A SOA statistical engine for biomedical data

Currently, large efforts are spent to develop standards and architectures useful to achieve more effective interoperability among medical information systems. Despite such efforts, there are no researches produced so far to directly analyse, with statistical methods, biomedical data represented as eXtensible Markup Language (XML) documents. Thus, the paper proposes an architecture which offers a twofold approach to the statistical analysis of XML data, i.e. via a web service and by extending the query languages used in XML databases. To show how the architecture can be used, a sample system is also reported. Finally, the paper ends by reporting the advantages and drawbacks of the proposed approach in comparison with classic statistical packages.     

Services surround you

Our daily life is pervaded by digital information and devices, not least the common mobile phone. However, a seamless connection between our physical world, such as a movie trailer on a screen in the main rail station and its digital counterparts, such as an online ticket service, remains difficult. In this paper, we present contextual bookmarks that enable users to capture information of interest with a mobile camera phone. Depending on the user’s context, the snapshot is mapped to a digital service such as ordering tickets for a movie theater close by or a link to the upcoming movie’s Web page.     

TiO2 thin films from titanium butoxide: Synthesis, Pt addition, structural stability, microelectronic processing and gas-sensing properties

TiO₂ thin films were prepared by spin-coating of a Ti butoxide-derived sol onto oxidized silicon wafers, followed by a heat-treatment at temperatures ranging from 500 to 800 °C. The film thickness after heat-treatment at 500 °C was 50 nm. Pt addition, with a Pt:Ti nominal atomic ratio ranging from 0.01 to 0.1, was achieved by adding solutions of Pt(II) acetylacetonate to the TiO₂ sols. The thin films were investigated by X-ray diffraction, evidencing that Pt promoted the structural transformation of the starting anatase phase of TiO₂ to rutile, with a more enhanced effect with increasing the Pt concentration and/or the heat-treatment temperature. High-resolution transmission electron microscopy evidenced that, when a Pt:Ti atomic ratio of 0.05 and a heat treatment at 500 °C were used, the TiO₂ contained both anatase and rutile phases and interspersed Pt nanocrystals (2–3 nm). This result allowed attributing the structural transformation in TiO₂ to the strain created by the Pt nanocrystals—a conclusion which was further corroborated by the observation that Pd-modified films, prepared under similar conditions, were only composed of anatase TiO₂ and did not contain any Pd nanocrystals. The films heat-treated at 500 °C were able to withstand a full microelectronic processing sequence, including dry etching for gas sensors sensitive area definition, Ti/Pt contact formation, and heater processing on the backside of the sensor substrates. H₂ gas-sensing tests evidenced that the anatase TiO₂ phase was much more sensitive than the rutile one. The presence of Pt further enhanced the gas-sensing properties, lowering the optimum sensor operation temperature to about 330 °C and allowing for the detection of a minimum H₂ concentration of about 1000 ppm.     

Existence,stability and robustness analysis of limit cycles in hybrid anti-lock braking systems

We investigate the stability and robustness properties of anti-lock braking systems (ABS) based on actuators with on/off dynamics. Namely, we propose a hybrid ABS controller which gives rise to an asymptotically stable limit cycle on the wheel slip. The proposed approach allows to derive exact information on the maximum allowable uncertainty in the measured variables which guarantee the cycle stability. Moreover, a structural stability analysis is performed with respect to different road conditions and to the actuator rate limit.     

Interactive multiscale tensor reconstruction for multiresolution volume visualization

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.     

High performance automotive chassis design: a topology optimization based approach

Automotive chassis design in view of car weight reduction is a challenging task due to the many performance targets that must be satisfied, in particular in terms of vehicle safety. In this paper a methodology for automotive chassis design in involving optimization techniques is presented. In particular, topology, topometry and size optimizations are coupled with fem analyses and adopted in cascade for reaching an optimum chassis configuration. The methodology is applied to the design process of a rear-central engine high performance vehicle chassis. The objective of the optimization process is the chassis weight reduction, yet in fulfilment of structural performance constraints as required by Ferrari standards. The results demonstrate the general applicability of the methodology presented for obtaining the general trusses layout and thicknesses distribution of the structure. The numerical model at this stage shows a significant weight reduction when compared to the chassis of the Ferrari F458 Italia.     

Solving satisfiability problems with preferences

Propositional satisfiability (SAT) is a success story in Computer Science and Artificial Intelligence: SAT solvers are currently used to solve problems in many different application domains, including planning and formal verification. The main reason for this success is that modern SAT solvers can successfully deal with problems having millions of variables. All these solvers are based on the Davis–Logemann–Loveland procedure (dll). In its original version, dll is a decision procedure, but it can be very easily modified in order to return one or all assignments satisfying the input set of clauses, assuming at least one exists. However, in many cases it is not enough to compute assignments satisfying all the input clauses: Indeed, the returned assignments have also to be “optimal” in some sense, e.g., they have to satisfy as many other constraints—expressed as preferences—as possible. In this paper we start with qualitative preferences on literals, defined as a partially ordered set (poset) of literals. Such a poset induces a poset on total assignments and leads to the definition of optimal model for a formula ψ as a minimal element of the poset on the models of ψ. We show (i) how dll can be extended in order to return one or all optimal models of ψ (once converted in clauses and assuming ψ is satisfiable), and (ii) how the same procedures can be used to compute optimal models wrt a qualitative preference on formulas and/or wrt a quantitative preference on literals or formulas. We implemented our ideas and we tested the resulting system on a variety of very challenging structured benchmarks. The results indicate that our implementation has comparable performances with other state-of-the-art systems, tailored for the specific problems we consider.