Co-design of Distributed Systems Using Skeleton and Autonomic Management Abstractions

We discuss how common problems arising with multi/many core distributed architectures can he effectively handled through co-design of parallel/distributed programming abstractions and of autonomic management of non-functional concerns. In particular, we demonstrate how restricted patterns (or skeletons) may be efficiently managed by rule-based autonomic managers. We discuss the basic principles underlying pattern+manager co-design, current implementations inspired by this approach and some result achieved with proof-or-concept, prototype.

“Shotgun” cloning of skin peptide precursors from skin secretion-derived cDNA libraries of the Fujian large-headed frog, Limnonectes fujianensis

Amphibian skin secretions are rich sources of biologically-active peptides and several studies involving molecular cloning of their biosynthetic precursors have revealed that many exhibit highly-conserved domain architectures with an associated high degree of primary structural conservation of the signal peptides. This conservation of primary structure is reflected at the level of nucleotide sequence — a finding that has permitted our group to design primers to these sites facilitating “shotgun” cloning using cDNA libraries from uninvestigated species. Here we describe the results of such an approach using a skin secretion-derived cDNA library from the Fujian large-headed frog, Limnonectes fujianensis, a completely unstudied species. In over 50 clones studied by this approach, 12 were found to encode peptides of different primary structure. Representatives of 5 different families of antimicrobial peptides derived from the skins of ranid frogs were found and these were brevinin-1 (n = 3), the ranatuerin-2 (n = 3), esculentin-2 (n = 1), temporin (n = 1) and chensinin (n = 1). Three clones encoded peptides that were novel with no homologues present in contemporary on-line databases. These included two related 16-mer peptides, named peptides SC-16a and b, and an unrelated 24-mer, named peptide AG-24. Preliminary biological characterisation of SC-16a has demonstrated an antimicrobial activity against Gram-negative bacteria with a minimal inhibitory concentration of 35 µM with no observable haemolysis up to 200 µM. This finding may suggest that this peptide represents a novel class of antimicrobial with little effect on eukaryotic membranes.

AdaBoost Multiple Feature Selection and Combination for Face Recognition

Gabor features have been recognized as one of the most successful face representations. Encouraged by the results given by this approach, other kind of facial representations based on Steerable Gaussian first order kernels and Harris corner detector are proposed in this paper. In order to reduce the high dimensional feature space, PCA and LDA techniques are employed. Once the features have been extracted, AdaBoost learning algorithm is used to select and combine the most representative features. The experimental results on XM2VTS database show an encouraging recognition rate, showing an important improvement with respect to face descriptors only based on Gabor filters.

2D Monolayer MoS2–Carbon Interoverlapped Superstructure: Engineering Ideal Atomic Interface for Lithium Ion Storage: Engineering Ideal Atomic Interface for Lithium Ion Storage

A novel strategy for the controlled synthesis of 2D MoS<inf>2</inf>/C hybrid nanosheets consisting of the alternative layer-by-layer interoverlapped single-layer MoS<inf>2</inf> and mesoporous carbon (m-C) is demonstrated. Such special hybrid nanosheets with a maximized MoS<inf>2</inf>/m-C interface contact show very good performance for lithium-ion batteries in terms of high reversible capacity, excellent rate capability, and outstanding cycling stability.

Predicting impact damage, residual strength and crashworthiness of composite structures

The development of the latest generation of wide-body carbon-fibre composite passenger aircraft has heralded a new era in the utilisation of these materials. The premise of superior specific strength and stiffness, corrosion and fatigue resistance, is tempered by high development costs, slow production rates and lengthy and expensive certification programmes. Substantial effort is currently being directed towards the development of new modelling and simulation tools, at all levels of the development cycle, to mitigate these shortcomings. One of the primary challenges is to reduce the extent of physical testing, in the certification process, by adopting a ‘certification by simulation’ approach. In essence, this aspirational objective requires the ability to reliably predict the evolution and progression of damage in composites. The aerospace industry has been at the forefront of developing advanced composites modelling tools. As the automotive industry transitions towards the increased use of composites in mass-produced vehicles, similar challenges in the modelling of composites will need to be addressed, particularly in the reliable prediction of crashworthiness. While thermoset composites have dominated the aerospace industry, thermoplastics composites are likely to emerge as the preferred solution for meeting the high-volume production demands of passenger road vehicles. This keynote presentation will outline recent progress and current challenges in the development of finite-element-based predictive modelling tools for capturing impact damage, residual strength and energy absorption capacity of thermoset and thermoplastic composites for crashworthiness assessments.