An Ultra-Low-Cost RCL-Meter.

An ultra-low-cost RCL meter, aimed at IoT applications, was developed, and was used to measure electrical components based on standard techniques without the need of additional electronics beyond the AVR® micro-controller hardware itself and high-level routines. The models and pseudo-routines required to measure admittance parameters are described, and a benchmark between the ATmega328P and ATmega32U4 AVR® micro-controllers was performed to validate the resistance and capacitance measurements. Both ATmega328P and ATmega32U4 micro-controllers could measure isolated resistances from 0.5 Ω to 80 MΩ and capacitances from 100 fF to 4.7 mF. Inductance measurements are estimated at between 0.2 mH to 1.5 H. The accuracy and range of the measurements of series and parallel RC networks are demonstrated. The relative accuracy (ar) and relative precision (pr) of the measurements were quantified. For the resistance measurements, typically ar, pr < 10% in the interval 100 Ω−100 MΩ. For the capacitance, measured in one of the modes (fast mode), ar < 20% and pr < 5% in the range 100 fF−10 nF, while for the other mode (transient mode), typically ar < 20% in the range 10 nF−10 mF and pr < 5% for 100 pF−10 mF. ar falls below 5% in some sub-ranges. The combination of the two capacitance modes allows for measurements in the range 100 fF−10 mF (11 orders of magnitude) with ar < 20%. Possible applications include the sensing of impedimetric sensor arrays targeted for wearable and in-body bioelectronics, smart agriculture, and smart cities, while complying with small form factor and low cost.

Non-Destructive Soluble Solids Content Determination for 'Rocha' Pear Based on VIS-SWNIR Spectroscopy under 'Real World' Sorting Facility Conditions.

In this paper we report a method to determine the soluble solids content (SSC) of 'Rocha' pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500-1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘ Brix and 1.09 ∘ Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more 'real world-like' conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.

Model selection for clustering of pharmacokinetic responses.

BACKGROUND AND OBJECTIVE: Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject's high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection. METHODS: We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood. RESULTS: Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses. CONCLUSIONS: In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community.

Flashing LEDs for Microalgal Production.

Flashing lights are next-generation tools to mitigate light attenuation and increase the photosynthetic efficiency of microalgal cultivation systems illuminated by light-emitting diodes (LEDs). Optimal flashing light conditions depend on the reaction kinetics and properties of the linear electron transfer chain, energy dissipation, and storage mechanisms of a phototroph. In particular, extremely short and intense light flashes potentially mitigate light attenuation in photobioreactors without impairing photosynthesis. Intelligently controlling flashing light units and selecting electronic components can maximize light emission and energy efficiency. We discuss the biological, physical, and technical properties of flashing lights for algal production. We combine recent findings about photosynthetic pathways, self-shading in photobioreactors, and developments in solid-state technology towards the biotechnological application of LEDs to microalgal production.

Characterization of human renal stones with MDCT: advantage of dual energy and limitations due to respiratory motion.

OBJECTIVE: Our aim was to determine, using CT attenuation values, the chemical composition of 241 human renal stones placed in a jelly phantom and to analyze the influence of respiratory motion on the classification. MATERIALS AND METHODS: The stones were placed in a jelly simulating the X-ray attenuation of the kidneys. A dynamic platform was used to apply to the phantom free-breathing motion (sinusoidal motion in z-axis) and motion due to lack of maintenance of a breath-hold (5 mm x s(-1) in z-axis). Determination of the chemical composition was performed with mean CT attenuation values obtained at 80 and 120 kV and with dual-energy CT attenuation values. RESULTS: Two hundred forty-one human urinary stones were classified into six groups: uric acid, cystine, struvite, weddellite (calcium oxalate dihydrate), whewellite (calcium oxalate monohydrate), and brushite. With no motion, the use of dual energy enabled differentiation of all of the types of stones with statistically significant differences. Uric acid (-20 +/- 22 H), cystine (106 +/- 19 H), struvite (271 +/- 16 H), weddellite (323 +/- 5 H), brushite (415 +/- 30 H), and whewellite (510 +/- 17 H) were identified as distinct groups. Motion-induced mean CT attenuation values were significantly different from those obtained with no motion. With motion, dual-energy CT attenuation values did not allow differentiation of all stone types. CONCLUSION: Dual-energy CT attenuation values can be used to predict the chemical composition of stones in vitro. However, when slight motion is applied to renal stones during image acquisition, the values become significantly different from those obtained with no motion. Consequently, confusion arises in differentiating stone types. A perfect breath-hold has to be performed for in vivo use of attenuation value to discern stone type.

Degradation of the z- resolution due to a longitudinal motion with a 64-channel CT scanner.

Isotropic acquisitions are routinely achievable with 64- channel CT scanners,. As it predecessors, it includes MultiPlanar Reformation (MPR) projection for the reconstruction of two-dimensional images and volume rendering for the creation of three dimensional images. The accuracy of images obtained with these postprocessing methods depends on the spatial resolution of image data acquired along the long axis of the patient (ie longitudinal, or z-inis spatial resolution). But physiologic motions can appear during a Computed Tomography (CT) exam and can leacd to a degradation of this spatial resolution. By using two different phantoms and a dynamic platform, we have studied the influence of a z-axis linear motion on the MPR images quality. Our results show that the corruption of the data results in the loss of information about the form, the contrast and/or the size of the scanned object. This corruption of data can lead to diagnostic errors by mimicking diseases or by masking physiologic details.