Seismic failure probability and vulnerability assessment of steel-concrete composite structures

Building collapse in earthquakes caused huge losses, both in human and economic terms. To assess the risk posed by using the composite members, this paper investigates seismic failure probability and vulnerability assessment of steel-concrete composite structures constituted by rectangular concrete filled steel tube (RCFT) columns and steel beams. To enable numerical simulation of RCFT-structure, the details of components modeling are developed using OpenSEES finite element analysis package and the validation of proposed procedure is investigated through comparisons with available experimental results. The seismic fragility and vulnerability curves of RCFT-structures are created through nonlinear dynamic analysis using an appropriate suite of ground motions for seismic loss assessment. These curves developed for three-, six- and nine-story prototypes of RCFT-structure. Fragility curves are an appropriate tool for representing the seismic failure probabilities and vulnerability curves demonstrate a probability of exceeding loss to a measure of ground motion intensity

Optimal Water Allocation from Subsurface Dams: A Risk-Based Optimization Approach

Subsurface dams, strongly advocated in the 1992 United Nations Agenda-21, have been widely studied to increase groundwater storage capacity. However, an optimal allocation of augmented water with the construction of the subsurface dams to compensate for the water shortage during dry periods has not so far been investigated. This study, therefore, presents a risk-based simulation–optimization framework to determine optimal water allocation with subsurface dams, which minimizes the risk of water shortage in different climatic conditions. The developed framework was evaluated in Al-Aswad falaj, an ancient water supply system in which a gently sloping underground channel was dug to convey water from an aquifer via the gravity force to the surface for irrigation of downstream agricultural zones. The groundwater dynamics were modeled using MODFLOW UnStructured-Grid. The data of boreholes were used to generate a three-dimensional stratigraphic model, which was used to define materials and elevations of five-layer grid cells. The validated groundwater model was employed to assess the effects of the subsurface dam on the discharge of the falaj. A Conditional Value-at-Risk optimization model was also developed to minimize the risk of water shortage for the augmented discharge on downstream agricultural zones. Results show that discharge of the falaj is significantly augmented with a long-term average increase of 46.51%. Moreover, it was found that the developed framework decreases the water shortage percentage in 5% of the worst cases from 87%, 75%, and 32% to 53%, 32%, and 0% under the current and augmented discharge in dry, normal, and wet periods, respectively

Do evolutionary algorithms indeed require random numbers? Extended study

An inherent part of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation, we discuss whether are random processes really needed in evolutionary algorithms. We use n periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that a certain class of deterministic processes can be used instead of random number generators without lowering of evolutionary algorithms performance. © Springer International Publishing Switzerland 2013

Correction to: Serotype distribution of Streptococcus pneumoniae among healthy carriers and clinical patients: a systematic review from Iran (European Journal of Clinical Microbiology & Infectious Diseases, (2020), 39, 12, (2257-2267), 10.1007/s10096-020-03963-z)

In the originally published article, the name of the 6th author was incorrectly presented as Hossein Abdiae. The correct name is Hossein Abdiaei, which is also given above. The original article has been corrected. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature

Byzantine Fireflies

This paper addresses the problem of synchronous beeping, as addressed by swarms of fireflies. We present Byzantine-resilient algorithms ensuring that the correct processes eventually beep synchronously despite a subset of nodes beeping asynchronously. We assume that n > 2f (n is the number of processes and f is the number of Byzantine processes) and that the initial state of the processes can be arbitrary (self-stabilization). We distinguish the cases where the beeping period is known, unknown or approximately known. We also consider the situation where the processes can produce light continuously. © Springer-Verlag Berlin Heidelberg 2015

High Performance Multicell Series Inverter-Fed Induction Motor Drive

This document is the Accepted Manuscript version of the following article: M. Khodja, D. Rahiel, M. B. Benabdallah, H. Merabet Boulouiha, A. Allali, A. Chaker, and M. Denai, ‘High-performance multicell series inverter-fed induction motor drive’, Electrical Engineering, Vol. 99 (3): 1121-1137, September 2017. The final publication is available at Springer via DOI: https://doi.org/10.1007/s00202-016-0472-4.The multilevel voltage-source inverter (VSI) topology of the series multicell converter developed in recent years has led to improved converter performance in terms of power density and efficiency. This converter reduces the voltage constraints between all cells, which results in a lower transmission losses, high switching frequencies and the improvement of the output voltage waveforms. This paper proposes an improved topology of the series multicell inverter which minimizes harmonics, reduces torque ripples and losses in a variable-speed induction motor drive. The flying capacitor multilevel inverter topology based on the classical and modified phase shift pulse width modulation (PSPWM, MPSPWM) techniques are applied in this paper to minimize harmonic distortion at the inverter output. Simulation results are presented for a 2-kW induction motor drive and the results obtained demonstrate reduced harmonics, improved transient responses and reference tracking performance of the voltage in the induction motor and consequently reduced torque ripplesPeer reviewe

Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

YesInnovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions

Attraction and diffusion in nature-inspired optimization algorithms

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype

Theoretical and practical convergence of a self-adaptive penalty algorithm for constrained global optimization

This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems.The authors would like to thank the referees, the Associate Editor and the Editor-in-Chief for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Funda¸c˜ao para a Ciˆencia e Tecnologia within the projects UID/CEC/00319/2013 and UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio