Differences in wearer response to garments for outdoor activity

The performance of garments for outdoor activity was compared. Three fabrics, each in garments for the upper body, matched garment/wearer dimensions, were worn by 10 athletically 'well-trained' males under controlled conditions (hot 32 +/- 2 degrees C, 20 +/- 2% relative humidity (RH); cold 8 +/- 2 degrees C, 40 +/- 2% RH) with physical (instrumental) and sensory responses obtained during the trials. Differences in human responses to the fabrics/garments included heart rate, core temperature during run (hot, cold), rest (hot) and walk (cold), heat content of the body, humidity under garments during rest and run and time to onset of sweating. No such differences were identified for change in body mass, core temperature during walk (hot) and rest (cold), skin temperature, temperature of skin covered by the garment, humidity under the garments during walk or for any perceptions (thermal sensations, thermal comfort of torso, exertion, wetness). The garment in single jersey wool fabric performed best in both hot and cold conditions. Effects of garments on wearers are often related to properties of the fabrics from which the garments are made. This study shows that only some differences in fabric properties result in measurable thermophysiological and perceptual responses of the garment wearers and underlines the difficulty in predicting performance of garments/persons from laboratory tests on fabrics.     

Matching-Pursuits Dissimilarity Measure for Shape-Based Comparison and Classification of High-Dimensional Data

In this paper, a new matching pursuits dissimilarity measure (MPDM) is presented that compares two signals using the information provided by their matching pursuits (MP) approximations, without requiring any prior domain knowledge. MPDM is a flexible and differentiable measure that can be used to perform shape-based comparisons and fuzzy clustering of very high-dimensional, possibly compressed, data. A novel prototype based classification algorithm, which is termed the computer aided minimization procedure (CAMP), is also proposed. The CAMP algorithm uses the MPDM with the competitive agglomeration (CA) fuzzy clustering algorithm to build reliable shape based prototypes for classification. MP is a well known sparse signal approximation technique, which is commonly used for video and image coding. The dictionary and coefficient information produced by MP has previously been used to define features to build discrimination and prototype based classifiers. However, existing MP based classification applications are quite problem domain specific, thus making their generalization to other problems quite difficult. The proposed CAMP algorithm is the first MP based classification system that requires no assumptions about the problem domain and builds a bridge between the MP and fuzzy clustering algorithms. Experimental results also show that the CAMP algorithm is more resilient to outliers in test data than the multilayer perceptron (MLP) and support-vector-machine (SVM) classifiers, as well as prototype-based classifiers using the Euclidean distance as their dissimilarity measure.     

Analysis of layered scattering materials by pulsed photothermal radiometry: application to photon propagation in tissue

A model of pulsed photothermal radiometry (PPTR) based on optical diffusion theory is presented for a turbid, two-layer, semi-infinite medium containing a surface layer whose optical absorption and scattering properties differ from that of the underlying layer. Assuming one-dimensional geometry, we develop expressions for the depth-dependent fluence distributions and radiant-energy-density profiles and for the time dependence of the PPTR signal. Experimental tests of the PPTR model in a series of layered phantoms of varying optical properties are described. The results of these tests are consistent with the model predictions.     

SATCHMORE: SATCHMO with RElevancy

We introduce a relevancy detection algorithm to be used in conjunction with the SATCHMO prover. The version of SATCHMO considered here is essentially a bidirectional prover, utilizing Prolog (back chaining) on Horn clauses and forward chaining on non-Horn clauses. Our extension, SATCHMORE (SATCHMO with RElevancy), addresses the major weakness of SATCHMO: the uncontrolled use of forward chaining. By marking potentially relevant clause head literals, and then requiring that all the head literals be marked relevant (be totally relevant) before a clause is used for forward chaining, SATCHMORE is able to guide the use of these rules. Furthermore, the relevancy testing is performed without extending the proof search beyond what is done in SATCHMO. A simple implementation of the extended SATCHMO can be written in Prolog. We describe our relevancy testing approach, present the implementation, prove soundness and completeness, and provide examples that demonstrate the power of relevancy testing.This research was partially supported by NSF Grants IRI-8805696 and CCR-9116203. This paper is a major revision of Wilson and Loveland (1989).     

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM-BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self-organization, and self-configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM-BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy-efficient clustering approach consolidated game theory as well as dual-cluster-head mode for WSNs energy-aware clustering by cuckoo optimization approach (EECM-COA), energy-aware clustering-based routing using multi-path reliable transmission with routing and control board (EECM-RCB-MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM-AR-TORA-FCS), passive multi-hop clustering algorithm (EECM-PMC), dynamic source routing protocol based on genetic algorithm-bacterial foraging optimization (DSR-GA-BFO).     

Sub‐Micron Polymeric Stomatocytes as Promising Templates for Confined Crystallization and Diffraction Experiments

The possibility of using sub‐micrometer polymeric stomatocytes is investigated to effectuate confined crystallization of inorganic compounds. These bowl‐shaped polymeric compartments facilitate confined crystallization while their glassy surfaces provide their crystalline cargos with convenient shielding from the electron beam's harsh effects during transmission electron microscopy experiments. Stomatocytes host the growth of a single nanocrystal per nanocavity, and the electron diffraction experiments reveal that their glassy membranes do not interfere with the diffraction patterns obtained from their crystalline cargos. Therefore, it is expected that the encapsulation and crystallization within these compartments can be considered as a promising template (nanovials) that hold and protect nanocrystals and protein clusters from the direct radiation damage before data acquisition, while they are examined by modern crystallography methodologies such as serial femtosecond crystallography.     

What is "special" about face perception?

There is growing evidence that face recognition is "special" but less certainty concerning the way in which it is special. The authors review and compare previous proposals and their own more recent hypothesis, that faces are recognized "holistically" (i.e., using relatively less part decomposition than other types of objects). This hypothesis, which can account for a variety of data from experiments on face memory, was tested with 4 new experiments on face perception. A selective attention paradigm and a masking paradigm were used to compare the perception of faces with the perception of inverted faces, words, and houses. Evidence was found of relatively less part-based shape representation for faces. The literatures on machine vision and single unit recording in monkey temporal cortex are also reviewed for converging evidence on face representation. The neuropsychological literature is reviewed for-evidence on the question of whether face representation differs in degree or kind from the representation of other types of objects.     

On the Complexity of Computing Winning Strategies for Finite Poset Games

This paper is concerned with the complexity of computing winning strategies for poset games. While it is reasonably clear that such strategies can be computed in PSPACE, we give a simple proof of this fact by a reduction to the game of geography. We also show how to formalize the reasoning about poset games in Skelley’s theory W₁¹\mathbf{W}_{1}^{1} for PSPACE reasoning. We conclude that W₁¹\mathbf{W}_{1}^{1} can use the “strategy stealing argument” to prove that in poset games with a supremum the first player always has a winning strategy.     

Interleaving solving and elicitation of constraint satisfaction problems based on expected cost

We consider Constraint Satisfaction Problems in which constraints can be initially incomplete, where it is unknown whether certain tuples satisfy the constraint or not. We assume that we can determine the satisfaction of such an unknown tuple, i.e., find out whether this tuple is in the constraint or not, but doing so incurs a known cost, which may vary between tuples. We also assume that we know the probability of an unknown tuple satisfying a constraint. We define algorithms for this problem, based on backtracking search. Specifically, we consider a simple iterative algorithm based on a cost limit on the unknowns that may be determined, and a more complex algorithm that delays determining an unknown in order to estimate better whether doing so is worthwhile. We show experimentally that the more sophisticated algorithms can greatly reduce the average cost.