INFLUENCE OF SPLICES ON THE STABILITY BEHAVIOUR OF COLUMNS AND FRAMES

Abstract. The paper presents a study on the influence of splice connections on the stability behaviour of compressed steel columns. The column is modelled as two independent prismatic parts connected by a rotational spring at the splice location and rotational and extensional springs at the column ends to represent the effect of the adjacent structure. The general behaviour is characterized using a polynomial Rayleigh-Ritz approximation substituted into the potential energy function, in combination with the Lagrange's method of undetermined multipliers, and based on this model the critical load is found. The load-carrying capacity is analysed with respect to the following variables: (i) location and rotational stiffness of the splice, (ii) change in the column section serial size and (iii) column end-restraints stiffness coefficients. A nonlinear regression model is developed to predict simple relationships between the critical load and the relevant column characteristics

INFLUENCE OF SPLICES ON THE STABILITY BEHAVIOUR OF COLUMNS AND FRAMES

Abstract. The paper presents a study on the influence of splice connections on the stability behaviour of compressed steel columns. The column is modelled as two independent prismatic parts connected by a rotational spring at the splice location and rotational and extensional springs at the column ends to represent the effect of the adjacent structure. The general behaviour is characterized using a polynomial Rayleigh-Ritz approximation substituted into the potential energy function, in combination with the Lagrange's method of undetermined multipliers, and based on this model the critical load is found. The load-carrying capacity is analysed with respect to the following variables: (i) location and rotational stiffness of the splice, (ii) change in the column section serial size and (iii) column end-restraints stiffness coefficients. A nonlinear regression model is developed to predict simple relationships between the critical load and the relevant column characteristics

Hybrid model for early identification post-Covid-19 sequelae

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.info:eu-repo/semantics/publishedVersio

OptFlux: an open-source software platform for in silico metabolic engineering

<p>Abstract</p> <p>Background</p> <p>Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications.</p> <p>Results</p> <p><it>OptFlux </it>is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes.</p> <p><it>OptFlux </it>also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms.</p> <p>The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. <it>OptFlux </it>has a visualization module that allows the analysis of the model structure that is compatible with the layout information of <it>Cell Designer</it>, allowing the superimposition of simulation results with the model graph.</p> <p>Conclusions</p> <p>The <it>OptFlux </it>software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community.</p> <p>Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.</p