NRP); and antibiotics with facilitated referral (AFR). Results Key elements that enabled the positive study result were: focusing on common and correctible causes of mortality; selecting a study population with high unmet public health need; early community mobilization to build awareness and support; emphasizing simplicity in the intervention technology and algorithms; using a traditional training approach appropriate to students with low literacy rates; requiring TBAs to demonstrate their competence before completing each workshop; and minimizing attrition of skills by retraining and reassessing the TBAs regularly throughout the study. Conclusion An effective NRP training model was created that is suitable for community-based neonatal interventions, in research or programmatic settings, and by practitioners with limited obstetric skills and low rates of literacy. PMID:22542215

[Exercise therapy as a therapeutic concept].

PubMed

Reer, R; Ziegler, M; Braumann, K-M

2005-08-01

Lack of exercise is a primary cause for today's level of morbidity and mortality in the Western world. Thus, exercise as a therapeutic modality has an important role. Beneficial effects of exercise have been extensively documented, specifically in primary and secondary prevention of coronary heart disease (CHD), diabetes mellitus, hypertension, disorders of fat metabolism, heart insufficiency, cancer, etc. A regular (at least 3 x per week) endurance training program of 30-40 min duration at an intensity of 65-70% of VO(2)max involving large muscle groups is recommended. The specific exercise activity can also positively affect individuals with orthopedic disease patterns, i.e., osteoporosis, back pain, postoperative rehabilitation, etc. Endurance strength training in the form of sequential training involving approx. 8-10 different exercises for the most important muscle groups 2 x per week is a suitable exercise therapy. One to three sets with 8-12 repetitions per exercise should be performed until volitional exhaustion of the trained muscle groups among healthy adults and 15-20 repetitions among older and cardiac patients. Apart from a positive effect on the locomotor system, this type of strength training has positive effects on CHD, diabetes mellitus, and cancer.

Query construction, entropy, and generalization in neural-network models

NASA Astrophysics Data System (ADS)

Sollich, Peter

1994-05-01

We study query construction algorithms, which aim at improving the generalization ability of systems that learn from examples by choosing optimal, nonredundant training sets. We set up a general probabilistic framework for deriving such algorithms from the requirement of optimizing a suitable objective function; specifically, we consider the objective functions entropy (or information gain) and generalization error. For two learning scenarios, the high-low game and the linear perceptron, we evaluate the generalization performance obtained by applying the corresponding query construction algorithms and compare it to training on random examples. We find qualitative differences between the two scenarios due to the different structure of the underlying rules (nonlinear and ``noninvertible'' versus linear); in particular, for the linear perceptron, random examples lead to the same generalization ability as a sequence of queries in the limit of an infinite number of examples. We also investigate learning algorithms which are ill matched to the learning environment and find that, in this case, minimum entropy queries can in fact yield a lower generalization ability than random examples. Finally, we study the efficiency of single queries and its dependence on the learning history, i.e., on whether the previous training examples were generated randomly or by querying, and the difference between globally and locally optimal query construction.

Automatic Classification of Time-variable X-Ray Sources

NASA Astrophysics Data System (ADS)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

A neural network for noise correlation classification

NASA Astrophysics Data System (ADS)

Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

2018-02-01

We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

Mission Suitability Testing of an Aircraft Simulator. Technical Report No. 75-12.

ERIC Educational Resources Information Center

Caro, Paul W.; And Others

The report describes a study conducted to evaluate Device 2B24, which simulates the UH-1 helicopter and an instrument flight environment, and to determine its suitability for cost-effectively accomplishing the instrument phase of Army rotary wing flight training and facilitating UH-1 helicopter transition training, aviator proficiency evaluation,…

Development and validation of a risk model for long-term mortality after percutaneous coronary intervention: The IDEA-BIO Study.

PubMed

van Boven, Nick; van Domburg, Ron T; Kardys, Isabella; Umans, Victor A; Akkerhuis, K Martijn; Lenzen, Mattie J; Valgimigli, Marco; Daemen, Joost; Zijlstra, Felix; Boersma, Eric; van Geuns, Robert-Jan

2018-03-01

We aimed to develop a model to predict long-term mortality after percutaneous coronary intervention (PCI), to aid in selecting patients with sufficient life expectancy to benefit from bioabsorbable scaffolds. Clinical trials are currently designed to demonstrate superiority of bioabsorbable scaffolds over metal devices up to 5 years after implantation. From 2000 to 2011, 19.532 consecutive patients underwent PCI in a tertiary referral hospital. Patients were randomly (2:1) divided into a training (N = 13,090) and validation (N = 6,442) set. Cox regression was used to identify determinants of long-term mortality in the training set and used to develop a risk model. Model performance was studied in the training and validation dataset. Median age was 63 years (IQR 54-72) and 72% were men. Median follow-up was 3.6 years (interquartile range [IQR] 2.4-6.8). The ratio elective vs. non-elective PCIs was 42/58. During 88,620 patient-years of follow-up, 3,156 deaths occurred, implying an incidence rate of 35.6 per 1,000. Estimated 5-year mortality was 12.9%.Regression analysis revealed age, body mass index, diabetes mellitus, renal insufficiency, prior myocardial infarction, PCI indication, lesion location, number of diseased vessels and cardiogenic shock at presentation as determinants of mortality. The long-term risk model showed good discrimination in the training and validation sets (c-indices 0.76 and 0.74), whereas calibration was appropriate. A simple risk model, containing 9 baseline clinical and angiographic variables effectively predicts long-term mortality after PCI and may possibly be used to select suitable patients for bioabsorbable scaffolds. © 2017 Wiley Periodicals, Inc.

Use of Combined A-Train Observations to Validate GEOS Model Simulated Dust Distributions During NAMMA

NASA Technical Reports Server (NTRS)

Nowottnick, E.

2007-01-01

During August 2006, the NASA African Multidisciplinary Analyses Mission (NAMMA) field experiment was conducted to characterize the structure of African Easterly Waves and their evolution into tropical storms. Mineral dust aerosols affect tropical storm development, although their exact role remains to be understood. To better understand the role of dust on tropical cyclogenesis, we have implemented a dust source, transport, and optical model in the NASA Goddard Earth Observing System (GEOS) atmospheric general circulation model and data assimilation system. Our dust source scheme is more physically based scheme than previous incarnations of the model, and we introduce improved dust optical and microphysical processes through inclusion of a detailed microphysical scheme. Here we use A-Train observations from MODIS, OMI, and CALIPSO with NAMMA DC-8 flight data to evaluate the simulated dust distributions and microphysical properties. Our goal is to synthesize the multi-spectral observations from the A-Train sensors to arrive at a consistent set of optical properties for the dust aerosols suitable for direct forcing calculations.

L2-Boosting algorithm applied to high-dimensional problems in genomic selection.

PubMed

González-Recio, Oscar; Weigel, Kent A; Gianola, Daniel; Naya, Hugo; Rosa, Guilherme J M

2010-06-01

The L(2)-Boosting algorithm is one of the most promising machine-learning techniques that has appeared in recent decades. It may be applied to high-dimensional problems such as whole-genome studies, and it is relatively simple from a computational point of view. In this study, we used this algorithm in a genomic selection context to make predictions of yet to be observed outcomes. Two data sets were used: (1) productive lifetime predicted transmitting abilities from 4702 Holstein sires genotyped for 32 611 single nucleotide polymorphisms (SNPs) derived from the Illumina BovineSNP50 BeadChip, and (2) progeny averages of food conversion rate, pre-corrected by environmental and mate effects, in 394 broilers genotyped for 3481 SNPs. Each of these data sets was split into training and testing sets, the latter comprising dairy or broiler sires whose ancestors were in the training set. Two weak learners, ordinary least squares (OLS) and non-parametric (NP) regression were used for the L2-Boosting algorithm, to provide a stringent evaluation of the procedure. This algorithm was compared with BL [Bayesian LASSO (least absolute shrinkage and selection operator)] and BayesA regression. Learning tasks were carried out in the training set, whereas validation of the models was performed in the testing set. Pearson correlations between predicted and observed responses in the dairy cattle (broiler) data set were 0.65 (0.33), 0.53 (0.37), 0.66 (0.26) and 0.63 (0.27) for OLS-Boosting, NP-Boosting, BL and BayesA, respectively. The smallest bias and mean-squared errors (MSEs) were obtained with OLS-Boosting in both the dairy cattle (0.08 and 1.08, respectively) and broiler (-0.011 and 0.006) data sets, respectively. In the dairy cattle data set, the BL was more accurate (bias=0.10 and MSE=1.10) than BayesA (bias=1.26 and MSE=2.81), whereas no differences between these two methods were found in the broiler data set. L2-Boosting with a suitable learner was found to be a competitive alternative for genomic selection applications, providing high accuracy and low bias in genomic-assisted evaluations with a relatively short computational time.

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.

PubMed

Ouyang, Wanli; Zeng, Xingyu; Wang, Xiaogang; Qiu, Shi; Luo, Ping; Tian, Yonglong; Li, Hongsheng; Yang, Shuo; Wang, Zhe; Li, Hongyang; Loy, Chen Change; Wang, Kun; Yan, Junjie; Tang, Xiaoou

2016-07-07

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [16], which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.

SASS Applied to Optimum Work Roll Profile Selection in the Hot Rolling of Wide Steel

NASA Astrophysics Data System (ADS)

Nolle, Lars

The quality of steel strip produced in a wide strip rolling mill depends heavily on the careful selection of initial ground work roll profiles for each of the mill stands in the finishing train. In the past, these profiles were determined by human experts, based on their knowledge and experience. In previous work, the profiles were successfully optimised using a self-organising migration algorithm (SOMA). In this research, SASS, a novel heuristic optimisation algorithm that has only one control parameter, has been used to find the optimum profiles for a simulated rolling mill. The resulting strip quality produced using the profiles found by SASS is compared with results from previous work and the quality produced using the original profile specifications. The best set of profiles found by SASS clearly outperformed the original set and performed equally well as SOMA without the need of finding a suitable set of control parameters.

Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model

PubMed Central

Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

2014-01-01

Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on top of the multiatlas concept for the HC segmentation. The method is based on a subject-specific set of 3-D optimal local maps (OLMs) that locally control the influence of each energy term of a hybrid active contour model (ACM). The complete set of the OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available data sets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method. PMID:27170866

Improving the food provided and food safety practices in out-of-school-hours services.

PubMed

Cooke, Lara; Sangster, Janice; Eccleston, Philippa

2007-04-01

Food provided and food safety and serving practices in out-of-school-hours (OOSH) services. Health promotion strategies, developed in partnership with an advisory committee, were directed at three main areas: supporting local services; developing statewide training and resources; and advocacy. Significant improvements were seen in the food provided, food safety and serving practices and the number of services with planned menus and nutrition and food safety policies. This project is one of the first implemented and evaluated in the OOSH setting. Statistically significant improvements were achieved in the food provided, food safety and serving practices, and menu and policy development. The project also increased the capacity of the OOSH sector to improve children's health by making suitable nutrition and food safety resources and training available to OOSH services across New South Wales.

Suitability of a structured Fundamental Movement Skills program for long day care centres: a process evaluation.

PubMed

Petrunoff, Nick; Lloyd, Beverley; Watson, Natalie; Morrisey, David

2009-04-01

Early childhood presents an opportunity to encourage development of Fundamental Movement Skills (FMS). Implementation of a structured program in the Long Day Care (LDC) setting presents challenges. Implementation of a structured FMS program FunMoves was assessed in LDC in metropolitan New South Wales. LDC staff attended a training session conducted by trained Health Promotion Officers (HPOs) and completed an evaluation. During implementation HPOs completed lesson observations. De-identified attendance data was collected and director and staff feedback on the program including barriers to implementation was obtained via questionnaire. Qualitative information relevant to process evaluation was obtained via open questions on questionnaires, and a de-brief diary recording feedback from directors and staff. Knowledge of FMS and FunMoves and staff confidence to deliver the program were high after training. On average, staff stated they ran lessons more than the suggested twice weekly and the majority of children attended 1-3 lessons per week. However, lesson delivery was not as designed, and staff found FunMoves disruptive and time consuming. Six directors and the majority of staff thought that FunMoves could be improved. Structured program delivery was hampered by contextual issues including significant staff turnover and program length and structure being at odds with the setting. Implementation could be enhanced by guidelines for more flexible delivery options including less structured approaches, shorter and simpler lessons, ongoing conversations with the early childhood sector, in-centre engagement of staff and post-training support.

Use of thermo‐coagulation as an alternative treatment modality in a ‘screen‐and‐treat’ programme of cervical screening in rural Malawi

PubMed Central

Kafwafwa, Savel; Brown, Hilary; Walker, Graeme; Madetsa, Belito; Deeny, Miriam; Kabota, Beatrice; Morton, David; Ter Haar, Reynier; Grant, Liz; Cubie, Heather A.

2016-01-01

The incidence of cervical cancer in Malawi is the highest in the world and projected to increase in the absence of interventions. Although government policy supports screening using visual inspection with acetic acid (VIA), screening provision is limited due to lack of infrastructure, trained personnel, and the cost and availability of gas for cryotherapy. Recently, thermo‐coagulation has been acknowledged as a safe and acceptable procedure suitable for low‐resource settings. We introduced thermo‐coagulation for treatment of VIA‐positive lesions as an alternative to cryotherapy within a cervical screening service based on VIA, coupled with appropriate, sustainable pathways of care for women with high‐grade lesions and cancers. Detailed planning was undertaken for VIA clinics, and approvals were obtained from the Ministry of Health, Regional and Village Chiefs. Educational resources were developed. Thermo‐coagulators were introduced into hospital and health centre settings, with theoretical and practical training in safe use and maintenance of equipment. A total of 7,088 previously unscreened women attended VIA clinics between October 2013 and March 2015. Screening clinics were held daily in the hospital and weekly in the health centres. Overall, VIA positivity was 6.1%. Almost 90% received same day treatment in the hospital setting, and 3‐ to 6‐month cure rates of more than 90% are observed. Thermo‐coagulation proved feasible and acceptable in this setting. Effective implementation requires comprehensive training and provider support, ongoing competency assessment, quality assurance and improvement audit. Thermo‐coagulation offers an effective alternative to cryotherapy and encouraged VIA screening of many more women. PMID:27006131

Use of thermo-coagulation as an alternative treatment modality in a 'screen-and-treat' programme of cervical screening in rural Malawi.

PubMed

Campbell, Christine; Kafwafwa, Savel; Brown, Hilary; Walker, Graeme; Madetsa, Belito; Deeny, Miriam; Kabota, Beatrice; Morton, David; Ter Haar, Reynier; Grant, Liz; Cubie, Heather A

2016-08-15

The incidence of cervical cancer in Malawi is the highest in the world and projected to increase in the absence of interventions. Although government policy supports screening using visual inspection with acetic acid (VIA), screening provision is limited due to lack of infrastructure, trained personnel, and the cost and availability of gas for cryotherapy. Recently, thermo-coagulation has been acknowledged as a safe and acceptable procedure suitable for low-resource settings. We introduced thermo-coagulation for treatment of VIA-positive lesions as an alternative to cryotherapy within a cervical screening service based on VIA, coupled with appropriate, sustainable pathways of care for women with high-grade lesions and cancers. Detailed planning was undertaken for VIA clinics, and approvals were obtained from the Ministry of Health, Regional and Village Chiefs. Educational resources were developed. Thermo-coagulators were introduced into hospital and health centre settings, with theoretical and practical training in safe use and maintenance of equipment. A total of 7,088 previously unscreened women attended VIA clinics between October 2013 and March 2015. Screening clinics were held daily in the hospital and weekly in the health centres. Overall, VIA positivity was 6.1%. Almost 90% received same day treatment in the hospital setting, and 3- to 6-month cure rates of more than 90% are observed. Thermo-coagulation proved feasible and acceptable in this setting. Effective implementation requires comprehensive training and provider support, ongoing competency assessment, quality assurance and improvement audit. Thermo-coagulation offers an effective alternative to cryotherapy and encouraged VIA screening of many more women. © 2016 The Authors International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.

Novel method of using dynamic electrical impedance signals for noninvasive diagnosis of knee osteoarthritis.

PubMed

Gajre, Suhas S; Anand, Sneh; Singh, U; Saxena, Rajendra K

2006-01-01

Osteoarthritis (OA) of knee is the most commonly occurring non-fatal irreversible disease, mainly in the elderly population and particularly in female. Various invasive and non-invasive methods are reported for the diagnosis of this articular cartilage pathology. Well known techniques such as X-ray, computed tomography, magnetic resonance imaging, arthroscopy and arthrography are having their disadvantages, and diagnosis of OA in early stages with simple effective noninvasive method is still a biomedical engineering problem. Analyzing knee joint noninvasive signals around knee might give simple solution for diagnosis of knee OA. We used electrical impedance data from knees to compare normal and osteoarthritic subjects during the most common dynamic conditions of the knee, i.e. walking and knee swing. It was found that there is substantial difference in the properties of the walking cycle (WC) and knee swing cycle (KS) signals. In experiments on 90 pathological (combined for KS and WC signals) and 72 normal signals (combined), suitable features were drawn. Then signals were used to classify as normal or pathological. Artificial multilayer feed forward neural network was trained using back propagation algorithm for the classification. On a training data set of 54 signals for KS signals, the classification efficiency for a test set of 54 was 70.37% and 85.19% with and without normalization respectively wrt base impedance. Similarly, the training set of 27 WC signals and test set of 27 signals resulted in 77.78% and 66.67% classification efficiency. The results indicate that dynamic electrical impedance signals have potential to be used as a novel method for noninvasive diagnosis of knee OA.

A paediatric cardiopulmonary resuscitation training project in Honduras.

PubMed

Urbano, Javier; Matamoros, Martha M; López-Herce, Jesús; Carrillo, Angel P; Ordóñez, Flora; Moral, Ramón; Mencía, Santiago

2010-04-01

It is possible that the exportation of North American and European models has hindered the creation of a structured cardiopulmonary resuscitation (CPR) training programme in developing countries. The objective of this paper is to describe the design and present the results of a European paediatric and neonatal CPR training programme adapted to Honduras. A paediatric CPR training project was set up in Honduras with the instructional and scientific support of the Spanish Group for Paediatric and Neonatal CPR. The programme was divided into four phases: CPR training and preparation of instructors; training for instructors; supervised teaching; and independent teaching. During the first phase, 24 Honduran doctors from paediatric intensive care, paediatric emergency and anaesthesiology departments attended the paediatric CPR course and 16 of them the course for preparation as instructors. The Honduran Paediatric and Neonatal CPR Group was formed. In the second phase, workshops were given by Honduran instructors and four of them attended a CPR course in Spain as trainee instructors. In the third phase, a CPR course was given in Honduras by the Honduran instructors, supervised by the Spanish team. In the final phase of independent teaching, eight courses were given, providing 177 students with training in CPR. The training of independent paediatric CPR groups with the collaboration and scientific assessment of an expert group could be a suitable model on which to base paediatric CPR training in Latin American developing countries. Copyright (c) 2010 Elsevier Ireland Ltd. All rights reserved.

Effect of postgraduate training on job and career satisfaction among health-system pharmacists.

PubMed

Padiyara, Rosalyn S; Komperda, Kathy E

2010-07-01

The effect of postgraduate training on job and career satisfaction among health-system pharmacists was evaluated. A mail-based questionnaire was sent to a random sample of pharmacist members of the American Society of Health-System Pharmacists. Previously validated questions for job and career satisfaction among pharmacists were utilized. The questionnaire was designed to obtain information regarding general employment, work environment, job satisfaction, career satisfaction, postgraduate training, and demographic characteristics. Pharmacists who had completed either a pharmacy residency or fellowship were classified as having postgraduate training. Questionnaires returned within two months of the original mailing date were included in the analysis. Responses from pharmacists who were retired, employed in a nonpharmacy career, or unemployed were excluded. Data were analyzed using SPSS software. Of the 2499 questionnaires mailed, 36 were undeliverable; 1058 were completed, yielding a response rate of 43%. Of these, 48 were excluded, resulting in 1010 questionnaires suitable for analysis. Approximately 37% of respondents indicated completion of postgraduate training. The most common practice setting was a community, not-for-profit hospital (40.9%). Overall, 90.7% of respondents indicated they were either satisfied or highly satisfied with their current employment. Approximately 45% of pharmacists with postgraduate training indicated they were highly satisfied with their employment, compared with 32.7% of pharmacists without postgraduate training (p < 0.001). Pharmacists who completed postgraduate training were more satisfied with their job than those who did not complete such training.

Assessment of the ability of wheelchair subjects with spinal cord injury to perform a specific protocol of shoulder training: a pilot study.

PubMed

Merolla, Giovanni; Dellabiancia, Fabio; Filippi, Maria Vittoria; De Santis, Elisa; Alpi, Daniele; Magrini, Paola; Porcellini, Giuseppe

2014-04-01

a regular program of exercises in subjects with spinal cord injury (SCI) can contribute to reduce the risk of upper extremities injuries. in this prospective laboratory study we tested the hypothesis that a training machine developed for able-body users is suitable for a shoulder training protocol in 11 paraplegic subjects with SCI. Overall subjects were assessed with the SCIM III, CS, DASH and standard shoulder examination. We set a protocol of shoulder exercises performed with a training machine. Overall subjects were able to perform the protocol but 2 did not complete the exercises n° 6 and 7. The position of the wheelchair during each exercise was recorded. Wheelchair position/loading level were significantly correlated with the protocol n° 2, 3 and 5 as well as BMI/loading level for the exercises n° 5 and 9 and age/loading level for the exercise n° 7. Clinical scores were neither correlated with loading nor with anthropometric data. FROM THE ANALYSIS OF DATA COLLECTED IN THIS STUDY ARISED THAT: 1) the training machine needs some adjustments for paraplegic subjects, 2) the training protocol was appropriate except for the exercises needing a torso-rotation and 3) the template for wheelchair position may be a valid guide for an optimal paraplegic shoulder training.

Subauditory Speech Recognition based on EMG/EPG Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

2003-01-01

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

Teaching and evaluation of ethics and professionalism

PubMed Central

Pauls, Merril A.

2012-01-01

Abstract Objective To document the scope of the teaching and evaluation of ethics and professionalism in Canadian family medicine postgraduate training programs, and to identify barriers to the teaching and evaluation of ethics and professionalism. Design A survey was developed in collaboration with the Committee on Ethics of the College of Family Physicians of Canada. The data are reported descriptively and in aggregate. Setting Canadian postgraduate family medicine training programs. Participants Between June and December of 2008, all 17 Canadian postgraduate family medicine training programs were invited to participate. Main outcome measures The first part of the survey explored the structure, resources, methods, scheduled hours, and barriers to teaching ethics and professionalism. The second section focused on end-of-rotation evaluations, other evaluation strategies, and barriers related to the evaluation of ethics and professionalism. Results Eighty-eight percent of programs completed the survey. Most respondents (87%) had learning objectives specifically for ethics and professionalism, and 87% had family doctors with training or interest in the area leading their efforts. Two-thirds of responding programs had less than 10 hours of scheduled instruction per year, and the most common barriers to effective teaching were the need for faculty development, competing learning needs, and lack of resident interest. Ninety-three percent of respondents assessed ethics and professionalism on their end-of-rotation evaluations, with 86% assessing specific domains. The most common barriers to evaluation were a lack of suitable tools and a lack of faculty comfort and interest. Conclusion By far most Canadian family medicine postgraduate training programs had learning objectives and designated faculty leads in ethics and professionalism, yet there was little curricular time dedicated to these areas and a perceived lack of resident interest and faculty expertise. Most programs evaluated ethics and professionalism as part of their end-of-rotation evaluations, but only a small number used novel means of evaluation, and most cited a lack of suitable assessment tools as an important barrier. PMID:23242906

Maintenance Research. Report 6. Maintenance Training.

ERIC Educational Resources Information Center

Louisiana State Dept. of Highways, Baton Rouge.

The main objective of the training research phase of the maintenance management study was to develop and test training methods suitable for highway maintenance supervisors. Supervisors were trained by one of five different methods (lecture, group discussion, programed instruction, programed workshops, audiovisual instruction). The report documents…

Development and assessment of users' satisfaction with the systemic lupus erythematosus disease activity index 2000 responder index-50 website.

PubMed

Touma, Zahi; Gladman, Dafna D; MacKinnon, Anne; Carette, Simon; Abu-Shakra, Mahmoud; Askanase, Anca; Nived, Ola; Hanly, John G; Landolt-Marticorena, Carolina; Tam, Lai-Shan; Toloza, Sergio; Nikpour, Mandana; Riddell, Claire; Steiman, Amanda; Eder, Lihi; Haddad, Amir; Barber, Claire; Urowitz, Murray B

2013-01-01

To describe the development of the Systemic Lupus Erythematosus Disease Activity Index 2000 Responder Index-50 (S2K RI-50) Website (www.s2k-ri-50.com) and to assess satisfaction with its training and examination modules among rheumatologists and rheumatology fellows. The development of the Website occurred in 3 phases. The first was a deployment phase that consisted of preparing the site map along with its content. The content included the S2K RI-50 training manual, the tests and corresponding question bank, and the online adaptive training module, along with the extensive site testing. The second phase included the participation of rheumatologists and trainees who completed the Website modules. The third was a quality assurance phase in which an online survey was developed to determine the satisfaction level of its users. Further modifications were implemented per participants' recommendations. The site has been online since it was registered in September 2010. Fourteen rheumatologists and rheumatology trainees from different centers reviewed and completed the material contained in the Website. The survey revealed acceptance among rheumatologists for the Website's content, design, and presentation. The Website was rated as user-friendly and useful in familiarizing investigators with the S2K RI-50. After completion of the training and examination modules, participants reported a suitable level of preparation to implement the S2K RI-50 in clinical trials and research settings in a timely manner. The Website includes training and examination modules that familiarize rheumatologists with the S2K RI-50 and assesses their competence to use the index. This prepares them for the use of the S2K RI-50 in clinical trials and research settings.

Transfer of Instrument Training and the Synthetic Flight Training System.

ERIC Educational Resources Information Center

Caro, Paul W.

One phase of an innovative flight training program, its development, and initial administration is described in this paper. The operational suitability test activities related to a determination of the transfer of instrument training value of the Army's Synthetic Flight Training System (SFTS) Device 2B24. Sixteen active Army members of an Officer…

How do cardiorespiratory fitness improvements vary with physical training modality in heart failure patients? A quantitative guide

PubMed Central

Smart, Neil A

2013-01-01

BACKGROUND: Peak oxygen consumption (VO2) is the gold standard measure of cardiorespiratory fitness and a reliable predictor of survival in chronic heart failure patients. Furthermore, any form of physical training usually improves cardiorespiratory fitness, although the magnitude of improvement in peak VO2 may vary across different training prescriptions. OBJECTIVE: To quantify, and subsequently rank, the magnitude of improvement in peak VO2 for different physical training prescriptions using data from published meta-analyses and randomized controlled trials. METHODS: Prospective randomized controlled parallel trials and meta-analyses of exercise training in chronic heart failure patients that provided data on change in peak VO2 for nine a priori comparative analyses were examined. RESULTS: All forms of physical training were beneficial, although the improvement in peak VO2 varied with modality. High-intensity interval exercise yielded the largest increase in peak VO2, followed in descending order by moderate-intensity aerobic exercise, functional electrical stimulation, inspiratory muscle training, combined aerobic and resistance training, and isolated resistance training. With regard to setting, the present study was unable to determine whether outpatient or unsupervised home exercise provided greater benefits in terms of peak VO2 improvment. CONCLUSIONS: Interval exercise is not suitable for all patients, especially the high-intensity variety; however, when indicated, this form of exercise should be adopted to optimize peak VO2 adaptations. Other forms of activity, such as functional electrical stimulation, may be more appropriate for patients who are not capable of high-intensity interval training, especially for severely deconditioned patients who are initially unable to exercise. PMID:24294043

Training Zambian traditional birth attendants to reduce neonatal mortality in the Lufwanyama Neonatal Survival Project (LUNESP).

PubMed

Gill, Christopher J; Guerina, Nicholas G; Mulenga, Charity; Knapp, Anna B; Mazala, Grace; Hamer, Davidson H

2012-07-01

To provide relevant details on how interventions in the Lufwanyama Neonatal Survival Project (LUNESP) were developed and how Zambian traditional birth attendants (TBAs) were trained to perform them. The study tested 2 interventions: a simplified version of the American Academy of Pediatrics' neonatal resuscitation protocol (NRP); and antibiotics with facilitated referral (AFR). Key elements that enabled the positive study result were: focusing on common and correctible causes of mortality; selecting a study population with high unmet public health need; early community mobilization to build awareness and support; emphasizing simplicity in the intervention technology and algorithms; using a traditional training approach appropriate to students with low literacy rates; requiring TBAs to demonstrate their competence before completing each workshop; and minimizing attrition of skills by retraining and reassessing the TBAs regularly throughout the study. An effective NRP training model was created that is suitable for community-based neonatal interventions, in research or programmatic settings, and by practitioners with limited obstetric skills and low rates of literacy. Clinicaltrials.gov NCT00518856. Copyright © 2012 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Automatic classification of time-variable X-ray sources

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

Lo, Kitty K.; Farrell, Sean; Murphy, Tara

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, andmore » other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.« less

Astronaut training manual

NASA Technical Reports Server (NTRS)

Coleman, E. A.

1980-01-01

Scientific information from previous space flights, space medicine, exercise physiology, and sports medicine was used to prepare a physical fitness manual suitable for use by members of the NASA astronaut population. A variety of scientifically valid exercise programs and activities suitable for the development of physical fitness are provided. Programs, activities, and supportive scientific data are presented in a concise, easy to read format so as to permit the user to select his or her mode of training with confidence and devote time previously spent experimenting with training routines to preparation for space flight. The programs and activities included were tested and shown to be effective and enjoyable.

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks.

PubMed

Maiti, Saumen; Erram, V C; Gupta, Gautam; Tiwari, Ram Krishna; Kulkarni, U D; Sangpal, R R

2013-04-01

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb's diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: "Very good", "Good", and "Unsuitable". The BNN model-based results suggest that groundwater quality falls mostly in the range of "Good" to "Very good" except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.

A Multispecialty Evaluation of Thiel Cadavers for Surgical Training.

PubMed

Yiasemidou, Marina; Roberts, David; Glassman, Daniel; Tomlinson, James; Biyani, Shekhar; Miskovic, Danilo

2017-05-01

Changes in UK legislation allow for surgical procedures to be performed on cadavers. The aim of this study was to assess Thiel cadavers as high-fidelity simulators and to examine their suitability for surgical training. Surgeons from various specialties were invited to attend a 1 day dissection workshop using Thiel cadavers. The surgeons completed a baseline questionnaire on cadaveric simulation. At the end of the workshop, they completed a similar questionnaire based on their experience with Thiel cadavers. Comparing the answers in the pre- and post-workshop questionnaires assessed whether using Thiel cadavers had changed the surgeons' opinions of cadaveric simulation. According to the 27 participants, simulation is important for surgical training and a full-procedure model is beneficial for all levels of training. Currently, there is dissatisfaction with existing models and a need for high-fidelity alternatives. After the workshop, surgeons concluded that Thiel cadavers are suitable for surgical simulation (p = 0.015). Thiel were found to be realistic (p < 0.001) to have reduced odour (p = 0.002) and be more cost-effective (p = 0.003). Ethical constraints were considered to be small. Thiel cadavers are suitable for training in most surgical specialties.

Effectuality of Cleaning Workers' Training and Cleaning Enterprises' Chemical Health Hazard Risk Profiling.

PubMed

Suleiman, Abdulqadir M; Svendsen, Kristin V H

2015-12-01

Goal-oriented communication of risk of hazards is necessary in order to reduce risk of workers' exposure to chemicals. Adequate training of workers and enterprise priority setting are essential elements. Cleaning enterprises have many challenges and the existing paradigms influence the risk levels of these enterprises. Information on organization and enterprises' prioritization in training programs was gathered from cleaning enterprises. A measure of enterprises' conceptual level of importance of chemical health hazards and a model for working out the risk index (RI) indicating enterprises' conceptual risk level was established and used to categorize the enterprises. In 72.3% of cases, training takes place concurrently with task performances and in 67.4% experienced workers conduct the trainings. There is disparity between employers' opinion on competence level of the workers and reality. Lower conceptual level of importance was observed for cleaning enterprises of different sizes compared with regional safety delegates and occupational hygienists. Risk index values show no difference in risk level between small and large enterprises. Training of cleaning workers lacks the prerequisite for suitability and effectiveness to counter risks of chemical health hazards. There is dereliction of duty by management in the sector resulting in a lack of competence among the cleaning workers. Instituting acceptable easily attainable safety competence level for cleaners will conduce to risk reduction, and enforcement of attainment of the competence level would be a positive step.

Projecting the Global Distribution of the Emerging Amphibian Fungal Pathogen, Batrachochytrium dendrobatidis, Based on IPCC Climate Futures

PubMed Central

Olson, Deanna H.; Blaustein, Andrew R.

2016-01-01

Projected changes in climate conditions are emerging as significant risk factors to numerous species, affecting habitat conditions and community interactions. Projections suggest species range shifts in response to climate change modifying environmental suitability and is supported by observational evidence. Both pathogens and their hosts can shift ranges with climate change. We consider how climate change may influence the distribution of the emerging infectious amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), a pathogen associated with worldwide amphibian population losses. Using an expanded global Bd database and a novel modeling approach, we examined a broad set of climate metrics to model the Bd-climate niche globally and regionally, then project how climate change may influence Bd distributions. Previous research showed that Bd distribution is dependent on climatic variables, in particular temperature. We trained a machine-learning model (random forest) with the most comprehensive global compilation of Bd sampling records (~5,000 site-level records, mid-2014 summary), including 13 climatic variables. We projected future Bd environmental suitability under IPCC scenarios. The learning model was trained with combined worldwide data (non-region specific) and also separately per region (region-specific). One goal of our study was to estimate of how Bd spatial risks may change under climate change based on the best available data. Our models supported differences in Bd-climate relationships among geographic regions. We projected that Bd ranges will shift into higher latitudes and altitudes due to increased environmental suitability in those regions under predicted climate change. Specifically, our model showed a broad expansion of areas environmentally suitable for establishment of Bd on amphibian hosts in the temperate zones of the Northern Hemisphere. Our projections are useful for the development of monitoring designs in these areas, especially for sensitive species and those vulnerable to multiple threats. PMID:27513565

Training set selection for the prediction of essential genes.

PubMed

Cheng, Jian; Xu, Zhao; Wu, Wenwu; Zhao, Li; Li, Xiangchen; Liu, Yanlin; Tao, Shiheng

2014-01-01

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

The Future of e-Learning in Medical Education: Current Trend and Future Opportunity

PubMed Central

2006-01-01

A wide range of e-learning modalities are widely integrated in medical education. However, some of the key questions related to the role of e-learning remain unanswered, such as (1) what is an effective approach to integrating technology into pre-clinical vs. clinical training?; (2) what evidence exists regarding the type and format of e-learning technology suitable for medical specialties and clinical settings?; (3) which design features are known to be effective in designing on-line patient simulation cases, tutorials, or clinical exams?; and (4) what guidelines exist for determining an appropriate blend of instructional strategies, including on-line learning, face-to-face instruction, and performance-based skill practices? Based on the existing literature and a variety of e-learning examples of synchronous learning tools and simulation technology, this paper addresses the following three questions: (1) what is the current trend of e-learning in medical education?; (2) what do we know about the effective use of e-learning?; and (3) what is the role of e-learning in facilitating newly emerging competency-based training? As e-learning continues to be widely integrated in training future physicians, it is critical that our efforts in conducting evaluative studies should target specific e-learning features that can best mediate intended learning goals and objectives. Without an evolving knowledge base on how best to design e-learning applications, the gap between what we know about technology use and how we deploy e-learning in training settings will continue to widen. PMID:19223995

Social workers' perceptions of barriers to interpersonal therapy implementation for treating postpartum depression in a primary care setting in Israel.

PubMed

Bina, Rena; Barak, Adi; Posmontier, Barbara; Glasser, Saralee; Cinamon, Tali

2018-01-01

Research on evidence-based practice (EBP) implementation in social work often neglects to include evaluation of application barriers. This qualitative study examined social workers' perspectives of provider- and organisational-related barriers to implementing a brief eight-session interpersonal therapy (IPT) intervention, a time-limited EBP that addresses reducing depressive symptoms and improving interpersonal functioning. Implementation took place in a primary care setting in Israel and was aimed at treating women who have postpartum depression (PPD) symptoms. Using purposeful sampling, 25 primary care licensed social workers were interviewed between IPT training and implementation regarding their perceived barriers to implementing IPT in practice. Data analysis was facilitated using a phenomenological approach, which entails identifying the shared themes and shared experiences of research participants regarding barriers to implementing IPT. Three themes emerged from the analysis of interviews: Perceived lack of flexibility of IPT intervention in comparison with more familiar methods social workers previously applied, specifically regarding the number of sessions and therapeutic topics included in the IPT protocol; insecurity and hesitance to gain experience with a new method of intervention; and organisational barriers, including difficulties with referrals, the perception of HMOs as health facilities not suitable for therapy, and time constraints. Addressing perceived barriers of social workers toward implementing EBPs, such as IPT for postpartum depression, during the training phase is crucial for enabling appropriate implementation. Future training should include examining practitioners' attitudes toward implementation of EBPs, as part of standardised training protocols. © 2017 John Wiley & Sons Ltd.

Coronary artery segmentation in X-ray angiograms using gabor filters and differential evolution.

PubMed

Cervantes-Sanchez, Fernando; Cruz-Aceves, Ivan; Hernandez-Aguirre, Arturo; Solorio-Meza, Sergio; Cordova-Fraga, Teodoro; Aviña-Cervantes, Juan Gabriel

2018-08-01

Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (DE) is proposed. Because the Gabor filters are governed by three different parameters, the optimal selection of those parameters is highly desirable in order to maximize the vessel detection rate while reducing the computational cost of the training stage. To obtain the optimal set of parameters for the Gabor filters, the area (Az) under the receiver operating characteristics curve is used as objective function. In the experimental results, the proposed method achieves an A z =0.9388 in a training set of 40 images, and for a test set of 40 images it obtains the highest performance with an A z =0.9538 compared with six state-of-the-art vessel detection methods. Finally, the proposed method achieves an accuracy of 0.9423 for vessel segmentation using the test set. In addition, the experimental results have also shown that the proposed method can be highly suitable for clinical decision support in terms of computational time and vessel segmentation performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

3D-QSAR modeling and molecular docking studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles

NASA Astrophysics Data System (ADS)

Ghaleb, Adib; Aouidate, Adnane; Ghamali, Mounir; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

2017-10-01

3D-QSAR (comparative molecular field analysis (CoMFA)) and comparative molecular similarity indices analysis (CoMSIA) were performed on novel 2,5 disubstituted 1,3,4-oxadiazoles analogues as anti-fungal agents. The CoMFA and CoMSIA models using 13 compounds in the training set gives Q2 values of 0.52 and 0.51 respectively, while R2 values of 0.92. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to determine a three-dimensional quantitative structure-activity relationship. Based on this study a set of new molecules with high predicted activities were designed. Surflex-docking confirmed the stability of predicted molecules in the receptor.

An automated system for positive reinforcement training of group-housed macaque monkeys at breeding and research facilities.

PubMed

Tulip, Jennifer; Zimmermann, Jonas B; Farningham, David; Jackson, Andrew

2017-06-15

Behavioural training through positive reinforcement techniques is a well-recognised refinement to laboratory animal welfare. Behavioural neuroscience research requires subjects to be trained to perform repetitions of specific behaviours for food/fluid reward. Some animals fail to perform at a sufficient level, limiting the amount of data that can be collected and increasing the number of animals required for each study. We have implemented automated positive reinforcement training systems (comprising a button press task with variable levels of difficulty using LED cues and a fluid reward) at the breeding facility and research facility, to compare performance across these different settings, to pre-screen animals for selection and refine training protocols. Animals learned 1- and 4-choice button tasks within weeks of home enclosure training, with some inter-individual differences. High performance levels (∼200-300 trials per 60min session at ∼80% correct) were obtained without food or fluid restriction. Moreover, training quickly transferred to a laboratory version of the task. Animals that acquired the task at the breeding facility subsequently performed better both in early home enclosure sessions upon arrival at the research facility, and also in laboratory sessions. Automated systems at the breeding facility may be used to pre-screen animals for suitability for behavioural neuroscience research. In combination with conventional training, both the breeding and research facility systems facilitate acquisition and transference of learning. Automated systems have the potential to refine training protocols and minimise requirements for food/fluid control. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Self-Organizing-Map Program for Analyzing Multivariate Data

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Jacob, Joseph C.; Block, Gary L.; Braverman, Amy J.

2005-01-01

SOM_VIS is a computer program for analysis and display of multidimensional sets of Earth-image data typified by the data acquired by the Multi-angle Imaging Spectro-Radiometer [MISR (a spaceborne instrument)]. In SOM_VIS, an enhanced self-organizing-map (SOM) algorithm is first used to project a multidimensional set of data into a nonuniform three-dimensional lattice structure. The lattice structure is mapped to a color space to obtain a color map for an image. The Voronoi cell-refinement algorithm is used to map the SOM lattice structure to various levels of color resolution. The final result is a false-color image in which similar colors represent similar characteristics across all its data dimensions. SOM_VIS provides a control panel for selection of a subset of suitably preprocessed MISR radiance data, and a control panel for choosing parameters to run SOM training. SOM_VIS also includes a component for displaying the false-color SOM image, a color map for the trained SOM lattice, a plot showing an original input vector in 36 dimensions of a selected pixel from the SOM image, the SOM vector that represents the input vector, and the Euclidean distance between the two vectors.

Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values.

PubMed

Valous, Nektarios A; Mendoza, Fernando; Sun, Da-Wen; Allen, Paul

2010-03-01

The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances. Mahalanobis distances and Pearson product moment correlations were used for feature selection. Six highly discriminating features were used as input to train the neural network. An adaptive feedforward multilayer perceptron classifier was employed to obtain a suitable mapping from the input dataset. The overall correct classification performance for the training, validation and test set were 90.3%, 94.4%, and 86.1%, respectively. The results confirm that the classification performance was satisfactory. Extracting the most informative features led to the recognition of a set of different but visually quite similar textural patterns based on quaternionic singular values. Copyright 2009 Elsevier Ltd. All rights reserved.

Developing a translational ecology workforce

USGS Publications Warehouse

Schwartz, Mark W.; Hiers, J. Kevin; Davis, Frank W.; Garfin, Gregg; Jackson, Stephen T.; Terando, Adam J.; Woodhouse, Connie A.; Morelli, Toni; Williamson, Matthew A.; Brunson, Mark W.

2017-01-01

We define a translational ecologist as a professional ecologist with diverse disciplinary expertise and skill sets, as well as a suitable personal disposition, who engages across social, professional, and disciplinary boundaries to partner with decision makers to achieve practical environmental solutions. Becoming a translational ecologist requires specific attention to obtaining critical non‐scientific disciplinary breadth and skills that are not typically gained through graduate‐level education. Here, we outline a need for individuals with broad training in interdisciplinary skills, use our personal experiences as a basis for assessing the types of interdisciplinary skills that would benefit potential translational ecologists, and present steps that interested ecologists may take toward becoming translational. Skills relevant to translational ecologists may be garnered through personal experiences, informal training, short courses, fellowships, and graduate programs, among others. We argue that a translational ecology workforce is needed to bridge the gap between science and natural resource decisions. Furthermore, we argue that this task is a cooperative responsibility of individuals interested in pursuing these careers, educational institutions interested in training scientists for professional roles outside of academia, and employers seeking to hire skilled workers who can foster stakeholder‐engaged decision making.

Optoelectronics applications in multimedia shooting training systems: SPARTAN

NASA Astrophysics Data System (ADS)

Glogowski, Tomasz; Hlosta, Pawel; Stepniak, Slawomir; Swiderski, Waldemar

2017-10-01

Multimedia shooting training systems are increasingly being used in the training of security staff and uniformed services. An advanced practicing-training system SPARTAN for simulation of small arms shooting has been designed and manufactured by Autocomp Management Ltd. and Military Institute of Armament Technology for the Polish Ministry of National Defence. SPARTAN is a stationary device designed to teach, monitor and evaluate the targeting of small arms and to prepare soldiers for: • firing the live ammunition at open ranges for combat targets and silhouettes • detection, classification and engagement of real targets upon different terrains, weather conditions and periods during the day • team work as a squad during the mission by using different types of arms • suitable reactions in untypical scenarios. Placed in any room the training set consists of: • the projection system that generates realistic 3D imaging of the battlefield (such as combat shooting range) in high-resolution • system that tracks weapons aiming points • sound system which delivers realistic mapping of acoustic surroundings • operator station with which the training is conducted and controlled • central processing unit based on PC computers equipped with specialist software realizing individual system functions • units of smart weapons equipped with radio communication modules, injection laser diodes and pneumatic reloading system. The system make possible training by firing in dynamic scenarios, using combat weapons and live ammunition against visible targets moving on a screen. The use of infrared camera for detecting the position of impact of a projectile.

Virtual reality 3D headset based on DMD light modulators

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

Bernacki, Bruce E.; Evans, Allan; Tang, Edward

We present the design of an immersion-type 3D headset suitable for virtual reality applications based upon digital micro-mirror devices (DMD). Our approach leverages silicon micro mirrors offering 720p resolution displays in a small form-factor. Supporting chip sets allow rapid integration of these devices into wearable displays with high resolution and low power consumption. Applications include night driving, piloting of UAVs, fusion of multiple sensors for pilots, training, vision diagnostics and consumer gaming. Our design is described in which light from the DMD is imaged to infinity and the user’s own eye lens forms a real image on the user’s retina.

Pharmacophore Based Virtual Screening Approach to Identify Selective PDE4B Inhibitors

PubMed Central

Gaurav, Anand; Gautam, Vertika

2017-01-01

Phosphodiesterase 4 (PDE4) has been established as a promising target in asthma and chronic obstructive pulmonary disease. PDE4B subtype selective inhibitors are known to reduce the dose limiting adverse effect associated with non-selective PDE4B inhibitors. This makes the development of PDE4B subtype selective inhibitors a desirable research goal. To achieve this goal, ligand based pharmacophore modeling approach is employed. Separate pharmacophore hypotheses for PDE4B and PDE4D inhibitors were generated using HypoGen algorithm and 106 PDE4 inhibitors from literature having thiopyrano [3,2-d] Pyrimidines, 2-arylpyrimidines, and triazines skeleton. Suitable training and test sets were created using the molecules as per the guidelines available for HypoGen program. Training set was used for hypothesis development while test set was used for validation purpose. Fisher validation was also used to test the significance of the developed hypothesis. The validated pharmacophore hypotheses for PDE4B and PDE4D inhibitors were used in sequential virtual screening of zinc database of drug like molecules to identify selective PDE4B inhibitors. The hits were screened for their estimated activity and fit value. The top hit was subjected to docking into the active sites of PDE4B and PDE4D to confirm its selectivity for PDE4B. The hits are proposed to be evaluated further using in-vitro assays. PMID:29201082

[Tutorial functions in 1st level nursing masters: cognitive study].

PubMed

Sarli, Maria Pompea; Burrai, Francesco; Cicolini, Giancarlo

2009-01-01

Tutorial functions are becoming increasingly more respondent to the requirements of student-based training. On the basis of national and international experiences, some active learning methods seem to be suitable for tutoring. Both basic and post-basic training can be directed towards satisfying the requirements of a society that is continually changing , forming professionals who are able to guarantee and maintain suitable levels of skill.In this study the results of a cognitive study, performed in various Italian universities where Masters in Nursing have already been implemented, are described. In particular, these results have shown the need for tutorial teaching methods that actively involve students in the training process.

Training in Basic Laparoscopic Surgical Skills: Residents Opinion of the New Nintendo Wii-U Laparoscopic Simulator.

PubMed

Overtoom, Evelien M; Jansen, Frank-Willem; van Santbrink, Evert J P; Schraffordt Koops, Steven E; Veersema, Sebastiaan; Schreuder, Henk W R

Serious games are new in the field of laparoscopic surgical training. We evaluate the residents׳ opinion of a new laparoscopic simulator for the Nintendo Wii-U platform. Prospective questionnaire study. Participants received a standardized introduction and completed level 3 and 4 of the game "Underground." They filled out a questionnaire concerning demographics and their opinion on realism, usefulness, suitability, haptic feedback, and home training-use of the game. Two tertiary teaching hospitals. Obstetrics and gynaecology residents postgraduate year 1 to 6 (n = 59) from several European countries. Subjects (n = 59) were divided into 2 groups based on laparoscopic experience: Group A (n = 38) and Group B (n = 21). The realism of different aspects of the game received mean scores around 3 on a 5-point Likert scale. The hand-eye coordination was regarded most useful for training with a mean of 3.92 (standard deviation 0.93) and the game was considered most suitable for residents in the first part of their postgraduate training with a mean of 3.73 (standard deviation 0.97). Both groups differed especially concerning their opinion of the usefulness of the game as a training tool. Most residents liked the new serious game for the Nintendo Wii-U. The usefulness and suitability as a laparoscopic training tool were rated at an acceptable to high level. However, the game does require improvements such as inclusion of a good scoring system before it can be integrated in resident training curricula. Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Real-time tumor motion estimation using respiratory surrogate via memory-based learning

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Berbeco, Ross I.; Xing, Lei

2012-08-01

Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95th percentile error of 3.4 mm on unseen test data. The average 3D error was further reduced to 1.4 mm when the model was tuned to its optimal setting for each respiratory trace. In one trace where a few outliers are present in the training data, the proposed method achieved an error reduction of as much as ∼50% compared with the best linear model (1.0 mm versus 2.1 mm). The memory-based learning technique is able to accurately capture the highly complex and nonlinear relations between tumor and surrogate motion in an efficient manner (a few milliseconds per estimate). Furthermore, the algorithm is particularly suitable to handle situations where the training data are contaminated by large errors or outliers. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates.

Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests

NASA Astrophysics Data System (ADS)

Adamczyk, Joanna; Osberger, Antonia

2015-05-01

Here we propose an approach to enhance the detection and assessment of forest disturbances in mountain areas based on red-edge reflectance. The research addresses the need for improved monitoring of areas included in the European Natura 2000 network. Thirty-eight vegetation indices (VI) are assessed for sensitivity to topographic variations. A separability analysis is performed for the resulting set of ten VI whereby two VI (PSSRc2, SR 800/550) are found most suitable for threshold-based OBIA classification. With a correlation analysis (SRCC) between VI and the training samples we identify Datt4 as suitable to represent the magnitude of forest disturbance. The provided information layers illustrate two combined phenomena that were derived by (1) an OBIA delineation and (2) continuous representation of the magnitude of forest disturbance. The satisfactory accuracy assessment results confirm that the approach is useful for operational tasks in the long-term monitoring of Norway spruce dominated forests in mountainous areas, with regard to forest disturbance.

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

2012-07-01

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

A neural network gravitational arc finder based on the Mediatrix filamentation method

NASA Astrophysics Data System (ADS)

Bom, C. R.; Makler, M.; Albuquerque, M. P.; Brandt, C. H.

2017-01-01

Context. Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. Aims: In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements that are derived from the Mediatrix filamentation method as entries to an artificial neural network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. Methods: The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. Results: In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input into the ANN after a preselection. However, this completeness drops to 70% on the HST images. The false detections are on the order of 3% of the objects detected in these images. Conclusions: The combination of Mediatrix measurements with an ANN is a promising tool for the pattern-recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.

DNA Everywhere. A Guide for Simplified Environmental Genomic DNA Extraction Suitable for Use in Remote Areas

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

Gabrielle N. Pecora; Francine C. Reid; Lauren M. Tom

2016-05-01

Collecting field samples from remote or geographically distant areas can be a financially and logistically challenging. With participation of a local organization where the samples are originated from, gDNA samples can be extracted from the field and shipped to a research institution for further processing and analysis. The ability to set up gDNA extraction capabilities in the field can drastically reduce cost and time when running long-term microbial studies with a large sample set. The method outlined here has developed a compact and affordable method for setting up a “laboratory” and extracting and shipping gDNA samples from anywhere in themore » world. This white paper explains the process of setting up the “laboratory”, choosing and training individuals with no prior scientific experience how to perform gDNA extractions and safe methods for shipping extracts to any research institution. All methods have been validated by the Andersen group at Lawrence Berkeley National Laboratory using the Berkeley Lab PhyloChip.« less

Pharmacist supply of sildenafil: pharmacists' experiences and perceptions on training and tools for supply.

PubMed

Braund, Rhiannon; Ratnayake, Kaushalya; Tong, Katie; Song, Jackie; Chai, Stephen; Gauld, Natalie

2018-06-01

Background In 2014, New Zealand reclassified sildenafil (for erectile dysfunction) to allow supply by specially trained pharmacists under strict criteria. Objective The study aimed to determine pharmacists' experiences and perspectives on the training for, and supply of sildenafil under this model. Setting New Zealand community pharmacy. Method This qualitative study captured data with a semi-structured interview used with purposively-sampled participants. A maximum variation sample was used to select a wide range of pharmacists working in various pharmacies, including pharmacists who were trained to provide sildenafil and those not trained to supply sildenafil. Consenting pharmacists were interviewed, with interviews audio-recorded and transcribed. Analysis used a framework approach. Main outcome measures Topics explored included: satisfaction and experience of the training; suitability and usability of the screening tools; experiences of the supply process and why some pharmacists chose not to become trained. Results Thirty-five pharmacists were interviewed. Training was seen as uncomplicated and the screening tools provided confidence that key consultation areas were covered. Most consultations reportedly took 15-20 min, some up to 60 min. Pharmacists reported being comfortable with the consultations. Many men requesting supply fell outside of the parameters, resulting in medical referral. This new model of supply was seen as a positive for pharmacists and their patients. Unaccredited pharmacists reported a perceived lack of interest from men, or ability to provide the service as reasons for not seeking accreditation. Conclusion New Zealand's model of pharmacist supply of sildenafil appears workable with some areas for improvement identified.

Exploration and practice of the cultivation of optoelectronic innovative talents based on the Students Innovation Training Program

NASA Astrophysics Data System (ADS)

Lei, Bing; Liu, Wei; Shi, Jianhua; Yao, Tianfu; Wang, Wei; Hu, Haojun

2017-08-01

The Students Innovation Training Program (SITP) has become an effective method to impel the teaching reform and improve undergraduate's innovative practical ability in Chinese colleges and universities, which is quite helpful for students to understand the social requirement, to grasp the basic means of scientific research and to improve their innovative practical ability and team work spirit. In this paper, three problems have been analyzed and discussed based on our organizing and instructing experience of SITP in recent years. Firstly, the SITP is a synthetically training project, and it is quite suitable to cultivate the students' innovative practical ability. Because SITP is similar to the real scientific research activity, and both of them include the steps of project application, solution design, research implementation and project summary etc. By making great efforts to these basic training steps, the undergraduates' innovative practical ability has been improved systemically. Secondly, a new talents cultivation system has been constructed based on SITP by integrating the subject competitions, graduation design and other conventional training activities, which is quite good to improve the training quality and decrease the total training class hours. Thirdly, a series of long-term effective operation and management guidelines have been established to ensure the SITP work normally, including doing a good job of project evaluation, setting up a reward and punishment system and creating a good atmosphere for innovation. In conclusion, great efforts have been made to enhance undergraduates' innovative ability, and the research results will provide useful reference for improving the training effects and reforming talents cultivating mode further.

Frontline over ivory tower: key competencies in community-based curricula.

PubMed

Millar, Adam; Malcolm, Janine; Cheng, Alice; Fine, Rebecca; Wong, Rene

2015-01-01

The Royal College of Physicians and Surgeons of Canada mandates that community experiences be incorporated into medicine-based specialties. Presently there is wide variability in community endocrine experiences across Canadian training programs. This is complicated by the paucity of literature providing guidance on what constitutes a 'community' rotation. A modified Delphi technique was used to determine the CanMEDS competencies best taught in a community endocrinology curriculum. The Delphi technique is a qualitative-research method that uses a series of questionnaires sent to a group of experts with controlled feedback provided by the researchers after each survey round. The experts in this study included endocrinology program directors, community endocrinologists, endocrinology residents and recent endocrinology graduates. Thirty four out of 44 competencies rated by the panel were deemed suitable for a community curriculum. The experts considered the "Manager" role best taught in the community, while they considered the community least suitable to learn the "Medical Expert" competency. To our knowledge, this is the first time the content of a community-based subspecialty curriculum was determined using the Delphi process in Canada. These findings suggest that community settings have potential to fill in gaps in residency training in regards to the CanMEDS Manager role. The results will aid program directors in designing competency-based community endocrinology rotations and competency-based community rotations in other medical subspecialty programs.

Receptive field optimisation and supervision of a fuzzy spiking neural network.

PubMed

Glackin, Cornelius; Maguire, Liam; McDaid, Liam; Sayers, Heather

2011-04-01

This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train firing rates and behave in a similar manner as fuzzy membership functions. The connectivity of the hidden and output layers in the fuzzy spiking neural network (FSNN) is representative of a fuzzy rule base. Fuzzy C-Means clustering is utilised to produce clusters that represent the antecedent part of the fuzzy rule base that aid classification of the feature data. Suitable cluster widths are determined using two strategies; subjective thresholding and evolutionary thresholding respectively. The former technique typically results in compact solutions in terms of the number of neurons, and is shown to be particularly suited to small data sets. In the latter technique a pool of cluster candidates is generated using Fuzzy C-Means clustering and then a genetic algorithm is employed to select the most suitable clusters and to specify cluster widths. In both scenarios, the network is supervised but learning only occurs locally as in the biological case. The advantages and disadvantages of the network topology for the Fisher Iris and Wisconsin Breast Cancer benchmark classification tasks are demonstrated and directions of current and future work are discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

Exploring the impact of wheelchair design on user function in a rural South African setting.

PubMed

Visagie, Surona; Duffield, Svenje; Unger, Mariaan

2015-01-01

Wheelchairs provide mobility that can enhance function and community integration. Function in a wheelchair is influenced by wheelchair design. To explore the impact of wheelchair design on user function and the variables that guided wheelchair prescription in the study setting. A mixed-method, descriptive design using convenience sampling was implemented. Quantitative data were collected from 30 wheelchair users using the functioning every day with a Wheelchair Scale and a Wheelchair Specification Checklist. Qualitative data were collected from ten therapists who prescribed wheelchairs to these users, through interviews. The Kruskal-Wallis test was used to identify relationships, and content analysis was undertaken to identify emerging themes in qualitative data. Wheelchairs with urban designs were issued to 25 (83%) participants. Wheelchair size, fit, support and functional features created challenges concerning transport, operating the wheelchair, performing personal tasks, and indoor and outdoor mobility. Users using wheelchairs designed for use in semi-rural environments achieved significantly better scores regarding the appropriateness of the prescribed wheelchair than those using wheelchairs designed for urban use ( p = <0.01). Therapists prescribed the basic, four-wheel folding frame design most often because of a lack of funding, lack of assessment, lack of skills and user choice. Issuing urban type wheelchairs to users living in rural settings might have a negative effect on users' functional outcomes. Comprehensive assessments, further training and research, on long term cost and quality of life implications, regarding provision of a suitable wheelchair versus a cheaper less suitable option is recommended.

Coral reef habitat response to climate change scenarios.

PubMed

Freeman, Lauren A; Kleypas, Joan A; Miller, Arthur J

2013-01-01

Coral reef ecosystems are threatened by both climate change and direct anthropogenic stress. Climate change will alter the physico-chemical environment that reefs currently occupy, leaving only limited regions that are conducive to reef habitation. Identifying these regions early may aid conservation efforts and inform decisions to transplant particular coral species or groups. Here a species distribution model (Maxent) is used to describe habitat suitable for coral reef growth. Two climate change scenarios (RCP4.5, RCP8.5) from the National Center for Atmospheric Research's Community Earth System Model were used with Maxent to determine environmental suitability for corals (order Scleractinia). Environmental input variables best at representing the limits of suitable reef growth regions were isolated using a principal component analysis. Climate-driven changes in suitable habitat depend strongly on the unique region of reefs used to train Maxent. Increased global habitat loss was predicted in both climate projections through the 21(st) century. A maximum habitat loss of 43% by 2100 was predicted in RCP4.5 and 82% in RCP8.5. When the model is trained solely with environmental data from the Caribbean/Atlantic, 83% of global habitat was lost by 2100 for RCP4.5 and 88% was lost for RCP8.5. Similarly, global runs trained only with Pacific Ocean reefs estimated that 60% of suitable habitat would be lost by 2100 in RCP4.5 and 90% in RCP8.5. When Maxent was trained solely with Indian Ocean reefs, suitable habitat worldwide increased by 38% in RCP4.5 by 2100 and 28% in RCP8.5 by 2050. Global habitat loss by 2100 was just 10% for RCP8.5. This projection suggests that shallow tropical sites in the Indian Ocean basin experience conditions today that are most similar to future projections of worldwide conditions. Indian Ocean reefs may thus be ideal candidate regions from which to select the best strands of coral for potential re-seeding efforts.

Membership generation using multilayer neural network

NASA Technical Reports Server (NTRS)

Kim, Jaeseok

1992-01-01

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

[A guide dog in the life of the blind--experiences, insights, considerations].

PubMed

Küpfer, R

1992-02-01

A guide dog user himself, the author nevertheless sets out to put forth in as unprejudiced a manner as possible the pros and cons of this mobility aid for blind persons. After a brief outline of the history and present situation of guide dog use in Germany, the author addresses the skills such a service animal must be provided with, dealing both with genetic and biographic issues relative to his or her suitability and training. Also, the team, conceived as a differentiated action entity, is set out in some detail due to its unique structure and dynamics, dealing also with its intricate relationship with the environment. Concluding, the author on the one hand addresses the psychologically important adjustment difficulties a person without previous experience in using this aid may be faced with, and on the other the partnership-based position of the guide dog in private and occupational social interaction.

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

2002-01-01

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

PubMed

Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael

2012-02-01

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Decentralization of CD4 testing in resource-limited settings: 7 years of experience in six African countries.

PubMed

Marinucci, F; Medina-Moreno, S; Paterniti, A D; Wattleworth, M; Redfield, R R

2011-05-01

Improving access to CD4 testing in resource-limited settings can be achieved through both centralized and decentralized testing networks. Decentralized testing models are more suitable for countries where the HIV epidemic affects a large portion of rural populations. Timely access to accurate CD4 results is crucial at the primary level of the health system. For the past 7 years, the Institute of Human Virology of the University of Maryland School of Medicine has implemented a flexible and sustainable three-phase model: (1) site assessment and improvement, (2) appropriate technology selection with capacity building through practical training and laboratory mentoring, and (3) quality management system strengthening and monitoring, to support accessibility to reliable CD4 counting at the point of service. CD4 testing capacity was established in 122 of 229 (53%) laboratories supported in Nigeria, Uganda, Kenya, Zambia, Tanzania, and Rwanda. Among those in rural settings, 46% (69/151) had CD4 testing available at site level, with a functioning flow cytometer installed at 28% (8/29) and 50% (61/122) of level 1 and level 2 sites, respectively. To strengthen local capacity, a total of 1,152 laboratory technicians were trained through 188 training sessions provided both on-site and at central locations. The overall quality of CD4 total testing procedure was assessed at 76% (92/121) of the laboratories, with 25% (23/92), 34% (31/92), and 33% (30/92) of them reporting excellent, good, and satisfactory performance. Balancing country-specific factors with the location of the clinic, number of patients, and the expected workload, was crucial in adapting this flexible model for decentralizing CD4 testing. The close collaboration with local governments and private vendors was key to successfully expanding access to CD4 testing within the framework of HIV care and treatment programs and for the sustainability of medical laboratories in resource-limited settings. Copyright © 2011 International Society for Advancement of Cytometry.

Agile convolutional neural network for pulmonary nodule classification using CT images.

PubMed

Zhao, Xinzhuo; Liu, Liyao; Qi, Shouliang; Teng, Yueyang; Li, Jianhua; Qian, Wei

2018-04-01

To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images. A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally. After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to [Formula: see text], the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used. This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

Automation Training Tools of the Future.

ERIC Educational Resources Information Center

Rehg, James

1986-01-01

Manufacturing isn't what it used to be, and the United States must ensure its position in the world trade market by educating factory workers in new automated systems. A computer manufacturing engineer outlines the training requirements of a modern workforce and details robotic training devices suitable for classroom use. (JN)

The Enterprise Training System and Training Content Analysis of Selected Manufacturing Companies in Taiwan, R.O.C.: A Case Study.

ERIC Educational Resources Information Center

Kuo, Mike Chu-Hsun

A study investigated the current enterprise training system in Taiwan and proposed suitable training suggestions for manufacturing industry through a carefully designed case study. Literature review and field study were used to gather research data. Interviews were conducted at four large manufacturing companies during the period October 1990 to…

Limited Effects of Set Shifting Training in Healthy Older Adults

PubMed Central

Grönholm-Nyman, Petra; Soveri, Anna; Rinne, Juha O.; Ek, Emilia; Nyholm, Alexandra; Stigsdotter Neely, Anna; Laine, Matti

2017-01-01

Our ability to flexibly shift between tasks or task sets declines in older age. As this decline may have adverse effects on everyday life of elderly people, it is of interest to study whether set shifting ability can be trained, and if training effects generalize to other cognitive tasks. Here, we report a randomized controlled trial where healthy older adults trained set shifting with three different set shifting tasks. The training group (n = 17) performed adaptive set shifting training for 5 weeks with three training sessions a week (45 min/session), while the active control group (n = 16) played three different computer games for the same period. Both groups underwent extensive pre- and post-testing and a 1-year follow-up. Compared to the controls, the training group showed significant improvements on the trained tasks. Evidence for near transfer in the training group was very limited, as it was seen only on overall accuracy on an untrained computerized set shifting task. No far transfer to other cognitive functions was observed. One year later, the training group was still better on the trained tasks but the single near transfer effect had vanished. The results suggest that computerized set shifting training in the elderly shows long-lasting effects on the trained tasks but very little benefit in terms of generalization. PMID:28386226

Economic evaluation of emergency obstetric care training: a systematic review.

PubMed

Banke-Thomas, Aduragbemi; Wilson-Jones, Megan; Madaj, Barbara; van den Broek, Nynke

2017-12-04

Training healthcare providers in Emergency Obstetric Care (EmOC) has been shown to be effective in improving their capacity to provide this critical care package for mothers and babies. However, little is known about the costs and cost-effectiveness of such training. Understanding costs and cost-effectiveness is essential in guaranteeing value-for-money in healthcare spending. This study systematically reviewed the available literature on cost and cost-effectiveness of EmOC trainings. Peer-reviewed and grey literature was searched for relevant papers published after 1990. Studies were included if they described an economic evaluation of EmOC training and the training cost data were available. Two reviewers independently searched, screened, and selected studies that met the inclusion criteria, with disagreements resolved by a third reviewer. Quality of studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards statement. For comparability, all costs in local currency were converted to International dollar (I$) equivalents using purchasing power parity conversion factors. The cost per training per participant was calculated. Narrative synthesis was used to summarise the available evidence on cost effectiveness. Fourteen studies (five full and nine partial economic evaluations) met the inclusion criteria. All five and two of the nine partial economic evaluations were of high quality. The majority of studies (13/14) were from low- and middle-income countries. Training equipment, per diems and resource person allowance were the most expensive components. Cost of training per person per day ranged from I$33 to I$90 when accommodation was required and from I$5 to I$21 when training was facility-based. Cost-effectiveness of training was assessed in 5 studies with differing measures of effectiveness (knowledge, skills, procedure cost and lives saved) making comparison difficult. Economic evaluations of EmOC training are limited. There is a need to scale-up and standardise processes that capture both cost and effectiveness of training and to agree on suitable economic evaluation models that allow for comparability across settings. PROSPERO_CRD42016041911 .

Short-term adaptations following Complex Training in team-sports: A meta-analysis

PubMed Central

Martinez-Rodriguez, Alejandro; Calleja-González, Julio; Alcaraz, Pedro E.

2017-01-01

Objective The purpose of this meta-analysis was to study the short-term adaptations on sprint and vertical jump (VJ) performance following Complex Training (CT) in team-sports. CT is a resistance training method aimed at developing both strength and power, which has a direct effect on sprint and VJ. It consists on alternating heavy resistance training exercises with plyometric/power ones, set for set, on the same workout. Methods A search of electronic databases up to July 2016 (PubMed-MEDLINE, SPORTDiscus, Web of Knowledge) was conducted. Inclusion criteria: 1) at least one CT intervention group; 2) training protocols ≥4-wks; 3) sample of team-sport players; 4) sprint or VJ as an outcome variable. Effect sizes (ES) of each intervention were calculated and subgroup analyses were performed. Results A total of 9 studies (13 CT groups) met the inclusion criteria. Medium effect sizes (ES) (ES = 0.73) were obtained for pre-post improvements in sprint, and small (ES = 0.41) in VJ, following CT. Experimental-groups presented better post-intervention sprint (ES = 1.01) and VJ (ES = 0.63) performance than control-groups. Sprint large ESs were exhibited in younger athletes (<20 years old; ES = 1.13); longer CT interventions (≥6 weeks; ES = 0.95); conditioning activities with intensities ≤85% 1RM (ES = 0.96) and protocols with frequencies of <3 sessions/week (ES = 0.84). Medium ESs were obtained in Division I players (ES = 0.76); training programs >12 total sessions (ES = 0.74). VJ Large ESs in programs with >12 total sessions (ES = 0.81). Medium ESs obtained for under-Division I individuals (ES = 0.56); protocols with intracomplex rest intervals ≥2 min (ES = 0.55); conditioning activities with intensities ≤85% 1RM (ES = 0.64); basketball/volleyball players (ES = 0.55). Small ESs were found for younger athletes (ES = 0.42); interventions ≥6 weeks (ES = 0.45). Conclusions CT interventions have positive medium effects on sprint performance and small effects on VJ in team-sport athletes. This training method is a suitable option to include in the season planning. PMID:28662108

Wire Detection Algorithms for Navigation

NASA Technical Reports Server (NTRS)

Kasturi, Rangachar; Camps, Octavia I.

2002-01-01

In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning method) is the need for a very good set of positive and negative examples since the performance depends on the quality of the training set.

Engineering teacher training models and experiences

NASA Astrophysics Data System (ADS)

González-Tirados, R. M.

2009-04-01

Institutions and Organisations that take training seriously and devote time, effort and resources, etc, to their own teams are more likely to succeed, since both initial teacher training and continuous improvement, studies, hours of group discussion, works on innovation and educational research, talks and permanent meetings, etc, will all serve to enhance teaching and its quality. Teachers will be able to introduce new components from previously taught classes into their university teaching which will contribute to improving their work and developing a suitable academic environment to include shared objectives, teachers and students. Moreover, this training will serve to enhance pedagogic innovation, new teaching-learning methodologies and contribute to getting teaching staff involved in respect of the guidelines set out by the EHEA. Bearing in mind that training and motivation can be key factors in any teacher's "performance", their productivity and the quality of their teaching, Teacher Training for a specific post inside the University Organisation is standard practice of so-called Human Resources management and an integral part of a teacher's work; it is a way of professionalising the teaching of the different branches of Engineering. At Madrid Polytechnic University, in the Institute of Educational Sciences (ICE), since it was founded in 1972, we have been working hard with university teaching staff. But it was not until 1992 after carrying out various studies on training needs that we planned and programmed different training actions, offering a wide range of possibilities. Thus, we designed and taught an "Initial Teacher Training Course", as it was first called in 1992, a programme basically aimed to train young Engineering teachers just setting out on their teaching career. In 2006, the name was changed to "Advanced University Teacher Training Course". Subsequently, with the appearance of the Bologna Declaration and the creation of the European Higher Education Area, we renewed the programme, content and methodology, teaching the course under the name of "Initial Teacher Training Course within the framework of the European Higher Education Area". Continuous Training means learning throughout one's life as an Engineering teacher. They are actions designed to update and improve teaching staff, and are systematically offered on the current issues of: Teaching Strategies, training for research, training for personal development, classroom innovations, etc. They are activities aimed at conceptual change, changing the way of teaching and bringing teaching staff up-to-date. At the same time, the Institution is at the disposal of all teaching staff as a meeting point to discuss issues in common, attend conferences, department meetings, etc. In this Congress we present a justification of both training models and their design together with some results obtained on: training needs, participation, how it is developing and to what extent students are profiting from it.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

AFOMP Policy No 5: career progression for clinical medical physicists in AFOMP countries.

PubMed

Round, W H; Stefanoyiannis, A P; Ng, K H; Rodriguez, L V; Thayalan, K; Han, Y; Tang, F; Fukuda, S; Srivastava, R; Krisanachinda, A; Shiau, A C; Deng, X

2015-06-01

This policy statement, which is the fifth of a series of documents being prepared by the Asia-Oceania Federation of Organizations for Medical Physics Professional Development Committee, gives guidance on how clinical medical physicists' careers should progress from their initial training to career end. It is not intended to be prescriptive as in some AFOMP countries career structures are already essentially defined by employment awards and because such matters will vary considerably from country to country depending on local culture, employment practices and legislation. It is intended to be advisory and set out options for member countries and employers of clinical medical physicists to develop suitable career structures.

Torque Control of a Rehabilitation Teaching Robot Using Magneto-Rheological Fluid Clutches

NASA Astrophysics Data System (ADS)

Hakogi, Hokuto; Ohaba, Motoyoshi; Kuramochi, Naimu; Yano, Hidenori

A new robot that makes use of MR-fluid clutches for simulating torque is proposed to provide an appropriate device for training physical therapy students in knee-joint rehabilitation. The feeling of torque provided by the robot is expected to correspond to the torque performance obtained by physical therapy experts in a clinical setting. The torque required for knee-joint rehabilitation, which is a function of the rotational angle and the rotational angular velocity of a knee movement, is modeled using a mechanical system composed of typical spring-mass-damper elements. The robot consists of two MR-fluid clutches, two induction motors, and a feedback control system. In the torque experiments, output torque is controlled using the spring and damper coefficients separately. The values of these coefficients are determined experimentally. The experimental results show that the robot would be suitable for training physical therapy students to experience similar torque feelings as needed in a clinical situation.

Laser bandwidth interlock capable of single pulse detection and rejection

DOEpatents

Armstrong, James P; Telford, Steven James; Lanning, Rodney Kay; Bayramian, Andrew James

2012-10-09

A pulse of laser light is switched out of a pulse train and spatially dispersed into its constituent wavelengths. The pulse is collimated to a suitable size and then diffracted by high groove density multilayer dielectric gratings. This imparts a different angle to each individual wavelength so that, when brought to the far field with a lens, the colors have spread out in a linear arrangement. The distance between wavelengths (resolution) can be tailored for the specific laser and application by altering the number of times the beam strikes the diffraction gratings, the groove density of the gratings and the focal length of the lens. End portions of the linear arrangement are each directed to a respective detector, which converts the signal to a 1 if the level meets a set-point, and a 0 if the level does not. If both detectors produces a 1, then the pulse train is allowed to propagate into an optical system.

Development of the Russian matrix sentence test.

PubMed

Warzybok, Anna; Zokoll, Melanie; Wardenga, Nina; Ozimek, Edward; Boboshko, Maria; Kollmeier, Birger

2015-01-01

To develop the Russian matrix sentence test for speech intelligibility measurements in noise. Test development included recordings, optimization of speech material, and evaluation to investigate the equivalency of the test lists and training. For each of the 500 test items, the speech intelligibility function, speech reception threshold (SRT: signal-to-noise ratio, SNR, that provides 50% speech intelligibility), and slope was obtained. The speech material was homogenized by applying level corrections. In evaluation measurements, speech intelligibility was measured at two fixed SNRs to compare list-specific intelligibility functions. To investigate the training effect and establish reference data, speech intelligibility was measured adaptively. Overall, 77 normal-hearing native Russian listeners. The optimization procedure decreased the spread in SRTs across words from 2.8 to 0.6 dB. Evaluation measurements confirmed that the 16 test lists were equivalent, with a mean SRT of -9.5 ± 0.2 dB and a slope of 13.8 ± 1.6%/dB. The reference SRT, -8.8 ± 0.8 dB for the open-set and -9.4 ± 0.8 dB for the closed-set format, increased slightly for noise levels above 75 dB SPL. The Russian matrix sentence test is suitable for accurate and reliable speech intelligibility measurements in noise.

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation.

PubMed

Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD's robustness and wide applicability.

Effects of Goal Setting on Performance and Job Satisfaction

ERIC Educational Resources Information Center

Ivancevich, John M.

1976-01-01

Studied the effect of goal-setting training on the performance and job satisfaction of sales personnel. One group was trained in participative goal setting; one group was trained in assigned goal setting; and one group received no training. Both trained groups showed temporary improvements in performance and job satisfaction. For availability see…

Adolescent Neurocognitive Development, Self-Regulation, and School-Based Drug Use Prevention

PubMed Central

Herzog, Thaddeus A.; Black, David S.; Zaman, Adnin; Riggs, Nathaniel R.; Sussman, Steve

2014-01-01

Adolescence is marked by several key development-related changes, including neurocognitive changes. Cognitive abilities associated with self-regulation are not fully developed until late adolescence or early adulthood whereas tendencies to take risks and seek thrilling and novel experience seem to increase significantly throughout this phase, resulting in a discrepancy between increased susceptibility to poor regulation and lower ability to exercise self-control. Increased vulnerability to drug use initiation, maintenance, and dependence during adolescence may be explained based on this imbalance in the self-regulation system. In this paper, we highlight the relevance of schools as a setting for delivering adolescent drug use prevention programs that are based on recent findings from neuroscience concerning adolescent brain development. We discuss evidence from school-based as well as laboratory research that suggests that suitable training may improve adolescents’ executive brain functions that underlie self-regulation abilities and, as a result, help prevent drug use and abuse. We note that considerable further research is needed in order (1) to determine that self-regulation training has effects at the neurocognitive level and (2) to effectively incorporate self-regulation training based on neuropsychological models into school-based programming. PMID:23408284

Adolescent neurocognitive development, self-regulation, and school-based drug use prevention.

PubMed

Pokhrel, Pallav; Herzog, Thaddeus A; Black, David S; Zaman, Adnin; Riggs, Nathaniel R; Sussman, Steve

2013-06-01

Adolescence is marked by several key development-related changes, including neurocognitive changes. Cognitive abilities associated with self-regulation are not fully developed until late adolescence or early adulthood whereas tendencies to take risks and seek thrilling and novel experience seem to increase significantly throughout this phase, resulting in a discrepancy between increased susceptibility to poor regulation and lower ability to exercise self-control. Increased vulnerability to drug use initiation, maintenance, and dependence during adolescence may be explained based on this imbalance in the self-regulation system. In this paper, we highlight the relevance of schools as a setting for delivering adolescent drug use prevention programs that are based on recent findings from neuroscience concerning adolescent brain development. We discuss evidence from school-based as well as laboratory research that suggests that suitable training may improve adolescents' executive brain functions that underlie self-regulation abilities and, as a result, help prevent drug use and abuse. We note that considerable further research is needed in order (1) to determine that self-regulation training has effects at the neurocognitive level and (2) to effectively incorporate self-regulation training based on neuropsychological models into school-based programming.

Construction of an isokinetic eccentric cycle ergometer for research and training.

PubMed

Elmer, Steven J; Martin, James C

2013-08-01

Eccentric cycling serves a useful exercise modality in clinical, research, and sport training settings. However, several constraints can make it difficult to use commercially available eccentric cycle ergometers. In this technical note, we describe the process by which we built an isokinetic eccentric cycle ergometer using exercise equipment modified with commonly available industrial parts. Specifically, we started with a used recumbent cycle ergometer and removed all the original parts leaving only the frame and seat. A 2.2 kW electric motor was attached to a transmission system that was then joined with the ergometer. The motor was controlled using a variable frequency drive, which allowed for control of a wide range of pedaling rates. The ergometer was also equipped with a power measurement device that quantified work, power, and pedaling rate and provided feedback to the individual performing the exercise. With these parts along with some custom fabrication, we were able to construct an isokinetic eccentric cycle ergometer suitable for research and training. This paper offers a guide for those individuals who plan to use eccentric cycle ergometry as an exercise modality and wish to construct their own ergometer.

Extracting duration information in a picture category decoding task using hidden Markov Models

NASA Astrophysics Data System (ADS)

Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg

2016-04-01

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

Artificial Intelligence in Prediction of Secondary Protein Structure Using CB513 Database

PubMed Central

Avdagic, Zikrija; Purisevic, Elvir; Omanovic, Samir; Coralic, Zlatan

2009-01-01

In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513. PMID:21347158

Fatty acid profile of plasma NEFA does not reflect adipose tissue fatty acid profile.

PubMed

Walker, Celia G; Browning, Lucy M; Stecher, Lynne; West, Annette L; Madden, Jackie; Jebb, Susan A; Calder, Philip C

2015-09-14

Adipose tissue (AT) fatty acid (FA) composition partly reflects habitual dietary intake. Circulating NEFA are mobilised from AT and might act as a minimally invasive surrogate marker of AT FA profile. Agreement between twenty-eight FA in AT and plasma NEFA was assessed using concordance coefficients in 204 male and female participants in a 12-month intervention using supplements to increase the intake of EPA and DHA. Concordance coefficients generally showed very poor agreement between AT FA and plasma NEFA at baseline SFA: 0·07; MUFA: 0·03; n-6 PUFA: 0·28; n-3 PUFA: 0·01). Participants were randomly divided into training (70 %) and validation (30 %) data sets, and models to predict AT and dietary FA were fitted using data from the training set, and their predictive ability was assessed using data from the validation set. AT n-6 PUFA and SFA were predicted from plasma NEFA with moderate accuracy (mean absolute percentage error n-6 PUFA: 11 % and SFA: 8 %), but predicted values were unable to distinguish between low, medium and high FA values, with only 25 % of n-6 PUFA and 33 % of SFA predicted values correctly assigned to the appropriate tertile group. Despite an association between AT and plasma NEFA EPA (P=0·001) and DHA (P=0·01) at baseline, there was no association after the intervention. To conclude, plasma NEFA are not a suitable surrogate for AT FA.

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

2017-10-01

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

[Hypertension and exercise. Sports methods for the hypertensive patient].

PubMed

Thiele, Holger; Pohlink, Carla; Schuler, Gerhard

2004-06-01

Physical exercise is of paramount therapeutic importance in nonpharmacological interventions of arterial hypertension. The extent and the effects of exercise on blood pressure lowering are analyzed according to the actual literature. Suitable and nonsuitable activities are considered. Dynamic isotonic endurance training is more effective than static isometric exercise. A rather low or moderate extent of endurance training lowers the systolic and diastolic blood pressure by approximately 5-11 mmHg and 3-8 mmHg, respectively. This effect of exercise can be achieved besides the favorable effects on other cardiovascular risk factors. Intensity of exercise should be monitored by the heart rate. The mean intensity should not exceed 70% of the maximal heart rate. An initial ergometry might be suitable for the planning of training recommendations.

Training for healthy older drivers : traffic tech.

DOT National Transportation Integrated Search

2013-05-01

The research described in this edition of "Traffic Tech" examined the effectiveness of four types of : training techniques designed to improve the driving performance : of normally aging adults. Each technique is suitable : for a broad cross-section ...

Fourier spatial frequency analysis for image classification: training the training set

NASA Astrophysics Data System (ADS)

Johnson, Timothy H.; Lhamo, Yigah; Shi, Lingyan; Alfano, Robert R.; Russell, Stewart

2016-04-01

The Directional Fourier Spatial Frequencies (DFSF) of a 2D image can identify similarity in spatial patterns within groups of related images. A Support Vector Machine (SVM) can then be used to classify images if the inter-image variance of the FSF in the training set is bounded. However, if variation in FSF increases with training set size, accuracy may decrease as the size of the training set increases. This calls for a method to identify a set of training images from among the originals that can form a vector basis for the entire class. Applying the Cauchy product method we extract the DFSF spectrum from radiographs of osteoporotic bone, and use it as a matched filter set to eliminate noise and image specific frequencies, and demonstrate that selection of a subset of superclassifiers from within a set of training images improves SVM accuracy. Central to this challenge is that the size of the search space can become computationally prohibitive for all but the smallest training sets. We are investigating methods to reduce the search space to identify an optimal subset of basis training images.

A retrospective analysis to determine the effect of independent treatment centres on the case mix for microsurgical training.

PubMed

Barsam, A; Heatley, C J; Sundaram, V; Toma, N M G

2008-05-01

To determine the effect of Independent Sector Treatment Centres (ISTC) on microsurgical training. A novel scoring protocol for stratification of cases suitable for microsurgical training was devised. This scoring protocol was applied to all patients who underwent cataract surgery on a single consultant dedicated training list between September and November 2004. These patients are representative of patients remaining on the waiting list after ISTC selection, that is, the residual case mix. Patients who underwent cataract surgery on the same consultant list in the same period in 2003 were also analysed when there was no ISTC or other waiting list initiative in operation. Data was available for 129 patients. Seventy three patients underwent cataract surgery between September and November 2003 and 56 patients underwent cataract surgery in the same period in 2004. Using the devised scoring protocol, the mean score in the 2003 group was 1.08 +/-1.75 (range, 0.0-10.5) and for the 2004 group the mean score was 2.31 +/-2.65 (range, 0.0-4.5). A Mann-Whitney test showed that there was a statistically significant difference between the scores in the two groups (P=0.0009). With Independent Sector Treatment Centre implementation the percentage of cases suitable only for consultants increased fourfold. The decrease in suitable cases for training as shown in this study is likely to have serious consequences on microsurgical training in the UK. We recommend that the results of this study are considered in any current or future plans for ISTC continuation and expansion.

38 CFR 21.6060 - Services and assistance.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., psychological, employment and personal adjustment counseling; (ii) Placement services to effect suitable...) Training at an institution of higher learning in a program of education that is not predominantly.... 3106(e). (Authority: 38 U.S.C. 1524(b)) Duration of Training ...

Utilizing feedback in adaptive SAR ATR systems

NASA Astrophysics Data System (ADS)

Horsfield, Owen; Blacknell, David

2009-05-01

Existing SAR ATR systems are usually trained off-line with samples of target imagery or CAD models, prior to conducting a mission. If the training data is not representative of mission conditions, then poor performance may result. In addition, it is difficult to acquire suitable training data for the many target types of interest. The Adaptive SAR ATR Problem Set (AdaptSAPS) program provides a MATLAB framework and image database for developing systems that adapt to mission conditions, meaning less reliance on accurate training data. A key function of an adaptive system is the ability to utilise truth feedback to improve performance, and it is this feature which AdaptSAPS is intended to exploit. This paper presents a new method for SAR ATR that does not use training data, based on supervised learning. This is achieved by using feature-based classification, and several new shadow features have been developed for this purpose. These features allow discrimination of vehicles from clutter, and classification of vehicles into two classes: targets, comprising military combat types, and non-targets, comprising bulldozers and trucks. The performance of the system is assessed using three baseline missions provided with AdaptSAPS, as well as three additional missions. All performance metrics indicate a distinct learning trend over the course of a mission, with most third and fourth quartile performance levels exceeding 85% correct classification. It has been demonstrated that these performance levels can be maintained even when truth feedback rates are reduced by up to 55% over the course of a mission.

The Irie Classroom Toolbox: developing a violence prevention, preschool teacher training program using evidence, theory, and practice.

PubMed

Baker-Henningham, Helen

2018-05-01

In this paper, I describe the development of the Irie Classroom Toolbox, a school-based violence prevention, teacher training program for use with children aged 3-6 years. In-depth interviews were conducted with Jamaican preschool teachers, who had participated in a trial of a classroom behavior management program, at posttest (n = 35) and 5 years later (n = 20). An on-going process evaluation was also conducted. Teachers' preferred behavior management strategies and training methods were documented, and enablers and barriers to implementation were identified. Teachers were most likely to adopt strategies that they liked, found easy to use, and were effective. These included paying attention to positive behavior and explicitly teaching children the expected behavior. Teachers preferred active, hands-on training strategies based on social-cognitive theories. Enablers to intervention implementation included positive teacher-facilitator relationships, choice, collaborative problem solving, teachers recognizing benefits of the intervention, group support, and provision of materials. Barriers to intervention implementation were also identified. These data were integrated with behavior change theory (i.e., the behavior change wheel and theoretical domains framework) to develop an intervention grounded in common core elements of evidence-based programs while also utilizing teachers' perspectives. The resulting program is a low cost, adaptable intervention that should be suitable for training preschool teachers in other low-resource settings. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of The New York Academy of Sciences.

The Canadian general surgery resident: defining current challenges for surgical leadership.

PubMed

Tomlinson, Corey; Labossière, Joseph; Rommens, Kenton; Birch, Daniel W

2012-08-01

Surgery training programs in Canada and the United States have recognized the need to modify current models of training and education. The shifting demographic of surgery trainees, lifestyle issues and an increased trend toward subspecialization are the major influences. To guide these important educational initiatives, a contemporary profile of Canadian general surgery residents and their impressions of training in Canada is required. We developed and distributed a questionnaire to residents in each Canadian general surgery training program, and residents responded during dedicated teaching time. In all, 186 surveys were returned for analysis (62% response rate). The average age of Canadian general surgery residents is 30 years, 38% are women, 41% are married, 18% have dependants younger than 18 years and 41% plan to add to or start a family during residency. Most (87%) residents plan to pursue postgraduate education. On completion of training, 74% of residents plan to stay in Canada and 49% want to practice in an academic setting. Almost half (42%) of residents identify a poor balance between work and personal life during residency. Forty-seven percent of respondents have appropriate access to mentorship, whereas 37% describe suitable access to career guidance and 40% identify the availability of appropriate social supports. Just over half (54%) believe the stress level during residency is manageable. This survey provides a profile of contemporary Canadian general surgery residents. Important challenges within the residency system are identified. Program directors and chairs of surgery are encouraged to recognize these challenges and intervene where appropriate.

Inventory of forest and rangeland and detection of forest stress

NASA Technical Reports Server (NTRS)

Heller, R. C. (Principal Investigator); Aldrich, R. C.; Weber, F. P.; Driscoll, R. S.

1972-01-01

The author has identified the following significant results. Seventy-two ground sensors were interfaced with three DCP'S at the Black Hills test site. Unfortunately, the transmitters had to be returned for modification and forestry sensed data is not available. The DCP's did operate properly from the Berkeley laboratory and data were recovered from the Goldstone and Alaska stations via Goddard. Replicated training sets and test sets have been selected from all three test site areas in preparation for the receipt of ERTS imagery and digital tapes. From 600 and 800 points have been selected at each site location and UTM coordinates determined. Templates are being made of these sets. As of September 1, ERTS-generated data had not been received and no statements can be made regarding quality or suitability for forest and range experiments. Aerial photography (scale 1:32,000) of the Manitou (226 C) and Black Hills (226 A) sites was taken with CIR in June. Various scales (1:2,000; 1:10,000; 1:20,000; and 1:40,000) of 70 mm photographs were obtained at Manitou with normal color, CIR, and panchromatic in August.

Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms

PubMed Central

Sher, Mazhar; Zhuang, Rachel; Demirci, Utkan; Asghar, Waseem

2017-01-01

Introduction There is a significant interest in developing inexpensive portable biosensing platforms for various applications including disease diagnostics, environmental monitoring, food safety, and water testing at the point-of-care (POC) settings. Current diagnostic assays available in the developed world require sophisticated laboratory infrastructure and expensive reagents. Hence, they are not suitable for resource-constrained settings with limited financial resources, basic health infrastructure, and few trained technicians. Cellulose and flexible transparency paper-based analytical devices have demonstrated enormous potential for developing robust, inexpensive and portable devices for disease diagnostics. These devices offer promising solutions to disease management in resource-constrained settings where the vast majority of the population cannot afford expensive and highly sophisticated treatment options. Areas covered In this review, the authors describe currently developed cellulose and flexible transparency paper-based microfluidic devices, device fabrication techniques, and sensing technologies that are integrated with these devices. The authors also discuss the limitations and challenges associated with these devices and their potential in clinical settings. Expert commentary In recent years, cellulose and flexible transparency paper-based microfluidic devices have demonstrated the potential to become future healthcare options despite a few limitations such as low sensitivity and reproducibility. PMID:28103450

Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms.

PubMed

Sher, Mazhar; Zhuang, Rachel; Demirci, Utkan; Asghar, Waseem

2017-04-01

There is a significant interest in developing inexpensive portable biosensing platforms for various applications including disease diagnostics, environmental monitoring, food safety, and water testing at the point-of-care (POC) settings. Current diagnostic assays available in the developed world require sophisticated laboratory infrastructure and expensive reagents. Hence, they are not suitable for resource-constrained settings with limited financial resources, basic health infrastructure, and few trained technicians. Cellulose and flexible transparency paper-based analytical devices have demonstrated enormous potential for developing robust, inexpensive and portable devices for disease diagnostics. These devices offer promising solutions to disease management in resource-constrained settings where the vast majority of the population cannot afford expensive and highly sophisticated treatment options. Areas covered: In this review, the authors describe currently developed cellulose and flexible transparency paper-based microfluidic devices, device fabrication techniques, and sensing technologies that are integrated with these devices. The authors also discuss the limitations and challenges associated with these devices and their potential in clinical settings. Expert commentary: In recent years, cellulose and flexible transparency paper-based microfluidic devices have demonstrated the potential to become future healthcare options despite a few limitations such as low sensitivity and reproducibility.

The dilemma of selecting suitable proximal carious lesions in primary molars for restoration using ART technique.

PubMed

Kemoli, A M; van Amerongen, W E

2011-03-01

To determine the examiner's accuracy in selecting proximal carious lesions in primary molars for restoration using the atraumatic restorative treatment (ART) approach. Intervention study. CLINICAL SETTING AND PARTICIPANTS: A total of 804 six to eight year-olds from 30 rural schools in Kenya participated in the study. Three examiners selected a total of 1,280 suitable proximal carious lesions in primary molars after examining 6,002 children from 30 schools randomly selected out of 142 schools in two divisions. Seven operators randomly paired on a daily basis with eight assistants restored the lesions. An explanation was provided for any cavity that was not restored. Pre-and post-operative radiographs of the cavities were also taken for evaluation. The examiner's choice of suitable proximal cavities restorable using the ART approach was related to the decision made to either restore or not during the operative stage. The radiographic findings of the selected cavities were also compared to the decision made by the operator. The results obtained were used to determine the examiner's accuracy in selecting suitable proximal cavities for restoration using the ART approach. The majority of the children recruited in the study were excluded due to absenteeism, pulpal-exposure or anxiety during the operative stage. Only 804 children received one restoration in their primary molars. The examiner's accuracy in selecting suitable ART-restorable cavities clinically was 94.9% and based on radiographic analysis was 91.7%. A trained and diligent examiner has a very good chance of selecting proximal carious lesions restorable with the use of ART approach, without the threat of dental pulpal-involvement during the excavation of caries.

A Preliminary Evaluation of a Short Online Training Workshop for TPACK Development

ERIC Educational Resources Information Center

Alsofyani, Mohammed Modeef; bin Aris, Baharuddin; Eynon, Rebecca

2013-01-01

National plans in higher education institutions are being developed in various aspects of the academic world for technology integration. Short online training has the potential for accelerating and facilitating the implementation of those plans. So far, a little is known about the suitability of this mode of training for faculty members'…

The Training Requirements of the Clothing Industry. A Survey of Selected Occupations.

ERIC Educational Resources Information Center

Berry, Kathleen M.; Kuhl, Dean H.

This survey was conducted in order to determine the training requirements of the clothing industry in South Australia. The results and findings are intended to be used as a means for upgrading and revising the Clothing Production Certificate Course and for providing suitable training programs for other key occupations within the industry. Survey…

Challenges in conducting psychiatry studies in India

PubMed Central

Kharawala, Saifuddin; Dalal, Jeroze

2011-01-01

A large number of psychiatry studies are conducted in India. Psychiatry studies are complex and present unique challenges in the Indian setting. Ethical issues pertaining to the risk of worsening of illness, use of placebo and validity of informed consents are commonly faced. Site selection can be difficult due to the relative paucity of ICH-GCP (International Conference on Harmonisation - Good Clinical Practice) trained psychiatry investigators in India. Recruitment can be challenging due to issues such as strict eligibility criteria, (lack of) availability of caregiver, illness-related considerations, etc. Assessment of the consent capacity of patients is not simple, while structured assessments are not commonly employed. As the illness fluctuates, the consent capacity may change, thus requiring continued assessment of consent capacity. Study patients run the risk of worsening of illness and suicide due to exposure to inactive treatments; this risk is counterbalanced by use of appropriate study designs, as well as the indirect psychotherapeutic support received. Psychiatry studies are associated with a high placebo response. This necessitates conduct of placebo-controlled studies despite the attendant difficulties. Also, the high placebo response is often the cause of failed trials. Rating scales are essential for assessment of drug response. Some rating instruments as well as some rater training procedures may not be suitable for the Indian setting. Technological advancements may increase the procedural complexity but improve the quality of ratings. Psychiatry studies present monitors and auditors with unique scenarios too. Utilization of psychiatry specific training and expertise is recommended to ensure successful conduct of these studies in India. PMID:21584176

[Simulation training in surgical education - application of virtual reality laparoscopic simulators in a surgical skills course].

PubMed

Lehmann, K S; Gröne, J; Lauscher, J C; Ritz, J-P; Holmer, C; Pohlen, U; Buhr, H-J

2012-04-01

Training and simulation are gaining importance in surgical education. Today, virtual reality surgery simulators provide sophisticated laparoscopic training scenarios and offer detailed assessment methods. This also makes simulators interesting for the application in surgical skills courses. The aim of the current study was to assess the suitability of a virtual surgery simulator for training and assessment in an established surgical training course. The study was conducted during the annual "Practical Course for Visceral Surgery" (Warnemuende, Germany). 36 of 108 course participants were assigned at random for the study. Training was conducted in 15 sessions over 5 days with 4 identical virtual surgery simulators (LapSim) and 2 standardised training tasks. The simulator measured 16 individual parameters and calculated 2 scores. Questionnaires were used to assess the test persons' laparoscopic experience, their training situation and the acceptance of the simulator training. Data were analysed with non-parametric tests. A subgroup analysis for laparoscopic experience was conducted in order to assess the simulator's construct validity and assessment capabilities. Median age was 32 (27 - 41) years; median professional experience was 3 (1 - 11) years. Typical laparoscopic learning curves with initial significant improvements and a subsequent plateau phase were measured over 5 days. The individual training sessions exhibited a rhythmic variability in the training results. A shorter night's sleep led to a marked drop in performance. The participants' different experience levels could clearly be discriminated ( ≤ 20 vs. > 20 laparoscopic operations; p ≤ 0.001). The questionnaire showed that the majority of the participants had limited training opportunities in their hospitals. The simulator training was very well accepted. However, the participants severely misjudged the real costs of the simulators that were used. The learning curve on the simulator was successfully mastered during the course. Construct validity could be demonstrated within the course setting. The simulator's assessment system can be of value for the assessment of laparoscopic training performance within surgical skills courses. Acceptance of the simulator training is high. However, simulators are currently too expensive to be used within a large training course. © Georg Thieme Verlag KG Stuttgart · New York.

Methods, compounds and systems for detecting a microorganism in a sample

DOEpatents

Colston, Jr, Bill W.; Fitch, J. Patrick; Gardner, Shea N.; Williams, Peter L.; Wagner, Mark C.

2016-09-06

Methods to identify a set of probe polynucleotides suitable for detecting a set of targets and in particular methods for identification of primers suitable for detection of target microorganisms related polynucleotides, set of polynucleotides and compositions, and related methods and systems for detection and/or identification of microorganisms in a sample.

European Project (Eu Train - No 226518-CP-1-2005-F-COMENIUS-C21) on Comenius 2.1. Program for a Better Teaching Practice of Students in Physics and Chemistry

NASA Astrophysics Data System (ADS)

Raykova, Zh.; Mitrikova, R.; Nikolov, St.; Dimova, Y.; Valtonen, S.; Lampiselka, J.; Kyyronen, L.; Krikmann, Ott; Susi, J.; Przegietka, K.; Turlo, J.

2007-04-01

Recent research shows that students' interest in science is decreasing dramatically. This places urgent demands to making science teaching better so as to stimulate interest in it. Future teachers who are to cope with the problem are the main figures in this process. Teaching practice as a fundamental part of then-university education is essential for their successful preparation as teachers. Searching for possibilities in this area led to the launch of this international project with partners from University of Helsinki, University of Jyvaskyla (Finland), the University of Plovdiv (Bulgaria), Copernicus University in Torun (Poland) and the University of Tartu (Estonia). The main objective of the project is to present guidelines for unified initial training of science teachers in partner countries and the possibility for mobility of trainees during their studies. The present study has made a comparison of the teaching practice in partners' countries. It has identified certain main principles for a future unified curriculum for initial training of science teachers. The comparison aims to create suitable conditions for mobility of students from partners' countries during their studies and to set up the grounds for a future collaboration in developing common principles, requirements and educational standards for the practical training of science teachers.

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

PubMed

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

2015-02-01

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

An Assessment of Worldview-2 Imagery for the Classification Of a Mixed Deciduous Forest

NASA Astrophysics Data System (ADS)

Carter, Nahid

Remote sensing provides a variety of methods for classifying forest communities and can be a valuable tool for the impact assessment of invasive species. The emerald ash borer (Agrilus planipennis) infestation of ash trees (Fraxinus) in the United States has resulted in the mortality of large stands of ash throughout the Northeast. This study assessed the suitability of multi-temporal Worldview-2 multispectral satellite imagery for classifying a mixed deciduous forest in Upstate New York. Training sites were collected using a Global Positioning System (GPS) receiver, with each training site consisting of a single tree of a corresponding class. Six classes were collected; Ash, Maple, Oak, Beech, Evergreen, and Other. Three different classifications were investigated on four data sets. A six class classification (6C), a two class classification consisting of ash and all other classes combined (2C), and a merging of the ash and maple classes for a five class classification (5C). The four data sets included Worldview-2 multispectral data collection from June 2010 (J-WV2) and September 2010 (S-WV2), a layer stacked data set using J-WV2 and S-WV2 (LS-WV2), and a reduced data set (RD-WV2). RD-WV2 was created using a statistical analysis of the processed and unprocessed imagery. Statistical analysis was used to reduce the dimensionality of the data and identify key bands to create a fourth data set (RD-WV2). Overall accuracy varied considerably depending upon the classification type, but results indicated that ash was confused with maple in a majority of the classifications. Ash was most accurately identified using the 2C classification and RD-WV2 data set (81.48%). A combination of the ash and maple classes yielded an accuracy of 89.41%. Future work should focus on separating the ash and maple classifiers by using data sources such as hyperspectral imagery, LiDAR, or extensive forest surveys.

An automated procedure to identify biomedical articles that contain cancer-associated gene variants.

PubMed

McDonald, Ryan; Scott Winters, R; Ankuda, Claire K; Murphy, Joan A; Rogers, Amy E; Pereira, Fernando; Greenblatt, Marc S; White, Peter S

2006-09-01

The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing "relevant" from "not relevant" abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents. Published 2006 Wiley-Liss, Inc.

The influence of learning environment on trainee pharmacy technicians' education and training experiences.

PubMed

Schafheutle, Ellen I; Jee, Samuel D; Willis, Sarah C

2017-12-16

In Great Britain (GB), pharmacy technicians (PTs) are registered professionals, with their education and training regulated; little is known about this or the learning environment in which it takes place. This study aimed to profile recently registered pre-registration trainee pharmacy technicians (PTPTs) in GB and capture views on PTPTs' training experiences, focussing on differences in community and hospital settings. A mixed methods study was conducted in 2013-14, following university ethics approval. One-to-one, semi-structured telephone interviews with face-to-face and distance education providers, and hospital and community pharmacy employers of PTPTs explored views on education delivery, work-based learning, and assessment. Interviews were transcribed verbatim, analysed thematically and findings informed design of a census survey of all 1457 recently registered PTs, investigating satisfaction with various aspects of their training. Quantitative data were analysed using SPSS v20, employing comparative statistics (Mann-Whitney U, Chi-Square). Six-hundred and forty-six questionnaires were returned (response rate 44.3%), 632 were usable. Three-quarters (75.9%) of respondents had trained in community; the majority (88.0%) were female, the average age was 35.26 ± 10.22. Those based in hospitals were more satisfied with their training: hospital trainees worked in larger teams and tended to be better supported, they had more study time, and were more likely to complete their training in the intended two-year period. Interviews with staff in 17 Further Education colleges, 6 distance providers, 16 community pharmacies and 15 NHS organisations confirmed survey findings and offered explanations into why differences in training experiences may exist. This study has identified differences between PTPTs' work-based experiences in hospital and community pharmacy. Perceiving PTPTs as 'apprentices' vs. 'employees' may define how their training is managed by employers. Clarity in PTs' roles, responsibilities, and expected competencies upon registration can ensure training is structured and delivered in a suitable and equitable manner across sectors. Copyright © 2017 Elsevier Inc. All rights reserved.

A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

NASA Astrophysics Data System (ADS)

Dzung Nguyen, Sy; Choi, Seung-Bok

2012-08-01

This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input-output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results.

Transfer and Use of Training Technology: A Model for Matching Training Approaches with Training Settings. Technical Report No. 74-24.

ERIC Educational Resources Information Center

Haverland, Edgar M.

The report describes a project designed to facilitate the transfer and utilization of training technology by developing a model for evaluating training approaches or innovtions in relation to the requirements, resources, and constraints of specific training settings. The model consists of two parallel sets of open-ended questions--one set…

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

PubMed

Möller, Christiane; Pijnenburg, Yolande A L; van der Flier, Wiesje M; Versteeg, Adriaan; Tijms, Betty; de Munck, Jan C; Hafkemeijer, Anne; Rombouts, Serge A R B; van der Grond, Jeroen; van Swieten, John; Dopper, Elise; Scheltens, Philip; Barkhof, Frederik; Vrenken, Hugo; Wink, Alle Meije

2016-06-01

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.

Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

PubMed

Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou

2018-03-27

P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. This study aimed to develop a method for acquiring more training data based on a collected small training set. A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.

Implanon NXT: Expert tips for best-practice insertion and removal.

PubMed

Pearson, Suzanne; Stewart, Mary; Bateson, Deborah

2017-03-01

The single rod etonogestrel contraceptive implant is available in Australia as Implanon NXT. It is a highly effective, long-acting reversible contraceptive method, which is suitable for most women across the reproductive lifespan. This article provides practical advice for clinicians who already insert and remove the contraceptive implant, as well as advice for those who have not yet acquired this procedural skill. Contraceptive implant procedures are usually performed in the general practice setting. Clinicians can support women in making an informed choice to have an implant by providing information about their benefits, side effects and risks, and timely access to insertion. Training in the procedures and compliance with procedural instructions are essential to minimise risks, including deep insertion and damage to neurovascular structures.

First Evidence of the Feasibility of Gaze-Contingent Attention Training for School Children with Autism

ERIC Educational Resources Information Center

Powell, Georgina; Wass, Sam V.; Erichsen, Jonathan T.; Leekam, Susan R.

2016-01-01

A number of authors have suggested that attention control may be a suitable target for cognitive training in children with autism spectrum disorder. This study provided the first evidence of the feasibility of such training using a battery of tasks intended to target visual attentional control in children with autism spectrum disorder within…

Postgraduate Training in Clinical Oncology. Report on a WHO Working Group (The Hague, The Netherlands, December 6-8, 1978).

ERIC Educational Resources Information Center

World Health Organization, Copenhagen (Denmark). Regional Office for Europe.

The 1978 report of the Working Group of Postgraduate Training in Clinical Oncology, convened by the World Health Organization (WHO) Regional Office for Europe in collaboration with the government of The Netherlands, is presented. The groups analyzed models of postgraduate training in clinical oncology and evaluated their suitability in relation to…

Cuatro Modelos para Disenar Actividades de Capacitacion de Docentes (Four Models to Design In-Service Teacher Training Activities).

ERIC Educational Resources Information Center

Valle, Victor M.

In designing inservice teacher training activities, it is necessary to apply educational principles and teaching and learning techniques which are suitable for adult education programs. Four models for designing inservice teacher training programs are the Malcom Knowles Model, the Leonard Nadler Model, the Cyril O. Houle Model, and the William R.…

[Postgraduate training for specialists in psychiatry and psychotherapy. Problem-based learning - evaluation of a pilot project].

PubMed

Rufer, M; Schnyder, U; Schirlo, C; Wengle, H; Gerke, W

2011-05-01

Problem-based learning (PBL) emphasizes the student's individual needs, their ability to solve complex clinical problems, and a professional attitude that facilitates communication among colleagues. Thus, PBL appears to provide a perfectly suitable didactic format for postgraduate training of medical specialties. To date, it is only rarely used in this area though. In a pilot project, we implemented PBL into the curriculum of postgraduate training in psychiatry and psychotherapy, and evaluated the program over a period of 12 months, using structured questionnaires. A total of 41 PBL courses were held, with 447 residents participating. Participants as well as tutors assessed 19 of 21 aspects as good or very good (5-point Likert scale, mean value >4). Overall, PBL was rated as highly suitable for advanced training (participants: 4.5±0.8; tutors: 5.0±0.2). The results of this pilot project suggest that PBL might be a useful element of multifaceted advanced training programs, strengthening their practical component and the applicability of knowledge in the daily clinical routine.

The effect of open kinetic chain knee extensor resistance training at different training loads on anterior knee laxity in the uninjured.

PubMed

Barcellona, Massimo G; Morrissey, Matthew C

2016-04-01

The commonly used open kinetic chain knee extensor (OKCKE) exercise loads the sagittal restraints to knee anterior tibial translation. To investigate the effect of different loads of OKCKE resistance training on anterior knee laxity (AKL) in the uninjured knee. non-clinical trial. Randomization into one of three supervised training groups occurred with training 3 times per week for 12 weeks. Subjects in the LOW and HIGH groups performed OKCKE resistance training at loads of 2 sets of 20 repetition maximum (RM) and 20 sets of 2RM, respectively. Subjects in the isokinetic training group (ISOK) performed isokinetic OKCKE resistance training using 2 sets of 20 maximal efforts. AKL was measured using the KT2000 arthrometer with concurrent measurement of lateral hamstrings muscle activity at baseline, 6 weeks and 12 weeks. Twenty six subjects participated (LOW n = 9, HIGH n = 10, ISOK n = 7). The main finding from this study is that a 12-week OKCKE resistance training programme at loads of 20 sets of 2RM, leads to an increase in manual maximal AKL. OKCKE resistance training at high loads (20 sets of 2RM) increases AKL while low load OKCKE resistance training (2 sets of 20RM) and isokinetic OKCKE resistance training at 2 sets of 20RM does not. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

PubMed Central

Bustos-Korts, Daniela; Malosetti, Marcos; Chapman, Scott; Biddulph, Ben; van Eeuwijk, Fred

2016-01-01

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel. PMID:27672112

On the predictive ability of mechanistic models for the Haitian cholera epidemic.

PubMed

Mari, Lorenzo; Bertuzzo, Enrico; Finger, Flavio; Casagrandi, Renato; Gatto, Marino; Rinaldo, Andrea

2015-03-06

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Quantitative analysis of single- vs. multiple-set programs in resistance training.

PubMed

Wolfe, Brian L; LeMura, Linda M; Cole, Phillip J

2004-02-01

The purpose of this study was to examine the existing research on single-set vs. multiple-set resistance training programs. Using the meta-analytic approach, we included studies that met the following criteria in our analysis: (a) at least 6 subjects per group; (b) subject groups consisting of single-set vs. multiple-set resistance training programs; (c) pretest and posttest strength measures; (d) training programs of 6 weeks or more; (e) apparently "healthy" individuals free from orthopedic limitations; and (f) published studies in English-language journals only. Sixteen studies generated 103 effect sizes (ESs) based on a total of 621 subjects, ranging in age from 15-71 years. Across all designs, intervention strategies, and categories, the pretest to posttest ES in muscular strength was (chi = 1.4 +/- 1.4; 95% confidence interval, 0.41-3.8; p < 0.001). The results of 2 x 2 analysis of variance revealed simple main effects for age, training status (trained vs. untrained), and research design (p < 0.001). No significant main effects were found for sex, program duration, and set end point. Significant interactions were found for training status and program duration (6-16 weeks vs. 17-40 weeks) and number of sets performed (single vs. multiple). The data indicated that trained individuals performing multiple sets generated significantly greater increases in strength (p < 0.001). For programs with an extended duration, multiple sets were superior to single sets (p < 0.05). This quantitative review indicates that single-set programs for an initial short training period in untrained individuals result in similar strength gains as multiple-set programs. However, as progression occurs and higher gains are desired, multiple-set programs are more effective.

Job Corps Vocational Offerings Review. Final Report.

ERIC Educational Resources Information Center

Manpower Administration (DOL), Washington, DC. Job Corps.

The relative effectiveness of current Job Corps vocational offerings was evaluated, and occupations suitable for addition to the Job Corps vocational curricula were identified. Characteristics of Job Corps enrollees and the occupations in which training was provided were identified. To assess their comparative effectiveness, training occupations…

A statistical approach to identify, monitor, and manage incomplete curated data sets.

PubMed

Howe, Douglas G

2018-04-02

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here. In this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval. This method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

PubMed Central

2011-01-01

Background Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). Results We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. Conclusions The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions. PMID:21429187

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

PubMed

Bernardes, Juliana S; Carbone, Alessandra; Zaverucha, Gerson

2011-03-23

Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

PubMed Central

Grossi, Giuliano; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD’s robustness and wide applicability. PMID:28103283

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

Peng, Jialin, E-mail: 2004pjl@163.com; Zhang, Hongbo; Hu, Peijun

Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiplemore » subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets. In addition, user operator variability experiments showed its good reproducibility. Conclusions: A multiregion-appearance based method is proposed and evaluated to segment liver. This approach does not require prior model construction and so eliminates the burdens associated with model construction and matching. The proposed method provides comparable results with state-of-the-art methods. Validation results suggest that it may be suitable for the clinical use.« less

A comparative study of two hazard handling training methods for novice drivers.

PubMed

Wang, Y B; Zhang, W; Salvendy, G

2010-10-01

The effectiveness of two hazard perception training methods, simulation-based error training (SET) and video-based guided error training (VGET), for novice drivers' hazard handling performance was tested, compared, and analyzed. Thirty-two novice drivers participated in the hazard perception training. Half of the participants were trained using SET by making errors and/or experiencing accidents while driving with a desktop simulator. The other half were trained using VGET by watching prerecorded video clips of errors and accidents that were made by other people. The two groups had exposure to equal numbers of errors for each training scenario. All the participants were tested and evaluated for hazard handling on a full cockpit driving simulator one week after training. Hazard handling performance and hazard response were measured in this transfer test. Both hazard handling performance scores and hazard response distances were significantly better for the SET group than the VGET group. Furthermore, the SET group had more metacognitive activities and intrinsic motivation. SET also seemed more effective in changing participants' confidence, but the result did not reach the significance level. SET exhibited a higher training effectiveness of hazard response and handling than VGET in the simulated transfer test. The superiority of SET might benefit from the higher levels of metacognition and intrinsic motivation during training, which was observed in the experiment. Future research should be conducted to assess whether the advantages of error training are still effective under real road conditions.

Using a situational judgement test for selection into dental core training: a preliminary analysis.

PubMed

Rowett, E; Patterson, F; Cousans, F; Elley, K

2017-05-12

Objective and setting This paper describes the evaluation of a pilot situational judgement test (SJT) for selection into UK Dental Core Training (DCT). The SJT's psychometric properties, group differences based on gender and ethnicity, and candidate reactions were assessed.Methods The SJT targets four non-academic attributes important for success in DCT. Data were collected alongside live selection processes from five Health Education England local teams in the UK (N = 386). Candidates completed the pilot SJT and an evaluation questionnaire to examine their reactions to the test.Results SJT scores were relatively normally distributed and showed acceptable levels of internal reliability (α = 0.68). Difficulty level and partial correlations between scenarios and SJT total score were in the expected ranges (64.61% to 90.03% and r = 0.06 to 0.41, respectively). No group differences were found for gender, and group differences between White and BME candidates were minimal. Most candidates perceived the SJT as relevant to the target role, appropriate and fair.Conclusions This study demonstrated the potential suitability of an SJT for use in DCT selection. Future research should replicate these preliminary findings in other cohorts, and assess the predictive validity of the SJT for predicting key training and practice-based outcomes.

Diploma Training for Chemical Technicians in Australia

NASA Astrophysics Data System (ADS)

Lampard, Mark G.

1999-07-01

We describe aspects of the present and past training of chemical technicians in Australia, with particular reference to that for senior technicians, technical officers, and those anticipating a career in laboratory management (i.e., diploma courses). We refer to the present study pathways for beginning science technicians leading to a full-time (or part-time equivalent) diploma course offered either by the State Departments of Technical and Further Education (TAFE) or by the universities. Credit for appropriate diploma subjects towards a university science degree is available. We emphasize the national unified nature of training according to the Australian Qualifications Framework (AQF), which sets syllabi for subjects in conjunction with the Australian Standards Framework (ASF) levels that depend on such factors as breadth, depth, and complexity of skills and knowledge, range of activities undertaken, degree to which tasks are routine or complex, level of judgment required, and level of autonomy and responsibility for others. Recognition of the two-year diploma with suitable chemical laboratory or technology experience is through the Royal Australian Chemical Institute (RACI), and the letters AT (Associate Technician) RACI denote the new grade of membership of the RACI, the equivalent of the ACS in America. Sample structures for a Certificate IV and Diploma of Chemical Laboratory Technology are given.

Set Shifting Training with Categorization Tasks

PubMed Central

Soveri, Anna; Waris, Otto; Laine, Matti

2013-01-01

The very few cognitive training studies targeting an important executive function, set shifting, have reported performance improvements that also generalized to untrained tasks. The present randomized controlled trial extends set shifting training research by comparing previously used cued training with uncued training. A computerized adaptation of the Wisconsin Card Sorting Test was utilized as the training task in a pretest-posttest experimental design involving three groups of university students. One group received uncued training (n = 14), another received cued training (n = 14) and the control group (n = 14) only participated in pre- and posttests. The uncued training group showed posttraining performance increases on their training task, but neither training group showed statistically significant transfer effects. Nevertheless, comparison of effect sizes for transfer effects indicated that our results did not differ significantly from the previous studies. Our results suggest that the cognitive effects of computerized set shifting training are mostly task-specific, and would preclude any robust generalization effects with this training. PMID:24324717

The use of lower resolution viewing devices for mammographic interpretation: implications for education and training.

PubMed

Chen, Yan; James, Jonathan J; Turnbull, Anne E; Gale, Alastair G

2015-10-01

To establish whether lower resolution, lower cost viewing devices have the potential to deliver mammographic interpretation training. On three occasions over eight months, fourteen consultant radiologists and reporting radiographers read forty challenging digital mammography screening cases on three different displays: a digital mammography workstation, a standard LCD monitor, and a smartphone. Standard image manipulation software was available for use on all three devices. Receiver operating characteristic (ROC) analysis and ANOVA (Analysis of Variance) were used to determine the significance of differences in performance between the viewing devices with/without the application of image manipulation software. The effect of reader's experience was also assessed. Performance was significantly higher (p < .05) on the mammography workstation compared to the other two viewing devices. When image manipulation software was applied to images viewed on the standard LCD monitor, performance improved to mirror levels seen on the mammography workstation with no significant difference between the two. Image interpretation on the smartphone was uniformly poor. Film reader experience had no significant effect on performance across all three viewing devices. Lower resolution standard LCD monitors combined with appropriate image manipulation software are capable of displaying mammographic pathology, and are potentially suitable for delivering mammographic interpretation training. • This study investigates potential devices for training in mammography interpretation. • Lower resolution standard LCD monitors are potentially suitable for mammographic interpretation training. • The effect of image manipulation tools on mammography workstation viewing is insignificant. • Reader experience had no significant effect on performance in all viewing devices. • Smart phones are not suitable for displaying mammograms.

A new training model for robot-assisted urethrovesical anastomosis and posterior muscle-fascial reconstruction: the Verona training technique.

PubMed

Cacciamani, G; De Marco, V; Siracusano, S; De Marchi, D; Bizzotto, L; Cerruto, M A; Motton, G; Porcaro, A B; Artibani, W

2017-06-01

A training model is usually needed to teach robotic surgical technique successfully. In this way, an ideal training model should mimic as much as possible the "in vivo" procedure and allow several consecutive surgical simulations. The goal of this study was to create a "wet lab" model suitable for RARP training programs, providing the simulation of the posterior fascial reconstruction. The second aim was to compare the original "Venezuelan" chicken model described by Sotelo to our training model. Our training model consists of performing an anastomosis, reproducing the surgical procedure in "vivo" as in RARP, between proventriculus and the proximal portion of the esophagus. A posterior fascial reconstruction simulating Rocco's stitch is performed between the tissues located under the posterior surface of the esophagus and the tissue represented by the serosa of the proventriculus. From 2014 to 2015, during 6 different full-immersion training courses, thirty-four surgeons performed the urethrovesical anastomosis using our model and the Sotelo's one. After the training period, each surgeon was asked to fill out a non-validated questionnaire to perform an evaluation of the differences between the two training models. Our model was judged the best model, in terms of similarity with urethral tissue and similarity with the anatomic unit urethra-pelvic wall. Our training model as reported by all trainees is easily reproducible and anatomically comparable with the urethrovesical anastomosis as performed during radical prostatectomy in humans. It is suitable for performing posterior fascial reconstruction reported by Rocco. In this context, our surgical training model could be routinely proposed in all robotic training courses to develop specific expertise in urethrovesical anastomosis with the reproducibility of the Rocco stitch.

Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.

PubMed

Lin, Hongli; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong

2014-12-01

The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Mammographic interpretation training in the UK: current difficulties and future outlook

NASA Astrophysics Data System (ADS)

Chen, Yan; Gale, Alastair G.; Scott, Hazel

2009-02-01

In the UK, most mammographic interpretation training needs to be undertaken where there is a mammo-alternator or other suitable light box; consequently limiting the time and places where training can take place. However, the gradual introduction of digital mammography is opening up new opportunities of providing such training without the restriction of current viewing devices. Whilst high-resolution monitors in appropriate viewing environments are de rigour for actual reporting; advantages of the digital image over film are in the flexibility of training opportunity afforded, e.g. training whenever, wherever suits the individual. A previous study indicated the possible potential for reporting mammographic cases utilising handheld devices with suitable interaction techniques. In a pilot study, a group of mammographers (n=4) were questioned in semi-structured interviews in order to help establish current UK film-readers' training profile. On the basis of the pilot study data, 109 Breast Screening Units (601 film readers) were approached to complete a structured questionnaire in order to establish the potential role of smaller computer devices in mammographic interpretation training (given the use of digital mammography). Subsequently, a study of radiologists' visual search behaviour in digital screening has begun. This has highlighted different image manipulations than found in structured experiments in this area and poses new challenges for visualising the inspection process. Overall the results indicate that using different display sizes for training is possible but is also a challenging task requiring novel interaction approaches.

Learning toward practical head pose estimation

NASA Astrophysics Data System (ADS)

Sang, Gaoli; He, Feixiang; Zhu, Rong; Xuan, Shibin

2017-08-01

Head pose is useful information for many face-related tasks, such as face recognition, behavior analysis, human-computer interfaces, etc. Existing head pose estimation methods usually assume that the face images have been well aligned or that sufficient and precise training data are available. In practical applications, however, these assumptions are very likely to be invalid. This paper first investigates the impact of the failure of these assumptions, i.e., misalignment of face images, uncertainty and undersampling of training data, on head pose estimation accuracy of state-of-the-art methods. A learning-based approach is then designed to enhance the robustness of head pose estimation to these factors. To cope with misalignment, instead of using hand-crafted features, it seeks suitable features by learning from a set of training data with a deep convolutional neural network (DCNN), such that the training data can be best classified into the correct head pose categories. To handle uncertainty and undersampling, it employs multivariate labeling distributions (MLDs) with dense sampling intervals to represent the head pose attributes of face images. The correlation between the features and the dense MLD representations of face images is approximated by a maximum entropy model, whose parameters are optimized on the given training data. To estimate the head pose of a face image, its MLD representation is first computed according to the model based on the features extracted from the image by the trained DCNN, and its head pose is then assumed to be the one corresponding to the peak in its MLD. Evaluation experiments on the Pointing'04, FacePix, Multi-PIE, and CASIA-PEAL databases prove the effectiveness and efficiency of the proposed method.

Surgical Thoracic Transplant Training: Super Fellowship-Is It Super?

PubMed

Makdisi, George; Makdisi, Tony; Caldeira, Christiano C; Wang, I-Wen

2017-10-11

The quality of training provided to thoracic transplant fellows is a critical step in the care of complex patients undergoing transplant. The training varies since it is not an accreditation council for graduate medical education accredited fellowship. A total of 104 heart or lung transplant program directors throughout the United States were sent a survey of 24 questions focusing on key aspects of training, fellowship training content and thoracic transplant job satisfaction. Out of the 104 programs surveyed 45 surveys (43%) were returned. In total, 26 programs offering a transplant fellowship were included in the survey. Among these programs 69% currently have fellows of which 56% are American Board of Thoracic Surgery board eligible. According to the United Network for Organ Sharing (UNOS) requirements, 46% of the programs do not meet the requirements to be qualified as a primary heart transplant surgeon. A total of 23% of lung transplant programs also perform less than the UNOS minimum requirements. Only 24% have extra-surgical curriculum. Out of the participating programs, only 38% of fellows secured a job in a hospital setting for performing transplants. An astounding 77% of replies site an unpredictable work schedule as the main reason that makes thoracic transplant a less than favorable profession among new graduates. Long hours were also a complaint of 69% of graduates who agreed that their personal life is affected by excessive work hours. Annually, almost half of all thoracic transplant programs perform fewer than the UNOS requirements to be a primary thoracic surgeon. This results in a majority of transplant fellows not finding a suitable transplant career. The current and future needs for highly qualified thoracic transplant surgeons will not be met through our existing training mechanisms. Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

[Primary, single-stage arterial switch operations at a newly-established, comprehensive congenital cardiac center performed in the neonatal age and beyond].

PubMed

Király, László; Tamás, Csaba

2015-06-21

Outcome of arterial switch operation for transposition of the great arteries with/without ventricular septal defect is a service key-performance-indicator. The aim of the authors was to assess patient characteristics and parameters in the perioperative course. In the setting of a newly-established, comprehensive tertiary-care center, primary complete repair was performed including associated anomalies, e.g. transverse arch repairs. Patients with d-transposition were grouped according to coexistence of ventricular septal defect. 118 arterial switch operations were performed between 2007 and 2014 with 96.62% survival (114/118). Ventricular septal defect and repair of associated anomalies did not yield worse outcome. Left ventricular re-training with late presentation necessitated mechanical circulatory support for 4.5±1.5 days. D-transposition is suitable for standardization of clinical algorithm and surgical technique. Quality standards contribute to excellent outcomes, minimize complications, and serve as blueprint for other neonatal open-heart procedures. Availability of mechanical circulatory support is key for single-stage left ventricular re-training beyond the neonatal period.

Targeting “hardly reached” people with chronic illness: a feasibility study of a person-centered self-management education approach

PubMed Central

Torenholt, Rikke; Helms Andersen, Tue; Møller, Birgitte Lund; Willaing, Ingrid

2018-01-01

Background Self-management education is critical to the development of successful health behavior changes related to chronic illness. However, people in high-risk groups attend less frequently or benefit less from patient education programs than do people with more socioeconomic advantages. Aim The aim was to test the feasibility of a participatory person-centered education approach and tool-kit targeting self-management of chronic illness in hardly reached people. Methods After participating in a training program, educators (n=77) tested the approach in practice. Data collection included online questionnaires for educators (n=65), observations of education sessions (n=7), and interviews with educators (n=11) and participants (n=22). Descriptive statistics were calculated. Transcripts of interviews and observations were analyzed using systematic text condensation. Feasibility was examined in terms of practicality, integration, suitability, and efficacy. Results Educators had a positive response to the approach and found that the tools supported involving participants in education and support. Participant satisfaction varied, depending on the ability of educators to integrate the tools into programs in a meaningful way. The tools provided time for reflection in the education process that benefited participants and educators alike. Educators found it challenging to allow participants to help set the agenda and to exchange experiences without educator control. Barriers to use reported by educators included lack of time for both training and preparation. Limitations The testing included varied groups of participants, some groups included members of hardly reached populations and others did not. Also, some tools were only tried in practice by a few educators. Conclusion The approach was feasible in terms of practicality, integration, acceptability, and efficacy and perceived by educators as suitable for both hardly reached participants and those who are less disadvantaged. Implementation of the approach requires time for training and preparation. PMID:29497283

Intelligent Computerized Training System

NASA Technical Reports Server (NTRS)

Wang, Lui; Baffes, Paul; Loftin, R. Bowen; Hua, Grace C.

1991-01-01

Intelligent computer-aided training system gives trainees same experience gained from best on-the-job training. Automated system designed to emulate behavior of experienced teacher devoting full time and attention to training novice. Proposes challenging training scenarios, monitors and evaluates trainee's actions, makes meaningful comments in response to errors, reponds to requests for information, gives hints when appropriate, and remembers strengths and weaknesses so it designs suitable exercises. Used to train flight-dynamics officers in deploying satellites from Space Shuttle. Adapted to training for variety of tasks and situations, simply by modifying one or at most two of its five modules. Helps to ensure continuous supply of trained specialists despite scarcity of experienced and skilled human trainers.

Non-parametric transient classification using adaptive wavelets

NASA Astrophysics Data System (ADS)

Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.

2015-11-01

Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.

Enhanced attentional gain as a mechanism for generalized perceptual learning in human visual cortex.

PubMed

Byers, Anna; Serences, John T

2014-09-01

Learning to better discriminate a specific visual feature (i.e., a specific orientation in a specific region of space) has been associated with plasticity in early visual areas (sensory modulation) and with improvements in the transmission of sensory information from early visual areas to downstream sensorimotor and decision regions (enhanced readout). However, in many real-world scenarios that require perceptual expertise, observers need to efficiently process numerous exemplars from a broad stimulus class as opposed to just a single stimulus feature. Some previous data suggest that perceptual learning leads to highly specific neural modulations that support the discrimination of specific trained features. However, the extent to which perceptual learning acts to improve the discriminability of a broad class of stimuli via the modulation of sensory responses in human visual cortex remains largely unknown. Here, we used functional MRI and a multivariate analysis method to reconstruct orientation-selective response profiles based on activation patterns in the early visual cortex before and after subjects learned to discriminate small offsets in a set of grating stimuli that were rendered in one of nine possible orientations. Behavioral performance improved across 10 training sessions, and there was a training-related increase in the amplitude of orientation-selective response profiles in V1, V2, and V3 when orientation was task relevant compared with when it was task irrelevant. These results suggest that generalized perceptual learning can lead to modified responses in the early visual cortex in a manner that is suitable for supporting improved discriminability of stimuli drawn from a large set of exemplars. Copyright © 2014 the American Physiological Society.

Single- vs. Multiple-Set Strength Training in Women.

ERIC Educational Resources Information Center

Schlumberger, Andreas; Stec, Justyna; Schmidtbleicher, Dietmar

2001-01-01

Compared the effects of single- and multiple-set strength training in women with basic experience in resistance training. Both training groups had significant strength improvements in leg extension. In the seated bench press, only the three-set group showed a significant increase in maximal strength. There were higher strength gains overall in the…

High School Weight Training: A Comprehensive Program.

ERIC Educational Resources Information Center

Viscounte, Roger; Long, Ken

1989-01-01

Describes a weight training program, suitable for the general student population and the student-athlete, which is designed to produce improvement in specific, measurable areas including bench press (upper body), leg press (lower body), vertical jump (explosiveness); and 40-yard dash (speed). Two detailed charts are included, with notes on their…

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

Handwritten word preprocessing for database adaptation

NASA Astrophysics Data System (ADS)

Oprean, Cristina; Likforman-Sulem, Laurence; Mokbel, Chafic

2013-01-01

Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level,...). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

Hypoglycemia alarm enhancement using data fusion.

PubMed

Skladnev, Victor N; Tarnavskii, Stanislav; McGregor, Thomas; Ghevondian, Nejhdeh; Gourlay, Steve; Jones, Timothy W

2010-01-01

The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features. Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions. The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels. Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms. 2010 Diabetes Technology Society.

Development and Evaluation of a Cognitive Training Game for Older People: A Design-based Approach.

PubMed

Lu, Ming-Hsin; Lin, Weijane; Yueh, Hsiu-Ping

2017-01-01

In the research field of cognitive aging, games have gained attention as training interventions to remediate age-related deficits. Cognitive training games on computer, video and mobile platforms have shown ample and positive support. However, the generalized effects are not agreed upon unanimously, and the game tasks are usually simple and decontextualized due to the limitations of measurements. This study adopted a qualitative approach of design-based research (DBR) to systematically review and pragmatically examine the regime, presentation and feedback design of a cognitive training game for older adults. An overview of the literature of cognitive aging and training games was conducted to form the theoretical conjectures of the design, and an iterative cycle and process were employed to develop a mobile game for older adults who are homebound or receiving care in a nursing home. Stakeholders, i.e., elderly users and institutional administrators, were invited to participate in the design process. Using two cycles of design and evaluation, a working prototype of an iPad-based app that accounted for the needs of elderly adults in terms of form, appearance and working function was developed and tested in the actual contexts of the participants' homes and an assisted living facility. The results showed that the cognitive training game developed in this study was accepted by the participants, and a high degree of satisfaction was noted. Moreover, the elements of the interface, including its size, layout and control flow, were tested and found to be suitable for use. This study contributes to the literature by providing design suggestions for such games, including the designs of the cognitive training structure, interface, interaction, instructions and feedback, based on empirical evidence collected in natural settings. This study further suggests that the effectiveness of cognitive training in mobile games be evaluated through field and physical testing on a larger scale in the future.

Development and Evaluation of a Cognitive Training Game for Older People: A Design-based Approach

PubMed Central

Lu, Ming-Hsin; Lin, Weijane; Yueh, Hsiu-Ping

2017-01-01

In the research field of cognitive aging, games have gained attention as training interventions to remediate age-related deficits. Cognitive training games on computer, video and mobile platforms have shown ample and positive support. However, the generalized effects are not agreed upon unanimously, and the game tasks are usually simple and decontextualized due to the limitations of measurements. This study adopted a qualitative approach of design-based research (DBR) to systematically review and pragmatically examine the regime, presentation and feedback design of a cognitive training game for older adults. An overview of the literature of cognitive aging and training games was conducted to form the theoretical conjectures of the design, and an iterative cycle and process were employed to develop a mobile game for older adults who are homebound or receiving care in a nursing home. Stakeholders, i.e., elderly users and institutional administrators, were invited to participate in the design process. Using two cycles of design and evaluation, a working prototype of an iPad-based app that accounted for the needs of elderly adults in terms of form, appearance and working function was developed and tested in the actual contexts of the participants' homes and an assisted living facility. The results showed that the cognitive training game developed in this study was accepted by the participants, and a high degree of satisfaction was noted. Moreover, the elements of the interface, including its size, layout and control flow, were tested and found to be suitable for use. This study contributes to the literature by providing design suggestions for such games, including the designs of the cognitive training structure, interface, interaction, instructions and feedback, based on empirical evidence collected in natural settings. This study further suggests that the effectiveness of cognitive training in mobile games be evaluated through field and physical testing on a larger scale in the future. PMID:29089914

Thinking Outside of Outpatient: Underutilized Settings for Psychotherapy Education.

PubMed

Blumenshine, Philip; Lenet, Alison E; Havel, Lauren K; Arbuckle, Melissa R; Cabaniss, Deborah L

2017-02-01

Although psychiatry residents are expected to achieve competency in conducting psychotherapy during their training, it is unclear how psychotherapy teaching is integrated across diverse clinical settings. Between January and March 2015, 177 psychiatry residency training directors were sent a survey asking about psychotherapy training practices in their programs, as well as perceived barriers to psychotherapy teaching. Eighty-two training directors (44%) completed the survey. While 95% indicated that psychotherapy was a formal learning objective for outpatient clinic rotations, fifty percent or fewer noted psychotherapy was a learning objective in other settings. Most program directors would like to see psychotherapy training included (particularly supportive psychotherapy and cognitive behavioral therapy) on inpatient (82%) and consultation-liaison settings (57%). The most common barriers identified to teaching psychotherapy in these settings were time and perceived inadequate staff training and interest. Non-outpatient rotations appear to be an underutilized setting for psychotherapy teaching.

Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal.

PubMed

Arjunan, Sridhar Poosapadi; Kumar, Dinesh Kant; Jayadeva J

2016-02-01

Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

Establishing Fire Safety Skills Using Behavioral Skills Training

ERIC Educational Resources Information Center

Houvouras, Andrew J., IV; Harvey, Mark T.

2014-01-01

The use of behavioral skills training (BST) to educate 3 adolescent boys on the risks of lighters and fire setting was evaluated using in situ assessment in a school setting. Two participants had a history of fire setting. After training, all participants adhered to established rules: (a) avoid a deactivated lighter, (b) leave the training area,…

A practical model for the train-set utilization: The case of Beijing-Tianjin passenger dedicated line in China

PubMed Central

Li, Xiaomeng; Yang, Zhuo

2017-01-01

As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved. PMID:28489933

Fuzzy set methods for object recognition in space applications

NASA Technical Reports Server (NTRS)

Keller, James M.

1991-01-01

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed.

Clinical bacteriology in low-resource settings: today's solutions.

PubMed

Ombelet, Sien; Ronat, Jean-Baptiste; Walsh, Timothy; Yansouni, Cedric P; Cox, Janneke; Vlieghe, Erika; Martiny, Delphine; Semret, Makeda; Vandenberg, Olivier; Jacobs, Jan

2018-03-05

Low-resource settings are disproportionately burdened by infectious diseases and antimicrobial resistance. Good quality clinical bacteriology through a well functioning reference laboratory network is necessary for effective resistance control, but low-resource settings face infrastructural, technical, and behavioural challenges in the implementation of clinical bacteriology. In this Personal View, we explore what constitutes successful implementation of clinical bacteriology in low-resource settings and describe a framework for implementation that is suitable for general referral hospitals in low-income and middle-income countries with a moderate infrastructure. Most microbiological techniques and equipment are not developed for the specific needs of such settings. Pending the arrival of a new generation diagnostics for these settings, we suggest focus on improving, adapting, and implementing conventional, culture-based techniques. Priorities in low-resource settings include harmonised, quality assured, and tropicalised equipment, consumables, and techniques, and rationalised bacterial identification and testing for antimicrobial resistance. Diagnostics should be integrated into clinical care and patient management; clinically relevant specimens must be appropriately selected and prioritised. Open-access training materials and information management tools should be developed. Also important is the need for onsite validation and field adoption of diagnostics in low-resource settings, with considerable shortening of the time between development and implementation of diagnostics. We argue that the implementation of clinical bacteriology in low-resource settings improves patient management, provides valuable surveillance for local antibiotic treatment guidelines and national policies, and supports containment of antimicrobial resistance and the prevention and control of hospital-acquired infections. Copyright © 2018 Elsevier Ltd. All rights reserved.

Looking for underlying features in automatic and reviewed seismic bulletins through a neural network

NASA Astrophysics Data System (ADS)

Carluccio, R.; Console, R.; Chiappini, M.; Chiappini, S.

2009-12-01

SEL1 bulletins are, among all IDC products, a fundamental tool for NDCs in their task of national assessment of compliance with the CTBT. This is because SEL1s are expected to be disseminated within 2 hours from the occurrence of any detected waveform event, and the National Authorities are supposed to take a political decision in nearly real time, especially in the case when the event could triggers the request for an on site inspection. In this context not only the rapidity, but also the reliability of the SEL1 is a fundamental requirement. Our last years experience gained in the comparison between SEL1 and Italian Seismic Bulletin events has shown that SEL1s usually contain a big fraction of bogus events (sometimes close to 50%). This is due to many factors, all related to the availability of processing data and to the fast automatic algorithms involved. On the other hand, REBs are much more reliable as proved by our experience. Therefore, in spite of their relevant time delay by which they are distributed, which prevents their real-time use, REBs can be still useful in a retrospective way as reference information for comparison with SEL1s. This study tries to set up a sort of logical filter on the SEL1s that, while maintaining the rapidity requirements, improves their reliability. Our idea is based on the assumption that the SEL1s are produced by systematic algorithm of phase association and therefore some patterns among the input and output data could exist and be recognized. Our approach was initially based on a set of rules suggested by human experts on their personal experience, and its application on large datasets on a global scale. Other approaches not involving human interaction (data mining techniques) do exist. This study refers specifically to a semi-automatic approach: fitting of multi-parametric relationships hidden in the data set, through the application of neural networks by an algorithm of supervised learning. Full SEL1 and REB bulletins from Jan 2005 to Oct 2008 have been inserted in a database, together with IMS stations availability information. Part of these data have been used to create two sets of independent data (learning and verifying) used to train a "feed-forward" supervised neural network. A network supervised training algorithm using "confirmation flag" values has been used. In order to optimize network training input a significant, not redundant subset of input parameters has been looked for with the help of a genetic algorithm search tool. A suitable 12 input subset has been found and a network architecture of 12-20-1 has thus been chosen and trained on a 15094 records data set. Different runs of training sequences have been conducted, all showing CCR (Correct Classification Rate) values of the order of 75% - 80%. The trained network behavior is shown in term of ROC curve and input-out success-error matrices. The results of the analysis on our testing and validating data groups appear promising.

Comprehensive simulation-enhanced training curriculum for an advanced minimally invasive procedure: a randomized controlled trial.

PubMed

Zevin, Boris; Dedy, Nicolas J; Bonrath, Esther M; Grantcharov, Teodor P

2017-05-01

There is no comprehensive simulation-enhanced training curriculum to address cognitive, psychomotor, and nontechnical skills for an advanced minimally invasive procedure. 1) To develop and provide evidence of validity for a comprehensive simulation-enhanced training (SET) curriculum for an advanced minimally invasive procedure; (2) to demonstrate transfer of acquired psychomotor skills from a simulation laboratory to live porcine model; and (3) to compare training outcomes of SET curriculum group and chief resident group. University. This prospective single-blinded, randomized, controlled trial allocated 20 intermediate-level surgery residents to receive either conventional training (control) or SET curriculum training (intervention). The SET curriculum consisted of cognitive, psychomotor, and nontechnical training modules. Psychomotor skills in a live anesthetized porcine model in the OR was the primary outcome. Knowledge of advanced minimally invasive and bariatric surgery and nontechnical skills in a simulated OR crisis scenario were the secondary outcomes. Residents in the SET curriculum group went on to perform a laparoscopic jejunojejunostomy in the OR. Cognitive, psychomotor, and nontechnical skills of SET curriculum group were also compared to a group of 12 chief surgery residents. SET curriculum group demonstrated superior psychomotor skills in a live porcine model (56 [47-62] versus 44 [38-53], P<.05) and superior nontechnical skills (41 [38-45] versus 31 [24-40], P<.01) compared with conventional training group. SET curriculum group and conventional training group demonstrated equivalent knowledge (14 [12-15] versus 13 [11-15], P = 0.47). SET curriculum group demonstrated equivalent psychomotor skills in the live porcine model and in the OR in a human patient (56 [47-62] versus 63 [61-68]; P = .21). SET curriculum group demonstrated inferior knowledge (13 [11-15] versus 16 [14-16]; P<.05), equivalent psychomotor skill (63 [61-68] versus 68 [62-74]; P = .50), and superior nontechnical skills (41 [38-45] versus 34 [27-35], P<.01) compared with chief resident group. Completion of the SET curriculum resulted in superior training outcomes, compared with conventional surgery training. Implementation of the SET curriculum can standardize training for an advanced minimally invasive procedure and can ensure that comprehensive proficiency milestones are met before exposure to patient care. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

The Application of Leap Motion in Astronaut Virtual Training

NASA Astrophysics Data System (ADS)

Qingchao, Xie; Jiangang, Chao

2017-03-01

With the development of computer vision, virtual reality has been applied in astronaut virtual training. As an advanced optic equipment to track hand, Leap Motion can provide precise and fluid tracking of hands. Leap Motion is suitable to be used as gesture input device in astronaut virtual training. This paper built an astronaut virtual training based Leap Motion, and established the mathematics model of hands occlusion. At last the ability of Leap Motion to handle occlusion was analysed. A virtual assembly simulation platform was developed for astronaut training, and occlusion gesture would influence the recognition process. The experimental result can guide astronaut virtual training.

The Handover Toolbox: a knowledge exchange and training platform for improving patient care.

PubMed

Drachsler, Hendrik; Kicken, Wendy; van der Klink, Marcel; Stoyanov, Slavi; Boshuizen, Henny P A; Barach, Paul

2012-12-01

Safe and effective patient handovers remain a global organisational and training challenge. Limited evidence supports available handover training programmes. Customisable training is a promising approach to improve the quality and sustainability of handover training and outcomes. We present a Handover Toolbox designed in the context of the European HANDOVER Project. The Toolbox aims to support physicians, nurses, individuals in health professions training, medical educators and handover experts by providing customised handover training tools for different clinical needs and contexts. The Handover Toolbox uses the Technology Enhanced Learning Design Process (TEL-DP), which encompasses user requirements analysis; writing personas; group concept mapping; analysis of suitable software; plus, minus, interesting rating; and usability testing. TEL-DP is aligned with participatory design approaches and ensures development occurs in close collaboration with, and engagement of, key stakeholders. Application of TEL-DP confirmed that the ideal formats of handover training differs for practicing professionals versus individuals in health profession education programmes. Training experts from different countries differed in their views on the optimal content and delivery of training. Analysis of suitable software identified ready-to-use systems that provide required functionalities and can be further customised to users' needs. Interest rating and usability testing resulted in improved usability, navigation and uptake of the Handover Toolbox. The design of the Handover Toolbox was based on a carefully led stakeholder participatory design using the TEL-DP approach. The Toolbox supports a customisable learning approach that allows trainers to design training that addresses the specific information needs of the various target groups. We offer recommendations regarding the application of the Handover Toolbox to medical educators.

Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation

DTIC Science & Technology

2010-01-01

classi- fication algorithms: simple random resampling (RRS), equal-instance random resampling (ERS), and network cross-validation ( NCV ). The first two... NCV procedure that eliminates overlap between test sets altogether. The procedure samples for k disjoint test sets that will be used for evaluation...propLabeled ∗ S) nodes from train Pool in f erenceSet =network − trainSet F = F ∪ < trainSet, test Set, in f erenceSet > end for output: F NCV addresses

A general architecture for intelligent training systems

NASA Technical Reports Server (NTRS)

Loftin, R. Bowen

1987-01-01

A preliminary design of a general architecture for autonomous intelligent training systems was developed. The architecture integrates expert system technology with teaching/training methodologies to permit the production of systems suitable for use by NASA, other government agencies, industry, and academia in the training of personnel for the performance of complex, mission-critical tasks. The proposed architecture consists of five elements: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The design of this architecture was guided and its efficacy tested through the development of a system for use by Mission Control Center Flight Dynamics Officers in training to perform Payload-Assist Module Deploys from the orbiter.

Wavelets and Elman Neural Networks for monitoring environmental variables

NASA Astrophysics Data System (ADS)

Ciarlini, Patrizia; Maniscalco, Umberto

2008-11-01

An application in cultural heritage is introduced. Wavelet decomposition and Neural Networks like virtual sensors are jointly used to simulate physical and chemical measurements in specific locations of a monument. Virtual sensors, suitably trained and tested, can substitute real sensors in monitoring the monument surface quality, while the real ones should be installed for a long time and at high costs. The application of the wavelet decomposition to the environmental data series allows getting the treatment of underlying temporal structure at low frequencies. Consequently a separate training of suitable Elman Neural Networks for high/low components can be performed, thus improving the networks convergence in learning time and measurement accuracy in working time.

Smartphone-Based System for Learning and Inferring Hearing Aid Settings.

PubMed

Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J

2016-10-01

Previous research has shown that hearing aid wearers can successfully self-train their instruments' gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the "untrained system," that is, the manufacturer's algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The "trained system" first learned each individual's preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. An experimental within-participants study. Participants used a prototype hearing system-comprising two hearing aids, Android smartphone, and body-worn gateway device-for ∼6 weeks. Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Participants were fitted and instructed to perform daily comparisons of settings ("listening evaluations") through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone-including environmental sound classification, sound level, and location-to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system ("trained settings") to those suggested by the hearing aids' untrained system ("untrained settings"). We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone. American Academy of Audiology

How well does multiple OCR error correction generalize?

NASA Astrophysics Data System (ADS)

Lund, William B.; Ringger, Eric K.; Walker, Daniel D.

2013-12-01

As the digitization of historical documents, such as newspapers, becomes more common, the need of the archive patron for accurate digital text from those documents increases. Building on our earlier work, the contributions of this paper are: 1. in demonstrating the applicability of novel methods for correcting optical character recognition (OCR) on disparate data sets, including a new synthetic training set, 2. enhancing the correction algorithm with novel features, and 3. assessing the data requirements of the correction learning method. First, we correct errors using conditional random fields (CRF) trained on synthetic training data sets in order to demonstrate the applicability of the methodology to unrelated test sets. Second, we show the strength of lexical features from the training sets on two unrelated test sets, yielding a relative reduction in word error rate on the test sets of 6.52%. New features capture the recurrence of hypothesis tokens and yield an additional relative reduction in WER of 2.30%. Further, we show that only 2.0% of the full training corpus of over 500,000 feature cases is needed to achieve correction results comparable to those using the entire training corpus, effectively reducing both the complexity of the training process and the learned correction model.

Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design.

PubMed

Xu, G; Hughes-Oliver, J M; Brooks, J D; Yeatts, J L; Baynes, R E

2013-01-01

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

New Directions in Customer Service. The Right Tool for the Job.

ERIC Educational Resources Information Center

State Univ. of New York, Albany. Rockefeller Coll.

This curriculum was designed to provide 20 hours of training to experienced employees (Certified Nursing Assistants or Home Health Aides) using seven stand-alone modules supported by training process guides. The materials are suitable for workplace literacy programs for adults with low levels of English literacy skills. The curriculum uses a…

Drill Press Operator: Instructor's Guide.

ERIC Educational Resources Information Center

Kagan, Alfred; And Others

The course is intended to help meet, in a relatively short time, the need for trained operators in metalworking. It can be used by students with little education or experience and is suitable for use in adult education programs and in manpower development and training programs. The course is designed to be completed in approximately 30 weeks and…

Dissociable effects of game elements on motivation and cognition in a task-switching training in middle childhood

PubMed Central

Dörrenbächer, Sandra; Müller, Philipp M.; Tröger, Johannes; Kray, Jutta

2014-01-01

Although motivational reinforcers are often used to enhance the attractiveness of trainings of cognitive control in children, little is known about how such motivational manipulations of the setting contribute to separate gains in motivation and cognitive-control performance. Here we provide a framework for systematically investigating the impact of a motivational video-game setting on the training motivation, the task performance, and the transfer success in a task-switching training in middle-aged children (8–11 years of age). We manipulated both the type of training (low-demanding/single-task training vs. high-demanding/task-switching training) as well as the motivational setting (low-motivational/without video-game elements vs. high-motivational/with video-game elements) separately from another. The results indicated that the addition of game elements to a training setting enhanced the intrinsic interest in task practice, independently of the cognitive demands placed by the training type. In the task-switching group, the high-motivational training setting led to an additional enhancement of task and switching performance during the training phase right from the outset. These motivation-induced benefits projected onto the switching performance in a switching situation different from the trained one (near-transfer measurement). However, in structurally dissimilar cognitive tasks (far-transfer measurement), the motivational gains only transferred to the response dynamics (speed of processing). Hence, the motivational setting clearly had a positive impact on the training motivation and on the paradigm-specific task-switching abilities; it did not, however, consistently generalize on broad cognitive processes. These findings shed new light on the conflation of motivation and cognition in childhood and may help to refine guidelines for designing adequate training interventions. PMID:25431564

LVQ and backpropagation neural networks applied to NASA SSME data

NASA Technical Reports Server (NTRS)

Doniere, Timothy F.; Dhawan, Atam P.

1993-01-01

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Landscape epidemiology in urban environments: The example of rodent-borne Trypanosoma in Niamey, Niger.

PubMed

Rossi, Jean-Pierre; Kadaouré, Ibrahima; Godefroid, Martin; Dobigny, Gauthier

2017-10-05

Trypanosomes are protozoan parasites found worldwide, infecting humans and animals. In the past decade, the number of reports on atypical human cases due to Trypanosoma lewisi or T. lewisi-like has increased urging to investigate the multiple factors driving the disease dynamics, particularly in cities where rodents and humans co-exist at high densities. In the present survey, we used a species distribution model, Maxent, to assess the spatial pattern of Trypanosoma-positive rodents in the city of Niamey. The explanatory variables were landscape metrics describing urban landscape composition and physiognomy computed from 8 land-cover classes. We computed the metrics around each data location using a set of circular buffers of increasing radii (20m, 40m, 60m, 80m and 100m). For each spatial resolution, we determined the optimal combination of feature class and regularization multipliers by fitting Maxent with the full dataset. Since our dataset was small (114 occurrences) we expected an important uncertainty associated to data partitioning into calibration and evaluation datasets. We thus performed 350 independent model runs with a training dataset representing a random subset of 80% of the occurrences and the optimal Maxent parameters. Each model yielded a map of habitat suitability over Niamey, which was transformed into a binary map implementing a threshold maximizing the sensitivity and the specificity. The resulting binary maps were combined to display the proportion of models that indicated a good environmental suitability for Trypanosoma-positive rodents. Maxent performed better with landscape metrics derived from buffers of 80m. Habitat suitability for Trypanosoma-positive rodents exhibited large patches linked to urban features such as patch richness and the proportion of landscape covered by concrete or tarred areas. Such inferences could be helpful in assessing areas at risk, setting of monitoring programs, public and medical staff awareness or even vaccination campaigns. Copyright © 2017 Elsevier B.V. All rights reserved.

The UXO Classification Demonstration at San Luis Obispo, CA

DTIC Science & Technology

2010-09-01

Set ................................45  2.17.2  Active Learning Training and Test Set ..........................................47  2.17.3  Extended...optimized algorithm by applying it to only the unlabeled data in the test set. 2.17.2 Active Learning Training and Test Set SIG also used active ... learning [12]. Active learning , an alternative approach for constructing a training set, is used in conjunction with either supervised or semi

Does rational selection of training and test sets improve the outcome of QSAR modeling?

PubMed

Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

2012-10-22

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

Teaching Health Center Graduate Medical Education Locations Predominantly Located in Federally Designated Underserved Areas.

PubMed

Barclift, Songhai C; Brown, Elizabeth J; Finnegan, Sean C; Cohen, Elena R; Klink, Kathleen

2016-05-01

Background The Teaching Health Center Graduate Medical Education (THCGME) program is an Affordable Care Act funding initiative designed to expand primary care residency training in community-based ambulatory settings. Statute suggests, but does not require, training in underserved settings. Residents who train in underserved settings are more likely to go on to practice in similar settings, and graduates more often than not practice near where they have trained. Objective The objective of this study was to describe and quantify federally designated clinical continuity training sites of the THCGME program. Methods Geographic locations of the training sites were collected and characterized as Health Professional Shortage Area, Medically Underserved Area, Population, or rural areas, and were compared with the distribution of Centers for Medicare and Medicaid Services (CMS)-funded training positions. Results More than half of the teaching health centers (57%) are located in states that are in the 4 quintiles with the lowest CMS-funded resident-to-population ratio. Of the 109 training sites identified, more than 70% are located in federally designated high-need areas. Conclusions The THCGME program is a model that funds residency training in community-based ambulatory settings. Statute suggests, but does not explicitly require, that training take place in underserved settings. Because the majority of the 109 clinical training sites of the 60 funded programs in 2014-2015 are located in federally designated underserved locations, the THCGME program deserves further study as a model to improve primary care distribution into high-need communities.

Molecular Biology Masterclasses--Developing Practical Skills and Building Links with Higher Education in Years 12/13

ERIC Educational Resources Information Center

Hooley, Paul; Cooper, Phillippa; Skidmore, Nick

2008-01-01

A one day practical course in molecular biology skills suitable for year 12/13 students is described. Colleagues from partner schools and colleges were trained by university staff in basic techniques and then collaborated in the design of a course suitable for their own students. Participants carried out a transformation of "E.coli"…

Introducing computer-assisted training sessions in the clinical skills lab at the Faculty of Medicine, Suez Canal University.

PubMed

Hosny, Somaya; Mishriky, Adel M; Youssef, Mirella

2008-01-01

The Faculty of Medicine, Suez Canal University clinical skills lab was established in 1981 as the first skills lab in Egypt to cope with innovation in medical education adopted since school inauguration in 1978. Students are trained using their peers or models. Training is done weekly, guided by checklists tested for validity and reliability and updated regularly. Students receive immediate feedback on their performance. Recently, the number of students has increased, leading to challenges in providing adequate supervision and training experiences. A project to design and implement a computer-assisted training (CAT) system seemed to be a plausible solution. To assess the quality of a newly developed CAT product, faculty and students' satisfaction with it, and its impact on the learning process. The project involved preparation of multimedia video-films with a web interface for links of different scientific materials. The project was implemented on second year students. A quality check was done to assess the product's scientific content, and technical quality using questionnaires filled by 84 faculty members (139 filled forms) and 175 students (924 filled forms). For assessment of impact, results of examinations after project implementation were compared with results of 2nd year students of previous 3 years. More faculty (96.3%) were satisfied with the product and considered its quality good to excellent, compared to 93.9% of students, p < 0.001. Most faculty (76.2%) have agreed on its suitability for self-learning, while most students considered the product would be suitable after modification. The percentage of students' failures was lower after project implementation, compared to previous 3 years, p < 0.05. CAT materials developed for training of second year students in skills lab proved to be of good scientific content and quality, and suitable for self-learning. Their use was associated with lower failure rates among students. A randomized trial is recommended to ascertain the effectiveness of its application.

Exploring virtual worlds for scenario-based repeated team training of cardiopulmonary resuscitation in medical students.

PubMed

Creutzfeldt, Johan; Hedman, Leif; Medin, Christopher; Heinrichs, Wm LeRoy; Felländer-Tsai, Li

2010-09-03

Contemporary learning technologies, such as massively multiplayer virtual worlds (MMVW), create new means for teaching and training. However, knowledge about the effectiveness of such training is incomplete, and there are no data regarding how students experience it. Cardiopulmonary resuscitation (CPR) is a field within medicine in high demand for new and effective training modalities. In addition to finding a feasible way to implement CPR training, our aim was to investigate how a serious game setting in a virtual world using avatars would influence medical students' subjective experiences as well as their retention of knowledge. An MMVW was refined and used in a study to train 12 medical students in CPR in 3-person teams in a repeated fashion 6 months apart. An exit questionnaire solicited reflections over their experiences. As the subjects trained in 4 CPR scenarios, measurements of self-efficacy, concentration, and mental strain were made in addition to measuring knowledge. Engagement modes and coping strategies were also studied. Parametric and nonparametric statistical analyses were carried out according to distribution of the data. The majority of the subjects reported that they had enjoyed the training, had found it to be suitable, and had learned something new, although several asked for more difficult and complex scenarios as well as a richer virtual environment. The mean values for knowledge dropped during the 6 months from 8.0/10 to 6.25/10 (P = .002). Self-efficacy increased from before to after each of the two training sessions, from 5.9/7 to 6.5/7 (P = .01) after the first and from 6.0/7 to 6.7/7 (P = .03) after the second. The mean perceived concentration value increased from 54.2/100 to 66.6/100 (P = .006), and in general the mental strain was found to be low to moderate (mean = 2.6/10). Using scenario-based virtual world team training with avatars to train medical students in multi-person CPR was feasible and showed promising results. Although we found no evidence of stimulated recall of CPR procedures in our test-retest study, the subjects were enthusiastic and reported increased concentration during the training. We also found that subjects' self-efficacy had increased after the training. Despite the need for further studies, these findings imply several possible uses of MMVW technology for future emergency medical training.

Effects of group metacognitive training (MCT) on mental capacity and functioning in patients with psychosis in a secure forensic psychiatric hospital: a prospective-cohort waiting list controlled study.

PubMed

Naughton, Marie; Nulty, Andrea; Abidin, Zareena; Davoren, Mary; O'Dwyer, Sarah; Kennedy, Harry G

2012-06-18

Metacognitive Training (MCT) is a manualised cognitive intervention for psychosis aimed at transferring knowledge of cognitive biases and providing corrective experiences. The aim of MCT is to facilitate symptom reduction and protect against relapse. In a naturalistic audit of clinical effectiveness we examined what effect group MCT has on mental capacity, symptoms of psychosis and global function in patients with a psychotic illness, when compared with a waiting list comparison group. Of 93 patients detained in a forensic mental health hospital under both forensic and civil mental health legislation, 19 were assessed as suitable for MCT and 11 commenced. These were compared with 8 waiting list patients also deemed suitable for group MCT who did not receive it in the study timeframe. The PANSS, GAF, MacArthur Competence Assessment Tool- Treatment (MacCAT-T) and MacArthur Competence Assessment Tool-Fitness to Plead (MacCAT-FP) were recorded at baseline and repeated after group MCT or following treatment as usual in the waiting list group. When baseline functioning was accounted for, patients that attended MCT improved in capacity to consent to treatment as assessed by the MacCAT-T (p = 0.019). The more sessions attended, the greater the improvements in capacity to consent to treatment, mainly due to improvement in MacCAT-T understanding (p = 0.014) and reasoning . The GAF score improved in patients who attended the MCT group when compared to the waiting list group (p = 0.038) but there were no changes in PANSS scores. Measures of functional mental capacity and global function can be used as outcome measures for MCT. MCT can be used successfully even in psychotic patients detained in a forensic setting. The restoration of elements of decision making capacity such as understanding and reasoning may be a hither-to unrecognised advantage of such treatment. Because pharmacotherapy can be optimised and there is likely to be enough time to complete the course, there are clear opportunities to benefit from such treatment programmes in forensic settings.

Effects of group metacognitive training (MCT) on mental capacity and functioning in patients with psychosis in a secure forensic psychiatric hospital: a prospective-cohort waiting list controlled study

PubMed Central

2012-01-01

Background Metacognitive Training (MCT) is a manualised cognitive intervention for psychosis aimed at transferring knowledge of cognitive biases and providing corrective experiences. The aim of MCT is to facilitate symptom reduction and protect against relapse. In a naturalistic audit of clinical effectiveness we examined what effect group MCT has on mental capacity, symptoms of psychosis and global function in patients with a psychotic illness, when compared with a waiting list comparison group. Methods Of 93 patients detained in a forensic mental health hospital under both forensic and civil mental health legislation, 19 were assessed as suitable for MCT and 11 commenced. These were compared with 8 waiting list patients also deemed suitable for group MCT who did not receive it in the study timeframe. The PANSS, GAF, MacArthur Competence Assessment Tool- Treatment (MacCAT-T) and MacArthur Competence Assessment Tool-Fitness to Plead (MacCAT-FP) were recorded at baseline and repeated after group MCT or following treatment as usual in the waiting list group. Results When baseline functioning was accounted for, patients that attended MCT improved in capacity to consent to treatment as assessed by the MacCAT-T (p = 0.019). The more sessions attended, the greater the improvements in capacity to consent to treatment, mainly due to improvement in MacCAT-T understanding (p = 0.014) and reasoning . The GAF score improved in patients who attended the MCT group when compared to the waiting list group (p = 0.038) but there were no changes in PANSS scores. Conclusion Measures of functional mental capacity and global function can be used as outcome measures for MCT. MCT can be used successfully even in psychotic patients detained in a forensic setting. The restoration of elements of decision making capacity such as understanding and reasoning may be a hither-to unrecognised advantage of such treatment. Because pharmacotherapy can be optimised and there is likely to be enough time to complete the course, there are clear opportunities to benefit from such treatment programmes in forensic settings. PMID:22709616

STACCATO: a novel solution to supernova photometric classification with biased training sets

NASA Astrophysics Data System (ADS)

Revsbech, E. A.; Trotta, R.; van Dyk, D. A.

2018-01-01

We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. - a diffusion map combined with a random forest classifier - to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the 'gold standard' of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe.

Resuscitation training in small-group setting – gender matters

PubMed Central

2013-01-01

Background Within cardiopulmonary resuscitation external chest compressions (ECC) are of outstanding importance. Frequent training in Basic Life Support (BLS) may improve the performance, but the perfect method or environment is still a matter of research. The objective of this study was to evaluate whether practical performance and retention of skills in resuscitation training may be influenced by the gender composition in learning groups. Methods Participants were allocated to three groups for standardized BLS-training: Female group (F): only female participants; Male group (M): only male participants; Standard group (S): male and female participants. All groups were trained with the standardized 4-step-approach method. Assessment of participants’ performance was done before training (t1), after one week (t2) and eight months later (t3) on a manikin in the same cardiac arrest single-rescuer-scenario. Participants were 251 Laypersons (mean age 21; SD 4; range 18–42 years; females 63%) without previous medical knowledge. Endpoints: compression rate 90-110/min; mean compression depth 38–51 mm. Standardized questionnaires were used for the evaluation of attitude and learning environment. Results After one week group F performed significantly better with respect to the achievement of the correct mean compression depth (F: 63% vs. S: 43%; p = 0.02). Moreover, groups F and S were the only groups which were able to improve their performance concerning the mean compression rate (t1: 35%; t3: 52%; p = 0.04). Female participants felt more comfortable in the female–only environment. Conclusions Resuscitation training in gender-segregated groups has an effect on individual performance with superior ECC skills in the female-only learning groups. Female participants could improve their skills by a more suitable learning environment, while male participants in the standard group felt less distracted by their peers than male participants in the male-only group. PMID:23590998

Comparison of the effects on dynamic balance and aerobic capacity between objective and subjective methods of high-intensity robot-assisted gait training in chronic stroke patients: a randomized controlled trial.

PubMed

Bae, Young-Hyeon; Lee, Suk Min; Ko, Mansoo

2017-05-01

Robot-assisted gait training (RAGT) is effective for improving dynamic balance and aerobic capacity, but previous RAGT method does not set suitable training intensity. Recently, high-intensity treadmill gait training at 70% of heart rate reserve (HRR) was used for improving aerobic capacity and dynamic balance. This study was designed to compare the effectiveness between objective and subjective methods of high-intensity RAGT for improving dynamic balance and aerobic capacity in chronic stroke. Subjects were randomly allocated into experimental (n = 17) and control (n = 17) groups. The experimental group underwent high-intensity RAGT at 70% of HRR, whereas the control group underwent high-intensity RAGT at an RPE of 15. Both groups received their assigned training for 30 min per session, 3 days per week for 6 weeks. All subjects also received an additional 30 min of conventional physical therapy. Before and after each of the 18 sessions, the dynamic balance and aerobic capacity of all subjects were evaluated by a blinded examiner. After training, Berg Balance Scale (BBS) and Timed Up and Go Test scores, VO 2 max, and VO 2 max/kg were significantly increased in both groups (p < 0.05). These variables in experimental group were significantly greater than control group. However, the BBS score was not significantly different between both groups. All subjects completed high-intensity RAGT. No adverse effect of training was observed in both groups. High-intensity RAGT at 70% of HRR significantly improved dynamic balance and aerobic capacity more than RAGT at RPE of 15. These results suggest that high-intensity RAGT at 70% of HRR is safe and effective for improving dynamic balance and aerobic capacity in chronic stroke.

CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

PubMed

Testa, Alison C; Hane, James K; Ellwood, Simon R; Oliver, Richard P

2015-03-11

The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes. We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available ( https://sourceforge.net/projects/codingquarry/ ), and suitable for incorporation into genome annotation pipelines.

Developing an indigenous surgical workforce for Australasia.

PubMed

Aramoana, Jaclyn; Alley, Patrick; Koea, Jonathan B

2013-12-01

Progress has been made in Australia and New Zealand to increase the numbers of indigenous students (Aboriginal, Torres Strait Islander and Maori) entering primary medical qualification courses. In New Zealand, up to 20 Maori are graduating annually, with similar numbers possible in Australia, creating a potential opportunity to develop an indigenous surgical workforce. A literature review identified factors utilized by medical schools to attract indigenous students into medical careers and the interventions necessary to ensure successful graduation. A further search identified those factors important in encouraging indigenous medical graduates to enter specialist training programmes and achieve faculty appointments. All medical schools have utilized elements of a 'pipeline approach' encompassing contact with students at secondary school level to encourage aspirational goals and assist with suitable subject selection. Bridging courses can ensure students leaving school have appropriate skill sets before entering medical degree courses. Extensive practical help is available during primary medical qualification study. The elements necessary for primary medical qualification success - dedicated and focused study, developing appropriate skill sets, mentoring, support, and an institutional and collegial commitment to success - are also the elements required for postgraduate achievement. The Royal Australasian College of Surgeons (RACS) is primarily involved in training rather than service provision. The increasing numbers of indigenous medical graduates in both Australia and New Zealand represent an opportunity for the College to contribute to improving indigenous health status by implementing specific measures to increase numbers of indigenous surgeons. © 2013 Royal Australasian College of Surgeons.

3D Printed Models of Cleft Palate Pathology for Surgical Education.

PubMed

Lioufas, Peter A; Quayle, Michelle R; Leong, James C; McMenamin, Paul G

2016-09-01

To explore the potential viability and limitations of 3D printed models of children with cleft palate deformity. The advantages of 3D printed replicas of normal anatomical specimens have previously been described. The creation of 3D prints displaying patient-specific anatomical pathology for surgical planning and interventions is an emerging field. Here we explored the possibility of taking rare pediatric radiographic data sets to create 3D prints for surgical education. Magnetic resonance imaging data of 2 children (8 and 14 months) were segmented, colored, and anonymized, and stereolothographic files were prepared for 3D printing on either multicolor plastic or powder 3D printers and multimaterial 3D printers. Two models were deemed of sufficient quality and anatomical accuracy to print unamended. One data set was further manipulated digitally to artificially extend the length of the cleft. Thus, 3 models were printed: 1 incomplete soft-palate deformity, 1 incomplete anterior palate deformity, and 1 complete cleft palate. All had cleft lip deformity. The single-material 3D prints are of sufficient quality to accurately identify the nature and extent of the deformities. Multimaterial prints were subsequently created, which could be valuable in surgical training. Improvements in the quality and resolution of radiographic imaging combined with the advent of multicolor multiproperty printer technology will make it feasible in the near future to print 3D replicas in materials that mimic the mechanical properties and color of live human tissue making them potentially suitable for surgical training.

Layoff Time Training: A Key to Upgrading Workforce Utilization and EEOC Affirmative Action. A Case Study in the Northern California Canning Industry. R & D Monograph 61.

ERIC Educational Resources Information Center

Aller, Curtis C.; And Others

An experimental and demonstration project was conducted over a five-year period in California to test the concept of lay-off time training to enable workers to qualify for promotion and increase their earnings. The canning industry was found to be a suitable area for this type of training since it had annual lay-offs followed by assured recalls to…

49 CFR 232.213 - Extended haul trains.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., DEPARTMENT OF TRANSPORTATION BRAKE SYSTEM SAFETY STANDARDS FOR FREIGHT AND OTHER NON-PASSENGER TRAINS AND... extended haul trains will originate and a description of the trains that will be operated as extended haul.... (5) The train shall have no more than one pick-up and one set-out en route, except for the set-out of...

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator); Knowlton, D. J.; Dean, M. E.

1981-01-01

A set of training statistics for the 30 meter resolution simulated thematic mapper MSS data was generated based on land use/land cover classes. In addition to this supervised data set, a nonsupervised multicluster block of training statistics is being defined in order to compare the classification results and evaluate the effect of the different training selection methods on classification performance. Two test data sets, defined using a stratified sampling procedure incorporating a grid system with dimensions of 50 lines by 50 columns, and another set based on an analyst supervised set of test fields were used to evaluate the classifications of the TMS data. The supervised training data set generated training statistics, and a per point Gaussian maximum likelihood classification of the 1979 TMS data was obtained. The August 1980 MSS data was radiometrically adjusted. The SAR data was redigitized and the SAR imagery was qualitatively analyzed.

Child health in low-resource settings: pathways through UK paediatric training.

PubMed

Goenka, Anu; Magnus, Dan; Rehman, Tanya; Williams, Bhanu; Long, Andrew; Allen, Steve J

2013-11-01

UK doctors training in paediatrics benefit from experience of child health in low-resource settings. Institutions in low-resource settings reciprocally benefit from hosting UK trainees. A wide variety of opportunities exist for trainees working in low-resource settings including clinical work, research and the development of transferable skills in management, education and training. This article explores a range of pathways for UK trainees to develop experience in low-resource settings. It is important for trainees to start planning a robust rationale early for global child health activities via established pathways, in the interests of their own professional development as well as UK service provision. In the future, run-through paediatric training may include core elements of global child health, as well as designated 'tracks' for those wishing to develop their career in global child health further. Hands-on experience in low-resource settings is a critical component of these training initiatives.

Online learning from input versus offline memory evolution in adult word learning: effects of neighborhood density and phonologically related practice.

PubMed

Storkel, Holly L; Bontempo, Daniel E; Pak, Natalie S

2014-10-01

In this study, the authors investigated adult word learning to determine how neighborhood density and practice across phonologically related training sets influence online learning from input during training versus offline memory evolution during no-training gaps. Sixty-one adults were randomly assigned to learn low- or high-density nonwords. Within each density condition, participants were trained on one set of words and then were trained on a second set of words, consisting of phonological neighbors of the first set. Learning was measured in a picture-naming test. Data were analyzed using multilevel modeling and spline regression. Steep learning during input was observed, with new words from dense neighborhoods and new words that were neighbors of recently learned words (i.e., second-set words) being learned better than other words. In terms of memory evolution, large and significant forgetting was observed during 1-week gaps in training. Effects of density and practice during memory evolution were opposite of those during input. Specifically, forgetting was greater for high-density and second-set words than for low-density and first-set words. High phonological similarity, regardless of source (i.e., known words or recent training), appears to facilitate online learning from input but seems to impede offline memory evolution.

Neural network evaluation of tokamak current profiles for real time control

NASA Astrophysics Data System (ADS)

Wróblewski, Dariusz

1997-02-01

Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental datais demonstrated.

Neural network evaluation of tokamak current profiles for real time control (abstract)

NASA Astrophysics Data System (ADS)

Wróblewski, Dariusz

1997-01-01

Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental data is demonstrated.

Heavy Equipment Operation Curriculum for Grades 11-12.

ERIC Educational Resources Information Center

Gubbins Associates, Hadlyme, CT.

This curriculum for the training of students in grades 11 and 12 is intended to provide a program of instruction in the operation and maintenance of heavy equipment for construction work and farms. It is also suitable for the training of adult learners interested in upgrading their skills or improving their opportunities to enter this labor…

Manpower, Personnel, and Training Assessment (MPTA) Handbook

DTIC Science & Technology

2015-11-01

Occupational Specialty (MOS), any Additional Skill Identifier (ASI) required, core knowledge, skills, and abilities ( KSAs ) required for the job...of training, usability assessments, interviews with Soldiers, and manpower modeling . Some guidelines on the type of questions to ask in this portion... modeling , and simulation activities into an efficient continuum. COICs are the operational effectiveness and operational suitability issues (not

Training Australian General Practitioners in Rural Public Health: Impact, Desirability and Adaptability of Hybrid Problem-Based Learning

ERIC Educational Resources Information Center

Gladman, Justin; Perkins, David

2013-01-01

Context and Objective: Australian rural general practitioners (GPs) require public health knowledge. This study explored the suitability of teaching complex public health issues related to Aboriginal health by way of a hybrid problem-based learning (PBL) model within an intensive training retreat for GP registrars, when numerous trainees have no…

An Inventory of U.S. Navy Courses Suitable for Use in Training Civiliam Personnel in Basic Technical Skills.

ERIC Educational Resources Information Center

Rogers, William A., Jr.; Nisos, Michael J.

An inventory of courses of study developed by the United States Navy which might be useful to other private and public institutions in training civilian students in basic technological skills is presented. Individual course reports contain the following information: course description, comments, course content (including blocks of instruction and…

Learner Support Requirements for Online Workplace Training in the South African Furniture Industry

ERIC Educational Resources Information Center

MacDonald, Iain S.; Bullen, Mark; Kozak, Robert A.

2010-01-01

A qualitative research project was conducted to evaluate the suitability of e-learning as a means of delivering training to workplace learners in the South African furniture manufacturing sector. Twenty learners participated in a three-month pilot e-learning course and were monitored throughout. While the study was designed primarily to…

Evaluation of Mathematics Curriculum in Primary Teacher Training Institute in Somalia. African Studies in Curriculum Development & Evaluation.

ERIC Educational Resources Information Center

Jama, Mohamed A. F.

This study sought to evaluate the mathematics curriculum of the Halane Teacher Training Institute in Somalia with a view toward: (1) determining its weaknesses and recommending measures for improvement; (2) examining its relevance to the present needs of the Somali society; (3) determining the suitability of instructional materials and other…

Engine Lathe Operator. Instructor's Guide. Part of Single-Tool Skills Program Series. Machine Industries Occupations.

ERIC Educational Resources Information Center

New York State Education Dept., Albany. Bureau of Secondary Curriculum Development.

Expected to help meet the need for trained operators in metalworking and suitable for use in the adult education programs of school districts, in manpower development and training programs, and in secondary schools, this guide consists of four sections: Introduction, General Job Content, Shop Projects, and Drawings for the Projects. General Job…

Machine Tool Technology. Automatic Screw Machine Troubleshooting & Set-Up Training Outlines [and] Basic Operator's Skills Set List.

ERIC Educational Resources Information Center

Anoka-Hennepin Technical Coll., Minneapolis, MN.

This set of two training outlines and one basic skills set list are designed for a machine tool technology program developed during a project to retrain defense industry workers at risk of job loss or dislocation because of conversion of the defense industry. The first troubleshooting training outline lists the categories of problems that develop…

On the box-counting dimension of the potential singular set for suitable weak solutions to the 3D Navier-Stokes equations

NASA Astrophysics Data System (ADS)

Wang, Yanqing; Wu, Gang

2017-05-01

In this paper, we are concerned with the upper box-counting dimension of the set of possible singular points in the space-time of suitable weak solutions to the 3D Navier-Stokes equations. By taking full advantage of the pressure \\Pi in terms of \

Virtual Reality Training System for Anytime/Anywhere Acquisition of Surgical Skills: A Pilot Study.

PubMed

Zahiri, Mohsen; Booton, Ryan; Nelson, Carl A; Oleynikov, Dmitry; Siu, Ka-Chun

2018-03-01

This article presents a hardware/software simulation environment suitable for anytime/anywhere surgical skills training. It blends the advantages of physical hardware and task analogs with the flexibility of virtual environments. This is further enhanced by a web-based implementation of training feedback accessible to both trainees and trainers. Our training system provides a self-paced and interactive means to attain proficiency in basic tasks that could potentially be applied across a spectrum of trainees from first responder field medical personnel to physicians. This results in a powerful training tool for surgical skills acquisition relevant to helping injured warfighters.

A mobile, high-throughput semi-automated system for testing cognition in large non-primate animal models of Huntington disease.

PubMed

McBride, Sebastian D; Perentos, Nicholas; Morton, A Jennifer

2016-05-30

For reasons of cost and ethical concerns, models of neurodegenerative disorders such as Huntington disease (HD) are currently being developed in farm animals, as an alternative to non-human primates. Developing reliable methods of testing cognitive function is essential to determining the usefulness of such models. Nevertheless, cognitive testing of farm animal species presents a unique set of challenges. The primary aims of this study were to develop and validate a mobile operant system suitable for high throughput cognitive testing of sheep. We designed a semi-automated testing system with the capability of presenting stimuli (visual, auditory) and reward at six spatial locations. Fourteen normal sheep were used to validate the system using a two-choice visual discrimination task. Four stages of training devised to acclimatise animals to the system are also presented. All sheep progressed rapidly through the training stages, over eight sessions. All sheep learned the 2CVDT and performed at least one reversal stage. The mean number of trials the sheep took to reach criterion in the first acquisition learning was 13.9±1.5 and for the reversal learning was 19.1±1.8. This is the first mobile semi-automated operant system developed for testing cognitive function in sheep. We have designed and validated an automated operant behavioural testing system suitable for high throughput cognitive testing in sheep and other medium-sized quadrupeds, such as pigs and dogs. Sheep performance in the two-choice visual discrimination task was very similar to that reported for non-human primates and strongly supports the use of farm animals as pre-clinical models for the study of neurodegenerative diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

PubMed Central

Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J.

2017-01-01

Background Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). Purpose To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. Research Design An experimental within-participants study. Participants used a prototype hearing system—comprising two hearing aids, Android smartphone, and body-worn gateway device—for ~6 weeks. Study Sample Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Intervention Participants were fitted and instructed to perform daily comparisons of settings (“listening evaluations”) through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone—including environmental sound classification, sound level, and location—to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system (“trained settings”) to those suggested by the hearing aids’ untrained system (“untrained settings”). Data Collection and Analysis We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Results Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. Conclusions The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone. PMID:27718350

Design of a Novel Low Cost Point of Care Tampon (POCkeT) Colposcope for Use in Resource Limited Settings

PubMed Central

Lam, Christopher T.; Krieger, Marlee S.; Gallagher, Jennifer E.; Asma, Betsy; Muasher, Lisa C.; Schmitt, John W.; Ramanujam, Nimmi

2015-01-01

Introduction Current guidelines by WHO for cervical cancer screening in low- and middle-income countries involves visual inspection with acetic acid (VIA) of the cervix, followed by treatment during the same visit or a subsequent visit with cryotherapy if a suspicious lesion is found. Implementation of these guidelines is hampered by a lack of: trained health workers, reliable technology, and access to screening facilities. A low cost ultra-portable Point of Care Tampon based digital colposcope (POCkeT Colposcope) for use at the community level setting, which has the unique form factor of a tampon, can be inserted into the vagina to capture images of the cervix, which are on par with that of a state of the art colposcope, at a fraction of the cost. A repository of images to be compiled that can be used to empower front line workers to become more effective through virtual dynamic training. By task shifting to the community setting, this technology could potentially provide significantly greater cervical screening access to where the most vulnerable women live. The POCkeT Colposcope’s concentric LED ring provides comparable white and green field illumination at a fraction of the electrical power required in commercial colposcopes. Evaluation with standard optical imaging targets to assess the POCkeT Colposcope against the state of the art digital colposcope and other VIAM technologies. Results Our POCkeT Colposcope has comparable resolving power, color reproduction accuracy, minimal lens distortion, and illumination when compared to commercially available colposcopes. In vitro and pilot in vivo imaging results are promising with our POCkeT Colposcope capturing comparable quality images to commercial systems. Conclusion The POCkeT Colposcope is capable of capturing images suitable for cervical lesion analysis. Our portable low cost system could potentially increase access to cervical cancer screening in limited resource settings through task shifting to community health workers. PMID:26332673

Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence

PubMed Central

Li, Mingzhong; Xue, Jianquan; Li, Yanchao; Tang, Shukai

2014-01-01

Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results. PMID:24772024

Quantitative structure-activity relationship of organosulphur compounds as soybean 15-lipoxygenase inhibitors using CoMFA and CoMSIA.

PubMed

Caballero, Julio; Fernández, Michael; Coll, Deysma

2010-12-01

Three-dimensional quantitative structure-activity relationship studies were carried out on a series of 28 organosulphur compounds as 15-lipoxygenase inhibitors using comparative molecular field analysis and comparative molecular similarity indices analysis. Quantitative information on structure-activity relationships is provided for further rational development and direction of selective synthesis. All models were carried out over a training set including 22 compounds. The best comparative molecular field analysis model only included steric field and had a good Q² = 0.789. Comparative molecular similarity indices analysis overcame the comparative molecular field analysis results: the best comparative molecular similarity indices analysis model also only included steric field and had a Q² = 0.894. In addition, this model predicted adequately the compounds contained in the test set. Furthermore, plots of steric comparative molecular similarity indices analysis field allowed conclusions to be drawn for the choice of suitable inhibitors. In this sense, our model should prove useful in future 15-lipoxygenase inhibitor design studies. © 2010 John Wiley & Sons A/S.

Prediction of the wall factor of arbitrary particle settling through various fluid media in a cylindrical tube using artificial intelligence.

PubMed

Li, Mingzhong; Zhang, Guodong; Xue, Jianquan; Li, Yanchao; Tang, Shukai

2014-01-01

Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results.

The construction of support vector machine classifier using the firefly algorithm.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

Optimal Sparse Upstream Sensor Placement for Hydrokinetic Turbines

NASA Astrophysics Data System (ADS)

Cavagnaro, Robert; Strom, Benjamin; Ross, Hannah; Hill, Craig; Polagye, Brian

2016-11-01

Accurate measurement of the flow field incident upon a hydrokinetic turbine is critical for performance evaluation during testing and setting boundary conditions in simulation. Additionally, turbine controllers may leverage real-time flow measurements. Particle image velocimetry (PIV) is capable of rendering a flow field over a wide spatial domain in a controlled, laboratory environment. However, PIV's lack of suitability for natural marine environments, high cost, and intensive post-processing diminish its potential for control applications. Conversely, sensors such as acoustic Doppler velocimeters (ADVs), are designed for field deployment and real-time measurement, but over a small spatial domain. Sparsity-promoting regression analysis such as LASSO is utilized to improve the efficacy of point measurements for real-time applications by determining optimal spatial placement for a small number of ADVs using a training set of PIV velocity fields and turbine data. The study is conducted in a flume (0.8 m2 cross-sectional area, 1 m/s flow) with laboratory-scale axial and cross-flow turbines. Predicted turbine performance utilizing the optimal sparse sensor network and associated regression model is compared to actual performance with corresponding PIV measurements.

Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

NASA Astrophysics Data System (ADS)

Bennett, C.; Dunne, J. F.; Trimby, S.; Richardson, D.

2017-02-01

A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 l, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

Issues in the Development and Evaluation of Cross-Cultural Training in a Business Setting.

ERIC Educational Resources Information Center

Broadbooks, Wendy J.

Issues in the development and evaluation of cross-cultural training in a business setting were investigated. Cross-cultural training and cross-cultural evaluation were defined as training and evaluation of training that involve the interaction of participants from two or more different countries. Two evaluations of a management development-type…

Optimization of genomic selection training populations with a genetic algorithm

USDA-ARS?s Scientific Manuscript database

In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

SwarmSight: Measuring the Temporal Progression of Animal Group Activity Levels from Natural Scene and Laboratory Videos

PubMed Central

Birgiolas, Justas; Jernigan, Christopher M.; Smith, Brian H.; Crook, Sharon M.

2016-01-01

We describe SwarmSight (available at: https://github.com/justasb/SwarmSight), a novel, open-source, Microsoft Windows software tool for quantitative assessment of the temporal progression of animal group activity levels from recorded videos. The tool utilizes a background subtraction machine vision algorithm and provides an activity metric that can be used to quantitatively assess and compare animal group behavior. Here we demonstrate the tool utility by analyzing defensive bee behavior as modulated by alarm pheromones, wild bird feeding onset and interruption, and cockroach nest finding activity. While more sophisticated, commercial software packages are available, SwarmSight provides a low-cost, open-source, and easy-to-use alternative that is suitable for a wide range of users, including minimally trained research technicians and behavioral science undergraduate students in classroom laboratory settings. PMID:27130170

Tai Chi Chuan: an ancient wisdom on exercise and health promotion.

PubMed

Lan, Ching; Lai, Jin-Shin; Chen, Ssu-Yuan

2002-01-01

Tai Chi Chuan (TCC) is a Chinese conditioning exercise and is well known for its slow and graceful movements. Recent investigations have found that TCC is beneficial to cardiorespiratory function, strength, balance, flexibility, microcirculation and psychological profile. The long-term practice of TCC can attenuate the age decline in physical function, and consequently it is a suitable exercise for the middle-aged and elderly individuals. TCC can be prescribed as an alternative exercise programme for selected patients with cardiovascular, orthopaedic, or neurological diseases, and can reduce the risk of falls in elderly individuals. The exercise intensity of TCC depends on training style, posture and duration. Participants can choose to perform a complete set of TCC or selected movements according to their needs. In conclusion, TCC has potential benefits in health promotion, and is appropriate for implementation in the community.

An exploratory clustering approach for extracting stride parameters from tracking collars on free-ranging wild animals.

PubMed

Dewhirst, Oliver P; Roskilly, Kyle; Hubel, Tatjana Y; Jordan, Neil R; Golabek, Krystyna A; McNutt, J Weldon; Wilson, Alan M

2017-02-01

Changes in stride frequency and length with speed are key parameters in animal locomotion research. They are commonly measured in a laboratory on a treadmill or by filming trained captive animals. Here, we show that a clustering approach can be used to extract these variables from data collected by a tracking collar containing a GPS module and tri-axis accelerometers and gyroscopes. The method enables stride parameters to be measured during free-ranging locomotion in natural habitats. As it does not require labelled data, it is particularly suitable for use with difficult to observe animals. The method was tested on large data sets collected from collars on free-ranging lions and African wild dogs and validated using a domestic dog. © 2017. Published by The Company of Biologists Ltd.

Automatic morphological classification of galaxy images

PubMed Central

Shamir, Lior

2009-01-01

We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbor rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with accuracy of ~90% compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects. PMID:20161594

Training a whole-book LSTM-based recognizer with an optimal training set

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2018-04-01

Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.

An easy-to-build, low-budget point-of-care ultrasound simulator: from Linux to a web-based solution.

PubMed

Damjanovic, Domagoj; Goebel, Ulrich; Fischer, Benedikt; Huth, Martin; Breger, Hartmut; Buerkle, Hartmut; Schmutz, Axel

2017-12-01

Hands-on training in point-of-care ultrasound (POC-US) should ideally comprise bedside teaching, as well as simulated clinical scenarios. High-fidelity phantoms and portable ultrasound simulation systems are commercially available, however, at considerable costs. This limits their suitability for medical schools. A Linux-based software for Emergency Department Ultrasound Simulation (edus2TM) was developed by Kulyk and Olszynski in 2011. Its feasibility for POC-US education has been well-documented, and shows good acceptance. An important limitation to an even more widespread use of edus2, however, may be due to the need for a virtual machine for WINDOWS ® systems. Our aim was to adapt the original software toward an HTML-based solution, thus making it affordable and applicable in any simulation setting. We created an HTML browser-based ultrasound simulation application, which reads the input of different sensors, triggering an ultrasound video to be displayed on a respective device. RFID tags, NFC tags, and QR Codes™ have been integrated into training phantoms or were attached to standardized patients. The RFID antenna was hidden in a mock ultrasound probe. The application is independent from the respective device. Our application was used successfully with different trigger/scanner combinations and mounted readily into simulated training scenarios. The application runs independently from operating systems or electronic devices. This low-cost, browser-based ultrasound simulator is easy-to-build, very adaptive, and independent from operating systems. It has the potential to facilitate POC-US training throughout the world, especially in resource-limited areas.

Preparation of Effective Operating Manuals to Support Waste Management Plant Operator Training

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

Brown, S. R.

2003-02-25

Effective plant operating manuals used in a formal training program can make the difference between a successful operation and a failure. Once the plant process design and control strategies have been fixed, equipment has been ordered, and the plant is constructed, the only major variable affecting success is the capability of plant operating personnel. It is essential that the myriad details concerning plant operation are documented in comprehensive operating manuals suitable for training the non-technical personnel that will operate the plant. These manuals must cover the fundamental principles of each unit operation including how each operates, what process variables aremore » important, and the impact of each variable on the overall process. In addition, operators must know the process control strategies, process interlocks, how to respond to alarms, each of the detailed procedures required to start up and optimize the plant, and every control loop-including when it is appropriate to take manual control. More than anything else, operating mistakes during the start-up phase can lead to substantial delays in achieving design processing rates as well as to problems with government authorities if environmental permit limits are exceeded. The only way to assure return on plant investment is to ensure plant operators have the knowledge to properly run the plant from the outset. A comprehensive set of operating manuals specifically targeted toward plant operators and supervisors written by experienced operating personnel is the only effective way to provide the necessary information for formal start-up training.« less

Nutritional recommendations for divers.

PubMed

Benardot, Dan; Zimmermann, Wes; Cox, Gregory R; Marks, Saul

2014-08-01

Competitive diving involves grace, power, balance, and flexibility, which all require satisfying daily energy and nutrient needs. Divers are short, well-muscled, and lean, giving them a distinct biomechanical advantage. Although little diving-specific nutrition research on performance and health outcomes exists, there is concern that divers are excessively focused on body weight and composition, which may result in reduced dietary intake to achieve desired physique goals. This will result in low energy availability, which may have a negative impact on their power-to-weight ratio and health risks. Evidence is increasing that restrictive dietary practices leading to low energy availability also result in micronutrient deficiencies, premature fatigue, frequent injuries, and poor athletic performance. On the basis of daily training demands, estimated energy requirements for male and female divers are 3,500 kcal and 2,650 kcal, respectively. Divers should consume a diet that provides 3-8 g/kg/day of carbohydrate, with the higher values accommodating growth and development. Total daily protein intake (1.2-1.7 g/kg) should be spread evenly throughout the day in 20 to 30 g amounts and timed appropriately after training sessions. Divers should consume nutrient-dense foods and fluids and, with medical supervision, certain dietary supplements (i.e., calcium and iron) may be advisable. Although sweat loss during indoor training is relatively low, divers should follow appropriate fluid-intake strategies to accommodate anticipated sweat losses in hot and humid outdoor settings. A multidisciplinary sports medicine team should be integral to the daily training environment, and suitable foods and fluids should be made available during prolonged practices and competitions.

Effects of interset whole-body vibration on bench press resistance training in trained and untrained individuals.

PubMed

Timon, Rafael; Collado-Mateo, Daniel; Olcina, Guillermo; Gusi, Narcis

2016-03-01

Previous studies have demonstrated positive effects of acute vibration exercise on concentric strength and power, but few have observed the effects of vibration exposure on resistance training. The aim of this study was to verify the effects of whole body vibration applied to the chest via hands on bench press resistance training in trained and untrained individuals. Nineteen participants (10 recreationally trained bodybuilders and 9 untrained students) performed two randomized sessions of resistance training on separate days. Each strength session consisted of 3 bench press sets with a load of 75% 1RM to failure in each set, with 2 minutes' rest between sets. All subjects performed the same strength training with either, vibration exposure (12 Hz, 4 mm) of 30 seconds immediately before each bench press set or without vibration. Number of total repetitions, kinematic parameters, blood lactate and perceived exertion were analyzed. In the untrained group, vibration exposure caused a significant increase in the mean velocity (from 0.36±0.02 to 0.39±0.03 m/s) and acceleration (from 0.75±0.10 to 0.86±0.09 m/s2), as well as a decrease in perceived effort (from 8±0.57 to 7.35±0.47) in the first bench press set, but no change was observed in the third bench press set. In the recreationally trained bodybuilders, vibration exposure did not cause any improvement on the performance of bench press resistance training. These results suggest that vibration exposure applied just before the bench press exercise could be a good practice to be implemented by untrained individuals in resistance training.

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

Automatic Classification of High Resolution Satellite Imagery - a Case Study for Urban Areas in the Kingdom of Saudi Arabia

NASA Astrophysics Data System (ADS)

Maas, A.; Alrajhi, M.; Alobeid, A.; Heipke, C.

2017-05-01

Updating topographic geospatial databases is often performed based on current remotely sensed images. To automatically extract the object information (labels) from the images, supervised classifiers are being employed. Decisions to be taken in this process concern the definition of the classes which should be recognised, the features to describe each class and the training data necessary in the learning part of classification. With a view to large scale topographic databases for fast developing urban areas in the Kingdom of Saudi Arabia we conducted a case study, which investigated the following two questions: (a) which set of features is best suitable for the classification?; (b) what is the added value of height information, e.g. derived from stereo imagery? Using stereoscopic GeoEye and Ikonos satellite data we investigate these two questions based on our research on label tolerant classification using logistic regression and partly incorrect training data. We show that in between five and ten features can be recommended to obtain a stable solution, that height information consistently yields an improved overall classification accuracy of about 5%, and that label noise can be successfully modelled and thus only marginally influences the classification results.

Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development

PubMed Central

Ayaz, Hasan; Onaral, Banu; Izzetoglu, Kurtulus; Shewokis, Patricia A.; McKendrick, Ryan; Parasuraman, Raja

2013-01-01

Functional near infrared spectroscopy (fNIRS) is a non-invasive, safe, and portable optical neuroimaging method that can be used to assess brain dynamics during skill acquisition and performance of complex work and everyday tasks. In this paper we describe neuroergonomic studies that illustrate the use of fNIRS in the examination of training-related brain dynamics and human performance assessment. We describe results of studies investigating cognitive workload in air traffic controllers, acquisition of dual verbal-spatial working memory skill, and development of expertise in piloting unmanned vehicles. These studies used conventional fNIRS devices in which the participants were tethered to the device while seated at a workstation. Consistent with the aims of mobile brain imaging (MoBI), we also describe a compact and battery-operated wireless fNIRS system that performs with similar accuracy as other established fNIRS devices. Our results indicate that both wired and wireless fNIRS systems allow for the examination of brain function in naturalistic settings, and thus are suitable for reliable human performance monitoring and training assessment. PMID:24385959

A new algorithm to detect earthquakes outside the seismic network: preliminary results

NASA Astrophysics Data System (ADS)

Giudicepietro, Flora; Esposito, Antonietta Maria; Ricciolino, Patrizia

2017-04-01

In this text we are going to present a new technique for detecting earthquakes outside the seismic network, which are often the cause of fault of automatic analysis system. Our goal is to develop a robust method that provides the discrimination result as quickly as possible. We discriminate local earthquakes from regional earthquakes, both recorded at SGG station, equipped with short period sensors, operated by Osservatorio Vesuviano (INGV) in the Southern Apennines (Italy). The technique uses a Multi Layer Perceptron (MLP) neural network with an architecture composed by an input layer, a hidden layer and a single node output layer. We pre-processed the data using the Linear Predictive Coding (LPC) technique to extract the spectral features of the signals in a compact form. We performed several experiments by shortening the signal window length. In particular, we used windows of 4, 2 and 1 seconds containing the onset of the local and the regional earthquakes. We used a dataset of 103 local earthquakes and 79 regional earthquakes, most of which occurred in Greece, Albania and Crete. We split the dataset into a training set, for the network training, and a testing set to evaluate the network's capacity of discrimination. In order to assess the network stability, we repeated this procedure six times, randomly changing the data composition of the training and testing set and the initial weights of the net. We estimated the performance of this method by calculating the average of correct detection percentages obtained for each of the six permutations. The average performances are 99.02%, 98.04% and 98.53%, which concern respectively the experiments carried out on 4, 2 and 1 seconds signal windows. The results show that our method is able to recognize the earthquakes outside the seismic network using only the first second of the seismic records, with a suitable percentage of correct detection. Therefore, this algorithm can be profitably used to make earthquake automatic analyses more robust and reliable. Finally, with appropriate tuning, it can be integrated in multi-parametric systems for monitoring high natural risk areas.

The effectiveness of three sets of school-based instructional materials and community training on the acquisition and generalization of community laundry skills by students with severe handicaps.

PubMed

Morrow, S A; Bates, P E

1987-01-01

This study examined the effectiveness of three sets of school-based instructional materials and community training on acquisition and generalization of a community laundry skill by nine students with severe handicaps. School-based instruction involved artificial materials (pictures), simulated materials (cardboard replica of a community washing machine), and natural materials (modified home model washing machine). Generalization assessments were conducted at two different community laundromats, on two machines represented fully by the school-based instructional materials and two machines not represented fully by these materials. After three phases of school-based instruction, the students were provided ten community training trials in one laundromat setting and a final assessment was conducted in both the trained and untrained community settings. A multiple probe design across students was used to evaluate the effectiveness of the three types of school instruction and community training. After systematic training, most of the students increased their laundry performance with all three sets of school-based materials; however, generalization of these acquired skills was limited in the two community settings. Direct training in one of the community settings resulted in more efficient acquisition of the laundry skills and enhanced generalization to the untrained laundromat setting for most of the students. Results of this study are discussed in regard to the issue of school versus community-based instruction and recommendations are made for future research in this area.

The Effects of Transfer in Teaching Vocabulary to School Children: An Analysis of the Dependencies between Lists of Trained and Non-Trained Words

ERIC Educational Resources Information Center

Frost, Jørgen; Ottem, Ernst; Hagtvet, Bente E.; Snow, Catherine E.

2016-01-01

In the present study, 81 Norwegian students were taught the meaning of words by the Word Generation (WG) method and 51 Norwegian students were taught by an approach inspired by the Thinking Schools (TS) concept. Two sets of words were used: a set of words to be trained and a set of non-trained control words. The two teaching methods yielded no…

Preventing a Relapse or Setting Goals? Elucidating the Impact of Post-Training Transfer Interventions on Training Transfer Performance

ERIC Educational Resources Information Center

Rahyuda, Agoes Ganesha; Soltani, Ebrahim; Syed, Jawad

2018-01-01

Based on a review of the literature on post-training transfer interventions, this paper offers a conceptual model that elucidates potential mechanisms through which two types of post-training transfer intervention (relapse prevention and proximal plus distal goal setting) influence the transfer of training. We explain how the application of…

Comparison of molecular breeding values based on within- and across-breed training in beef cattle.

PubMed

Kachman, Stephen D; Spangler, Matthew L; Bennett, Gary L; Hanford, Kathryn J; Kuehn, Larry A; Snelling, Warren M; Thallman, R Mark; Saatchi, Mahdi; Garrick, Dorian J; Schnabel, Robert D; Taylor, Jeremy F; Pollak, E John

2013-08-16

Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set.

VHR satellite multitemporal data to extract cultural landscape changes in the roman site of Grumentum

NASA Astrophysics Data System (ADS)

masini, nicola; Lasaponara, Rosa

2013-04-01

The papers deals with the use of VHR satellite multitemporal data set to extract cultural landscape changes in the roman site of Grumentum Grumentum is an ancient town, 50 km south of Potenza, located near the roman road of Via Herculea which connected the Venusia, in the north est of Basilicata, with Heraclea in the Ionian coast. The first settlement date back to the 6th century BC. It was resettled by the Romans in the 3rd century BC. Its urban fabric which evidences a long history from the Republican age to late Antiquity (III BC-V AD) is composed of the typical urban pattern of cardi and decumani. Its excavated ruins include a large amphitheatre, a theatre, the thermae, the Forum and some temples. There are many techniques nowadays available to capture and record differences in two or more images. In this paper we focus and apply the two main approaches which can be distinguished into : (i) unsupervised and (ii) supervised change detection methods. Unsupervised change detection methods are generally based on the transformation of the two multispectral images in to a single band or multiband image which are further analyzed to identify changes Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) a pixel-by-pixel comparison is performed, (iii). Identification of changes according to the magnitude an direction (positive /negative). Unsupervised change detection are generally based on the transformation of the two multispectral images into a single band or multiband image which are further analyzed to identify changes. Than the separation between changed and unchanged classes is obtained from the magnitude of the resulting spectral change vectors by means of empirical or theoretical well founded approaches Supervised change detection methods are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers. Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) supervised classification is performed on the single dates or on the map obtained as the difference of two dates, (iii). Identification of changes according to the magnitude an direction (positive /negative). Supervised change detection are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers, therefore these algorithms require a preliminary knowledge necessary: (i) to generate representative parameters for each class of interest; and (ii) to carry out the training stage Advantages and disadvantages of the supervised and unsupervised approaches are discuss. Finally results from the the satellite multitemporal dataset was also integrated with aerial photos from historical archive in order to expand the time window of the investigation and capture landscape changes occurred from the Agrarian Reform, in the 50s, up today.

Vocational Education and Training--An Engine for Economic Growth and a Vehicle for Social Inclusion?

ERIC Educational Resources Information Center

Nilsson, Anders

2010-01-01

Vocational education and training (VET) has in recent years enjoyed a revival for two major reasons. Firstly, it is regarded as a suitable means of promoting economic growth. Secondly, it is seen as a potentially powerful tool for fostering social inclusion. In this review, these assumed effects are critically examined on the basis of the vastly…

Effects of draught load exercise and training on calcium homeostasis in horses.

PubMed

Vervuert, I; Coenen, M; Zamhöfer, J

2005-01-01

This study was conducted to investigate the effects of draught load exercise on calcium (Ca) homeostasis in young horses. Five 2-year-old untrained Standardbred horses were studied in a 4-month training programme. All exercise workouts were performed on a treadmill at a 6% incline and with a constant draught load of 40 kg (0.44 kN). The training programme started with a standardized exercise test (SET 1; six incremental steps of 5 min duration each, first step 1.38 m/s, stepwise increase by 0.56 m/s). A training programme was then initiated which consisted of low-speed exercise sessions (LSE; constant velocity at 1.67 m/s for 60 min, 48 training sessions in total). After the 16th and 48th LSE sessions, SETs (SET 2: middle of training period, SET 3: finishing training period) were performed again under the identical test protocol of SET 1. Blood samples for blood lactate, plasma total Ca, blood ionized calcium (Ca(2+)), blood pH, plasma inorganic phosphorus (P(i)) and plasma intact parathyroid hormone (PTH) were collected before, during and after SETs, and before and after the first, 16th, 32nd and 48th LSE sessions. During SETs there was a decrease in ionized Ca(2+) and a rise in lactate, P(i) and intact PTH. The LSEs resulted in an increase in pH and P(i), whereas lactate, ionized Ca(2+), total Ca and intact PTH were not affected. No changes in Ca metabolism were detected in the course of training. Results of this study suggest that the type of exercise influences Ca homeostasis and intact PTH response, but that these effects are not influenced in the course of the training period.

Two Validated Ways of Improving the Ability of Decision-Making in Emergencies; Results from a Literature Review

PubMed Central

Khorram-Manesh, Amir; Berlin, Johan; Carlström, Eric

2016-01-01

The aim of the current review wasto study the existing knowledge about decision-making and to identify and describe validated training tools.A comprehensive literature review was conducted by using the following keywords: decision-making, emergencies, disasters, crisis management, training, exercises, simulation, validated, real-time, command and control, communication, collaboration, and multi-disciplinary in combination or as an isolated word. Two validated training systems developed in Sweden, 3 level collaboration (3LC) and MacSim, were identified and studied in light of the literature review in order to identify how decision-making can be trained. The training models fulfilled six of the eight identified characteristics of training for decision-making.Based on the results, these training models contained methods suitable to train for decision-making. PMID:27878123

Team Training and Retention of Skills Acquired Above Real Time Training on a Flight Simulator

NASA Technical Reports Server (NTRS)

Ali, Syed Friasat; Guckenberger, Dutch; Crane, Peter; Rossi, Marcia; Williams, Mayard; Williams, Jason; Archer, Matt

2000-01-01

Above Real-Time Training (ARTT) is the training acquired on a real time simulator when it is modified to present events at a faster pace than normal. The experiments related to training of pilots performed by NASA engineers (Kolf in 1973, Hoey in 1976) and others (Guckenberger, Crane and their associates in the nineties) have shown that in comparison with the real time training (RTT), ARTT provides the following benefits: increased rate of skill acquisition, reduced simulator and aircraft training time, and more effective training for emergency procedures. Two sets of experiments have been performed; they are reported in professional conferences and the respective papers are included in this report. The retention of effects of ARTT has been studied in the first set of experiments and the use of ARTT as top-off training has been examined in the second set of experiments. In ARTT, the pace of events was 1.5 times the pace in RTT. In both sets of experiments, university students were trained to perform an aerial gunnery task. The training unit was equipped with a joystick and a throttle. The student acted as a nose gunner in a hypothetical two place attack aircraft. The flight simulation software was installed on a Universal Distributed Interactive Simulator platform supplied by ECC International of Orlando, Florida. In the first set of experiments, two training programs RTT or ART7 were used. Students were then tested in real time on more demanding scenarios: either immediately after training or two days later. The effects of ARTT did not decrease over a two day retention interval and ARTT was more time efficient than real time training. Therefore, equal test performance could be achieved with less clock-time spent in the simulator. In the second set of experiments three training programs RTT or ARTT or RARTT, were used. In RTT, students received 36 minutes of real time training. In ARTT, students received 36 minutes of above real time training. In RARTT, students received 18 minutes of real time training and 18 minutes of above real time training as top-off training. Students were then tested in real time on more demanding scenarios. The use of ARTT as top-off training after RTT offered better training than RTT alone or ARTT alone. It is, however, suggested that a similar experiment be conducted on a relatively more complex task with a larger sample of participants. Within the proposed duration of the research effort, the setting up of experiments and trial runs on using ARTT for team training were also scheduled but they could not be accomplished due to extra ordinary challenges faced in developing the required software configuration. Team training is, however, scheduled in a future study sponsored by NASA at Tuskegee University.

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

2015-04-01

Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

Upper and Lower Urinary Tract Endoscopy Training on Thiel-embalmed Cadavers.

PubMed

Bele, Uros; Kelc, Robi

2016-07-01

To evaluate Thiel-embalmed cadavers as a new training model for urological endoscopy procedures. Twelve urologists performed upper and lower urinary tract endoscopies on 5 different Thiel-embalmed cadavers to evaluate this potentially new training model in urological endoscopic procedural training. Using a 5-point Likert scale, the participants assessed the quality of the tissue and the overall experience of the endoscopy in comparison to a live patient procedure. Thiel-embalmed cadavers have shown to mimic live patient endoscopy of the upper and lower urinary tract in terms of almost identical overall anatomical conditions and manipulation characteristics of the tissue. The mucosa of the urethra and ureters showed similar colors and consistency in comparison to a live patient, whereas bladder mucosa was lacking the visibility of the vessels, thus was unsuitable for identifying any mucosal abnormalities. The flexibility of the muscles allowed for proper patient positioning, whereas the loss of muscle tonus made ureteroscopy more difficult although sufficiently comparable to the procedure done in a live patient. Thiel-embalmed cadavers have already been proven to be a suitable training model for several medical procedures. They are known for preserving tissue color, consistency, and flexibility without the irritant odors or risk of infection, which make them resemble live patients with real-life surgical challenges. The results of our study strongly suggest that despite some minor drawbacks, Thiel-embalmed cadavers are a suitable simulation model for initial training of urethrocystoscopy and ureteroscopy. Copyright © 2016 Elsevier Inc. All rights reserved.

Systematic review of skills transfer after surgical simulation-based training.

PubMed

Dawe, S R; Pena, G N; Windsor, J A; Broeders, J A J L; Cregan, P C; Hewett, P J; Maddern, G J

2014-08-01

Simulation-based training assumes that skills are directly transferable to the patient-based setting, but few studies have correlated simulated performance with surgical performance. A systematic search strategy was undertaken to find studies published since the last systematic review, published in 2007. Inclusion of articles was determined using a predetermined protocol, independent assessment by two reviewers and a final consensus decision. Studies that reported on the use of surgical simulation-based training and assessed the transferability of the acquired skills to a patient-based setting were included. Twenty-seven randomized clinical trials and seven non-randomized comparative studies were included. Fourteen studies investigated laparoscopic procedures, 13 endoscopic procedures and seven other procedures. These studies provided strong evidence that participants who reached proficiency in simulation-based training performed better in the patient-based setting than their counterparts who did not have simulation-based training. Simulation-based training was equally as effective as patient-based training for colonoscopy, laparoscopic camera navigation and endoscopic sinus surgery in the patient-based setting. These studies strengthen the evidence that simulation-based training, as part of a structured programme and incorporating predetermined proficiency levels, results in skills transfer to the operative setting. © 2014 BJS Society Ltd. Published by John Wiley & Sons Ltd.

A pharmacy carer support service: obtaining new insight into carers in the community.

PubMed

McMillan, Sara S; King, Michelle A; Stapleton, Helen; Sav, Adem; Kelly, Fiona; Wheeler, Amanda J

2018-05-06

Unpaid carers have many and varied responsibilities in society, which can include medication management for the person they support. However, the potential for Australian community pharmacies to better assist carers is relatively unexplored. This mixed-methods study investigated the acceptability of a local carer support service by trained community pharmacy staff, including issues regarding the implementation and impact of this service. Staff from 11 community pharmacies in South East Queensland, Australia, were trained to deliver a six-step carer support service between September 2016 and March 2017. Pharmacies were supported by a carer and pharmacist mentor pair and asked to recruit up to six carers each. Evaluations of staff training were descriptively analysed. Semi-structured interviews were undertaken with pharmacy staff, and interview transcripts were analysed thematically. Staff training evaluations were positive; participants acquired new information about carers and rated the service highly in terms of its importance within the pharmacy setting. Feedback was obtained on how to improve the training, such as further opportunities for role-play. Seven staff members were interviewed, and data analysis revealed two main themes: (1) implementation of the carer support service and (2) perceived impact on pharmacy staff. Positive attitudes towards recognising and supporting carers, and training and mentoring were identified with community pharmacies viewed as a suitable place for delivering this new service. New insights into the impact of caring were widely reported, which staff had not appreciated from previous carer interactions. Structural issues, including space and time pressures, and a lack of awareness about the types of support currently available to carers were emphasised. Pharmacy staff are well positioned to support carers. Engaging carers in conversation to better understand their needs is a small step with potential for big gains, including a more empathetic understanding of their individual circumstances and overall well-being. © 2018 Royal Pharmaceutical Society.

Differences in Physiological Responses to Interval Training in Cyclists With and Without Interval Training Experience

PubMed Central

Hebisz, Rafal; Borkowski, Jacek; Zatoń, Marek

2016-01-01

Abstract The aim of this study was to determine differences in glycolytic metabolite concentrations and work output in response to an all-out interval training session in 23 cyclists with at least 2 years of interval training experience (E) and those inexperienced (IE) in this form of training. The intervention involved subsequent sets of maximal intensity exercise on a cycle ergometer. Each set comprised four 30 s repetitions interspersed with 90 s recovery periods; sets were repeated when blood pH returned to 7.3. Measurements of post-exercise hydrogen (H+) and lactate ion (LA-) concentrations and work output were taken. The experienced cyclists performed significantly more sets of maximal efforts than the inexperienced athletes (5.8 ± 1.2 vs. 4.3 ± 0.9 sets, respectively). Work output decreased in each subsequent set in the IE group and only in the last set in the E group. Distribution of power output changed only in the E group; power decreased in the initial repetitions of set only to increase in the final repetitions. H+ concentration decreased in the third, penultimate, and last sets in the E group and in each subsequent set in the IE group. LA- decreased in the last set in both groups. In conclusion, the experienced cyclists were able to repeatedly induce elevated levels of lactic acidosis. Power output distribution changed with decreased acid–base imbalance. In this way, this group could compensate for a decreased anaerobic metabolism. The above factors allowed cyclists experienced in interval training to perform more sets of maximal exercise without a decrease in power output compared with inexperienced cyclists. PMID:28149346

An accelerated training method for back propagation networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O. (Inventor)

1993-01-01

The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

Improving Personal Selling

ERIC Educational Resources Information Center

Rosenbloom, Bert

1977-01-01

Good personal selling results from a carefully planned program consisting of three basic elements: selecting people who are suitable for particular sales positions, providing training, and devising an appropriate plan for compensation. (LBH)

Re-designing a mechanism for higher speed: A case history from textile machinery

NASA Astrophysics Data System (ADS)

Douglas, S. S.; Rooney, G. T.

The generation of general mechanism design software which is the formulation of suitable objective functions is discussed. There is a consistent drive towards higher speeds in the development of industrial sewing machines. This led to experimental analyses of dynamic performance and to a search for improved design methods. The experimental work highlighted the need for smoothness of motion at high speed, component inertias, and frame structural stiffness. Smoothness is associated with transmission properties and harmonic analysis. These are added to other design requirements of synchronization, mechanism size, and function. Some of the mechanism trains in overedte sewing machines are shown. All these trains are designed by digital optimization. The design software combines analysis of the sewing machine mechanisms, formulation of objectives innumerical terms, and suitable mathematical optimization ttechniques.

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-07-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations and in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, using either magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-04-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations as well as in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, either using magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r-band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte-Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Data Programming: Creating Large Training Sets, Quickly.

PubMed

Ratner, Alexander; De Sa, Christopher; Wu, Sen; Selsam, Daniel; Ré, Christopher

2016-12-01

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions , which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can "denoise" the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.

Data Programming: Creating Large Training Sets, Quickly

PubMed Central

Ratner, Alexander; De Sa, Christopher; Wu, Sen; Selsam, Daniel; Ré, Christopher

2018-01-01

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can “denoise” the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable. PMID:29872252

Task Analysis of Tactical Leadership Skills for Bradley Infantry Fighting Vehicle Leaders

DTIC Science & Technology

1986-10-01

The Bradley Leader Trainer is conceptualized as a device or set of de - vices that can be used to teach Bradley leaders to perform their full set of...experts. The task list was examined to de - termine critical training requirements, requirements for training device sup- port of this training, and...Functions/ j ITask | |Task | |Task | [Training j , To Further De - | ;Critical Train- | iTninir

Shooting with sound: optimizing an affordable ballistic gelatin recipe in a graded ultrasound phantom education program.

PubMed

Tanious, Shariff F; Cline, Jamie; Cavin, Jennifer; Davidson, Nathan; Coleman, J Keegan; Goodmurphy, Craig W

2015-06-01

The goal of this study was to investigate the durability and longevity of gelatin formulas for the production of staged ultrasound phantoms for education. Gelatin phantoms were prepared from Knox gelatin (Kraft Foods, Northfield, IL) and a standard 10%-by-mass ordinance gelatin solution. Phantoms were durability tested by compressing to a 2-cm depth until cracking was visible. Additionally, 16 containers with varying combinations of phenol, container type, and storage location were tested for longevity against desiccation and molding. Once formulation was determined, 4 stages of phantoms from novice to clinically relevant were poured, and clinicians with ultrasound training ranked them on a 7-point Likert scale based on task difficulty, phantom suitability, and fidelity. On durability testing, the ballistic gelatin outperformed the Knox gelatin by more than 200 compressions. On longevity testing, gelatin with a 0.5% phenol concentration stored with a lid and refrigeration lasted longest, whereas containers without a lid had desiccation within 1 month, and those without phenol became moldy within 6 weeks. Ballistic gelatin was more expensive when buying in small quantities but was 7.4% less expensive when buying in bulk. The staged phantoms were deemed suitable for training, but clinicians did not consistently rank the phantoms in the intended order of 1 to 4 (44%). Refrigerated and sealed ballistic gelatin with phenol was a cost-effective method for creating in-house staged ultrasound phantoms suitable for large-scale ultrasound educational training needs. Clinician ranking of phantoms may be influenced by current training methods that favor biological tissue scanning as easier. © 2015 by the American Institute of Ultrasound in Medicine.

Principles to Consider in Defining New Directions in Internal Medicine Training and Certification

PubMed Central

Turner, Barbara J; Centor, Robert M; Rosenthal, Gary E

2006-01-01

SGIM endoreses seven principles related to current thinking about internal medicine training: 1) internal medicine requires a full three years of residency training before subspecialization; 2) internal medicine residency programs must dramatically increase support for training in the ambulatory setting and offer equivalent opportunities for training in both inpatient and outpatient medicine; 3) in settings where adequate support and time are devoted to ambulatory training, the third year of residency could offer an opportunity to develop further expertise or mastery in a specific type or setting of care; 4) further certification in specific specialties within internal medicine requires the completion of an approved fellowship program; 5) areas of mastery in internal medicine can be demonstrated through modified board certification and recertification examinations; 6) certification processes throughout internal medicine should focus increasingly on demonstration of clinical competence through adherence to validated standards of care within and across practice settings; and 7) regardless of the setting in which General Internists practice, we should unite to promote the critical role that this specialty serves in patient care. PMID:16637826

Optimization of Training Sets for Neural-Net Processing of Characteristic Patterns from Vibrating Solids

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

2001-01-01

Artificial neural networks have been used for a number of years to process holography-generated characteristic patterns of vibrating structures. This technology depends critically on the selection and the conditioning of the training sets. A scaling operation called folding is discussed for conditioning training sets optimally for training feed-forward neural networks to process characteristic fringe patterns. Folding allows feed-forward nets to be trained easily to detect damage-induced vibration-displacement-distribution changes as small as 10 nm. A specific application to aerospace of neural-net processing of characteristic patterns is presented to motivate the conditioning and optimization effort.

The Use of Qualitative Case Studies as an Experiential Teaching Method in the Training of Pre-Service Teachers

ERIC Educational Resources Information Center

Arseven, Ilhami

2018-01-01

This study presents the suitability of case studies, which is a qualitative research method and can be used as a teaching method in the training of pre-service teachers, for experiential learning theory. The basic view of experiential learning theory on learning and the qualitative case study paradigm are consistent with each other within the…

BUMPER v1.0: a Bayesian user-friendly model for palaeo-environmental reconstruction

NASA Astrophysics Data System (ADS)

Holden, Philip B.; Birks, H. John B.; Brooks, Stephen J.; Bush, Mark B.; Hwang, Grace M.; Matthews-Bird, Frazer; Valencia, Bryan G.; van Woesik, Robert

2017-02-01

We describe the Bayesian user-friendly model for palaeo-environmental reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring ˜ 2 s to build a 100-taxon model from a 100-site training set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training sets under ideal assumptions. We then use these to demonstrate the sensitivity of reconstructions to the characteristics of the training set, considering assemblage richness, taxon tolerances, and the number of training sites. We find that a useful guideline for the size of a training set is to provide, on average, at least 10 samples of each taxon. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. An identically configured model is used in each application, the only change being the input files that provide the training-set environment and taxon-count data. The performance of BUMPER is shown to be comparable with weighted average partial least squares (WAPLS) in each case. Additional artificial datasets are constructed with similar characteristics to the real data, and these are used to explore the reasons for the differing performances of the different training sets.

Study on the continuing education innovative talents training mode of civil engineering major

NASA Astrophysics Data System (ADS)

Sun, Shengnan; Su, Zhibin; Cui, Shicai

2017-12-01

According to the characteristics of civil engineering professional continuing education, continuing education of innovative talents training mode suitable for the characteristics of our school is put forward in this paper. The characteristics of the model include: the education of professional basic courses and specialized courses should be paid attention to; engineering training should be strengthened and engineering quality should be trained; the concept of large civil engineering should be highlighted, the specialized areas should be broadened, and the curriculum system should be reconstructed; the mechanism of personnel training program should be constructed by the employers, the domestic highlevel institutions and our university. It is hoped that the new training model will promote the development of continuing education of civil engineering specialty in our university.

Comparison of molecular breeding values based on within- and across-breed training in beef cattle

PubMed Central

2013-01-01

Background Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. Methods Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. Results With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. Conclusions Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set. PMID:23953034

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling.

PubMed

Mainali, Kumar P; Warren, Dan L; Dhileepan, Kunjithapatham; McConnachie, Andrew; Strathie, Lorraine; Hassan, Gul; Karki, Debendra; Shrestha, Bharat B; Parmesan, Camille

2015-12-01

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our 'best' model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. © 2015 John Wiley & Sons Ltd.

a Fully Automated Pipeline for Classification Tasks with AN Application to Remote Sensing

NASA Astrophysics Data System (ADS)

Suzuki, K.; Claesen, M.; Takeda, H.; De Moor, B.

2016-06-01

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed `shallow' machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

3D Printed Models of Cleft Palate Pathology for Surgical Education

PubMed Central

Lioufas, Peter A.; Quayle, Michelle R.; Leong, James C.

2016-01-01

Objective: To explore the potential viability and limitations of 3D printed models of children with cleft palate deformity. Background: The advantages of 3D printed replicas of normal anatomical specimens have previously been described. The creation of 3D prints displaying patient-specific anatomical pathology for surgical planning and interventions is an emerging field. Here we explored the possibility of taking rare pediatric radiographic data sets to create 3D prints for surgical education. Methods: Magnetic resonance imaging data of 2 children (8 and 14 months) were segmented, colored, and anonymized, and stereolothographic files were prepared for 3D printing on either multicolor plastic or powder 3D printers and multimaterial 3D printers. Results: Two models were deemed of sufficient quality and anatomical accuracy to print unamended. One data set was further manipulated digitally to artificially extend the length of the cleft. Thus, 3 models were printed: 1 incomplete soft-palate deformity, 1 incomplete anterior palate deformity, and 1 complete cleft palate. All had cleft lip deformity. The single-material 3D prints are of sufficient quality to accurately identify the nature and extent of the deformities. Multimaterial prints were subsequently created, which could be valuable in surgical training. Conclusion: Improvements in the quality and resolution of radiographic imaging combined with the advent of multicolor multiproperty printer technology will make it feasible in the near future to print 3D replicas in materials that mimic the mechanical properties and color of live human tissue making them potentially suitable for surgical training. PMID:27757345

Target discrimination method for SAR images based on semisupervised co-training

NASA Astrophysics Data System (ADS)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

Sample Selection for Training Cascade Detectors.

PubMed

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

Biochemical markers of bone metabolism and risk of dorsal metacarpal disease in 2-year-old Thoroughbreds.

PubMed

Jackson, B F; Lonnell, C; Verheyen, K L P; Dyson, P; Pfeiffer, D U; Price, J S

2005-01-01

Dorsal metacarpal disease (DMD) is a common problem in 2-year-old racehorses and results in loss of a significant number of days from training. Biochemical markers of bone cell activity measured early in the training season could have value for identifying 2-year-old Thoroughbred racehorses that develop DMD. To determine the association between serum concentrations of osteocalcin, the carboxyterminal propeptide of type I collagen (PICP) and the carboxyterminal cross-linked telopeptide of type I collagen (ICTP) measured early in the training season and the risk of DMD. Blood samples were collected from 165 two-year-old Thoroughbreds during late November/early December. Osteocalcin and PICP were measured as markers of bone formation, and ICTP as a marker of bone resorption. Training and veterinary records for each horse were monitored over the following training/racing season (10 months). Cases were defined as an episode where signs of DMD were sufficiently severe for a horse to miss at least 5 consecutive days of training. Classification tree and logistic regression analysis were used to identify the most important factors suitable for prediction of DMD risk. There were 24 cases of DMD during the season (14.6% cumulative incidence), with an average time to recognition of approximately 6 months (May). The earliest recognised case was in February and the latest in September. Osteocalcin and ICTP concentrations in the early stages of the training season were significantly higher in horses that subsequently developed DMD (P = 0.017 and 0.019, respectively). DMD cases were also significantly older compared to noncases (21.04 vs. 20.44 months, P = 0.023). Using a multivariable logistic regression model, it was possible to postulate a set of diagnostic rules to predict the likelihood of DMD injury during the season. This suggested that horses with ICTP concentrations above 12365 ug/l and older than 20.5 months are 2.6 times more likely to develop DMD. The measurement of the bone resorption marker ICTP could be useful for identification of 2-year-olds at increased risk of developing DMD. These findings, together with other strategies such as modification of training regimens, e.g. early introduction of short distances of high-speed exercise into the training programme, could help reduce the days lost to training as a result of DMD.

[Identification of spill oil species based on low concentration synchronous fluorescence spectra and RBF neural network].

PubMed

Liu, Qian-qian; Wang, Chun-yan; Shi, Xiao-feng; Li, Wen-dong; Luan, Xiao-ning; Hou, Shi-lin; Zhang, Jin-liang; Zheng, Rong-er

2012-04-01

In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.

Problem solving therapy - use and effectiveness in general practice.

PubMed

Pierce, David

2012-09-01

Problem solving therapy (PST) is one of the focused psychological strategies supported by Medicare for use by appropriately trained general practitioners. This article reviews the evidence base for PST and its use in the general practice setting. Problem solving therapy involves patients learning or reactivating problem solving skills. These skills can then be applied to specific life problems associated with psychological and somatic symptoms. Problem solving therapy is suitable for use in general practice for patients experiencing common mental health conditions and has been shown to be as effective in the treatment of depression as antidepressants. Problem solving therapy involves a series of sequential stages. The clinician assists the patient to develop new empowering skills, and then supports them to work through the stages of therapy to determine and implement the solution selected by the patient. Many experienced GPs will identify their own existing problem solving skills. Learning about PST may involve refining and focusing these skills.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Maslanik, J. A.; Key, J. R.

1987-01-01

A definition is undertaken of the spectral and spatial characteristics of clouds and surface conditions in the polar regions, and to the creation of calibrated, geometrically correct data sets suitable for quantitative analysis. Ways are explored in which this information can be applied to cloud classifications as new methods or as extensions to existing classification schemes. A methodology is developed that uses automated techniques to merge Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data, and to apply first-order calibration and zenith angle corrections to the AVHRR imagery. Cloud cover and surface types are manually interpreted, and manual methods are used to define relatively pure training areas to describe the textural and multispectral characteristics of clouds over several surface conditions. The effects of viewing angle and bidirectional reflectance differences are studied for several classes, and the effectiveness of some key components of existing classification schemes is tested.

Extraction of UMLS® Concepts Using Apache cTAKES™ for German Language.

PubMed

Becker, Matthias; Böckmann, Britta

2016-01-01

Automatic information extraction of medical concepts and classification with semantic standards from medical reports is useful for standardization and for clinical research. This paper presents an approach for an UMLS concept extraction with a customized natural language processing pipeline for German clinical notes using Apache cTAKES. The objectives are, to test the natural language processing tool for German language if it is suitable to identify UMLS concepts and map these with SNOMED-CT. The German UMLS database and German OpenNLP models extended the natural language processing pipeline, so the pipeline can normalize to domain ontologies such as SNOMED-CT using the German concepts. For testing, the ShARe/CLEF eHealth 2013 training dataset translated into German was used. The implemented algorithms are tested with a set of 199 German reports, obtaining a result of average 0.36 F1 measure without German stemming, pre- and post-processing of the reports.

Teaching resources for dermatology on the WWW--quiz system and dynamic lecture scripts using a HTTP-database demon.

PubMed Central

Bittorf, A.; Diepgen, T. L.

1996-01-01

The World Wide Web (WWW) is becoming the major way of acquiring information in all scientific disciplines as well as in business. It is very well suitable for fast distribution and exchange of up to date teaching resources. However, to date most teaching applications on the Web do not use its full power by integrating interactive components. We have set up a computer based training (CBT) framework for Dermatology, which consists of dynamic lecture scripts, case reports, an atlas and a quiz system. All these components heavily rely on an underlying image database that permits the creation of dynamic documents. We used a demon process that keeps the database open and can be accessed using HTTP to achieve better performance and avoid the overhead involved by starting CGI-processes. The result of our evaluation was very encouraging. Images Figure 3 PMID:8947625

Galilean Moons, Kepler's Third Law, and the Mass of Jupiter

NASA Astrophysics Data System (ADS)

Bates, Alan

2013-10-01

Simulations of physical systems are widely available online, with no cost, and are ready to be used in our classrooms. ,2 Such simulations offer an accessible tool that can be used for a range of interactive learning activities. The Jovian Moons Applet2 allows the user to track the position of Jupiter's four Galilean moons with a variety of viewing options. For this activity, data are obtained from the orbital period and orbital radii charts. Earlier experiments have used telescopes to capture the orbital motion of the Galilean moons,3 although observation of astronomical events and the measurement of quantities may be difficult to achieve due to a combination of cost, training, and observing conditions. The applet allows a suitable set of data to be generated and data analysis that verifies Kepler's third law of planetary motion, which leads to a calculated value for the mass of Jupiter.

Nuclear Materials Science

NASA Astrophysics Data System (ADS)

Whittle, Karl

2016-06-01

Concerns around global warming have led to a nuclear renaissance in many countries, meanwhile the nuclear industry is warning already of a need to train more nuclear engineers and scientists, who are needed in a range of areas from healthcare and radiation detection to space exploration and advanced materials as well as for the nuclear power industry. Here Karl Whittle provides a solid overview of the intersection of nuclear engineering and materials science at a level approachable by advanced students from materials, engineering and physics. The text explains the unique aspects needed in the design and implementation of materials for use in demanding nuclear settings. In addition to material properties and their interaction with radiation the book covers a range of topics including reactor design, fuels, fusion, future technologies and lessons learned from past incidents. Accompanied by problems, videos and teaching aids the book is suitable for a course text in nuclear materials and a reference for those already working in the field.

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

PubMed Central

Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J.

2018-01-01

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future. PMID:29538331

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

PubMed

Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

2018-03-14

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

Challenges of predicting the potential distribution of a slow-spreading invader: a habitat suitability map for an invasive riparian tree

USGS Publications Warehouse

Jarnevich, Catherine S.; Reynolds, Lindsay V.

2011-01-01

Understanding the potential spread of invasive species is essential for land managers to prevent their establishment and restore impacted habitat. Habitat suitability modeling provides a tool for researchers and managers to understand the potential extent of invasive species spread. Our goal was to use habitat suitability modeling to map potential habitat of the riparian plant invader, Russian olive (Elaeagnus angustifolia). Russian olive has invaded riparian habitat across North America and is continuing to expand its range. We compiled 11 disparate datasets for Russian olive presence locations (n = 1,051 points and 139 polygons) in the western US and used Maximum entropy (Maxent) modeling to develop two habitat suitability maps for Russian olive in the western United States: one with coarse-scale water data and one with fine-scale water data. Our models were able to accurately predict current suitable Russian olive habitat (Coarse model: training AUC = 0.938, test AUC = 0.907; Fine model: training AUC = 0.923, test AUC = 0.885). Distance to water was the most important predictor for Russian olive presence in our coarse-scale water model, but it was only the fifth most important variable in the fine-scale model, suggesting that when water bodies are considered on a fine scale, Russian olive does not necessarily rely on water. Our model predicted that Russian olive has suitable habitat further west from its current distribution, expanding into the west coast and central North America. Our methodology proves useful for identifying potential future areas of invasion. Model results may be influenced by locations of cultivated individuals and sampling bias. Further study is needed to examine the potential for Russian olive to invade beyond its current range. Habitat suitability modeling provides an essential tool for enhancing our understanding of invasive species spread.

A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework.

PubMed

Wei, Shengjing; Chen, Xiang; Yang, Xidong; Cao, Shuai; Zhang, Xu

2016-04-19

Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user's training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system.

Coping with challenging behaviours of children with autism: effectiveness of brief training workshop for frontline staff in special education settings.

PubMed

Ling, C Y M; Mak, W W S

2012-03-01

The present study examined the effectiveness of three staff training elements: psychoeducation (PE) on autism, introduction of functional behavioural analysis (FBA) and emotional management (EM), on the reaction of challenging behaviours for frontline staff towards children with autism in Hong Kong special education settings. A sample of 311 frontline staff in educational settings was recruited to one of the three conditions: control, PE-FBA and PE-FBA-EM groups. A total of 175 participants completed all three sets of questionnaires during pre-training, immediate post-training and 1-month follow-up. Findings showed that the one-session staff training workshop increased staff knowledge of autism and perceived efficacy but decrease helping behavioural intention. In spite of the limited effectiveness of a one-session staff training workshop, continued staff training is still necessary for the improvement of service quality. Further exploration on how to change emotion response of staff is important. © 2011 The Authors. Journal of Intellectual Disability Research © 2011 Blackwell Publishing Ltd.

Semi-supervised SVM for individual tree crown species classification

NASA Astrophysics Data System (ADS)

Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

2015-12-01

In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

PubMed Central

Dähne, Sven; Wilbert, Niko; Wiskott, Laurenz

2014-01-01

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world. PMID:24810948

Multi-level deep supervised networks for retinal vessel segmentation.

PubMed

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.

PubMed

Kalderstam, Jonas; Edén, Patrik; Bendahl, Pär-Ola; Strand, Carina; Fernö, Mårten; Ohlsson, Mattias

2013-06-01

The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior. Copyright © 2013 Elsevier B.V. All rights reserved.

Effects of training set selection on pain recognition via facial expressions

NASA Astrophysics Data System (ADS)

Shier, Warren A.; Yanushkevich, Svetlana N.

2016-07-01

This paper presents an approach to pain expression classification based on Gabor energy filters with Support Vector Machines (SVMs), followed by analyzing the effects of training set variations on the systems classification rate. This approach is tested on the UNBC-McMaster Shoulder Pain Archive, which consists of spontaneous pain images, hand labelled using the Prkachin and Solomon Pain Intensity scale. In this paper, the subjects pain intensity level has been quantized into three disjoint groups: no pain, weak pain and strong pain. The results of experiments show that Gabor energy filters with SVMs provide comparable or better results to previous filter- based pain recognition methods, with precision rates of 74%, 30% and 78% for no pain, weak pain and strong pain, respectively. The study of effects of intra-class skew, or changing the number of images per subject, show that both completely removing and over-representing poor quality subjects in the training set has little effect on the overall accuracy of the system. This result suggests that poor quality subjects could be removed from the training set to save offline training time and that SVM is robust not only to outliers in training data, but also to significant amounts of poor quality data mixed into the training sets.

International standards for programmes of training in intensive care medicine in Europe.

PubMed

2011-03-01

To develop internationally harmonised standards for programmes of training in intensive care medicine (ICM). Standards were developed by using consensus techniques. A nine-member nominal group of European intensive care experts developed a preliminary set of standards. These were revised and refined through a modified Delphi process involving 28 European national coordinators representing national training organisations using a combination of moderated discussion meetings, email, and a Web-based tool for determining the level of agreement with each proposed standard, and whether the standard could be achieved in the respondent's country. The nominal group developed an initial set of 52 possible standards which underwent four iterations to achieve maximal consensus. All national coordinators approved a final set of 29 standards in four domains: training centres, training programmes, selection of trainees, and trainers' profiles. Only three standards were considered immediately achievable by all countries, demonstrating a willingness to aspire to quality rather than merely setting a minimum level. Nine proposed standards which did not achieve full consensus were identified as potential candidates for future review. This preliminary set of clearly defined and agreed standards provides a transparent framework for assuring the quality of training programmes, and a foundation for international harmonisation and quality improvement of training in ICM.

Acute effects of verbal feedback on upper-body performance in elite athletes.

PubMed

Argus, Christos K; Gill, Nicholas D; Keogh, Justin Wl; Hopkins, Will G

2011-12-01

Argus, CK, Gill, ND, Keogh, JWL, and Hopkins, WG. Acute effects of verbal feedback on upper-body performance in elite athletes. J Strength Cond Res 25(12): 3282-3287, 2011-Improved training quality has the potential to enhance training adaptations. Previous research suggests that receiving feedback improves single-effort maximal strength and power tasks, but whether quality of a training session with repeated efforts can be improved remains unclear. The purpose of this investigation was to determine the effects of verbal feedback on upper-body performance in a resistance training session consisting of multiple sets and repetitions in well-trained athletes. Nine elite rugby union athletes were assessed using the bench throw exercise on 4 separate occasions each separated by 7 days. Each athlete completed 2 sessions consisting of 3 sets of 4 repetitions of the bench throw with feedback provided after each repetition and 2 identical sessions where no feedback was provided after each repetition. When feedback was received, there was a small increase of 1.8% (90% confidence limits, ±2.7%) and 1.3% (±0.7%) in mean peak power and velocity when averaged over the 3 sets. When individual sets were compared, there was a tendency toward the improvements in mean peak power being greater in the second and third sets. These results indicate that providing verbal feedback produced acute improvements in upper-body power output of well-trained athletes. The benefits of feedback may be greatest in the latter sets of training and could improve training quality and result in greater long-term adaptation.

Effect of creatine supplementation and drop-set resistance training in untrained aging adults.

PubMed

Johannsmeyer, Sarah; Candow, Darren G; Brahms, C Markus; Michel, Deborah; Zello, Gordon A

2016-10-01

To investigate the effects of creatine supplementation and drop-set resistance training in untrained aging adults. Participants were randomized to one of two groups: Creatine (CR: n=14, 7 females, 7 males; 58.0±3.0yrs, 0.1g/kg/day of creatine+0.1g/kg/day of maltodextrin) or Placebo (PLA: n=17, 7 females, 10 males; age: 57.6±5.0yrs, 0.2g/kg/day of maltodextrin) during 12weeks of drop-set resistance training (3days/week; 2 sets of leg press, chest press, hack squat and lat pull-down exercises performed to muscle fatigue at 80% baseline 1-repetition maximum [1-RM] immediately followed by repetitions to muscle fatigue at 30% baseline 1-RM). Prior to and following training and supplementation, assessments were made for body composition, muscle strength, muscle endurance, tasks of functionality, muscle protein catabolism and diet. Drop-set resistance training improved muscle mass, muscle strength, muscle endurance and tasks of functionality (p<0.05). The addition of creatine to drop-set resistance training significantly increased body mass (p=0.002) and muscle mass (p=0.007) compared to placebo. Males on creatine increased muscle strength (lat pull-down only) to a greater extent than females on creatine (p=0.005). Creatine enabled males to resistance train at a greater capacity over time compared to males on placebo (p=0.049) and females on creatine (p=0.012). Males on creatine (p=0.019) and females on placebo (p=0.014) decreased 3-MH compared to females on creatine. The addition of creatine to drop-set resistance training augments the gains in muscle mass from resistance training alone. Creatine is more effective in untrained aging males compared to untrained aging females. Copyright © 2016 Elsevier Inc. All rights reserved.

Linear Vector Quantisation and Uniform Circular Arrays based decoupled two-dimensional angle of arrival estimation

NASA Astrophysics Data System (ADS)

Ndaw, Joseph D.; Faye, Andre; Maïga, Amadou S.

2017-05-01

Artificial neural networks (ANN)-based models are efficient ways of source localisation. However very large training sets are needed to precisely estimate two-dimensional Direction of arrival (2D-DOA) with ANN models. In this paper we present a fast artificial neural network approach for 2D-DOA estimation with reduced training sets sizes. We exploit the symmetry properties of Uniform Circular Arrays (UCA) to build two different datasets for elevation and azimuth angles. Linear Vector Quantisation (LVQ) neural networks are then sequentially trained on each dataset to separately estimate elevation and azimuth angles. A multilevel training process is applied to further reduce the training sets sizes.

A tutorial platform suitable for surgical simulator training (SimMentor).

PubMed

Røtnes, Jan Sigurd; Kaasa, Johannes; Westgaard, Geir; Eriksen, Eivind Myrold; Hvidsten, Per Oyvind; Strøm, Kyrre; Sørhus, Vidar; Halbwachs, Yvon; Haug, Einar; Grimnes, Morten; Fontenelle, Hugues; Ekeberg, Tom; Thomassen, Jan B; Elle, Ole Jakob; Fosse, Erik

2002-01-01

The introduction of simulators in surgical training entails the need to develop pedagogic platforms adapted to the potentials and limitations provided by the information technology. As a solution to the technical challenges in treating all possible interaction events and to obtain a suitable pedagogic approach, we have developed a pedagogic platform for surgical training, SimMentor. In SimMentor the procedure to be practiced is divided into a number of natural phases. The trainee will practice on one phase at a time, however he can select the sequence of phases arbitrarily. A phase is taught by letting the trainee alternate freely between 2 modes: 1: A 3-dimensional animated guidance designed for learning the objectives and challenges in a procedure. 2: An interactive training session through the instrument manipulator device designed for training motoric responses based on visual and tactile responses produced by the simulator. The two modes are interfaced with the same virtual reality platform, thus SimMentor allows a seamless transition between the modes. We have developed a prototype simulator for robotic assisted endoscopic CABG (Coronary Artery Bypass Grafting) procedure by first focusing on the anastomosis part of the operation. Tissue, suture and instrument models have been developed and integrated with a simulated model of a beating heart comprises the elements in the simulator engine that is used in construction a training platform for learning different methods for performing a coronary anastomosis procedure. The platform is designed for integrating the following features: 1) practical approach to handle interactivity events with flexible-objects 3D simulators, 2) methods for quantitative evaluations of performance, 3) didactic presentations, 4) effective ways of producing diversity of clinical and pathological training scenarios.

Skill Transfer and Virtual Training for IND Response Decision-Making: Project Summary and Next Steps

DTIC Science & Technology

2016-04-12

are likely to be very productive partners—independent video - game developers and academic game degree programs—are not familiar with working with...experimental validation. • Independent Video - Game Developers. Small companies and individuals that pursue video - game design and development can be...complexity, such as an improvised nuclear device (IND) detonation. The effort has examined game - based training methods to determine their suitability

Rhabdomyolysis After Out-of-Water Exercise in an Elite Adolescent Water Polo Player Carrying the IL-6 174C Allele Single-Nucleotide Polymorphism.

PubMed

Eliakim, Alon; Ben Zaken, Sigal; Meckel, Yoav; Yamin, Chen; Dror, Nitzan; Nemet, Dan

2015-12-01

We present an adolescent elite water polo player who despite a genetic predisposition to develop exercise-induced severe muscle damage due to carrying the IL-6 174C allele single-nucleotide polymorphism, developed acute rhabdomyolysis only after a vigorous out-of-water training, suggesting that water polo training may be more suitable for genetically predisposed athletes.

Long-Term Abstract Learning of Attentional Set

ERIC Educational Resources Information Center

Leber, Andrew B.; Kawahara, Jun-Ichiro; Gabari, Yuji

2009-01-01

How does past experience influence visual search strategy (i.e., attentional set)? Recent reports have shown that, when given the option to use 1 of 2 attentional sets, observers persist with the set previously required in a training phase. Here, 2 related questions are addressed. First, does the training effect result only from perseveration with…

Exer-Genie(Registered Trademark) Exercise Device Hardware Evaluation

NASA Technical Reports Server (NTRS)

Schaffner, Grant; Sharp,Carwyn; Stroud, Leah

2008-01-01

An engineering evaluation was performed on the ExerGenie(r) exercise device to quantify its capabilities and limitations to address questions from the Constellation Program. Three subjects performed rowing and circuit training sessions to assess the suitability of the device for aerobic exercise. Three subjects performed a resistive exercise session to assess the suitability of the device for resistive exercise. Since 1 subject performed both aerobic and resistive exercise sessions, a total of 5 subjects participated.

Geropsychology Training in a VA Nursing Home Setting

ERIC Educational Resources Information Center

Karel, Michele J.; Moye, Jennifer

2005-01-01

There is a growing need for professional psychology training in nursing home settings, and nursing homes provide a rich environment for teaching geropsychology competencies. We describe the nursing home training component of our Department of Veterans Affairs (VA) Predoctoral Internship and Geropsychology Postdoctoral Fellowship programs. Our…

Exercise order affects the total training volume and the ratings of perceived exertion in response to a super-set resistance training session

PubMed Central

Balsamo, Sandor; Tibana, Ramires Alsamir; Nascimento, Dahan da Cunha; de Farias, Gleyverton Landim; Petruccelli, Zeno; de Santana, Frederico dos Santos; Martins, Otávio Vanni; de Aguiar, Fernando; Pereira, Guilherme Borges; de Souza, Jéssica Cardoso; Prestes, Jonato

2012-01-01

The super-set is a widely used resistance training method consisting of exercises for agonist and antagonist muscles with limited or no rest interval between them – for example, bench press followed by bent-over rows. In this sense, the aim of the present study was to compare the effects of different super-set exercise sequences on the total training volume. A secondary aim was to evaluate the ratings of perceived exertion and fatigue index in response to different exercise order. On separate testing days, twelve resistance-trained men, aged 23.0 ± 4.3 years, height 174.8 ± 6.75 cm, body mass 77.8 ± 13.27 kg, body fat 12.0% ± 4.7%, were submitted to a super-set method by using two different exercise orders: quadriceps (leg extension) + hamstrings (leg curl) (QH) or hamstrings (leg curl) + quadriceps (leg extension) (HQ). Sessions consisted of three sets with a ten-repetition maximum load with 90 seconds rest between sets. Results revealed that the total training volume was higher for the HQ exercise order (P = 0.02) with lower perceived exertion than the inverse order (P = 0.04). These results suggest that HQ exercise order involving lower limbs may benefit practitioners interested in reaching a higher total training volume with lower ratings of perceived exertion compared with the leg extension plus leg curl order. PMID:22371654

How large a training set is needed to develop a classifier for microarray data?

PubMed

Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

2008-01-01

A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

Waste disposal technology transfer matching requirement clusters for waste disposal facilities in China.

PubMed

Dorn, Thomas; Nelles, Michael; Flamme, Sabine; Jinming, Cai

2012-11-01

Even though technology transfer has been part of development aid programmes for many decades, it has more often than not failed to come to fruition. One reason is the absence of simple guidelines or decision making tools that help operators or plant owners to decide on the most suitable technology to adopt. Practical suggestions for choosing the most suitable technology to combat a specific problem are hard to get and technology drawbacks are not sufficiently highlighted. Western counterparts in technology transfer or development projects often underestimate or don't sufficiently account for the high investment costs for the imported incineration plant; the differing nature of Chinese MSW; the need for trained manpower; and the need to treat flue gas, bunker leakage water, and ash, all of which contain highly toxic elements. This article sets out requirements for municipal solid waste disposal plant owner/operators in China as well as giving an attribute assessment for the prevalent waste disposal plant types in order to assist individual decision makers in their evaluation process for what plant type might be most suitable in a given situation. There is no 'best' plant for all needs and purposes, and requirement constellations rely on generalisations meaning they cannot be blindly applied, but an alignment of a type of plant to a type of owner or operator can realistically be achieved. To this end, a four-step approach is suggested and a technology matrix is set out to ease the choice of technology to transfer and avoid past errors. The four steps are (1) Identification of plant owner/operator requirement clusters; (2) Determination of different municipal solid waste (MSW) treatment plant attributes; (3) Development of a matrix matching requirement clusters to plant attributes; (4) Application of Quality Function Deployment Method to aid in technology localisation. The technology transfer matrices thus derived show significant performance differences between the various technologies available. It is hoped that the resulting research can build a bridge between technology transfer research and waste disposal research in order to enhance the exchange of more sustainable solutions in future. Copyright © 2012 Elsevier Ltd. All rights reserved.

On the diverse outcome of communication partner training of significant others of people with aphasia: an experimental study of six cases.

PubMed

Eriksson, Karin; Hartelius, Lena; Saldert, Charlotta

2016-07-01

Communication partner training (CPT) has been shown to improve the communicative environment of people with aphasia. Interaction-focused training is one type of training that provides an individualized intervention to participants. Although shown to be effective, outcomes have mostly been evaluated in non-experimental case studies. The aim of the controlled experimental intervention study was to evaluate an individualized approach in a CPT programme directed to significant others of people with aphasia. Specifically the effects on conversation partners' ability to support the person with aphasia in conversation and on the individuals with aphasias' perception of their functional communication were explored. Six dyads consisting of a person with aphasia and a significant other were included in a replicated single-subject design with multiple baselines across individuals. The intervention followed the interaction-focused communication training programme included in Supporting Partners of People with Aphasia in Relationships and Conversation (SPARRC). The main elements of the training consisted of supervised viewing of the couples' own video-recorded natural interaction and the formulation of individual goals for the adaptation of particular communicative strategies. Outcome was measured via blinded ratings of filmed conversational interaction obtained once a week throughout the different phases of baseline, intervention and follow-up. A rating scale to assess overall quality of conversation was used, taking into account both transfer of information and social aspects of conversation. Measures of perceived functional communication in the persons with aphasia were also collected from the individuals with aphasia and their conversation partners. The results were mixed, with two of the six participants showing small improvements in ability to support their partner with aphasia in conversation. Half the participants with aphasia and half the significant others reported improvements on perceived functional communication in the person with aphasia after intervention, but no changes were statistically significant. This study adds to the growing body of research concerning CPT by pinpointing the importance of careful consideration regarding set-up of training, suitability of participants and evaluation of outcome. © 2016 Royal College of Speech and Language Therapists.

Personal Computer-less (PC-less) Microcontroller Training Kit

NASA Astrophysics Data System (ADS)

Somantri, Y.; Wahyudin, D.; Fushilat, I.

2018-02-01

The need of microcontroller training kit is necessary for practical work of students of electrical engineering education. However, to use available training kit not only costly but also does not meet the need of laboratory requirements. An affordable and portable microcontroller kit could answer such problem. This paper explains the design and development of Personal Computer Less (PC-Less) Microcontroller Training Kit. It was developed based on Lattepanda processor and Arduino microcontroller as target. The training kit equipped with advanced input-output interfaces that adopted the concept of low cost and low power system. The preliminary usability testing proved this device can be used as a tool for microcontroller programming and industrial automation training. By adopting the concept of portability, the device could be operated in the rural area which electricity and computer infrastructure are limited. Furthermore, the training kit is suitable for student of electrical engineering student from university and vocational high school.

Performance Measures for Adaptive Decisioning Systems

DTIC Science & Technology

1991-09-11

set to hypothesis space mapping best approximates the known map. Two assumptions, a sufficiently representative training set and the ability of the...successful prediction of LINEXT performance. The LINEXT algorithm above performs the decision space mapping on the training-set ele- ments exactly. For a

String Scale Gauge Coupling Unification with Vector-Like Exotics and Noncanonical U(1)Y Normalization

NASA Astrophysics Data System (ADS)

Barger, V.; Jiang, Jing; Langacker, Paul; Li, Tianjun

We use a new approach to study string scale gauge coupling unification systematically, allowing both the possibility of noncanonical U(1)Y normalization and the existence of vector-like particles whose quantum numbers are the same as those of the Standard Model (SM) fermions and their Hermitian conjugates and the SM adjoint particles. We first give all the independent sets (Yi) of particles that can be employed to achieve SU(3)C and SU(2)L string scale gauge coupling unification and calculate their masses. Second, for a noncanonical U(1)Y normalization, we obtain string scale SU(3)C ×SU(2)L ×U(1)Y gauge coupling unification by choosing suitable U(1)Y normalizations for each of the Yi sets. Alternatively, for the canonical U(1)Y normalization, we achieve string scale gauge coupling unification by considering suitable combinations of the Yi sets or by introducing additional independent sets (Zi), that do not affect the SU(3)C ×SU(2)L unification at tree level, and then choosing suitable combinations, one from the Yi sets and one from the Zi sets. We also briefly discuss string scale gauge coupling unification in models with higher Kac-Moody levels for SU(2)L or SU(3)C.

Mining big data sets of plankton images: a zero-shot learning approach to retrieve labels without training data

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.

Coordinating a national rangeland monitoring training program: Success and lessons learned

USDA-ARS?s Scientific Manuscript database

One of the best ways to ensure quality of information gathered in a rangeland monitoring program is through a strong and uniform set of trainings. Curriculum development and delivery of monitoring trainings poses unique challenges that are not seen in academic settings. Participants come from a rang...

Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: an annotation and machine learning study.

PubMed

Skeppstedt, Maria; Kvist, Maria; Nilsson, Gunnar H; Dalianis, Hercules

2014-06-01

Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction. There are a number of named entity recognition studies on English clinical text, but less work has been carried out on clinical text in other languages. This study was performed on Swedish health records, and focused on four entities that are highly relevant for constructing a patient overview and for medical hypothesis generation, namely the entities: Disorder, Finding, Pharmaceutical Drug and Body Structure. The study had two aims: to explore how well named entity recognition methods previously applied to English clinical text perform on similar texts written in Swedish; and to evaluate whether it is meaningful to divide the more general category Medical Problem, which has been used in a number of previous studies, into the two more granular entities, Disorder and Finding. Clinical notes from a Swedish internal medicine emergency unit were annotated for the four selected entity categories, and the inter-annotator agreement between two pairs of annotators was measured, resulting in an average F-score of 0.79 for Disorder, 0.66 for Finding, 0.90 for Pharmaceutical Drug and 0.80 for Body Structure. A subset of the developed corpus was thereafter used for finding suitable features for training a conditional random fields model. Finally, a new model was trained on this subset, using the best features and settings, and its ability to generalise to held-out data was evaluated. This final model obtained an F-score of 0.81 for Disorder, 0.69 for Finding, 0.88 for Pharmaceutical Drug, 0.85 for Body Structure and 0.78 for the combined category Disorder+Finding. The obtained results, which are in line with or slightly lower than those for similar studies on English clinical text, many of them conducted using a larger training data set, show that the approaches used for English are also suitable for Swedish clinical text. However, a small proportion of the errors made by the model are less likely to occur in English text, showing that results might be improved by further tailoring the system to clinical Swedish. The entity recognition results for the individual entities Disorder and Finding show that it is meaningful to separate the general category Medical Problem into these two more granular entity types, e.g. for knowledge mining of co-morbidity relations and disorder-finding relations. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Cognitive responses to hypobaric hypoxia: implications for aviation training

PubMed Central

Neuhaus, Christopher; Hinkelbein, Jochen

2014-01-01

The aim of this narrative review is to provide an overview on cognitive responses to hypobaric hypoxia and to show relevant implications for aviation training. A principal element of hypoxia-awareness training is the intentional evocation of hypoxia symptoms during specific training sessions within a safe and controlled environment. Repetitive training should enable pilots to learn and recognize their personal hypoxia symptoms. A time span of 3–6 years is generally considered suitable to refresh knowledge of the more subtle and early symptoms especially. Currently, there are two different technical approaches available to induce hypoxia during training: hypobaric chamber training and reduced-oxygen breathing devices. Hypoxia training for aircrew is extremely important and effective, and the hypoxia symptoms should be emphasized clearly to aircrews. The use of tight-fitting masks, leak checks, and equipment checks should be taught to all aircrew and reinforced regularly. It is noteworthy that there are major differences in the required quality and quantity of hypoxia training for both military and civilian pilots. PMID:25419162

Controlled randomised trial of visual biofeedback versus muscle training without a visual display for intractable constipation.

PubMed Central

Koutsomanis, D; Lennard-Jones, J E; Roy, A J; Kamm, M A

1995-01-01

Training to contract the abdominal muscles effectively and to relax the pelvic floor during defecation straining helps some patients with severe constipation. Hitherto all such training has used a visible or audible signal of sphincter muscle activity as a biofeedback method to assist in relaxation. A randomised controlled trial comparing the outcome of muscular training without any biofeedback device with the same training supplemented by an electromyographic (EMG) record visible to the patient is reported. Significant symptomatic improvement was noted and electromyographic measurements confirmed a decrease in pelvic floor muscle activity during defecation straining after treatment in both groups. The outcome was similar in the two treatment groups. Muscular coordination training using personal instruction and encouragement without a visual display is thus a potentially successful treatment suitable for outpatient use by paramedical personnel. PMID:7672690

Haptic interface of web-based training system for interventional radiology procedures

NASA Astrophysics Data System (ADS)

Ma, Xin; Lu, Yiping; Loe, KiaFock; Nowinski, Wieslaw L.

2004-05-01

The existing web-based medical training systems and surgical simulators can provide affordable and accessible medical training curriculum, but they seldom offer the trainee realistic and affordable haptic feedback. Therefore, they cannot offer the trainee a suitable practicing environment. In this paper, a haptic solution for interventional radiology (IR) procedures is proposed. System architecture of a web-based training system for IR procedures is briefly presented first. Then, the mechanical structure, the working principle and the application of a haptic device are discussed in detail. The haptic device works as an interface between the training environment and the trainees and is placed at the end user side. With the system, the user can be trained on the interventional radiology procedures - navigating catheters, inflating balloons, deploying coils and placing stents on the web and get surgical haptic feedback in real time.

Simultaneous estimation of ramipril, acetylsalicylic acid and atorvastatin calcium by chemometrics assisted UV-spectrophotometric method in capsules.

PubMed

Sankar, A S Kamatchi; Vetrichelvan, Thangarasu; Venkappaya, Devashya

2011-09-01

In the present work, three different spectrophotometric methods for simultaneous estimation of ramipril, aspirin and atorvastatin calcium in raw materials and in formulations are described. Overlapped data was quantitatively resolved by using chemometric methods, viz. inverse least squares (ILS), principal component regression (PCR) and partial least squares (PLS). Calibrations were constructed using the absorption data matrix corresponding to the concentration data matrix. The linearity range was found to be 1-5, 10-50 and 2-10 μg mL-1 for ramipril, aspirin and atorvastatin calcium, respectively. The absorbance matrix was obtained by measuring the zero-order absorbance in the wavelength range between 210 and 320 nm. A training set design of the concentration data corresponding to the ramipril, aspirin and atorvastatin calcium mixtures was organized statistically to maximize the information content from the spectra and to minimize the error of multivariate calibrations. By applying the respective algorithms for PLS 1, PCR and ILS to the measured spectra of the calibration set, a suitable model was obtained. This model was selected on the basis of RMSECV and RMSEP values. The same was applied to the prediction set and capsule formulation. Mean recoveries of the commercial formulation set together with the figures of merit (calibration sensitivity, selectivity, limit of detection, limit of quantification and analytical sensitivity) were estimated. Validity of the proposed approaches was successfully assessed for analyses of drugs in the various prepared physical mixtures and formulations.

Manual cleaning of hospital mattresses: an observational study comparing high- and low-resource settings.

PubMed

Hopman, J; Hakizimana, B; Meintjes, W A J; Nillessen, M; de Both, E; Voss, A; Mehtar, S

2016-01-01

Hospital-associated infections (HAIs) are more frequently encountered in low- than in high-resource settings. There is a need to identify and implement feasible and sustainable approaches to strengthen HAI prevention in low-resource settings. To evaluate the biological contamination of routinely cleaned mattresses in both high- and low-resource settings. In this two-stage observational study, routine manual bed cleaning was evaluated at two university hospitals using adenosine triphosphate (ATP). Standardized training of cleaning personnel was achieved in both high- and low-resource settings. Qualitative analysis of the cleaning process was performed to identify predictors of cleaning outcome in low-resource settings. Mattresses in low-resource settings were highly contaminated prior to cleaning. Cleaning significantly reduced biological contamination of mattresses in low-resource settings (P < 0.0001). After training, the contamination observed after cleaning in both the high- and low-resource settings seemed comparable. Cleaning with appropriate type of cleaning materials reduced the contamination of mattresses adequately. Predictors for mattresses that remained contaminated in a low-resource setting included: type of product used, type of ward, training, and the level of contamination prior to cleaning. In low-resource settings mattresses were highly contaminated as noted by ATP levels. Routine manual cleaning by trained staff can be as effective in a low-resource setting as in a high-resource setting. We recommend a multi-modal cleaning strategy that consists of training of domestic services staff, availability of adequate time to clean beds between patients, and application of the correct type of cleaning products. Copyright © 2015 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Teaching between-class generalization of toy play behavior to handicapped children.

PubMed Central

Haring, T G

1985-01-01

In this study, young children with severe and moderate handicaps were taught to generalize play responses. A multiple baseline across responses design, replicated with four children, was used to assess the effects of generalization training within four sets of toys on generalization to untrained toys from four other sets. The responses taught were unique for each set of toys. Across the four participants, training to generalize within-toy sets resulted in complete between-class generalization in 11 sets, partial generalization in 3 sets, and no generalization in 2 sets. No generalization occurred to another class of toys that differed from the previous sets in that they produced a reaction to the play movement (e.g., pianos). Implications for conducting research using strategies based on class interrelationships in training contexts are discussed. PMID:4019349

[Current status on management and needs related to education and training programs set for new employees at the provincial Centers for Disease Control and Prevention, in China].

PubMed

Ma, J; Meng, X D; Luo, H M; Zhou, H C; Qu, S L; Liu, X T; Dai, Z

2016-06-01

In order to understand the current management status on education/training and needs for training among new employees working at the provincial CDC in China during 2012-2014, so as to provide basis for setting up related programs at the CDC levels. Based on data gathered through questionnaire surveys run by CDCs from 32 provincial and 5 specifically-designated cities, microsoft excel was used to analyze the current status on management of education and training, for new employees. There were 156 management staff members working on education and training programs in 36 CDCs, with 70% of them having received intermediate or higher levels of education. Large differences were seen on equipment of training hardware in different regions. There were 1 214 teaching staff with 66 percent in the fields or related professional areas on public health, in 2014. 5084 new employees conducted pre/post training programs, from 2012 to 2014 with funding as 750 thousand RMB Yuan. 99.5% of the new employees expressed the needs for further training while. 74% of the new staff members expecting a 2-5 day training program to be implemented. 79% of the new staff members claimed that practice as the most appropriate method for training. Institutional programs set for education and training at the CDCs need to be clarified, with management team organized. It is important to provide more financial support on both hardware, software and human resources related to training programs which are set for new stuff members at all levels of CDCs.

Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems

PubMed Central

Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

2016-01-01

The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance–performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system. PMID:27598390

Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems.

PubMed

Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

2016-01-01

The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance-performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.

Baryonic effects in cosmic shear tomography: PCA parametrization and importance of extreme baryonic models

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

Mohammed, Irshad; Gnedin, Nickolay Y.

Baryonic effects are amongst the most severe systematics to the tomographic analysis of weak lensing data which is the principal probe in many future generations of cosmological surveys like LSST, Euclid etc.. Modeling or parameterizing these effects is essential in order to extract valuable constraints on cosmological parameters. In a recent paper, Eifler et al. (2015) suggested a reduction technique for baryonic effects by conducting a principal component analysis (PCA) and removing the largest baryonic eigenmodes from the data. In this article, we conducted the investigation further and addressed two critical aspects. Firstly, we performed the analysis by separating the simulations into training and test sets, computing a minimal set of principle components from the training set and examining the fits on the test set. We found that using only four parameters, corresponding to the four largest eigenmodes of the training set, the test sets can be fitted thoroughly with an RMSmore » $$\\sim 0.0011$$. Secondly, we explored the significance of outliers, the most exotic/extreme baryonic scenarios, in this method. We found that excluding the outliers from the training set results in a relatively bad fit and degraded the RMS by nearly a factor of 3. Therefore, for a direct employment of this method to the tomographic analysis of the weak lensing data, the principle components should be derived from a training set that comprises adequately exotic but reasonable models such that the reality is included inside the parameter domain sampled by the training set. The baryonic effects can be parameterized as the coefficients of these principle components and should be marginalized over the cosmological parameter space.« less

Disentangling multidimensional spatio-temporal data into their common and aberrant responses

DOE PAGES

Chang, Young Hwan; Korkola, James; Amin, Dhara N.; ...

2015-04-22

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures andmore » how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.« less

The Dundee Ready Education Environment Measure (DREEM): a review of its adoption and use.

PubMed

Miles, Susan; Swift, Louise; Leinster, Sam J

2012-01-01

The Dundee Ready Education Environment Measure (DREEM) was published in 1997 as a tool to evaluate educational environments of medical schools and other health training settings and a recent review concluded that it was the most suitable such instrument. This study aimed to review the settings and purposes to which the DREEM has been applied and the approaches used to analyse and report it, with a view to guiding future users towards appropriate methodology. A systematic literature review was conducted using the Web of Knowledge databases of all articles reporting DREEM data between 1997 and 4 January 2011. The review found 40 publications, using data from 20 countries. DREEM is used in evaluation for diagnostic purposes, comparison between different groups and comparison with ideal/expected scores. A variety of non-parametric and parametric statistical methods have been applied, but their use is inconsistent. DREEM has been used internationally for different purposes and is regarded as a useful tool by users. However, reporting and analysis differs between publications. This lack of uniformity makes comparison between institutions difficult. Most users of DREEM are not statisticians and there is a need for informed guidelines on its reporting and statistical analysis.

Quantitative assessment of developmental levels in overarm throwing using wearable inertial sensing technology.

PubMed

Grimpampi, Eleni; Masci, Ilaria; Pesce, Caterina; Vannozzi, Giuseppe

2016-09-01

Motor proficiency in childhood has been recently recognised as a public health determinant, having a potential impact on the physical activity level and possible sedentary behaviour of the child later in life. Among fundamental motor skills, ballistic skills assessment based on in-field quantitative observations is progressively needed in the motor development community. The aim of this study was to propose an in-field quantitative approach to identify different developmental levels in overarm throwing. Fifty-eight children aged 5-10 years performed an overarm throwing task while wearing three inertial sensors located at the wrist, trunk and pelvis level and were then categorised using a developmental sequence of overarm throwing. A set of biomechanical parameters were defined and analysed using multivariate statistics to evaluate whether they can be used as developmental indicators. Trunk and pelvis angular velocities and time durations before the ball release showed increasing/decreasing trends with increasing developmental level. Significant differences between developmental level pairs were observed for selected biomechanical parameters. The results support the suitability and feasibility of objective developmental measures in ecological learning contexts, suggesting their potential supportiveness to motor learning experiences in educational and youth sports training settings.

Combined 3D-QSAR Modeling and Molecular Docking Studies on Pyrrole-Indolin-2-ones as Aurora A Kinase Inhibitors

PubMed Central

Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

2011-01-01

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r2cv values of 0.726 and 0.566, and r2 values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed. PMID:21673910

Combined 3D-QSAR modeling and molecular docking studies on pyrrole-indolin-2-ones as Aurora A kinase inhibitors.

PubMed

Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

2011-01-01

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r(2) (cv) values of 0.726 and 0.566, and r(2) values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed.

Distributed computing methodology for training neural networks in an image-guided diagnostic application.

PubMed

Plagianakos, V P; Magoulas, G D; Vrahatis, M N

2006-03-01

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

[Russian treadmill BD-1 as a backup of the NASA TVIS].

PubMed

Iarmanova, E N; Kozlovskaia, I B; Bogomolov, V V; Rumiantseva, O N; Sukhachev, V I; Mel'nik, K A

2006-01-01

Already during the early ISS increments malfunctioning of NASA TVIS (treadmill with vibration isolation system) posed major problems for regular crew training and particularly scamper, one of the key exercises on the Russian physical training program. During ISS increment-3, TVIS unscheduled repairs took virtually all the training time. In search for TVIS backup, Russian and NASA engineers considered jointly Russian treadmill BD-1, originally designed for Russian "shuttle" Buran and accepted it as a suitable backup in case of complete TVIS failure. To enter into the "dialogue" with BD-1, i.e., to record and downlink training data, the treadmill speed indicator, a part of the treadmill stand, was replaced by PC.

[Multiprofessional family-system training programme in psychiatry--effects on team cooperation and staff strain].

PubMed

Zwack, Julika; Schweitzer, Jochen

2008-01-01

How does the interdisciplinary cooperation of psychiatric staff members change after a multiprofessional family systems training programme? Semi-structured interviews were conducted with 49 staff members. Quantitative questionnaires were used to assess burnout (Maslach Burnout Inventory, MBI) and team climate (Team-Klima-Inventar, TKI). The multiprofessional training intensifies interdisciplinary cooperation. It results in an increased appreciation of the nurses involved and in a redistribution of therapeutic tasks between nurses, psychologists and physicians. Staff burnout decreased during the research period, while task orientation and participative security within teams increased. The multiprofessional family systems training appears suitable to improve quality of patient care and interdisciplinary cooperation and to reduce staff burnout.

Combining multiple positive training sets to generate confidence scores for protein-protein interactions.

PubMed

Yu, Jingkai; Finley, Russell L

2009-01-01

High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space. We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.

Workforce Skills Development and Engagement in Training through Skill Sets: Literature Review. Occasional Paper

ERIC Educational Resources Information Center

Mills, John; Bowman, Kaye; Crean, David; Ranshaw, Danielle

2012-01-01

This literature review examines the available research on skill sets. It provides background for a larger research project "Workforce skills development and engagement in training through skill sets," the report of which will be released early next year. This paper outlines the origin of skill sets and explains the difference between…

Improving a Lecture-Size Molecular Model Set by Repurposing Used Whiteboard Markers

ERIC Educational Resources Information Center

Dragojlovic, Veljko

2015-01-01

Preparation of an inexpensive model set from whiteboard markers and either HGS molecular model set or atoms made of wood is described. The model set is relatively easy to prepare and is sufficiently large to be suitable as an instructor set for use in lectures.

A simple method to derive bounds on the size and to train multilayer neural networks

NASA Technical Reports Server (NTRS)

Sartori, Michael A.; Antsaklis, Panos J.

1991-01-01

A new derivation is presented for the bounds on the size of a multilayer neural network to exactly implement an arbitrary training set; namely, the training set can be implemented with zero error with two layers and with the number of the hidden-layer neurons equal to no.1 is greater than p - 1. The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving no.1 + 1 linear equations. The method presented exactly solves (M), the multilayer neural network training problem, for any arbitrary training set.

Surveillance system and method having parameter estimation and operating mode partitioning

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2003-01-01

A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.

Differential Effects of Heavy Versus Moderate Loads on Measures of Strength and Hypertrophy in Resistance-Trained Men.

PubMed

Schoenfeld, Brad J; Contreras, Bret; Vigotsky, Andrew D; Peterson, Mark

2016-12-01

The purpose of the present study was to evaluate muscular adaptations between heavy- and moderate-load resistance training (RT) with all other variables controlled between conditions. Nineteen resistance-trained men were randomly assigned to either a strength-type RT routine (HEAVY) that trained in a loading range of 2-4 repetitions per set (n = 10) or a hypertrophy-type RT routine (MODERATE) that trained in a loading range of 8-12 repetitions per set (n = 9). Training was carried out 3 days a week for 8 weeks. Both groups performed 3 sets of 7 exercises for the major muscle groups of the upper and lower body. Subjects were tested pre- and post-study for: 1 repetition maximum (RM) strength in the bench press and squat, upper body muscle endurance, and muscle thickness of the elbow flexors, elbow extensors, and lateral thigh. Results showed statistically greater increases in 1RM squat strength favoring HEAVY compared to MODERATE. Alternatively, statistically greater increases in lateral thigh muscle thickness were noted for MODERATE versus HEAVY. These findings indicate that heavy load training is superior for maximal strength goals while moderate load training is more suited to hypertrophy-related goals when an equal number of sets are performed between conditions.

Programming "loose training" as a strategy to facilitate language generalization.

PubMed Central

Campbell, C R; Stremel-Campbell, K

1982-01-01

This study investigated the generalization of spontaneous complex language behavior across a nontraining setting and the durability of generalization as a result of programming and "loose training" strategy. A within-subject, across-behaviors multiple-baseline design was used to examine the performance of two moderately retarded students in the use of is/are across three syntactic structures (i.e., "wh" questions, "yes/no" reversal questions, and statements). The language training procedure used in this study represented a functional example of programming "loose training." The procedure involved conducting concurrent language training within the context of an academic training task, and establishing a functional reduction in stimulus control by permitting the student to initiate a language response based on a wide array of naturally occurring stimulus events. Concurrent probes were conducted in the free play setting to assess the immediate generalization and the durability of the language behaviors. The results demonstrated that "loose training" was effective in establishing a specific set of language responses with the participants of this investigation. Further, both students demonstrated spontaneous use of the language behavior in the free play generalization setting and a trend was clearly evident for generalization to continue across time. Thus, the methods used appear to be successful for training the use of is/are in three syntactic structures. PMID:7118759

Analysis of precision and accuracy in a simple model of machine learning

NASA Astrophysics Data System (ADS)

Lee, Julian

2017-12-01

Machine learning is a procedure where a model for the world is constructed from a training set of examples. It is important that the model should capture relevant features of the training set, and at the same time make correct prediction for examples not included in the training set. I consider the polynomial regression, the simplest method of learning, and analyze the accuracy and precision for different levels of the model complexity.

HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set.

PubMed

Nunes, Kelly; Zheng, Xiuwen; Torres, Margareth; Moraes, Maria Elisa; Piovezan, Bruno Z; Pontes, Gerlandia N; Kimura, Lilian; Carnavalli, Juliana E P; Mingroni Netto, Regina C; Meyer, Diogo

2016-03-01

Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy. Copyright © 2016 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

The efficacy of a whole body sprint-interval training intervention in an office setting: A feasibility study.

PubMed

Gurd, Brendon J; Patel, Jugal; Edgett, Brittany A; Scribbans, Trisha D; Quadrilatero, Joe; Fischer, Steven L

2018-05-28

Whole body sprint-interval training (WB-SIT) represents a mode of exercise training that is both time-efficient and does not require access to an exercise facility. The current study examined the feasibility of implementing a WB-SIT intervention in a workplace setting. A total of 747 employees from a large office building were invited to participate with 31 individuals being enrolled in the study. Anthropometrics, aerobic fitness, core and upper body strength, and lower body mobility were assessed before and after a 12-week exercise intervention consisting of 2-4 training sessions per week. Each training session required participants to complete 8, 20-second intervals (separated by 10 seconds of rest) of whole body exercise. Proportion of participation was 4.2% while the response rate was 35% (11/31 participants completed post training testing). In responders, compliance to prescribed training was 83±17%, and significant (p <  0.05) improvements were observed for aerobic fitness, push-up performance and lower body mobility. These results demonstrate the efficacy of WB-FIT for improving fitness and mobility in an office setting, but highlight the difficulties in achieving high rates of participation and response in this setting.

Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling.

PubMed

Ding, Meng; Fan, Guolian

2015-11-01

We present new multilayer joint gait-pose manifolds (multilayer JGPMs) for complex human gait motion modeling, where three latent variables are defined jointly in a low-dimensional manifold to represent a variety of body configurations. Specifically, the pose variable (along the pose manifold) denotes a specific stage in a walking cycle; the gait variable (along the gait manifold) represents different walking styles; and the linear scale variable characterizes the maximum stride in a walking cycle. We discuss two kinds of topological priors for coupling the pose and gait manifolds, i.e., cylindrical and toroidal, to examine their effectiveness and suitability for motion modeling. We resort to a topologically-constrained Gaussian process (GP) latent variable model to learn the multilayer JGPMs where two new techniques are introduced to facilitate model learning under limited training data. First is training data diversification that creates a set of simulated motion data with different strides. Second is the topology-aware local learning to speed up model learning by taking advantage of the local topological structure. The experimental results on the Carnegie Mellon University motion capture data demonstrate the advantages of our proposed multilayer models over several existing GP-based motion models in terms of the overall performance of human gait motion modeling.

Local linear discriminant analysis framework using sample neighbors.

PubMed

Fan, Zizhu; Xu, Yong; Zhang, David

2011-07-01

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach. The algorithms of LDA usually perform well under the following two assumptions. The first assumption is that the global data structure is consistent with the local data structure. The second assumption is that the input data classes are Gaussian distributions. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose an improved LDA framework, the local LDA (LLDA), which can perform well without needing to satisfy the above two assumptions. Our LLDA framework can effectively capture the local structure of samples. According to different types of local data structure, our LLDA framework incorporates several different forms of linear feature extraction approaches, such as the classical LDA and principal component analysis. The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to image recognition, such as face recognition. Our algorithms need to train only a small portion of the whole training set before testing a sample. They are suitable for learning large-scale databases especially when the input data dimensions are very high and can achieve high classification accuracy. Extensive experiments show that the proposed algorithms can obtain good classification results.

Amateur boxing: physical and physiological attributes.

PubMed

Chaabène, Helmi; Tabben, Montassar; Mkaouer, Bessem; Franchini, Emerson; Negra, Yassine; Hammami, Mehrez; Amara, Samiha; Chaabène, Raja Bouguezzi; Hachana, Younés

2015-03-01

Boxing is one of the oldest combat sports. The aim of the current review is to critically analyze the amateur boxer's physical and physiological characteristics and to provide practical recommendations for training as well as new areas of scientific research. High-level male and female boxers show a propensity for low body fat levels. Although studies on boxer somatotypes are limited, the available information shows that elite-level male boxers are characterized by a higher proportion of mesomorphy with a well-developed muscle mass and a low body fat level. To help support the overall metabolic demands of a boxing match and to accelerate the recovery process between rounds, athletes of both sexes require a high level of cardiorespiratory fitness. International boxers show a high peak and mean anaerobic power output. Muscle strength in both the upper and lower limbs is paramount for a fighter's victory and is one of the keys to success in boxing. As boxing punches are brief actions and very dynamic, high-level boxing performance requires well-developed muscle power in both the upper and lower limbs. Albeit limited, the available studies reveal that isometric strength is linked to high-level boxing performance. Future investigations into the physical and physiological attributes of boxers are required to enrich the current data set and to help create a suitable training program.

Machine learning-based screening of complex molecules for polymer solar cells

NASA Astrophysics Data System (ADS)

Jørgensen, Peter Bjørn; Mesta, Murat; Shil, Suranjan; García Lastra, Juan Maria; Jacobsen, Karsten Wedel; Thygesen, Kristian Sommer; Schmidt, Mikkel N.

2018-06-01

Polymer solar cells admit numerous potential advantages including low energy payback time and scalable high-speed manufacturing, but the power conversion efficiency is currently lower than for their inorganic counterparts. In a Phenyl-C_61-Butyric-Acid-Methyl-Ester (PCBM)-based blended polymer solar cell, the optical gap of the polymer and the energetic alignment of the lowest unoccupied molecular orbital (LUMO) of the polymer and the PCBM are crucial for the device efficiency. Searching for new and better materials for polymer solar cells is a computationally costly affair using density functional theory (DFT) calculations. In this work, we propose a screening procedure using a simple string representation for a promising class of donor-acceptor polymers in conjunction with a grammar variational autoencoder. The model is trained on a dataset of 3989 monomers obtained from DFT calculations and is able to predict LUMO and the lowest optical transition energy for unseen molecules with mean absolute errors of 43 and 74 meV, respectively, without knowledge of the atomic positions. We demonstrate the merit of the model for generating new molecules with the desired LUMO and optical gap energies which increases the chance of finding suitable polymers by more than a factor of five in comparison to the randomised search used in gathering the training set.

The production of audiovisual teaching tools in minimally invasive surgery.

PubMed

Tolerton, Sarah K; Hugh, Thomas J; Cosman, Peter H

2012-01-01

Audiovisual learning resources have become valuable adjuncts to formal teaching in surgical training. This report discusses the process and challenges of preparing an audiovisual teaching tool for laparoscopic cholecystectomy. The relative value in surgical education and training, for both the creator and viewer are addressed. This audiovisual teaching resource was prepared as part of the Master of Surgery program at the University of Sydney, Australia. The different methods of video production used to create operative teaching tools are discussed. Collating and editing material for an audiovisual teaching resource can be a time-consuming and technically challenging process. However, quality learning resources can now be produced even with limited prior video editing experience. With minimal cost and suitable guidance to ensure clinically relevant content, most surgeons should be able to produce short, high-quality education videos of both open and minimally invasive surgery. Despite the challenges faced during production of audiovisual teaching tools, these resources are now relatively easy to produce using readily available software. These resources are particularly attractive to surgical trainees when real time operative footage is used. They serve as valuable adjuncts to formal teaching, particularly in the setting of minimally invasive surgery. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Discovery and quantitative structure-activity relationship study of lepidopteran HMG-CoA reductase inhibitors as selective insecticides.

PubMed

Zang, Yang-Yang; Li, Yuan-Mei; Yin, Yue; Chen, Shan-Shan; Kai, Zhen-Peng

2017-09-01

In a previous study we have demonstrated that insect 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) can be a potential selective insecticide target. Three series of inhibitors were designed on the basis of the difference in HMGR structures from Homo sapiens and Manduca sexta, with the aim of discovering potent selective insecticide candidates. An in vitro bioassay showed that gem-difluoromethylenated statin analogues have potent effects on JH biosynthesis of M. sexta and high selectivity between H. sapiens and M. sexta. All series II compounds {1,3,5-trisubstituted [4-tert-butyl 2-(5,5-difluoro-2,2-dimethyl-6-vinyl-4-yl) acetate] pyrazoles} have some effect on JH biosynthesis, whereas most of them are inactive on human HMGR. In particular, the IC 50 value of compound II-12 (37.8 nm) is lower than that of lovastatin (99.5 nm) and similar to that of rosuvastatin (24.2 nm). An in vivo bioassay showed that I-1, I-2, I-3 and II-12 are potential selective insecticides, especially for lepidopteran pest control. A predictable and statistically meaningful CoMFA model of 23 inhibitors (20 as training sets and three as test sets) was obtained with a value of q 2 and r 2 of 0.66 and 0.996 respectively. The final model suggested that a potent insect HMGR inhibitor should contain suitable small and non-electronegative groups in the ring part, and electronegative groups in the side chain. Four analogues were discovered as potent selective lepidopteran HMGR inhibitors, which can specifically be used for lepidopteran pest control. The CoMFA model will be useful for the design of new selective insect HMGR inhibitors that are structurally related to the training set compounds. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Education and Training for Clinical Neuropsychologists in Integrated Care Settings.

PubMed

Roper, Brad L; Block, Cady K; Osborn, Katie; Ready, Rebecca E

2018-05-01

The increasing importance of integrated care necessitates that education and training experiences prepare clinical neuropsychologists for competent practice in integrated care settings, which includes (a) general competence related to an integrated/interdisciplinary approach and (b) competence specific to the setting. Formal neuropsychology training prepares neuropsychologists with a wide range of knowledge and skills in assessment, intervention, teaching/supervision, and research that are relevant to such settings. However, less attention has been paid to the knowledge and skills that directly address functioning within integrated teams, such as the ability to develop, maintain, and expand collaboration across disciplines, bidirectional clinical-research translation and implementation in integrated team settings, and how such collaboration contributes to clinical and research activities. Foundational knowledge and skills relevant to interdisciplinary systems have been articulated as part of competencies for entry into clinical neuropsychology, but their emphasis in education and training programs is unclear. Recommendations and resources are provided regarding how competencies relevant to integrated care can be provided across the continuum of education and training (i.e., doctoral, internship, postdoctoral, and post-licensure).

A virtual reality assessment and training system for unilateral neglect.

PubMed

Kim, Kwanguk; Kim, Jaehun; Ku, Jeonghun; Kim, Deog Young; Chang, Won Hyek; Shin, Dong Ik; Lee, Jang Han; Kim, In Young; Kim, Sun I

2004-12-01

Patients with unilateral neglect have problems reporting, responding, or orienting to novel or meaningful stimuli that is presented to the side opposite to that of a brain lesion. This creates a serous problem in regards to daily living activities. However, the established methods for assessing and training of unilateral neglect patients have several deficits. Recently, virtual reality (VR) technologies have been used as an assessment and treatment tool for rehabilitation. Hence, this study designed a VR system to assess and train unilateral neglect patients. In addition, the suitability and feasibility of our VR system for unilateral neglect patients was verified.

Structure: Suitable Staffing and Training of Functional Specialists within the United States Army Reserve Civil Affairs Force

DTIC Science & Technology

2011-12-01

military organizations with capabilities to obtain simultaneous strategic and operational flexibility. This idea allows leaders to provide a tailor...that could provide immediate and proper treatment .119 A brief perspective of the 426th CA BN’s deployment in 2009 provides another example of CA...specialty areas. USAR civil affairs functional specialty cells train to provide expertise in political, military, economic, social, infrastructure, and

Analyzing the Utilization of Interferon-Gamma Screening for Tuberculosis at Recruit Training Command, Great Lakes

DTIC Science & Technology

2006-05-31

the project through a business case analysis conducted on this same subject. CAPT Monestersky, Preventive Medicine; LCDR Jacobs, Occupational Medicine...TB and LTBI (Taylor, Nolan, & Blumberg , Interferon-y screening for TB 8 2005). They are based on science founded in the 1 9 th century. The current...identify individuals who may not be suitable for service. For the majority who are suitable, the "P" days allow time to complete necessary business

The GOBLET training portal: a global repository of bioinformatics training materials, courses and trainers

PubMed Central

Corpas, Manuel; Jimenez, Rafael C.; Bongcam-Rudloff, Erik; Budd, Aidan; Brazas, Michelle D.; Fernandes, Pedro L.; Gaeta, Bruno; van Gelder, Celia; Korpelainen, Eija; Lewitter, Fran; McGrath, Annette; MacLean, Daniel; Palagi, Patricia M.; Rother, Kristian; Taylor, Jan; Via, Allegra; Watson, Mick; Schneider, Maria Victoria; Attwood, Teresa K.

2015-01-01

Summary: Rapid technological advances have led to an explosion of biomedical data in recent years. The pace of change has inspired new collaborative approaches for sharing materials and resources to help train life scientists both in the use of cutting-edge bioinformatics tools and databases and in how to analyse and interpret large datasets. A prototype platform for sharing such training resources was recently created by the Bioinformatics Training Network (BTN). Building on this work, we have created a centralized portal for sharing training materials and courses, including a catalogue of trainers and course organizers, and an announcement service for training events. For course organizers, the portal provides opportunities to promote their training events; for trainers, the portal offers an environment for sharing materials, for gaining visibility for their work and promoting their skills; for trainees, it offers a convenient one-stop shop for finding suitable training resources and identifying relevant training events and activities locally and worldwide. Availability and implementation: http://mygoblet.org/training-portal Contact: manuel.corpas@tgac.ac.uk PMID:25189782

Methodological issues in the design and evaluation of supported communication for aphasia training: a cluster-controlled feasibility study

PubMed Central

Horton, Simon; Clark, Allan; Barton, Garry; Lane, Kathleen; Pomeroy, Valerie M

2016-01-01

Objective To assess the feasibility and acceptability of training stroke service staff to provide supported communication for people with moderate–severe aphasia in the acute phase; assess the suitability of outcome measures; collect data to inform sample size and Health Economic evaluation in a definitive trial. Design Phase II cluster-controlled, observer-blinded feasibility study. Settings In-patient stroke rehabilitation units in the UK matched for bed numbers and staffing were assigned to control and intervention conditions. Participants 70 stroke rehabilitation staff from all professional groups, excluding doctors, were recruited. 20 patients with moderate-severe aphasia were recruited. Intervention Supported communication for aphasia training, adapted to the stroke unit context versus usual care. Training was supplemented by a staff learning log, refresher sessions and provision of communication resources. Main outcome measures Feasibility of recruitment and acceptability of the intervention and of measures required to assess outcomes and Health Economic evaluation in a definitive trial. Staff outcomes: Measure of Support in Conversation; patient outcomes: Stroke and Aphasia Quality of Life Scale; Communicative Access Measure for Stroke; Therapy Outcome Measures for aphasia; EQ-5D-3L was used to assess health outcomes. Results Feasibility of staff recruitment was demonstrated. Training in the intervention was carried out with 28 staff and was found to be acceptable in qualitative reports. 20 patients consented to take part, 6 withdrew. 18 underwent all measures at baseline; 16 at discharge; and 14 at 6-month follow-up. Of 175 patients screened 71% were deemed to be ineligible, either lacking capacity or too unwell to participate. Poor completion rates impacted on assessment of patient outcomes. We were able to collect sufficient data at baseline, discharge and follow-up for economic evaluation. Conclusions The feasibility study informed components of the intervention and implementation in day-to-day practice. Modifications to the design are needed before a definitive cluster-randomised trial can be undertaken. Trial registration number ISRCTN37002304; Results. PMID:27091825

Introduction of Syphilis Point-of-Care Tests, from Pilot Study to National Programme Implementation in Zambia: A Qualitative Study of Healthcare Workers' Perspectives on Testing, Training and Quality Assurance.

PubMed

Ansbro, Éimhín M; Gill, Michelle M; Reynolds, Joanna; Shelley, Katharine D; Strasser, Susan; Sripipatana, Tabitha; Tshaka Ncube, Alexander; Tembo Mumba, Grace; Terris-Prestholt, Fern; Peeling, Rosanna W; Mabey, David

2015-01-01

Syphilis affects 1.4 million pregnant women globally each year. Maternal syphilis causes congenital syphilis in over half of affected pregnancies, leading to early foetal loss, pregnancy complications, stillbirth and neonatal death. Syphilis is under-diagnosed in pregnant women. Point-of-care rapid syphilis tests (RST) allow for same-day treatment and address logistical barriers to testing encountered with standard Rapid Plasma Reagin testing. Recent literature emphasises successful introduction of new health technologies requires healthcare worker (HCW) acceptance, effective training, quality monitoring and robust health systems. Following a successful pilot, the Zambian Ministry of Health (MoH) adopted RST into policy, integrating them into prevention of mother-to-child transmission of HIV clinics in four underserved Zambian districts. We compare HCW experiences, including challenges encountered in scaling up from a highly supported NGO-led pilot to a large-scale MoH-led national programme. Questionnaires were administered through structured interviews of 16 HCWs in two pilot districts and 24 HCWs in two different rollout districts. Supplementary data were gathered via stakeholder interviews, clinic registers and supervisory visits. Using a conceptual framework adapted from health technology literature, we explored RST acceptance and usability. Quantitative data were analysed using descriptive statistics. Key themes in qualitative data were explored using template analysis. Overall, HCWs accepted RST as learnable, suitable, effective tools to improve antenatal services, which were usable in diverse clinical settings. Changes in training, supervision and quality monitoring models between pilot and rollout may have influenced rollout HCW acceptance and compromised testing quality. While quality monitoring was integrated into national policy and training, implementation was limited during rollout despite financial support and mentorship. We illustrate that new health technology pilot research can rapidly translate into policy change and scale-up. However, training, supervision and quality assurance models should be reviewed and strengthened as rollout of the Zambian RST programme continues.

Introduction of Syphilis Point-of-Care Tests, from Pilot Study to National Programme Implementation in Zambia: A Qualitative Study of Healthcare Workers’ Perspectives on Testing, Training and Quality Assurance

PubMed Central

Ansbro, Éimhín M.; Gill, Michelle M.; Reynolds, Joanna; Shelley, Katharine D.; Strasser, Susan; Sripipatana, Tabitha; Ncube, Alexander Tshaka; Tembo Mumba, Grace; Terris-Prestholt, Fern; Peeling, Rosanna W.; Mabey, David

2015-01-01

Syphilis affects 1.4 million pregnant women globally each year. Maternal syphilis causes congenital syphilis in over half of affected pregnancies, leading to early foetal loss, pregnancy complications, stillbirth and neonatal death. Syphilis is under-diagnosed in pregnant women. Point-of-care rapid syphilis tests (RST) allow for same-day treatment and address logistical barriers to testing encountered with standard Rapid Plasma Reagin testing. Recent literature emphasises successful introduction of new health technologies requires healthcare worker (HCW) acceptance, effective training, quality monitoring and robust health systems. Following a successful pilot, the Zambian Ministry of Health (MoH) adopted RST into policy, integrating them into prevention of mother-to-child transmission of HIV clinics in four underserved Zambian districts. We compare HCW experiences, including challenges encountered in scaling up from a highly supported NGO-led pilot to a large-scale MoH-led national programme. Questionnaires were administered through structured interviews of 16 HCWs in two pilot districts and 24 HCWs in two different rollout districts. Supplementary data were gathered via stakeholder interviews, clinic registers and supervisory visits. Using a conceptual framework adapted from health technology literature, we explored RST acceptance and usability. Quantitative data were analysed using descriptive statistics. Key themes in qualitative data were explored using template analysis. Overall, HCWs accepted RST as learnable, suitable, effective tools to improve antenatal services, which were usable in diverse clinical settings. Changes in training, supervision and quality monitoring models between pilot and rollout may have influenced rollout HCW acceptance and compromised testing quality. While quality monitoring was integrated into national policy and training, implementation was limited during rollout despite financial support and mentorship. We illustrate that new health technology pilot research can rapidly translate into policy change and scale-up. However, training, supervision and quality assurance models should be reviewed and strengthened as rollout of the Zambian RST programme continues. PMID:26030741

Re-Conceptualization of Modified Angoff Standard Setting: Unified Statistical, Measurement, Cognitive, and Social Psychological Theories

ERIC Educational Resources Information Center

Iyioke, Ifeoma Chika

2013-01-01

This dissertation describes a design for training, in accordance with probability judgment heuristics principles, for the Angoff standard setting method. The new training with instruction, practice, and feedback tailored to the probability judgment heuristics principles was called the Heuristic training and the prevailing Angoff method training…

Replacing Maladaptive Speech with Verbal Labeling Responses: An Analysis of Generalized Responding.

ERIC Educational Resources Information Center

Foxx, R. M.; And Others

1988-01-01

Three mentally handicapped students (aged 13, 36, and 40) with maladaptive speech received training to answer questions with verbal labels. The results of their cues-pause-point training showed that the students replaced their maladaptive speech with correct labels (answers) to questions in the training setting and three generalization settings.…

A Model for Teaching Rational Behavior Therapy in a Public School Setting.

ERIC Educational Resources Information Center

Patton, Patricia L.

A training model for the use of rational behavior therapy (RBT) with emotionally disturbed adolescents in a school setting is presented, including a structured, didactic format consisting of five basic RBT training techniques. The training sessions, lasting 10 weeks each, are described. Also presented is the organization for the actual classroom…

A novel classifier based on three preoperative tumor markers predicting the cancer-specific survival of gastric cancer (CEA, CA19-9 and CA72-4).

PubMed

Guo, Jing; Chen, Shangxiang; Li, Shun; Sun, Xiaowei; Li, Wei; Zhou, Zhiwei; Chen, Yingbo; Xu, Dazhi

2018-01-12

Several studies have highlighted the prognostic value of the individual and the various combinations of the tumor markers for gastric cancer (GC). Our study was designed to assess establish a new novel model incorporating carcino-embryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 72-4 (CA72-4). A total of 1,566 GC patients (Primary cohort) between Jan 2000 and July 2013 were analyzed. The Primary cohort was randomly divided into Training set (n=783) and Validation set (n=783). A three-tumor marker classifier was developed in the Training set and validated in the Validation set by multivariate regression and risk-score analysis. We have identified a three-tumor marker classifier (including CEA, CA19-9 and CA72-4) for the cancer specific survival (CSS) of GC (p<0.001). Consistent results were obtained in the both Training set and Validation set. Multivariate analysis showed that the classifier was an independent predictor of GC (All p value <0.001 in the Training set, Validation set and Primary cohort). Furthermore, when the leave-one-out approach was performed, the classifier showed superior predictive value to the individual or two of them (with the highest AUC (Area Under Curve); 0.618 for the Training set, and 0.625 for the Validation set), which ascertained its predictive value. Our three-tumor marker classifier is closely associated with the CSS of GC and may serve as a novel model for future decisions concerning treatments.

Application of the Repetitions in Reserve-Based Rating of Perceived Exertion Scale for Resistance Training

PubMed Central

Cronin, John; Storey, Adam; Zourdos, Michael C.

2016-01-01

ABSTRACT RATINGS OF PERCEIVED EXERTION ARE A VALID METHOD OF ESTIMATING THE INTENSITY OF A RESISTANCE TRAINING EXERCISE OR SESSION. SCORES ARE GIVEN AFTER COMPLETION OF AN EXERCISE OR TRAINING SESSION FOR THE PURPOSES OF ATHLETE MONITORING. HOWEVER, A NEWLY DEVELOPED SCALE BASED ON HOW MANY REPETITIONS ARE REMAINING AT THE COMPLETION OF A SET MAY BE A MORE PRECISE TOOL. THIS APPROACH ADJUSTS LOADS AUTOMATICALLY TO MATCH ATHLETE CAPABILITIES ON A SET-TO-SET BASIS AND MAY MORE ACCURATELY GAUGE INTENSITY AT NEAR-LIMIT LOADS. THIS ARTICLE OUTLINES HOW TO INCORPORATE THIS NOVEL SCALE INTO A TRAINING PLAN. PMID:27531969

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Supporting the Cybercrime Investigation Process: Effective Discrimination of Source Code Authors Based on Byte-Level Information

NASA Astrophysics Data System (ADS)

Frantzeskou, Georgia; Stamatatos, Efstathios; Gritzalis, Stefanos

Source code authorship analysis is the particular field that attempts to identify the author of a computer program by treating each program as a linguistically analyzable entity. This is usually based on other undisputed program samples from the same author. There are several cases where the application of such a method could be of a major benefit, such as tracing the source of code left in the system after a cyber attack, authorship disputes, proof of authorship in court, etc. In this paper, we present our approach which is based on byte-level n-gram profiles and is an extension of a method that has been successfully applied to natural language text authorship attribution. We propose a simplified profile and a new similarity measure which is less complicated than the algorithm followed in text authorship attribution and it seems more suitable for source code identification since is better able to deal with very small training sets. Experiments were performed on two different data sets, one with programs written in C++ and the second with programs written in Java. Unlike the traditional language-dependent metrics used by previous studies, our approach can be applied to any programming language with no additional cost. The presented accuracy rates are much better than the best reported results for the same data sets.

An integrated fiber-optic probe combined with support vector regression for fast estimation of optical properties of turbid media.

PubMed

Zhou, Yang; Fu, Xiaping; Ying, Yibin; Fang, Zhenhuan

2015-06-23

A fiber-optic probe system was developed to estimate the optical properties of turbid media based on spatially resolved diffuse reflectance. Because of the limitations in numerical calculation of radiative transfer equation (RTE), diffusion approximation (DA) and Monte Carlo simulations (MC), support vector regression (SVR) was introduced to model the relationship between diffuse reflectance values and optical properties. The SVR models of four collection fibers were trained by phantoms in calibration set with a wide range of optical properties which represented products of different applications, then the optical properties of phantoms in prediction set were predicted after an optimal searching on SVR models. The results indicated that the SVR model was capable of describing the relationship with little deviation in forward validation. The correlation coefficient (R) of reduced scattering coefficient μ'(s) and absorption coefficient μ(a) in the prediction set were 0.9907 and 0.9980, respectively. The root mean square errors of prediction (RMSEP) of μ'(s) and μ(a) in inverse validation were 0.411 cm(-1) and 0.338 cm(-1), respectively. The results indicated that the integrated fiber-optic probe system combined with SVR model were suitable for fast and accurate estimation of optical properties of turbid media based on spatially resolved diffuse reflectance. Copyright © 2015 Elsevier B.V. All rights reserved.

Species distribution modelling for Rhipicephalus microplus (Acari: Ixodidae) in Benin, West Africa: comparing datasets and modelling algorithms.

PubMed

De Clercq, E M; Leta, S; Estrada-Peña, A; Madder, M; Adehan, S; Vanwambeke, S O

2015-01-01

Rhipicephalus microplus is one of the most widely distributed and economically important ticks, transmitting Babesia bigemina, B. bovis and Anaplasma marginale. It was recently introduced to West Africa on live animals originating from Brazil. Knowing the precise environmental suitability for the tick would allow veterinary health officials to draft vector control strategies for different regions of the country. To test the performance of modelling algorithms and different sets of environmental explanatory variables, species distribution models for this tick species in Benin were developed using generalized linear models, linear discriminant analysis and random forests. The training data for these models were a dataset containing reported absence or presence in 104 farms, randomly selected across Benin. These farms were sampled at the end of the rainy season, which corresponds with an annual peak in tick abundance. Two environmental datasets for the country of Benin were compared: one based on interpolated climate data (WorldClim) and one based on remotely sensed images (MODIS). The pixel size for both environmental datasets was 1 km. Highly suitable areas occurred mainly along the warmer and humid coast extending northwards to central Benin. The northern hot and drier areas were found to be unsuitable. The models developed and tested on data from the entire country were generally found to perform well, having an AUC value greater than 0.92. Although statistically significant, only small differences in accuracy measures were found between the modelling algorithms, or between the environmental datasets. The resulting risk maps differed nonetheless. Models based on interpolated climate suggested gradual variations in habitat suitability, while those based on remotely sensed data indicated a sharper contrast between suitable and unsuitable areas, and a patchy distribution of the suitable areas. Remotely sensed data yielded more spatial detail in the predictions. When computing accuracy measures on a subset of data along the invasion front, the modelling technique Random Forest outperformed the other modelling approaches, and results with MODIS-derived variables were better than those using WorldClim data. The high environmental suitability for R. microplus in the southern half of Benin raises concern at the regional level for animal health, including its potential to substantially alter transmission risk of Babesia bovis. The northern part of Benin appeared overall of low environmental suitability. Continuous surveillance in the transition zone however remains relevant, in relation to important cattle movements in the region, and to the invasive character of R. microplus. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Comparing two methods to promote generalization of receptive identification in children with autism spectrum disorders.

PubMed

Dufour, Marie-Michèle; Lanovaz, Marc J

2017-11-01

The purpose of our study was to compare the effects of serial and concurrent training on the generalization of receptive identification in children with autism spectrum disorders (ASD). We taught one to three pairs of stimulus sets to nine children with ASD between the ages of three and six. One stimulus set within each pair was taught using concurrent training and the other using serial training. We alternated the training sessions within a multielement design and staggered the introduction of subsequent pairs for each participant as in a multiple baseline design. Overall, six participants generalized at least one stimulus set more rapidly with concurrent training whereas two participants showed generalization more rapidly with serial training. Our results differ from other comparison studies on the topic and indicate that practitioners should consider assessing the effects of both procedures prior to teaching receptive identification to children with ASD.

Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

PubMed

Raaijmakers, Steven F; Baars, Martine; Paas, Fred; van Merriënboer, Jeroen J G; van Gog, Tamara

2018-01-01

Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, and-importantly-such training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.

Training self‐assessment and task‐selection skills to foster self‐regulated learning: Do trained skills transfer across domains?

PubMed Central

Baars, Martine; Paas, Fred; van Merriënboer, Jeroen J. G.; van Gog, Tamara

2018-01-01

Summary Students' ability to accurately self‐assess their performance and select a suitable subsequent learning task in response is imperative for effective self‐regulated learning. Video modeling examples have proven effective for training self‐assessment and task‐selection skills, and—importantly—such training fostered self‐regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task‐selection rule or a more general heuristic task‐selection rule in biology would transfer to self‐regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task‐selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self‐regulated learning in math. Future research should investigate how to support transfer of task‐selection skills across domains. PMID:29610547

"Functional" Inspiratory and Core Muscle Training Enhances Running Performance and Economy.

PubMed

Tong, Tomas K; McConnell, Alison K; Lin, Hua; Nie, Jinlei; Zhang, Haifeng; Wang, Jiayuan

2016-10-01

Tong, TK, McConnell, AK, Lin, H, Nie, J, Zhang, H, and Wang, J. "Functional" inspiratory and core muscle training enhances running performance and economy. J Strength Cond Res 30(10): 2942-2951, 2016-We compared the effects of two 6-week high-intensity interval training interventions. Under the control condition (CON), only interval training was undertaken, whereas under the intervention condition (ICT), interval training sessions were followed immediately by core training, which was combined with simultaneous inspiratory muscle training (IMT)-"functional" IMT. Sixteen recreational runners were allocated to either ICT or CON groups. Before the intervention phase, both groups undertook a 4-week program of "foundation" IMT to control for the known ergogenic effect of IMT (30 inspiratory efforts at 50% maximal static inspiratory pressure [P0] per set, 2 sets per day, 6 days per week). The subsequent 6-week interval running training phase consisted of 3-4 sessions per week. In addition, the ICT group undertook 4 inspiratory-loaded core exercises (10 repetitions per set, 2 sets per day, inspiratory load set at 50% post-IMT P0) immediately after each interval training session. The CON group received neither core training nor functional IMT. After the intervention phase, global inspiratory and core muscle functions increased in both groups (p ≤ 0.05), as evidenced by P0 and a sport-specific endurance plank test (SEPT) performance, respectively. Compared with CON, the ICT group showed larger improvements in SEPT, running economy at the speed of the onset of blood lactate accumulation, and 1-hour running performance (3.04% vs. 1.57%, p ≤ 0.05). The changes in these variables were interindividually correlated (r ≥ 0.57, n = 16, p ≤ 0.05). Such findings suggest that the addition of inspiratory-loaded core conditioning into a high-intensity interval training program augments the influence of the interval program on endurance running performance and that this may be underpinned by an improvement in running economy.

Training transfer: a systematic review of the impact of inner setting factors.

PubMed

Jackson, Carrie B; Brabson, Laurel A; Quetsch, Lauren B; Herschell, Amy D

2018-06-19

Consistent with Baldwin and Ford's model (Pers Psychol 41(1):63-105, 1988), training transfer is defined as the generalization of learning from a training to everyday practice in the workplace. The purpose of this review was to examine the influence of work-environment factors, one component of the model hypothesized to influence training transfer within behavioral health. An electronic literature search guided by the Consolidated Framework for Implementation Research's inner setting domain was conducted was conducted on Medline OVID, Medline EMBASE, and PsycINFO databases. Of 9184 unique articles, 169 full-text versions of articles were screened for eligibility, yielding 26 articles meeting inclusion criteria. Results from the 26 studies revealed that overall, having more positive networks and communication, culture, implementation climate, and readiness for implementation can facilitate training transfer. Although few studies have examined the impact of inner setting factors on training transfer, these results suggest organizational context is important to consider with training efforts. These findings have important implications for individuals in the broader health professions educational field.

High Intensity High Volume Interval Training Improves Endurance Performance and Induces a Nearly Complete Slow-to-Fast Fiber Transformation on the mRNA Level.

PubMed

Eigendorf, Julian; May, Marcus; Friedrich, Jan; Engeli, Stefan; Maassen, Norbert; Gros, Gerolf; Meissner, Joachim D

2018-01-01

We present here a longitudinal study determining the effects of two 3 week-periods of high intensity high volume interval training (HIHVT) (90 intervals of 6 s cycling at 250% maximum power, P max /24 s) on a cycle ergometer. HIHVT was evaluated by comparing performance tests before and after the entire training (baseline, BSL, and endpoint, END) and between the two training sets (intermediate, INT). The mRNA expression levels of myosin heavy chain (MHC) isoforms and markers of energy metabolism were analyzed in M. vastus lateralis biopsies by quantitative real-time PCR. In incremental tests peak power (P peak ) was increased, whereas V ˙ O 2peak was unaltered. Prolonged time-to-exhaustion was found in endurance tests with 65 and 80% P max at INT and END. No changes in blood levels of lipid metabolites were detected. Training-induced decreases of hematocrit indicate hypervolemia. A shift from slow MHCI/β to fast MHCIIa mRNA expression occurred after the first and second training set. The mRNA expression of peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α), a master regulator of oxidative energy metabolism, decreased after the second training set. In agreement, a significant decrease was also found for citrate synthase mRNA after the second training set, indicating reduced oxidative capacity. However, mRNA expression levels of glycolytic marker enzyme glyceraldehyde-3-phosphate dehydrogenase did not change after the first and second training set. HIHVT induced a nearly complete slow-to-fast fiber type transformation on the mRNA level, which, however, cannot account for the improvements of performance parameters. The latter might be explained by the well-known effects of hypervolemia on exercise performance.

EEG-guided meditation: A personalized approach.

PubMed

Fingelkurts, Andrew A; Fingelkurts, Alexander A; Kallio-Tamminen, Tarja

2015-12-01

The therapeutic potential of meditation for physical and mental well-being is well documented, however the possibility of adverse effects warrants further discussion of the suitability of any particular meditation practice for every given participant. This concern highlights the need for a personalized approach in the meditation practice adjusted for a concrete individual. This can be done by using an objective screening procedure that detects the weak and strong cognitive skills in brain function, thus helping design a tailored meditation training protocol. Quantitative electroencephalogram (qEEG) is a suitable tool that allows identification of individual neurophysiological types. Using qEEG screening can aid developing a meditation training program that maximizes results and minimizes risk of potential negative effects. This brief theoretical-conceptual review provides a discussion of the problem and presents some illustrative results on the usage of qEEG screening for the guidance of mediation personalization. Copyright © 2015 Elsevier Ltd. All rights reserved.

Training practices and ergogenic aids used by male bodybuilders.

PubMed

Hackett, Daniel A; Johnson, Nathan A; Chow, Chin-Moi

2013-06-01

Bodybuilding involves performing a series of poses on stage where the competitor is judged on aesthetic muscular appearance. The purpose of this study was to describe training practices and ergogenic aids used by competitive bodybuilders and to determine whether training practices comply with current recommendations for muscular hypertrophy. A web-based survey was completed by 127 competitive male bodybuilders. The results showed that during the off-season phase of training (OFF), the majority of respondents performed 3-6 sets per exercise (95.3%), 7-12 repetition maximum (RM) per set (77.0%), and 61- to 120-seconds recovery between sets and exercises (68.6%). However, training practices changed 6 weeks before competition (PRE), where there was an increased number of respondents who reported undertaking 3-4 sets per exercise at the expense of 5-6 sets per exercise (p < 0.001), an increase in the number reporting 10-15RM per set from 7-9RM per set (p < 0.001), and an increase in the number reporting 30-60 seconds vs. 61-180 seconds recovery between sets and exercises (p < 0.001). Anabolic steroid use was high among respondents competing in amateur competitions (56 of 73 respondents), whereas dietary supplementation was used by all respondents. The findings of this study demonstrate that competitive bodybuilders comply with current resistance exercise recommendations for muscular hypertrophy; however, these changed before competition during which there is a reduction resistance training volume and intensity. This alteration, in addition to an increase in aerobic exercise volume, is purportedly used to increase muscle definition. However, these practices may increase the risk of muscle mass loss in natural compared with amateur bodybuilders who reportedly use drugs known to preserve muscle mass.

PDF4LHC recommendations for LHC Run II

DOE PAGES

Butterworth, Jon; Carrazza, Stefano; Cooper-Sarkar, Amanda; ...

2016-01-06

We provide an updated recommendation for the usage of sets of parton distribution functions (PDFs) and the assessment of PDF and PDF+αs uncertainties suitable for applications at the LHC Run II. We review developments since the previous PDF4LHC recommendation, and discuss and compare the new generation of PDFs, which include substantial information from experimental data from the Run I of the LHC. We then propose a new prescription for the combination of a suitable subset of the available PDF sets, which is presented in terms of a single combined PDF set. Lastly, we finally discuss tools which allow for themore » delivery of this combined set in terms of optimized sets of Hessian eigenvectors or Monte Carlo replicas, and their usage, and provide some examples of their application to LHC phenomenology.« less

Project ACE Activity Sets. Book I: Grades 3, 4, and 5.

ERIC Educational Resources Information Center

Eden City Schools, NC.

Eleven activity sets suitable for supplementing social studies units in grades 3, 4, and 5 are presented. Each set lists appropriate resources, concepts, general objectives and instructional objectives for each activity within the set. Grade 3 sets are "You Can Help Conserve Our Natural Resources,""Urban Decay and Urban…

Importance of eccentric actions in performance adaptations to resistance training

NASA Technical Reports Server (NTRS)

Dudley, Gary A.; Miller, Bruce J.; Buchanan, Paul; Tesch, Per A.

1991-01-01

The importance of eccentric (ecc) muscle actions in resistance training for the maintenance of muscle strength and mass in hypogravity was investigated in experiments in which human subjects, divided into three groups, were asked to perform four-five sets of 6 to 12 repetitions (rep) per set of three leg press and leg extension exercises, 2 days each weeks for 19 weeks. One group, labeled 'con', performed each rep with only concentric (con) actions, while group con/ecc with performed each rep with only ecc actions; the third group, con/con, performed twice as many sets with only con actions. Control subjects did not train. It was found that resistance training wih both con and ecc actions induced greater increases in muscle strength than did training with only con actions.

Teaching adolescents with severe disabilities to use the public telephone.

PubMed

Test, D W; Spooner, F; Keul, P K; Grossi, T

1990-04-01

Two adolescents with severe disabilities served as participants in a study conducted to train in the use of the public telephone to call home. Participants were trained to complete a 17-step task analysis using a training package which consisted of total task presentation in conjunction with a four-level prompting procedure (i.e., independent, verbal, verbal + gesture, verbal + guidance). All instruction took place in a public setting (e.g., a shopping mall) with generalization probes taken in two alternative settings (e.g., a movie theater and a convenience store). A multiple probe across individuals design demonstrated the training package was successful in teaching participants to use the telephone to call home. In addition, newly acquired skills generalized to the two untrained settings. Implications for community-based training are discussed.

Project ACE Activity Sets. Book II: Grades 6 and 7.

ERIC Educational Resources Information Center

Eden City Schools, NC.

The document contains eight activity sets suitable for grades 6 and 7. Topics focus on governmental, social, and educational systems in foreign countries. Each activity set contains background reading materials, resources, concepts, general objectives, and instructional objectives. Grade 6 sets are "Soviet Youth Organizations,""How…

Effects of number of training generations on genomic prediction for various traits in a layer chicken population.

PubMed

Weng, Ziqing; Wolc, Anna; Shen, Xia; Fernando, Rohan L; Dekkers, Jack C M; Arango, Jesus; Settar, Petek; Fulton, Janet E; O'Sullivan, Neil P; Garrick, Dorian J

2016-03-19

Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line. Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated. On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions. The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.

Lack of training threatening drilling talent supply

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

Von Flatern, R.

When oil prices crashed in the mid-1980s, the industry tightened budgets. Among the austerity measures taken to survive the consequences of low product prices was an end to expensive, long-term investment training of drilling engineers. In the absence of traditional sources of trained drilling talent, forward-looking contractors are creating their own training programs. The paper describes the activities of some companies who are setting up their own training programs, and an alliance being set up by Chevron and Amoco for training. The paper also discusses training drilling managers, third-party trainers, and the consequences for the industry that does not renewmore » its inventory of people.« less

Training in interprofessional collaboration: pedagogic innovation in family medicine units.

PubMed

Paré, Line; Maziade, Jean; Pelletier, Francine; Houle, Nathalie; Iloko-Fundi, Maximilien

2012-04-01

A number of agencies that accredit university health sciences programs recently added standards for the acquisition of knowledge and skills with respect to interprofessional collaboration. Within primary care settings there are no practical training programs that allow students from different disciplines to develop competencies in this area. The training program was developed within family medicine units affiliated with Université Laval in Quebec for family medicine residents and trainees from various disciplines to develop competencies in patient-centred, interprofessional collaborative practice in primary care. Based on adult learning theories, the program was divided into 3 phases--preparing family medicine unit professionals, training preceptors, and training the residents and trainees. The program's pedagogic strategies allowed participants to learn with, from, and about one another while preparing them to engage in contemporary primary care practices. A combination of quantitative and qualitative methods was used to evaluate the implementation process and the immediate results of the training program. The training program had a positive effect on both the clinical settings and the students. Preparation of clinical settings is an important issue that must be considered when planning practical interprofessional training.

Best practices in bioinformatics training for life scientists.

PubMed

Via, Allegra; Blicher, Thomas; Bongcam-Rudloff, Erik; Brazas, Michelle D; Brooksbank, Cath; Budd, Aidan; De Las Rivas, Javier; Dreyer, Jacqueline; Fernandes, Pedro L; van Gelder, Celia; Jacob, Joachim; Jimenez, Rafael C; Loveland, Jane; Moran, Federico; Mulder, Nicola; Nyrönen, Tommi; Rother, Kristian; Schneider, Maria Victoria; Attwood, Teresa K

2013-09-01

The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.

Best practices in bioinformatics training for life scientists

PubMed Central

Blicher, Thomas; Bongcam-Rudloff, Erik; Brazas, Michelle D.; Brooksbank, Cath; Budd, Aidan; De Las Rivas, Javier; Dreyer, Jacqueline; Fernandes, Pedro L.; van Gelder, Celia; Jacob, Joachim; Jimenez, Rafael C.; Loveland, Jane; Moran, Federico; Mulder, Nicola; Nyrönen, Tommi; Rother, Kristian; Schneider, Maria Victoria; Attwood, Teresa K.

2013-01-01

The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists. PMID:23803301

Emergence of Tacts following Mand Training in Young Children with Autism

ERIC Educational Resources Information Center

Egan, Claire E.; Barnes-Holmes, Dermot

2009-01-01

This study sought to examine the effects of training mands on the emergence of tacts with the same response forms. Results indicated that training adjective sets as mands resulted in the emergence of adjective sets as tacts under modified, but not standard, antecedent conditions. The findings suggested that the apparent functional independence of…

Using a Process Social Skills Training Approach with Adolescents with Mild Intellectual Disabilities in a High School Setting.

ERIC Educational Resources Information Center

O'Reilly, Mark F.; Glynn, Dawn

1995-01-01

A process social skills training approach was implemented and evaluated with two high school students having mild intellectual disabilities and social skills deficits. The intervention package was successful in promoting generalization of targeted social skills from the training setting to the classroom for both students. Participants had…

Bringing the Science of Team Training to School-Based Teams

ERIC Educational Resources Information Center

Benishek, Lauren E.; Gregory, Megan E.; Hodges, Karin; Newell, Markeda; Hughes, Ashley M.; Marlow, Shannon; Lacerenza, Christina; Rosenfield, Sylvia; Salas, Eduardo

2016-01-01

Teams are ubiquitous in schools in the 21st Century; yet training for effective teaming within these settings has lagged behind. The authors of this article developed 5 modules, grounded in the science of team training and adapted from an evidence-based curriculum used in medical settings called TeamSTEPPS®, to prepare instructional and…

Training a Retarded Client's Mother and Teacher through Sequenced Instructions to Establish Self-Feeding.

ERIC Educational Resources Information Center

Kissel, Robert C.; And Others

1980-01-01

A parent and teacher were trained in home and school settings to administer a self-feeding program to a profoundly retarded adult woman. During training, an increase in both the parent and teacher's appropriate use of instruction and attention occurred, and a high stable rate of self-feeding responses developed across settings. (Author)

A youth-led reproductive health program in a university setting.

PubMed

Djalalinia, Shirin; Ramezani Tehrani, Fahimeh; Malekafzali, Hossein; Hashemi, Zeynab; Peykari, Niloofar

2015-01-01

Reproductive health problems affect youths in all countries. There is an urgent need to enhance youths reproductive health services to provide a healthy life for this group. In this regard, the present study aimed to evaluate the Reproductive Health Peer Education Program based on the opinion of university students. This interventional study was conducted in Qazvin University of Medical Sciences through the peer education method. The participants of this study were 24 peer educators who received training in a 40 hour peer educator training course. The peer education program was implemented in the university. In order to evaluate this community- based intervention, 329 students were selected through the stratified sampling method and their opinion was assessed. Descriptive statistical methods were used by SPSS software for data analysis. The results of the study revealed that peer education was accepted by 64.7% (n= 213) of the students, according to their opinion. The educational priorities of the students were as follows: pre-marriage counseling (78%, n= 166); STI/AIDS (17%, n= 36); and contraception (5%, n= 11). The peer education program was recognized as the most required reproductive health service in the university by 55.3% (n= 118) of the students. They believed that the most important duties of the peer educators were: education (33.5%, n= 71); counseling (30.4%, n= 65); referring to a counseling center (21.6%, n= 46) and referring to a therapeutic center (14.5%, n= 31). Also, the students stated that confidentiality (53%, n= 113), suitable communication (26%, n= 55) and sufficient knowledge (21%, n= 45) were desired characteristics for the peer educators. According to the students' opinion, peer education could provide suitable reproductive health services and could also be beneficial for reproductive health promotion and might reinforce positive behaviors in youths. Reproductive health peer- counseling is a sensitive process, and it is best to be conducted under the supervision of specialists.

Patient education and rehabilitation after hip arthroplasty in an Italian spa center: a pilot study on its feasibility

NASA Astrophysics Data System (ADS)

Musumeci, Alfredo; Pranovi, Giulia; Masiero, Stefano

2018-05-01

Nowadays, some spa centers are suitable for providing rehabilitative and preventive treatment in association with traditional spa therapy. This study aims to evaluate the feasibility and the effectiveness of an intensive rehabilitation program after hip arthroplasty in an Italian spa center. Early after total hip arthroplasty for severe osteoarthritis (≤ 10 days after the intervention), 12 consecutive patients (5 males and 7 females) aged between 50 and 85 years were enrolled for this study. All the patients performed a 2-week thermal multimodal rehabilitation program, which consisted of education and physical rehabilitative measures. Patients had 2-h and half/day session of land-based and hydrokinesitherapy (aquatic therapy) consisted in active and passive joint mobilization, respiratory and functional re-education exercises, gait and balance training, resistance exercise, and power training mainly for the upper limb associated to physical therapy modalities (electrotherapy and low-level laser therapy). An educational program was performed to both patients and families. Both before and after the rehabilitation treatment, patients underwent clinical evaluation, hip flexion/abduction range of motion, and Numeric Pain Rating Scale. Harris Hip Score (HHS) and SF-12 questionnaires (physical—PCS-12—and mental health component—MCS-12) were also administered. After the 2-week thermal spa treatment, hip flexion/abduction improved significantly (p < 0.05), but there was no statistically significant reduction in pain (p = 0.350). The HHS score improved significantly from 62.6 ± 12.8 to 82.15 ± 12.7 (p < 0.05), and the PCS-12 score from 36.37 ± 8.4 to 43.61 ± 8.95 (p < 0.05). There was no adverse event during spa treatment. After total hip arthroplasty, patients who underwent an intensive post-acute multimodal rehabilitation program showed an improvement in motor and functional recovery and a positive impact on quality of life. Therefore, we believe that the thermal setting is a suitable place for providing intensive rehabilitative treatment in orthopedic musculoskeletal disability.

Predictive genomics DNA profiling for athletic performance.

PubMed

Kambouris, Marios; Ntalouka, Foteini; Ziogas, Georgios; Maffulli, Nicola

2012-12-01

Genes control biological processes such as muscle, cartilage and bone formation, muscle energy production and metabolism (mitochondriogenesis, lactic acid removal), blood and tissue oxygenation (erythropoiesis, angiogenesis, vasodilatation), all essential in sport and athletic performance. DNA sequence variations in such genes confer genetic advantages that can be exploited, or genetic 'barriers' that could be overcome to achieve optimal athletic performance. Predictive Genomic DNA Profiling for athletic performance reveals genetic variations that may be associated with better suitability for endurance, strength and speed sports, vulnerability to sports-related injuries and individualized nutritional requirements. Knowledge of genetic 'suitability' in respect to endurance capacity or strength and speed would lead to appropriate sport and athletic activity selection. Knowledge of genetic advantages and barriers would 'direct' an individualized training program, nutritional plan and nutritional supplementation to achieving optimal performance, overcoming 'barriers' that results from intense exercise and pressure under competition with minimum waste of time and energy and avoidance of health risks (hypertension, cardiovascular disease, inflammation, and musculoskeletal injuries) related to exercise, training and competition. Predictive Genomics DNA profiling for Athletics and Sports performance is developing into a tool for athletic activity and sport selection and for the formulation of individualized and personalized training and nutritional programs to optimize health and performance for the athlete. Human DNA sequences are patentable in some countries, while in others DNA testing methodologies [unless proprietary], are non patentable. On the other hand, gene and variant selection, genotype interpretation and the risk and suitability assigning algorithms based on the specific Genomic variants used are amenable to patent protection.

Effect of core stability training on throwing velocity in female handball players.

PubMed

Saeterbakken, Atle H; van den Tillaar, Roland; Seiler, Stephen

2011-03-01

The purpose was to study the effect of a sling exercise training (SET)-based core stability program on maximal throwing velocity among female handball players. Twenty-four female high-school handball players (16.6 ± 0.3 years, 63 ± 6 kg, and 169 ± 7 cm) participated and were initially divided into a SET training group (n = 14) and a control group (CON, n = 10). Both groups performed their regular handball training for 6 weeks. In addition, twice a week, the SET group performed a progressive core stability-training program consisting of 6 unstable closed kinetic chain exercises. Maximal throwing velocity was measured before and after the training period using photocells. Maximal throwing velocity significantly increased 4.9% from 17.9 ± 0.5 to 18.8 ± 0.4 m·s in the SET group after the training period (p < 0.01), but was unchanged in the control group (17.1 ± 0.4 vs. 16.9 ± 0.4 m·s). These results suggest that core stability training using unstable, closed kinetic chain movements can significantly improve maximal throwing velocity. A stronger and more stable lumbopelvic-hip complex may contribute to higher rotational velocity in multisegmental movements. Strength coaches can incorporate exercises exposing the joints for destabilization force during training in closed kinetic chain exercises. This may encourage an effective neuromuscular pattern and increase force production and can improve a highly specific performance task such as throwing.

Illumination estimation via thin-plate spline interpolation.

PubMed

Shi, Lilong; Xiong, Weihua; Funt, Brian

2011-05-01

Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.

Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records.

PubMed

Forsyth, Alexander W; Barzilay, Regina; Hughes, Kevin S; Lui, Dickson; Lorenz, Karl A; Enzinger, Andrea; Tulsky, James A; Lindvall, Charlotta

2018-06-01

Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes. The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning. The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes. We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications. Copyright © 2018 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry.

PubMed

Orlandi, Silvia; Reyes Garcia, Carlos Alberto; Bandini, Andrea; Donzelli, Gianpaolo; Manfredi, Claudia

2016-11-01

Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques. Twenty-two acoustical parameters are estimated in more than 3000 cry units from cry recordings of 28 full-term and 10 preterm newborns. Feature extraction is performed through the BioVoice dedicated software tool, developed at the Biomedical Engineering Lab, University of Firenze, Italy. Classification and pattern recognition is based on genetic algorithms for the selection of the best attributes. Training is performed comparing four classifiers: Logistic Curve, Multilayer Perceptron, Support Vector Machine, and Random Forest and three different testing options: full training set, 10-fold cross-validation, and 66% split. Results show that the best feature set is made up by 10 parameters capable to assess differences between preterm and full-term newborns with about 87% of accuracy. Best results are obtained with the Random Forest method (receiver operating characteristic area, 0.94). These 10 cry features might convey important additional information to assist the clinical specialist in the diagnosis and follow-up of possible delays or disorders in the neurologic development due to premature birth in this extremely vulnerable population of patients. The proposed approach is a first step toward an automatic infant cry recognition system for fast and proper identification of risk in preterm babies. Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Modeling suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in North and South America’s coastal waters

USGS Publications Warehouse

Evangelista, Paul H.; Young, Nicholas E.; Schofield, Pamela J.; Jarnevich, Catherine S.

2016-01-01

We used two common correlative species-distribution models to predict suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in the western Atlantic and eastern Pacific Oceans. The Generalized Linear Model (GLM) and the Maximum Entropy (Maxent) model were applied using the Software for Assisted Habitat Modeling. We compared models developed using native occurrences, using non-native occurrences, and using both native and non-native occurrences. Models were trained using occurrence data collected before 2010 and evaluated with occurrence data collected from the invaded range during or after 2010. We considered a total of 22 marine environmental variables. Models built with non-native only or both native and non-native occurrence data outperformed those that used only native occurrences. Evaluation metrics based on the independent test data were highest for models that used both native and non-native occurrences. Bathymetry was the strongest environmental predictor for all models and showed increasing suitability as ocean floor depth decreased, with salinity ranking the second strongest predictor for models that used native and both native and non-native occurrences, indicating low habitat suitability for salinities <30. Our model results also suggest that red lionfish could continue to invade southern latitudes in the western Atlantic Ocean and may establish localized populations in the eastern Pacific Ocean. We reiterate the importance in the choice of the training data source (native, non-native, or native/non-native) used to develop correlative species distribution models for invasive species.

Multiscale 3D Shape Analysis using Spherical Wavelets

PubMed Central

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2013-01-01

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data. PMID:16685992

Multiscale 3D shape analysis using spherical wavelets.

PubMed

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen R

2005-01-01

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.

An evaluation of open set recognition for FLIR images

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2015-05-01

Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.

A review of factors affecting the transfer of sexual and reproductive health training into practice in low and lower-middle income country humanitarian settings.

PubMed

Beek, Kristen; Dawson, Angela; Whelan, Anna

2017-01-01

A lack of access to sexual and reproductive health (SRH) care is the leading cause of morbidity and mortality among displaced women and girls of reproductive age. Efforts to address this public health emergency in humanitarian settings have included the widespread delivery of training programmes to address gaps in health worker capacity for SRH. There remains a lack of data on the factors which may affect the ability of health workers to apply SRH knowledge and skills gained through training programmes in humanitarian contexts. We searched four electronic databases and ten key organizations' websites to locate literature on SRH training for humanitarian settings in low and lower-middle income countries. Papers were examined using content analysis to identify factors which contribute to health workers' capacity to transfer SRH knowledge, skills and attitudes learned in training into practice in humanitarian settings. Seven studies were included in this review. Six research papers focused on the response stage of humanitarian crises and five papers featured the disaster context of conflict. A range of SRH components were addressed including maternal, newborn health and sexual violence. The review identified factors, including appropriate resourcing, organisational support and confidence in health care workers that were found to facilitate the transfer of learning. The findings suggest the presence of factors that moderate the transfer of training at the individual, training, organisational, socio-cultural, political and health system levels. Supportive strategies are necessary to best assist trainees to apply newly acquired knowledge and skills in their work settings. These interventions must address factors that moderate the success of learning transfer. Findings from this review suggest that these are related to the individual trainee, the training program itself and the workplace as well as the broader environmental context. Organisations which provide SRH training for humanitarian emergencies should work to identify the system of moderating factors that affect training transfer in their setting and employ evidence-based strategies to ameliorate these.

Analyzing the Relative Cost, Effectiveness and Suitability of Synchronous Training Versus Traditional On-site Training Approaches (Joint Applied Project)

DTIC Science & Technology

2012-03-01

approaches. 39 LIST OF REFERENCES Acevedo, D. P. (n.d.). Video Teleconferencing: The Future Ahead. Retrieved January 28, 2011, from http://ac...blog.tandberg.com/publicsector/index.php/2009/05/videoconferencing- takes-telework-to-the-next-level/ The eLearning Guild. (2002). The e...Learning Development Time Ration Survey. Retrieved July 15, 2011, from THe Elearning Guild: http://www.elearningguild.com/pdf/1/time%20to%20develop

Army Support to the United States Border Patrol in the 21st Century

DTIC Science & Technology

2011-05-19

and Lieutenant Colonel (Promotable) Clifford J. Weinstein (United States Marine Corps). Thank you for letting me travel this important journey and...Operating Bases in Deming and Playas , New Mexico. The 4-14 CAV was preparing for its deployment to the Joint Readiness Training Center (JRTC) at Fort Polk...write strategic policy. Once a suitable bench of key planners comes back to USBP, they can travel throughout the UCs and train other agents across the

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

PubMed

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

Preconstructing Suspicion and Recasting Masculinity in Preschool Settings

ERIC Educational Resources Information Center

Pruit, John C.

2014-01-01

Although there is literature explaining how female ethnographers negotiate male-dominated research settings, there is a lack of literature explaining how male ethnographers negotiate female-dominated settings. It is, more or less, taken for granted the research settings males choose will be suitable for them. The field of early childhood…

Single versus multiple sets of resistance exercise: a meta-regression.

PubMed

Krieger, James W

2009-09-01

There has been considerable debate over the optimal number of sets per exercise to improve musculoskeletal strength during a resistance exercise program. The purpose of this study was to use hierarchical, random-effects meta-regression to compare the effects of single and multiple sets per exercise on dynamic strength. English-language studies comparing single with multiple sets per exercise, while controlling for other variables, were considered eligible for inclusion. The analysis comprised 92 effect sizes (ESs) nested within 30 treatment groups and 14 studies. Multiple sets were associated with a larger ES than a single set (difference = 0.26 +/- 0.05; confidence interval [CI]: 0.15, 0.37; p < 0.0001). In a dose-response model, 2 to 3 sets per exercise were associated with a significantly greater ES than 1 set (difference = 0.25 +/- 0.06; CI: 0.14, 0.37; p = 0.0001). There was no significant difference between 1 set per exercise and 4 to 6 sets per exercise (difference = 0.35 +/- 0.25; CI: -0.05, 0.74; p = 0.17) or between 2 to 3 sets per exercise and 4 to 6 sets per exercise (difference = 0.09 +/- 0.20; CI: -0.31, 0.50; p = 0.64). There were no interactions between set volume and training program duration, subject training status, or whether the upper or lower body was trained. Sensitivity analysis revealed no highly influential studies, and no evidence of publication bias was observed. In conclusion, 2 to 3 sets per exercise are associated with 46% greater strength gains than 1 set, in both trained and untrained subjects.

AIDS is everybody's business: reaching people at work: programmes in Uganda, India and Zimbabwe.

PubMed

1992-09-01

The AIDS advice of Ajonye Fermina Acuba, a trainer with the Federation of Uganda Employers (FUE), is provided in a serious of questions and answers. Other workplace experiences in Zimbabwe and India are reported. Questions were asked about the nature of the AIDS program in Uganda, the secrets of the program's success, the experiences of educators, and progress since 1988. FUE is nationally active with 150 member companies and 900 volunteer employees trained for peer education. Success was tied to proper selection of trainers, who were picked by union representatives and department heads. Training was over 3 days. 75% are men, but training is conducted for men and women together. success is attributed to the type of training and followup. Common problems overcome during training concern talking about changing sexual behavior. Employees initially believe educational efforts are only to promote condoms, but when risk reduction through any method is emphasized, the barriers are removed. Educators talk repeatedly with interested persons. Trainers requested better training to handle "first aid" situations before referral. Managers need specialized training programs. In Zimbabwe, commercial farm owners are engaging in AIDS educational activities through the Commercial Farmers' Union. 4500 farm owners and managers are represented. The program has operated since 1986 by providing volunteer coordinators from branch associations to initiate discussion with village leaders and later the community. AIDS committees are set up at the village level. Education focused on the fatal nature of the disease and lack of cure, the relationship with sexually transmitted diseases (STDS) which transmission can be prevented with condoms, the danger to women of sterility from STDs, and the price of not preventing through education is having to care for relatives' children. Stigma has been thus reduced. In India, the AIDS Research Foundation of India (AFRI), which is financed by local companies, reports that company directors listen to the message about caring for their workers better by meeting at social and civic clubs. Education focuses on the impact on business profits and the solution of establishing prevention programs. Companies are encouraged to work together. AFRI trains staff who are placed in Company personnel offices to encourage education within the Company and find suitable solutions for each company.

Challenging behaviour: an action plan for education and training.

PubMed

Farrell, Gerald A; Salmon, Peter

Nurses and other health care staff frequently encounter a range of aggressive and other 'challenging behaviours' at work from clients and colleagues. In response to staff concerns, an abundance of state and national policies are now available but it is left up to individual employers to decide how best to implement them at a local level. In this paper we offer an education and training model which is conceptually sound, practical in application, and suitable for health care staff at different levels in the organisation. The importance of understanding challenging behaviour from an interactional perspective, and the educational principles on which training should be founded, are discussed. Finally, the cost of training and the need for program evaluation are considered.

Residents' perceived needs in communication skills training across in- and outpatient clinical settings.

PubMed

Junod Perron, Noelle; Sommer, Johanna; Hudelson, Patricia; Demaurex, Florence; Luthy, Christophe; Louis-Simonet, Martine; Nendaz, Mathieu; De Grave, Willem; Dolmans, Diana; Van der Vleuten, Cees

2009-05-01

Residents' perceived needs in communication skills training are important to identify before designing context-specific training programmes, since learrners' perceived needs can influence the effectiveness of training. To explore residents' perceptions of their training needs and training experiences around communication skills, and whether these differ between residents training in inpatient and outpatient clinical settings. Four focus groups (FG) and a self-administered questionnaire were conducted with residents working in in- and outpatient medical service settings at a Swiss University Hospital. Focus groups explored residents' perceptions of their communication needs, their past training experiences and suggestions for future training programmes in communication skills. Transcripts were analysed in a thematic way using qualitative analytic approaches. All residents from both settings were asked to complete a questionnaire that queried their sociodemographics and amount of prior training in communication skills. In focus groups, outpatient residents felt that communication skills were especially useful in addressing chronic diseases and social issues. In contrast, inpatient residents emphasized the importance of good communication skills for dealing with family conflicts and end-of-life issues. Felt needs reflected residents' differing service priorities: outpatient residents saw the need for skills to structure the consultation and explore patients' perspectives in order to build therapeutic alliances, whereas inpatient residents wanted techniques to help them break bad news, provide information and increase their own well-being. The survey's overall response rate was 56%. Its data showed that outpatient residents received more training in communication skills and more of them than inpatient residents considered communication skills training to be useful (100% vs 74%). Outpatient residents' perceived needs in communication skills were more patient-centered than the needs perceived by inpatient residents. Residents' perceived needs for communication skills may differ not only because of their differing service priorities but also because of differences in their previous experiences with communication skills training. These factors should be taken into account when designing a training programme in communication skills.

Vocational Rehabilitation for Persons with Rheumatoid Arthritis.

ERIC Educational Resources Information Center

Allaire, Saralynn H.

1998-01-01

Useful vocational rehabilitation strategies for persons with rheumatoid arthritis include (1) management of symptoms and reduction of energy demand; (2) reasonable job accommodations; (3) identification of suitable jobs and necessary training; and (4) enhancement of self-advocacy skills. (SK)

An Analysis of Training Focused on Improving SMART Goal Setting for Specific Employee Groups

ERIC Educational Resources Information Center

Worden, Jeannie M.

2014-01-01

This quantitative study examined the proficiency of employee SMART goal setting following the intervention of employee SMART goal setting training. Current challenges in higher education substantiate the need for employees to align their performance with the mission, vision, and strategic directions of the organization. A performance management…

Educational Preparation and Experiences in the Industrial-Occupational Setting: A Qualitative Study of Athletic Training Graduates' Perspectives

ERIC Educational Resources Information Center

Schilling, Jim F.

2011-01-01

Context: The industrial-occupational setting provides a workplace of substantial potential for the athletic training graduate. Acquiring input from entry-level athletic trainers (ATs) pertaining to experiences, knowledge, and skills necessary to be successful in the industrial-occupational setting is critical information for future Athletic…

Competence feedback improves CBT competence in trainee therapists: A randomized controlled pilot study.

PubMed

Weck, Florian; Kaufmann, Yvonne M; Höfling, Volkmar

2017-07-01

The development and improvement of therapeutic competencies are central aims in psychotherapy training; however, little is known about which training interventions are suitable for the improvement of competencies. In the current pilot study, the efficacy of feedback regarding therapeutic competencies was investigated in cognitive behavioural therapy (CBT). Totally 19 trainee therapists and 19 patients were allocated randomly to a competence feedback group (CFG) or control group (CG). Two experienced clinicians and feedback providers who were blind to the treatment conditions independently evaluated therapeutic competencies on the Cognitive Therapy Scale at five treatment times (i.e., at Sessions 1, 5, 9, 13, and 17). Whereas CFG and CG included regular supervision, only therapists in the CFG additionally received written qualitative and quantitative feedback regarding their demonstrated competencies in conducting CBT during treatment. We found a significant Time × Group interaction effect (η² = .09), which indicates a larger competence increase in the CFG in comparison to the CG. Competence feedback was demonstrated to be suitable for the improvement of therapeutic competencies in CBT. These findings may have important implications for psychotherapy training, clinical practice, and psychotherapy research. However, further research is necessary to ensure the replicability and generalizability of the findings.

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

NASA Astrophysics Data System (ADS)

Li, Manchun; Ma, Lei; Blaschke, Thomas; Cheng, Liang; Tiede, Dirk

2016-07-01

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.

Future enhanced clinical role of pharmacists in Emergency Departments in England: multi-site observational evaluation.

PubMed

Hughes, Elizabeth; Terry, David; Huynh, Chi; Petridis, Konstantinos; Aiello, Matthew; Mazard, Louis; Ubhi, Hirminder; Terry, Alex; Wilson, Keith; Sinclair, Anthony

2017-08-01

Background There are concerns about maintaining appropriate clinical staffing levels in Emergency Departments. Pharmacists may be one possible solution. Objective To determine if Emergency Department attendees could be clinically managed by pharmacists with or without advanced clinical practice training. Setting Prospective 49 site cross-sectional observational study of patients attending Emergency Departments in England. Method Pharmacist data collectors identified patient attendance at their Emergency Department, recorded anonymized details of 400 cases and categorized each into one of four possible options: cases which could be managed by a community pharmacist; could be managed by a hospital pharmacist independent prescriber; could be managed by a hospital pharmacist independent prescriber with additional clinical training; or medical team only (unsuitable for pharmacists to manage). Impact indices sensitive to both workload and proportion of pharmacist manageable cases were calculated for each clinical group. Main outcome measure Proportion of cases which could be managed by a pharmacist. Results 18,613 cases were observed from 49 sites. 726 (3.9%) of cases were judged suitable for clinical management by community pharmacists, 719 (3.9%) by pharmacist prescribers, 5202 (27.9%) by pharmacist prescribers with further training, and 11,966 (64.3%) for medical team only. Impact Indices of the most frequent clinical groupings were general medicine (13.18) and orthopaedics (9.69). Conclusion The proportion of Emergency Department cases that could potentially be managed by a pharmacist was 36%. Greatest potential for pharmacist management was in general medicine and orthopaedics (usually minor trauma). Findings support the case for extending the clinical role of pharmacists.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

The effects of training with loads that maximise power output and individualised repetitions vs. traditional power training

PubMed Central

Moya-Ramón, M.; Hernández-Davó, J. L.; Fernandez-Fernandez, J.; Sabido, R.

2017-01-01

Background It has been suggested that strength training effects (i.e. neural or structural) vary, depending on the total repetitions performed and velocity loss in each training set. Purpose The aim of this study is to compare the effects of two training programmes (i.e. one with loads that maximise power output and individualised repetitions, and the other following traditional power training). Methods Twenty-five males were divided into three groups (optimum power [OP = 10], traditional training [TT = 9] and control group [CG = 6]). The training load used for OP was individualised using loads that maximised power output (41.7% ± 5.8 of one repetition maximum [1RM]) and repetitions at maximum power (4 to 9 repetitions, or ‘reps’). Volume (sets x repetitions) was the same for both experimental groups, while intensity for TT was that needed to perform only 50% of the maximum number of possible repetitions (i.e. 61.1%–66.6% of 1RM). The training programme ran over 11 weeks (2 sessions per week; 4–5 sets per session; 3-minute rests between sets), with pre-, intermediate and post-tests which included: anthropometry, 1RM, peak power output (PPO) with 30%, 40% and 50% of 1RM in the bench press throw, and salivary testosterone (ST) and cortisol (SC) concentrations. Rate of perceived exertion (RPE) and power output were recorded in all sessions. Results Following the intermediate test, PPO was increased in the OP group for each load (10.9%–13.2%). Following the post-test, both experimental groups had increased 1RM (11.8%–13.8%) and PPO for each load (14.1%–19.6%). Significant decreases in PPO were found for the TT group during all sets (4.9%–15.4%), along with significantly higher RPE (37%). Conclusion OP appears to be a more efficient method of training, with less neuromuscular fatigue and lower RPE. PMID:29053725

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction.

PubMed

Wu, X; Lund, M S; Sun, D; Zhang, Q; Su, G

2015-10-01

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less-related animals is favourable for reliability of genomic prediction. © 2015 Blackwell Verlag GmbH.

Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Wagstaff, Kiri L.

2011-01-01

This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines high-confidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensor-network environments.

New public QSAR model for carcinogenicity

PubMed Central

2010-01-01

Background One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. Results Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. Conclusion Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible. PMID:20678182

Training Decisions Technology Analysis

DTIC Science & Technology

1992-06-01

4.5.1 Relational Data Base Management 69 4.5.2 TASCS Data Content 69 4.5.3 Relationships with TDS 69 4.6 Other Air Force Modeling R&D 70 4.6.1 Time ...executive decision making were first developed by M. S. Scott Morton in the early 1970’s who, at that time , termed them " management decision systems" (Scott...Allocations to Training Settings o Managers ’ Preferences for Task Allocations to Training Settings o Times Required to Training Tasks in Various

Training instructional skills with paraprofessional service providers at a community-based habilitation setting.

PubMed

Wood, Amanda L; Luiselli, James K; Harchik, Alan E

2007-11-01

The present study evaluates a training program with paraprofessional service providers at a community-based habilitation setting. Four staff were taught to implement alternative and augmentative communication instruction with an adult who had autism and mental retardation through a combination of instruction, demonstration, behavior rehearsal, and performance feedback. Training was conducted under natural conditions at the adult's group home residence. Three of the four staff were able to maintain near-100% instructional accuracy following initial training. The results add to the limited research literature concerning community-based training of direct-care personnel.

The Role of Malaria Microscopy Training and Refresher Training Courses in Malaria Control Program in Iran during 2001 - 2011.

PubMed

Nateghpour, M; Edrissian, Ghh; Raeisi, A; Motevalli-Haghi, A; Farivar, L; Mohseni, Gh; Rahimi-Froushani, A

2012-01-01

Malaria is still one of the most important infectious diseases in the world. The disease also is a public health problem in south and southeast of Iran. This study programmed to show the correlation between regular malaria microscopy training and refresher training courses and control of malaria in Iran. Three types of training courses were conducted in this programme including; five - day, ten - day and bimonthly training courses. Each of the training courses contained theoretical and practical sections and training impact was evaluated by practical examination and multiple-choice quizzes through pre and post tests. Distribution pattern of the participants in the training and refresher training courses showed that the most participants were from Sistan & Baluchistan and Hormozgan provinces where malaria is endemic and most cases of the infection come out from these malarious areas. A total of 695 identified individuals were participated in the training courses. A significant conversely correlation was found between conducting malaria microscopy training courses and annual malaria cases in Iran. Conducting a suitable programme for malaria microscopy training and refresher training plays an important role in the control of malaria in endemic areas. Obviously, the decrease of malaria cases in Iran has been achieved due to some activities that malaria diagnosis training was one of them.

[Endurance training in hypertension is effective. Which form of sports, frequency and intensity of training? When should adjuvant chemotherapy be considered?].

PubMed

Ketelhut, R G

1998-12-10

Physical activity in the form of endurance training is highly recommendable for hypertensives. Both suitable and unsuitable forms of sports are identified. From various points of view, two one-hour sessions per week would appear to be optimal. The intensity of the activity should be oriented to the heart rate, and, for safety's sake, prior ergometric evaluation should be carried out. As a rule of thumb, the heart rate should not exceed 70% of the maximum rate during exercise. If the blood pressure should nevertheless increase too much, appropriate pharmacological treatment is indicated.

Accreditation in radiation protection for cardiologists and interventionalists.

PubMed

Vano, E; Gonzalez, L

2005-01-01

Training in radiation protection is widely recognised as one of the basic components of optimisation programmes for medical exposures. Occupational and patient radiation risks in interventional radiology can be quite high and international bodies have shown concern on this item. Following recommendations of the International Commission on Radiological Protection and in accordance with the European Directive on medical exposures, some initiatives for training in radiation protection took place in Spain and Luxembourg. These provided practitioners of interventional radiology adequate theoretical and practical training in radiation protection. The main outcome of the pilot courses organised to this end is discussed, concluding its suitability to implement the European Directive in practice.

Effects of a Modified German Volume Training Program on Muscular Hypertrophy and Strength.

PubMed

Amirthalingam, Theban; Mavros, Yorgi; Wilson, Guy C; Clarke, Jillian L; Mitchell, Lachlan; Hackett, Daniel A

2017-11-01

Amirthalingam, T, Mavros, Y, Wilson, GC, Clarke, JL, Mitchell, L, and Hackett, DA. Effects of a modified German volume training program on muscular hypertrophy and strength. J Strength Cond Res 31(11): 3109-3119, 2017-German Volume Training (GVT), or the 10 sets method, has been used for decades by weightlifters to increase muscle mass. To date, no study has directly examined the training adaptations after GVT. The purpose of this study was to investigate the effect of a modified GVT intervention on muscular hypertrophy and strength. Nineteen healthy men were randomly assign to 6 weeks of 10 or 5 sets of 10 repetitions for specific compound resistance exercises included in a split routine performed 3 times per week. Total and regional lean body mass, muscle thickness, and muscle strength were measured before and after the training program. Across groups, there were significant increases in lean body mass measures, however, greater increases in trunk (p = 0.043; effect size [ES] = -0.21) and arm (p = 0.083; ES = -0.25) lean body mass favored the 5-SET group. No significant increases were found for leg lean body mass or measures of muscle thickness across groups. Significant increases were found across groups for muscular strength, with greater increases in the 5-SET group for bench press (p = 0.014; ES = -0.43) and lat pull-down (p = 0.003; ES = -0.54). It seems that the modified GVT program is no more effective than performing 5 sets per exercise for increasing muscle hypertrophy and strength. To maximize hypertrophic training effects, it is recommended that 4-6 sets per exercise be performed, as it seems gains will plateau beyond this set range and may even regress due to overtraining.

3D active shape models of human brain structures: application to patient-specific mesh generation

NASA Astrophysics Data System (ADS)

Ravikumar, Nishant; Castro-Mateos, Isaac; Pozo, Jose M.; Frangi, Alejandro F.; Taylor, Zeike A.

2015-03-01

The use of biomechanics-based numerical simulations has attracted growing interest in recent years for computer-aided diagnosis and treatment planning. With this in mind, a method for automatic mesh generation of brain structures of interest, using statistical models of shape (SSM) and appearance (SAM), for personalised computational modelling is presented. SSMs are constructed as point distribution models (PDMs) while SAMs are trained using intensity profiles sampled from a training set of T1-weighted magnetic resonance images. The brain structures of interest are, the cortical surface (cerebrum, cerebellum & brainstem), lateral ventricles and falx-cerebri membrane. Two methods for establishing correspondences across the training set of shapes are investigated and compared (based on SSM quality): the Coherent Point Drift (CPD) point-set registration method and B-spline mesh-to-mesh registration method. The MNI-305 (Montreal Neurological Institute) average brain atlas is used to generate the template mesh, which is deformed and registered to each training case, to establish correspondence over the training set of shapes. 18 healthy patients' T1-weightedMRimages form the training set used to generate the SSM and SAM. Both model-training and model-fitting are performed over multiple brain structures simultaneously. Compactness and generalisation errors of the BSpline-SSM and CPD-SSM are evaluated and used to quantitatively compare the SSMs. Leave-one-out cross validation is used to evaluate SSM quality in terms of these measures. The mesh-based SSM is found to generalise better and is more compact, relative to the CPD-based SSM. Quality of the best-fit model instance from the trained SSMs, to test cases are evaluated using the Hausdorff distance (HD) and mean absolute surface distance (MASD) metrics.

The "global surgeon": is it time for modifications in the American surgical training paradigm?

PubMed

Ginwalla, Rashna F; Rustin, Rudolph B

2015-01-01

"Global surgery" is becoming an increasingly popular concept not only for new trainees, but also for established surgeons. The need to provide surgical care in low-resource settings is laudable, but the American surgical training system currently does not impart the breadth of skills required to provide quality care. We propose one possible model for a surgical fellowship program that provides those trainees who desire to practice in these settings a comprehensive experience that encompasses not only broad technical skills but also the opportunity to engage in policy and programmatic development and implementation. This is a descriptive commentary based on personal experience and a review of the literature. The proposed model is 2 years long, and can either be done after general surgery training as an additional "global surgery" fellowship or as part of a 3 + 2 general surgery + global surgery system. It would incorporate training in general surgery as well as orthopedics, urology, obstetrics & gynecology, neurosurgery, plastics & reconstructive surgery, as well as dedicated time for health systems training. Incorporating such training in a low-resource setting would be a requirement of such a program, in order to obtain field experience. Global surgery is a key word these days in attracting young trainees to academic surgical residency programs, yet they are subsequently inadequately trained to provide the required surgical services in these low-resource settings. Dedicated programmatic changes are required to allow those who choose to practice in these settings to obtain the full breadth of training needed to become safe, competent surgeons in such environments. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Interoceptive conditioning in rats: effects of using a single training dose or a set of 5 different doses of nicotine.

PubMed

Pittenger, Steven T; Bevins, Rick A

2013-12-01

Interoceptive conditioning contributes to the tenacity of nicotine dependence. Previous research investigating nicotine as an interoceptive stimulus has typically employed administration of a single training dose of nicotine over an extended time. This approach has allowed for careful study of the nicotine stimulus. In humans, the nicotine stimulus is unlikely to be fixed across learning episodes. Thus, from a translational perspective, systematic variation of nicotine dose in training might better approximate interoceptive conditioning in humans. Notably, training with a class or set of discrete exteroceptive stimuli (e.g., different pictures of cars) produces interesting behavioral differences relative to training with a single stimulus. The present study sought to determine whether similar differences would occur if a set of nicotine stimuli were used in place of a single dose. To investigate this question, one group of male Sprague-Dawley rats was trained on a discriminated goal-tracking task with a set of nicotine doses (0.05, 0.125, 0.2, 0.275, and 0.35mg/kg). A second group received the standard protocol of training with a single nicotine dose (0.2mg/kg). On each nicotine session, there was intermittent access to liquid sucrose (26%) in a conditioning chamber. On intermixed saline sessions, sucrose was withheld. We examined acquisition, subsequent extinction, transfer of extinction, nicotine generalization, and mecamylamine blockade. Both groups reliably discriminated between nicotine and saline sessions, were sensitive to non-reinforcement, displayed transfer of extinction, demonstrated dose-dependent nicotine generalization, and responding was blocked by mecamylamine. There were no significant differences between the two groups. The unique nature of an interoceptive pharmacological stimulus and the challenges posed for studying the impact of training with a set of interoceptive stimuli are discussed. © 2013.

Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

NASA Astrophysics Data System (ADS)

Liu, J.; Lan, T.; Qin, H.

2017-10-01

Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class-imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class-imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class-balanced effect of Time-Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry-INTerferometry (POINT) system.

Effect of Single Setting versus Multiple Setting Training on Learning to Shop in a Department Store.

ERIC Educational Resources Information Center

Westling, David L.; And Others

1990-01-01

Fifteen students, age 13-21, with moderate to profound mental retardation received shopping skills training in either 1 or 3 department stores. A study of operational behaviors, social behaviors, number of settings in which criterion performance was achieved, and number of sessions required to achieve criterion found no significant differences…

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

PubMed

Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew G; Blum, Michael; Mongan, John

2017-02-01

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

Multi-model blending

DOEpatents

Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

2016-10-18

A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

The Effect of Different Resistance Training Load Schemes on Strength and Body Composition in Trained Men

PubMed Central

Lopes, Charles Ricardo; Aoki, Marcelo Saldanha; Crisp, Alex Harley; de Mattos, Renê Scarpari; Lins, Miguel Alves; da Mota, Gustavo Ribeiro; Schoenfeld, Brad Jon; Marchetti, Paulo Henrique

2017-01-01

Abstract The purpose of this study was to evaluate the impact of moderate-load (10 RM) and low-load (20 RM) resistance training schemes on maximal strength and body composition. Sixteen resistance-trained men were randomly assigned to 1 of 2 groups: a moderate-load group (n = 8) or a low-load group (n = 8). The resistance training schemes consisted of 8 exercises performed 4 times per week for 6 weeks. In order to equate the number of repetitions performed by each group, the moderate load group performed 6 sets of 10 RM, while the low load group performed 3 sets of 20 RM. Between-group differences were evaluated using a 2-way ANOVA and independent t-tests. There was no difference in the weekly total load lifted (sets × reps × kg) between the 2 groups. Both groups equally improved maximal strength and measures of body composition after 6 weeks of resistance training, with no significant between-group differences detected. In conclusion, both moderate-load and low-load resistance training schemes, similar for the total load lifted, induced a similar improvement in maximal strength and body composition in resistance-trained men. PMID:28828088

Training in interprofessional collaboration

PubMed Central

Paré, Line; Maziade, Jean; Pelletier, Francine; Houle, Nathalie; Iloko-Fundi, Maximilien

2012-01-01

Abstract Problem addressed A number of agencies that accredit university health sciences programs recently added standards for the acquisition of knowledge and skills with respect to interprofessional collaboration. Within primary care settings there are no practical training programs that allow students from different disciplines to develop competencies in this area. Objective of the program The training program was developed within family medicine units affiliated with Université Laval in Quebec for family medicine residents and trainees from various disciplines to develop competencies in patient-centred, interprofessional collaborative practice in primary care. Program description Based on adult learning theories, the program was divided into 3 phases—preparing family medicine unit professionals, training preceptors, and training the residents and trainees. The program’s pedagogic strategies allowed participants to learn with, from, and about one another while preparing them to engage in contemporary primary care practices. A combination of quantitative and qualitative methods was used to evaluate the implementation process and the immediate results of the training program. Conclusion The training program had a positive effect on both the clinical settings and the students. Preparation of clinical settings is an important issue that must be considered when planning practical interprofessional training. PMID:22611607

Decision Document for the Storm Water Outfalls/Industrial Wastewater Treatment Plant, Pesticide Rinse Area, Old Fire Fighting Training Pit, Illicit PCB Dump Site, and the Battery Acid Pit Fort Lewis, Washington

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

Cantrell, Kirk J; Liikala, Terry L; Strenge, Dennis L

PNNL conducted independent site evaluations for four sites at Fort Lewis, Washington, to determine their suitability for closure on behalf of the installation. These sites were recommended for ''No Further Action'' by previous investigators and included the Storm Water Outfalls/Industrial Waste Water Treatment Plant (IWTP), the Pesticide Rinse Area, the Old Fire Fighting Training Pit, and the Illicit PCB Dump Site.

Decision Document for the Storm Water Outfalls/Industrial Wastewater Treatment Plant, Pesticide Rinse Area, Old Fire Fighting Training Pit, Illicit PCB Dump Site, and the Battery Acid Pit Fort Lewis, Washington

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

Cantrell, Kirk J.; Liikala, Terry L.; Strenge, Dennis L.

PNNL conducted independent site evaluations for four sites at Fort Lewis, Washington, to determine their suitability for closure on behalf of the installation. These sites were recommended for "No Further Action" by previous invesitgators and included the Storm Water Outfalls/Industrial Waste Water Treatment Plant (IWTP), the Pesticide Rinse Area, the Old Fire Fighting Training Pit, and the Illicit PCB Dump Site.

Evaluation of Material Nonlinearities Using Rectangular Pulse Trains for Excitation

NASA Astrophysics Data System (ADS)

Chaziachmetovas, Andrius; Svilainis, Linas; Kybartas, Darius; Aleksandrovas, Arturas; Liaukonis, Dobilas

Aim of the presented investigation was to evaluate the suitability of the rectangular pulse trains for nonlinear material parameters study. It was assumed that if duty cycle of the excitation is 50% then second harmonic is significantly reduced. Excitation signal frequency was fixed to the A/D sampling frequency and signal carefully gated to reduce the signal leak into neighbouring frequency bins. Sine wave correlation was used to extract the harmonics content. Results of nonlinear parameters measurement for several materials are given as performance comparison.

Effects of cluster vs. traditional plyometric training sets on maximal-intensity exercise performance.

PubMed

Asadi, Abbas; Ramírez-Campillo, Rodrigo

2016-01-01

The aim of this study was to compare the effects of 6-week cluster versus traditional plyometric training sets on jumping ability, sprint and agility performance. Thirteen college students were assigned to a cluster sets group (N=6) or traditional sets group (N=7). Both training groups completed the same training program. The traditional group completed five sets of 20 repetitions with 2min of rest between sets each session, while the cluster group completed five sets of 20 [2×10] repetitions with 30/90-s rest each session. Subjects were evaluated for countermovement jump (CMJ), standing long jump (SLJ), t test, 20-m and 40-m sprint test performance before and after the intervention. Both groups had similar improvements (P<0.05) in CMJ, SLJ, t test, 20-m, and 40-m sprint. However, the magnitude of improvement in CMJ, SLJ and t test was greater for the cluster group (effect size [ES]=1.24, 0.81 and 1.38, respectively) compared to the traditional group (ES=0.84, 0.60 and 0.55). Conversely, the magnitude of improvement in 20-m and 40-m sprint test was greater for the traditional group (ES=1.59 and 0.96, respectively) compared to the cluster group (ES=0.94 and 0.75, respectively). Although both plyometric training methods improved lower body maximal-intensity exercise performance, the traditional sets methods resulted in greater adaptations in sprint performance, while the cluster sets method resulted in greater jump and agility adaptations. Copyright © 2016 The Lithuanian University of Health Sciences. Production and hosting by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Postdoctoral training in posttraumatic stress disorder research.

PubMed

Sloan, Denise M; Vogt, Dawne; Wisco, Blair E; Keane, Terence M

2015-03-01

Postdoctoral training is increasingly common in the field of psychology. Although many individuals pursue postdoctoral training in psychology, guidelines for research training programs at this level do not exist. The rapid advances in the field, particularly with respect to genetics, neuroimaging, and data analytic approaches, require clinical scientists to possess knowledge and expertise across a broad array of areas. Postdoctoral training is often needed to acquire such a skill set. This paper describes a postdoctoral training program designed for individuals pursuing academic careers in traumatic stress disorders research. In this paper, we describe the structure of our training program, challenges we have faced during the 15 years of its existence, and how we have addressed these challenges. We conclude with a presentation of outcome data for the training program and a discussion of how training programs in other settings might be structured. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Effects of dialectical behavior therapy skills training on outcomes for mental health staff in a child and adolescent residential setting

PubMed Central

Haynos, Ann F.; Fruzzetti, Alan E.; Anderson, Calli; Briggs, David; Walenta, Jason

2017-01-01

Training in Dialectical Behavior Therapy (DBT) skills coaching is desirable for staff in psychiatric settings, due to the efficacy of DBT in treating difficult patient populations. In such settings, training resources are typically limited, and staff turnover is high, necessitating brief training. This study evaluated the effects of a brief training in DBT skills coaching for nursing staff working in a child and adolescent psychiatric residential program. Nursing staff (n = 22) completed assessments of DBT skill knowledge, burnout, and stigma towards patients with borderline personality disorder (BPD) before and after a six-week DBT skills coaching training. Repeated measure ANOVAs were conducted to examine changes on all measures from pre- to post- treatment and hierarchical linear regressions to examine relationships between pre- training DBT knowledge, burnout, and BPD stigma and these same measures post-training. The brief DBT skill coaching training significantly increased DBT knowledge (p = .007) and decreased staff personal (p = .02) and work (p = .03) burnout and stigma towards BPD patients (p = .02). Burnout indices and BPD stigma were highly correlated at both time points (p < .001); however, while pre-training BPD stigma significantly predicted post-training client burnout (p = .04), pre-training burnout did not predict post-training BPD stigma. These findings suggest that brief training of psychiatric nursing staff in DBT skills and coaching techniques can result in significant benefits, including reduced staff burnout and stigma toward patients with BPD-related problems, and that reducing BPD stigma may particularly promote lower burnout. PMID:28751925

Effects of dialectical behavior therapy skills training on outcomes for mental health staff in a child and adolescent residential setting.

PubMed

Haynos, Ann F; Fruzzetti, Alan E; Anderson, Calli; Briggs, David; Walenta, Jason

2016-04-01

Training in Dialectical Behavior Therapy (DBT) skills coaching is desirable for staff in psychiatric settings, due to the efficacy of DBT in treating difficult patient populations. In such settings, training resources are typically limited, and staff turnover is high, necessitating brief training. This study evaluated the effects of a brief training in DBT skills coaching for nursing staff working in a child and adolescent psychiatric residential program. Nursing staff ( n = 22) completed assessments of DBT skill knowledge, burnout, and stigma towards patients with borderline personality disorder (BPD) before and after a six-week DBT skills coaching training. Repeated measure ANOVAs were conducted to examine changes on all measures from pre- to post- treatment and hierarchical linear regressions to examine relationships between pre- training DBT knowledge, burnout, and BPD stigma and these same measures post-training. The brief DBT skill coaching training significantly increased DBT knowledge ( p = .007) and decreased staff personal ( p = .02) and work ( p = .03) burnout and stigma towards BPD patients ( p = .02). Burnout indices and BPD stigma were highly correlated at both time points ( p < .001); however, while pre-training BPD stigma significantly predicted post-training client burnout ( p = .04), pre-training burnout did not predict post-training BPD stigma. These findings suggest that brief training of psychiatric nursing staff in DBT skills and coaching techniques can result in significant benefits, including reduced staff burnout and stigma toward patients with BPD-related problems, and that reducing BPD stigma may particularly promote lower burnout.

Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

NASA Astrophysics Data System (ADS)

Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.

2013-03-01

Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

Traditional and pyramidal resistance training systems improve muscle quality and metabolic biomarkers in older women: A randomized crossover study.

PubMed

Ribeiro, Alex S; Schoenfeld, Brad J; Souza, Mariana F; Tomeleri, Crisieli M; Venturini, Danielle; Barbosa, Décio S; Cyrino, Edilson S

2016-06-15

The purpose of this study was to compare the effect of RT performed in a pyramid (PR) and traditional (TD) straight set training system on muscle quality and metabolic biomarkers in older women. Twenty-five physically independent older women (67.6±5.1years, 65.9±11.1kg, 154.7±5.8cm) performed a RT program in TD and PR training systems in a balanced crossover design. Measurements of muscle quality, serum levels of C-reactive protein (CRP), glucose (GLU), total cholesterol, high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and triglycerides (TG) were obtained at different moments. The TD program consisted of 3 sets of 8-12 repetitions maximum (RM) with a constant weight for the 3 sets, whereas the PR training consisted of 3 sets of 12/10/8 RM with incremental weight for each set. The training was performed in 2 phases of 8weeks each, with a 12-week washout period between phases. Significant (P<0.05) improvements were observed in both groups for muscle quality (TD=+8.6% vs. PR=+6.8%), GLU (TD=-4.5% vs. PR=-1.9%), TG (TD=-18.0% vs. PR=-11.7%), HDL-C (TD=+10.6 vs. PR=+7.8%), LDL-C (TD=-23.3% vs. PR=-21.0%), and CRP (TD=-19.4% vs. PR=-14.3%) with no differences between training systems. These results suggest that RT improves muscle quality and metabolic biomarkers of older women independently of the training system. Copyright © 2016 Elsevier Inc. All rights reserved.

Parent Training on Generalized Use of Behavior Analytic Strategies for Decreasing the Problem Behavior of Children with Autism Spectrum Disorder: A Data-Based Case Study

ERIC Educational Resources Information Center

Crone, Regina M.; Mehta, Smita Shukla

2016-01-01

Setting variables such as location of parent training, programming with common stimuli, generalization of discrete responses to non-trained settings, and subsequent reduction in child problem behavior may influence the effectiveness of interventions. The purpose of this study was to evaluate the effectiveness of home-versus clinic-based training…

Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.

PubMed

Ngo, Tuan Anh; Lu, Zhi; Carneiro, Gustavo

2017-01-01

We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.