A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit

This study provides an innovative approach to improve deep learning (DL) models for spectral data processing with the use of chemometrics knowledge. The technique proposes pre-filtering the outliers using the Hotelling’s T2 and Q statistics obtained with partial least-square (PLS) analysis and spectral data augmentation in the variable domain to improve the predictive performance of DL models made on spectral data. The data augmentation is carried out by stacking the same data pre-processed with several pre-processing techniques such as standard normal variate, 1st derivatives, 2nd derivatives and their combinations. The performance of the approach is demonstrated on a real near-infrared (NIR) data set related to dry matter (DM) prediction in mango fruit. The data set consisted of a total 11,961 spectra and reference DM measurements. The results showed that removing the outliers and augmenting spectral data improved the predictive performance of DL models. Furthermore, this innovative approach not only improved DL models but attained the lowest root mean squared error of prediction (RMSEP) on the mango data set i.e., 0.79% compared to the best known RMSEP of 0.84%. Further, by removing outliers from the test set the RMSEP decreased to 0.75%. Several chemometrics approaches can complement DL models and should be widely explored in conjunction.info:eu-repo/semantics/publishedVersio

Deep chemometrics: validation and transfer of a global deep near‐infrared fruit model to use it on a new portable instrument

Recently, a large near-infrared spectroscopy data set for mango fruit quality assessment was made available online. Based on that data, a deep learning (DL) model outperformed all major chemometrics and machine learning approaches. However, in earlier studies, the model validation was limited to the test set from the same data set which was measured with the same instru ment on samples from a similar origin. From a DL perspective, once a model is trained it is expected to generalise well when applied to a new batch of data. Hence, this study aims to validate the generalisability performance of the earlier developed DL model related to DM prediction in mango on a different test set measured in a local laboratory setting, with a different instrument. At first, the performance of the old DL model was presented. Later, a new DL model was crafted to cover the seasonal variability related to fruit harvest season. Finally, a DL model transfer method was performed to use the model on a new instrument. The direct application of the old DL model led to a higher error compared to the PLS model. However, the performance of the DL model was improved drastically when it was tuned to cover the seasonal variability. The updated DL model performed the best compared to the implementation of a new PLS model or updating the existing PLS model. A final root-mean-square error prediction (RMSEP) of 0.518% was reached. This result supports that, in the availability of large data sets, DL modelling can outperform chemometrics approaches.info:eu-repo/semantics/publishedVersio

Realizing transfer learning for updating deep learning models of spectral data to be used in new scenarios

This study presents the concept of transfer learning (TL) to the chemometrics community for updating DL models related to spectral data, particularly when a pre-trained DL model needs to be used in a scenario having unseen variability. This is the typical situation where classical chemometrics models require some form of re-calibration or update. In TL, the network architecture and weights from the pre-trained DL model are complemented by adding extra fully connected (FC) layers when dealing with the new data. Such extra FC layers are expected to learn the variability of the new scenario and adjust the output of the main architecture. Furthermore, three approaches of TL were compared, first where the weights from the initial model were left untrained and the only the newly added FC layers could be retrained. The second was when the weights from the initial model could be retrained alongside the new FC layers. The third was when the weights from the initial model could be re-trained with no extra FC layers added. The TL was shown using two real cases related to near-infrared spectroscopy i.e., mango fruit analysis and melamine production monitoring. In the case of mango, the model needs to be updated to cover a new seasonal variability for dry matter prediction, while, for the melamine case, the model needs to be updated for the change in the recipe of the production material. The results showed that the proposed TL approaches successfully updated the DL models to new scenarios for both the mango and melamine cases presented. The TL performed better when the weights from the old model were retrained. Furthermore, TL outperformed three recent benchmark approaches to model updating. TL has the potential to make DL models widely useable, sharable, and scalable.info:eu-repo/semantics/publishedVersio

A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks

Deep spectral modelling for regression and classification is gaining popularity in the chemometrics domain. A major topic in the deep learning (DL) modelling of spectral data is the choice and optimization of the deep neural network architecture suitable for the specific task of spectral modelling. Although there are several recent research articles already available in the chemometric domain showing advanced approaches to deep spectral modelling, currently, there is a lack of hands-on tutorial articles in this space that supply the non-expert user with practical tools to learn and implement advanced DL optimization methodologies aimed a spectral data. Hence, this tutorial article aims a reducing the gap between the non-expert user of DL in the chemometric community and the implementation of DL models for daily usage. This tutorial supplies a quick introduction to the state-of-the-art deep spectral modelling and related DL concepts and presents a set of methodologies aimed a DL hyperparameters' optimization. To this end, this tutorial shows two practical examples on how to implement and optimize two DL models for spectral regression and classification tasks. The models are implemented in python and Tensorflow and the complete code is supplied in the form of two complementary notebooks.info:eu-repo/semantics/publishedVersio

Oscillator models of the solar cycle: Towards the development of inversion methods

This article reviews some of the leading results obtained in solar dynamo physics by using temporal oscillator models as a tool to interpret observational data and dynamo model predictions. We discuss how solar observational data such as the sunspot number is used to infer the leading quantities responsible for the solar variability during the last few centuries. Moreover, we discuss the advantages and difficulties of using inversion methods (or backward methods) over forward methods to interpret the solar dynamo data. We argue that this approach could help us to have a better insight about the leading physical processes responsible for solar dynamo, in a similar manner as helioseismology has helped to achieve a better insight on the thermodynamic structure and flow dynamics in the Sun's interior.Comment: 28 pages; 16 figures, ISSI Workshop 11-15 November 2013 - The Solar Cycle, http://www.issibern.ch/program/workshops.htm

Como se financiam as PME em Portugal?

Mestrado em Controlo de Gestão e Avaliação de DesempenhoO tecido empresarial português é composto essencialmente por micro, pequenas e médias empresas. Desta forma, o principal objetivo do presente trabalho consiste em analisar de que forma se financiam as pequenas e médias empresas em Portugal, isto é, quais os fatores que influenciam as decisões de financiamento. Ao longo dos últimos anos, têm surgido diversos estudos sobre a temática de Estrutura de Capital, sendo que o tema ganhou força após o trabalho inicial realizado por Modigliani e Miller. Após o trabalho destes autores, foram surgindo cada vez mais estudos com o intuito de conseguir chegar a uma estrutura de capital ideal, contudo sem consenso. Assim, surgiram novas teorias, como a teoria do Trade-off, teoria dos Custos de Agência e teoria da Pecking Order. Através dos resultados obtidos, será posteriormente verificado se existem evidencias capazes de associar os resultados obtidos às principais teorias da Estrutura de Capital. A amostra para o presente estudo foi recolhida através da base de dados Sistema de Análise de Balanços Ibéricos (SABI), sendo composta por 5.907 empresas, no período compreendido entre 2014 a 2020, proporcionando um painel balanceado com 41.349 observações. O tratamento dos dados fez uso dos modelos baseados na metodologia de dados em painel. Os resultados empíricos apurados evidenciam que as PME portuguesas apresentam uma combinação entre as teorias Trade-Off e Pecking Order. As variáveis mais significativas para o presente estudo são: a dimensão, a composição do ativo, a rendibilidade e o crescimento do ativo.The Portuguese business community is composed essentially of micro, small and medium sized companies. Thus, the main objective of this work is to analyze how small and medium sized companies in Portugal are financed, that is, what factors influence financing decisions. Over the past few years, several studies have emerged on the subject of Capital Structure, and the theme gained strength after the initial work done by Modigliani and Miller. After the work of these authors, more and more studies have emerged with the purpose of reaching an ideal capital structure, however, without consensus. Thus, new theories have emerged, such as the Trade-Off theory, Agency Cost theory, and Pecking Order theory. Through the results obtained, it will be later verified if there is evidence capable of associating the results obtained with the main theories of Capital Structure. The sample for the present study was collected through the Iberian Balance Sheet Analysis System (SABI) database, and is made up of 5,907 companies, for the period between 2014 and 2020, providing a balanced panel with 41,349 observations. The data treatment made use of models based on panel data methodology. The empirical results show that Portuguese SMEs present a combination between Trade-Off and Pecking Order theories. The most significant variables for this study are: size, asset composition, profitability and asset growth.info:eu-repo/semantics/publishedVersio

ANÁLISE INSTITUCIONAL E APOIO INSTITUCIONAL: COLETIVIZAÇÃO COMO PRÁTICA DE RESISTÊNCIA

Introduction: This article stems from the analysis of the political, ethical, and ideological crisis fueled by the advancement of neoliberal logic and its effects on the weakening of the Unified Health System (SUS). The legitimation of practices that undermine social guarantees raises questions about the existence of a health system that aspires to be universal, comprehensive, and equitable. Objective: Seeking to highlight strategies of resistance against the dismantling of SUS, this article presents Institutional Analysis and Institutional Support as mechanisms capable of mobilizing counter-forces to the neoliberal logic in health management and care practices. Method: This is a conceptual review that brings together the tools of Institutional Analysis and Institutional Support as resources for counter-practices within SUS. Result: The work to be carried out using these tools, in the context of crisis and neoliberal advance, involves activating collective action to critically examine work and institutional implications. Conclusion: Supporting collectives, therefore, emerges as a supportive action to contribute to the production of a healthcare system that serves as a stronghold of resistance against the dismantling of public policies.Introdução: O presente artigo parte da análise da crise política, ética e ideológica promovida pelo avanço da lógica neoliberal e seus efeitos de fragilização do Sistema Único de Saúde (SUS). A legitimação de práticas de devastação de garantias sociais, coloca em xeque a existência de um sistema de saúde que se quer universal, integral e com equidade. Objetivo: Buscando evidenciar estratégias de resistência ao desmonte do SUS, apresenta a Análise Institucional e o Apoio Institucional como dispositivos capazes de mobilizar forças destituintes da lógica neoliberal nas práticas de gestão e de cuidado no campo da saúde. Método: Trata-se de uma revisão conceitual que aproxima as ferramentas da Análise Institucional e Apoio Institucional como recursos de práticas destituintes no SUS. Resultado: O trabalho a ser realizado a partir destas ferramentas, no contexto de crise e avanço neoliberal, é o de acionar a ação coletiva para colocar o trabalho e as implicações institucionais em análise. Conclusão: Apoiar coletivos, portanto, se apresenta como uma ação de suporte para a produção de uma saúde que seja trincheira de resistência ao desmonte das políticas públicas

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.Funding Agency CEOT strategic project UID/Multi/00631/2019 project OtiCalFrut ALG-01-0247-FEDER-033652 Ideias em Caixa 2010, CAIXA GERAL DE DEPOSITOS Fundacao para a Ciencia e a Tecnologia (Ciencia)info:eu-repo/semantics/publishedVersio

Grand Minima Under the Light of a Low Order Dynamo Model

In this work we use a low order dynamo model and study under which conditions can it reproduce solar grand minima. We begin by building the phase space of a proxy for the toroidal component of the solar magnetic field and we develop a model, derived from mean field dynamo theory, that gives the time evolution of the toroidal field. This model is characterized by a non-linear oscillator whose coefficients retain most of the physics behind dynamo theory. In the derivation of the model we also include stochastic oscillations in the $\alpha$ effect. We found no evidences that stochastic fluctuations in a linear $\alpha$ effect can trigger grand minima episodes in this model. In contrast, the model used points out that possible mechanism that can trigger grand minima should involve the meridional circulation, magnetic diffusivity or field intensification by buoyancy driven instabilities.Comment: 12 pages, 5 figures, Space Climate Symposium 3, Finland (in press JASTP). Version 3.0 incorporates new figures and reference