Strong Networks Grow Distance Learning

This article presents a snapshot of one state’s experience with connectivity from the early 1980s to the present and illustrates how distance learning has utilized that infrastructure to grow to serve more than 100,000 Ohioans. In early 1980s, most of Ohio’s telecommunications traffic traveled on dial-up connections. Ohio’s history of formidable statewide networking began in 1987, when Compuserve and OARnet (Ohio Academic Resources Network) were among few regional networks in existence. Through various mergers and acquisitions, Compuserve became Worldcom, AOL, MCI-Worldcom, and, finally, Verizon. OARnet became the Third Frontier Network (TFN) in 2004 and now is referred to as OSCnet and Broadband Ohio Network (BON). OARnet was created in 1987 by the Ohio Board of Regents to provide statewide connectivity to resources at the Ohio Supercomputer Center (OSC). In later years, the network extended support to the 89 member institutions of the Ohio Library and Information Network (OhioLINK), and the 83 colleges and universities of the Ohio Learning Network (OLN), a consortium offering blended, online, and distance education. OLN provides faculty development, infrastructure support via Collaborative Learning Environments (CLE), and various student support services and grants

Numerické simulace ustáleného stavu rotorové soustavy se dvěma trhlinami uložené v radiálních aktivních magnetických ložiskách

The steady-state response of a two-crack rotor system is investigated by computational simulations in the paper. The rotor system supported by radial active magnetic bearings is excited by centrifugal forces of discs unbalances. A flexibility matrix of Bernoulli beam element takes into account coupling phenomena between directions of vibration. The phenomenon occurs in cracked rotors. The couplings express relations between vibration in different directions, i.e. bending-torsion, bending-longitudinal, and torsion-longitudinal. The flexibility matrix elements of the cracked rotor are derived using the concepts of fracture mechanics for transverse surface crack. Partial opening/closing of cracks implemented in motion equations is determined by sign of stress intensity factor for mode I. The factor is computed for the crack edge. The motion equations are nonlinear because the system response depends on breathing of cracks and nonlinear force coupling is introduced by radial active magnetic bearings. Parametric studies of the system response were carried out in order to examine influence of various angles between two cracks. Several recommendations for detection of cracks and monitoring of the cracked rotor are suggested.V této práci jsou užity výpočetní simulace ke zkoumání ustálené složky odezvy na buzení odstředivými silami nevyvážených disků rotorové soustavy se dvěma trhlinami, jenž je uložená v radiálních aktivních magnetických ložiskách. Matice poddajnosti Bernoulliho nosníkového prvku je upravena tak, aby se uvážily všechny vazby v kmitání, které existují v rotoru s trhlinou, tj. ohybově-torzní, ohybově-podélné a torzně-podélné. Prvky matice poddajnosti s trhlinou jsou odvozeny na základě teorie lomové mechaniky pro příčnou povrchovou trhlinu. Částečné otevření/zavření trhlin zahrnuté do pohybové rovnice je řízeno podle znaménka součinitele intenzity napětí I. módu zatížení vypočítaného na hraně trhliny. Pohybové rovnice rotorové soustavy jsou nelineární kvůli odezvě závislé na dýchání trhlin a nelineární silové vazbě zavedené radiálními aktivními magnetickými ložisky. Byla provedena parametrická studie s cílem zkoumat vliv různých hodnot úhlu mezi trhlinami na ustálený stav odezvy rotorové soustavy. Taktéž jsou prezentovány doporučení pro detekci a monitorování rotoru se dvěma trhlinami

Prevalence of specific learning disabilities among Gujarati medium primary school children

Introduction: Learning disability in children is an assorted group of disorders where the individual unpredictably fails to proficiently attain, regain, and use information. Objective: The objective of this study was to measure the prevalence of specific learning disabilities (SpLDs) such as dyslexia and dysgraphia among the Gujarati medium primary schoolchildren. Methods: A cross-sectional study was conducted in public schools of Gujarati medium among children aged 7–12 years from the second, third, and fourth standard. After obtaining a sociodemographic profile, a multilevel screening approach that begins with the identification of educational backwardness followed by exclusion of vision, hearing impairment, chronic health conditions, and subnormal intelligence was carried out among these children. In the last stage, remaining children were subjected to the National Institute of Mental Health and Neurosciences test for SpLDs. Results: The prevalence of SpLDs was 9.6% in sampled children, whereas 7.4%, 8.6%, and 7.1% had dyslexia, dysgraphia, and dyscalculia, respectively. Among children diagnosed with SpLD, 65.7% (n=25/38) of children had a combination of all three types of SpLDs. Conclusions: This study suggests that the prevalence of SpLDs in public schools is the same as private schools and metro center. We have used a multilevel screening approach that can be utilized for early identification of children with SpLD by Rashtriya Bal Swasthya Karyakram team without imposing load on specialist services. There is a need for sensitization of school teacher for timely referral, remediation strategies, and policy interventions to improve school performance and to reduce dropouts of schoolchildren

Oseltamivir induced sinus bradycardia: an area of potential concern

Oseltamivir was approved for the prevention and treatment of influenza in 1999 by the USFDA (US Food and Drug Administration). The use of Oseltamivir is increasing rapidly all over the world, especially after the 2009 “Swine Flu” pandemic. Less data is published as far as the cardiovascular side effects of Oseltamivir are concerned, but it could be associated with some serious cardiovascular side effects. This study presented a case series of 5 cases suspected to be suffering from seasonal influenza H1N1 (“Swine Flu”), who developed sinus bradycardia while they were on Oseltamivir therapy

Application of quasilinearization to industrial management systems

Call number: LD2668 .T4 1969 M44Master of Scienc

Detection of Pecan Weevil Larvae in Pecan Nutmeat Using Multispectral Imaging System

This project utilizes multispectral imaging techniques to detect and identify pecan weevil larvae in pecan nutmeat. Diffuse reflectance measurements were obtained for weevil larvae and pecan nutmeat using a VIS/NIR spectrometer. PCA and derivative analysis were performed to identify the spectral wavelengths that best differentiated pecan nutmeat from larvae. The four potential wavelengths in the spectral range of silicon CCD were identified as 855nm, 902nm, 940nm and 981 nm. The images were acquired with a NIR enhanced camera. The images acquired at 980nm showed significant gray scale contrast between pecan nutmeat and larvae. These images were then processed using masking and morphological processing. This method was compared to a novel active contour based image segmentation algorithm. The contour based algorithm produced much better segmentation results and should be used instead of simple masking operation. Classification accuracy of 84% was obtained for the training images and 74% for the testing images.Biosystems and Agricultural Engineerin

Resilient geotechnical asset management

There is overwhelming evidence that the development of new, technically sound, engineered and fit-for-purpose critical physical infrastructure is vital for economic growth and stability. With many countries targeting significant levels of capital investment in energy, transport, communications, flood management and water and waste water infrastructure, there is a vital need for asset management frameworks that can provide both robust and resilient asset support. Currently, asset management tools focus predominantly on data management, deterioration modelling, condition assessment, risk, as well as economic factors (such as whole-life costing and developing investment plans). Some also consider the vulnerabilities of a network to climate change and extreme weather events such as flooding. However, rather than taking a long term view, asset management strategies are often short term, typically five years or less. What is needed is a long-term approach, which will ensure assets are safe, secure and resilient to what the future may hold in 20, or even 50 years’ time. The thesis describes the development of a ‘Resilience Assessment Framework’ which provides a platform to appraise resilience of geotechnical assets in the planning stage of asset management by considering how geotechnical assets (specifically for transport infrastructure) designed and built today will perform in the light of socio-economic, environmental, political, technological changes and shock events in the future. This framework intends to assist in strategic level decision-making by enabling long term planning and management of geotechnical assets and help future proof transport infrastructure. The proposed framework is validated using two real case studies to demonstrate its use and applicability in the field of geotechnical asset management

Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression

This paper aims at determining the various economic and non-economic factors that can influence the voting behaviour in the forthcoming United States Presidential Election using Lasso regression, a Machine learning algorithm. Even though contemporary discussions on the subject of the United States Presidential Election suggest that the level of unemployment in the economy will be a significant factor in determining the result of the election, in our study, it has been found that the rate of unemployment will not be the only significant factor in forecasting the election. However, various other economic factors such as the inflation rate, rate of economic growth, and exchange rates will not have a significant influence on the election result. The June Gallup Rating, is not the only significant factor for determining the result of the forthcoming presidential election. In addition to the June Gallup Rating, various other non-economic factors such as the performance of the contesting political parties in the midterm elections, Campaign spending by the contesting parties and scandals of the Incumbent President will also play a significant role in determining the result of the forthcoming United States Presidential Election. The paper explores the influence of all the aforementioned economic and non-economic factors on the voting behaviour of the voters in the forthcoming United States Presidential Election. The proposed Lasso Regression model forecasts that the vote share for the incumbent Republican Party to be 41.63% in the 2020 US presidential election. This means that the incumbent party is most likely to lose the upcoming election

Prediction for the 2020 United States Presidential Election using Linear Regression Model

The paper identifies various crucial factors, economic and non-economic, essential for predicting the 2020 United States presidential election results. Although it has been suggested by the contemporary discussions on the subject of United States presidential election that inflation rate, unemployment rate, and other such economic factors will play an important role in determining who will win the forthcoming United States Presidential Elections in November, it has been found in this study that, non-economic variables have a significant influence on the voting behaviour. Various non-economic factors like the performance of the contesting political parties in the midterm elections, the June Gallup Rating for the incumbent President, Average Gallup rating during the tenure of the incumbent President, Gallup Index, and Scandals of the Incumbent President were found to have a massive impact on the election outcomes. In the research conducted by Lewis-Beck and Rice (1982) , it was proposed that the Gallup rating for the Incumbent President, obtained in the month of June of the election year, is a significant factor in determining the results of the Presidential Elections. The major reason behind obtaining the Gallup Rating in June of the election year, post-primaries and pre-conventions, is that it is a relative political calm period. However, it has been found in this study that despite the existence of a relationship between the vote share of the incumbent President and his Gallup rating for June, the said Gallup rating cannot be used as the only factor for forecasting the results of the Presidential Election. The influence of all the aforementioned economic and non-economic factors and some other factors on the voter's voting behavior in the forthcoming United States Presidential Election is analyzed in this paper. The proposed regression model in the paper forecasts that Republican party candidate Donald Trump would receive a vote share of 46.74 ± 2.638%