ABSTRACT
Climate change has diversified negative implications on environmental sustainability and water availability. Assessing the impacts of climate change is crucial to enhance resilience and future preparedness particularly at a watershed scale. Therefore, the goal of this study is to evaluate the impact of climate change on the water balance components and extreme events in Piabanha watershed in the Brazilian Atlantic Forest. In this study, extreme climate change scenarios were developed using a wide array of global climate models acquired from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Reports (AR6). Two extreme climate change scenarios, DryHot and WetCool, were integrated into the Soil and Water Assessment Tools (SWAT) hydrological model to evaluate their impacts on the hydrological dynamics in the watershed. The baseline SWAT model was first developed and evaluated using different model performance evaluation metrics such as coefficient of determination (R2), Nash-Sutcliffe (NSC), and Kling-Gupta efficiency coefficient (KGE). The model results illustrated an excellent model performance with metric values reaching 0.89 and 0.64 for monthly and daily time steps respectively in the calibration (2008 to 2017) and validation (2018 to 2023) periods. The findings of future climate change impacts assessment underscored an increase in temperature and shifts in precipitation patterns. In terms of streamflow, high-flow events may experience a 47.3 % increase, while low-flows could see an 76.6 % reduction. In the DryHot scenario, annual precipitation declines from 1657 to 1420 mm, with evapotranspiration reaching 54 % of precipitation, marking a 9 % rise compared to the baseline. Such changes could induce water stress in plants and lead to modifications on structural attributes of the ecosystem recognized as the Atlantic rainforest. This study established boundaries concerning the effects of climate change and highlighted the need for proactive adaptation strategies and mitigation measures to minimize the potential adverse impacts in the study watershed.
ABSTRACT
The estimation of defects positioning occurring in the interface between different materials is performed by using an artificial neural network modeled as an inverse heat conduction problem. Identifying contact failures in the bonding process of different materials is crucial in many engineering applications, ranging from manufacturing, preventive inspection and even failure diagnosis. This can be modeled as an inverse heat conduction problem in multilayered media, where thermography temperature measurements from an exposed surface of the media are available. This work solves this inverse problem with an artificial neural network that receives these experimental data as input and outputs the thermalphysical properties of the adhesive layer, where defects can occur. An autoencoder is used to reduce the dimension of the transient 1D thermography data, where its latent space represents the experimental data in a lower dimension, then these reduced data are used as input to a fully connected multilayer perceptron network. Results indicate that this is a promising approach due to the good accuracy and low computational cost observed. In addition, by including different noise levels within a defined range in the training process, the network can generalize the experimental data input and estimate the positioning of defects with similar quality.
Subject(s)
Algorithms , Neural Networks, ComputerABSTRACT
This work presents different applications in progress with the aid of the atomic force microscopy (AFM) technique for biomedical and biotechnological applications, comprising both the acquisition of three-dimensional images and spectroscopic force measurements, in the following systems: first, low-density lipoprotein (LDL)-glycosaminoglycans; second, lectins-polysaccharides; third, mycobacterium leprae cellular wall and Vesicular Stomatites Virus (VSV) with fibronectin laminin, and lipidic membranes; fourth, DNA-complex; and fifth, actin, as well as the development of surface functionalizing protocols and image restoration by means of mathematical techniques.
