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Tomato fruit is susceptible to chilling injury (CI) during its postharvest handling at low temperature. The symptoms caused by this physiological disorder have been commonly evaluated by visual inspection at a macro-observation scale on fruit surface; however, the structure at deeper scales is also affected by CI. This work aimed to propose a descriptive model of the CI development in tomato tissue under the micro-scale, micro-nano-scale and nano-scale approaches using fractal analysis. For that, quality and fractal parameters were determined. In this sense, light microscopy, Environmental Scanning Electron Microscopy (ESEM) and Atomic Force Microscopy (AFM) were applied to analyse micro-, micro-nano- and nano-scales, respectively. Results showed that the morphology of tomato tissue at the micro-scale level was properly described by the multifractal behaviour. Also, generalised fractal dimension (Dq=0) and texture fractal dimension (FD) of CI-damaged pericarp and cuticle were higher (1.659, 1.601 and 1.746, respectively) in comparison to non-chilled samples (1.606, 1.578 and 1.644, respectively); however, FD was unsuitable to detect morphological changes at the nano-scale. On the other hand, lacunarity represented an appropriate fractal parameter to detect CI symptoms at the nano-scale due to differences observed between damaged and regular ripe tissue (0.044 and 0.025, respectively). The proposed multi-scale approach could improve the understanding of CI as a complex disorder to the development of novel techniques to avoid this postharvest issue at different observation scales.

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BACKGROUND: Although the benefits of breast screening and early diagnosis are known for reducing breast cancer mortality rates, the effects and risks of low radiation doses to the cells in the breast are still ongoing topics of study. PURPOSE: To study specific energy distributions ( f ( z , D g ) $f(z,D_{g})$ ) in cytoplasm and nuclei of cells corresponding to glandular tissue for different x-ray breast imaging modalities. METHODS: A cubic lattice (500 µm length side) containing 4064 spherical cells was irradiated with photons loaded from phase space files with varying glandular voxel doses ( D g $D_{g}$ ). Specific energy distributions were scored for nucleus and cytoplasm compartments using the PENELOPE (v. 2018) + penEasy (v. 2020) Monte Carlo (MC) code. The phase space files, generated in part I of this work, were obtained from MC simulations in a voxelized anthropomorphic phantom corresponding to glandular voxels for different breast imaging modalities, including digital mammography (DM), digital breast tomosynthesis (DBT), contrast enhanced digital mammography (CEDM) and breast CT (BCT). RESULTS: In general, the average specific energy in nuclei is higher than the respective glandular dose scored in the same region, by up to 10%. The specific energy distributions for nucleus and cytoplasm are directly related to the magnitude of the glandular dose in the voxel ( D g $D_{g}$ ), with little dependence on the spatial location. For similar D g $D_{g}$ values, f ( z , D g ) $f(z,D_{g})$ for nuclei is different between DM/DBT and CEDM/BCT, indicating that distinct x-ray spectra play significant roles in f ( z , D g ) $f(z,D_{g})$ . In addition, this behavior is also present when the specific energy distribution ( F g ( z ) $F_{g}(z)$ ) is considered taking into account the GDD in the breast. CONCLUSIONS: Microdosimetry studies are complementary to the traditional macroscopic breast dosimetry based on the mean glandular dose (MGD). For the same MGD, the specific energy distribution in glandular tissue varies between breast imaging modalities, indicating that this effect could be considered for studying the risks of exposing the breast to ionizing radiation.

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In image classification, few-shot learning deals with recognizing visual categories from a few tagged examples. The degree of expressiveness of the encoded features in this scenario is a crucial question that needs to be addressed in the models being trained. Recent approaches have achieved encouraging results in improving few-shot models in deep learning, but designing a competitive and simple architecture is challenging, especially considering its requirement in many practical applications. This work proposes an improved few-shot model based on a multi-layer feature fusion (FMLF) method. The presented approach includes extended feature extraction and fusion mechanisms in the Convolutional Neural Network (CNN) backbone, as well as an effective metric to compute the divergences in the end. In order to evaluate the proposed method, a challenging visual classification problem, maize crop insect classification with specific pests and beneficial categories, is addressed, serving both as a test of our model and as a means to propose a novel dataset. Experiments were carried out to compare the results with ResNet50, VGG16, and MobileNetv2, used as feature extraction backbones, and the FMLF method demonstrated higher accuracy with fewer parameters. The proposed FMLF method improved accuracy scores by up to 3.62% in one-shot and 2.82% in five-shot classification tasks compared to a traditional backbone, which uses only global image features.

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Precise instrument segmentation aids surgeons to navigate the body more easily and increases patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to: (1) a complex surgical environment, and (2) model design trade-off in terms of both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios. The Robust Medical Instrument Segmentation 2019 challenge (ROBUST-MIS) provides more than 10,000 frames with surgical tools in different clinical settings. In this paper, we propose a light-weight single stage instance segmentation model complemented with a convolutional block attention module for achieving both faster and accurate inference. We further improve accuracy through data augmentation and optimal anchor localization strategies. To our knowledge, this is the first work that explicitly focuses on both real-time performance and improved accuracy. Our approach out-performed top team performances in the most recent edition of ROBUST-MIS challenge with over 44% improvement on area-based multi-instance dice metric MI_DSC and 39% on distance-based multi-instance normalized surface dice MI_NSD. We also demonstrate real-time performance (>60 frames-per-second) with different but competitive variants of our final approach.

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Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

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This work applies a multi-scale approach to the microencapsulation by interfacial polymerization. Such microencapsulation is used to produce fertilizers, pesticides and drugs. In this study, variations at three different scales (molecular, microscopic and macroscopic) of product design (i.e., product variables, process variables and properties) are considered simultaneously. We quantify the effect of the formulation, composition and pH change on the microcapsules' properties. Additionally, the method of measuring the strength of the microcapsules by crushing a sample of microcapsules' suspension was tested. Results show that the xylene release rate in the microcapsules decreases when the amine functionality is greater due to a stronger crosslinking. Such degree of crosslinking increases the compression force over the microcapsules and improves their appearance. When high levels of amine concentration are used, the initial pH values in the reaction are also high which leads to agglomeration. This study provides a possible explanation to the aggregation based on the kinetic and thermodynamic controls in reactions and shows that the pH measurements account for the polyurea reaction and carbamate formation, which is a reason why this is not a suitable method to study kinetics of polymerization. Finally, the method used to measure the compressive strength of the microcapsules detected differences in formulations and composition with low sensibility.

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Epiphytic and epixylic lichens respond negatively to forest degradation, climate change and pollution, but those effects may depend on functional traits or interact with the stage of tree decay. Disentangling the main drivers of lichen communities remains a challenge in regions where lichens are diverse and poorly known, as the case of Patagonian temperate forests. We used a multi-scale approach to evaluate the relationship between environmental variables, tree decay stage and lichens. We sampled lichens across three increasing scales (tree â‰ª site â‰ª landscape) by selecting 19 landscape units, where trees in four decay stages (snags, logs, cavity trees and healthy trees) were selected within sampling plots. A total of 35 predictors were measured over different scales, including 25 remote sensing indices of forest conditions, climate and air pollutants. Structural Equation Models were used to test the causal linkages of predictors with lichens, distinguishing functional categories (size, growth and reproductive strategy). A total of 69 lichen species were recorded. Cavity trees and logs supported the largest diversity, while snags and healthy trees had the lowest diversity. Functional lichen groups responded differently to fine-scale variables, including the diameter, height, density and pH of trees. Air pollutants affected species with sexual and mixed strategies. Lichens were sensitive to precipitation, temperature and wind speed, with foliose and sexual species responding positively to the latter. The abundance of all species and macrolichens increased with tree senescence and decreased with canopy continuity. Lichens occupying snags and logs responded negatively to primary productivity and tree senescence, but positively to soil organic matter. Our findings suggest: i) the functional structure of lichen communities varies non-linearly with the wood decay process; ii) the reproductive strategy influences the sensitivity to air pollutants, iii) climate variables influence dispersal and colonization of woody substrates; and iv) forest structure/succession interacts with tree decay.

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BACKGROUND: We propose a general approach to the analysis of multivariate health outcome data where geo-coding at different spatial scales is available. We propose multiscale joint models which address the links between individual outcomes and also allow for correlation between areas. The models are highly novel in that they exploit survey data to provide multiscale estimates of the prevalences in small areas for a range of disease outcomes. Results The models incorporate both disease specific, and common disease spatially structured components. The multiple scales envisaged is where individual survey data is used to model regional prevalences or risks at an aggregate scale. This approach involves the use of survey weights as predictors within our Bayesian multivariate models. Missingness has to be addressed within these models and we use predictive inference which exploits the correlation between diseases to provide estimates of missing prevalances. The Case study we examine is from the National Health Survey of Chile where geocoding to Province level is available. In that survey, diabetes, Hypertension, obesity and elevated low-density cholesterol (LDL) are available but differential missingness requires that aggregation of estimates and also the assumption of smoothed sampling weights at the aggregate level. Conclusions: The methodology proposed is highly novel and flexibly handles multiple disease outcomes at individual and aggregated levels (i.e., multiscale joint models). The missingness mechanism adopted provides realistic estimates for inclusion in the aggregate model at Provincia level. The spatial structure of four diseases within Provincias has marked spatial differentiation, with diabetes and hypertension strongly clustered in central Provincias and obesity and LDL more clustered in the southern areas.

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The spatial structure of the fish diversity and site-scale and landscape-scale environmental effects were investigated across hierarchical levels in tropical coastal ecosystems. Total diversity (γ) was hierarchically partitioned into α and ß components using both the additive and multiplicative methods. A model selection based on the AICc was applied to generalized linear mixed models relating diversity measures to environmental variables and including random effects for hierarchical levels and season. Short-term seasonal effects were negligible. Spatial effects were more relevant at the site level and negligible at the subregion level, due to the high spatial heterogeneity and the natural pooling of ecosystems, respectively. Site-scale environmental effects were more relevant at the subregion level, with eutrophic conditions (continental influence) favoring the species richness (α and γ) and higher absence of species (ßA) in oligotrophic conditions (marine influence). At the system level, the positive effect of the distance from the ocean on γ and higher ßA in oligotrophic conditions reinforced the positive continental influence on fish diversity. Environmental homogenization processes were most likely associated with the negative effect of the pasture cover on α at the system level, and γ and ßA at the site level. The negative effect of the forest cover on the later diversity measure evidenced its relevance to maintain richer but more similar assemblages, whereas the positive continental influence was most likely due to the loss of stenohaline marine species. This study evidenced that disentangling spatial, land use, and marine vs. continental effects on diversity components is critical to understand the primary determinants of the fish diversity in tropical coastal ecosystems.

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Land-use change modifies the spatial structure of terrestrial landscapes, potentially shaping the distribution, abundance and diversity of remaining species assemblages. Non-human primates can be particularly vulnerable to landscape disturbances, but our understanding of this topic is far from complete. Here we reviewed all available studies on primates' responses to landscape structure. We found 34 studies of 71 primate species (24 genera and 10 families) that used a landscape approach. Most studies (82%) were from Neotropical forests, with howler monkeys being the most frequently studied taxon (56% of studies). All studies but one used a site-landscape or a patch-landscape study design, and frequently (34% of studies) measured landscape variables within a given radius from the edge of focal patches. Altogether, the 34 studies reported 188 responses to 17 landscape-scale metrics. However, the majority of the studies (62%) quantified landscape predictors within a single spatial scale, potentially missing significant primate-landscape responses. To assess such responses accurately, landscape metrics need to be measured at the optimal scale, i.e. the spatial extent at which the primate-landscape relationship is strongest (so-called 'scale of effect'). Only 21% of studies calculated the scale of effect through multiscale approaches. Interestingly, the vast majority of studies that do not assess the scale of effect mainly reported null effects of landscape structure on primates, while most of the studies based on optimal scales found significant responses. These significant responses were primarily to landscape composition variables rather than landscape configuration variables. In particular, primates generally show positive responses to increasing forest cover, landscape quality indices and matrix permeability. By contrast, primates show weak responses to landscape configuration. In addition, half of the studies showing significant responses to landscape configuration metrics did not control for the effect of forest cover. As configuration metrics are often correlated with forest cover, this means that documented configuration effects may simply be driven by landscape-scale forest loss. Our findings suggest that forest loss (not fragmentation) is a major threat to primates, and thus, preventing deforestation (e.g. through creation of reserves) and increasing forest cover through restoration is critically needed to mitigate the impact of land-use change on our closest relatives. Increasing matrix functionality can also be critical, for instance by promoting anthropogenic land covers that are similar to primates' habitat.

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Urban adaptation to climate change is likely to emerge from the responses of residents, authorities, and infrastructure providers to the impact of flooding, water scarcity, and other climate-related hazards. These responses are, in part, modulated by political relationships under cultural norms that dominate the institutional and collective decisions of public and private actors. The legacy of these decisions, which are often associated with investment in hard and soft infrastructure, has lasting consequences that influence current and future vulnerabilities. Making those decisions visible, and tractable is, therefore, an urgent research and political challenge in vulnerability assessments. In this work, we present a modeling framework to explore scenarios of institutional decision-making and socio-political processes and the resultant effects on spatial patterns of vulnerability. The approach entails using multi-criteria decision analysis, agent-based models, and geographic information simulation. The approach allows for the exploration of uncertainties, spatial patterns, thresholds, and the sensitivities of vulnerability outcomes to different policy scenarios. Here, we present the operationalization of the framework through an intentionally simplified model example of the governance of water in Mexico City. We discuss results from this example as part of a larger effort to empirically implement the framework to explore sociohydrological risk patterns and trade-offs of vulnerability in real urban landscapes.

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Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. Therefore, in this paper, we propose multi-scale wavelet Shannon entropy as a discriminative fault feature to improve the diagnosis accuracy of bearing fault under variable work conditions. To compute the multi-scale wavelet entropy, we consider integrating stationary wavelet packet transform with both dispersion (SWPDE) and permutation (SWPPE) entropies. The multi-scale entropy features extracted by our proposed methods are then passed on to the kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. In the end, both the SWPDE-KELM and the SWPPE-KELM methods are evaluated on two bearing vibration signal databases. We compare these two feature extraction methods to a recently proposed method called stationary wavelet packet singular value entropy (SWPSVE). Based on our results, we can say that the diagnosis accuracy obtained by the SWPDE-KELM method is slightly better than the SWPPE-KELM method and they both significantly outperform the SWPSVE-KELM method.

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Bearing fault diagnosis methods play an important role in rotating machine health monitoring. In recent years, various intelligent fault diagnosis methods have been proposed, which are mainly based on the features extraction method combined with either shallow or deep learning methods. During the last few years, Shannon entropy features have been widely used in machine health monitoring, improving the accuracy of the bearing fault diagnosis process. Therefore, in this paper, we consider the combination of multi-scale stationary wavelet packet analysis with the Fourier amplitude spectrum to obtain a new discriminative Shannon entropy feature that we call stationary wavelet packet Fourier entropy (SWPFE). Features extracted by our SWPFE method are then passed onto a shallow kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. The proposed method was applied on two experimental vibration signal databases of a rolling element bearing and compared to two recently proposed methods called stationary wavelet packet permutation entropy (SWPPE) and stationary wavelet packet dispersion entropy (SWPPE). Based on our results, we can say that the proposed method is able to achieve better accuracy levels than both the SWPPE and SWPDE methods using fewer failure features. Further, as our method does not require any hyperparameter calibration step, it is less dependent on user experience/expertise.

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Due to the availability of Web 2.0 technologies, volunteered geographic information (VGI) is on the rise. This new type of data is available on many topics and on different scales. Thus, it has become interesting for research. This article deals with the collective potential of VGI and remote sensing to detect peri-urbanization in the conservation zone of Mexico City. On the one hand, remote sensing identifies horizontal urban expansion, and on the other hand, VGI of ecological complaints provides data about informal settlements. This enables the combination of top-down approaches (remote sensing) and bottom-up approaches (ecological complaints). Within the analysis, we identify areas of high urbanization as well as complaint densities and bring them together in a multi-scale analysis using Geographic Information Systems (GIS). Furthermore, we investigate the influence of settlement patterns and main roads on the peri-urbanization process in Mexico City using OpenStreetMap. Peri-urbanization is detected especially in the transition zone between the urban and rural (conservation) area and near main roads as well as settlements.

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Autism Spectrum Disorder (ASD) can be understood as a social interaction disorder. This requires researchers to take a "second-person" stance and to use experimental setups based on bidirectional interactions. The present work offers a quantitative description of movement patterns exhibited during computer-mediated real-time sensorimotor interaction in 10 dyads of adult participants, each consisting of one control individual (CTRL) and one individual with high-functioning autism (HFA). We applied time-series analyses to their movements and found two main results. First, multi-scale coordination between participants was present. Second, despite this dyadic alignment and our previous finding that individuals with HFA can be equally sensitive to the other's presence, individuals' movements differed in style: in contrast to CTRLs, HFA participants appeared less inclined to sustain mutual interaction and instead explored the virtual environment more generally. This finding is consistent with social motivation deficit accounts of ASD, as well as with hypersensitivity-motivated avoidance of overstimulation. Our research demonstrates the utility of time series analyses for the second-person stance and complements previous work focused on non-dynamical and performance-based variables.

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This paper seeks to reinterpret current tenets of functional analytic psychotherapy (FAP) using some assumptions of molar behaviorism. In it, a conceptual alternative to the molecular approach is proposed to explain the mechanisms of change of FAP. To achieve this goal, the utility and limitations of using discrete responses and stimuli as units of analysis are discussed. As an alternative, this paper suggests, as proposed by molar behaviorism, using activities as the unit of analysis. The use of activities as the unit of analysis would allow analyses of clinical behavior as choices in different time scales and measurements of clinical behavior using time allocation. Using choice behavior to analyze data allows analyses of behavior in terms of its short-term and long-term value. It is argued that time allocation as a unit of measure is more appropriate than the rate of behavior because it allows the continuous measurement of behavior and comparisons of disparate behaviors as they occur during therapy sessions. Finally, a multi-scale analysis would allow the articulation of long-term and short-term contingencies that are related to therapy and behavioral change.

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This work represents a study of a mathematical model that describes the biological response to different mechanical stimuli in a cellular dynamics model for bone remodelling. The biological system discussed herein consists of three specialised cellular types, responsive osteoblasts, active osteoblasts and osteoclasts, three types of signalling molecules, transforming growth factor beta (TGF-ß), receptor activator of nuclear factor kappa-b ligand (RANKL) and osteoprotegerin (OPG) and the parathyroid hormone (PTH). Three proposals for mechanical stimuli were tested: strain energy density (SED), hydrostatic and deviatoric parts of SED. The model was tested in a two-dimensional geometry of a standard human femur. The spatial discretization was performed by the finite element method while the temporal evolution of the variables was calculated by the 4th order Runge-Kutta method. The obtained results represent the temporal evolution of the apparent density distribution and the mean apparent density and thickness for the cortical bone after 600 days of remodelling simulation. The main contributions of this paper are the coupling of mechanical and biological models and the exploration of how the different mechanical stimuli affect the cellular activity in different types of physical activities. The results revealed that hydrostatic SED stimulus was able to form more cortical bone than deviatoric SED and total SED stimuli. The computational model confirms how different mechanical stimuli can impact in the balance of bone homeostasis.

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Identification and extraction of vortical structures and of waves in a disorganised flow is a mayor challenge in the study of turbulence. We present a study of the spatio-temporal behavior of turbulent flows in the presence of different restitutive forces. We show how to compute and analyse the spatio-temporal spectrum from data stemming from numerical simulations and from laboratory experiments. Four cases are considered: homogeneous and isotropic turbulence, rotating turbulence, stratified turbulence, and water wave turbulence. For homogeneous and isotropic turbulence, the spectrum allows identification of sweeping by the large-scale flow. For rotating and for stratified turbulence, the spectrum allows identification of the waves, precise quantification of the energy in the waves and in the turbulent eddies, and identification of physical mechanisms such as Doppler shift and wave absorption in critical layers. Finally, in water wave turbulence the spectrum shows a transition from gravity-capillary waves to bound waves as the amplitude of the forcing is increased.

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The theory of transport in porous media such as clays depends on the level of description. On the macroscopic scale,hydrodynamics equations are used. These continuous descriptions are convenient to model the fluid motion in a confined system. Nevertheless, they are valid only if the pores of the material are much larger than the molecular size of the components of the system. Another approach consists in using molecular descriptions. These two methods which correspond to different levels of description are complementary. The link between them can be clarified by using a coarse-graining procedure where the microscopic laws are averaged over fast variables to get the long time macroscopic laws. We present such an approach in the case of clays. Firstly, we detail the various levels of description and the relations among them, by emphasizing the validity domain of the hydrodynamic equations. Secondly, we focus on the case of dehydrated clays where hydrodynamics is not relevant. We show that it is possible to derive a simple model for the motion of the cesium ion based on the difference on time scale between the solvent and the solute particles.

A teoria de transporte em meios porosos tais como argilasdepende do nível de descrição. Na escala macroscópica, equações da hidrodinâmica são utilizadas. Tais descrições a níveldo contínuo são convenientes para tratar o movimento do fluido em sistemas confinados. No entanto, tais equações são válidas se os poros do material são muito maiores do que as moléculas das componentes do sistema. Uma outra abordagem consiste em usar descrições moleculares. Esses dois métodos que correspondem a diferentes níveis de descriçãosão complementares. A ligação entre eles pode ser elucidada usando um procedimento de mudança de escala onde são tomadas médias das leis microscópicas sobre as variáveis rápidas para se obter as leis macroscópicas para tempos longos. Apresentamos esta abordagem no caso de argilas. Primeiramente apresentamos em detalhes os vários níveis de descrição bem como as relações entre eles, enfatizando o domínio de validade das equações hidrodinâmicas. Em seguida, focamos no caso de argilas desidratadas onde a hidrodinâmica não é relevante. Mostramos que é possível derivar um modelo simples para o movimento dos íons césio baseado na diferença entre as escalas de tempo do solvente e das partículas do soluto.

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In this study, a multiphysical description of fluid transport through osteo-articular porous media is presented. Adapted from the model of Moyne and Murad, which is intended to describe clayey materials behaviour, this multiscale modelling allows for the derivation of the macroscopic response of the tissue from microscopical information. First the model is described. At the pore scale, electrohydrodynamics equations governing the electrolyte movement are coupled with local electrostatics (Gauss-Poisson equation), and ionic transport equations. Using a change of variables and an asymptotic expansion method, the macroscopic description is carried out. Results of this model are used to show the importance of couplings effects on the mechanotransduction of compact bone remodelling.

Neste estudo uma descrição multifísica do transporte de fluidos em meios porosos osteo articulares é apresentada. Adaptado a partir do modelo de Moyne e Murad proposto para descrever o comportamento de materiais argilosos a modelagem multiescala permite a derivação da resposta macroscópica do tecido a partir da informação microscópica. Na primeira parte o modelo é apresentado. Na escala do poro as equações da eletro-hidrodinâmica governantes do movimento dos eletrolitos são acopladas com a eletrostática local (equação de Gauss-Poisson) e as equações de transporte iônico. Usando uma mudança de variáveis e o método de expansão assintótica a derivação macroscópica é conduzida. Resultados do modelo proposto são usados para salientar a importância dos efeitos de acoplamento sobre a transdução mecânica da remodelagem de ossos compactados.

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