Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.

COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries

Electrochemical Engineering of All-Vanadium Redox Flow Batteries for Reduced Ionic and Water Crossover via Experimental Diagnostics and Multiscale Modeling

Scalable and open architecture of redox flow batteries (RFBs) is a promising solution for large-scale energy storage. Among many chemistries developed for RFBs, all-vanadium redox flow batteries (VRFBs) currently show great potential for widespread commercialization. VRFBs utilize vanadium ions with different oxidation states in the negative and positive electrolytes; this characteristic frees them from irreversible capacity decay as a function of electroactive species transport through the membrane (i.e. crossover). However, crossover of vanadium ions and water during the charge/discharge cycling not only results in a lost discharge capacity, but also has real-time influence on the cell performance.Several parameters affect solute and solvent crossover during cycling. In this dissertation, experimental data along with multiscale computational modeling tailored to quantify the contributions to capacity decay stemming from ion-exchange membrane properties (e.g. equivalent weight and degree of reinforcement), flow field design, electrolyte properties, and operating conditions. A major focus has been to understand the effect of the electrode/membrane interface on the capacity decay and contact resistance. Novel ex-situ conductivity cells have been devised to assess ionic conductivity of the ion-exchange membranes along with electrolytes leading to details on the impact of interfacial phenomena on ionic conductivity and crossover.To quantify the long-term influence of crossover, a unique set-up (we call it IonCrG: Ionic Crossover Gauge) was built and fabricated enabling real-time measurement of the ionic transport across the polymeric membrane using ultraviolet-visible (UV/Vis) spectroscopy. The IonCrG enables separation of contributions to crossover emerging from concentration and electrostatic potential gradients. To investigate the instantaneous impact of crossover on the performance, a real-time current density distribution diagnostic has been implemented for measuring the in-plane current density distribution.The insights gained from this suite of experimental diagnostics and multiscale modeling have inspired design of systems with enhanced performance and greatly decreased crossover losses. Novel cell topologies along with asymmetric electrolyte compositions were designed and engineered for mitigating the ionic crossover during the operation of VRFBs. The cell architecture as well as the electrolyte configuration proposed in this dissertation provides an inexpensive and passive solution for retaining capacity during extended cycling of aqueous RFBs

Next Generation Teaching and Learning ??? Technologies and Trends

The landscape of teaching and learning has been radically shifted in the last 15 years by the advent of web technologies, which enabled the emergence of Learning Management Systems (LMS). These systems changed the educational paradigm by extending the classroom borders, capturing and persisting course content and giving instructors more flexibility and access to students and other resources. However, they also constrained and limited the evolution of teaching and learning by imposing a traditional, instructional framework. With the advent of Web 2.0 technologies, participation and collaboration have become predominant experiences on the Web. The teaching and learning community, as a whole, has been late to capitalize on these technologies in the classroom. Part of this trend is due to constraints in the technology (LMS), and part is due to the fact that participatory media tools require an additional shift in educational paradigms, from instructional, on-the-pulpit type of teaching, to a student-centered, adaptive environment where students can contribute to the course material and learn from one another. This panel will discuss the next generation of teaching and learning, involving more lightweight, modular systems to empower instructors to be flexible, explore new student-centered paradigms, and plug and play tools as needed. We will also discuss how the iSchools are and should be increasingly involved in studying these new forms, formulating best practices and supporting the needs of teachers as they move toward more collaborative learning environments

A hybrid computational approach for seismic energy demand prediction

In this paper, a hybrid genetic programming (GP) with multiple genes is implemented for developing prediction models of spectral energy demands. A multi-objective strategy is used for maximizing the accuracy and minimizing the complexity of the models. Both structural properties and earthquake characteristics are considered in prediction models of four demand parameters. Here, the earthquake records are classified based on soil type assuming that different soil classes have linear relationships in terms of GP genes. Therefore, linear regression analysis is used to connect genes for different soil types, which results in a total of sixteen prediction models. The accuracy and effectiveness of these models were assessed using different performance metrics and their performance was compared with several other models. The results indicate that not only the proposed models are simple, but also they outperform other spectral energy demand models proposed in the literature

Energy-based numerical models for assessment of soil liquefaction

AbstractThis study presents promising variants of genetic programming (GP), namely linear genetic programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction resistance of sandy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction (capacity energy) and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction

Structural Health Monitoring in Composite Structures: A Comprehensive Review.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented

Consolidation assessment using Multi Expression Programming

© 2019 Elsevier B.V. In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called Multi-Expression Programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models

Benchmarking business analytics techniques in Big Data

Technological developments and the growing dependence of organizations and society in the world of the internet led to the growth and variety of data. This growth and variety have become a challenge to the traditional techniques of Business Analytics. In this project, we conducted a benchmarking process that aimed to assess the performance of some Data Mining tools, like RapidMiner, in Big Data environment. Firstly, was analyzed a study where a group of Data Mining tools are evaluated and determined what is the best Data Mining tool, according to the evaluation criteria. After that, the best two tools considered in the study are analyzed regarding their ability to analyze data in a Big Data environment. Finally, studies were carried out on the evaluations of the RapidMiner and KNIME tools for their performance in the Big Data environment.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains -NORTE-01-0145-FEDER-000026

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

© 2019 Elsevier B.V. In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design