Instituto Tecnológico de Aragón

Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest's flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.

Neuro-occlusal Rehabilitation (N.O.R.) is a discipline of the stomatognathic medicine that defends early treatments of functional malocclusions, such as unilateral crossbite, for the correction of craniofacial development, avoiding surgical procedures later in life. Nevertheless, N.O.R.'s advances have not been proved analytically yet due to the difficulties of evaluate the mechanical response after the treatment. This study aims to evaluate computationally the effect of N.O.R.'s treatments during childhood. Therefore, bilateral chewing and maximum intercuspation occlusion were modelled through a detailed finite element model of a paediatric craniofacial complex, before and after different selective grinding-alternatives. This model was subjected to the muscular forces derived from a musculoskeletal model and was validated by the occlusal contacts recorded experimentally. This approach yielded errors below 2% and reproduced successfully the occlusal, muscular, functional, and mechanical imbalance before the therapies. Treatment strategies balanced the occlusal plane and reduced the periodontal overpressure (>4.7 kPa) and the mandibular over deformation (>0.002 ε) on the crossed side. Based on the principles of the mechanostat theory of bone remodelling and the pressure-tension theory of tooth movement, these findings could also demonstrate how N.O.R.'s treatments correct the malocclusion and the asymmetrical development of the craniofacial complex. Besides, N.O.R.'s treatments slightly modified the stress state and functions of the temporomandibular joints, facilitating the chewing by the unaccustomed side. These findings provide important biomechanical insights into the use of N.O.R.'s treatments for the correction of unilateral crossbite, but also encourage the application of computing methods in biomedical research and clinical practice. This article is protected by copyright. All rights reserved.

This study describes the preparation and characterization of full atomistic models of amorphous cellulose and calcium carbonate (CaCO 3 ) nanocomposite to assess its mechanical properties within and beyond the elastic limit via molecular dynamics simulations. The interactions by hydrogen bond and conformation of the cellulose molecules from the assessment of torsional angles were specifically monitored during the tensile stretching simulations to get deep understanding of the possible structural changes produced in the material during the deformation. On the one hand, the results showed a favorable interaction of the cellulose matrix with the calcium carbonate nanoparticle, with the electrostatic contribution being dominant over the van der Waals component. The determined mechanical elastic constants indicated that the inclusion of the CaCO 3 nanoparticle provided an increase on the rigidity of the composite system of 15%, 18% and 19% in the Young, shear or bulk modulus, respectively. On the other hand, using extension and compression simulations, the recovery capacity of the material systems was also assessed in terms of plastic deformation. The elastoplastic behavior was observed for either the neat or the CaCO 3 nanocomposite, with an elastic limit around 2.5%. The results also showed that the presence of the CaCO 3 nanoparticle produced higher values of plastic deformation in the composite material compared to the neat cellulose system and thus decreased the flexibility of the material. A hysteresis mechanism was identified together with irreversible conformational changes on the cellulose molecules which would explain the plastic deformation observed on the cellulosic systems. It was concluded that the higher plastic deformations observed in the nanocomposite system would be a result of the disruption of the network of hydrogen bonds and the associated decrease on the number of possible interactions. Graphical Abstract

Simulation-based analyses are becoming increasingly vital for the development of cyber-physical systems. Co-simulation is one such technique, enabling the coupling of specialized simulation tools through an orchestration algorithm. The orchestrator describes how to coordinate the simulation of multiple simulation tools. The simulation result depends on the orchestration algorithm that must stabilize algebraic loops, choose the simulation resolution, and adhere to each simulation tool’s implementation. This paper describes how to verify that an orchestration algorithm respects all contracts related to the simulation tool’s implementation and how to synthesize such tailored orchestration algorithms. The approaches work for complex and adaptive co-simulation scenarios and have been applied to several real-world case studies.

IntroductionUnlike colorectal cancer (CRC), few studies have explored the predictive value of genetic risk scores (GRS) in the development of colorectal adenomas (CRA), either alone or in combination with other demographic and clinical factors.Methods In this study, genomic DNA from 613 Spanish Caucasian patients with CRA and 829 polyp-free individuals was genotyped for 88 single-nucleotide polymorphisms (SNPs) associated with CRC risk using the MassArray™ (Sequenom) platform. After applying a multivariate logistic regression model, five SNPs were selected to calculate the GRS. Regression models adjusted by sex, age, family history of CRC, chronic use of NSAIDs, low-dose ASA, and consumption of tobacco were built in order to study the association between GRS and CRA risk. We evaluated the discriminatory capacity using the area under the receiver operating characteristic curve (AUC). The interactions between demographic information and GRS were also analyzed.ResultsSignificant associations between high GRS values and risk of CRA for analyzed models were observed. In particular, patients with higher GRS values had 2.3–2.6-fold increase in risk of CRA compared to patients with middle values. Combining sex and age with the GRS significantly increased the discriminatory accuracy of the univariate model with GRS alone. The best model achieved an AUC value of 0.665 (95% CI: 0.63–0.69). The GRS showed a different behavior depending on sex and age.Conclusion Our findings showed that, besides sex and age, GRS is an important risk factor for development of CRA and may be useful for CRC risk stratification and adaptation of screening programs.

Intracellular bacterial infections alter the normal functionality of human host cells and tissues. Infection can also modify the mechanical properties of host cells, altering the mechanical equilibrium of tissues. In order to advance our understanding of host–pathogen interactions, simplified in vitro models are normally used. However, in vitro studies present certain limitations that can be alleviated by the use of computer-based models. As complementary tools these computational models, in conjunction with in vitro experiments, can enhance our understanding of the mechanisms of action underlying infection processes. In this work, we extend our previous computer-based model to simulate infection of epithelial cells with the intracellular bacterial pathogen Listeria monocytogenes. We found that forces generated by host cells play a regulatory role in the mechanobiological response to infection. After infection, in silico cells alter their mechanical properties in order to achieve a new mechanical equilibrium. The model pointed the key role of cell–cell and cell–extracellular matrix interactions in the mechanical competition of bacterial infection. The obtained results provide a more detailed description of cell and tissue responses to infection, and could help inform future studies focused on controlling bacterial dissemination and the outcome of infection processes.

The reduction of plant pest treatments contributes to a more sustainable agriculture. However, to be effective, the application of these treatments must be performed at the correct phenological stage of the plants. In this paper, we present the comparison of physical and ML models to predict the phenological stage of vineyards. The performance of both shows an average R2 above 0.94. However, the physical models do not generalize well and they cannot be easily improved by the inclusion of new datasets as ML models do.

Industrial manufacturing management can benefit from the use of modeling. For a correct representation of the manufacturing process and the subsequent management, the models must incorporate the effect of the uncertainty propagation throughout the stages considered. In this paper, the proposed methodology for uncertainty management uses a nonintrusive method that is based on building a deterministic physics-informed real-time model for the a posteriori computation of output uncertainties. This model is built using tensor factorization as the Model Order Reduction technique. It includes as model parameters: material properties, process operations, and those random and epistemic uncertainties of known variables. The resulting model is used off-line to identify sensitivities and therefore to unify uncertainty management across the material transformation process. This method is presented by its direct application to an automotive door seal manufactured by continuous co-extrusion of several rubbers and reinforcement (metal strip and glass fiber thread).

Digital Twins (DTs) are real-time digital models that allow for self-diagnosis, self-optimization and self-configuration without the need for human input or intervention. While DTs are a central aspect of the ongoing fourth industrial revolution (I4.0), this leap forward may be reserved for the established, large-cap companies since the adoption of digital technologies among Small and Medium-size Enterprises (SMEs) is still modest. The aim of the H2020 European Project ”DIGITbrain” is to support a modular construction of DTs by reusing their fundamental building blocks, i.e., the Models that describe the behavior of the DT, their associated Algorithms and the Data required for the evaluation. By offering these building blocks as a service via a DT Platform (a Digital Twin Environment), the technical barriers among SMEs to adopt these technologies are lowered. This paper describes how digital models can be classified, reused and authored on such DT Platforms. Through experimental analyses of three industrial cases, the paper exemplifies how DTs are employed in relation to product assembly of agricultural robots, polymer injection molding, as well as laser-cutting and sheet-metal forming of aluminum.

Combining multiple biomarkers to provide predictive models with a greater discriminatory ability is a discipline that has received attention in recent years. Choosing the probability threshold that corresponds to the highest combined marker accuracy is key in disease diagnosis. The Youden index is a statistical metric that provides an appropriate synthetic index for diagnostic accuracy and a good criterion for choosing a cut-off point to dichotomize a biomarker. In this study, we present a new stepwise algorithm for linearly combining continuous biomarkers to maximize the Youden index. To investigate the performance of our algorithm, we analyzed a wide range of simulated scenarios and compared its performance with that of five other linear combination methods in the literature (a stepwise approach introduced by Yin and Tian, the min-max approach, logistic regression, a parametric approach under multivariate normality and a non-parametric kernel smoothing approach). The obtained results show that our proposed stepwise approach showed similar results to other algorithms in normal simulated scenarios and outperforms all other algorithms in non-normal simulated scenarios. In scenarios of biomarkers with the same means and a different covariance matrix for the diseased and non-diseased population, the min-max approach outperforms the rest. The methods were also applied on two real datasets (to discriminate Duchenne muscular dystrophy and prostate cancer), whose results also showed a higher predictive ability in our algorithm in the prostate cancer database.

Polymer/silica (PS) nanocomposites are, among numerous combinations of inorganic/organic nanocomposites, one of the most important materials reported in the literature and have been employed in a wide variety of applications. Due to this great interest in the scientific and industry community, knowledge about their physiochemistry allows for a better understanding of their development and improvement. One area of interest found in biopolymers is silica, where silica nanoparticles can be used to increase their mechanical properties and give them higher opportunities to replace synthetic plastics. With this aim in mind, molecular dynamics (MD) simulations were used to predict the structure and mechanical properties of the interphase region and nanocomposite systems of polycaprolactone (PCL), a common poly(hydroxy acid) type biopolymer, reinforced with silica nanoparticles. Two types of nanoparticles were studied to assess the effect of PEGylation: hydroxyl (ungrafted) and polyethylene glycol (PEG) (grafted or PEGylated) functionalized silica. The interaction energy between the nanoparticle and the polymeric matrix was determined, showing an increase of the affinity between each component due to the PEGylation of the nanoparticle. Through the analysis of polymer density profiles, the structure and thickness of the interphase region were determined, and it was observed that PEGylation increased the interphase thickness from 10.80 Å to 13.04 Å while it decreased the peak and average polymer density of the interphase region. Using compressed and expanded molecular models of the neat PCL polymer, the mechanical properties of the interphase region were related to its density through an interpolation model, and mechanical property profiles were obtained, from which the average values of the Young's modulus, Poisson's ratio and shear modulus of the interphase region were calculated. Finally, the mechanical properties of the nanocomposites were determined by molecular mechanics simulations, showing that the silica nanoparticles increased the stiffness of the composite system to about 7-8% with respect to that of the neat polymer, having a 2.09% weight of bare silica or 2.82% weight of PEGylated silica. PEGylation did not show an additional effect on the overall mechanical properties. A mean field micromechanics model (Mori-Tanaka) corroborated the properties calculated for the interphase region using MD simulations. It was concluded that the PEGylation of the nanoparticle improved the affinity, and thus the dispersion, of the silica nanoparticles towards the PCL matrix, but with no further increase in the mechanical properties of the composite.

Demand uncertainty is a critical point that leads to the development of increasingly complex forecasting models. The set of activities aimed at providing estimates of a company's future sales is called demand forecasting and are a necessary element in decision making on the company planning

A novel experimental methodology is developed for the characterization of the vulcanization and foaming processes of an ethylene propylene diene (EPDM) cellular rubber and for establishing the relationship of its physical and mechanical property evolution with vulcanization and foaming process temperature. To establish this relationship, the vulcanization and foaming reaction kinetics and their coupling have been determined, as well as important parameters in the behavior of the material, such as conductivity, specific heat capacity and coefficients of expansion and foaming. This aforementioned strategy allows the setting of a material model that can be implemented into finite-element (FE) codes to reproduce the material changes during the vulcanization and foaming processes. The material model developed reproduces with enough accuracy the coupling of chemical kinetics of vulcanization and foaming reactions. The results provided by the numerical material model fit a similar trend, and values with an accuracy of 90–99 % to those observed in the experiments conducted for the determination of the cellular rubber expansion in function of the temperature. Moreover, the cellular rubber expansion values agree with the structural analysis of vulcanized and foamed samples at different isothermal temperatures and with the proportional loss of mechanical properties in the function of the vulcanization and foaming degree.

Demand forecasting is a very important feature in companies since it provides valuable information for taking decisions about planning, pricing and business growth strategies. In this paper, multi-country demand forecasting for a company of the construction industry is carried out. In the case study, we analyze different external variables from open data sources of each country in order to reduce uncertainty in demand forecasting. The analysis is carried out with different univariate and multivariate methods. The results show how the consideration of external variables improves the forecasts. Finally, forecasts of different kinds of demand are evaluated using Syntetos, Boylan and Croston classification methodology. Key words: Demand forecasting, forecast models, multivariate methods, accuracy indicators

Background: Micro-texturing is an increasingly used technique that aims at improving the functional behaviour of components during their useful life, and it is applied in different industrial manufacturing processes for different purposes, such as reducing friction on dynamic rubber seals for pneumatic equipment, among others. Micro-texturing is produced on polymer components by transfer from the mould and might critically increase the adhesion and friction between the moulded rubber part with the mould, provoking issues during demoulding, both on the mould itself and on the rubber part. The mould design, the coating release agent applied to the mould surface, and the operational parameters of the moulding/demoulding process, are fundamental aspects to avoid problems and guarantee a correct texture transfer during the demoulding process. Methods: In this work, the lack of knowledge about demoulding processes was addressed with an in-house test rig and a robust experimental procedure to measure demoulding forces (DFs) as well as the final quality of the moulded part, between thermoset polymers and moulds. After the characterization of several Sol-Gel coating formulations (inorganic; hybrid) the influence of several parameters was analysed experimentally, i.e.: Sol-Gel efficiency, texture effects, pattern geometry, roughness and material compound. Results: The results obtained from the experimental studies revealed that texture depth is the most critical geometrical parameter, showing high scatter among the selected compounds. Finally, the experimental results were used to compute a model through reduced order modelling (ROM) technique for the prediction of DFs. Conclusions: The characterization of DFs in a laboratory, with a specific device operated by a universal testing machine (UTM), provided valuable information that allows a fast and optimized introduction of texturing in rubber components. Selection of a novel Sol-Gel coating and the use of the ROM technique contributed to speed up implementation for mass production.

Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications.

The purpose of this manuscript is to present the Smart Driving Service (SDS), a customized mobile application, and a complex microservices framework intended not only for professional drivers but also for novel people who need help during the driving time in their long-distance journeys. The European regulation on driving times, breaks and rest periods for drivers engaged in the carriage of freight is implemented in the system. Additionally, it is necessary to have a feedback report to detect the behavior of drivers and what to do differently to improve driving. This issue is addressed by implementing a Route Performance Index (RPI) to measure the driver compliance. The proposed service has been running in a production stage for 6 months with a reduction in consumption of 2 liters/100 km. Considering that the company runs more than 100M km per year, the savings in fuel are relevant apart from the environmental impact reduction.

Different options that rely on fracture mechanics are currently used in engineering during the design and assessment of components. One of the most important aspects is the time taken for a crack to extend to its critical size. If this time is long enough, a design concept based on inspection intervals can be applied, as is it the case of a railway axle component. To define inspection intervals that ensure the continuous and safe operation of a damage-tolerant railway axle, a reliable estimation of its fatigue crack growth life is required. Due to the uncertainties involved in the fatigue process, inspections must be devised not only considering the uncertainties in the performance of the inspection technique, but also based on a probabilistic lifespan prediction. From this premise, this paper presents a procedure for determination of inspection intervals that uses a conservative fatigue crack growth life estimation based on the lifespan probability distribution. A practical example to illustrate the reliability-based inspection planning methodology in a railway axle under random bending loading is given. The inspection intervals are further assessed in terms of overall probability of detecting cracks in successive inspections and in terms of probability of failure, considering the probability of detection curve of the non-destructive testing technique. The procedure developed provides recommendation for the definition of inspection intervals and associated inspection techniques.