Production and characterization of pure and Cr<Superscript>3+</Superscript>-doped hydroxyapatite for biomedical applications as fluorescent probes

Pure and Cr³⁺-doped hydroxyapatite (HAP) were prepared via chemical precipitation route. The XRD measurements revealed that the typical HAP powder pattern was obtained. SEM analysis indicated that aggregates of nanoparticles were formed. EDX analysis indicates that the [Ca]/[P] concentration ratio was higher than the expected values but can be explained by the presence of carbonate groups as dopants. The optical absorption spectra of the doped samples presented absorption bands typical of Cr³⁺ occupying to different crystalline sites. From the position of the bands, it was possible to estimate the crystal field parameters for both sites of Cr³⁺ in the HAP matrix. The emission spectra of the Cr-doped samples were also investigated and typical transitions of the dopant ion, in trivalent state, were identified. The potential use of the Cr³⁺-HAP as fluorescent probes for medical applications was discussed.     

DC-Amplifier-Input-Offset-Voltage Control in a Constant-Temperature Thermoresistive-Sensor-Measurement Instrument

In this paper, the effect of dc amplifier input offset voltage on the static and dynamic performances of a generic constant-temperature thermoresistive-sensor-feedback circuit (CTC) is reviewed. A negative-feedback automatic control circuit for the input offset voltage is proposed in order to assure a stable operation of the CTC and to obtain optimal response time. Experimental evaluation results of the proposed circuit performance are presented     

Multiple biological sequence alignment in heterogeneous multicore clusters with user-selectable task allocation policies

Multiple Sequence Alignment (MSA) is an important problem in Bioinformatics that aims to align more than two sequences in order to emphasize similarity regions. This problem is known to be NP-Hard, so heuristic methods are used to solve it. DIALIGN-TX is an iterative heuristic method for MSA that generates alignments by concatenating ungapped regions with high similarity. Usually, the first phase of MSA algorithms is parallelized by distributing several independent tasks among the nodes. Even though heterogeneous multicore clusters are becoming very common nowadays, very few task allocation policies were proposed for this type of architecture. This paper proposes an MPI/OpenMP master/slave parallel strategy to run DIALIGN-TX in heterogeneous multicore clusters, with several allocation policies. We show that an appropriate choice of the master node has great impact on the overall system performance. Also, the results obtained in a heterogeneous multicore cluster composed of 4 nodes (30 cores), with real sequence sets show that the execution time can be drastically reduced when the appropriate allocation policy is used.     

Robust image-based visual servoing using invariant visual information

This paper deals with the use of invariant visual features for visual servoing. New features are proposed to control the 6 degrees of freedom of a robotic system with better linearizing properties and robustness to noise than the state of the art in image-based visual servoing. We show in this paper that by using these features the behavior of image-based visual servoing in task space can be significantly improved. Several experimental results are provided and validate our proposal.     

Detecting malicious tweets in trending topics using a statistical analysis of language

Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-of-the-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.     

Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices

This paper addresses challenges relating to applying data mining techniques to detect stock price manipulations and extends previous results by incorporating the analysis of intraday trade prices in addition to closing prices for the investigation of trade-based manipulations. In particular, this work extends previous results on the topic by analysing empirical evidence in normal and manipulated hourly data and the particular characteristics of intraday trades within suspicious hours. Furthermore, the analytical models described in this paper reinforce the results of previous market manipulation studies that are based on traditional statistical and econometrical methods providing an alternative portfolio of methods and techniques originating from the data mining and knowledge discovery areas. With the application of the analytical approach described in this paper, it is possible to identify new fraud manipulation pattern characteristics encoded as decision trees which can be readily employed in fraud detection systems. The paper also proposes a number of policy recommendations towards increasing the effectiveness of the operational processes executed by stock exchange fraud departments and regulatory authorities.     

Study of Nanostructured Array of Antidots Using Pulsed Magnetic Fields

The absence of the superparamagnetic limit in nanostructured antidots makes them strong candidates for ultra-high density recording media. In this work, nanoporous alumina templates (NpATs), with average pore diameters ～35 nm and separation ～100 nm, were grown using a two-step anodization method. A Ni₈₀Fe₂₀ thin film of 6.5 nm was then sputtered on top of such NpATs, building an antidot network. A detailed study of the magnetoresistance (MR) (fields up to 25 T and temperatures down to 77 K) was performed. The antidot network sample revealed an anomalous MR and R(T) behaviour at T _M ～250 K arising from a spin-flip transition occurring in a thin iron oxide layer.     

Measuring Magnetoresistance in a 2D Intergranular Magnetic-Semiconducting Material

A new 2D intergranular semiconducting-magnetic material obtained by depositing magnetic clusters of permalloy in a doped Si surface, is presented. The material, exhibiting ohmic contacts, has an ample range of properties and versatility by simply controlling deposition time. We can have clusters separated by relatively large, intermediate distances or a continuous film. This permits to study the magnetic and electrical properties of the clusters mediated by currents through the n-doped Si. We observe anisotropic and giant magnetoresistances of and between clusters. Also, by preventing oxidation of the Si surface, we suggest the possibility of formation of magnetic Si up to 205 K. The material reported here is technologically promising since it is grown directly on Si and its magnetoresistance of up to 1.5% is obtained at low fields and RT. The spin transfer occurs for long distances since the current flows coherently from cluster to cluster via the Si matrix. The method developed may be also adequate to study superconductivity properties of isolated or weakly percolative clusters by coupling through Josephson currents. The system is diluted when clusters are separated on average by 50 nm. This could help to better understand dilute magnetic semiconductor materials in general. We also put forward a method to distinguish univocally giant from anisotropic magnetoresistance by depositing a thin film of gold and comparing magnetic properties by spin transfer through Si or metal.     

Conformance notions for the coordination of interaction components

In component-based development, software components are taken as units of composition. Nevertheless, to achieve the widely disseminated status of components being plugged together as simple ‘Lego Pieces’, the integration of components must be carefully designed, systematised and verified; only this can ensure reliable architectures. In this work, we propose some conformance notions to predict the precise nature of some forms of composition, considering coordination patterns used in the integration. These notions are formalised in terms of the denotational semantics of the process algebra CSP, and assist the designer in common activities during integration, such as the substitution of component specifications by implementations, contract adaptations, and system extensions. To support mechanical verifications using FDR (a model-checker for CSP), we derive test characterisations from the denotational definitions of conformance. We illustrate the application of these notions through a systematic composition strategy of software frameworks, and we mechanically verify the preservation of behavioural component properties in these compositions. Moreover, we characterise the well-formedness of a coordination pattern used in this strategy at the design stage, before components are assembled.