Influence of Field Effects on the Performance of InGaAs-Based Terahertz Radiation Detectors

A detailed electrical characterization of high-performance bow-tie InGaAs-based terahertz detectors is presented along with simulation results. The local surface potential and tunnelling current were scanned over the surfaces of the detectors by means of Kelvin probe force microscopy (KPFM) and scanning tunnelling microscopy (STM), which also enabled the determination of the Fermi level. Current-voltage curves were measured and modelled using the Synopsys Sentaurus TCAD package to gain deeper insight into the processes involved in detector operation. In addition, we performed finite-difference time-domain (FDTD) simulations to reveal features related to changes in the electric field due to the metal detector contacts. The investigation revealed that field-effect-induced conductivity modulation is a possible mechanism contributing to the high sensitivity of the studied detectors.     

A collaborative study of a method for the enumeration of probiotic bifidobacteria in animal feed

An enumeration method to be used as an official control method in the framework of Council Directive 70/524/EEC for probiotic bifidobacteria used as feed additives was validated. Seventeen laboratories in 11 European Countries carried out a collaborative study. A spread plate method following BS ISO 15214:1998 using four different agars, Man Rogosa Sharpe (MRS), acidified MRS, MRS with triphenyl tetrazolium chloride (TTC) and a selective bifidobacteria medium, was validated. Precision data in terms of repeatability (r) and reproducibility (R) of the method for each medium using different feeding stuffs with a high and a low inoculation level were determined. Bifidobacteria were present in the samples as a single component or in mixtures with other probiotics. The enumeration of bifidobacteria on all agars showed a relative standard deviation of repeatability (RSD(r)) between 1.2% and 6.3% and a relative standard deviation of reproducibility (RSD(R)) between 2.6% and 8.7%. MRS agar was preferred, followed by acidified MRS and MRS+TTC agar. The selective bifidobacteria medium gave similar counts as the MRS media. For routine analysis, the use of MRS agar with supplementation of cysteine hydrochloride (the selective bifidobacteria medium without antibiotics) is recommended. Depending on the presence and concentration of other probiotics such as enterococci, lactobacilli and pediococci, acidified MRS or MRS+TTC agar is recommended. The selective bifidobacteria medium was selective for bifidobacteria.An official control method for enumeration of probiotic bifidobacteria as a single component and in mixtures with other probiotic microorganisms in feeding stuffs was validated. The methodology is not applicable to mineral feed. The results are intended for consideration for adaptation as CEN and ISO standards.     

Detection of GMO in food products in Brazil: the INCQS experience

Regulations for the use and labeling of genetically modified organism products and derived ingredients are being implemented worldwide, what demands reliable and accurate methods to detect genetically modified organisms (GMO) in raw materials and food products. This study aimed at monitoring products derived from GMO in the Brazilian market using detection methods for the presence of Roundup Ready soybean, Bt176 and MON 810 maize. The results demonstrate for the first time the presence of GM-soy in Brazilian food products, reinforcing the need for the development of accurate quantitative methods in routine analyses.     

An exact approach for portfolio selection with transaction costs and rounds

We consider a single-period mean-safety portfolio selection problem with transaction costs and integer constraints on the quantities selected for the securities (rounds). We propose an exact approach based on the partition of the initial problem into two subproblems and the use of a simple local search heuristic to obtain an initial solution. To the best of our knowledge, no optimal algorithms have been proposed in the literature for this problem. The proposed approach is simple, general and easily adaptable to other problems. An extensive experimental analysis based on real data from the main international Stock Exchange Markets is performed. The results show, on average, an impressive improvement with respect to the computational time and space memory required by CPLEX 7.0. We also show that the solution of the first subproblem can be used on its own as an extremely effective heuristic.     

Effect of 475°C embrittlement on the low cycle fatigue behaviour of lean duplex stainless steels

Lean duplex stainless steels (LDSSs) with lower nickel and molybdenum are less susceptible to suffer spinodal decomposition than standard duplex stainless steels. It is the purpose of this work to study the effect of thermal embrittlement on the low cycle fatigue behaviour of 2 LDSSs with different Cr_eq and Ni_eq. The correlation between the fatigue behaviour and the dislocation structure is attempted. Transmission electron microscopy was used to observe the dislocation microstructure. Additionally, STEM‐EDS technique in conjunction with Vickers microhardness measurements was used to characterize the amplitude of the spinodal decomposition. The results show that the LDSS with lower Cr_eq and Ni_eq values exhibits improved fatigue properties in the as received and aged conditions. Furthermore, it is important to emphasize that with an adequate volume fraction of phases in LDSSs, the ageing treatment leads to an increase in strength without causing a great detriment in low cycle fatigue life.     

Exploiting group symmetry in truss topology optimization

We consider semidefinite programming (SDP) formulations of certain truss topology optimization problems, where a lower bound is imposed on the fundamental frequency of vibration of the truss structure. These SDP formulations were introduced in Ohsaki et al. (Comp. Meth. Appl. Mech. Eng. 180:203–217, 1999). We show how one may automatically obtain symmetric designs, by eliminating the ‘redundant’ symmetry in the SDP problem formulation. This has the advantage that the original SDP problem is substantially reduced in size for trusses with large symmetry groups.     

Superparamagnetic relaxation evidences large surface contribution for the magnetic anisotropy of MnFe<Subscript>2</Subscript>0<Subscript>4</Subscript> nanoparticles of ferrofluids

Manganese ferrite nanoparticles, in the size range 3.3–9.0 nm, are prepared by a hydrothermal coprecipitation process and peptized in aqueous solution. The magnetization curves recorded at room temperature on diluted colloidal sols allow characterizing the distribution of magnetic moment by using a simple Langevin formalism. Mössbauer spectroscopy measurements performed on powder samples at 77 K exhibit a quadrupolar doublet which intensity grows at the expense of the hyperfine sextet pattern as the nanoparticles mean size decreases. The magnetic dynamics behavior is then investigated by measurements of magnetic hysteretic properties at 5 K and temperature dependence of the zero field cooling (ZFC) susceptibility. The values found for the effective anisotropy constant and the dependence of the irreversibility field, inversely proportional to the reference size, clearly indicate that the magnetic anisotropy of our nanoparticles finds its origin on the disordered surface layer.     

Effect of nitrogen and vanadium on austenite grain growth kinetics of a low alloy steel

Austenite grain growth kinetics in a steel containing 0.4% C, 1.8% Cr with different nitrogen contents (in the range 0.0038–0.0412%) and a micralloying addition of 0.078% V were investigated. The investigations were carried out in an austenitising temperature range of 840–1200 °C for 30 min. The results of investigations showed that N promotes the grain growth of austenite. The microalloying addition of vanadium protects the austenite grain growth because of carbonitride V(C,N) precipitation and the grain boundary pinning effect of undissolved particles of V(C,N). Using a thermodynamic model, the carbonitride V(C,N) content, undissolved at the austenitising temperature was calculated. At temperatures when a coarsening and dissolution of carbonitride occurs, the austenite grains start to growth. The effect of nitrogen on the type of chord length distribution of austenite grains was analysed.     

Cobalt nanoparticles prepared by three different methods

This work aimed to study cobalt nanoparticles (Co-NPs) preparation using three different methods in order to evaluate the effect of synthesis variables that can influence the nanoparticle size distribution and particle shape. The synthesised nanoparticles were characterised by Transmission Electron Microscopy. The first synthesis employed decomposition of Co₂(CO)₈, at high temperatures. This procedure resulted in spherical nanoparticles with low size distribution. The size of Co-NPs could be tuned by modification of precursor/surfactant, nevertheless the stirring and injection time influenced the size distribution. Using polyol process, at high temperatures, it was produced undefined-shape nanoparticles. This result suggests that the solution composition, i.e. the amount of trioctylphosphine and oleic acid was not suitable to control both size and shape of nanoparticles. Finally, the method based on reduction with NaBH₄ resulted spherical nanoparticles with tiny sizes, indicating that in this case a variation on amount of reductant would be more efficient on the particle size control than a variation in concentration of oleic acid. These results indicated that, for each method, a different variable exists for the control of the distribution size and the shape of the formed particles.     

A multivariate fuzzy c-means method

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.