Nanowires and Nanostructures that Grow like Polymer Molecules

Unique properties (e.g., rubber elasticity, viscoelasticity, folding, reptation) determine the utility of polymer molecules and derive from their morphology (i.e., one‐dimensional connectivity and large aspect ratios) and flexibility. Crystals do not display similar properties because they have smaller aspect ratios, they are rigid, and they are often too large and heavy to be colloidally stable. We argue, with the support of recent experimental studies, that these limitations are not fundamental and that they might be overcome by growth processes that mimic polymerization. Furthermore, we (i) discuss the similarities between crystallization and polymerization, (ii) critically review the existing experimental evidence of polymer‐like growth kinetic and behavior in crystals and nanostructures, and (iii) propose heuristic guidelines for the synthesis of “polymer‐like” crystals and assemblies. Understanding these anisotropic materials at the boundary between molecules and solids will determine whether we can confer the unique properties of polymer molecules to crystals, expanding them with topology, dynamics, and information and not just tuning them with size.     

THERMOELASTIC DEFORMATION OF MICROSTRETCH ELASTIC BEAMS

This article addresses the problem of thermal stresses for isotropic microstretch elastic cylinders subjected to a temperature distribution that is linear in the axial coordinate. A direct method is used to reduce the problem to solving plane strain problems. The results are used to study the deformation of circular cylinders.     

Applied statistics for extreme wind estimate

The assessment of extreme wind speeds is a crucial issue for securing structural safety of wind turbines and inquiring largest loads to which turbines must be prepared to undergo. International standards suggest applying the Gumbel method of fitting the annual maxima to their theoretic probability distribution. Yet, often, wind databases are too short to apply such methods with statistical significance, and other procedures are commonly adopted [such as peaks over threshold (POT) and independent storms], which involve dependency on arbitrary thresholds for filtering data and issues of sub‐asymptocity, i.e. how well the selected dataset fits to density functions describing the distribution of peaks or extreme values. The present paper aims at contributing to such currently ongoing debate, providing a statistical analysis of the application of POT and independent storms methods on wind time series of various lengths from different geographical areas. The CERN data analysis framework ROOT has been employed for guaranteeing excellent standards of computational precision and wealth of statistical information. Analysis of uncertainties in the wind speeds estimates and tests of the goodness of fit of the datasets to the proper distributions have been carried on. An algorithm for choosing the optimum thresholds was developed, which encapsules and compromises the statistical complexity of the methods. A declustering procedure has been carried on for discriminating proper peaks in the POT method: it has been tested that such declustering provides a dramatic improvement of the statistical quality of the method. Copyright © 2014 John Wiley & Sons, Ltd.     

Statistical analysis of the stereolithographic process to improve the accuracy

Stereolithography (SL) is a widely used technology in the field of rapid prototyping. However, the dimensional accuracy of SL products is today still limited; therefore, this technology needs to be optimized for high precision applications. This paper presents a statistical analysis of the stereolithographic process, in order to find out the combination of parameters leading to the best accuracy of the manufactured parts. A particular benchmark was designed and a global error index was introduced to evaluate the global distortion of built parts. The Taguchi methodology was employed for the optimization. A Viper Si₂ machine by 3D Systems was used in both the modalities allowed from this system: Normal and High Resolution.Moreover, a detailed analysis of the resin polymerization mechanism was performed; from this study it emerged that the post-curing process is not always necessary if the process parameters are chosen for not having uncured areas.     

Mechanical behaviour of FRP-confined masonry by testing of full-scale columns

The vulnerability of masonry constructions under seismic forces, or more generally under the mechanical actions during the centuries, has been highlighted in the last years by several events that caused the loss of significant heritage buildings. Faced with this difficulty, the use of composite materials, fiber reinforced polymers (FRP) may be a solution for mitigating the vulnerability of masonry buildings. This solution has been tested in the laboratory by researchers in the last decade. In particular, studies regarding elements such as walls, arches and vaults, strengthened with FRP materials are available. A few numbers of studies are known for columns, which have been tested only as small or middle scale samples. The current state of the art does not report studies on FRP-confined masonry columns tested in real scale. The research presents the results of an experimental program performed on full-scale masonry columns strengthened with different composite systems. The same kind of study had been previously performed by the authors on medium scale masonry columns, using the same materials for both the masonry core and for the FRP system. Prismatic columns with a square cross section were subjected to compression tests according to the following test schemes: two control unconfined columns; column with continuous wrapping by using unidirectional glass FRP (GFRP) sheets; column with discontinuous wrapping by using GFRP unidirectional sheets; column with continuous GFRP wrapping and internal carbon FRP bars bonded in the transverse directions; column wrapped with continuous alkali resistant GFRP grid and steel spikes bonded together in lime based matrix. The experimental results are presented and discussed in the paper along with the comparison with the results obtained from the experimental tests on medium scale specimens. The comparison between experimental data and theoretical predictions provided by the analytical model found in the guidelines of the CNR technical document is also illustrated.     

Evaluation of a machine learning classifier for metamodels

Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as reusability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a machine learning classifier for metamodel repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics. More importantly, we improve the original AURORA tool by changing its internal design. Experimental results demonstrate that the proposed amendment is beneficial to the classification of metamodels. We also compared our approach with two baseline algorithms, namely gradient boosted decision tree and support vector machines. Eventually, we see that AURORA outperforms the baselines with respect to various quality metrics.

     

Biexponential and diffusional kurtosis imaging, and generalised diffusion-tensor imaging (GDTI) with rank-4 tensors: a study in a group of healthy subjects

Object Clinical diffusion imaging is based on two assumptions of limited validity: that the radial projections of the diffusion propagator are Gaussian, and that a single directional diffusivity maximum exists in each voxel. The former can be removed using the biexponential and diffusional kurtosis models, the latter using generalised diffusion-tensor imaging. This study provides normative data for these three models. Materials and methods Eighteen healthy subjects were imaged. Maps of the biexponential parameters D _fast, D _slow and f _slow, of D and K from the diffusional kurtosis model, and of diffusivity D′ were obtained. Maps of generalised anisotropy (GA) and scaled entropy(SE) were also generated, for second and fourth rank tensors. Normative values were obtained for 26 regions. Results In grey versus white matter, D _slow and D′ were higher and D _fast, f _slow and K were lower. With respect to maps of D′, anatomical contrast was stronger in maps of D _slow and K. Elevating tensor rank increased SE, generally more significantly than GA, in: anterior limb of internal capsule, corpus callosum, deep frontal and subcortical white matter, along superior longitudinal fasciculus and cingulum. Conclusion The values reported herein can be used for reference in future studies and in clinical settings.     

The Genetic,Environmental, and Immunopathological Complexity of Autoantibody-Negative Rheumatoid Arthritis

Differences in clinical presentation, response to treatment, and long-term outcomes between autoantibody-positive and -negative rheumatoid arthritis (RA) highlight the need for a better comprehension of the immunopathogenic events underlying the two disease subtypes. Whilst the drivers and perpetuators of autoimmunity in autoantibody-positive RA have started to be disclosed, autoantibody-negative RA remains puzzling, also due its wide phenotypic heterogeneity and its possible misdiagnosis. Genetic susceptibility appears to mostly rely on class I HLA genes and a number of yet unidentified non-HLA loci. On the background of such variable genetic predisposition, multiple exogeneous, endogenous, and stochastic factors, some of which are not shared with autoantibody-positive RA, contribute to the onset of the inflammatory cascade. In a proportion of the patients, the immunopathology of synovitis, at least in the initial stages, appears largely myeloid driven, with abundant production of proinflammatory cytokines and only minor involvement of cells of the adaptive immune system. Better understanding of the complexity of autoantibody-negative RA is still needed in order to open new avenues for targeted intervention and improve clinical outcomes.     

Investigation of terrain and wake effects on the performance of wind farms in complex terrain using numerical and experimental data

In this work, the combination effects on wind turbine performances of wakes and terrain‐driven flow are investigated. The test case is a subcluster of four turbines from a wind farm sited in southern Italy in a very complex terrain. The layout, the inter‐turbine distance and the wind rose result in a challenging performance scenery. The subcluster is analyzed, when the wind blows from the west, through computational fluid dynamics numerical simulations and experimental supervisory control and data acquisition data mining. Two wind intensity regimes and several simulation setups are employed. It is shown that the main effect of the terrain is the northward distortion of the wake of the upstream turbine. This explains the non‐trivial yawing patterns of the cluster and the fact that the wake line affects the overall performances of the subcluster less than it would do in flat terrain. It is further shown that the presence of the rest of the subcluster in operation southward deviates the wake line of the upstream turbine. The dependency on wind intensity of these directional distortions allows to estimate the relative importance of wakes and terrain‐driven flow. A bijective feedback between models and data is established and a convincing framework is constructed, for separating and assessing the effect of the terrain and of the single and multiple wake. Copyright © 2017 John Wiley & Sons, Ltd.     

Enhanced Magnetic Anisotropy for Reprogrammable High-Force-Density Microactuators

The precise control of magnetic properties at the microscale has transformative potential in healthcare and human-robot interaction. This research focuses on understanding the magnetic interactions in nanostructure assemblies responsible for microactuation. By combining experimental measurements and micromagnetic simulations, the interactions in both nanocube and nanochain assemblies are elucidated. Hysteresis measurements and first-order reversal curves (FORC) reveal that the spatial arrangement of these assemblies governs their collective magnetism. A critical concentration threshold is observed where a transition from ferromagnetic-like to antiferromagnetic-like coupling occurs. Leveraging the high uniaxial anisotropy of 1D nanochains, the remanent magnetization of assembled chain structures is maximized for efficient magneto-mechanical energy transduction. By utilizing an optimized magnetic nanostructure concentration, a flexible film is fabricated, and its significantly enhanced mechanical deformation response to a small magnetic field, surpassing conventional particle-based samples by a factor of five, is demonstrated. Demonstrating excellent transduction efficiency, visible deformations such as bending and S-shaped twisting modes are achieved with an applied field of less than 400 Oe. Furthermore, the reprogrammability of the actuator, achieving a U-shaped bending mode by altering its magnetization profile, is showcased. This research provides valuable insights for designing reconfigurable and effective microactuators and devices at significantly smaller scales than previously possible.