On probability distributions of contact force magnitudes in loaded dense granular media

This paper discusses possible models for probability distributions of contact force magnitudes in loaded granular media. Many authors have studied such distributions, based on experiments with real particles as well as simulations in 2D and 3D. This has led to various and partly contradicting suggestions for the form of those distributions, which are described in the present paper. Its new theoretical investigations start from the empirically justified assumption that the components of contact forces follow exponential distributions with a certain dependence structure. This leads to distributions of force magnitudes similar to Gamma distributions with shape parameters depending on space dimension, which is in good agreement to results from experiments and numerical simulations. Also the analytical and statistical difficulties of the problem of determination of distributions of force magnitudes are discussed.In a discussion of Stefan Luding and D.S. the idea arose to consider dependent force components. Niels Kruyt supported our work by sending his papers and by patient discussions via e-mail and a careful reading of an earlier version of this paper.We had a very useful discussion with Farhang Radjai about the problem P(0)=0 and experiments with real disks. Finally, we are grateful to Tomaso Aste for leading our attention to infinitely divisible distributions.     

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.     

Modeling and computer simulation of ultrasonic/sonic driller/corer (USDC)

Simulation and analytical models for the ultrasonic/sonic drill/corer (USDC) are described in this paper. The USDC was developed as a tool for in-situ rock sampling and analysis in support of the NASA planetary exploration program. The USDC uses a novel drive mechanism, which transfers ultrasonic vibrations of a piezoelectric actuator into larger oscillations of a free-flying mass (free-mass). The free-mass impact on the drill bit creates a stress pulse at the drill tip/rock interface causing fracture in the rock. The main parts of the device (transducer, free-mass, bit, and rock) and the interactions between them were analyzed and numerically modeled to explore the drive mechanism. Each of these interactions is normally described by a time-dependent 2- or 3-D model involving slowly converging solutions, which makes the conventional approach unsuitable for USDC optimization studies. A simplified integrated model using tabulated data was developed to simulate the operation of the USDC on desktop PC and successfully predicted the characteristics of the device under a variety of conditions. The simulated results of the model and the experimental data used to verify the model are presented.     

Some novel classifiers designed using prototypes extracted by a newscheme based on self-organizing feature map

We propose two new comprehensive schemes for designing prototype-based classifiers. The scheme addresses all major issues (number of prototypes, generation of prototypes, and utilization of the prototypes) involved in the design of a prototype-based classifier. First we use Kohonen's self-organizing feature map (SOFM) algorithm to produce a minimum number (equal to the number of classes) of initial prototypes. Then we use a dynamic prototype generation and tuning algorithm (DYNAGEN) involving merging, splitting, deleting, and retraining of the prototypes to generate an adequate number of useful prototypes. These prototypes are used to design a "1 nearest multiple prototype (1-NMP)" classifier. Though the classifier performs quite well, it cannot reasonably deal with large variation of variance among the data from different classes. To overcome this deficiency we design a "1 most similar prototype (1-MSP)" classifier. We use the prototypes generated by the SOFM-based DYNAGEN algorithm and associate with each of them a zone of influence. A norm (Euclidean)-induced similarity measure is used for this. The prototypes and their zones of influence are fine-tuned by minimizing an error function. Both classifiers are trained and tested using several data sets, and a consistent improvement in performance of the latter over the former has been observed. We also compared our classifiers with some benchmark results available in the literature.     

Tele-rheumatology: telephone follow up and cyberclinic

This paper reviews here the main practical applications of telemedicine with particular reference to telephone care by health professionals to patients. A pilot project of out-patient telephone follow-up service for continuity of ambulatory rheumatology patients is described and recommendations are provided for those keen to develop such a service. In a later section newer technologies such as the potential practical applications of the internet and e-mail are described with the setting up of a cyber project for rheumatology and osteoporosis patients. It is noted that despite enthusiasm and development of such new projects, conventional clinicians need further longer term observation to grasp the advantages and pitfalls before more widespread use of telemedicine becomes commonplace.     

Experimental set-up for measuring diffusive and advective transport of radon through building materials

This study describes an approach for measuring and modelling diffusive and advective transport of radon through building materials. The goal of these measurements and model calculations is to improve our understanding concerning the factors influencing the transport of radon through building materials. To reach this goal, a number of experiments have to be conducted. These experiments, including measurements in a large cylinder for creating diffusive and advective transport of radon under controlled, 'dwelling-like' conditions, are described here and the initial results are presented. A better understanding about the transport of radon through building materials will lead to more effective ways to decrease or to prevent the entrance of radon into dwellings.     

Stemming via Distribution-Based Word Segregation for Classification and Retrieval

A novel corpus-based method for stemmer refinement, which can provide improvement in both classification and retrieval, is described. The method models the given words as generated from a multinomial distribution over the topics available in the corpus and includes a procedurelike sequential hypothesis testing that enables grouping together distributionally similar words. The system can refine any stemmer, and its strength can be controlled with parameters that reflect the amount of tolerance to be allowed in computing the similarity between the distributions of two words. Although obtaining the morphological roots of the given words is not the primary objective, the algorithm automatically does that to some extent. Despite a huge reduction in dictionary size, classification accuracies are seen to improve significantly when the proposed system is applied on some existing stemmers for classifying 20 Newsgroups and WebKB data. The refinements obtained are also suitable for cross-corpus stemming. Regarding retrieval, its superiority is extensively demonstrated with respect to four existing methods     

Role of Direct Reduced Iron Fines in Nitrogen Removal from Electric Arc Furnace Steel

Electric arc furnace steel contains about 70‐120 ppm nitrogen. There is no suitable method for nitrogen removal from electric arc furnace steel up to the level desired for good quality bars and flat rolled products (30 ppm max). The existing process based on vacuum degassing can remove only up to 20% of nitrogen in steel. In the present study DRI fines have been injected into a steel bath which can drift out nitrogen in steel through production of fine CO bubbles in‐situ on reaction with residual FeO in DRI fines and C in bath. For high and medium carbon steel, nitrogen got reduced to 30 ppm and 60 ppm respectively where initial nitrogen was 150 – 200 ppm in steel. Nitrogen removal also depends upon bath depth and addition level of DRI.