Damage Detection Based On Damping Analysis Of Ambient Vibration Data

Enabling an automated, remote and rapid detection of structural damage, sensor-based structural health monitoring is becoming a powerful tool for maintenance of civil engineering structures. In this study, a baseline-free, time-domain damage detection method was developed for concrete structures, which is based on analysis of nonlinear damping from measured structural vibration responses. The efficacy of the proposed method was demonstrated through a large-scale concrete bridge model subjected to different levels of seismic damage caused by shaking table tests. By applying the random decrement signature technique, the proposed method successfully identified, from its ambient vibration responses, nonlinear damping of the bridge associated with the seismic damage. The amount of the nonlinear damping increases as the seismic damage becomes more severe. This paper also compares the damage detection results with those obtained by stiffness-based methods, demonstrating a strong correlation between the increase in nonlinear damping and the decrease in structural stiffness associated with the increase in damage severity

Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits

peer-reviewedIncluding all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods.

Peer reviewedNumerous statistical machine learning methods suitable for application to highly correlated features, as exists for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN) and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated (a60, alpha s1 CN, alpha s2 CN, kappa CN, alpha lactalbumin, and beta lactoglobulin B), while NN and RR were the best algorithms for 3 traits each (RCT, k20, and heat stability, and a30, beta CN, and beta lactoglobulin A, respectively), PLSR was best for pH and LASSO was best for CN micelle size. When traits were divided into two classes, SVM had the greatest accuracy for the majority of the traits investigated. While the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error when compared to PLSR from between 0.18% (kappa CN) to 3.67% (heat stability). The use of modern statistical ML methods for trait prediction from MIRS may improve the prediction accuracy for some traits

Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics - Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows' diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows' diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model.Comment: 27 pages, 9 figure

Estimation of body condition score change in dairy cows in a seasonal calving pasture-based system using routinely available milk mid-infrared spectra and machine learning techniques

peer-reviewe

Supplementary Figure 1.docx

Supplementary Figure paper Frizzarin et al. 2024</p

Técnicas utilizadas para detecção e quantificação de aflatoxina M1 no Leite

Aflatoxina é a denominação dada a um grupo de substâncias tóxicas, produzidas principalmente por dois fungos Aspergillus flavus e Aspergillus parasiticus, que se desenvolvem sobre muitos produtos agrícolas e alimentos quando as condições de umidade do produto, umidade relativa do ar e temperatura ambiente são favoráveis. As aflatoxinas podem apresentar diferentes formas, sendo que no leite, apresentam-se como M1 e M2, resultantes do metabolismo das aflatoxinas B1 e B2. A aflatoxina M1 (AFM1) é classificada como possível carcinógeno para o homem, por isso, a ocorrência desta no leite de vacas lactantes é uma questão de saúde pública, diversas técnicas são utilizadas para sua detecção e quantificação. Essas técnicas incluem as físicoquímicas como a cromatografia em camada delgada e cromatografia líquida de alta eficiência e as técnicas biológicas que incluem os imunoensaios, como o radioimunoensaio e o ELISA. Deste modo, estre trabalho objetivou apresentar as técnicas utilizadas para determinação de aflatoxina M1 e M2 no leite

O pensamento político de Fernando Henrique Cardoso

Este artigo buscou evidenciar o pensamento político de Fernando HenriqueCardoso, através da análise imanente de sua produção teórica, artigos e discursosna senatoria, abrangendo o período que vai da segunda metade da década de 70até princípios dos anos 80. Destacamos suas teses sobre a ditadura militar (1964-1985), parametradas pela teoria do autoritarismo, e sobre a chamada aberturapolítica (pós-1974).This article had as purpose to elucidate Fernando Henrique Cardoso´s politicalthought, presented from the seventies to the early eighties, through immanentanalysis of his theoretical work, articles published in newspapers, interviews andpolitical speeches. We have focused on his theses about the military dictatorship(1964-1985), which take the theory of authoritarianism as a parameter and theprocess of political opening (post-1974)

Técnicas utilizadas para detecção e quantificação de aflatoxina M1 no Leite

Aflatoxina é a denominação dada a um grupo de substâncias tóxicas, produzidas principalmente por dois fungos Aspergillus flavus e Aspergillus parasiticus, que se desenvolvem sobre muitos produtos agrícolas e alimentos quando as condições de umidade do produto, umidade relativa do ar e temperatura ambiente são favoráveis. As aflatoxinas podem apresentar diferentes formas, sendo que no leite, apresentam-se como M1 e M2, resultantes do metabolismo das aflatoxinas B1 e B2. A aflatoxina M1 (AFM1) é classificada como possível carcinógeno para o homem, por isso, a ocorrência desta no leite de vacas lactantes é uma questão de saúde pública, diversas técnicas são utilizadas para sua detecção e quantificação. Essas técnicas incluem as físicoquímicas como a cromatografia em camada delgada e cromatografia líquida de alta eficiência e as técnicas biológicas que incluem os imunoensaios, como o radioimunoensaio e o ELISA. Deste modo, estre trabalho objetivou apresentar as técnicas utilizadas para determinação de aflatoxina M1 e M2 no leite