Observer-based offset-free internal model control

A linear feedback control structure is proposed that allows internal model control design principles to be applied to unstable and marginally stable plants. The control structure comprises an observer using an augmented plant model, state estimate feedback and disturbance estimate feedback. Conditions are given for both nominal internal stability and offset-free action even in the case of plant-model mismatch. The Youla parameterization is recovered as a limiting case with reduced order observers. The simple design methodology is illustrated for a marginally stable plant with delay

A study of Machine Learning models for Clinical Coding of Medical Reports at CodiEsp 2020

The task of identifying one or more diseases associated with a patient’s clinical condition is often very complex, even for doctors and specialists. This process is usually time-consuming and has to take into account different aspects of what has occurred, including symptoms elicited and previous healthcare situations. The medical diagnosis is often provided to patients in the form of written paper without any correlation with a national or international standard. Even if the WHO (World Health Organization) released the ICD10 international glossary of diseases, almost no doctor has enough time to manually associate the patient’s clinical history with international codes. The CodiEsp task at CLEF 2020 addressed this issue by proposing the development of an automatic system to deal with this task. Our solution investigated different machine learning strategies in order to identify an approach to face that challenge. The main outcomes of the experiments showed that a strategy based on BERT for pre-filtering and one based on BiLSTMCNN-SelfAttention for classification provide valuable results. We carried out several experiments on a subset of the training set for tuning the final model submitted to the challenge. In particular, we analyzed the impact of the algorithm, the input encoding strategy, and the thresholds for multi-label classification. A set of experiments has been carried out also during a post hoc analysis. The experiments confirmed that the strategy submitted to the CodiEsp task is the best performing one among those evaluated, and it allowed us to obtain a final mean average error value on the test set equal to 0.202. To support future developments of the proposed approach and the replicability of the experiments we decided to make the source code publicly accessible

A deep learning model for the analysis of medical reports in ICD-10 clinical coding task

The practice of assigning a uniquely identifiable and easily traceable code to pathology from medical diagnoses is an added value to the current modality of archiving health data collected to build the clinical history of each of us. Unfortunately, the enormous amount of possible pathologies and medical conditions has led to the realization of extremely wide international codifications that are difficult to consult even for a human being. This difficulty makes the practice of annotation of diagnoses with ICD-10 codes very cumbersome and rarely performed. In order to support this operation, a classification model was proposed, able to analyze medical diagnoses written in natural language and automatically assign one or more international reference codes. The model has been evaluated on a dataset released in the Spanish language for the eHealth challenge (CodiEsp) of the international conference CLEF 2020, but it could be extended to any language with latin characters. We proposed a model based on a two-step classification process based on BERT and BiLSTM. Although still far from an accuracy sufficient to do without a licensed physician opinion, the results obtained show the feasibility of the task and are a starting point for future studies in this direction

A comparison of services for intent and entity recognition for conversational recommender systems

Conversational Recommender Systems (CoRSs) are becoming increasingly popular. However, designing and developing a CoRS is a challenging task since it requires multi-disciplinary skills. Even though several third-party services are available for supporting the creation of a CoRS, a comparative study of these platforms for the specific recommendation task is not available yet. In this work, we focus our attention on two crucial steps of the Conversational Recommendation (CoR) process, namely Intent and Entity Recognition. We compared four of the most popular services, both commercial and open source. Furthermore, we proposed two custom-made solutions for Entity Recognition, whose aim is to overcome the limitations of the other services. Results are very interesting and give a clear picture of the strengths and weaknesses of each solution

IDeS Method Applied to an Innovative Motorbike—Applying Topology Optimization and Augmented Reality

This study is on the conception of the DS700 HYBRID project by the application of the Industrial Design Structure method (IDeS), which applies different tools sourced from engineering and style departments, including QFD and SDE, used to create the concept of a hybrid motorbike that could reach the market in the near future. SDE is an engineering approach for the design and development of industrial design projects, and it finds important applications in the automotive sector. In addition, analysis tools such as QFD, comprising benchmarking and top-flop analysis are carried out to maximize the creative process. The key characteristics of the bike and the degree of innovation are identified and outlined, the market segment is identified, and the stylistic trends that are most suitable for a naked motorbike of the future are analyzed. In the second part the styling of each superstructure and of all the components of the vehicle is carried out. Afterwards the aesthetics and engineering perspectives are accounted for to complete the project. This is achieved with modelling and computing tools such as 3D CAD, visual renderings, and FEM simulations, and virtual prototyping thanks to augmented reality (AR), and finally physical prototyping with the use of additive manufacturing (AM). The result is a product conception able to compete in the present challenging market, with a design that is technically feasible and also reaches new lightness targets for efficiency

Similarity Patterns and Stability of Environmental Response in Sunflower Hybrids

The rationale for the following research was to analyse the response of sunflower hybrids to different sowing dates and to evaluate hybrid response to critical environmental conditions. The data used are from an experiment conducted in a location-year combination over a period of two years (2007-09) in southern Italy. Eleven hybrids were tested following a randomized complete block design with three replications at each location-year combination. Eight agronomic characters including seed oil content were recorded. Classification and ordination procedures were used to investigate hybrid performance in relation to three different sowing dates. Combined analysis of variance showed that hybrids, location-year combination, sowing date and their interactions were highly significant for all characters. Hybrid performances were classified by cluster analysis into groups that were differentiable in terms of means and stability. The first three components accounted for 74%, 82%, and 87% of the total variation for the first, second and third sowing date respectively. Plotting component one against component two along Euclidean axes should therefore provide a reasonable representation of the spatial arrangements of hybrid performances in the original multi-dimensional space. The applied statistical method gives full information on hybrid performances similarity

Diversity and relationships in key traits for functional and apparent quality in a collection of eggplant: fruit phenolics content, antioxidant activity, polyphenol oxidase activity, and browning

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Agricultural and Food Chemistry, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work seehttp://dx.doi.org/10.1021/jf402429kEggplant (Solanum melongena) varieties with increased levels of phenolics in the fruit present enhanced functional quality, but may display greater fruit flesh browning. We evaluated 18 eggplant accessions for fruit total phenolics content, chlorogenic acid content, DPPH scavenging activity, polyphenol oxidase (PPO) activity, liquid extract browning, and fruit flesh browning. For all the traits we found a high diversity, with differences among accessions of up to 3.36-fold for fruit flesh browning. Variation in total content in phenolics and in chlorogenic acid content accounted only for 18.9% and 6.0% in the variation in fruit flesh browning, and PPO activity was not significantly correlated with fruit flesh browning. Liquid extract browning was highly correlated with chlorogenic acid content (r = 0.852). Principal components analysis (PCA) identified four groups of accessions with different profiles for the traits studied. Results suggest that it is possible to develop new eggplant varieties with improved functional and apparent quality.This project has been funded by Universitat Politecnica de Valencia through the grants SP20120681 and PAID-06-11 Nr. 2082, and by Ministerio de Economia y Competitividad Grant AGL2012-34213 (jointly funded by FEDER).Plazas Ávila, MDLO.; López Gresa, MP.; Vilanova Navarro, S.; Torres Vidal, C.; Hurtado Ricart, M.; Gramazio, P.; Andújar Pérez, I.... (2013). Diversity and relationships in key traits for functional and apparent quality in a collection of eggplant: fruit phenolics content, antioxidant activity, polyphenol oxidase activity, and browning. Journal of Agricultural and Food Chemistry. 61(37):8871-8879. https://doi.org/10.1021/jf402429kS88718879613