Synthesis, crystal structures, hydrogen bonding graph-sets and theoretical studies of nickel (+II) co-ordinations with pyridine-2,6-dicarboxamide oxime

The pyridine-2,6-dicarboxamide oxime, C7H9N5O2, was Synthesis and  characterises with 1H NMR and FTIR spectroscopy . The reaction of this ligand with nickel (II) perchlorate yielded green crystals of formula  [Ni(C<sub>7</sub>H<sub>9</sub>N<sub>5</sub>O<sub>2</sub>)<sub>2</sub>]<sup>2+</sup>,2[ClO<sub>4</sub>]-, which crystallized in the monoclinic space group C2/c with a = 14.915(2), b = 0.895(2), c = 8.205(1) Å, β = 114.69(1), and Z = 4. The complex consists of discrete cations (+II) and one perchlorate anion, the  cations existing in a slightly distorted octahedral  complex with bonding through the heterocyclic and oxime nitrogen atoms. The structure is held together through N-H…O, O-H…O and C-H...O hydrogen bonds occurring  between the coordinated oxime  molecules and the perchlorate counter-ion. Computational investigations of nickel(II) complex are done by using M062X method with 6-31+G(d)(LANL2DZ) basis set in vacuo.Keywords: Oxime complexe; Crystal structure; Hydrogen-bonding graph-set; DFT; M062X method; 6-31+G(d)(LANL2DZ) basis

Synthesis, quantum chemical computations and x-ray crystallographic studies of a new complex based of manganese (+II)

The ligand oxime, C7H9N5O2, was Synthesis and characterises with different characterization methods such as 1H NMR and FTIR spectroscopy. The complexation of this ligand with manganese (II) perchlorate yielded pink crystals of formula [Mn (C7H9N5O2)2]2+, 2[ClO4]-, which crystallized in the monoclinic space group P21/n with a = 12.824(3), b=13.799(2), c=15.441(4)Å, β = 100.17(2), and Z = 4. The complex consists of cations (+II) and two perchlorate anions, the cations part existing in a slightly distorted octahedral complex. Computational investigations of manganese (II) complex are done by using the DFTmethod with B3LYP functional in conjunction with the 6-31G(d,p) and lanl2dz basis sets in the gas phase imposing the C1 and C2v symmetries.Keywords: Manganese complex; Crystal structure; DFT method; B3LYP functional; 6-31G(d,p) and (LANL2DZ) basi

Team-optimal distributed MMSE estimation in general and tree networks

We construct team-optimal estimation algorithms over distributed networks for state estimation in the finite-horizon mean-square error (MSE) sense. Here, we have a distributed collection of agents with processing and cooperation capabilities. These agents observe noisy samples of a desired state through a linear model and seek to learn this state by interacting with each other. Although this problem has attracted significant attention and been studied extensively in fields including machine learning and signal processing, all the well-known strategies do not achieve team-optimal learning performance in the finite-horizon MSE sense. To this end, we formulate the finite-horizon distributed minimum MSE (MMSE) when there is no restriction on the size of the disclosed information, i.e., oracle performance, over an arbitrary network topology. Subsequently, we show that exchange of local estimates is sufficient to achieve the oracle performance only over certain network topologies. By inspecting these network structures, we propose recursive algorithms achieving the oracle performance through the disclosure of local estimates. For practical implementations we also provide approaches to reduce the complexity of the algorithms through the time-windowing of the observations. Finally, in the numerical examples, we demonstrate the superior performance of the introduced algorithms in the finite-horizon MSE sense due to optimal estimation. © 2017 Elsevier Inc

Stochastic subgradient algorithms for strongly convex optimization over distributed networks

We study diffusion and consensus based optimization of a sum of unknown convex objective functions over distributed networks. The only access to these functions is through stochastic gradient oracles, each of which is only available at a different node; and a limited number of gradient oracle calls is allowed at each node. In this framework, we introduce a convex optimization algorithm based on stochastic subgradient descent (SSD) updates. We use a carefully designed time-dependent weighted averaging of the SSD iterates, which yields a convergence rate of O N ffiffiffi N p (1s)T after T gradient updates for each node on a network of N nodes, where 0 ≤ σ < 1 denotes the second largest singular value of the communication matrix. This rate of convergence matches the performance lower bound up to constant terms. Similar to the SSD algorithm, the computational complexity of the proposed algorithm also scales linearly with the dimensionality of the data. Furthermore, the communication load of the proposed method is the same as the communication load of the SSD algorithm. Thus, the proposed algorithm is highly efficient in terms of complexity and communication load. We illustrate the merits of the algorithm with respect to the state-of-art methods over benchmark real life data sets. © 2017 IEEE

Twice-universal piecewise linear regression via infinite depth context trees

We investigate the problem of sequential piecewise linear regression from a competitive framework. For an arbitrary and unknown data length n, we first introduce a method to partition the regressor space. Particularly, we present a recursive method that divides the regressor space into O(n) disjoint regions that can result in approximately 1.5n different piecewise linear models on the regressor space. For each region, we introduce a universal linear regressor whose performance is nearly as well as the best linear regressor whose parameters are set non-causally. We then use an infinite depth context tree to represent all piecewise linear models and introduce a universal algorithm to achieve the performance of the best piecewise linear model that can be selected in hindsight. In this sense, the introduced algorithm is twice-universal such that it sequentially achieves the performance of the best model that uses the optimal regression parameters. Our algorithm achieves this performance only with a computational complexity upper bounded by O(n) in the worst-case and O(log(n)) under certain regularity conditions. We provide the explicit description of the algorithm as well as the upper bounds on the regret with respect to the best nonlinear and piecewise linear models, and demonstrate the performance of the algorithm through simulations. © 2015 IEEE

Communication efficient channel estimation over distributed networks

We study diffusion based channel estimation in distributed architectures suitable for various communication applications such as cognitive radios. Although the demand for distributed processing is steadily growing, these architectures require a substantial amount of communication among their nodes (or processing elements) causing significant energy consumption and increase in carbon footprint. Due to growing awareness of telecommunication industry's impact on the environment, the need to mitigate this problem is indisputable. To this end, we introduce algorithms significantly reducing the communication load between distributed nodes, which is the main cause in energy consumption, while providing outstanding performance. In this framework, after each node produces its local estimate of the communication channel, a single bit or a couple of bits of information is generated using certain random projections. This newly generated data is diffused and then used in neighboring nodes to recover the original full information, i.e., the channel estimate of the desired communication channel. We provide the complete state-space description of these algorithms and demonstrate the substantial gains through our experiments. © 2014 IEEE

Energy consumption forecasting via order preserving pattern matching

We study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, which can be subsequently used to optimize energy consumption. To this end, we use the energy consumption history of the users and define finite state (FS) predictors according to the relative ordering patterns of these past observations. In order to alleviate the overfitting problems, we generate equivalence classes by tying several states in a nested manner. Using the resulting equivalence classes, we obtain a doubly exponential number of different FS predictors, one among which achieves the smallest accumulated loss, hence is optimal for the prediction task. We then introduce an algorithm to achieve the performance of this FS predictor among all doubly exponential number of FS predictors with a significantly reduced computational complexity. Our approach is generic in the sense that different tying configurations and loss functions can be incorporated into our framework in a straightforward manner. We illustrate the merits of the proposed algorithm using the real life energy usage data. © 2014 IEEE

Pathogenesis and management of Buerger's disease

Buerger’s disease or thromboangiitis obliterans causes pain, ulceration, or gangrene in the lower or upper extremity. It is associated with chronic cigarette smoking and is believed to be an immune mediated vasculitis. The pathogenesis is still unknown but recent postulate of its association with odontal bacteria has generated much renewed interest. Despite its recognition more than a century ago, little progress has been made in its treatment. Until the pathogenesis is elucidated, abstinence from cigarette is the only effective therapy