Interest of molecular functionalization for electrochemical storage

Despite great interests in electrochemical energy storage systems for numerous applications, considerable challenges remain to be overcome. Among the various approaches to improving the stability, safety, performance, and cost of these systems, molecular functionalization has recently been proved an attractive method that allows the tuning of material surface reactivity while retaining the properties of the bulk material. For this purpose, the reduction of aryldiazonium salt, which is a versatile method, is considered suitable; it forms robust covalent bonds with the material surface, however, with the formation of multilayer structures and sp³ defects (for carbon substrate) that can be detrimental to the electronic conductivity. Alternatively, non-covalent molecular functionalization based on π–π interactions using aromatic ring units has been proposed. In this review, the various advances in molecular functionalization concerning the current limitations in lithium-ion batteries and electrochemical capacitors are discussed. According to the targeted applications and required properties, both covalent and non-covalent functionalization methods have proved to be very efficient and versatile. Fundamental aspects to achieve a better understanding of the functionalization reactions as well as molecular layer properties and their effects on the electrochemical performance are also discussed. Finally, perspectives are proposed for future implementation of molecular functionalization in the field of electrochemical storage.

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An adaptive method based on the improved LPA-ICI algorithm for MRI enhancement

Various diseases are diagnosed using medical imaging used for analysing internal anatomical structures. However, medical images are susceptible to noise introduced in both acquisition and transmission processes. We propose an adaptive data-driven image denoising algorithm based on an improvement of the intersection of confidence intervals (ICI), called relative ICI (RICI) algorithm. The 2D mask of the adaptive size and shape is calculated for each image pixel independently, and utilized in the design of the 2D local polynomial approximation (LPA) filters. Denoising performances, in terms of the PSNR, are compared to the original ICI-based method, as well as to the fixed sized filtering. The proposed adaptive RICI-based denoising outperformed the original ICI-based method by up to 1.32 dB, and the fixed size filtering by up to 6.48 dB. Furthermore, since the denoising of each image pixel is done locally and independently, the method is easy to parallelize.     

A SLIT THROUGH PHOTOGRAPHY

Post-war experimental and conceptual photography in former Yugoslavia has only rarely been the subject of detailed study and interpretation. In considering this period, it is necessary to take into account several factors, including the absence of permanent exhibition spaces for photography, the lack of magazines in which photographic themes were presented and discussed, the impossibility of studying the field of photography and, finally, the inadequate knowledge and application of contemporary criticism and theories of photography. Nevertheless, from the mid-1950s onwards it is possible to note a variety of innovations in the field, in terms of both form and subject-matter. This article considers rare instances of institutional support for progressive photography-related events and unique, intellectual-poetic works. After the break-up of Yugoslavia, there is a dominant tendency to nationalize art created in the former state, thus ignoring the specific Yugoslav cultural field as well as the European context. Based on a methodology which surpasses the national (but still acknowledges it) and searches for meaning within the broader socio-political space to which art is referring, the research aims to change the paradigm of the peripheral position and general ignorance of the circumstances under which this innovative practice emerges.     

Recent advances in controlled modification of the size and morphology of metal-organic frameworks

Advances in metal-organic frameworks (MOFs) resulted in significant contributions to diverse applications such as carbon capture, gas storage, heat transformation and separation along with emerging applications toward catalysis, medical imaging, drug delivery, and sensing. The unique in situ and ex situ structural features of MOFs can be tailored by conceptual selection of the organic (e.g., ligand) and inorganic (e.g., metal) components. Here, we provide a comprehensive review on the synthesis and characterization of MOFs, particularly with respect to controlling their size and morphology. A better understanding of the specific size and morphological parameters of MOFs will help initiate a new era for their real-world applications. Most importantly, this assessment will help develop novel synthesis methods for MOFs and their hybrid/porous materials counterparts with considerably improved properties in targeted applications.

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BallotMaps: detecting name bias in alphabetically ordered ballot papers

The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections.     

Shortcut fusion rules for the derivation of circular and higher-order programs

Functional programs often combine separate parts using intermediate data structures for communicating results. Programs so defined are modular, easier to understand and maintain, but suffer from inefficiencies due to the generation of those gluing data structures. To eliminate such redundant data structures, some program transformation techniques have been proposed. One such technique is shortcut fusion, and has been studied in the context of both pure and monadic functional programs. In this paper, we study several shortcut fusion extensions, so that, alternatively, circular or higher-order programs are derived. These extensions are also provided for effect-free programs and monadic ones. Our work results in a set of generic calculation rules, that are widely applicable, and whose correctness is formally established.     

Spoken emotion recognition through optimum-path forest classification using glottal features

A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks – multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5 M and 5 F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.     

Shrinking‐horizon dynamic programming

We describe a heuristic control policy for a general finite‐horizon stochastic control problem, which can be used when the current process disturbance is not conditionally independent of the previous disturbances, given the current state. At each time step, we approximate the distribution of future disturbances (conditioned on what has been observed) by a product distribution with the same marginals. We then carry out dynamic programming (DP), using this modified future disturbance distribution, to find an optimal policy, and in particular, the optimal current action. We then execute only the optimal current action. At the next step, we update the conditional distribution, and repeat the process, this time with a horizon reduced by one step. (This explains the name ‘shrinking‐horizon dynamic programming’). We explain how the method can be thought of as an extension of model predictive control, and illustrate our method on two variations on a revenue management problem. Copyright © 2010 John Wiley & Sons, Ltd.     

Vehicle license plate tilt correction based on the straight line fitting method and minimizing variance of coordinates of projection points

Tilt correction is a very crucial and inevitable task in the automatic recognition of the vehicle license plate (VLP). In this paper, according to the least square fitting with perpendicular offsets (LSFPO), the VLP region is fitted to a straight line. After the line slope is obtained, rotation angle of the VLP is estimated. Then the whole image is rotated for tilt correction in horizontal direction by this angle. Tilt correction in vertical direction by minimizing the variance of coordinates of the projection points is proposed. Character segmentation is performed after horizontal correction and character points are projected along the vertical direction after shear transform. Despite the success of VLP detection approaches in the past decades, a few of them can effectively locate license plate (LP), even when vehicle bodies and LPs have similar color. A common drawback of color-based VLP detection is the failure to detect the boundaries or border of LPs. In this paper, we propose a modified recursive labeling algorithm for solving this problem and detecting candidate regions. According to different colored LP, these candidate regions may include LP regions. Geometrical properties of the LP such as area, bounding box and aspect-ratio are then used for classification. Various LP images were used with a variety of conditions to test the proposed method and results are presented to prove its effectiveness.