Systems-based accident analysis in the led outdoor activity domain : application and evaluation of a risk management framework

Safety-compromising accidents occur regularly in the led outdoor activity domain. Formal accident analysis is an accepted means of understanding such events and improving safety. Despite this, there remains no universally accepted framework for collecting and analysing accident data in the led outdoor activity domain. This article presents an application of Rasmussen's risk management framework to the analysis of the Lyme Bay sea canoeing incident. This involved the development of an Accimap, the outputs of which were used to evaluate seven predictions made by the framework. The Accimap output was also compared to an analysis using an existing model from the led outdoor activity domain. In conclusion, the Accimap output was found to be more comprehensive and supported all seven of the risk management framework's predictions, suggesting that it shows promise as a theoretically underpinned approach for analysing, and learning from, accidents in the led outdoor activity domain.

Frequency Domain Characterization Of Sliding Mode Control Of An Inverter Used In DSTATCOM Application

In this paper, the commonly used switching schemes for sliding mode control of power converters is analyzed and designed in the frequency domain. Particular application of a distribution static compensator (DSTATCOM) in voltage control mode is investigated in a power distribution system. Tsypkin's method and describing function is used to obtain the switching conditions for the two-level and three-level voltage source inverters. Magnitude conditions of carrier signals are developed for robust switching of the inverter under carrier-based modulation scheme of sliding mode control. The existence of border collision bifurcation is identified to avoid the complex switching states of the inverter. The load bus voltage of an unbalanced three-phase nonstiff radial distribution system is controlled using the proposed carrier-based design. The results are validated using PSCAD/EMTDC simulation studies and through a scaled laboratory model of DSTATCOM that is developed for experimental verification

Ontologies derived from Wikipedia : a framework for comparison

Since its debut in 2001 Wikipedia has attracted the attention of many researchers in different fields. In recent years researchers in the area of ontology learning have realised the huge potential of Wikipedia as a source of semi-structured knowledge and several systems have used it as their main source of knowledge. However, the techniques used to extract semantic information vary greatly, as do the resulting ontologies. This paper introduces a framework to compare ontology learning systems that use Wikipedia as their main source of knowledge. Six prominent systems are compared and contrasted using the framework.

Contribution of transmembrane domain V amino acids to β1l-adrenoceptor activity and affinity

Introduction. There are two binding sites on the β1-adrenoceptor (AR), β1H and β1L corresponding to high and low affinity binding sites respectively, which can be activated to cause cardiostimulation. Some β-blockers that block β1AR and β2ARs can activate β1LARs at higher concentrations than those required to cause blockade. The β2AR does not form a corresponding low affinity binding site and therefore we postulated that heterologous amino acids are responsible for the formation of β1LAR. Aim. To investigate whether heterologous amino acids of transmembrane domain V (TMDV) of β1AR and β2ARs contribute to β1LAR. Methods. β1ARs, β2ARs and mutant β1ARs containing all (β1(β2TMDV)AR) or single amino acids of TMDV of the β2AR were prepared and stably expressed in Chinese Hamster Ovary cells. Concentration-effect curves for cyclicAMP accumulation were carried out for (-)-CGP12177 in the absence or presence of (-)-bupranolol. Results. The potencies (pEC50) of (-)-CGP12177 were β2AR (9.24 ± 0.14, n = 5), β1(V230I)AR (9.07 ± 0.07, n = 10), β1(β2TMDV)AR (8.86 ± 0.10, n = 15), β1(R222Q)AR (8.09 ± 0.29, n = 6), β1AR (8.00 ± 0.11, n = 11). The affinities (pKB) of (-)-bupranolol were β2AR (9.82 ± 0.52, n = 5), β1(V230I)AR (7.64 ± 0.12, n = 8), β1(β2TMV)AR (8.06 ± 0.17, n = 8), β1(R222Q)AR (7.33 ± 0.23, n = 5), β1AR (7.23 ± 0.23, n = 5). Discussion. The potency of (-)-CGP12177 was higher at β2AR than at β1AR consistent with activation through a low affinity site at the β1AR (β1LAR). The presence of V230 in β1AR accounted for the lower potency of (-)-CGP 12177. The affinity of (-)-bupranolol was lower at β1AR compared to β2AR. The presence of V230 in β1AR accounted in part for the lower affinity. In conclusion TMDV of the β1AR contributes in part to the low affinity binding site of β1AR.

Contribution of transmembrane domain V amino acids to β1l-adrenoceptor activity and affinity

There are two binding sites on the β1-adrenoceptor (AR), β1H and β1L corresponding to high and low affinity binding sites respectively, which can be activated to cause cardiostimulation (reviewed Kaumann and Molenaar, 2008). Some β-blockers that block β1AR and β2ARs can activate β1LARs at higher concentrations than those required to cause blockade. The β2AR does not form a corresponding low affinity binding site (Baker et al 2002) and therefore we postulated that heterologous amino acids are responsible for the formation of β1LAR. Our aim was to investigate whether heterologous amino acids of transmembrane domain V (TMDV) of β1AR and β2ARs contribute to β1LAR. β1ARs, β2ARs and mutant β1ARs containing all (β1(β2TMDV)AR) or single amino acids of TMDV of the β2AR were prepared and stably expressed in Chinese Hamster Ovary cells. Concentration-effect curves for cyclicAMP accumulation were carried out for (-)-CGP12177 or (-)-isoprenaline in the absence or presence of (-)-bupranolol. _______________________________________________________________________ (-)-CGP 12177 (-)-Bupranolol affinity (pKB) pEC50 vs (-)-CGP 12177 vs (-)-isoprenaline _______________________________________________________________________ β1AR 8.00 ± 0.11 (11) 7.23 ± 0.23 (5) 9.52 ± 0.28 (5) β2AR (high density) 9.24 ± 0.14 (5) 9.82 ± 0.52 (8) xPaulxxxxxxx β2AR (low density) no effect β1(β2TMV)AR 8.86 ± 0.10 (15) 8.06 ± 0.17 (8) 9.08 ± 0.22 (6) β1(V230I)AR 9.07 ± 0.07 (10) 7.64 ± 0.12 (8) 9.36 ± 0.28 (9) β1(R222Q)AR 8.09 ± 0.29 (6) 7.33 ± 0.23 (5) 9.36 ± 0.08 (6) β1(V230A)AR 7.59 ± 0.09 (6) 7.32 ± 0.24 (4) 8.62 ± 0.18 (5) _______________________________________________________________________ The potency of (-)-CGP12177 was higher at β2AR than at β1AR consistent with activation through a low affinity site at the β1AR (β1LAR) but not β2AR. The presence of V230 in β1AR accounted for the lower potency of (-)-CGP 12177. The affinity of (-)-bupranolol at β1AR and mutants was higher when determined with (-)-isoprenaline than with (-)-CGP 12177. The affinity of (-)-bupranolol determined against (-)-CGP 12177 was lower at β1AR compared to β2AR. The presence of V230 in β1AR accounted in part for the lower affinity. In conclusion V230 of the β1AR contributes in part to the low affinity binding site of β1AR. Baker JG, Hall IP, Hill SJ (2002). Pharmacological characterization of CGP12177 at the human β2-adrenoceptor. Br J Pharmacol 137, 400−408 Kaumann AJ, Molenaar P (2008) The low-affinity site of the β1-adrenoceptor and its relevance to cardiovascular pharmacology. Pharmacol Ther 118, 303-336

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Noise robust voice activity detection using normal probability testing and time-domain histogram analysis

This paper presents a method of voice activity detection (VAD) suitable for high noise scenarios, based on the fusion of two complementary systems. The first system uses a proposed non-Gaussianity score (NGS) feature based on normal probability testing. The second system employs a histogram distance score (HDS) feature that detects changes in the signal through conducting a template-based similarity measure between adjacent frames. The decision outputs by the two systems are then merged using an open-by-reconstruction fusion stage. Accuracy of the proposed method was compared to several baseline VAD methods on a database created using real recordings of a variety of high-noise environments.

Par4 is a coactivator for a splice isoform-specific transcriptional activation domain in WT1

The Wilms’ tumor suppressor protein WT1 is a transcriptional regulator involved in differentiation and the regulation of cell growth. WT1 is subject to alternative splicing, one isoform including a 17–amino acid region that is specific to mammals. The function of this 17–amino acid insertion is not clear, however. Here, we describe a transcriptional activation domain in WT1 that is specific to the WT1 splice isoform that contains the 17–amino acid insertion. We show that the function of this domain in transcriptional activation is dependent on a specific interaction with the prostate apoptosis response factor par4. A mutation in WT1 found in Wilms’ tumor disturbs the interaction with par4 and disrupts the function of the activation domain. Analysis of WT1 derivatives in cells treated to induce par4 expression showed a strong correlation between the transcription function of the WT1 17–amino acid insertion and the ability of WT1 to regulate cell survival and proliferation. Our results provide a molecular mechanism by which alternative splicing of WT1 can regulate cell growth in development and disease.

Noise robust voice activity detection using features extracted from the time-domain autocorrelation function

This paper presents a method of voice activity detection (VAD) for high noise scenarios, using a noise robust voiced speech detection feature. The developed method is based on the fusion of two systems. The first system utilises the maximum peak of the normalised time-domain autocorrelation function (MaxPeak). The second zone system uses a novel combination of cross-correlation and zero-crossing rate of the normalised autocorrelation to approximate a measure of signal pitch and periodicity (CrossCorr) that is hypothesised to be noise robust. The score outputs by the two systems are then merged using weighted sum fusion to create the proposed autocorrelation zero-crossing rate (AZR) VAD. Accuracy of AZR was compared to state of the art and standardised VAD methods and was shown to outperform the best performing system with an average relative improvement of 24.8% in half-total error rate (HTER) on the QUT-NOISE-TIMIT database created using real recordings from high-noise environments.

Mum, why are the potholes 8 feet apart? Repetitive wheel loads from heavy vehicles in the spatial domain

This paper presents the results of testing to determine pavement forces from three heavy vehicles (HVs). The HVs were instrumented to measure their wheel forces. A “novel roughness” value of the roads during testing is also derived. The various dynamic pavement forces are presented according to the range of novel roughness of pavement surfacings encountered during testing. The paper then examines the relationship between the two derived wavelengths predominant within the HV suspensions; those of axle hop and body-bounce. How these may be considered as contributing to spatial repetition of pavement forces from HVs is discussed. The paper concludes that pavement models need to be revised since dynamic forces from HVs in particular are not generally considered in current pavement design.

Feature-domain super-resolution for IRIS recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the captured iris images, which significantly degrades iris recognition performance. Superresolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, all existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values. This paper considers transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. This is the first paper to investigate the possibility of feature domain super-resolution for iris recognition, and experiments confirm the validity of the proposed approach.

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.