Abstract. Anisotropic wet chemical etching of quartz is a bulk micromachining process for the fabrication of Micro-Electro-Mechanical Systems (MEMS), such as resonators and temperature sensors. Despite the success of the Continuous Cellular Automaton (CCA) for the simulation of wet etching of silicon, the simulation of the same process for quartz has received little attention -especially from an atomistic perspective-resulting in a lack of accurate modeling tools. This paper analyzes the crystallographic structure of the main surface orientations of quartz and proposes a novel classification of the surface atoms as well as an Evolutionary Algorithm (EA) to determine suitable values for the corresponding atomistic removal rates. Not only the presented Evolutionary Continuous Cellular Automaton (ECCA) reproduces the correct macroscopic etch rate distribution for quartz hemispheres but it is also capable of performing fast and accurate 3D simulations of MEMS structures. This is shown by several comparisons between simulated and experimental results and, in particular, by a detailed, quantitative comparison for an extensive collection of trench profiles.

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Due to the high intensification of poultry production in recent years, white chicken breast striping is one of the most frequently seen myopathies. The aim of this research was to develop a spectrophotometry-based sensor to detect white striping physiopathy in chicken breast meat in whole chicken carcasses with skin. Experiments were carried out using normal and white striping breasts. In order to understand the mechanism involved in this physiopathy, the different tissues that conform each breast were analyzed. Permittivity in radiofrequency (40 Hz to 1 MHz) was measured using two different sensors; a sensor with two flat plates to analyze the whole breast with skin (NB or WSB), and a two needles with blunt-ended sensor to analyze the different surface tissues of the skinless breast. In the microwave range (500 MHz to 20 GHz), permittivity was measured as just was described for the two needles with blunt-ended sensor. Moreover, fatty acids composition was determined by calorimetry techniques from −40 °C to 50 °C at 5 °C/min after previously freeze-drying the samples, and pH, microstructure by Cryo-SEM and binocular loupe structure were also analyzed. The results showed that the white striping physiopathy consists of the partial breakdown of the pectoral muscle causing an increase in fatty acids, reducing the quality of the meat. It was possible to detect white striping physiopathy in chicken carcasses with skin using spectrophotometry of radiofrequency spectra.

Abstract-Current PET systems with fully digital trigger rely on early digitization of detector signals and the use of digital processors, usually FPGAs, for recognition of valid gamma events on single detectors. Timestamps are assigned and later used for coincidence analysis. In order to maintain a decent timing resolution for events detected on different acquisition boards, it is necessary that local timestamps on different FPGAs be synchronized. Sub-nanosecond accuracy is mandatory if we want this effect to be negligible on overall timing resolution. This is usually achieved by connecting all boards to a common backplane with a precise clock delivery network; however, this approach forces a rigid structure on the whole PET system and may pose scalability problems.As an alternative, we propose a backplane-less PET system architecture in which DAQ boards are connected by single fullduplex high-speed data links. Data encoding with embedded clock is used to correct frequency differences between local oscillators. Timestamp synchronization between FPGAs with clock period resolution is maintained by means of data transfers in a way similar to the IEEE 1588 standard. Finer resolution is achieved by reflection of received clocks and phase difference measurement on the transmitter. It is crucial that data transceivers have very low latency uncertainty in order to achieve the desired timestamp accuracy; we discuss the availability of off-the-shelf hardware for these implementations.

The trend in meat consumption has changed drastically in the last years, mainly due to the relationship of red and processed meats with cancer and cardiovascular diseases, which has caused a substantial growth in poultry meat consumption, 8% in 2016. Therefore, poultry production has suffered an intensification that has led to an increase in the incidence of internal malformations in chickens and turkeys for fattening, especially in the pectoral muscles, as Deep Pectoral Myopathy (DPM). Currently, industry is not able to detect DPM breasts when sold as whole carcasses. In this context, the use of dielectric spectroscopy, complemented by a deep study of the chemical, biochemical and microstructural transformations of the muscle and the effect that these changes have on the electrical dispersions in radiofrequency range, may become feasible for online DPM detection. For this paper, non-damaged and affected by DPM chicken breasts (pectoralis major and pectoralis minor) was analysed. Permittivity in radiofrequency and microwave ranges were measured in the different tissues: pectoralis minor, major and skin in order to characterize them. Moreover, proteins content, ion content and pH were measured. With this data, a sensor for measuring the permittivity of chicken whole carcass with skin was developed; it consists of two pairs of two flat plates sensor connected to an impedance Agilent analyzer 4294A and can measure the permittivity from 40 Hz to 1 MHz. The results demonstrated the feasibility of the permittivity in radiofrequency range as an identification technique of chicken breasts affected by DPM. Highlights > A deep microstructural study of normal and DPM chicken meat has been carried out > Lactate content of each category has been related to the ε'  > The relation of proteins content of each category with the ε'  has been obtained > Dielectric properties of normal and DPM poultry meat were obtained in RF and MW ranges > A non-destructive sensor able to detect DPM in whole carcasses has been developed *Highlights (for review)

We report a first assessment of image quality enhancement achieved by the implementation of depth of interaction detection with monolithic crystals. The method of interaction depth measurement is based on analogue computation of the standard deviation with an enhanced charge divider readout. This technique of depth of interaction detection was developed in order to provide fast and determination of this parameter at a reasonable increase of detector cost. The detector consists of an large-sized monolithic scintillator coupled to a position sensitive photomultiplier tube. A special design feature is the flat-topped pyramidal shape of the crystal. This reduces image compression near the edges of the scintillator. We studied the image enhancement qualitatively with a FDG filled hot spot phantom and quantitatively by displacing a single point source along a radial axis. An important uniformity improvement was observed for the reconstructed image of the hot spot phantom when depth of interaction correction was applied. A moderate improvement of the spatial resolution was observed when reconstructing the images of the point source with depth of interaction correction.

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ElsevierMontoliu Álvaro, C.; Ferrando Jódar, N.; Gosalves Tomas, MA.; Cerdá Boluda, J.; Colom Palero, RJ. (2013 AbstractThe use of atomistic methods, such as the Continuous Cellular Automaton (CCA), is currently regarded as a computationally efficient and experimentally accurate approach for the simulation of anisotropic etching of various substrates in the manufacture of Micro-electro-mechanical Systems (MEMS). However, when the features of the chemical process are modified, a time-consuming calibration process needs to be used to transform the new macroscopic etch rates into a corresponding set of atomistic rates. Furthermore, changing the substrate requires a labor-intensive effort to reclassify most atomistic neighborhoods. In this context, the Level Set (LS) method provides an alternative approach where the macroscopic forces affecting the front evolution are directly applied at the discrete level, thus avoiding the need for reclassification and/or calibration. Correspondingly, we present a fullyoperational Sparse Field Method (SFM) implementation of the LS approach, discussing in detail the algorithm and providing a thorough characterization of the computational cost and simulation accuracy, including a comparison to the performance by the most recent CCA model. We conclude that the SFM implementation achieves similar accuracy as the CCA method with less fluctuations in the etch front and requiring roughly 4 times less memory. Although SFM can be up to 2 times slower than CCA for the simulation of anisotropic etchants, it can also be up to 10 times faster than CCA for isotropic etchants. In addition, we present a parallel, GPU-based implementation (gSFM) and compare it to an optimized, multi-core CPU version (cSFM), demonstrating that the SFM algorithm can be successfully parallelized and the simulation times consequently reduced, while keeping the accuracy of the simulations. Although modern multicore CPUs provide an acceptable option, the massively parallel architecture of modern GPUs is more suitable, as reflected by computational times for gSFM up to 7.4 times faster than for cSFM.