Predicting rainfall and irrigation requirements of corn in Ecuador.

This work is a case study whose objective is prediction of irrigation needs of corn crops in different regions of Ecuador; being this a fundamental basic food for the country's economy, as in the remaining countries of the Andean area. The proposed methodology seeks to help improving the quality of corn crop. Specifically, we propose the application of regression models, within the framework of Functional Data Analysis (FDA), to predict the amount of rainfall (scalar response variable) in the places with the highest production of corn in Ecuador, as a function of functional covariates such as temperature and wind speed. From the estimation of the amount of rainfall, effective precipitation is calculated. This is the fraction of water used by the crops, from which the value of real evapotranspiration or ETc is obtained and, more importantly, the irrigation requirements at each stage of the corn crop, for its adequate physiological development. Application of regression models based on functional basis, Functional Principal Components (FPC) or Functional Partial Least Squares (FPLS) for scalar response variable, allows us to use the information of variables such as wind speed and temperature (of functional nature) in a better way than using multivariate models, for predicting the amount of rainfall, obtaining, as a result, very explicative models, defined by a high goodness of fit (R2=0.97, with 6 significant parameters and an error of 0.14) and practical utility. The model has been also applied to North Peru regions, obtaining rainfall prediction errors between 9% and 22%. Thus, the geographical limitations of the model could be the Andean regions with similar climate. In addition, this study proposes the application of FDA exploratory analysis and FDA outlier detection techniques as a common and useful practice in the specific domain of rainfall prediction studies, prior to applying the regression models.

Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain.

Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 103 and 106 SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

TTS package: Computational tools for the application of the Time Temperature Superposition principle.

The TTS package has been developed in R software to predict the mechanical properties of viscoelastic materials, at short and long observation times/frequencies by applying the Time Temperature Superposition (TTS) principle. TTS is a physical principle used in material science to estimate mechanical properties beyond the experimental range of observed times/frequencies by shifting data curves obtained at other temperatures relative to a reference temperature in the dataset. It is a methodology related to accelerated life-tests and reliability, whereas the TTS library is one of the first open source computational tool to apply the TTS principle. This R package provides free computational tools to obtain master curves that characterize materials from a thermal-mechanical approach. The TTS package also proposes, implements and explains our own method to obtain the shift factors and the master curve in a TTS analysis, based on horizontal shifting of the first derivative function of viscoelastic properties. This procedure provides shift factors estimates and smooth master curve estimates using B-spline fitting, in a fully automatic way, without assuming any parametric expression. Williams-Landel-Ferry (WLF) and Arrhenius TTS parametric models are also implemented in the TTS package. They can be fitted from shifts obtained by the our first derivative based method.

Considerations on the programmed functional life (one generation) of a green artificial reef in terms of the sustainability of the modified ecosystem.

The installation of artificial reefs serves to enhance marine ecosystems, although it also modifies them. These changes do not have to be irreversible, since it is possible to treat the functional life of an artificial reef (AR) as a variable factor to be determined, with the objective of contributing to the sustainability of the ecosystem. The quest for sustainability does not end with the manufacture and installation of the AR units. It is also necessary to analyse the sustainability of the modified ecosystem, through the production of services. This leads to consider the medium-term return of the ecosystem to its initial state, once the functional life of the ARs expires. This paper presents and justifies an AR design/composition for limited functional life. It is the result of acting on the base material, the concrete, with the objective of limiting the useful life to one social generation. Four different dosages were proposed for such a purpose. They were subjected to mechanical tests (compressive strength and absorption after immersion), including an innovative abrasion-resistant one. The results allow estimating the functional life of the four types of concrete from the design variables (density, compactness, and quantity of water and cement as well as its relation). To this end linear regression models and clustering techniques were applied. The described procedure leads to an AR design for limited functional life.

Application of residuals from purification of bivalve molluscs in Galician to facilitate marine ecosystem resiliency through artificial reefs with shells - One generation.

The seas and oceans of the planet provide a wide range of essential resources. However, marine ecosystems are undergoing severe degradation due to the unsustainable exploitation and consumption patterns of the linear economy. On the other hand, many economic activities linked to the sea generate a large amount of waste, leading to negative impacts, such as the cost of treating or disposing of this waste. A case in point is bivalve mollusc production: a purification process is needed to avoid the risk of diseases through faecal contamination. The present work proposes an innovative procedure to convert this waste, calcium carbonate as calcite and aragonite allotropic types, into by-products. These by-products can be used to manufacture green artificial reefs, partially replacing concrete aggregates with a sustainable alternative to the geological sources of CaCO3. By installing these reefs, marine ecosystems could be created in a sustainable way and an innovative approach based on the circular economy could be taken towards protecting them. To this end, different concrete mixtures with bivalve shells are proposed. Although this study had been carried out for Galicia (NW Spain), the methodology followed could also be valid for other regions. A physicochemical characterisation of the waste from purifying the bivalves, including oysters, mussels, clams and scallops, was performed. Statistical and multi-criteria analyses were done in order to select the best dosage. Both have provided justification for using a mixture of shells with a predominance of calcite (oyster, scallop) instead of shells with a predominance of aragonite. The multi-criteria analysis served to identify the two best alternatives with dosages in which the medium aggregates were substituted with shells mainly from oysters, with a predominance of calcite. Finally, the statistical analysis played a role in estimating the compressive strength and water absorption of each mixture from the design parameter values.

Modeling the number of people infected with SARS-COV-2 from wastewater viral load in Northwest Spain.

The quantification of the SARS-CoV-2 RNA load in wastewater has emerged as a useful tool to monitor COVID-19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruña (NW Spain), where wastewater from a treatment plant was analyzed to track the epidemic dynamics in a population of 369,098 inhabitants. Viral load detected in the wastewater and the epidemiological data from A Coruña health system served as main sources for statistical models developing. Regression models described here allowed us to estimate the number of infected people (R2 = 0.9), including symptomatic and asymptomatic individuals. These models have helped to understand the real magnitude of the epidemic in a population at any given time and have been used as an effective early warning tool for predicting outbreaks in A Coruña municipality. The methodology of the present work could be used to develop a similar wastewater-based epidemiological model to track the evolution of the COVID-19 epidemic anywhere in the world where centralized water-based sanitation systems exist.

Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing.

The analysis of thermal processes associated to the curing of adhesives and the study of mechanical behavior once cured, provide key information to choose the best option for any specific application. The proposed methodology for the curing characterization, based on thermal analysis and rheology, is described through the comparison of three commercial adhesives. The experimental techniques used here are Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC) and Rheology. TGA provides information about the thermal stability and filler content, DSC allows the evaluation of some thermal events associated to the cure reaction and to thermal changes of the cured material when subjected to temperature changes. Rheology complements the information of the thermal transformations from a mechanical point of view. Thus, the curing reaction can be tracked through the elastic modulus (mainly the storage modulus), the phase angle and the gap. In addition, it is also shown that although DSC is of no use to study the curing of moisture curable adhesives, it is a very convenient method to evaluate the low temperature glass transition of amorphous systems.

An artificial-vision- and statistical-learning-based method for studying the biodegradation of type I collagen scaffolds in bone regeneration systems.

This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects' circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.

Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications.

This methodology permits to simulate the performance of different Poly(D,L-lactide-co-glycolide) copolymer formulations (PDLGA) in the human body, to identify the more influencing variables on hydrolytic degradation and, thus, to estimate biopolymer degradation level. The PDLGA characteristic degradation trends, caused by hydrolysis processes, have been studied to define their future biomedical applications as dental scaffolds. For this purpose, the mass loss, pH, glass transition temperature (Tg) and absorbed water mass of the different biopolymers have been obtained from samples into a phosphate-buffered saline solution (PBS) with initial pH of 7.4, at 37°C (human body conditions). The mass loss has been defined as the variable that characterize the biopolymer degradation level. Its dependence relationship with respect to time, pH and biopolymer formulation has been modelled using statistical learning tools. Namely, generalized additive models (GAM) and nonlinear mixed-effects regression with logistic and asymptotic functions have been applied. GAM model provides information about the relevant variables and the parametric functions that relate mass loss, pH and time. Mixed effects are introduced to model and estimate the degradation properties, and to compare the PDLGA biopolymer populations. The degradation path for each polymer formulation has been estimated and compared with respect to the others for helping to use the proper polymer for each specific medical application, performing selection criteria. It was found that the mass loss differences in PDLGA copolymers are strongly related with the way the pH decay versus time, due to carboxylic acid groups formation. This may occur in those environments in which the degradation products remain relatively confined with the non degraded mass. This is the case emulated with the present experimental procedure. The results show that PDLGA polymers degradation degree, in terms of half life and degradation rate, is increasing when acid termination is included, when DL-lactide molar ratio is reduced, decreasing the midpoint viscosity, or when glycolide is not included.

Correction: Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications.

[This corrects the article DOI: 10.1371/journal.pone.0204004.].

Dynamic rheological comparison of silicones for podiatry applications.

PURPOSE: This work shows an effective methodology to evaluate the dynamic viscoelastic behavior of silicones for application in podiatry. The aim is to characterize, compare their viscoelastic properties according to the dynamic stresses they can be presumably subjected when used in podiatry orthotic applications. These results provide a deeper insight which extends the previous creep-recovery results to the world of dynamic stresses developed in physical activity. In this context, it shoulod be taken into account that an orthoses can subjected to a set of static and dynamic shear and compressive forces. METHODS: Two different podiatric silicones, Blanda-blanda and Master, from Herbitas, are characterized by dynamic rheological methods. Three kinds of rheological tests are considered: shear stress sweep, compression frequency sweep and shear frequency sweep, all the three with simultaneous control of the static force at three different levels. The static force represents a static load like that produced by the weight of a human body on a shoe insole. In a practical sense, dynamic stresses are related to physical activity and are needed to evaluate the frequency effect on the viscoelastic behavior of the material. It is considered that the dynamic stresses can be applied in compression and shear since, in practice, the way the stresses are applied in real life depends on the orthoses geometry and its exact location with respect to the foot and shoe. The effects of static and dynamic loads are individualized and compared to each other through the relations between the elastic constants for isotropic materials. CONCLUSIONS: The overall proposed experimental methodology can provide very insightful information for better selection of materials in podiatry applications. This study focuses on the rheological characterization to choose the right silicone for each podiatric application, taking into account the dynamic viscoelastic requirements associated to the physical activity of user. Accordingly, one soft and one hard silicones of common use in podiatry were tested. Each of the two silicones exhibit not only different moduli values, but also, a different kind of dependence of the dynamic moduli with respect to the static load. In the case of the soft sample a linear trend is observed but in the case of of the hard one the dependence is of the power law type. Moreover, these samples exhibit very different Poisson's coefficient values for compression stresses lower than 20 kPa, and almost the same values for stresses above 40 kPa. That different dependence of the Poisson's ratio on the static load should also be taken into account for material selection in customized podiatry applications, where static and dynamic loads are strongly dependent on the individual weight and activity.

Statistical Modeling Applied to Deformation-Relaxation Processes in a Composite Biopolymer Network Induced by Magnetic Field.

This study investigated a methodology based on image processing and statistics to characterize and model the deformation upon controlled and uniform magnetic field and the relaxation under zero field of droplets observed in aqueous solutions of sodium alginate incorporating magnetic maghemite nanoparticles stabilized by adsorption of citrate ions. The changes of droplet geometry were statistically analyzed using a new approach based on the data obtained from optical microscopy, image processing, nonlinear regression, evolutionary optimization, analysis of variance and resampling. Image enhancement and then image segmentation (Gaussian mixture modeling) processes were applied to extract features with reliable information of droplets dimensions from optical micrographs. The droplets deformation and relaxation trends were accurately adjusted by the Kohlrausch-Williams-Watts (KWW) function and a mean relaxation time was obtained by fitting the time evolution of geometry parameters. It was found to be proportional to the initial radius of the spherical droplets and was associated to interfacial tension.

Creep analysis of silicone for podiatry applications.

PURPOSE: This work shows an effective methodology to characterize the creep-recovery behavior of silicones before their application in podiatry. The aim is to characterize, model and compare the creep-recovery properties of different types of silicone used in podiatry orthotics. METHODS: Creep-recovery phenomena of silicones used in podiatry orthotics is characterized by dynamic mechanical analysis (DMA). Silicones provided by Herbitas are compared by observing their viscoelastic properties by Functional Data Analysis (FDA) and nonlinear regression. The relationship between strain and time is modeled by fixed and mixed effects nonlinear regression to compare easily and intuitively podiatry silicones. RESULTS: Functional ANOVA and Kohlrausch-Willians-Watts (KWW) model with fixed and mixed effects allows us to compare different silicones observing the values of fitting parameters and their physical meaning. The differences between silicones are related to the variations of breadth of creep-recovery time distribution and instantaneous deformation-permanent strain. Nevertheless, the mean creep-relaxation time is the same for all the studied silicones. Silicones used in palliative orthoses have higher instantaneous deformation-permanent strain and narrower creep-recovery distribution. CONCLUSIONS: The proposed methodology based on DMA, FDA and nonlinear regression is an useful tool to characterize and choose the proper silicone for each podiatry application according to their viscoelastic properties.