Enhanced resistive switching and multilevel behavior in bilayered HfAlO/HfAlOx structures for non-volatile memory applications

In this work, hafnium aluminum oxide (HfAlO) thin films were deposited by ion beam sputtering deposition technique on Si substrate. The presence of oxygen vacancies in the HfAlOx layer deposited in oxygen deficient environment is evidenced from the photoluminescence spectra. Furthermore, HfAlO(oxygen rich)/HfAlOx(oxygen poor) bilayer structures exhibit multilevel resistive switching (RS), and the switching ratio becomes more prominent with increasing the HfAlO layer thickness. The bilayer structure with HfAlO/HfAlOx thickness of 30/40 nm displays the enhanced multilevel resistive switching characteristics, where the high resistance state/ intermediate resistance state (IRS) and IRS/low resistance state resistance ratios are 102 and 5 105 , respectively. The switching mechanisms in the bilayer structures were investigated by the temperature dependence of the three resistance states. This study revealed that the multilevel RS is attributed to the coupling of ionic conduction and the metallic conduction, being the first associated to the formation and rupture of conductive filaments related to oxygen vacancies and the second with the formation of a metallic filament. Moreover, the bilayer structures exhibit good endurance and stability in time.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Estudo da influência de nanotubos de carbono funcionalizados nas propriedades de revestimentos de poliuretano

Dissertação de mestrado em Propriedades e Tecnologias de Polímeros

Pointlike reducibility of pseudovarieties of the form V*D

In this paper, we investigate the reducibility property of semidirect products of the form V *D relatively to (pointlike) systems of equations of the form x1 =...= xn, where D denotes the pseudovariety of definite semigroups. We establish a connection between pointlike reducibility of V*D and the pointlike reducibility of the pseudovariety V. In particular, for the canonical signature consisting of the multiplication and the (omega-1)-power, we show that V*D is pointlike-reducible when V is pointlike-reducible.

Light-controlled resistive switching in laser-assisted annealed Ba0.8Sr0.2TiO3 thin films

In this work, Ba0.8Sr0.2TiO3 (BST)/ITO structures were grown on glass substrate and laser assisted annealing (LAA) was performed to promote the crystallization of BST. Atomic force microscopy and X-ray diffraction studies confirm the crack free and polycrystalline perovskite phase of BST. White light controlled resistive switching (RS) effect in Au/BST/ITO device is investigated. The device displays the electroforming-free bipolar RS characteristics and are explained by the modulationof the width and height of barrier at the BST/ITO interface via ferroelectric polarization. Moreover, the RS effect is signifi- cantly improved under white light illumination compared to that in the dark. The enhanced RS and photovoltaic effects are explained by considering depolarization field and charge distribution at the interface. The devices exhibit stable retention characteristics with low currents (mA), which make them attractive for non volatile memory devices.

Arquitetura Flutuante: projetar uma habitação-tipo para um ambiente em transformação

Dissertação de mestrado integrado em Arquitectura

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Urothelial bladder cancer progression: lessons learned from the bench

Urothelial bladder carcinoma (UBC) is an intricate malignancy with a variable natural history and clinical behavior. Despite developments in diagnosis/prognosis refinement and treatment modalities, the recurrence rate is high, and progression from non-muscle to muscle invasive UBC commonly leads to metastasis. Moreover, patients with muscle-invasive or extra-vesical disease often fail the standard chemotherapy treatment, and overall survival rates are poor. Thus, UBC remains a challenge in the oncology field, representing an ideal candidate for research on biomarkers that could identify patients at increased risk of recurrence, progression, and chemo-refractoriness. However, progress toward personalized medicine has been hampered by the unique genetic complexity of UBC. Recent genome-wide expression and sequencing studies have brought new insights into its molecular features, pathogenesis and clinical diversity, revealing a landscape where classical pathology is intersected by the novel and heterogeneous molecular groups. Hence, it seems plausible to postulate that only an integrated signature of prognostic/predictive biomarkers inherent in different cancer hallmarks will reach clinical validation. In this review, we have summarized ours and others' research into novel putative biomarkers of progression and chemoresistance that encompass several hallmarks of cancer: tumor neovascularization, invasion and metastasis, and energy metabolism reprogramming of the tumor microenvironment.

Systems biology approaches for the design of novel Saccharomyces cerevisiae winemaking strains for enhanced flavour compounds synthesis

Tese de Doutoramento em Biologia Ambiental e Molecular

HOXA9 as a key regulator in glioma initiation, aggressiveness and response to therapy

Tese de Doutoramento em Ciências da Saúde

Factoriality and the pin-reutenauer procedure

We consider implicit signatures over finite semigroups determined by sets of pseudonatural numbers. We prove that, under relatively simple hypotheses on a pseudovariety V of semigroups, the finitely generated free algebra for the largest such signature is closed under taking factors within the free pro-V semigroup on the same set of generators. Furthermore, we show that the natural analogue of the Pin-Reutenauer descriptive procedure for the closure of a rational language in the free group with respect to the profinite topology holds for the pseudovariety of all finite semigroups. As an application, we establish that a pseudovariety enjoys this property if and only if it is full.

Ensaios de controlo de qualidade em sabões e sabonetes

Dissertação de mestrado em Técnicas de Caracterização e Análise Química

Expectativa dos e das adolescentes relativas ao futuro: influência dos estereótipos de género

Dissertação de mestrado em Estudos da Criança (área de especialização em Intervenção Psicossocial com Crianças, Jovens e Famílias)

New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.