Operator's, organizational, direct support and general support maintenance manual : electronic shop, transportable, AN/TSM-126A.

"1 May 1981."

Approaches and Strategy for Cancer Research and Surveillance Data: Integration, Information Pipeline, Data Models, and Informatics Opportunities

Thesis (Ph.D.)--University of Washington, 2016-04

Molecular systematics of the Philippine malaria vector Anopheles flavirostris

Allozyme and molecular sequence data from the malaria vector Anopheles flavirostris (Ludlow) (Diptera: Culicidae) were analysed from 34 sites throughout the Philippines, including the type locality, to test the hypothesis that this taxon is a single panmictic species. A finer-scaled allozyme study, of mainly Luzon samples, revealed no fixed genetic differences in sympatric sites and only low levels of variation. We obtained data from partial sequences for the internal transcribed spacer 2 (ITS2) (483 bp), the third domain (D3) (330 bp) of the 28S ribosomal DNA subunit and cytochrome c oxidase subunit I (COI) of mitochondrial DNA (261 bp). No sequence variation was observed for ITS2, only a one base pair difference was observed between Philippine and Indonesian D3 sequences and An. flavirostris sequences were unique, confirming their diagnostic value for this taxon. Sixteen COI haplotypes were identified, giving 25 parsimony informative sites. Neighbour-Joining, Maximum Parsimony, Maximum Likelihood and Bayesian phylogenetic analysis of COI sequences for An. flavirostris and outgroup taxa revealed strong branch support for the monophyly of An. flavirostris, thus confirming that Philippine populations of this taxon comprise a single separate species within the Minimus Subgroup of the Funestus Group. Variation in the behaviour of An. flavirostris is likely to be intraspecific rather than interspecific in origin. © 2006 The Royal Entomological Society.

The real-time refinement calculus: A foundation for machine-independent real-time programming

The real-time refinement calculus is an extension of the standard refinement calculus in which programs are developed from a precondition plus post-condition style of specification. In addition to adapting standard refinement rules to be valid in the real-time context, specific rules are required for the timing constructs such as delays and deadlines. Because many real-time programs may be nonterminating, a further extension is to allow nonterminating repetitions. A real-time specification constrains not only what values should be output, but when they should be output. Hence for a program to implement such a specification, it must guarantee to output values by the specified times. With standard programming languages such guarantees cannot be made without taking into account the timing characteristics of the implementation of the program on a particular machine. To avoid having to consider such details during the refinement process, we have extended our real-time programming language with a deadline command. The deadline command takes no time to execute and always guarantees to meet the specified time; if the deadline has already passed the deadline command is infeasible (miraculous in Dijkstra's terminology). When such a realtime program is compiled for a particular machine, one needs to ensure that all execution paths leading to a deadline are guaranteed to reach it by the specified time. We consider this checking as part of an extended compilation phase. The addition of the deadline command restores for the real-time language the advantage of machine independence enjoyed by non-real-time programming languages.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

A methodology for implementing a decision support system:a small company case study

The importance of non-technical factors in the design and implementation of information systems has been increasingly recognised by both researchers and practitioners, and recent literature highlights the need for new tools and techniques with an organisational, rather than technical, focus. The gap between what is technically possible and what is generally practised, is particularly wide in the sales and marketing field. This research describes the design and implementation of a decision support system (DSS) for marketing planning and control in a small, but complex company and examines the nature of the difficulties encountered. An intermediary with functional, rather than technical, expertise is used as a strategy for overcoming these by taking control of the whole of the systems design and implementation cycle. Given the practical nature of the research, an action research approach is adopted with the researcher undertaking this role. This approach provides a detailed case study of what actually happens during the DSS development cycle, allowing the influence of organisational factors to be captured. The findings of the research show how the main focus of the intermediary's role needs to be adapted over the systems development cycle; from coordination and liaison in the pre-design and design stages, to systems champion during the first part of the implementation stage, and finally to catalyst to ensure that the DSS is integrated into the decision-making process. Two practical marketing exercises are undertaken which illustrate the nature of the gap between the provision of information and its use. The lack of a formal approach to planning and control is shown to have a significant effect on the way the DSS is used and the role of the intermediary is extended successfully to accommodate this factor. This leads to the conclusion that for the DSS to play a fully effective role, small firms may need to introduce more structure into their marketing planning, and that the role of the intermediary, or Information Coordinator, should include the responsibility for introducing new techniques and ideas to aid with this.

Clinical decision support system for point of care use: ontology driven design and software implementation

OBJECTIVES: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. METHODS: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications' functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. RESULTS: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms-desktop and handheld computers. CONCLUSIONS: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.

Biomedical events extraction using the hidden vector state model

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. Methods and material: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results. Results: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events. Conclusions: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

Uncoordinated coexisting IEEE 802.15.4 networks for machine to machine communications

IEEE 802.15.4 standard has been proposed for low power wireless personal area networks. It can be used as an important component in machine to machine (M2M) networks for data collection, monitoring and controlling functions. With an increasing number of machine devices enabled by M2M technology and equipped with 802.15.4 radios, it is likely that multiple 802.15.4 networks may be deployed closely, for example, to collect data for smart metering at residential or enterprise areas. In such scenarios, supporting reliable communications for monitoring and controlling applications is a big challenge. The problem becomes more severe due to the potential hidden terminals when the operations of multiple 802.15.4 networks are uncoordinated. In this paper, we investigate this problem from three typical scenarios and propose an analytic model to reveal how performance of coexisting 802.15.4 networks may be affected by uncoordinated operations under these scenarios. Simulations will be used to validate the analytic model. It is observed that uncoordinated operations may lead to a significant degradation of system performance in M2M applications. With the proposed analytic model, we also investigate the performance limits of the 802.15.4 networks, and the conditions under which coordinated operations may be required to support M2M applications. © 2012 Springer Science + Business Media, LLC.

Attachment anxiety and friendship group identification under attachment threat:the moderating role of priming support network expectations

We examined the role of priming participants' own network expectations on their subsequent identification with their friendship group. We examined this prime alongside attachment anxiety and attachment threat, as predictors of friendship group identification. Previous research has suggested that attachment anxiety is associated with negative network expectations. In this study, we extended this work to show that when a network expectation prime was absent, higher attachment anxiety was associated with lower group identification under attachment threat, compared to a control condition. However, when expectations of support network were primed, attachment threat no longer affected group identification, so that only attachment anxiety predicted group identification. This suggests that priming participants who are high in attachment anxiety with their own network expectancies (which are negative), results in participants dis-identifying with their friendship group, regardless of whether or not they have experienced attachment threat. © 2012 Elsevier Ltd.

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

An investigation of support effects on the Pt-catalysed selective transformation of α,β-unsaturated substrates

Pt catalyst series were prepared on mesoporous SBA-15, SBA-16, KIT-6, true liquidcrystal-templated meso-macroporous SBA-15 and a commercial, low surface area silicasupport. Support structure can be easily fabricated using surfactant templating as a mode ofstringent control on porosity, surface area and internal structure. The impact of varying Pt-support physicochemical properties was systematically studied for the selective transformation of allylic substrates under chemoselective oxidation and hydrogenation regimes, a class of reactions highly applicable to industry. Pt-based heterogeneous catalysts are well-known for their utilisation in the hydrogenation of α,β-unsaturated aldehydes,although the mode of action and lack of systematic studies in the literature fuels continuing debate into the role of Pt nanoparticles and support choice for this area. This project attempts to shed some light on several frequently asked questions in this field. Successful support synthesis and stability after Pt impregnation is confirmed through HRTEM, XRD and N2 porosimetry. Decreasing metal loading promoted dispersion values,regardless of support choice, with surface PtO2 content also showing visible enhancement.Increasing support surface area and mesoporosity exhibited the following trend on Pt dispersion augmentation; low surface area commercial silica < true liquid crystal-templated SBA-15 < SBA-15 < SBA-16 ~ KIT-6. For the selective oxidation of cinnamyl alcohol,increasing PtO2 surface population confers substantial rate enhancements, with turnover frequencies evidencing PtO2 to be the active species .In the Pt-catalysed hydrogenation of cinnamaldehyde, strong support insensitivity was observed towards catalytic activity; as turnover frequencies normalised to Pt metal reveal constant values. However, structure sensitivity to the desired unsaturated alcohol arose,evidencing the requirement of flat, extended Pt (111) facets for C=O hydrogenation. Pt/SBA-15 proved the most selective, reflecting suppressed cinnamyl alcohol hydrogenation, with DRIFTS and in-situ ATR-IR evidencing the key role of support polarity in re-orientation of cinnamaldehyde to favour di-σCO adsorption and C=O versus C=C hydrogenation. High pressures increased activity, whilst a dramatic shift in selectivity from dominant C=C (1 bar)to C=O hydrogenation (10 bar) was also observed, attributed to surface crowding and suppression of di-σCC and η4 di-σCO+πC=C cinnamaldehyde binding modes.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

Abstractions to support dynamic adaptation of communication frameworks for user-centric communication

The convergence of data, audio and video on IP networks is changing the way individuals, groups and organizations communicate. This diversity of communication media presents opportunities for creating synergistic collaborative communications. This form of collaborative communication is however not without its challenges. The increasing number of communication service providers coupled with a combinatorial mix of offered services, varying Quality-of-Service and oscillating pricing of services increases the complexity for the user to manage and maintain ‘always best’ priced or performance services. Consumers have to manually manage and adapt their communication in line with differences in services across devices, networks and media while ensuring that the usage remain consistent with their intended goals. This dissertation proposes a novel user-centric approach to address this problem. The proposed approach aims to reduce the aforementioned complexity to the user by (1) providing high-level abstractions and a policy based methodology for automated selection of the communication services guided by high-level user policies and (2) providing services through the seamless integration of multiple communication service providers and providing an extensible framework to support the integration of multiple communication service providers. The approach was implemented in the Communication Virtual Machine (CVM), a model-driven technology for realizing communication applications. The CVM includes the Network Communication Broker, the layer responsible for providing a network-independent API to the upper layers of CVM. The initial prototype for the NCB supported only a single communication framework which limited the number, quality and types of services available. Experimental evaluation of the approach show the additional overhead of the approach is minimal compared to the individual communication services frameworks. Additionally the automated approach proposed out performed the individual communication services frameworks for cross framework switching.