Interplay Between Doping,Morphology, and Lattice Thermal Conductivity in PEDOT:PSS

Organic materials for thermoelectric (TE) applications have attracted a fair amount of attention in recent years due to remarkable advances achieved in terms of their figure of merit, ZT: a value of 0.42 has been reported by for poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) films treated with dimethyl sulfoxide, while 0.25 has been obtained for PEDOT:Tosylate. In this study various PEDOT morphologies are investigated, considering both neutral and doped (bipolaronic) samples, by means of classical molecular dynamics, by taking advantage of a recently developed all-atom force field. In the case of bare PEDOT, it is found that changing the distribution of chain lengths affects the thermal conductivity of neutral and doped samples in a different way: longer chain lengths result in higher conductivities in the neutral scenario, whereas an intermediate chain length gives the highest value in the bipolaronic case. The role of PSS in the bipolaronic state, as compared to the simpler case where the counterion is Cl⁻, is discussed.     

e-Infrastructures for e-Science: A Global View

In the last 10?years, a new way of doing science is spreading in the world thank to the development of virtual research communities across many geographic and administrative boundaries. A virtual research community is a widely dispersed group of researchers and associated scientific instruments working together in a common virtual environment. This new kind of scientific environment, usually addressed as a ??collaboratory??, is based on the availability of high-speed networks and broadband access, advanced virtual tools and Grid-middleware technologies which, altogether, are the elements of the e-Infrastructures. The European Commission has heavily invested in promoting this new way of collaboration among scientists funding several international projects with the aim of creating e-Infrastructures to enable the European Research Area and connect the European researchers with their colleagues based in Africa, Asia and Latin America. In this paper we describe the actual status of these e-Infrastructures and present a complete picture of the virtual research communities currently using them. Information on the scientific domains and on the applications supported are provided together with their geographic distribution.     

Fluid-dynamic design optimization of hydraulic proportional directional valves

This article proposes an effective methodology for the fluid-dynamic design optimization of the sliding spool of a hydraulic proportional directional valve: the goal is the minimization of the flow force at a prescribed flow rate, so as to reduce the required opening force while keeping the operation features unchanged. A full three-dimensional model of the flow field within the valve is employed to accurately predict the flow force acting on the spool. A theoretical analysis, based on both the axial momentum equation and flow simulations, is conducted to define the design parameters, which need to be properly selected in order to reduce the flow force without significantly affecting the flow rate. A genetic algorithm, coupled with a computational fluid dynamics flow solver, is employed to minimize the flow force acting on the valve spool at the maximum opening. A comparison with a typical single-objective optimization algorithm is performed to evaluate performance and effectiveness of the employed genetic algorithm. The optimized spool develops a maximum flow force which is smaller than that produced by the commercially available valve, mainly due to some major modifications occurring in the discharge section. Reducing the flow force and thus the electromagnetic force exerted by the solenoid actuators allows the operational range of direct (single-stage) driven valves to be enlarged.     

Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems

Cloud computing has recently emerged as a new paradigm to provide computing services through large-size data centers where customers may run their applications in a virtualized environment. The advantages of cloud in terms of flexibility and economy encourage many enterprises to migrate from local data centers to cloud platforms, thus contributing to the success of such infrastructures. However, as size and complexity of cloud infrastructures grow, scalability issues arise in monitoring and management processes. Scalability issues are exacerbated because available solutions typically consider each virtual machine (VM) as a black box with independent characteristics, which is monitored at a fine-grained granularity level for management purposes, thus generating huge amounts of data to handle. We claim that scalability issues can be addressed by leveraging the similarity between VMs in terms of resource usage patterns. In this paper, we propose an automated methodology to cluster similar VMs starting from their resource usage information, assuming no knowledge of the software executed on them. This is an innovative methodology that combines the Bhattacharyya distance and ensemble techniques to provide a stable evaluation of similarity between probability distributions of multiple VM resource usage, considering both system- and network-related data. We evaluate the methodology through a set of experiments on data coming from an enterprise data center. We show that our proposal achieves high and stable performance in automatic VMs clustering, with a significant reduction in the amount of data collected which allows to lighten the monitoring requirements of a cloud data center.     

Multiple functionalization of fluorescent nanoparticles for specific biolabeling and drug delivery of dopamine

The development of fluorescent biolabels for specific targeting and controlled drug release is of paramount importance in biological applications due to their potential in the generation of novel tools for simultaneous diagnosis and treatment of diseases. Dopamine is a neurotransmitter involved in several neurological diseases, such as Parkinson's disease and attention deficit hyperactivity disorder (ADHD), and the controlled delivery of its agonists already proved to have beneficial effects both in vitro and in vivo. Here, we report the synthesis and multiple functionalization of highly fluorescent CdSe/CdS quantum rods for specific biolabeling and controlled drug release. After being transferred into aqueous media, the nanocrystals were made highly biocompatible through PEG conjugation and covered by a carbohydrate shell, which allowed specific GLUT-1 recognition. Controlled attachment of dopamine through an ester bond also allowed hydrolysis by esterases, yielding a smart nanotool for specific biolabeling and controlled drug release.     

Modeling natural anti-inflammatory compounds by molecular topology

One of the main pharmacological problems today in the treatment of chronic inflammation diseases consists of the fact that anti-inflammatory drugs usually exhibit side effects. The natural products offer a great hope in the identification of bioactive lead compounds and their development into drugs for treating inflammatory diseases. Computer-aided drug design has proved to be a very useful tool for discovering new drugs and, specifically, Molecular Topology has become a good technique for such a goal. A topological-mathematical model, obtained by linear discriminant analysis, has been developed for the search of new anti-inflammatory natural compounds. An external validation obtained with the remaining compounds (those not used in building up the model), has been carried out. Finally, a virtual screening on natural products was performed and 74 compounds showed actual anti-inflammatory activity. From them, 54 had been previously described as anti-inflammatory in the literature. This can be seen as a plus in the model validation and as a reinforcement of the role of Molecular Topology as an efficient tool for the discovery of new anti-inflammatory natural compounds.     

Optimization models for the dynamic facility location and allocation problem

The design of logistic distribution systems is one of the most critical and strategic issues in industrial facility management. The aim of this study is to develop and apply innovative mixed integer programming optimization models to design and manage dynamic (i.e. multi-period) multi-stage and multi-commodity location allocation problems (LAP). LAP belong to the NP-hard complexity class of decision problems, and the generic occurrence requires simultaneous determination of the number of logistic facilities (e.g. production plants, warehousing systems, distribution centres), their locations, and assignment of customer demand to them. The proposed models use a mixed integer linear programming solver to find solutions in complex industrial applications even when several entities are involved (production plants, distribution centres, customers, etc.). Lastly, the application of the proposed models to a significant case study is presented and discussed.     

Operational planning models for distribution networks

The design and optimisation of a logistic network deals with a wide set of decisions, e.g. the determination of the best location and capacity of the different logistic facilities (production plants, distribution centres, transit points, wholesalers, etc.), the allocation of the product demand coming from customers in presence (or absence) of fractionable flows of material, the determination of the best transportation mode (truck, rail, etc.) as well as loading and routing of vehicles. These decisions involve multiple stages of a distribution network: customers-regional distribution centres (RDC), RDCs-central distribution centres (CDC) and CDCs-production plants and sources, in presence of multiple products and the variable time (i.e. time-dependent product demand and flows of material). This paper presents a top-down methodology that joins the strategic planning, the tactical planning and the operational planning of distribution networks with a special focus on the development of effective heuristic methods to face the vehicle routing problem. Original models and heuristic algorithms for the operational planning are illustrated. The impact of the strategic and tactical decisions on the performance of the operational planning is evaluated by the application of the proposed hierarchical approach to two realistic case studies. Obtained results are illustrated in a what-if experimental analysis conducted on multiple problem settings and realistic scenarios.     

The threshold value of group similarity in the formation of cellular manufacturing systems

Cellular manufacturing is an effective alternative to batch-type production systems where different products are intermittently produced in small lot sizes with frequent setups, large in-process storage quantities, long production lead times, decreasing throughputs, and complex planning and control functions. An effective approach to forming manufacturing cells and introducing families of similar parts, consequently increasing production volumes and machine utilisation, is the use of similarity coefficients in conjunction with clustering procedures. In a similarity coefficients-based approach, the results of the clustering analysis depend on the minimum admissible level of similarity adopted for the generic group of clustered items. This is the so-called threshold value of group similarity. The aim of this paper is to identify effective values of the threshold value of group similarity to help practitioners and managers of manufacturing systems form machine groups and related part families. The proposed threshold values for a given similarity coefficient are based on calculation of the percentile of aggregations generated by the adopted clustering algorithm. The importance of the proposed measure of group similarity has been demonstrated by experimental analysis conducted on a large set of significant instances of the cell formation problem in the literature. This analysis can also support the best determination of this percentile-based cut value especially when the number of manufacturing cells is not known in advance.     

Swelling properties and bioactivity of silica gel/pHEMA nanocomposites

A novel hydrogel based on 2-hydroxyethyl- methacrilate and SiO₂ nanoparticles was prepared. The filler was added at a concentration of 30% w/w of silica nanoparticles to the mass of polymer. The composite material was characterised as far as concerns swelling behaviour in comparison to pHEMA. Swelling ratio of modified pHEMA was higher. Bioactivity of both SiO₂ nanoparticles and the modified hydrogel was evaluated by soaking samples into a simulated body fluid (SBF). FT-IR spectroscopy, scanning electron microscopy (SEM) and energy dispersive system (EDS) results suggest silica nanoparticles keep bioactive in the polymer. SiO₂ filler in a p(HEMA) matrix makes the composite bioactive. Therefore, these composites can be used to make bioactive scaffold for bone engineering.