Ultrafast measurement of transient electroosmotic flow in microfluidics

We present a non-intrusive molecular dye based method, i.e., laser-induced fluorescence photobleaching anemometer (LIFPA), to significantly increase temporal resolution (TR) for velocity measurement of fast transient electrokinetic flows. To our knowledge, the TR has been for the first time achieved to 5–10 μs, about 100 times better than that published from state-of-the-art micro particle image velocimetry (μPIV), which is currently the most widely used velocimetry in the microfluidics community. The new method provides us with new opportunities to study experimentally the fundamental phenomena of unsteady electrokinetics (EK) and to validate relevant theoretical models. One application of the new method is demonstrated by measuring the rise time of DC electroosmotic flows (EOFs) in a microcapillary of 10 μm in diameter.     

Filtering artificial texts with statistical machine learning techniques

Fake content is flourishing on the Internet, ranging from basic random word salads to web scraping. Most of this fake content is generated for the purpose of nourishing fake web sites aimed at biasing search engine indexes: at the scale of a search engine, using automatically generated texts render such sites harder to detect than using copies of existing pages. In this paper, we present three methods aimed at distinguishing natural texts from artificially generated ones: the first method uses basic lexicometric features, the second one uses standard language models and the third one is based on a relative entropy measure which captures short range dependencies between words. Our experiments show that lexicometric features and language models are efficient to detect most generated texts, but fail to detect texts that are generated with high order Markov models. By comparison our relative entropy scoring algorithm, especially when trained on a large corpus, allows us to detect these “hard” text generators with a high degree of accuracy.     

Analysis of chaos and FRET reaction in split-and-recombine microreactors

The mixing and reaction performance of a split-and-recombine (SAR) microreactor was enhanced on modification of the geometric configuration. A rotation of fluid is induced on shrinking the structure of the splitting and reorientation region in alternate directions, thus improving the pattern of multi-lamination and enhancing the chaos of the fluid. To design and to analyze systematically the performance of the reactors, an effective method involving chaotic analysis and fluorescent resonant-energy transfer (FRET) is proposed. The structural design of a passive microreactor to generate an effective contact between the reagents is of great practical significance. SAR microreactors of four types with various microstructures were designed to illustrate the effects of geometric patterns (i.e., arrangement and dimensions) on mixing and reaction. Through analysis of the chaos, we revealed numerically the dynamic mixing governed by multi-lamination and chaotic mechanisms in the devices. The results show that specific structural designs induce rotation and rearrangement of fluids, thus elongating their material interface; the mixing of the fluids consequently improved. We investigated the hybridization of two complementarily labeled oligonucleotides in the devices by means of FRET. How the devices affected the rate of hybridization was thereby assessed, verifying that FRET is a technique capable of estimating the practical applicability of these devices.     

Infrastructure Federation Through Virtualized Delegation of Resources and Services

Infrastructure federation is becoming an increasingly important issue for modern Distributed Computing Infrastructures (DCIs): Dynamic elasticity of quasi-static Grid environments, incorporation of special-purpose resources into commoditized Cloud infrastructures, cross-community collaboration for increasingly diverging areas of modern e-Science, and Cloud Bursting pose major challenges on the technical level for many resource and middleware providers. Especially with respect to increasing costs of operating data centers, the intelligent yet automated and secure sharing of resources is a key factor for success. With the D-Grid Scheduler Interoperability (DGSI) project within the German D-Grid Initiative, we provide a strategic technology for the automatically negotiated, SLA-secured, dynamically provisioned federation of resources and services for Grid-and Cloud-type infrastructures. This goal is achieved by complementing current DCI schedulers with the ability to federate infrastructure for the temporary leasing of resources and rechanneling of workloads. In this work, we describe the overall architecture and SLA-secured negotiation protocols within DGSI and depict an advanced mechanism for resource delegation through means of dynamically provisioned, virtualized middleware. Through this methodology, we provide the technological foundation for intelligent capacity planning and workload management in a cross-infrastructure fashion.     

On the quantum and randomized approximation of linear functionals on function spaces

We deal with quantum and randomized algorithms for approximating a class of linear continuous functionals. The functionals are defined on a H?lder space of functions f of d variables with r continuous partial derivatives, the rth derivative being a H?lder function with exponent ρ. For a certain class of such linear problems (which includes the integration problem), we define algorithms based on partitioning the domain of f into a large number of small subdomains, and making use of the well-known quantum or randomized algorithms for summation of real numbers. For N information evaluations (quantum queries in the quantum setting), we show upper bounds on the error of order N ^−(γ+1) in the quantum setting, and N ^−(γ+1/2) in the randomized setting, where γ = (r + ρ)/d is the regularity parameter. Hence, we obtain for a wider class of linear problems the same upper bounds as those known for the integration problem. We give examples of functionals satisfying the assumptions, among which we discuss functionals defined on the solution of Fredholm integral equations of the second kind, with complete information about the kernel. We also provide lower bounds, showing in some cases sharpness of the obtained results, and compare the power of quantum, randomized and deterministic algorithms for the exemplary problems.     

Genetic interacting multiple model algorithm based on <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> filter for maneuvering target tracking

In order to have a good performance for maneuvering target tracking, a genetic interacting multiple model (GIMM) algorithm based on the H _∞ filter is proposed in this paper. It introduces the H _∞ filter as model-conditional filter, which keeps its robustness by constantly adjusting parameters, to improve the performance and the precision. Meanwhile, it optimizes model probabilities using the genetic algorithm (GA), chooses sub-models which are close to true models from a set of models, adjusts the number of models and parameters in real-time, reduces excessive competition, and improves the performance of the algorithm. The simulation results indicate that, the algorithm has higher tracking accuracy and stronger robustness than the standard IMM algorithm.     

COSAR: hybrid reasoning for context-aware activity recognition

Human activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module.     

Non-functional properties in the model-driven development of service-oriented systems

Systems based on the service-oriented architecture (SOA) principles have become an important cornerstone of the development of enterprise-scale software applications. They are characterized by separating functions into distinct software units, called services, which can be published, requested and dynamically combined in the production of business applications. Service-oriented systems (SOSs) promise high flexibility, improved maintainability, and simple re-use of functionality. Achieving these properties requires an understanding not only of the individual artifacts of the system but also their integration. In this context, non-functional aspects play an important role and should be analyzed and modeled as early as possible in the development cycle. In this paper, we discuss modeling of non-functional aspects of service-oriented systems, and the use of these models for analysis and deployment. Our contribution in this paper is threefold. First, we show how services and service compositions may be modeled in UML by using a profile for SOA (UML4SOA) and how non-functional properties of service-oriented systems can be represented using the non-functional extension of UML4SOA (UML4SOA-NFP) and the MARTE profile. This enables modeling of performance, security and reliable messaging. Second, we discuss formal analysis of models which respect this design, in particular we consider performance estimates and reliability analysis using the stochastically timed process algebra PEPA as the underlying analytical engine. Last but not least, our models are the source for the application of deployment mechanisms which comprise model-to-model and model-to-text transformations implemented in the framework VIATRA. All techniques presented in this work are illustrated by a running example from an eUniversity case study.     

Implementation of a strain energy-based nonlinear finite element in the object-oriented environment

The objective of the paper is to describe a novel finite element computational method based on a strain energy density function and to implement it in the object-oriented environment. The original energy-based finite element was put into the known standard framework of classes and handled in a different manner. The nonlinear properties of material are defined with a modified strain energy density function. The local relaxation procedure proposed as a method used to resolve a nonlinear problem is implemented in C++ language. The hexahedral element with eight nodes as well as the adaptation of the nonlinear finite element is introduced. The chosen numerical model is made of nearly incompressible hyperelastic material. The application of the proposed element is shown on the example of a rectangular parallelepiped with a hollow port.     

Improving practical exact string matching

We present improved variations of the BNDM algorithm for exact string matching. At each alignment our bit-parallel algorithms process a q-gram before testing the state variable. In addition we apply reading a 2-gram in one instruction. Our point of view is practical efficiency of algorithms. Our experiments show that the new variations are faster than earlier algorithms in many cases.