Frontally mediated inhibitory processing and white matter microstructure: age and alcoholism effects

RationaleThe NOGO P3 event-related potential is a sensitive marker of alcoholism, relates to EEG oscillation in the δ and θ frequency ranges, and reflects activation of an inhibitory processing network. Degradation of white matter tracts related to age or alcoholism should negatively affect the oscillatory activity within the network.ObjectiveThis study aims to evaluate the effect of alcoholism and age on δ and θ oscillations and the relationship between these oscillations and measures of white matter microstructural integrity.MethodsData from ten long-term alcoholics to 25 nonalcoholic controls were used to derive P3 from Fz, Cz, and Pz using a visual GO/NOGO protocol. Total power and across trial phase synchrony measures were calculated for δ and θ frequencies. DTI, 1.5 T, data formed the basis of quantitative fiber tracking in the left and right cingulate bundles and the genu and splenium of the corpus callosum. Fractional anisotropy and diffusivity (λL and λT) measures were calculated from each tract.ResultsNOGO P3 amplitude and δ power at Cz were smaller in alcoholics than controls. Lower δ total power was related to higher λT in the left and right cingulate bundles. GO P3 amplitude was lower and GO P3 latency was longer with advancing age, but none of the time-frequency analysis measures displayed significant age or diagnosis effects.ConclusionsThe relation of δ total power at CZ with λT in the cingulate bundles provides correlational evidence for a functional role of fronto-parietal white matter tracts in inhibitory processing

Knowledge representation and text mining in biomedical, healthcare, and political domains

Knowledge representation and text mining can be employed to discover new knowledge and develop services by using the massive amounts of text gathered by modern information systems. The applied methods should take into account the domain-specific nature of knowledge. This thesis explores knowledge representation and text mining in three application domains. Biomolecular events can be described very precisely and concisely with appropriate representation schemes. Protein–protein interactions are commonly modelled in biological databases as binary relationships, whereas the complex relationships used in text mining are rich in information. The experimental results of this thesis show that complex relationships can be reduced to binary relationships and that it is possible to reconstruct complex relationships from mixtures of linguistically similar relationships. This encourages the extraction of complex relationships from the scientific literature even if binary relationships are required by the application at hand. The experimental results on cross-validation schemes for pair-input data help to understand how existing knowledge regarding dependent instances (such those concerning protein–protein pairs) can be leveraged to improve the generalisation performance estimates of learned models. Healthcare documents and news articles contain knowledge that is more difficult to model than biomolecular events and tend to have larger vocabularies than biomedical scientific articles. This thesis describes an ontology that models patient education documents and their content in order to improve the availability and quality of such documents. The experimental results of this thesis also show that the Recall-Oriented Understudy for Gisting Evaluation measures are a viable option for the automatic evaluation of textual patient record summarisation methods and that the area under the receiver operating characteristic curve can be used in a large-scale sentiment analysis. The sentiment analysis of Reuters news corpora suggests that the Western mainstream media portrays China negatively in politics-related articles but not in general, which provides new evidence to consider in the debate over the image of China in the Western media

Critique of Architectures for Long-Term Digital Preservation

Evolving technology and fading human memory threaten the long-term intelligibility of many kinds of documents. Furthermore, some records are susceptible to improper alterations that make them untrustworthy. Trusted Digital Repositories (TDRs) and Trustworthy Digital Objects (TDOs) seem to be the only broadly applicable digital preservation methodologies proposed. We argue that the TDR approach has shortfalls as a method for long-term digital preservation of sensitive information. Comparison of TDR and TDO methodologies suggests differentiating near-term preservation measures from what is needed for the long term. TDO methodology addresses these needs, providing for making digital documents durably intelligible. It uses EDP standards for a few file formats and XML structures for text documents. For other information formats, intelligibility is assured by using a virtual computer. To protect sensitive information—content whose inappropriate alteration might mislead its readers, the integrity and authenticity of each TDO is made testable by embedded public-key cryptographic message digests and signatures. Key authenticity is protected recursively in a social hierarchy. The proper focus for long-term preservation technology is signed packages that each combine a record collection with its metadata and that also bind context—Trustworthy Digital Objects.

What happens during a blackout: Consequences of a prolonged and wide-ranging power outage

Power outages in Europe and North America in recent years have given a lasting impression of the vulnerability of modern and high-tech societies. Although the power supply was interrupted for a week at most and only locally, massive functional and supply disruptions, threats to public order and damage amounting to billions of euros have already become apparent. This book shows what consequences a prolonged and widespread power blackout could have on society and its critical infrastructures and how Germany is prepared for such a large-scale disaster. By means of comprehensive consequence analyses, the authors drastically demonstrate that after only a few days, the supply of the population with (vital) goods and services can no longer be guaranteed in the affected area. It is also made clear that considerable efforts are required to increase the sustainability of critical infrastructures and to further optimise the capacities of the national disaster management system. The book is based on TAB Report Nr. 141 "Gefährdung und Verletzbarkeit moderner Gesellschaften - am Beispiel eines großräumigen und langandauernden Ausfalls der Stromversorgung"

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

A study of teaching automation for marine engineers

This dissertation is a research into a study of teaching automation for marine engineers which is conducted at the Institute of Marine Technology (IMT)of the Union of Myanmar as related to the Fundamentals of Automation, Instrumentation and Control systems, module 9 of IMO model course 7.02. This course is now included in the mandatory part of STCW Code A of the revised STCW Convention. An examination is made of the fundamentals of ship automation and a brief overview is given of the modem developments in ship automation system. This includes modem developments in main engine automation, navigation/ bridge control, integrated control ship, condition monitoring systems and programmable controller. The author has attempted to analyse the present syllabus on Automation, Instrumentation and Control Systems ( AICS ) course conducted at IMT and related subjects conducted in recent education and training schemes for marine engineers in Myanmar. Comparisons of the IMO model course and IMT’s AICS course are presented emphasising entry standards, subject outline and detailed teaching syllabus. Then the author proposes ways and means to improve the course to meet the requirements of the IMO model course. The author also suggests the promotion of some related subjects to support the AICS course by using teaching aids and some courses which are recently available in IMT. The modem developments in ship automation are very rapid and dramatic. In this regard, a brief syllabus for the near future is also proposed to cope with modem developments. A number of recommendations are also made to harmonise with the course to be promoted

An interactive human centered data science approach towards crime pattern analysis

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis