Rocket engine analog simulation

Mathematical equations simulate the operation of a rocket engine, simulate destructive and nondestructive tests to verify engine design feasibility, and investigate nonlinear variations in engine performance

Shallow Neural Network for Biometrics from the ECG-WATCH

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate

NEW VARIETIES OF DURUM WHEAT (TRITICUM DURUM DESF.) IN SICILY: EVALUATION OF GENOTYPE-ENVIRONMENT INTERACTION

Durum wheat (Triticum durum Desf.) is the typical cereal crop in semi-arid Italian environments, and Sicily, is one of the regions of southern Italy where the cultivation of durum wheat is very spread. The thesis was the comparison of 14 genotypes of durum wheat located in 11 locations typical of the Sicilian cropland in the years 2006-2011. The analysis of the results has allowed to study the productive and qualitative behavior of the tested genotypes. In addition, the study of interaction genotype-environment, has allowed to verify the yield stability in quantity and the quality of the grain by the individual genotypes

THE MOSAIC OF THE FRIGIDARIUM OF “VILLA BONANNO” IN PALERMO: MINERALOGICAL AND PETROGRAPHIC ANALYSES FOR IN SITU CONSERVATION AND RESTORATION INTERVENTIONS

The topic of this study was the mineralogical and petrographic characterization of bedding mortars (made of different layers) and tesserae of Roman age (3rd century A.D.), taken from the mosaic of the Frigidarium of “Villa Bonanno”, brought to light by archaeological excavations conducted in the historical centre of Palermo. The collected samples have been analysed by thin-section optical microscopy (PLM), and scanning electron microscopy coupled with energy dispersive spectrometry (SEM-EDS). The study was aimed to define the “recipe” (composition of aggregate and binder, aggregate size distribution, aggregate/binder ratio), in order to assess the provenance of raw materials (supply site/area) and to acquire useful information in order to formulate "restoration mortars" that should be most comparable with the original ones, for replacements and/or integration. Rock types constituting the coloured tesserae were also characterized by thin-section optical microscopy. The mineralogical and petrographic investigations allowed establishing two different recipes used for the formulation of the studied mortars in terms of both compositional and textural features. The aggregate is composed by diverse proportions of detritic calcareous granules (both bioclasts and limestone fragments deriving from the local outcropping biocalcarenites and limestones), siliceous sand (monocrystalline quartz, chert and quartzarenite fragments), volcanic ash (pozzolana) and sometimes cocciopesto. The resulting hydraulic binder was the product of the „pozzolanic reaction‟ between volcanic ash and the aerial lime (specifically made by the calcination of locally available magnesian limestone or dolostone). The coloured tesserae can be all classified as compact limestones of Mesozoic or Cenozoic age, likely of local provenance

Double Channel Neural Non Invasive Blood Pressure Prediction

Cardiovascular Diseases represent the leading cause of deaths in the world. Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention. This work applies the neural network output-error (NNOE) model to ABP forecasting. Three input configurations are proposed based on ECG and PPG for estimating both systolic and diastolic blood pressures. The double channel configuration is the best performing one by means of the mean absolute error w.r.t the corresponding invasive blood pressure signal (IBP); indeed, it is also proven to be compliant with the ANSI/AAMI/ISO 81060-2:2013 regulation for non invasive ABP techniques. Both ECG and PPG correlations to IBP signal are further analyzed using Spearman’s correlation coefficient. Despite it suggests PPG is more closely related to ABP, its regression performance is worse than ECG input configuration one. However, this behavior can be explained looking to human biology and ABP computation, which is based on peaks (systoles) and valleys (diastoles) extraction

Electrochemical incineration of oxalic acid in the presence of NaCl

Recent researches have demonstrated that electrochemical methods offer an attractive alternative to traditional routes for treating wastewaters containing toxic or/and refractory organic pollutants. The effectiveness of the electrochemical treatment depends on many factors including the presence in solution of specie able to act as mediators. In particular, the effect of chloride ions on the performances of the process has been the object of numerous researches. However, up to now, many practical and theoretical aspects about this argument are not completely clear. In the present work, the anodic incineration of oxalic acid (OA) in the presence of NaCl has been investigated with the aim of studying in a systematic way the influence of numerous parameters, such as the current density, the flow rate, the OA, the NaCl concentrations and the pH on the performances of the process and to individuate the optimal operative conditions. Oxalic acid was chosen as model substrate for its low reactivity toward anodic oxidation, which also results in an incomplete mineralization of more complex organics. Furthermore, the oxidation of this simple molecule does not involve the formation of stable intermediates thus giving rise to a more easy rationalizing of experimental results. Since the effect of NaCl on the process is expected to depend on the nature of the electrodic material, two very different anodes were used: the Ti/IrO2-Ta2O5 which presents a quite low oxygen overpotential and boron doped diamond (BDD) which is probably one of the more promising materials for the electrochemical incineration

Distinctive Traits of Four Apulian Traditional Agri-Food Product (TAP) Cheeses Manufactured at the Same Dairy Plant

This study aimed to highlight the distinctive features of four Traditional Agri-food Products (TAP), namely, Caprino, Pecorino, Vaccino, and Cacioricotta cheeses produced at the same dairy plant to reveal any possible relationships between their microbiological and biochemical character-istics. Two distinct natural whey starter (NWS) cultures were used during Caprino and Vaccino cheesemaking, whereas no starter was used for the other cheeses. Cacioricotta retained the highest concentrations of salt and residual carbohydrates. Lactic acid bacteria dominated the microbiota of the cheeses. Furthermore, staphylococci represented an additional dominant microbial population in Cacioricotta. Although culture-dependent analysis showed that the use of NWS cultures only slightly affected the microbial community of cheeses, 16S metagenetic analysis showed that Lactobacillus helveticus dominated both the NWS cultures and the corresponding Caprino and Vaccino cheeses. This analysis indicated that Staphylococcus equorum and Streptococcus thermophilus dominated Cacioricotta and Pecorino cheeses, respectively. The highest peptidase activities were found in either Caprino or Vaccino. Enzymes involved in the catabolism of free amino acids and esterase showed the highest activity in Pecorino cheese. Each cheese showed a distinct profile of volatile organic compounds, with Pecorino being the richest cheese in carboxylic acids, ketones, and esters, related to lipolysis. The results of this study contribute to valorizing and safeguarding these TAP cheeses, sustaining local farming

Smart Analogue Sampler for the Optical Module of a Cherenkov Neutrino Detector

A transient waveform sampler/recorder IC has been developed and realized in AMS C35B4 technology. This chip has been designed to fit the needs of a proposal for a front-end architecture for the readout of the anode signal of the photomultipliers in an underwater neutrino telescope. The design is based around a 3 channels x 32 cells switched capacitor array unit sampling its voltage inputs at 200MHz external clock rate and transferring the stored analogue voltage samples to its single analogue output at 1/10th of the sampling rate. This unit is replicated inside the ASIC providing 4 independent analogue sampling queues for signal transients up to 32 x 5 ns and a fifth unit storing transients up to 128 x 5 ns. A micro-pipelined unit, based on Muller C-gates, controls the 5 independent samplers. This paper briefly summarizes the complete front-end architecture and discusses in more detail the internal structure of the ASIC and its first functional tests

Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network

Stator-based faults are one of the most common faults among induction motors (IMs). The conventional approach to IM control and protection employs current sensors installed on the motor. Recently, most studies have focused on fault detection by means of stator current. This paper presents an application of the Growing Curvilinear Component Analysis (GCCA) neural network aided by the Extended Park Vector Approach (EPVA) for the purpose of transforming the three-phase current signals. The GCCA is a growing neural based technique specifically designed to detect and follow changes in the input distribution, e.g. stator faults. In particular, the GCCA has proven its capability of correctly identifying and tracking stator inter-turn fault in IMs. To this purpose, the three-phase stator currents have been acquired from IMs, which start at healthy operating state and, evolve to different fault severities (up to 10%) under different loading conditions. Data has been transformed using the EPVA and pre-processed to extract statistical time domain features. To calibrate the GCCA neural network, a topological manifold analysis has been carried out to study the input features. The efficacy of the proposed method has been verified experimentally using IM with l.lkW rating and has potential for IMs with different manufacturing conditions

A survey on data integration for multi-omics sample clustering

Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the latter case, a few reviews refer to some outdated or no longer available methods, whereas others lack the description of relevant clustering metrics to compare the main approaches. This work provides a general overview of the major techniques in this area, divided into four groups: graph, dimensionality reduction, statistical and neural-based. Besides, eight tools have been tested both on a synthetic and a real biological dataset. An extensive performance comparison has been provided using four clustering evaluation scores: Peak Signal-to-Noise Ratio (PSNR), Davies-Bouldin(DB) index, Silhouette value and the harmonic mean of cluster purity and efficiency. The best results were obtained by using the dimensionality reduction, either explicitly or implicitly, as in the neural architecture