Mixed ligand cobalt(III) complexes of tetradentate N,N′-ethylenebis(acetylacetonimine) and flexidentate isonitroso-β-diketones and isonitrosomonoketones

A series of mixed ligand cobalt(III) complexes having the general formula Co(EA)X [where EA = dianion of N,N′-ethylenebis(acetylacetonimine) and X = anion of isonitroso-acetylacetone, IAA; isonitrosobenzoylacetone, IBA; isonitrosodibenzoylmethane, IDBM; isonitrosoethylacetoacetate, IEA; isonitrosoacetoacetanillide, IAN; isonitrosoethylmethylketone, IEMK; isonitrosobenzylmethylketone, IBMK and isonitrosopropiophenone, IPP] have been synthesised and characterised. A facial-cis-β structure (cis with respect to the coordinated two oxygen atoms of EA) with N,N,N,O,O,O ligational environment has been assigned for the complexes. The characterisation of the complexes has been based upon chemical analysis, electrical conductivity, magnetic moment, IR, PMR and electronic spectra.

Impact of metal on the DNA photocleavage activity and cytotoxicity of ferrocenyl terpyridine 3d metal complexes

Ferrocenyl terpyridine 3d metal complexes and their analogues, viz. [M(Fc-tpy)(2)](ClO(4))(2) (1-4), [Zn(Ph-tpy)(2)](ClO(4))(2) (5) and [Zn(Fc-dpa)(2)]X(2) (X = ClO(4), 6; PF6, 6a), where M = Fe(II) in 1, Co(II) in 2, Cu(II) in 3 and Zn(II) in 4, Fc-tpy is 4'-ferrocenyl-2,2': 6', 2 `'-terpyridine, Ph-tpy is 4'-phenyl-2,2': 6', 2 `'-terpyridine and Fc-dpa is ferrocenyl-N,N-dipicolylmethanamine, are prepared and their DNA binding and photocleavage activity in visible light studied. Complexes 2, 4, 5 and 6a that are structurally characterized by X-ray crystallography show distorted octahedral geometry with the terpyridyl ligands binding to the metal in a meridional fashion, with Fc-dpa in 6a showing a facial binding mode. The Fc-tpy complexes display a charge transfer band in the visible region. The ferrocenyl (Fc) complexes show a quasi-reversible Fc(+)-Fc redox couple within 0.48 to 0.66 V vs. SCE in DMF-0.1 M TBAP. The DNA binding constants of the complexes are similar to 10(4) M(-1). Thermal denaturation and viscometric data suggest DNA surface binding through electrostatic interaction by the positively charged complexes. Barring the Cu(II) complex 3, the complexes do not show any chemical nuclease activity in the presence of glutathione. Complexes 1-4 exhibit significant plasmid DNA photocleavage activity in visible light via a photoredox pathway. Complex 5, without the Fc moiety, does not show any DNA photocleavage activity. The Zn(II) complex 4 shows a significant PDT effect in HeLa cancer cells giving an IC(50) value of 7.5 mu M in visible light, while being less toxic in the dark (IC(50) = 49 mu M).

Prediction of inter-laminar stresses in composite honeycomb sandwich panels under mechanical loading using Variational Asymptotic Method

This work focuses on the formulation of an asymptotically correct theory for symmetric composite honeycomb sandwich plate structures. In these panels, transverse stresses tremendously influence design. The conventional 2-D finite elements cannot predict the thickness-wise distributions of transverse shear or normal stresses and 3-D displacements. Unfortunately, the use of the more accurate three-dimensional finite elements is computationally prohibitive. The development of the present theory is based on the Variational Asymptotic Method (VAM). Its unique features are the identification and utilization of additional small parameters associated with the anisotropy and non-homogeneity of composite sandwich plate structures. These parameters are ratios of smallness of the thickness of both facial layers to that of the core and smallness of 3-D stiffness coefficients of the core to that of the face sheets. Finally, anisotropy in the core and face sheets is addressed by the small parameters within the 3-D stiffness matrices. Numerical results are illustrated for several sample problems. The 3-D responses recovered using VAM-based model are obtained in a much more computationally efficient manner than, and are in agreement with, those of available 3-D elasticity solutions and 3-D FE solutions of MSC NASTRAN. (c) 2012 Elsevier Ltd. All rights reserved.

An adaptation framework for head-pose classification in dynamic multi-view scenarios

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.

Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images

Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Redox-Active Metal-Organic Frameworks: Highly Stable Charge-Separated States through Strut/Guest-to-Strut Electron Transfer

Molecular organization of donor and acceptor chromophores in self-assembled materials is of paramount interest in the field of photovoltaics or mimicry of natural light-harvesting systems. With this in mind, a redox-active porous interpenetrated metal-organic framework (MOF), {Cd(bpdc)(bpNDI)]4.5H(2)ODMF}(n) (1) has been constructed from a mixed chromophoric system. The -oxo-bridged secondary building unit, {Cd-2(-OCO)(2)}, guides the parallel alignment of bpNDI (N,N-di(4-pyridyl)-1,4,5,8-naphthalenediimide) acceptor linkers, which are tethered with bpdc (bpdcH(2)=4,4-biphenyldicarboxylic acid) linkers of another entangled net in the framework, resulting in photochromic behaviour through inter-net electron transfer. Encapsulation of electron-donating aromatic molecules in the electron-deficient channels of 1 leads to a perfect donor-acceptor co-facial organization, resulting in long-lived charge-separated states of bpNDI. Furthermore, 1 and guest encapsulated species are characterised through electrochemical studies for understanding of their redox properties.

Low Resolution Face Recognition Across Variations in Pose and Illumination

We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.