Distributed and compressed MIKEY mode to secure end-to-end communications in the Internet of things.

Multimedia Internet KEYing protocol (MIKEY) aims at establishing secure credentials between two communicating entities. However, existing MIKEY modes fail to meet the requirements of low-power and low-processing devices. To address this issue, we combine two previously proposed approaches to introduce a new distributed and compressed MIKEY mode for the Internet of Things. Indeed, relying on a cooperative approach, a set of third parties is used to discharge the constrained nodes from heavy computational operations. Doing so, the preshared mode is used in the constrained part of network, while the public key mode is used in the unconstrained part of the network. Furthermore, to mitigate the communication cost we introduce a new header compression scheme that reduces the size of MIKEY’s header from 12 Bytes to 3 Bytes in the best compression case. Preliminary results show that our proposed mode is energy preserving whereas its security properties are preserved untouched.

PCA-FNN based performance prediction for water injection in oilfields.

In order to solve the problem of uncertain cycle of water injection in the oilfield, this paper proposed a numerical method based on PCA-FNN, so that it can forecast the effective cycle of water injection. PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data. The correctness of PCA-FNN model is verified by the real injection statistics data from 116 wells of an oilfield, the result shows that the average absolute error and relative error of the test are 1.97 months and 10.75% respectively. The testing accuracy has been greatly improved by PCA-FNN model compare with the FNN which has not been processed by PCA and multiple liner regression method. Therefore, PCA-FNN method is reliable to forecast the effectiveness cycle of water injection and it can be used as an decision-making reference method for the engineers.

A Comparison of Geo-tagging in Mobile Internet Browsing Applications on iOS and Android.

Nowadays there is almost no crime committed without a trace of digital evidence, and since the advanced functionality of mobile devices today can be exploited to assist in crime, the need for mobile forensics is imperative. Many of the mobile applications available today, including internet browsers, will request the user’s permission to access their current location when in use. This geolocation data is subsequently stored and managed by that application's underlying database files. If recovered from a device during a forensic investigation, such GPS evidence and track points could hold major evidentiary value for a case. The aim of this paper is to examine and compare to what extent geolocation data is available from the iOS and Android operating systems. We focus particularly on geolocation data recovered from internet browsing applications, comparing the native Safari and Browser apps with Google Chrome, downloaded on to both platforms. All browsers were used over a period of several days at various locations to generate comparable test data for analysis. Results show considerable differences not only in the storage locations and formats, but also in the amount of geolocation data stored by different browsers and on different operating systems.

POWKist: visualising cultural heritage linked datasets.

A collaboration between dot.rural at the University of Aberdeen and the iSchool at Northumbria University, POWkist is a pilot-study exploring potential usages of currently available linked datasets within the cultural heritage domain. Many privately-held family history collections (shoebox archives) remain vulnerable unless a sustainable, affordable and accessible model of citizen-archivist digital preservation can be offered. Citizen-historians have used the web as a platform to preserve cultural heritage, however with no accessible or sustainable model these digital footprints have been ad hoc and rarely connected to broader historical research. Similarly, current approaches to connecting material on the web by exploiting linked datasets do not take into account the data characteristics of the cultural heritage domain. Funded by Semantic Media, the POWKist project is investigating how best to capture, curate, connect and present the contents of citizen-historians’ shoebox archives in an accessible and sustainable online collection. Using the Curios platform - an open-source digital archive - we have digitised a collection relating to a prisoner of war during WWII (1939-1945). Following a series of user group workshops, POWkist is now connecting these ‘made digital’ items with the broader web using a semantic technology model and identifying appropriate linked datasets of relevant content such as DBPedia (an archived linked dataset of Wikipedia) and Ordnance Survey Open Data. We are analysing the characteristics of cultural heritage linked datasets, so that these materials are better visualised, contextualised and presented in an attractive and comprehensive user interface. Our paper will consider the issues we have identified, the solutions we are developing and include a demonstration of our work-in-progress.

Utilising presence in places to support mobile interaction

Physical places are given contextual meaning by the objects and people that make up the space. Presence in physical places can be utilised to support mobile interaction by making access to media and notifications on a smartphone easier and more visible to other people. Smartphone interfaces can be extended into the physical world in a meaningful way by anchoring digital content to artefacts, and interactions situated around physical artefacts can provide contextual meaning to private manipulations with a mobile device. Additionally, places themselves are designed to support a set of tasks, and the logical structure of places can be used to organise content on the smartphone. Menus that adapt the functionality of a smartphone can support the user by presenting the tools most likely to be needed just-in-time, so that information needs can be satisfied quickly and with little cognitive effort. Furthermore, places are often shared with people whom the user knows, and the smartphone can facilitate social situations by providing access to content that stimulates conversation. However, the smartphone can disrupt a collaborative environment, by alerting the user with unimportant notifications, or sucking the user in to the digital world with attractive content that is only shown on a private screen. Sharing smartphone content on a situated display creates an inclusive and unobtrusive user experience, and can increase focus on a primary task by allowing content to be read at a glance. Mobile interaction situated around artefacts of personal places is investigated as a way to support users to access content from their smartphone while managing their physical presence. A menu that adapts to personal places is evaluated to reduce the time and effort of app navigation, and coordinating smartphone content on a situated display is found to support social engagement and the negotiation of notifications. Improving the sensing of smartphone users in places is a challenge that is out-with the scope of this thesis. Instead, interaction designers and developers should be provided with low-cost positioning tools that utilise presence in places, and enable quantitative and qualitative data to be collected in user evaluations. Two lightweight positioning tools are developed with the low-cost sensors that are currently available: The Microsoft Kinect depth sensor allows movements of a smartphone user to be tracked in a limited area of a place, and Bluetooth beacons enable the larger context of a place to be detected. Positioning experiments with each sensor are performed to highlight the capabilities and limitations of current sensing techniques for designing interactions with a smartphone. Both tools enable prototypes to be built with a rapid prototyping approach, and mobile interactions can be tested with more advanced sensing techniques as they become available. Sensing technologies are becoming pervasive, and it will soon be possible to perform reliable place detection in-the-wild. Novel interactions that utilise presence in places can support smartphone users by making access to useful functionality easy and more visible to the people who matter most in everyday life.

Garbage collection optimization for non uniform memory access architectures

Cache-coherent non uniform memory access (ccNUMA) architecture is a standard design pattern for contemporary multicore processors, and future generations of architectures are likely to be NUMA. NUMA architectures create new challenges for managed runtime systems. Memory-intensive applications use the system’s distributed memory banks to allocate data, and the automatic memory manager collects garbage left in these memory banks. The garbage collector may need to access remote memory banks, which entails access latency overhead and potential bandwidth saturation for the interconnection between memory banks. This dissertation makes five significant contributions to garbage collection on NUMA systems, with a case study implementation using the Hotspot Java Virtual Machine. It empirically studies data locality for a Stop-The-World garbage collector when tracing connected objects in NUMA heaps. First, it identifies a locality richness which exists naturally in connected objects that contain a root object and its reachable set— ‘rooted sub-graphs’. Second, this dissertation leverages the locality characteristic of rooted sub-graphs to develop a new NUMA-aware garbage collection mechanism. A garbage collector thread processes a local root and its reachable set, which is likely to have a large number of objects in the same NUMA node. Third, a garbage collector thread steals references from sibling threads that run on the same NUMA node to improve data locality. This research evaluates the new NUMA-aware garbage collector using seven benchmarks of an established real-world DaCapo benchmark suite. In addition, evaluation involves a widely used SPECjbb benchmark and Neo4J graph database Java benchmark, as well as an artificial benchmark. The results of the NUMA-aware garbage collector on a multi-hop NUMA architecture show an average of 15% performance improvement. Furthermore, this performance gain is shown to be as a result of an improved NUMA memory access in a ccNUMA system. Fourth, the existing Hotspot JVM adaptive policy for configuring the number of garbage collection threads is shown to be suboptimal for current NUMA machines. The policy uses outdated assumptions and it generates a constant thread count. In fact, the Hotspot JVM still uses this policy in the production version. This research shows that the optimal number of garbage collection threads is application-specific and configuring the optimal number of garbage collection threads yields better collection throughput than the default policy. Fifth, this dissertation designs and implements a runtime technique, which involves heuristics from dynamic collection behavior to calculate an optimal number of garbage collector threads for each collection cycle. The results show an average of 21% improvements to the garbage collection performance for DaCapo benchmarks.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

NETCODE: an XOR-based warning dissemination scheme for vehicular wireless networks

The next generation of vehicles will be equipped with automated Accident Warning Systems (AWSs) capable of warning neighbouring vehicles about hazards that might lead to accidents. The key enabling technology for these systems is the Vehicular Ad-hoc Networks (VANET) but the dynamics of such networks make the crucial timely delivery of warning messages challenging. While most previously attempted implementations have used broadcast-based data dissemination schemes, these do not cope well as data traffic load or network density increases. This problem of sending warning messages in a timely manner is addressed by employing a network coding technique in this thesis. The proposed NETwork COded DissEmination (NETCODE) is a VANET-based AWS responsible for generating and sending warnings to the vehicles on the road. NETCODE offers an XOR-based data dissemination scheme that sends multiple warning in a single transmission and therefore, reduces the total number of transmissions required to send the same number of warnings that broadcast schemes send. Hence, it reduces contention and collisions in the network improving the delivery time of the warnings. The first part of this research (Chapters 3 and 4) asserts that in order to build a warning system, it is needful to ascertain the system requirements, information to be exchanged, and protocols best suited for communication between vehicles. Therefore, a study of these factors along with a review of existing proposals identifying their strength and weakness is carried out. Then an analysis of existing broadcast-based warning is conducted which concludes that although this is the most straightforward scheme, loading can result an effective collapse, resulting in unacceptably long transmission delays. The second part of this research (Chapter 5) proposes the NETCODE design, including the main contribution of this thesis, a pair of encoding and decoding algorithms that makes the use of an XOR-based technique to reduce transmission overheads and thus allows warnings to get delivered in time. The final part of this research (Chapters 6--8) evaluates the performance of the proposed scheme as to how it reduces the number of transmissions in the network in response to growing data traffic load and network density and investigates its capacity to detect potential accidents. The evaluations use a custom-built simulator to model real-world scenarios such as city areas, junctions, roundabouts, motorways and so on. The study shows that the reduction in the number of transmissions helps reduce competition in the network significantly and this allows vehicles to deliver warning messages more rapidly to their neighbours. It also examines the relative performance of NETCODE when handling both sudden event-driven and longer-term periodic messages in diverse scenarios under stress caused by increasing numbers of vehicles and transmissions per vehicle. This work confirms the thesis' primary contention that XOR-based network coding provides a potential solution on which a more efficient AWS data dissemination scheme can be built.

Probabilistic verification of satellite systems for mission critical applications

In this thesis, we present a quantitative approach using probabilistic verification techniques for the analysis of reliability, availability, maintainability, and safety (RAMS) properties of satellite systems. The subject of our research is satellites used in mission critical industrial applications. A strong case for using probabilistic model checking to support RAMS analysis of satellite systems is made by our verification results. This study is intended to build a foundation to help reliability engineers with a basic background in model checking to apply probabilistic model checking to small satellite systems. We make two major contributions. One of these is the approach of RAMS analysis to satellite systems. In the past, RAMS analysis has been extensively applied to the field of electrical and electronics engineering. It allows system designers and reliability engineers to predict the likelihood of failures from the indication of historical or current operational data. There is a high potential for the application of RAMS analysis in the field of space science and engineering. However, there is a lack of standardisation and suitable procedures for the correct study of RAMS characteristics for satellite systems. This thesis considers the promising application of RAMS analysis to the case of satellite design, use, and maintenance, focusing on its system segments. Data collection and verification procedures are discussed, and a number of considerations are also presented on how to predict the probability of failure. Our second contribution is leveraging the power of probabilistic model checking to analyse satellite systems. We present techniques for analysing satellite systems that differ from the more common quantitative approaches based on traditional simulation and testing. These techniques have not been applied in this context before. We present the use of probabilistic techniques via a suite of detailed examples, together with their analysis. Our presentation is done in an incremental manner: in terms of complexity of application domains and system models, and a detailed PRISM model of each scenario. We also provide results from practical work together with a discussion about future improvements.

Transmission electron tomography: quality assessment and enhancement for three-dimensional imaging of nanostructures

Nanotechnology has revolutionised humanity's capability in building microscopic systems by manipulating materials on a molecular and atomic scale. Nan-osystems are becoming increasingly smaller and more complex from the chemical perspective which increases the demand for microscopic characterisation techniques. Among others, transmission electron microscopy (TEM) is an indispensable tool that is increasingly used to study the structures of nanosystems down to the molecular and atomic scale. However, despite the effectivity of this tool, it can only provide 2-dimensional projection (shadow) images of the 3D structure, leaving the 3-dimensional information hidden which can lead to incomplete or erroneous characterization. One very promising inspection method is Electron Tomography (ET), which is rapidly becoming an important tool to explore the 3D nano-world. ET provides (sub-)nanometer resolution in all three dimensions of the sample under investigation. However, the fidelity of the ET tomogram that is achieved by current ET reconstruction procedures remains a major challenge. This thesis addresses the assessment and advancement of electron tomographic methods to enable high-fidelity three-dimensional investigations. A quality assessment investigation was conducted to provide a quality quantitative analysis of the main established ET reconstruction algorithms and to study the influence of the experimental conditions on the quality of the reconstructed ET tomogram. Regular shaped nanoparticles were used as a ground-truth for this study. It is concluded that the fidelity of the post-reconstruction quantitative analysis and segmentation is limited, mainly by the fidelity of the reconstructed ET tomogram. This motivates the development of an improved tomographic reconstruction process. In this thesis, a novel ET method was proposed, named dictionary learning electron tomography (DLET). DLET is based on the recent mathematical theorem of compressed sensing (CS) which employs the sparsity of ET tomograms to enable accurate reconstruction from undersampled (S)TEM tilt series. DLET learns the sparsifying transform (dictionary) in an adaptive way and reconstructs the tomogram simultaneously from highly undersampled tilt series. In this method, the sparsity is applied on overlapping image patches favouring local structures. Furthermore, the dictionary is adapted to the specific tomogram instance, thereby favouring better sparsity and consequently higher quality reconstructions. The reconstruction algorithm is based on an alternating procedure that learns the sparsifying dictionary and employs it to remove artifacts and noise in one step, and then restores the tomogram data in the other step. Simulation and real ET experiments of several morphologies are performed with a variety of setups. Reconstruction results validate its efficiency in both noiseless and noisy cases and show that it yields an improved reconstruction quality with fast convergence. The proposed method enables the recovery of high-fidelity information without the need to worry about what sparsifying transform to select or whether the images used strictly follow the pre-conditions of a certain transform (e.g. strictly piecewise constant for Total Variation minimisation). This can also avoid artifacts that can be introduced by specific sparsifying transforms (e.g. the staircase artifacts the may result when using Total Variation minimisation). Moreover, this thesis shows how reliable elementally sensitive tomography using EELS is possible with the aid of both appropriate use of Dual electron energy loss spectroscopy (DualEELS) and the DLET compressed sensing algorithm to make the best use of the limited data volume and signal to noise inherent in core-loss electron energy loss spectroscopy (EELS) from nanoparticles of an industrially important material. Taken together, the results presented in this thesis demonstrates how high-fidelity ET reconstructions can be achieved using a compressed sensing approach.

An ontology for risk management of digital collections

Maintaining accessibility to and understanding of digital information over time is a complex challenge that often requires contributions and interventions from a variety of individuals and organizations. The processes of preservation planning and evaluation are fundamentally implicit and share similar complexity. Both demand comprehensive knowledge and understanding of every aspect of to-be-preserved content and the contexts within which preservation is undertaken. Consequently, means are required for the identification, documentation and association of those properties of data, representation and management mechanisms that in combination lend value, facilitate interaction and influence the preservation process. These properties may be almost limitless in terms of diversity, but are integral to the establishment of classes of risk exposure, and the planning and deployment of appropriate preservation strategies. We explore several research objectives within the course of this thesis. Our main objective is the conception of an ontology for risk management of digital collections. Incorporated within this are our aims to survey the contexts within which preservation has been undertaken successfully, the development of an appropriate methodology for risk management, the evaluation of existing preservation evaluation approaches and metrics, the structuring of best practice knowledge and lastly the demonstration of a range of tools that utilise our findings. We describe a mixed methodology that uses interview and survey, extensive content analysis, practical case study and iterative software and ontology development. We build on a robust foundation, the development of the Digital Repository Audit Method Based on Risk Assessment. We summarise the extent of the challenge facing the digital preservation community (and by extension users and creators of digital materials from many disciplines and operational contexts) and present the case for a comprehensive and extensible knowledge base of best practice. These challenges are manifested in the scale of data growth, the increasing complexity and the increasing onus on communities with no formal training to offer assurances of data management and sustainability. These collectively imply a challenge that demands an intuitive and adaptable means of evaluating digital preservation efforts. The need for individuals and organisations to validate the legitimacy of their own efforts is particularly prioritised. We introduce our approach, based on risk management. Risk is an expression of the likelihood of a negative outcome, and an expression of the impact of such an occurrence. We describe how risk management may be considered synonymous with preservation activity, a persistent effort to negate the dangers posed to information availability, usability and sustainability. Risk can be characterised according to associated goals, activities, responsibilities and policies in terms of both their manifestation and mitigation. They have the capacity to be deconstructed into their atomic units and responsibility for their resolution delegated appropriately. We continue to describe how the manifestation of risks typically spans an entire organisational environment, and as the focus of our analysis risk safeguards against omissions that may occur when pursuing functional, departmental or role-based assessment. We discuss the importance of relating risk-factors, through the risks themselves or associated system elements. To do so will yield the preservation best-practice knowledge base that is conspicuously lacking within the international digital preservation community. We present as research outcomes an encapsulation of preservation practice (and explicitly defined best practice) as a series of case studies, in turn distilled into atomic, related information elements. We conduct our analyses in the formal evaluation of memory institutions in the UK, US and continental Europe. Furthermore we showcase a series of applications that use the fruits of this research as their intellectual foundation. Finally we document our results in a range of technical reports and conference and journal articles. We present evidence of preservation approaches and infrastructures from a series of case studies conducted in a range of international preservation environments. We then aggregate this into a linked data structure entitled PORRO, an ontology relating preservation repository, object and risk characteristics, intended to support preservation decision making and evaluation. The methodology leading to this ontology is outlined, and lessons are exposed by revisiting legacy studies and exposing the resource and associated applications to evaluation by the digital preservation community.

Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs.

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network.

How well does B-type natriuretic peptide predict death and cardiac events in patients with heart failure: systematic review

Objective To assess how well B-type natriuretic peptide (BNP) predicts prognosis in patients with heart failure. Design Systematic review of studies assessing BNP for prognosis m patients with heart failure or asymptomatic patients. Data sources Electronic searches of Medline and Embase from January 1994 to March 2004 and reference lists of included studies. Study selection and data extraction We included all studies that estimated the relation between BNP measurement and the risk of death, cardiac death, sudden death, or cardiovascular event in patients with heart failure or asymptomatic patients, including initial values and changes in values in response to treatment. Multivariable models that included both BNP and left ventricular ejection fraction as predictors were used to compare the prognostic value of each variable. Two reviewers independently selected studies and extracted data. Data synthesis 19 studies used BNP to estimate the relative risk of death or cardiovascular events in heart failure patients and five studies in asymptomatic patients. In heart failure patients, each 100 pg/ml increase was associated with a 35% increase in the relative risk of death. BNP was used in 35 multivariable models of prognosis. In nine of the models, it was the only variable to reach significance-that is, other variables contained no prognostic information beyond that of BNP. Even allowing for the scale of the variables, it seems to be a strong indicator of risk. Conclusion Although systematic reviews of prognostic studies have inherent difficulties, including die possibility of publication bias, the results of the studies in this review show that BNP is a strong prognostic indicator for both asymptomatic patients mid for patients with heart failure at all stages of disease.

EDI e a fatura eletrónica redes colaborativas e desmaterialização

O Electronic Data Interchange (EDI) oferece um vasto leque de oportunidades para as empresas se tornarem mais competitivas. Não só na ótica dos processos de negócio de bens e serviços, como nas práticas organizacionais e de gestão. A adoção de tecnologias de informação e comunicação do comércio eletrónico B2B, visando o aumento da eficácia e eficiência na troca de documentos de negócio, possibilita a melhoria da colaboração entre agentes económicos de uma cadeia produtiva de bens ou serviços. O comércio eletrónico permitiu uma profunda transformação das práticas comerciais tradicionais, onde o EDI influenciou positivamente na forma de conduzir o negócio. Um conceito atualmente emergente é o de fatura eletrónica, essencial para o desenvolvimento da relação entre entidades (B2B). A desmaterialização da fatura acarreta inúmeros benefícios para as entidades envolvidas, tais como: a redução de custos, a redução do impacto ambiental e o aumento da produtividade e da transparência nos processos de negócio. Esta dissertação pretende abordar em detalhe as temáticas referentes ao EDI e à fatura eletrónica, no que concerne ao conceito, às mensagens, aos standards utilizados, aos benefícios e custos envolvidos, e a toda a sua envolvência ao longo de uma cadeia de abastecimento. Com base nestes conceitos, é apresentado um caso de estudo de sucesso na empresa nacional Sonae®, que pretende abordar todo o programa EDI da empresa. O esforço despendido na implementação do programa e na adoção da fatura eletrónica legal na Sonae® supera em larga escala os objetivos pretendidos, devido aos benefícios inerentes à utilização do EDI. A fatura eletrónica representa não só o conceito de troca de documentos via eletrónica, como também uma nova forma de conduzir os negócios.