Asymmetry studies in Lambda 0/Lambda 0-bar, Xi-/Xi+ and Omega-/Omega+ production

We present a study on hyperon/anti-hyperon production asymmetries in the framework of the recombination model. The production asymmetries for Lambda 0/Lambda 0-bar, Xi-/Xi+ and Omega-/Omega+ are studied as a function of x_F. Predictions of the model are compared to preliminary data on hyperon/anti-hyperon production asymmetries in 500 GeV/c pi- p interactions from the Fermilab E791 experiment. The model predicts a growing asymmetry with the number of valence quarks shared by the target and the produced hyperons in the x_F < 0 region. In the positive x_F region, the model predicts constant asymmetries for Lambda 0/Lambda 0-bar and Omega-/Omega+ production and a growing asymmetry with x_F for Xi-/Xi+. We found a qualitatively good agreement between the model predictions and data, showing that recombination is a competitive mechanism in the hadronization process.Comment: One reference correcte

Daily locomotion recognition and prediction: A kinematic data-based machine learning approach

More versatile, user-independent tools for recognizing and predicting locomotion modes (LMs) and LM transitions (LMTs) in natural gaits are still needed. This study tackles these challenges by proposing an automatic, user-independent recognition and prediction tool using easily wearable kinematic motion sensors for innovatively classifying several LMs (walking direction, level-ground walking, ascend and descend stairs, and ascend and descend ramps) and respective LMTs. We compared diverse state-of-the-art feature processing and dimensionality reduction methods and machine-learning classifiers to find an effective tool for recognition and prediction of LMs and LMTs. The comparison included kinematic patterns from 10 able-bodied subjects. The more accurate tools were achieved using min-max scaling [-1; 1] interval and 'mRMR plus forward selection' algorithm for feature normalization and dimensionality reduction, respectively, and Gaussian support vector machine classifier. The developed tool was accurate in the recognition (accuracy >99% and >96%) and prediction (accuracy >99% and >93%) of daily LMs and LMTs, respectively, using exclusively kinematic data. The use of kinematic data yielded an effective recognition and prediction tool, predicting the LMs and LMTs one-step-ahead. This timely prediction is relevant for assistive devices providing personalized assistance in daily scenarios. The kinematic data-based machine learning tool innovatively addresses several LMs and LMTs while allowing the user to self-select the leading limb to perform LMTs, ensuring a natural gait.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015 and SFRH/BD/147878/2019, by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Defect analysis and alignment quantification of line arrays prepared by directed self-assembly of a block copolymer

Trabajo presentado al XXVIII Metrology, Inspection, and Process Control for Microlithography, celebrado en California (US) en 2014.Different linear patterns obtained from the directed self-assembly of the block copolymer (BCP) polystyrene-b-polyethylene oxide (PS-b-PEO) were analysed and compared. The hexagonal phase PS-b-PEO in a thin film exhibits linear pattern morphology, by conventional solvent annealing in an atmosphere saturated in chloroform. The surface energy of the silicon substrates was varied using surface functionalization of a self-assembly monolayer (SAM) and a polymer brush, chosen to investigate the influence of the surface energy on the self-assembly of the BCP. The linear patterns formed were analyzed with innovative image analysis software specifically developed in our laboratory to identify elements and defects of line arrays from block copolymer self-assembly. The technique starts by performing dimensional metrology to calculate the pitch size and estimate the linewidth of the lines. Secondly, the methodology allows identification and quantification of typical defects observable in BCP systems, such as turning points, disclination or branching points, break or lone points and end points. The defect density and the quantification of the alignment were estimated using our technique. The methodology presented here represents a step forward in dimensional metrology and defect analysis of BCP DSA systems and can be readily used to analyze other lithographic or non-lithographic patterns.The research leading to these results received funding from the European Union FP7 under the project LAMAND (grant agreement n° 245565), the Spanish MICIN under the project TAPHOR (contract nr. MAT2012-31392) and the Science Foundation Ireland under grant number 09/SIRG/I1615.Peer Reviewe

Order quantification of hexagonal periodic arrays fabricated by in situ solvent-assisted nanoimprint lithography of block copolymers

arXiv:1403.2250v1Directed self-assembly of block copolymer polystyrene-b-polyethylene oxide (PS-b-PEO) thin film was achieved by a one-pot methodology of solvent vapor assisted nanoimprint lithography (SAIL). Simultaneous solvent-anneal and imprinting of a PS-b-PEO thin film on silicon without surface pre-treatments yielded a 250 nm line grating decorated with 20 nm diameter nanodots array over a large surface area of up to 4' wafer scale. The grazing-incidence small-angle x-ray scattering diffraction pattern showed the fidelity of the NIL stamp pattern replication and confirmed the periodicity of the BCP of 40 nm. The order of the hexagonally arranged nanodot lattice was quantified by SEM image analysis using the opposite partner method and compared to conventionally solvent-annealed block copolymer films. The imprint-based SAIL methodology thus demonstrated an improvement in ordering of the nanodot lattice of up to 50%, and allows significant time and cost reduction in the processing of these structures.The research leading to these results received funding from the European Union FP7 under the project LAMAND (grant agreement n° 245565), NANOFUNCTION (grant agreement no. 257375, FP7-ICT-2009-5) and by the Spanish Ministry of Economics and Competitiveness under project TAPHOR contract no. MAT2012-31392 (Plan Nacional de I + D + I (2008–2011)Peer Reviewe