Evaluation of physicochemical and glycaemic properties of commercial plant-based milk substitutes

The market for plant-based dairy-type products is growing as consumers replace bovine milk in their diet, for medical reasons or as a lifestyle choice. A screening of 17 different commercial plant-based milk substitutes based on different cereals, nuts and legumes was performed, including the evaluation of physicochemical and glycaemic properties. Half of the analysed samples had low or no protein contents (<0.5 %). Only samples based on soya showed considerable high protein contents, matching the value of cow’s milk (3.7 %). An in-vitro method was used to predict the glycaemic index. In general, the glycaemic index values ranged from 47 for bovine milk to 64 (almond-based) and up to 100 for rice-based samples. Most of the plant-based milk substitutes were highly unstable with separation rates up to 54.39 %/h. This study demonstrated that nutritional and physicochemical properties of plant-based milk substitutes are strongly dependent on the plant source, processing and fortification. Most products showed low nutritional qualities. Therefore, consumer awareness is important when plant-based milk substitutes are used as an alternative to cow’s milk in the diet.

Singularity and multifractal characterization of signals with wavelets. Application to clay soil images

In the field of multiscale analysis of signals, including images, the wavelet transform is one of the most attractive and powerful tool due to its ability to focus on signals structures at different scales. Wavelet Transform at different scales is successfully applied to image characterization (which can be applied to a watermarking scheme) and multiscale singularity detection and processing. In this work we show further research of computation of multifractals properties such as the multifractal spectrum (D(alpha)) applied to dye stained images of natural terrain. This can be useful for statically describing preferential flow path geometry.

Degradation and sorption of fipronil and atrazine in Latossols with organic residues from sugarcane crop.

ABSTRACT: Organic residues from sugarcane crop and processing (vinasse, boiler ash, cake filter, and straw) are commonly applied or left on the soil to enhance its fertility. However, they can influence pesticide degradation and sorption. The objective of this study was to assess the effect of adding these organic residues on the degradation and sorption of fipronil and atrazine in two soils of the State of Mato Grosso do Sul, MS, Brazil. The degradation experiment was carried out with laboratory-incubated (40 days; 28°C; 70% field capacity) soils (0-10cm). The batch equilibration method was used to determine sorption. Fipronil (half-life values of 15-105 days) showed to be more persistent than atrazine (7-17 days). Vinasse application to the soil favored fipronil and atrazine degradation, whereas cake filter application decreased the degradation rates for both pesticides. Values for sorption coefficients (Kd) were determined for fipronil (5.1-13.2mL g-1) and atrazine (0.5-1.5mL g-1). Only straw and cake filter residues enhanced fipronil sorption when added to the soil, whereas all sugarcane residues increased atrazine sorption. RESUMO: Resíduos orgânicos do cultivo e processamento da cana-de-açúcar (vinhaça, cinzas, torta de filtro e palha) são usualmente aplicados ou deixados no solo para aumentar sua fertilidade, mas eles podem influenciar na degradação e sorção de agrotóxicos. O objetivo deste estudo foi avaliar o efeito da adição desses resíduos orgânicos no solo sobre a degradação e sorção do fipronil e da atrazina em dois solos no Estado de Mato Grosso do Sul, MS, Brasil. O experimento de degradação foi realizado com solos (0-10cm) incubados em laboratório (40 dias; 28°C; 70% da capacidade de campo). Para determinar a sorção, foi usado o método da batelada. Fipronil mostrou ser mais persistente (valores de meia-vida entre 15-105 dias) que atrazina (7-17 dias). O solo com adição de vinhaça favoreceu a degradação de fipronil e atrazina, enquanto adição da torta de filtro desacelerou o processo. Os valores do coeficiente de sorção (Kd) foram determinados para fipronil (5,1-13,2mL g-1) e atrazina (0,5-1,5mL g-1). Apenas os resíduos palha e torta de filtro aumentaram a sorção de fipronil quando adicionados ao solo, enquanto todos os resíduos aumentaram a sorção de atrazina.

Analysis and Evaluation of Tiny Machine Learning applications

The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection and processing of data in real time. This emerging field, which is the end of a lot of innovation, is ready to speed up its growth in the coming years. In this thesis, we deploy three model on a microcontroller. For the model, datasets are retrieved from an online repository and are preprocessed as per our requirement. The model is then trained on the split of preprocessed data at its best to get the most accuracy out of it. Later the trained model is converted to C language to make it possible to deploy on the microcontroller. Finally, we take step towards incorporating the model into the microcontroller by implementing and evaluating an interface for the user to utilize the microcontroller’s sensors. In our thesis, we will have 4 chapters. The first will give us an introduction of TinyML. The second chapter will help setup the TinyML Environment. The third chapter will be about a major use of TinyML in Wake Word Detection. The final chapter will deal with Gesture Recognition in TinyML.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

Deep and near space tracking stations in support of lunar and planetary exploration missions

The aim of this dissertation is to describe the methodologies required to design, operate, and validate the performance of ground stations dedicated to near and deep space tracking, as well as the models developed to process the signals acquired, from raw data to the output parameters of the orbit determination of spacecraft. This work is framed in the context of lunar and planetary exploration missions by addressing the challenges in receiving and processing radiometric data for radio science investigations and navigation purposes. These challenges include the designing of an appropriate back-end to read, convert and store the antenna voltages, the definition of appropriate methodologies for pre-processing, calibration, and estimation of radiometric data for the extraction of information on the spacecraft state, and the definition and integration of accurate models of the spacecraft dynamics to evaluate the goodness of the recorded signals. Additionally, the experimental design of acquisition strategies to perform direct comparison between ground stations is described and discussed. In particular, the evaluation of the differential performance between stations requires the designing of a dedicated tracking campaign to maximize the overlap of the recorded datasets at the receivers, making it possible to correlate the received signals and isolate the contribution of the ground segment to the noise in the single link. Finally, in support of the methodologies and models presented, results from the validation and design work performed on the Deep Space Network (DSN) affiliated nodes DSS-69 and DSS-17 will also be reported.

Quantitative susceptibility mapping as biomarker of neurodegeneration: methodological models and clinical applications

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance technique that can quantify in vivo biomarkers of pathology, such as alteration in iron and myelin concentration. It allows for the comparison of magnetic susceptibility properties within and between different subject groups. In this thesis, QSM acquisition and processing pipeline are discussed, together with clinical and methodological applications of QSM to neurodegeneration. In designing the studies, significant emphasis was placed on results reproducibility and interpretability. The first project focuses on the investigation of cortical regions in amyotrophic lateral sclerosis. By examining various histogram susceptibility properties, a pattern of increased iron content was revealed in patients with amyotrophic lateral sclerosis compared to controls and other neurodegenerative disorders. Moreover, there was a correlation between susceptibility and upper motor neuron impairment, particularly in patients experiencing rapid disease progression. Similarly, in the second application, QSM was used to examine cortical and sub-cortical areas in individuals with myotonic dystrophy type 1. The thalamus and brainstem were identified as structures of interest, with relevant correlations with clinical and laboratory data such as neurological evaluation and sleep records. In the third project, a robust pipeline for assessing radiomic susceptibility-based features reliability was implemented within a cohort of patients with multiple sclerosis and healthy controls. Lastly, a deep learning super-resolution model was applied to QSM images of healthy controls. The employed model demonstrated excellent generalization abilities and outperformed traditional up-sampling methods, without requiring a customized re-training. Across the three disorders investigated, it was evident that QSM is capable of distinguishing between patient groups and healthy controls while establishing correlations between imaging measurements and clinical data. These studies lay the foundation for future research, with the ultimate goal of achieving earlier and less invasive diagnoses of neurodegenerative disorders within the context of personalized medicine.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Development and scale-up studies of bio-based poly(ester-amide)s

Plastics are polymers of conventional and extensive use in our day-to-day life. This is due to their light weight, adaptability to different uses and low prices. A downside of such extensive use is the environmental pollution arising from plastic production and disposal. Indeed, many commodity polymers are produced from non-renewable resources while other do not bio-degrade after their end-of-life disposal. Consequently, the ideal polymer comes from renewable raw materials and bio-degrades after its disposal, meaning that it would do little or no harm to the environment from the beginning to the end of its life cycle. In this thesis project a class of bio-based and bio-degradable co-polymers, namely poly(ester-amide)s, was investigated because of their tunable mechanical and bio-degradation properties as well as their renewable origin. Such polymers were synthetized and characterized thermically and mechanically. Furthermore, a scale-up procedure was developed and applied to one polymer and processing trials were made with the material obtained after scale-up.

Template-based fluoroethylenepropylene piezoelectrets with tubular channels for transducer applications

We describe the concept, the fabrication, and the most relevant properties of a piezoelectric-polymer system: Two fluoroethylenepropylene (FEP) films with good electret properties are laminated around a specifically designed and prepared polytetrafluoroethylene (PTFE) template at 300 degrees C. After removing the PTFE template, a two-layer FEP film with open tubular channels is obtained. For electric charging, the two-layer FEP system is subjected to a high electric field. The resulting dielectric barrier discharges inside the tubular channels yield a ferroelectret with high piezoelectricity. d(33) coefficients of up to 160 pC/N have already been achieved on the ferroelectret films. After charging at suitable elevated temperatures, the piezoelectricity is stable at temperatures of at least 130 degrees C. Advantages of the transducer films include ease of fabrication at laboratory or industrial scales, a wide range of possible geometrical and processing parameters, straightforward control of the uniformity of the polymer system, flexibility, and versatility of the soft ferroelectrets, and a large potential for device applications e.g., in the areas of biomedicine, communications, production engineering, sensor systems, environmental monitoring, etc.

A study of cell disruption of Candida mogii by glass bead mill for the recovery of xylose reductase

The release of xylose reductase (XR) from Candida mogii by cell disruption in a glass beads mill was studied using an experimental design. Statistical analysis of the results indicated that XR volumetric activity increases by using lower glass beads diameter and cell concentration, and by increasing the number of agitation pulses. Based on results attained in experimental design, assays were carried out aiming at the maximization of XR release. Under optimized conditions (300 mu m glass beads, 45 g/l of cell concentration and 50 pulses), the XR volumetric activity reach 0.683 U/ml. Disruption with glass beads showed to be the most efficient method for XR release when compared to sonication process. The highest specific activity (0.175 U/mg of protein) was found in extracts obtained by suspension freezing and thawing, which suggests that this method can be used as a selective process of cell disruption for XR release. (c) 2008 Elsevier B.V. All rights reserved.

Kinetics of the xylitol crystallization in hydro-alcoholic solution

Nyvlt method Was used to determine the kinetic parameters of commercial xylitol in ethanol:water (50:50 %w/w) Solution by batch cooling crystallization. The kinetic exponents (n, g and in) and the system kinetic constant (B(N)) were determined. Model experiments were carried Out in order to verify the combined effects of saturation temperatures (40, 50 and 60 degrees C) and cooling rates (0.10, 0.25 and 0.50 degrees C/min) on these parameters. The fitting between experimental and Calculated crystal sizes has 11.30% mean deviation. (C) 2007 Elsevier B.V. All rights reserved.

Mechanical properties of Y-TPZ ceramics obtained by liquid phase sintering using bioglass as additive

Yttria stabilized tetragonal zirconia (Y-TZP) ceramics were sintered by liquid phase sintering at low temperatures using bioglass as sintering additive. ZrO2-bioglass ceramics were prepared by mixing a ZrO2 stabilized with 3 Mol%Y2O3 and different amounts of bioglass based on 3CaO center dot P2O5-MgO-SiO2 system. Mixtures were compacted by uniaxial cold pressing and sintered in air, at 1200 and 1300 degrees C for 120 min. The influence of the bioglass content on the densification, tetragonal phase stability, bending strength, hardness and fracture toughness was investigated. The ceramics sintered at 1300 degrees C and prepared by addition of 3% of bioglass, exhibited the highest strength of 435 MPa, hardness of 1170 HV and fracture toughness of 6.3 MPa m(1/2). These results are related to the low monoclinic phase content, high relative density and the presence of the thermal residual stress generated between the ZrO2-matrix and bioglass grain boundary, contributing to the activation of the toughening mechanisms. (c) 2007 Elsevier B.V. All rights reserved.

Properties of Y-TZP/Al(2)O(3) ceramic nanocomposites obtained by high-energy ball milling

In this work, the synthesis of Y(2)O(3) stabilized tetragonal zirconia polycrystals (Y-TZP)-alumina (Al(2)O(3)) powder mixture was performed by high-energy ball milling and the sintering behavior of this composite was investigated. In order to understand the phase transformations occurring during ball milling, samples were collected after different milling times, from 1 to 60 h. The milled powders were compacted by cold uniaxial pressing and sintered at 1400 and 1600 degrees C. Both powders and sintered samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry analysis (EDS) and mechanical properties. Fully dense samples were obtained after sintering at 1600 degrees C, while the samples sintered at 1400 degrees C presented a full density for powder mixtures milled for 30 and 60 h. Fracture toughness and Vickers hardnessvalues of the Y-T-ZP/Al(2)O(3) nanocomposite were improved due to dispersed Al(2)O(3) grains and reduced ZrO(2) grain size. Samples sintered at 1400 degrees C, based on powders milled for 60 h, presented high K(IC) and hardness values near to 8.0 Mpan(1/2) and 15 GPa, respectively (C) 2008 Elsevier B.V. All rights reserved

Effects of temperature at different organic loading levels on the performance of a fluidized-bed anaerobic sequencing batch bioreactor

An investigation was performed on the effect of temperature and organic load on the stability and efficiency of a 1.8-L fluidized-bed anaerobic sequencing batch reactor (ASBR), containing granulated biomass. Assays were carried out employing superficial up How velocity of 7 m/h, total cycle length of 6 h and synthetic wastewater volume of 1.3 L per cycle. The fluidized-bed ASH was operated at 15, 20, 25 and 30 degrees C with influent organic matter concentrations of 500 and 1000 mgCOD/L The system showed stability under all conditions and presented filtered samples removal efficiency ranging from 79 to 86%. A first-order kinetic model could be fitted to the experimental values of the organic matter concentration profiles. The specific kinetic parameter values of this model ranged from 0.0435 to 0.2360 L/(gTVS h) at the implemented operation conditions. in addition, from the slope of an Arrhenius plot, the activation energy values were calculated to be 16,729 and 12,673 cal/mol for operation with 500 and 1000 mgCOD/L, respectively. These results show that treatment of synthetic wastewater. with concentration of 500 mgCOD/L, was more sensitive to temperature variations than treatment of the same residue with concentration of 1000 mgCOD/L. Comparing the activation energy value for operation at 500 mgCOD/L with the value obtained by Agibert et al. (S.A. Agibert, M.B. Moreira, S.M. Ratusznei, J.A.D. Rodrigues, M. Zaiat, E. Foresti. Influence of temperature on performance of an ASBBR with circulation applied to treatment of low-strength wastewater. journal of Applied Biochemistry and Biotechnology, 136 (2007) 193-206) in an ASBBR treating the same wastewater at the same concentration, the value obtained in the fluidized-bed ASBR showed to be superior, indicating that treatment of synthetic wastewater in a reactor containing granulated biomass was more sensitive to temperature variations than the treatment using immobilized biomass. (c) 2008 Elsevier B.V. All rights reserved.