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.     

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.     

A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.     

Modeling heavy metal mass releases from urban battery litter

Consumer batteries littered on urban pavements release metals of environmental significance (Ag, Cd, Cr, Cu, Hg, Li, Mn, Ni, Pd, Ti, Zn) to stormwater runoff. Predicting the mass loading of any one metal is difficult because of the random composition of battery litter. However, when littering is modeled as a conditional filtered Poisson process, bounds may be estimated for the mean and variance of site mass loading for any metal if the site litter rate and battery product contributions are known. Site-specific data on the battery brand distribution in litter can improve load estimates, but statistics computed from 5500 littered batteries collected in the Cleveland area may be used to approximate the brand distribution. Zinc load calculations based on battery litter size, type and brand discretizations are implemented in a model titled BLML and illustrated for a case-study location. Results indicate that, at some urban sites, zinc released from battery litter can be the largest source of zinc in urban pavement runoff.     

AlphaPilot: autonomous drone racing

Autonomous Robots - This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The...