Strategies for increasing evaporative cooling during simulated desert patrol mission

The study evaluated the efficiency of two heat dissipation strategies under simulated desert patrol missions. Ten men participated in four trials, during which they walked on a treadmill (45°C, 20% relative humidity), carrying a load of 35 kg; two 50-min walks were separated by a 20-min rest. Cooling strategies, provided by an ambient air-ventilated vest (active cooling condition, AC), or water spraying of the skin during the rest (passive cooling condition, PC), in addition to reduced clothing and open zippers, were compared to conditions with full protective (FP) clothing and naked condition (NC). Skin temperature was higher during NC (37.9 ± 0.4°C; p < 0.001), and rectal temperature and heart rate were higher during FP (38.6 ± 0.4°C, p < 0.001 and 145 ± 12, p < 0.001, respectively), compared to other conditions. Four subjects terminated the trial prematurely due to signs of heat exhaustion in FP. Both cooling strategies substantially improved evaporative cooling.     

Semantic integration of enterprise information systems using meta-metadata ontology

This paper proposes a non-domain-specific metadata ontology as a core component in a semantic model-based document management system (DMS), a potential contender towards the enterprise information systems of the next generation. What we developed is the core semantic component of an ontology-driven DMS, providing a robust semantic base for describing documents’ metadata. We also enabled semantic services such as automated semantic translation of metadata from one domain to another. The core semantic base consists of three semantic layers, each one serving a different view of documents’ metadata. The core semantic component’s base layer represents a non-domain-specific metadata ontology founded on ebRIM specification. The main purpose of this ontology is to serve as a meta-metadata ontology for other domain-specific metadata ontologies. The base semantic layer provides a generic metadata view. For the sake of enabling domain-specific views of documents’ metadata, we implemented two domain-specific metadata ontologies, semantically layered on top of ebRIM, serving domain-specific views of the metadata. In order to enable semantic translation of metadata from one domain to another, we established model-to-model mappings between these semantic layers by introducing SWRL rules. Having the semantic translation of metadata automated not only allows for effortless switching between different metadata views, but also opens the door for automating the process of documents long-term archiving. For the case study, we chose judicial domain as a promising ground for improving the efficiency of the judiciary by introducing the semantics in this field.     

A Monge-Ampère enhancement for semi-Lagrangian methods

Demanding the compatibility of semi-Lagrangian trajectory schemes with the fundamental Euler expansion formula leads to the Monge-Ampère (MA) nonlinear second-order partial differential equation. Given standard estimates of the departure points of flow trajectories, solving the associated MA problem provides a corrected solution satisfying a discrete Lagrangian form of the mass continuity equation to round-off error. The impact of the MA enhancement is discussed in two diverse limits of fluid dynamics applications: passive tracer advection in a steady cellular flow and in fully developed turbulence. Improvements of the overall accuracy of simulations depend on the problem and can be substantial.     

Information systems in modeling interactive computations on granules

In this paper, we discuss the importance of information systems in modeling interactive computations performed on (complex) granules and we propose a formal approach to interactive computations based on generalized information systems and rough sets which can be combined with other soft computing paradigms such as fuzzy sets or evolutionary computing, but also with machine learning and data mining techniques. Information systems are treated as dynamic granules used for representing the results of the interaction of attributes with the environment. Two kinds of attributes are distinguished, namely, the perception attributes, including sensory attributes, and the action attributes. Sensory attributes are the basic perception attributes, other perception attributes are constructed on the basis of the sensory ones. Actions are activated when their guards, being often complex and vague concepts, are satisfied to a satisfactory degree. The guards can be approximated on the basis of measurements performed by sensory attributes rather than defined exactly. Satisfiability degrees for guards are results of reasoning called the adaptive judgment. The approximations are induced using hierarchical modeling. We show that information systems can be used for modeling more advanced forms of interactions in hierarchical modeling. The role of hierarchical interactions is emphasized in the modeling of interactive computations. Some illustrative examples of interactions used in the ACT-R 6.0 system are reported. ACT-R 6.0 is based on a cognitive architecture and can be treated as an example of a highly interactive complex granule which can be involved in hierarchical interactions. For modeling of interactive computations, we propose much more general information systems than the studied dynamic information systems (see, e.g., Ciucci (2010) [8] and Pa?asiński and Pancerz (2010) [32]). For example, the dynamic information systems are making it possible to consider incremental changes in information systems. However, they do not contain the perception and action attributes necessary for modeling interactive computations, in particular for modeling intrastep interactions.     

Dynamic normal forms and dynamic characteristic polynomial

We present the first fully dynamic algorithm for computing the characteristic polynomial of a matrix. In the generic symmetric case, our algorithm supports rank-one updates in O(n²logn) randomized time and queries in constant time, whereas in the general case the algorithm works in O(n²klogn) randomized time, where k is the number of invariant factors of the matrix. The algorithm is based on the first dynamic algorithm for computing normal forms of a matrix such as the Frobenius normal form or the tridiagonal symmetric form. The algorithm can be extended to solve the matrix eigenproblem with relative error 2^−b in additional O(nlog²nlogb) time. Furthermore, it can be used to dynamically maintain the singular value decomposition (SVD) of a generic matrix. Together with the algorithm, the hardness of the problem is studied. For the symmetric case, we present an Ω(n²) lower bound for rank-one updates and an Ω(n) lower bound for element updates.     

Processor Efficient Parallel Matching

In this paper we consider the problem of finding perfect matchings in parallel. We present a RNC algorithm with almost optimal work with respect to sequential algorithms, i.e., it uses O(n ^ω ) processors, where ω is the matrix multiplication exponent. Our algorithm is based on an RNC algorithm for computing determinant of a degree one polynomial matrix which is of independent interest. Research supported by KBN grant 1P03A01830.     

On constructing an optimal consensus clustering from multiple clusterings

Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mining. In this paper, we formalize a set-theoretic model for computing such a similarity measure. Roughly speaking, in this model we have k>1 partitions (clusters) of the same data set each containing the same number of sets and the goal is to align the sets in each partition to minimize a similarity measure. For k=2, a polynomial-time solution was proposed by Gusfield (Information Processing Letters 82 (2002) 159-164). In this paper, we show that the problem is MAX-SNP-hard for k=3 even if each partition in each cluster contains no more than 2 elements and provide a -approximation algorithm for the problem for any k.     

Computational models of myocardial endomysial collagen arrangement

Collagen extracellular matrix is one of the factors related to high passive stiffness of cardiac muscle. However, the architecture and the mechanical aspects of the cardiac collagen matrix are not completely known. In particular, endomysial collagen contribution to the passive mechanics of cardiac muscle as well as its micro anatomical arrangement is still a matter of debate. In order to investigate mechanical and structural properties of endomysial collagen, we consider two alternative computational models of some specific aspects of the cardiac muscle. These two models represent two different views of endomysial collagen distribution: (1) the traditional view and (2) a new view suggested by the data obtained from scanning electron microscopy (SEM) in NaOH macerated samples (a method for isolating collagen from the other tissue). We model the myocardial tissue as a net of spring elements representing the cardiomyocytes together with the endomysial collagen distribution. Each element is a viscous elastic spring, characterized by an elastic and a viscous constant. We connect these springs to imitate the interconnections between collagen fibers. Then we apply to the net of springs some external forces of suitable magnitude and direction, obtaining an extension of the net itself. In our setting, the ratio forces magnitude /net extension is intended to model the stress /strain ratio of a microscopical portion of the myocardial tissue. To solve the problem of the correct identification of the values of the different parameters involved, we use an artificial neural network approach. In particular, we use this technique to learn, given a distribution of external forces, the elastic constants of the springs needed to obtain a desired extension as an equilibrium position. Our experimental findings show that, in the model of collagen distribution structured according to the new view, a given stress /strain ratio (of the net of springs, in the sense specified above) is obtained with much smaller (w.r.t. the other model, corresponding to the traditional view) elasticity constants of the springs. This seems to indicate that by an appropriate structure, a given stiffness of the myocardial tissue can be obtained with endomysial collagen fibers of much smaller size.     

Model-based tracking by classification in a tiny discrete pose space

A method is presented for tracking 3D objects as they transform rigidly in space within a sparse range image sequence. The method operates in discrete space and exploits the coherence across image frames that results from the relationship between known bounds on the object's velocity and the sensor frame rate. These motion bounds allow the interframe transformation space to be reduced to a reasonable and indeed tiny size, comprising only tens or hundreds of possible states. The tracking problem is in this way cast into a classification framework, effectively trading off localization precision for runtime efficiency and robustness. The method has been implemented and tested extensively on a variety of freeform objects within a sparse range data stream comprising only a few hundred points per image. It has been shown to compare favorably against continuous domain iterative closest point (ICP) tracking methods, performing both more efficiently and more robustly. A hybrid method has also been implemented that executes a small number of ICP iterations following the initial discrete classification phase. This hybrid method is both more efficient than the ICP alone and more robust than either the discrete classification method or the ICP separately