A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Predictive accuracy of original and recalibrated Framingham risk score in the Swiss population.

OBJECTIVE: To compare the predictive accuracy of the original and recalibrated Framingham risk function on current morbidity from coronary heart disease (CHD) and mortality data from the Swiss population. METHODS: Data from the CoLaus study, a cross-sectional, population-based study conducted between 2003 and 2006 on 5,773 participants aged 35-74 without CHD were used to recalibrate the Framingham risk function. The predicted number of events from each risk function were compared with those issued from local MONICA incidence rates and official mortality data from Switzerland. RESULTS: With the original risk function, 57.3%, 21.2%, 16.4% and 5.1% of men and 94.9%, 3.8%, 1.2% and 0.1% of women were at very low (<6%), low (6-10%), intermediate (10-20%) and high (>20%) risk, respectively. With the recalibrated risk function, the corresponding values were 84.7%, 10.3%, 4.3% and 0.6% in men and 99.5%, 0.4%, 0.0% and 0.1% in women, respectively. The number of CHD events over 10 years predicted by the original Framingham risk function was 2-3 fold higher than predicted by mortality+case fatality or by MONICA incidence rates (men: 191 vs. 92 and 51 events, respectively). The recalibrated risk function provided more reasonable estimates, albeit slightly overestimated (92 events, 5-95th percentile: 26-223 events); sensitivity analyses showed that the magnitude of the overestimation was between 0.4 and 2.2 in men, and 0.7 and 3.3 in women. CONCLUSION: The recalibrated Framingham risk function provides a reasonable alternative to assess CHD risk in men, but not in women.

Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

The glasses of the transept's rosette of the Cathedral of Tarragona: characterization, classification and decay.

The glasses of the rosette forming the main window of the transept of the Gothic Cathedral of Tarragona have been characterised by means of SEM/EDS, XRD, FTIR and electronic microprobe. The multivariate statistical treatment of these data allow to establish a classification of the samples forming groups having an historical significance and reflecting ancient restorations. Furthermore, the decay patterns and mechanisms have been determined and the weathering by-products characterised. It has been demonstrated a clear influence of the bioactivity in the decay of these glasses, which activity is partially controlled by the chemical composition of the glasses.

The glasses of the transept's rosette of the Cathedral of Tarragona: characterization, classification and decay.

The glasses of the rosette forming the main window of the transept of the Gothic Cathedral of Tarragona have been characterised by means of SEM/EDS, XRD, FTIR and electronic microprobe. The multivariate statistical treatment of these data allow to establish a classification of the samples forming groups having an historical significance and reflecting ancient restorations. Furthermore, the decay patterns and mechanisms have been determined and the weathering by-products characterised. It has been demonstrated a clear influence of the bioactivity in the decay of these glasses, which activity is partially controlled by the chemical composition of the glasses.

Iowa Roads and Streets Functional Classification Manual, 1970

This manual provides a set of procedural rules and regulations for use in functionally classifying all roads and streets in Iowa according to the character of service they are intended to provide. Functional classification is a requirement of House File 394 (Functional Highway Classification Bill) enacted by the 63rd General Assembly of the Iowa Legislature. Functional classification is defined in this Bill as: "The grouping of roads and streets into systems according to the character of service they will be expected to provide, and the assignment of jurisdiction over each class to the governmental unit having primary interest in each type of service."

Characterization and multivariate classification of grapes and wines of two Cabernet Sauvignon clones

The objective of this work was to assess and characterize two clones, 169 and 685, of Cabernet Sauvignon grapes and to evaluate the wine produced from these grapes. The experiment was carried out in São Joaquim, SC, Brazil, during the 2009 harvest season. During grape ripening, the evolution of physical-chemical properties, phenolic compounds, organic acids, and anthocyanins was evaluated. During grape harvest, yield components were determined for each clone. Individual and total phenolics, individual and total anthocyanins, and antioxidant activity were evaluated for wine. The clones were also assessed regarding the duration of their phenological cycle. During ripening, the evolution of phenolic compounds and of physical-chemical parameters was similar for both clones; however, during harvest, significant differences were observed regarding yield, number of bunches per plant and berries per bunch, leaf area, and organic acid, polyphenol, and anthocyanin content. The wines produced from these clones showed significant differences regarding chemical composition. The clones showed similar phenological cycle and responses to bioclimatic parameters. Principal component analysis shows that clone 685 is strongly correlated with color characteristics, mainly monomeric anthocyanins, while clone 169 is correlated with individual phenolic compounds.

Approximate polytope ensemble for one-class classification

In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.

Pre-stimulus beta oscillations within left posterior sylvian regions impact auditory temporal order judgment accuracy.

Both neural and behavioral responses to stimuli are influenced by the state of the brain immediately preceding their presentation, notably by pre-stimulus oscillatory activity. Using frequency analysis of high-density electroencephalogram coupled with source estimations, the present study investigated the role of pre-stimulus oscillatory activity in auditory spatial temporal order judgments (TOJ). Oscillations within the beta range (i.e. 18-23Hz) were significantly stronger before accurate than inaccurate TOJ trials. Distributed source estimations identified bilateral posterior sylvian regions as the principal contributors to pre-stimulus beta oscillations. Activity within the left posterior sylvian region was significantly stronger before accurate than inaccurate TOJ trials. We discuss our results in terms of a modulation of sensory gating mechanisms mediated by beta activity.

Accuracy profile validation of a new analytical method for butane measurement using headspace-gas chromatography-mass spectrometry.

The aim of our study was to provide an innovative HS-GC/MS method applicable to the routine determination of butane concentration in forensic toxicology laboratories. The main drawback of the GC/MS methods discussed in literature concerning butane measurement was the absence of a specific butane internal standard necessary to perform quantification. Because no stable isotope of butane is commercially available, it is essential to develop a new approach by an in situ generation of standards. To avoid the manipulation of a stable isotope-labelled gas, we have chosen to generate in situ an internal labelled standard gas (C(4)H(9)D) following the basis of the stoichiometric formation of butane by the reaction of deuterated water (D(2)O) with Grignard reagent butylmagnesium chloride (C(4)H(9)MgCl). This method allows a precise measurement of butane concentration and therefore, a full validation by accuracy profile was presented.

Soybean crop area estimation by Modis/Evi data

The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics.

Evaluating spine micro-architectural texture (via TBS) discriminates major osteoporotic fractures from controls both as well as and independent of site matched BMD: the Eastern European TBS study.

The aim of the study was to assess the clinical performance of the model combining areal bone mineral density (aBMD) at spine and microarchitecural texture (TBS) for the detection of the osteoporotic fracture. The Eastern European Study is a multicenter study (Serbia, Bulgaria, Romania and Ukraine) evaluating the role of TBS in routine clinical practice as a complement to aBMD. All scans were acquired on Hologic Discovery and GE Prodigy densitometers in a routine clinical manner. The additional clinical values of aBMD and TBS were analyzed using a two steps classification tree approach (aBMD followed by TBS tertiles) for all type of osteoporotic fracture (All-OP Fx). Sensitivity, specificity and accuracy of fracture detection as well as the Net Reclassification Index (NRI) were calculated. This study involves 1031 women subjects aged 45 and older recruited in east European countries. Clinical centers were cross-calibrated in terms of BMD and TBS. As expected, areal BMD (aBMD) at spine and TBS were only moderately correlated (r (2) = 0.19). Prevalence rate for All-OP Fx was 26 %. Subjects with fracture have significant lower TBS and aBMD than subjects without fracture (p < 0.01). TBS remains associated with the fracture even after adjustment for age and aBMD with an OR of 1.27 [1.07-1.51]. When using aBMD T-score of -2.5 and the lowest TBS tertile thresholds, both BMD and TBS were similar in terms of sensitivity (35 vs. 39 %), specificity (78 vs. 80 %) and accuracy (64 vs. 66 %). aBMD and TBS combination, induced a significant improvement in sensitivity (+28 %) and accuracy (+17 %) compared to aBMD alone whereas a moderate improvement was observed in terms of specificity (+9 %). The overall combination gain was 36 % as expressed using the NRI. aBMD and TBS combination decrease significantly the number of subjects needed to diagnose from 7 for aBMD alone to 2. In a multi-centre Eastern European cohort, we have shown that the use of TBS in addition to the aBMD permit to reclassified correctly more than one-third of the overall subjects. Furthermore, the number of subjects needed to diagnose fell to 2 subjects. Economical studies have to be performed to evaluate the gain induced by the use of TBS for the healthcare system.

Accuracy of indirect estimation of power output from uphill performance in cycling.

PURPOSE: To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest. METHODS: Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3-6.3 km, slope 4.4-10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France. RESULTS: Overall, the difference between Pmes and Pest was -0.95% (95%CI: -10.4%, +8.5%) for all trials and 0.24% (-6.1%, +6.6%) in conditions without wind (<2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial. CONCLUSIONS: Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.

Classification and fingerprinting of different berries based on biochemical profiling and antioxidant capacity

The objective of this work was to evaluate the biochemical composition of six berry types belonging to Fragaria, Rubus, Vaccinium and Ribes genus. Fruit samples were collected in triplicate (50 fruit each) from 18 different species or cultivars of the mentioned genera, during three years (2008 to 2010). Content of individual sugars, organic acids, flavonols, and phenolic acids were determined by high performance liquid chromatography (HPLC) analysis, while total phenolics (TPC) and total antioxidant capacity (TAC), by using spectrophotometry. Principal component analysis (PCA) and hierarchical cluster analysis (CA) were performed to evaluate the differences in fruit biochemical profile. The highest contents of bioactive components were found in Ribes nigrum and in Fragaria vesca, Rubus plicatus, and Vaccinium myrtillus. PCA and CA were able to partially discriminate between berries on the basis of their biochemical composition. Individual and total sugars, myricetin, ellagic acid, TPC and TAC showed the highest impact on biochemical composition of the berry fruits. CA separated blackberry, raspberry, and blueberry as isolate groups, while classification of strawberry, black and red currant in a specific group has not occurred. There is a large variability both between and within the different types of berries. Metabolite fingerprinting of the evaluated berries showed unique biochemical profiles and specific combination of bioactive compound contents.