Multistep electrochemical reduction path of clusters [Os3(CO)10(α-diimine)]: comparison of electrochemical and photochemical Os-Os(α-diimine) bond cleavage

Electrochemical reduction of the triangular clusters [Os-3(CO)(10)(alpha-dimine)] (alpha-dimine = 2,2'-bipyridine (bpy), 2,2'-bipyrimidine (bpym)) and [Os-3(CO)(10)(mu-bpym) ReBr(CO)(3)] produces primarily the corresponding radical anions. Their stability is strongly determined by the pi acceptor ability of the reducible alpha-dimine ligand, which decreases in the order mu-bpym > bpym >> bpy. Along this series, increasing delocalisation of the odd electron density in the radical anion over the Os(alpha-dimine) chelate ring causes weakening of the axial (CO)(4)Os-Os(CO)(2)(alpha-dimine) bond and its facile cleavage for alpha-diimine = bpy. In contrast, the cluster radical anion is inherently stable for the bridging bpym ligand, the strongest pi-acceptor in the studied series. In the absence of the partial delocalisation of the unpaired electron over the Re( bpym) chelate bond, the Os-3-core of the radical anion remains intact only at low temperatures. Subsequent one-electron reduction of [Os-3(CO)(10)(bpym)](center dot-) at T = 223 K gives the open-triosmium core (= Os-3*) dianion, [Os-3*(CO)(10)(bpym)](2-). Its oxidation leads to the recovery of parent [Os-3(CO)(10)( bpym)]. At room temperature, [Os-3*( CO)(10)(bpym)](2-) is formed along a two-electron (ECE) reduction path. The chemical step (C) results in the formation of an open- core radical anion that is directly reducible at the cathodic potential of the parent cluster in the second electrochemical (E) step. In weakly coordinating tetrahydrofuran, [Os-3*(CO)(10)( bpym)](2-) rapidly attacks yet non- reduced parent cluster molecules, producing the relatively stable open- core dimer [Os-3*(CO)(10)(bpym)](2)(2-) featuring two open- triangle cluster moieties connected with an ( bpym) Os - Os( bpym) bond. In butyronitrile, [Os-3*( CO)(10)(bpym)](2-) is stabilised by the solvent and the dimer [Os-3*(CO)(10)(bpym)](2)(2-) is then mainly formed by reoxidation of the dianion on reverse potential scan. The more reactive cluster [Os-3(CO)(10)(bpy)] follows the same reduction path, as supported by spectroelectrochemical results and additional valuable evidence obtained from cyclic voltammetric scans. The ultimate process in the reduction mechanism is fragmentation of the cluster core triggered by the reduction of the dimer [Os-3*(CO)(10)(alpha- diimine)](2)(2-). The products formed are [Os-2(CO)(8)](2-) and {Os(CO)(2)(alpha- diimine)}(2). The latter dinuclear fragments constitute a linear polymeric chain [Os( CO)(2)(alpha-dimine)] n that is further reducible at the alpha-dimine ligands. For alpha-dimine = bpy, the charged polymer is capable of reducing carbon dioxide. The electrochemical opening of the triosmium core in the [Os-3( CO)(10)(alpha-dimine)] clusters exhibits several common features with their photochemistry. The same Os-alpha-dimine bond dissociates in both cases but the intimate mechanisms are different.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Use of the statistical properties of the wavelet-transform coefficients for optimization of integration time in Fourier transform spectrometry

We show that an analysis of the mean and variance of discrete wavelet coefficients of coaveraged time-domain interferograms can be used as a specification for determining when to stop coaveraging. We also show that, if a prediction model built in the wavelet domain is used to determine the composition of unknown samples, a stopping criterion for the coaveraging process can be developed with respect to the uncertainty tolerated in the prediction.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

Interference cancellation in two-path successive relay system with network coding

This paper proposes a novel interference cancellation algorithm for the two-path successive relay system using network coding. The two-path successive relay scheme was proposed recently to achieve full date rate transmission with half-duplex relays. Due to the simultaneous data transmission at the relay and source nodes, the two-path relay suffers from the so-called inter-relay interference (IRI) which may significantly degrade the system performance. In this paper, we propose to use the network coding to remove the IRI such that the interference is first encoded with the network coding at the relay nodes and later removed at the destination. The network coding has low complexity and can well suppress the IRI. Numerical simulations show that the proposed algorithm has better performance than existing approaches.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

L1 transfer in the acquisition of manner and path in Spanish by native speakers of English

In this article the authors argue that L1 transfer from English is not only important in the early stages of L2 acquisition of Spanish, but remains influential in later stages if there is not enough positive evidence for the learners to progress in their development (Lefebvre, White, & Jourdan, 2006). The findings are based on analyses of path and manner of movement in stories told by British students of Spanish (N = 68) of three different proficiency levels. Verbs that conflate motion and path, on the one hand, are mastered early, possibly because the existence of Latinate path verbs, such as enter and ascend in English, facilitate their early acquisition by British learners of Spanish. Contrary to the findings of Cadierno (2004) and Cadierno and Ruiz (2006), the encoding of manner, in particular in boundary crossing contexts, seems to pose enormous difficulties, even among students who had been abroad on a placement in a Spanish-speaking country prior to the data collection. An analysis of the frequency of manner verbs in Spanish corpora shows that one of the key reasons why students struggle with manner is that manner verbs are so infrequent in Spanish. The authors claim that scarce positive evidence in the language exposed to and little or no negative evidence are responsible for the long-lasting effect of transfer on the expression of manner.

Path planning of robots in noisy workspaces using learning automata

The problem of a manipulator operating in a noisy workspace and required to move from an initial fixed position P0 to a final position Pf is considered. However, Pf is corrupted by noise, giving rise to Pˆf, which may be obtained by sensors. The use of learning automata is proposed to tackle this problem. An automaton is placed at each joint of the manipulator which moves according to the action chosen by the automaton (forward, backward, stationary) at each instant. The simultaneous reward or penalty of the automata enables avoiding any inverse kinematics computations that would be necessary if the distance of each joint from the final position had to be calculated. Three variable-structure learning algorithms are used, i.e., the discretized linear reward-penalty (DLR-P, the linear reward-penalty (LR-P ) and a nonlinear scheme. Each algorithm is separately tested with two (forward, backward) and three forward, backward, stationary) actions.

Tendering: optimization and rationality

There have been various techniques published for optimizing the net present value of tenders by use of discounted cash flow theory and linear programming. These approaches to tendering appear to have been largely ignored by the industry. This paper utilises six case studies of tendering practice in order to establish the reasons for this apparent disregard. Tendering is demonstrated to be a market orientated function with many subjective judgements being made regarding a firm's environment. Detailed consideration of 'internal' factors such as cash flow are therefore judged to be unjustified. Systems theory is then drawn upon and applied to the separate processes of estimating and tendering. Estimating is seen as taking place in a relatively sheltered environment and as such operates as a relatively closed system. Tendering, however, takes place in a changing and dynamic environment and as such must operate as a relatively open system. The use of sophisticated methods to optimize the value of tenders is then identified as being dependent upon the assumption of rationality, which is justified in the case of a relatively closed system (i.e. estimating), but not for a relatively open system (i.e. tendering).