Steric and rotational constraints in the X-ray structure of the animalarial drug amodiaquine

In the crystal structure of the antimalarial drug amodiaquine, the bonds linking the quinoline and the phenyl groups show partial double-bond character. The partial double-bond character of the two exocyclic bonds, together with stereochemical constraints, reduce flexibility of the two ring systems of the molecule. The dihedral angle between the two ring planes is lowest compared to those in the antileukaemic drug amsacrine and its derivatives. CPK-modelling studies suggest the way amodiaquine can bind to DNA. Stacking interaction between the quinoline and phenyl groups of independent molecules and the hydrogen-bond network stabilize the crystal structure.

Variable Elimination in Linear Constraints

Gauss and Fourier have together provided us with the essential techniques for symbolic computation with linear arithmetic constraints over the reals and the rationals. These variable elimination techniques for linear constraints have particular significance in the context of constraint logic programming languages that have been developed in recent years. Variable elimination in linear equations (Guassian Elimination) is a fundamental technique in computational linear algebra and is therefore quite familiar to most of us. Elimination in linear inequalities (Fourier Elimination), on the other hand, is intimately related to polyhedral theory and aspects of linear programming that are not quite as familiar. In addition, the high complexity of elimination in inequalities has forces the consideration of intricate specializations of Fourier's original method. The intent of this survey article is to acquaint the reader with these connections and developments. The latter part of the article dwells on the thesis that variable elimination in linear constraints over the reals extends quite naturally to constraints in certain discrete domains.

On the diversity and complexity of Zero Forcing receivers for MIMO Zero Padded systems

Zero padded systems with linear receivers are shown to be robust and amenable to fast implementations in single antenna scenarios. In this paper, properties of such systems are investigated when multiple antennas are present at both ends of the communication link. In particular, their diversity and complexity are evaluated for precoded transmissions. The linear receivers are shown to exploit multipath and receive diversities, even in the absence of any coding at the transmitter. Use of additional redundancy to improve performance is considered and the effect of transmission rate on diversity order is analyzed. Low complexity implementations of Zero Forcing receivers are devised to further enhance their applicability.

Qualitative theorem proving in linear constraints

We know, from the classical work of Tarski on real closed fields, that elimination is, in principle, a fundamental engine for mechanized deduction. But, in practice, the high complexity of elimination algorithms has limited their use in the realization of mechanical theorem proving. We advocate qualitative theorem proving, where elimination is attractive since most processes of reasoning take place through the elimination of middle terms, and because the computational complexity of the proof is not an issue. Indeed what we need is the existence of the proof and not its mechanization. In this paper, we treat the linear case and illustrate the power of this paradigm by giving extremely simple proofs of two central theorems in the complexity and geometry of linear programming.

Robust Approach for Identification of Bad Data in State Estimation Using SLP Technique

This paper proposes a new approach for solving the state estimation problem. The approach is aimed at producing a robust estimator that rejects bad data, even if they are associated with leverage-point measurements. This is achieved by solving a sequence of Linear Programming (LP) problems. Optimization is carried via a new algorithm which is a combination of “upper bound optimization technique" and “an improved algorithm for discrete linear approximation". In this formulation of the LP problem, in addition to the constraints corresponding to the measurement set, constraints corresponding to bounds of state variables are also involved, which enables the LP problem more efficient in rejecting bad data, even if they are associated with leverage-point measurements. Results of the proposed estimator on IEEE 39-bus system and a 24-bus EHV equivalent system of the southern Indian grid are presented for illustrative purpose.

Optimal Power Allocation Policies for Low Powered Wireless Communication Systems

We consider optimal average power allocation policies in a wireless channel in the presence of individual delay constraints on the transmitted packets. Power is consumed in transmission of data only. We consider the case when the power used in transmission is a linear function of the data transmitted. The transmission channel may experience multipath fading. We have developed a computationally efficient online algorithm, when there is same hard delay constraint for all packets. Later on, we generalize it to the case when there are multiple real time streams with different hard deadline constraints. Our algorithm uses linear programming and has very low complexity.

Constraints based analysis of extended cybernetic models

The cybernetic modeling framework provides an interesting approach to model the regulatory phenomena occurring in microorganisms. In the present work, we adopt a constraints based approach to analyze the nonlinear behavior of the extended equations of the cybernetic model. We first show that the cybernetic model exhibits linear growth behavior under the constraint of no resource allocation for the induction of the key enzyme. We then quantify the maximum achievable specific growth rate of microorganisms on mixtures of substitutable substrates under various kinds of regulation and show its use in gaining an understanding of the regulatory strategies of microorganisms. Finally, we show that Saccharomyces cerevisiae exhibits suboptimal dynamic growth with a long diauxic lag phase when growing on a mixture of glucose and galactose and discuss on its potential to achieve optimal growth with a significantly reduced diauxic lag period. The analysis carried out in the present study illustrates the utility of adopting a constraints based approach to understand the dynamic growth strategies of microorganisms. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

Distribution of black nodes at various levels in a linear quadtree

The distribution of black leaf nodes at each level of a linear quadtree is of significant interest in the context of estimation of time and space complexities of linear quadtree based algorithms. The maximum number of black nodes of a given level that can be fitted in a square grid of size 2n × 2n can readily be estimated from the ratio of areas. We show that the actual value of the maximum number of nodes of a level is much less than the maximum obtained from the ratio of the areas. This is due to the fact that the number of nodes possible at a level k, 0≤k≤n − 1, should consider the sum of areas occupied by the actual number of nodes present at levels k + 1, k + 2, …, n − 1.

Hydrothermal phase equilibria studies in Ln2O3---H2O systems and synthesis of cubic lanthanide oxides

Phase diagrams for the systems Ln2O3---H2O (Ln = La, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Lu and Y) studied at 5000 to 10,000 psi and temperature range of 200–900°C, show that Ln(OH)3 hexagonal and LnOOH monoclinic are the only stable phases from Nd to Ho. The cubic oxide phase (C---Ln2O3) is stable for systems of Er, Tm, Yb and Lu, with no evidence of its equilibrium in the systems of lighter lanthanides. Using strong acids, HNO3 and HCOOH, as mineralisers the cubic oxides could be stabilised from Eu down to Lu. Solid solution phases of CeO2---Y2O3 and Eu2O3---Y2O3 have also been synthesised with HNO3 as mineraliser, since these compounds have promising use as solid electrolyte and phosphor materials respectively.

Functional programming systems revisited

Functional Programming (FP) systems are modified and extended to form Nondeterministic Functional Programming (NFP) systems in which nondeterministic programs can be specified and both deterministic and nondeterministic programs can be verified essentially within the system. It is shown that the algebra of NFP programs has simpler laws in comparison with the algebra of FP programs. "Regular" forms are introduced to put forward a disciplined way of reasoning about programs. Finally, an alternative definition of "linear" forms is proposed for reasoning about recursively defined programs. This definition, when used to test the linearity of forms, results in simpler verification conditions than those generated by the original definition of linear forms.

Optimal simultaneous maximum a posterioriestimation of states, noise statistics and parameters I. Algorithm

The simultaneous state and parameter estimation problem for a linear discrete-time system with unknown noise statistics is treated as a large-scale optimization problem. The a posterioriprobability density function is maximized directly with respect to the states and parameters subject to the constraint of the system dynamics. The resulting optimization problem is too large for any of the standard non-linear programming techniques and hence an hierarchical optimization approach is proposed. It turns out that the states can be computed at the first levelfor given noise and system parameters. These, in turn, are to be modified at the second level.The states are to be computed from a large system of linear equations and two solution methods are considered for solving these equations, limiting the horizon to a suitable length. The resulting algorithm is a filter-smoother, suitable for off-line as well as on-line state estimation for given noise and system parameters. The second level problem is split up into two, one for modifying the noise statistics and the other for modifying the system parameters. An adaptive relaxation technique is proposed for modifying the noise statistics and a modified Gauss-Newton technique is used to adjust the system parameters.

On various types of decoupling for time-varying system

This paper deals with the problem of decoupling a class of linear time-varying multi-variable systems, based on the defining property that the impulse response matrix of a decoupled system is diagonal. Depending on the properties of the coefficient matrices of the vector differential equation of the open-loop system, the system may be uniformly or totally decoupled. The necessary and sufficient conditions that permit a system to be uniformly or totally decoupled by state variable feedback are given. The main contribution of this paper is the precise definition of these two classes of decoupling and a rigorous derivation of the necessary and sufficient conditions which show the necessity of requiring that the system be of constant ordered rank with respect to observability. A simple example illustrates the importance of having several definitions of decoupling. Finally, the results are specialized to the case of time invariant systems.

Symmetries and constraints in generalized Hamiltonian dynamics

A general analysis of symmetries and constraints for singular Lagrangian systems is given. It is shown that symmetry transformations can be expressed as canonical transformations in phase space, even for such systems. The relation of symmetries to generators, constraints, commutators, and Dirac brackets is clarified.

A Team of Continuous-Action Learning Automata for Noise-Tolerant Learning of Half-Spaces

Learning automata are adaptive decision making devices that are found useful in a variety of machine learning and pattern recognition applications. Although most learning automata methods deal with the case of finitely many actions for the automaton, there are also models of continuous-action-set learning automata (CALA). A team of such CALA can be useful in stochastic optimization problems where one has access only to noise-corrupted values of the objective function. In this paper, we present a novel formulation for noise-tolerant learning of linear classifiers using a CALA team. We consider the general case of nonuniform noise, where the probability that the class label of an example is wrong may be a function of the feature vector of the example. The objective is to learn the underlying separating hyperplane given only such noisy examples. We present an algorithm employing a team of CALA and prove, under some conditions on the class conditional densities, that the algorithm achieves noise-tolerant learning as long as the probability of wrong label for any example is less than 0.5. We also present some empirical results to illustrate the effectiveness of the algorithm.

Power dissipation for systems with junctions of multiple quantum wires

We study power dissipation for systems of multiple quantum wires meeting at a junction, in terms of a current splitting matrix (M) describing the junction. We present a unified framework for studying dissipation for wires with either interacting electrons (i.e., Tomonaga-Luttinger liquid wires with Fermi-liquid leads) or noninteracting electrons. We show that for a given matrix M, the eigenvalues of (MM)-M-T characterize the dissipation, and the eigenvectors identify the combinations of bias voltages which need to be applied to the different wires in order to maximize the dissipation associated with the junction. We use our analysis to propose and study some microscopic models of a dissipative junction which employ the edge states of a quantum Hall liquid. These models realize some specific forms of the M matrix whose entries depends on the tunneling amplitudes between the different edges.