Classification of railway bridges based on criticality and vulnerability factors

Bridges are currently rated individually for maintenance and repair action according to the structural conditions of their elements. Dealing with thousands of bridges and the many factors that cause deterioration, makes this rating process extremely complicated. The current simplified but practical methods are not accurate enough. On the other hand, the sophisticated, more accurate methods are only used for a single or particular bridge type. It is therefore necessary to develop a practical and accurate rating system for a network of bridges. The first most important step in achieving this aim is to classify bridges based on the differences in nature and the unique characteristics of the critical factors and the relationship between them, for a network of bridges. Critical factors and vulnerable elements will be identified and placed in different categories. This classification method will be used to develop a new practical rating method for a network of railway bridges based on criticality and vulnerability analysis. This rating system will be more accurate and economical as well as improve the safety and serviceability of railway bridges.

Refinements and developments on the stratospheric dust database and classification scheme

Greater than 750 individual particles have now been selected from collection flags housed in the JSC Cosmic Dust Curatorial Facility and most have been documented in the Cosmic Dust Catalogs [1]. As increasing numbers of particles are placed in Cosmic Dust Collections, and a greater diversity of particles are introduced to the stratosphere through natural and man-made processes (e.g. decaying orbits of space debris [2]), there is an even greater need for a classification scheme to encompass all stratospheric particles rather than only extraterrestrial particles. The fundamental requirements for a suitable classification scheme have been outlined in earlier communications [3,4]. A quantitative survey of particles on collection flag W7017 indicates that there is some bias in the number of samples selected within a given category for the Cosmic Dust Catalog [5]. However, the sample diversity within this selection is still appropriate for the development of a reliable classification scheme. In this paper, we extend the earlier works on stratospheric particle classification to include particles collected during the period May 1981 to November 1983.

Australian Aboriginal words in English : their origin and meaning [Second edition]

Australian Aboriginal Words in English records the Aboriginal contribution to Australian English and provides a fascinating insight into the contact between the first Australians and European settlers. The words are grouped according to subject, and for each one there is information on the Aboriginal language from which it derives, the date of its first written use in English, and its present meaning and pronunciation. This book brings them together and provides the fullest available information about their Aboriginal background and their Australian English History.

Australian Aboriginal Words in English : their Origin and Meaning [First edition]

This extensive reference provides authoritative information about the history of over 400 words from Aboriginal languages, offering the fullest available information about their Aboriginal background and Australian English history. The book begins with a general history of the 250 Australian aboriginal languages, including profiles of the languages that have been most significant as sources for borrowing. The words are then grouped according to subject: birds, fish, edible flora, dwellings, etc., with each work listed in a dictionary-style entry. The book concludes by addressing how words changed in English, and discusses English words that have since been adopted into Aboriginal languages.

Multifocal urothelial cancers with the mutator phenotype are of monoclonal origin and require panurothelial treatment for tumor clearance

Purpose: UC is a disease of the entire urothelium, characterized by multiplicity and multifocality. The clonal relationship among multiple UCs has implications regarding adjuvant chemotherapy. It has been investigated in studies of chromosomal alteration and single gene mutation. However, these genetic changes can occur in unrelated tumors under similar carcinogenic selection pressures. Tumors with high MSI have numerous DNA mutations, of which many provide no selection benefit. While these tumors represent an ideal model for studying UC clonality, their low frequency has prevented their previous investigation. Materials and Methods: We investigated 32 upper and lower urinary tract UCs with high MSI and 4 nonUC primary cancers in 9 patients. We used the high frequency and specificity of individual DNA mutations in these tumors (MSI at 17 loci) and the early timing of epigenetic events (methylation of 7 gene promoters) to investigate tumor clonality. Results: Molecular alterations varied among tumors from different primary organs but they appeared related in the UCs of all 9 patients. While 7 patients had a high degree of concordance among UCs, in 2 the UCs shared only a few similar alterations. Genetic and epigenetic abnormalities were frequently found in normal urothelial samples. Conclusions: Multiple UCs in each patient appeared to arise from a single clone. The molecular order of tumor development varied from the timing of clinical presentation and suggested that residual malignant cells persist in the urinary tract despite apparent curative surgery. These cells lead to subsequent tumor relapse and new methods are required to detect and eradicate them.

Impact of information on intentions to vaccinate in a potential epidemic : Swine-origin Influenza A (H1N1)

Vaccination campaigns to prevent the spread of epidemics are successful only if the targeted populations subscribe to the recommendations of health authorities. However, because compulsory vaccination is hardly conceivable in modern democracies, governments need to convince their populations through efficient and persuasive information campaigns. In the context of the swine-origin A (H1N1) 2009 pandemic, we use an interactive study among the general public in the South of France, with 175 participants, to explore what type of information can induce change in vaccination intentions at both aggregate and individual levels. We find that individual attitudes to vaccination are based on rational appraisal of the situation, and that it is information of a purely scientific nature that has the only significant positive effect on intention to vaccinate.

Read classification for next generation sequencing

Next Generation Sequencing (NGS) has revolutionised molec- ular biology, allowing routine clinical sequencing. NGS data consists of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. The common bacterium Staphylococcus aureus may cause severe and life-threatening infections in humans, with some strains exhibiting antibiotic resistance. Here we apply an SVM classifier to the important problem of distinguishing S. aureus sequencing projects from other pathogens, including closely related Staphylococci. Using a sequence k-mer representation, we achieve precision and recall above 95%, implicating features with important functional associations.

Cardiomyopathy classification : ongoing debate in the genomics era

Cardiomyopathies represent a group of diseases of the myocardium of the heart and include diseases both primarily of the cardiac muscle and systemic diseases leading to adverse effects on the heart muscle size, shape, and function. Traditionally cardiomyopathies were defined according to phenotypical appearance. Now, as our understanding of the pathophysiology of the different entities classified under each of the different phenotypes improves and our knowledge of the molecular and genetic basis for these entities progresses, the traditional classifications seem oversimplistic and do not reflect current understanding of this myriad of diseases and disease processes. Although our knowledge of the exact basis of many of the disease processes of cardiomyopathies is still in its infancy, it is important to have a classification system that has the ability to incorporate the coming tide of molecular and genetic information. This paper discusses how the traditional classification of cardiomyopathies based on morphology has evolved due to rapid advances in our understanding of the genetic and molecular basis for many of these clinical entities.

Timing, origin and emplacement dynamics of mass flows offshore of SE Montserrat in the last 110 ka : implications for landslide and tsunami hazards, eruption history, and volcanic island evolution

Mass flows on volcanic islands generated by volcanic lava dome collapse and by larger-volume flank collapse can be highly dangerous locally and may generate tsunamis that threaten a wider area. It is therefore important to understand their frequency, emplacement dynamics, and relationship to volcanic eruption cycles. The best record of mass flow on volcanic islands may be found offshore, where most material is deposited and where intervening hemipelagic sediment aids dating. Here we analyze what is arguably the most comprehensive sediment core data set collected offshore from a volcanic island. The cores are located southeast of Montserrat, on which the Soufriere Hills volcano has been erupting since 1995. The cores provide a record of mass flow events during the last 110 thousand years. Older mass flow deposits differ significantly from those generated by the repeated lava dome collapses observed since 1995. The oldest mass flow deposit originated through collapse of the basaltic South Soufriere Hills at 103-110 ka, some 20-30 ka after eruptions formed this volcanic center. A ∼1.8 km3 blocky debris avalanche deposit that extends from a chute in the island shelf records a particularly deep-seated failure. It likely formed from a collapse of almost equal amounts of volcanic edifice and coeval carbonate shelf, emplacing a mixed bioclastic-andesitic turbidite in a complex series of stages. This study illustrates how volcanic island growth and collapse involved extensive, large-volume submarine mass flows with highly variable composition. Runout turbidites indicate that mass flows are emplaced either in multiple stages or as single events.

Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms

Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts.

Retrieving dynamic origin-destination matrices from Bluetooth data

The Bluetooth technology is being increasingly used, among the Automated Vehicle Identification Systems, to retrieve important information about urban networks. Because the movement of Bluetooth-equipped vehicles can be monitored, throughout the network of Bluetooth sensors, this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. Some of the main challenges inherent to Bluetooth data are, first, that Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be estimated. Second, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold: to give an overview of the issues inherent to the Bluetooth technology, through the analysis of the data available from the Bluetooth sensors in Brisbane; and to propose a method for retrieving the itineraries of the individual Bluetooth vehicles. We argue that estimating these latent itineraries, accurately, is a crucial step toward the retrieval of accurate dynamic Origin Destination Matrices.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.