Inorganic-organic hybrids of the p,p'-diphenylmethylenediphosphinate ligand with bivalent metals: a new 2D-layered phenylphosphinate zinc(II) complex

By reaction of Zn(CH₃COO)₂ with p,p′-diphenylmethylenediphosphinic acid in water a new inorganic–organic polymeric hybrid of formula [Zn(CH₂(P(Ph)O₂)₂)] has been synthesized and completely characterized. The X-ray analysis established that the structure consists of 2D-layered polymeric array, the 2D-sheets being built up through strong covalent linkages between the zinc metal and the oxygen donors of the phenylphosphinate ligand. The 2D-layers, which are featuring a mesh-net fashion, present voids of various dimensionality, up to 24-membered rings. The organic parts of the hybrid ligand, namely the phenyl rings, are shielding the inorganic skeleton of the layers, preventing the propagation of the polymer in the third dimension. No water molecules are present in the lattice, both of coordination and crystallization. Crystal data are: monoclinic, P2₁Ic, a=11.840(2), b=9.646(9), c=12.516(5) Å, β=95.03(2), V=1423.9(15) ų, Z=4. The solid material has been characterized by ³¹P MAS NMR spectroscopy and thermogravimetric analysis.

Layered fragment architecture for situational methodology engineering

Many organizations make use of information system development methodologies to guide their staff in developing computerised information systems. This thesis contributes to methodology engineering research by introducing a number of important innovations in methodology fragment architectures.

A layered security approach for cloud computing infrastructure

This paper introduces a practical security model based on key security considerations by looking at a number of infrastructure aspects of Cloud Computing such as SaaS, Utility, Web, Platform and Managed Services, Service commerce platforms and Internet Integration which was introduced with a concise literature review. The purpose of this paper is to offer a macro level solution for identified common infrastructure security requirements. This model with a number of emerged patterns can be applied to infrastructure aspect of Cloud Computing as a proposed shared security approach in system development life cycle focusing on the plan-built-run scope.

Transparent uv-shielding films from organic small molecule-intercalated layered double hydroxide nanoplatelets/polymer composite

In this study, layered double hydroxide (LDH) with nitrate as the interlayer anion has been partially exfoliated in dimethyl sulfoxide (DMSO). Atomic force microscopy (AFM) images showed that both the lateral size and the thickness of the LDH nanoplatelets were decreased after DMSO treatment. Formation of transparent LDH suspension in DMSO was observed. Taking this advantage, we have prepared transparent LDH/ethylene-vinyl alcohol copolymer (EVOH) nanocomposite films using DMSO as the processing solvent. Organic small molecules, UV absorbers, were intercalated into the LDH interlayers to incorporate the UV-shielding property into the transparent composite films. The thermal stability of UV absorbers was considerably improved after intercalation, which was attributed to the electrostatic interaction between the guest UV absorbers and the host LDH layers. The prepared composite films were flexible and exhibited excellent UV-shielding capability, but had transmittance as high as 90% in the visible region. The effect of LDH filler on thermal and mechanical properties of the composite films was also examined.

Visible transparent, UV-blocking polymer nanocomposite films containing nano-sized organic-LDH (Layered Double Hydroxide) hybrid

A nano-sized Mg2Al layered double hydroxide (LDH) was used for encapsulating an organic UV absorber, 2-hydroxy-4- methoxybenzeophenone-5-sulfonic acid (HMBS), to produce HMBS@LDH hybrid nano-platelets. Upon dispersing this organic-inorganic hybrid LDH into ethylene-vinyl alcohol copolymer (EVOH) for film casting, a thin polymer
nanocomposite film that is UV opaque but highly transparent to visible light (higher than 90%) was formed. Thermogravimetry (TG) analysis confirmed that the intercalation of HMBS into LDH considerably increased the thermal stability of HMBS. Such an improvement was attributed to the strong guest-host interaction between the HMBS anions and the LDH layers. Also, the nanocomposite films were flexible and had good mechanical properties.

Partial exfoliation of layered double hydroxides in DMSO : a route to transparent polymer nanocomposites

Layered double hydroxides (LDHs), either having nitrate counter anions or intercalated with organic molecules, have been for the first time partially exfoliated in dimethyl sulfoxide (DMSO) to form a transparent suspension. Atomic force microscopy (AFM) images showed that both the lateral size and the thickness of the LDH nanoplatelets were decreased after the exfoliation. The organic-LDHs maintained their intercalation characteristics, i.e. the thermal stability improvement of the incorporated organic anions, after the exfoliation in DMSO. Transparent ethylene-vinyl alcohol copolymer (EVOH) nanocomposite films containing partially exfoliated LDHs intercalated with UV absorbers were prepared using DMSO as the processing solvent. As the first reported example of a highly transparent LDH/polymer composite, the obtained composite film had a visible light transmittance of 90% (comparable to that of the pure matrix), was flexible and exhibited an excellent UV-shielding capability and thermal stability.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

Layered dynamic probabilistic networks for spatio-temporal modelling

In applications such as tracking and surveillance in large spatial environments, there is a need for representing dynamic and noisy data and at the same time dealing with them at different levels of detail. In the spatial domain, there has been work dealing with these two issues separately, however, there is no existing common framework for dealing with both of them. In this paper, we propose a new representation framework called the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN), capable of handling uncertainty and representing spatial data at various levels of detail. The framework is thus particularly suited to applications in wide-area environments which are characterised by large region size, complex spatial layout and multiple sensors/cameras. For example, a building has three levels: entry/exit to the building, entry/exit between rooms and moving within rooms. To avoid the problem of a relatively large state space associated with a large spatial environment, the LDPN explicitly encodes the hierarchy of connected spatial locations, making it scalable to the size of the environment being modelled. There are three main advantages of the LDPN. First, the reduction in state space makes it suitable for dealing with wide area surveillance involving multiple sensors. Second, it offers a hierarchy of intervals for indexing temporal data. Lastly, the explicit representation of intermediate sub-goals allows for the extension of the framework to easily represent group interactions by allowing coupling between sub-goal layers of different individuals or objects. We describe an adaptation of the likelihood sampling inference scheme for the LDPN, and illustrate its use in a hypothetical surveillance scenario.

A GA-PSO layered encoding evolutionary approach to 0/1 knapsack optimization

This paper presents a layered encoding cascade evolutionary approach to solve a 0/1 knapsack optimization problem. A layered encoding structure is proposed and developed based on the schema theorem and the concepts of cascade correlation and multi-population evolutionary algorithms. Genetic algorithm (GA) and particle swarm optimization (PSO) are combined with the proposed layered encoding structure to form a generic optimization model denoted as LGAPSO. In order to enhance the finding of both local and global optimum in the evolutionary search, the model adopts hill climbing evaluation criteria, feature of strength Pareto evolutionary approach (SPEA) as well as nondominated spread lengthen criteria. Four different sizes benchmark knapsack problems are studied using the proposed LGAPSO model. The performance of LGAPSO is compared to that of the ordinary multi-objective optimizers such as VEGA, NSGA, NPGA and SPEA. The proposed LGAPSO model is shown to be efficient in improving the search of knapsack’s optimum, capable of gaining better Pareto trade-off front.

A layered-encoding cascade optimization approach to product-mix planning in high-mix-low-volume manufacturing

High-mix-low-volume (HMLV) production is currently a worldwide manufacturing trend. It requires a high degree of customization in the manufacturing process to produce a wide range of products in low quantity in order to meet customers' demand for more variety and choices of products. Such a kind of business environment has increased the conversion time and decreased the production efficiency due to frequent production changeover. In this paper, a layered-encoding cascade optimization (LECO) approach is proposed to develop an HMLV product-mix optimizer that exhibits the benefits of low conversion time, high productivity, and high equipment efficiency. Specifically, the genetic algorithm (GA) and particle swarm optimization (PSO) techniques are employed as optimizers for different decision layers in different LECO models. Each GA and PSO optimizer is studied and compared. A number of hypothetical and real data sets from a manufacturing plant are used to evaluate the performance of the proposed GA and PSO optimizers. The results indicate that, with a proper selection of the GA and PSO optimizers, the LECO approach is able to generate high-quality product-mix plans to meet the production demands in HMLV manufacturing environments.

An interactive genetic algorithm approach to MMIC low noise amplifier design using a layered encoding structure

In this paper, an interactive genetic algorithm (IGA) approach is developed to optimize design variables for a monolithic microwave integrated circuit (MMIC) low noise amplifier. A layered encoding structure is employed to the problem representation in genetic algorithm to allow human intervention in the circuit design variable tuning process. The MMIC amplifier design is synthesized using the Agilent Advance Design System (ADS), and the IGA is proposed to tune the design variables in order to meet multiple constraints and objectives such as noise figure, current and simulated power gain. The developed IGA is compared with other optimization techniques from ADS. The results showed that the IGA performs better in achieving most of the involved objectives.