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

Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm

Bidirectional inductive power transfer (BIPT) system facilitates contactless power transfer between two sides and across an air-gap, through weak magnetic coupling. Typically, this system is nonlinear high order system which includes nonlinear switch components and resonant networks, developing of accurate model is a challenging task. In this paper, a novel technique for parameter identification of a BIPT system is presented by using chaotic-enhanced fruit fly optimization algorithm (CFOA). The fruit fly optimization algorithm (FOA) is a new meta-heuristic technique based on the swarm behavior of the fruit fly. This paper proposes a novel CFOA, which employs chaotic sequence to enhance the global optimization capacity of original FOA. The parameter identification of the BIPT system is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured values. All the 11 parameters of this system (Lpi, LT, Lsi, Lso, CT, Cs, M, Rpi, RT, Rsi and Rso) can be identified simultaneously using measured input–output data. Simulations show that the proposed parameter identification technique is robust to measurements noise and variation of operation condition and thus it is suitable for practical application

Level of Knowledge about Human Papillomavirus Infection among Women of Kashan City, Iran

Abstract Aims: A few studies concentrate on the level of knowledge of HPV. This study was conducted to evaluate the level of knowledge about HPV, its risk factors, and its relation with cervical cancer in women of Kashan City, Iran. Instrument & Methods: This descriptive cross-sectional study was conducted in January 2015 in the population of the women of Kashan City, Iran, and 200 persons were selected by simple sampling method. The level of knowledge about HPV and cervical cancer were measured using a questionnaire with 10 questions about knowledge. The data was analyzed in SPSS 16 software by Chi-square, Exact Fisher and Kruskal-Wallis tests. Findings: Most of the participants (152 persons; 76) had “weak, 26 participants (13) had “moderate” and only 22 participants (11) had “strong” level of knowledge about HPV. There were significant differences between the level of knowledge according to educational level (p=0.014) and professional status (p<0.001) but there were no differences according to marital status (p=0.9) and age (p>0.05). In all the questions, the most frequent answer was “don’t know”. The participants had some knowledge about “HPV causing cervical cancer” (34.5), “HPV causing genital warts” (38), “sexually transmission of HPV” (37.5) and “increased risk of getting HPV by extramarital sexual affairs” (43.5) Conclusion: The level of knowledge about HPV, genital warts, and ways of infection transmission and its preventions in women of Kashan City, Iran, is insufficient

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

SPAM detection: Naïve bayesian classification and RPN expression-based LGP approaches compared

An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters. © Springer International Publishing Switzerland 2016

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

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