Synthesis and Evaluation of Blends Formed by Polymeric Crown Ethers and a Fullerene-Containing Primary Ammonium Salt in Organic Thin Films

The synthesis of a series of polymers and cyclopolymers bearing crown ethers of differing structure and affinities towards primary ammonium ions is discussed. These polymers have been tested in their efficiency to form structurally homogeneous thin films when blended with an amphiphilic C₆₀ compound containing a primary ammonium ion functional group. The X-ray reflectivity characterization of the films revealed that the polymer bearing the crown ether with the least affinity for primary ammonium ions, but having the highest degree of polymerization, is the most effective in forming structurally homogeneous thin films.     

From "macro" to "micro" manipulation: models and experiments

This paper addresses various problems related to manipulation in the micro domain, a field which is increasingly important for research and application. Grasping and manipulating parts with size ranging between a few micrometers and about 1 millimeter (defined in this paper as "micro parts") are required for an increasing number of applications: the assembly of micro systems and micro machines; and the operation in tiny and unpredictable environments, such as for inspection and interventions in pipes and for micro surgery. The aim of this work is to find out similarities and differences between traditional manipulation and micro manipulation, by investigating which requirements are still valid and which must be redefined when the object size scales down. The similarities between the two application domains "macro" and "micro" are pointed out along with the differences, and both are taken into account for the evaluation of different grasping typologies. Dedicated models for the adhesion forces arising at the micro level are presented, preliminarily tested, and discussed.     

Computing the energy balance of a statue of bronze: The San Marco's horses as a case study

The energy balance is discussed for the complex case of bronze or marble statuary horses and particularly the four horses of San Marco in Venice. Having computed the coefficient of exchange between bronze and the atmosphere, the daytime overheating is evaluated for different seasons and orientations of the statues. The equations can be solved with reasonable approximation and both the past and future overheatings can be evaluated as a function of the growing surface weathering and blackening. Therefore, the present analysis points out the chief characteristics of both the diurnal cycle and the variation over the centuries of the response of the bronzes to atmospheric variables.     

Simple digital control improving dynamic performance of powerfactor preregulators

This paper presents the practical implementation of a fully digital control for boost power factor preregulators (PFPs). The control algorithm, which is simple and fast, provides a significant improvement in the system's dynamic performance compared to the usual analog control techniques. The paper discusses the design criteria and the actions taken for the implementation of the digital control, which is performed by means of a standard microcontroller (Siemens 80C166). The effectiveness of the approach is assessed by experimental tests     

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.     

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O₂ consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.     

Parametric study for the growth of carbon nanotubes by catalytic chemical vapor deposition in a fluidized bed reactor

Multiwalled carbon nanotubes have been produced from H₂-C₂H₄ mixtures on Fe-SiO₂ catalysts by a fluidized bed catalytic chemical vapor deposition process. Various parameters such as the catalyst preparation, the residence time, the run duration, the temperature, the H₂:C₂H₄ ratio, the amount of metal deposited on the support have been examined. The influence of these parameters on the deposited carbon yield is reported, together with observations of the produced material. This process allows an homogeneously distributed deposition of nanotubes (10-20 nm diameter), that remain anchored to the support.     

Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Fuzzy clustering enables the simultaneous membership of objects in two or more clusters. This is particularly pertinent where time series are concerned, because very often patterns of time series change over time. Thus, a time series might belong to different clusters over different periods of time, in which case, crisp clustering is unable to capture this multi-cluster membership. In this paper, we adopt a Fuzzy C-Medoids approach to clustering time series based on autoregressive estimates of models fitted to the time series. We illustrate very good performance of this approach in a range of simulation studies. By means of two applications, we also show the usefulness of this clustering approach in the air pollution monitoring, by considering air pollution time series, i.e., CO time series, CO₂ time series and NO time series monitored on world and urban scales. In particular, we show that, by considering in the clustering process, the autoregressive representation of these air pollution time series, we are able to detect possible information redundancy in the monitoring networks and then, decreasing the number of monitoring stations, to reduce the monitoring costs and then to increase the monitoring efficiency of the networks.