Regionalization methods dependent on hydrological models comprise techniques for transferring calibrated parameters in instrumented watersheds (donor basins) to non-instrumented watersheds (target basins). There is a lack of flow regionalization studies in regions with humid subtropical and hot temperate climates, and one of the main novelties of this research is to assess the regionalization of low flows in Paraná in the south of Brazil. In addition to filling this gap, this research presents innovative artificial-intelligence techniques for transferring parameters from hydrological models. This study aims to evaluate regionalization methods for transferring GR4J parameters and predicting river flow in catchments from the south of Brazil. We created a dataset for the state of Paraná with daily hydrological time series (precipitation, evapotranspiration, and river flow) and watershed physiographic and climatological indices for 126 catchments. Rigorous quality-controlling techniques were applied to recover data from 1979 to 2020. The regionalization methods compared in this study are based on simple spatial proximity, physiographic–climatic similarity, and regression by random forest techniques. Direct regression of Q₉₅ was calculated using random forest techniques and compared with indirect methods, i.e. using regionalization of GR4J parameters. A set of 100 basins was used to train the regionalization models, and another 26 catchments (pseudo-non-instrumented) were used to evaluate and compare the performance of regionalizations. The GR4J model showed acceptable performances for the sample of 126 catchments, with 65 % of watersheds presenting a log-transformed Nash–Sutcliffe coefficient greater than 0.70 during the validation period. According to the evaluation carried out for the sample of 26 basins, regionalization based on physiographic–climatic similarity was shown to be the most robust method for the prediction of daily and Q₉₅ reference flow in basins from the state of Paraná. When increasing the number of donor basins, the method based on spatial proximity has comparable performance to the method based on physiographic–climatic similarity. Based on the physiographic–climatic characteristics of the basins, it was possible to classify six distinct groups of watersheds in Paraná. Each group shows similarities in forest cover, urban area, number of days with more than 150 mm of precipitation, and average duration of consecutive dry days. Although the physiographic–climatic similarity method obtained the best performance, the use of machine learning algorithms to regionalize the model parameters had good performance using climatic and physiographic indices as inputs. This research represents a proof of concept that basins without flow monitoring can have a good approximation of streamflow if physiographic–climatic information is provided.

Algorithmic models have been proposed to explain adaptive behavior of bone to loading; however, these models have not been applied to explain the biomechanics of short dental implants. Purpose of present study was to simulate bone remodeling around single implants of different lengths using mechanoregulatory tissue differentiation model derived from the Stanford theory, using finite elements analysis (FEA) and to validate the theoretical prediction with the clinical findings of crestal bone loss. Loading cycles were applied on 7-, 10-, or 13-mm-long dental implants to simulate daily mastication and bone remodeling was assessed by changes in the strain energy density of bone after a 3, 6, and 12 months of function. Moreover, clinical findings of marginal bone loss in 45 patients rehabilitated with same implant designs used in the simulation (n = 15) were computed to validate the theoretical results. FEA analysis showed that although the bone density values reduced over time in the cortical bone for all groups, bone remodeling was independent of implant length. Clinical data showed a similar pattern of bone resorption compared with the data generated from mathematical analyses, independent of implant length. The results of this study showed that the mechanoregulatory tissue model could be employed in monitoring the morphological changes in bone that is subjected to biomechanical loads. In addition, the implant length did not influence the bone remodeling around single dental implants during the first year of loading.

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-β), 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.

This study aims to understand the response of the trophic state index (TSI) of a reservoir under different watershed land-use scenarios. A novel methodology was applied to the Verde River Basin in the south of Brazil, which is exploited by agricultural activity and has a reservoir for water supply. An ecohydrological model was used to calculate daily nutrient concentrations in the water system, which allowed the determination of the reservoir’s TSI. The authors propose a new TSI known as TSI_mod, considering phosphorus, nitrogen, and chlorophyll in its determination. The results indicate that TSI_mod may be more appropriate for classifying highly impacted environments when compared to the index currently used in Brazil. The Verde River Reservoir was classified as mesotrophic under the current land-use scenario and eutrophic under the proposed scenarios. The framework developed in this study can be replicated in other watersheds to evaluate the impacts of land-use change on water quality.

Patient-specific finite element (FE) models are constructed in a way that boundary conditions, material and geometric FE models respect the unique characteristics of the patients, offering a more reliable and realistic structural analysis. This study presents an image-based construction process of a patient-specific FE model applied to a femur with a subtrochanteric fracture. Available procedures are systematized and described in greater depth aiming to investigate the difficulties and possibilities in the development of this type of model. A set of image processing techniques (segmentation, labeling, morphological operations and registering) were performed for volumetric reconstruction and registration of femoral fragments by assessing computed tomography (CT) images in a semi supervised procedure. The CT numbers feed the FE mesh with a non-homogeneous distribution of elasticity modulus. A FE analysis and a bone remodeling simulation with pharmacological stimulus based on the dynamics of bone cells populations were also developed for this study. The FE analysis revealed that the principal stresses provided results close to those obtained using a generic homogeneous model, with a difference of about 2%. In contrast, principal strains presented significant differences, up to 12 and 21 % for the maximum tensile and compressive principal strain values, respectively. The critical values of tensile principal strains were located in the superior part of the femoral neck, a typical region of femoral fractures, and maximum principal strains 2 and 3 (compressive) were concentrated in the diaphyseal region, rather close to the actual fracture. These results corroborate previous works indicating that the strain-based failure criterion is more successful in capturing the bone fracture sites when compared to stress-based criteria. The attempt to reconstruct the material state before the failure as implemented here constitutes an innovative way to analyze and reconstruct bone fractures. In the bone remodeling simulation, the inclusion of an antiresorptive agent stimulus produced cortical bone thickening, an increase in the BMD biomarker, and an overall increase in the bone ash density, just as expected in an osteoporosis therapeutic treatment. In conclusion, this study highlights the advantages of subject-specific FE models compared to generic ones in the study of abnormal clinical cases, evaluation of fracture risk, and optimization of surgical intervention. On the other hand, the high level of human intervention and expertise involved in patient-specific FE analyses hinders the incorporation of this technique into clinical practice.