Robustness of the partitions in grouping multi-item inventories

A recent work obtained closed-form solutions to the.problem of optimally grouping a multi-item inventory into subgroups with a common order cycle per group, when the distribution by value of the inventory could be described by a Pareto function. This paper studies the sensitivity of the optimal subgroup boundaries so obtained. Closed-form expressions have been developed to find intervals for the subgroup boundaries for any given level of suboptimality. Graphs have been provided to aid the user in selecting a cost-effective level of aggregation and choosing appropriate subgroup boundaries for a whole range of inventory distributions. The results of sensitivity analyses demonstrate the availability of flexibility in the partition boundaries and the cost-effectiveness of any stock control system through three groups, and thus also provide a theoretical support to the intuitive ABC system of classifying the items.

UAV Grouping and Coordination Tactics for Ground Attack Missions

Grouping and coordination tactics for ground attack missions by a heterogeneous mix of reconnaissance, enemy suppression, and attack unmanned aerial vehicles (UAVs) is presented. Dubins' paths are used to determine the optimal number of attack UAVs and their positional and heading freedoms, as functions of weapon seeker range and field of view. A generic battlefield scenario with layered defense is created and the tactics are evaluated on a Group Flyer simulation platform for both nominal and off-nominal conditions.

Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Quaternary unary operators

Unary operators are functions of a single variable. Realization of quaternary unary operators (QUOs) using quaternary multiplexer (QMUX) is presented in this paper. QUOs are divided into eight groups on the basis of the number of change overs in the output for an input sequence of 0, 1, 2, 3. This grouping reduces the hardware required to realize them. QMUX with two, three, and four input lines are proposed for the realization of QUOs belonging to the eight groups. A systematic procedure for the selection of QMUX and the implementation of the QUOs are given. The QMUXs are designed using CMOS ICs. The hardware required for their implementation is also discussed.

Conformational Characterization of S-Methyl Dithiocarbazate by Infrared Spectra and Vibrational Assignments

Infrared spectra are recorded for S-methyl dithiocarbazate and its N-deuterated compound in two molecular conformations in the solid state and in solution between 4000 and 30 cm−1. The assignments have been supported from a complete normal coordinate analysis; the conformation sensitive bands of the –CSNHNH2 grouping are discussed. The assignments are compared with those of related molecules to check the internal consistency and to obtain the pattern of the characteristic bands of thiocarbazoyl (–CSNHNH2) group. The magnitudes of the C–N and S–CH3 torsional barriers are estimated from the force constants.

Vibrational and 1H NMR spectra of N-(2-pyridyl)-thioformamide and N-(2-pyridyl)thioacetamide

The Raman and infrared spectra of N-(2-pyridyl) thioformamide and N-(2-pyridyl)-thioacetamide have been measured. The assignment of the bands is aided by the complete normal coordinate treatment for all the vibrations of N-(2-pyridyl)thioformamide and its N-deuterated molecule using a Urey—Bradley force function for the in-plane vibrations and a valence force function for the out of plane vibrations. Variable temperature 1H NMR study of the two pyridylthionamides has also been performed. It is inferred that while N-(2-pyridyl)thioformamide favours a cis —CSNH— group, the other compound favours a trans —CSNH— grouping at ambient temperature.

The line transect method for estimating densities of large mammals in a tropical deciduous forest: An evaluation of models and field experiments

We have evaluated techniques of estimating animal density through direct counts using line transects during 1988-92 in the tropical deciduous forests of Mudumalai Sanctuary in southern India for four species of large herbivorous mammals, namely, chital (Axis axis), sambar (Cervus unicolor), Asian elephant (Elephas maximus) and gaur (Bos gauras). Density estimates derived from the Fourier Series and the Half-Normal models consistently had the lowest coefficient of variation. These two models also generated similar mean density estimates. For the Fourier Series estimator, appropriate cut-off widths for analysing line transect data for the four species are suggested. Grouping data into various distance classes did not produce any appreciable differences in estimates of mean density or their variances, although model fit is generally better when data are placed in fewer groups. The sampling effort needed to achieve a desired precision (coefficient of variation) in the density estimate is derived. A sampling effort of 800 km of transects returned a 10% coefficient of variation on estimate for chital; for the other species a higher effort was needed to achieve this level of precision. There was no statistically significant relationship between detectability of a group and the size of the group for any species. Density estimates along roads were generally significantly different from those in the interior af the forest, indicating that road-side counts may not be appropriate for most species.

A blind indic script recognizer for multi-script documents

We report a hierarchical blind script identifier for 11 different Indian scripts. An initial grouping of the 11 scripts is accomplished at the first level of this hierarchy. At the subsequent level, we recognize the script in each group. The various nodes of this tree use different feature-classifier combinations. A database of 20,000 words of different font styles and sizes is collected and used for each script. Effectiveness of Gabor and Discrete Cosine Transform features has been independently, evaluated using nearest neighbor linear discriminant and support vector machine classifiers. The minimum and maximum accuracies obtained, using this hierarchical mechanism, are 92.2% and 97.6%, respectively.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Localized agglutinin staining in muscle capillaries from normal and very old atrophic human muscle using winged bean ( Psophocarpus tetragonolobus ) lectin

The winged bean (Psophocarpus tetragonolobus) agglutinin (total lectin) and its basic (WBA I) and acidic isoform (WBA II) were used to analyze capillaries in sections from human muscle. The microvessels were clearly labeled after incubation with the lectins in both normal muscle and in old muscles with age-related type II atrophy or muscle fiber grouping. Muscle fibers, nerves, and connective tissue remained unstained. The total lectin detected muscle capillaries from all blood group AB0 individuals. The isoform WBA I reacted only with blood vessels in blood group A and B individuals, while the blood vessels in blood group 0 individuals were demonstrated with WBA II. WBA I staining was inhibited by p-nitrophenyl α-galactopyranoside and N-acetylgalactosamine, whereas 2′-fucosyllactose and preincubation with an antibody against type-1 chain H abolished capillary staining with WBA II. The study demonstrates the usefulness of WBA as a marker of capillaries in human muscle.

Revised Cam-Clay Model

The Cam-clay models, or any other plasticity-based models, do not make distinction between the mode of stress transfer in coarse- and fine-grained soils. An examination of behavior at micro level in fine-grained soils, from the consideration of load transfer through physico-chemical interactions, suggests that the plastic compressions result from the grouping of particles into larger clusters and that elastic compressions result from the decrease in the spacing between particles. During shearing, these clusters gradually get dismembered, releasing the locked-in energy. The effect of such dismembering of clusters can be easily incorporated into the original Cam-clay model, and better predictions can be obtained with the associated flow rule, itself, for both normally and over consolidated states. The method essentially defines the hardening of yield surfaces with internal changes in the spacing between particles, instead of changes in externally observed plastic strains. The approach describes the behavior of over consolidated soils as yielding along successfively hardening Roscoe surfaces with gradually varying plastic properties.

Self accomodation morphology of martensite variants in Zr---2.5wt%Nb alloy

The role of self accomodation of the different mertensite variants controlling the morphologies of the Zr---2.5wt%Nb alloy martensite has been examined. Three distinct types of grouping of martensite variants have been found to be predominantly present. Crystallographic descriptions of these groups have been provided and the degrees of self accomodation for these have been estimated and compared with those corresponding to other possible variant groupings around the symmetry axes of the parent phase. The frequently observed 3-variant group, which shows an “indentation mark” morphology when viewed along left angle bracket111right-pointing angle bracketβ directions in the transmission electron microscope, has been seen to have the highest degree of self accomodation amongst the cases considered. Based on the observations made, a growth sequence leading to the formation of the final martensitic structure has been proposed.

Tropical Clays .1. Index Properties And Microstructural Aspects

Soils showing changes in plasticity characteristics upon driving form an important group in tropical soils. These changes are attributed to the grouping of particles into aggregates either due to mineralogy or presence of cementing agents and/or pore fluid characteristics. These changes are found to be permanent. In this paper, the effect of these changes leading to changes in index properties is discussed. The coefficient of permeability has been found to be comparable at liquid limit water content for different soils of varying liquid limit values. Permeability is an indirect reflection of microstructure and indicates the flow rate, which depends upon pore geometry. Other mechanical properties like compressibility and shear strength also depend upon pore geometry. These microstructural aspects of liquid limit as a reference state for the analysis of engineering behavior of tropical soils are examined in detail.

Variable Sparsity Kernel Learning

This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assume that the given kernels are grouped and employ l(1) norm regularization for promoting sparsity within RKHS norms of each group and l(s), s >= 2 norm regularization for promoting non-sparse combinations across groups. Various sparsity levels in combining the kernels can be achieved by varying the grouping of kernels-hence we name the formulations as Variable Sparsity Kernel Learning (VSKL) formulations. While previous attempts have a non-convex formulation, here we present a convex formulation which admits efficient Mirror-Descent (MD) based solving techniques. The proposed MD based algorithm optimizes over product of simplices and has a computational complexity of O (m(2)n(tot) log n(max)/epsilon(2)) where m is no. training data points, n(max), n(tot) are the maximum no. kernels in any group, total no. kernels respectively and epsilon is the error in approximating the objective. A detailed proof of convergence of the algorithm is also presented. Experimental results show that the VSKL formulations are well-suited for multi-modal learning tasks like object categorization. Results also show that the MD based algorithm outperforms state-of-the-art MKL solvers in terms of computational efficiency.

Knowledge-based clustering

In the knowledge-based clustering approaches reported in the literature, explicit know ledge, typically in the form of a set of concepts, is used in computing similarity or conceptual cohesiveness between objects and in grouping them. We propose a knowledge-based clustering approach in which the domain knowledge is also used in the pattern representation phase of clustering. We argue that such a knowledge-based pattern representation scheme reduces the complexity of similarity computation and grouping phases. We present a knowledge-based clustering algorithm for grouping hooks in a library.