Using Generalized Beam Theory to Assess the Behavior of Curved Thin-Walled Members

In this work, the first-order behavior of naturally curved thin-walled bars with circular axis, without pre-twist, is assessed with the help of the Generalized Beam Theory (GBT) formulation previously developed by the authors. With respect to the previous work, which dealt with simple cross-sections, the present paper presents a method to obtain the deformation modes for arbitrary flat-walled cross-sections. Despite the complexity involved in this generalization, the standard GBT kinematic assumptions are kept, since they are essential to (i) subdivide the modes in a meaningful way and (ii) reduce the number of DOFs necessary to obtain accurate solutions. It is shown that the curvature of the bar influences significantly the deformation mode shapes. Furthermore, a standard displacement-based finite element (FE) is employed to solve several examples that highlight the peculiar behavior of curved members. For validation and comparison purposes, results obtained using shell FE models are provided. Finally, the superiority of a mixed GBT-based FE format is demonstrated

Advanced data analysis methods to optimize crop management decisions

The lack of knowledge of limiting factors and optimal management practices at the field level is one of the main reasons for the inefficient use of inputs and low productivity, profitability, and sustainability of agricultural systems. Agricultural research aims to update and improve crop management recommendations to match the spatiotemporal variability and the dynamism of production systems. The advances in remote sensing, precision agriculture, the adoption of information and communication technologies by farmers, and the ability to collect and process large amounts of data create an opportunity to reimagine agricultural research and extension. Advanced data analysis methods are needed to take full advantage of the new data sources and other technological innovations. Therefore, the objectives of this Ph.D. research were i) to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs, ii) investigate the spatial variability of optimal input rates in on-farm precision experimentation and the potential economic benefit of site-specific input management, iii) develop a data-driven decision support system for maize in Mexico The first chapter addresses the need for scalable and accurate methods to develop imagery-based high-throughput phenotyping in breeding programs. Images were acquired with unmanned aerial vehicles twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks were developed to predict the maturity date. The first using a single date, and the second using the five best image dates identified by the first model. The proposed neural network architectures were validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves the limitations of previous research and can be used at scale in commercial breeding programs. The second chapter demonstrates how on-farm precision experimentation can be a valuable tool for estimating in-field variation of optimal input rates and improving agronomic decisions. Within-field variability of crop yield levels has been extensively investigated, but the spatial variability of crop yield responses to agronomic treatments is less understood. Mixed geographically weighted regression models were used to estimate local yield response functions. The methodology was applied to investigate the spatial variability in corn response to nitrogen and seed rates in four cornfields in Illinois, USA. The results showed that spatial heterogeneity of model parameters was significant in all four fields evaluated. On average, the root mean squared error of the fitted yield decreased from 1.2 Mg ha-1 in the non-spatial global model to 0.7 Mg ha-1 in the geographically weighted regression model, and the r-squared increased from 10% to 68%. The average potential gain of using optimized uniform rates of seed and nitrogen was US$65.00 ha-1, while the added potential gain of the site-specific application was US$ 58.00 ha-1. The reported results encourage more research on response-based input management recommendations instead of the still widespread focus on yield-based algorithms. The third chapter integrates domain knowledge and explainable machine learning methods to optimize management decisions using observational data. The data comes from the Sustainable Modernization of Traditional Agriculture (MasAgro) project in the southern state of Chiapas - Mexico. The dataset was assembled using field observations, including yield, cultivars and management, and environment variables from soil mapping and gridded weather datasets. Random forest models were trained with the dataset and explained up to 75% of the variation. However, the ability of the model to predict crop performance in future weather scenarios was limited. Overall, nitrogen was the management decision that influenced yields the most, with different yield responses depending on the year and variety. This research exemplifies the use of explainable machine learning to offer farmers the opportunity to benchmark their management decisions with peers in similar growing conditions and visualize what would have happened if they made different decisions

New concepts in breast cancer genomics and genetics

Massively parallel DNA and RNA sequencing approaches have generated data on thousands of breast cancer genomes. In this review, we consider progress largely from the perspective of new concepts and hypotheses raised so far. These include challenges to the multistep model of breast carcinogenesis and the discovery of new defects in DNA repair through sequence analysis. Issues for functional genomics include the development of strategies to differentiate between mutations that are likely to drive carcinogenesis and bystander background mutations, as well as the importance of mechanistic studies that examine the role of mutations in genes with roles in splicing, histone methylation, and long non-coding RNA function. The application of genome-annotated patient-derived breast cancer xenografts as a potentially more reliable preclinical model is also discussed. Finally, we address the challenge of extracting medical value from genomic data. A weakness of many datasets is inadequate clinical annotation, which hampers the establishment of links between the mutation spectra and the efficacy of drugs or disease phenotypes. Tools such as dGene and the DGIdb are being developed to identify possible druggable mutations, but these programs are a work in progress since extensive molecular pharmacology is required to develop successful ‘genome-forward’ clinical trials. Examples are emerging, however, including targeting HER2 in HER2 mutant breast cancer and mutant ESR1 in ESR1 endocrine refractory luminal-type breast cancer. Finally, the integration of DNA- and RNA-based sequencing studies with mass spectrometry-based peptide sequencing and an unbiased determination of post-translational modifications promises a more complete view of the biochemistry of breast cancer cells and points toward a new discovery horizon in our understanding of the pathophysiology of this complex disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-014-0460-4) contains supplementary material, which is available to authorized users

Utilização de QOS para priorização de tráfego de voz em links wan

O trabalho aqui apresentado, pretende apresentar modelos de customização e aplicação de QoS (Quality of Service), utilizando políticas escolhidas e de acordo com suas especificações e aplicabilidade nos cenários simulados, permitindo a implementação mais eficiente para os diversos tráfegos tempo real, como voz, e sua interação com o tráfego de dados. Aplicando solução visando a priorização de tráfico de pacotes de voz RTP (Real Time Protocol) em links WAN. (Wide Area Networks). Para isso serão analisados três cenários que simulam condições semelhantes às encontradas em grandes redes, efetuando comparações entre as tecnologias utilizadas visando escolher a mais indicada para utilização

Low-complexity DCD-based sparse recovery algorithms

Sparse recovery techniques find applications in many areas. Real-time implementation of such techniques has been recently an important area for research. In this paper, we propose computationally efficient techniques based on dichotomous coordinate descent (DCD) iterations for recovery of sparse complex-valued signals. We first consider $\ell_2 \ell_1$ optimization that can incorporate \emph{a priori} information on the solution in the form of a weight vector. We propose a DCD-based algorithm for $\ell_2 \ell_1$ optimization with a fixed $\ell_1$-regularization, and then efficiently incorporate it in reweighting iterations using a \emph{warm start} at each iteration. We then exploit homotopy by sampling the regularization parameter and arrive at an algorithm that, in each homotopy iteration, performs the $\ell_2 \ell_1$ optimization on the current support with a fixed regularization parameter and then updates the support by adding/removing elements. We propose efficient rules for adding and removing the elements. The performance of the homotopy algorithm is further improved with the reweighting. We then propose an algorithm for $\ell_2 \ell_0$ optimization that exploits homotopy for the $\ell_0$ regularization; it alternates between the least-squares (LS) optimization on the support and the support update, for which we also propose an efficient rule. The algorithm complexity is reduced when DCD iterations with a \emph{warm start} are used for the LS optimization, and, as most of the DCD operations are additions and bit-shifts, it is especially suited to real time implementation. The proposed algorithms are investigated in channel estimation scenarios and compared with known sparse recovery techniques such as the matching pursuit (MP) and YALL1 algorithms. The numerical examples show that the proposed techniques achieve a mean-squared error smaller than that of the YALL1 algorithm and complexity comparable to that of the MP algorithm

Assessment Of Reproducibility Of Sanders Classification For Calcaneal Fractures

To assess intra- and interobserver reproducibility of Sanders Classification System of calcaneal fractures among experienced and less experienced observers. Methods: Forty-six CT scans of intra-articular calcaneal fractures were reviewed. Four observers, two with ten years of experience in foot and ankle surgery and two third-year residents in Orthopedics and Traumatology classified the fractures on two separate occasions three weeks apart from each other. The intra and inter-observer reliability was analyzed using the Kappa index. Results: There was good intraobserver reliability for the two experienced observers and one less experienced observer (Kappa values 0.640, 0.632 and 0.629, respectively). The interobserver reliability was fair between the experienced observers (Kappa = 0.289) and moderate among the less experienced observers (Kappa = 0.527). Conclusions: The Sanders Classification System showed good intraobserver reliability, but interobserver reproducibility below the ideal level, both among experienced and less experienced observers.242909

Linear and bifurcation analyses combining shell and GBT-based beam finite elements

This paper concerns a general and very efficient approach to model thin-walled members with complex geometries (including taper and/or connected through joints), which combines standard shell and GBT-based finite elements. This approach (i) allows a straightforward modelling of complex geometries and (ii) is very efficient from a computational point of view, as the shell model substructures can be condensed out of the global equilibrium equations. The capabilities of the proposed approach are demonstrated through several examples concerning the linear and bifurcation (linear stability) analyses of (i) members with tapered segments, (ii) members with holes and (iii) beam-column assemblies. The results obtained are compared with full shell finite element model solutions and an excellent match is obtained.The first author gratefully acknowledges the financial support of FCT (Fundac¸ ˜ao para a Ciˆencia e a Tecnologia, Portugal), through the doctoral scholarship SFRH/BD/130515/2017

Pull-out behaviour of Glass-Fibre Reinforced Polymer perforated plate connectors embedded in concrete. Part I: Experimental program

The Glass Fibre Reinforced Polymer (GFRP) connectors studied in this work were previously proposed by the authors for connecting the outer Steel Fibre Reinforced Self-Compacting Concrete (SFRSCC) layers of sandwich panels for prefabricated modular housing. In this building system, SFRSCC was used to totally eliminate the need for conventional reinforcement and to decrease the thickness of the panel's outer layers, with consequent reduction of the global self-weigh of the panels, while GFRP connectors aimed to significantly decrease thermal bridging effects. For a reliable design of the structural elements that make use of these connectors, the mechanical behaviour of this connection should be known and taken into account. The present paper summarizes the results obtained in an experimental research devoted to the assessment of the behaviour of GFRP-SFRSCC connection by performing pullout tests with specimens representative of the developed sandwich panel. The specimens were designed to examine the influence of the number and geometry of holes executed in the GFRP connector that assure the connection between these two materials.This work is part of the research project QREN number 5387, LEGOUSE, involving the companies Mota-Engil, CiviTest, the ISISE/University of Minho and PIEP. The first author would like to thank the financial support provided by PAIP/UNILA. The third author wish to acknowledge the grant SFRH/BSAB/114302/2016 provided by FCT.info:eu-repo/semantics/publishedVersio