Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions.     

Adaptive control of a continuously variable transmission subject to wear

Continuously variable transmissions (CVT) require an electronic controller for real application: it must reach a targeted transmission speed ratio in spite of wear. Based on our recent model based for high speed steel belt CVT, an improved feedback linearization design is proposed with an on-line adaption of the main nonlinear map in a neural associative memory. The adaptation is required because the stability condition depends of the accuracy of the estimated nonlinear map. Experimental results show that the adaptation allows the speed ratio controller to remain stable regardless of the wear accumulated by the CVT transmission.     

Discrete distributions when modeling the disability severity score of motor victims

Many European countries apply score systems to evaluate the disability severity of non-fatal motor victims under the law of third-party liability. The score is a non-negative integer with an upper bound at 100 that increases with severity. It may be automatically converted into financial terms and thus also reflects the compensation cost for disability. In this paper, standard and zero-altered discrete regression models are applied to model the disability severity score of victims. An application using data from Spain is provided in which the hurdle-Negative Binomial regression was the preferred method. The effects of victims’ characteristics, type of road user and recovery duration are examined. The results suggest that the expected permanent disability severity is higher for older women with long recovery periods. The results provide traffic decision makers with a model to quantify the compensation cost savings due to disability severity reductions.     

Investigating Pathogenicity and Virulence of Staphylococcus pettenkoferi: An Emerging Pathogen

Staphylococcus pettenkoferi is a coagulase-negative Staphylococcus identified in 2002 that has been implicated in human diseases as an opportunistic pathogenic bacterium. Its multiresistant character is becoming a major health problem, yet the pathogenicity of S. pettenkoferi is poorly characterized. In this study, the pathogenicity of a S. pettenkoferi clinical isolate from diabetic foot osteomyelitis was compared with a Staphylococcus aureus strain in various in vitro and in vivo experiments. Growth kinetics were compared against S. aureus, and bacteria survival was assessed in the RAW 264.7 murine macrophage cell line, the THP-1 human leukemia monocytic cell line, and the HaCaT human keratinocyte cell line. Ex vivo analysis was performed in whole blood survival assays and in vivo assays via the infection model of zebrafish embryos. Moreover, whole-genome analysis was performed. Our results show that S. pettenkoferi was able to survive in human blood, human keratinocytes, murine macrophages, and human macrophages. S. pettenkoferi demonstrated its virulence by causing substantial embryo mortality in the zebrafish model. Genomic analysis revealed virulence factors such as biofilm-encoding genes (e.g., icaABCD; rsbUVW) and regulator-encoding genes (e.g., agr, mgrA, sarA, saeS) well characterized in S. aureus. This study thus advances the knowledge of this under-investigated pathogen and validates the zebrafish infection model for this bacterium.     

FTIR studies of conformational disorder: crystal perfecting in long chain n-alkanes

The low temperature FTIR spectra of long chain n-alkanes C₁₉₈H₃₉₈ and C₂₄₆H₄₉₄, crystallised from solution in both extended and once-folded forms, have been investigated in the CH₂ wagging region. Successive annealing and cooling stages result in a significant reduction in the number of non-planar C-C bond conformers in all cases. This is evidence of a ‘perfecting’ process within the crystals. The time dependence of this behaviour shows that, on raising the annealing temperature, the level of disorder reaches a maximum before declining. In addition, in a continuous heating run on once-folded C₁₉₈H₃₉₈, all types of conformational disorder except end-gauche bonds are shown to pass through a maximum near 118 °C, in the region of the transformation to extended chain crystals.     

Computing effective properties of random heterogeneous materials on heterogeneous parallel processors

In recent decades, finite element (FE) techniques have been extensively used for predicting effective properties of random heterogeneous materials. In the case of very complex microstructures, the choice of numerical methods for the solution of this problem can offer some advantages over classical analytical approaches, and it allows the use of digital images obtained from real material samples (e.g., using computed tomography). On the other hand, having a large number of elements is often necessary for properly describing complex microstructures, ultimately leading to extremely time-consuming computations and high memory requirements. With the final objective of reducing these limitations, we improved an existing freely available FE code for the computation of effective conductivity (electrical and thermal) of microstructure digital models. To allow execution on hardware combining multi-core CPUs and a GPU, we first translated the original algorithm from Fortran to C, and we subdivided it into software components. Then, we enhanced the C version of the algorithm for parallel processing with heterogeneous processors. With the goal of maximizing the obtained performances and limiting resource consumption, we utilized a software architecture based on stream processing, event-driven scheduling, and dynamic load balancing. The parallel processing version of the algorithm has been validated using a simple microstructure consisting of a single sphere located at the centre of a cubic box, yielding consistent results. Finally, the code was used for the calculation of the effective thermal conductivity of a digital model of a real sample (a ceramic foam obtained using X-ray computed tomography). On a computer equipped with dual hexa-core Intel Xeon X5670 processors and an NVIDIA Tesla C2050, the parallel application version features near to linear speed-up progression when using only the CPU cores. It executes more than 20 times faster when additionally using the GPU.     

Towards a generalized physicochemical framework

Process models used for activated sludge, anaerobic digestion and in general wastewater treatment plant process design and optimization have traditionally focused on important biokinetic conversions. There is a growing realization that abiotic processes occurring in the wastewater (i.e. 'solvent') have a fundamental effect on plant performance. These processes include weak acid-base reactions (ionization), spontaneous or chemical dose-induced precipitate formation and chemical redox conversions, which influence pH, gas transfer, and directly or indirectly the biokinetic processes themselves. There is a large amount of fundamental information available (from chemical and other disciplines), which, due to its complexity and its diverse sources (originating from many different water and process environments), cannot be readily used in wastewater process design as yet. This position paper outlines the need, the methods, available knowledge and the fundamental approaches that would help to focus the effort of research groups to develop a physicochemical framework specifically in support of whole-plant process modeling. The findings are that, in general, existing models such as produced by the International Water Association for biological processes are limited by omission of key corrections such as non-ideal acid-base behavior, as well as major processes (e.g., ion precipitation). While the underlying chemistry is well understood, its applicability to wastewater applications is less well known. This justifies important further research, with both experimental and model development activities to clarify an approach to modeling of physicochemical processes.