Submerged floating tunnels (SFTs) for Norwegian fjords

AbstractSubmerged floating tunnels (SFTs) weigh roughly the same as the surrounding water. The loads on the tunnel depend on the variation of the forces on the tunnel. The forces come from variation in traffic, current, temperature, waves, weight of water, weight of concrete, growth on the tunnel, wear of asphalt, dust and debris, relaxation of prestress and shrinkage and creep in the concrete. The last six variations are slow and can be counteracted by altering weights in the tunnel.All structures above sea level are subject to gravity, which tends to limit their spans. In SFTs buoyancy counteracts gravity. This speaks for longer spans, but the slope of roads limit the depth to raise ratio of downward arched SFTs. This tends to limit the free spans. In this article the design of SFTs is discussed. Finally there is a comparison between materials needed for two SFTs and a suspension bridge between Vallavik and Bu in Eidfjord in western Norway. The fjord is ∼500 m deep.It is many years since the author did serious research on submerged tunnels. He has written this contribution in the vain hope that some of his ideas on submerged floating tunnels might be of value to somebody. The author got the idea of SFTs at the technical university in Trondheim from his teacher Erik Ødegård [1]. However the idea is much older. E.J Reed, MP. applied for a patent in 1884. Up to 1968 the following Norwegian names are connected to the idea: Olsen 1923, Sam Lorgen 1968, Sverre Mo 1968.The author apologizes for not mentioning many other Norwegians who have done valuable work on SFTs. His best publication on submerged tunnels is [2]. It has a list of 32 references

Backwards Inheritance in Medieval Scandinavian Law

acceptedVersio

Machine learning, unsupervised learning and stain normalization in digital nephropathology

Chronic kidney disease is a serious health challenge and still, the field of study lacks awareness and funding. Improving the efficiency of diagnosing chronic disease is important. Machine learning can be used for various tasks in order to make CKD diagnosis more efficient. If the disease is discovered quickly it can be possible to reverse changes. In this project, we explore techniques that can improve clustering of glomeruli images. The current thesis evaluates the effects of applying stain normalization to nephropathological data in order to improve unsupervised learning cluster- ing. A unsupervised learning pipeline was implemented in order to evaluate the effects of using stain normalization techniques with different reference images. The stain normalization techniques that were implemented are: Reinhard stain normalization, Macenko stain normalization and Structure preserving color normalization. The evaluation of these methods was done by measuring clustering results from the unsupervised learning pipeline, using the Adjusted Rand Index metric. The results indicate that using these techniques will increase the cluster agreement between results and true labels for the data. Six reference images were used for each stain nor- malization technique. The average Adjusted Rand Index score for all ref- erence images was increased using all three stain normalization techniques. The best performing method overall was the Reinhard stain normalization technique. This method gave both the highest single experiment and aver- age score. The other normalization methods both have one score close to zero (unsuccessful clustering), and structure preserving color normalization would outperform the Reinhard method if this single clustering was more successful.Chronic kidney disease is a serious health challenge and still, the field of study lacks awareness and funding. Improving the efficiency of diagnosing chronic disease is important. Machine learning can be used for various tasks in order to make CKD diagnosis more efficient. If the disease is discovered quickly it can be possible to reverse changes. In this project, we explore techniques that can improve clustering of glomeruli images. The current thesis evaluates the effects of applying stain normalization to nephropathological data in order to improve unsupervised learning cluster- ing. A unsupervised learning pipeline was implemented in order to evaluate the effects of using stain normalization techniques with different reference images. The stain normalization techniques that were implemented are: Reinhard stain normalization, Macenko stain normalization and Structure preserving color normalization. The evaluation of these methods was done by measuring clustering results from the unsupervised learning pipeline, using the Adjusted Rand Index metric. The results indicate that using these techniques will increase the cluster agreement between results and true labels for the data. Six reference images were used for each stain nor- malization technique. The average Adjusted Rand Index score for all ref- erence images was increased using all three stain normalization techniques. The best performing method overall was the Reinhard stain normalization technique. This method gave both the highest single experiment and aver- age score. The other normalization methods both have one score close to zero (unsuccessful clustering), and structure preserving color normalization would outperform the Reinhard method if this single clustering was more successful

An Electromyographic and Motion Analysis Study of an Elliptical Trainer

Americans are increasingly interested in exercising to increase fitness and reduce the risks of disease. One of the latest machines used to accomplish this goal is the elliptical trainer, a combination stair stepper, treadmill, exercise cycle, and cross-country ski machine. The purpose of this study was to describe muscle activity and joint range of motion while moving both forward and backward on an elliptical trainer at different inclines. Six subjects between the ages of twenty-two and twenty-five years rode an elliptical trainer backwards and forwards at different inclines for four trials. Electromyographic activity of eight lower extremity muscles was calculated along with lower extremity joint angles while performing the stride. From our results, we concluded that with changing inclines and direction, the electromyographical data from the lower extremity muscles was variable. Neither changes in direction nor incline produced consistent changes in EMG activity. Range of motion of the hip and knee increased as the incline increased. No differences in range of motion were noted when changing from backward striding to forward striding

Exploring the Relationship Between Feeling of Rightness and Recall: A Study Challenging Dual Process Theory

Tidlig forskning på bedømming- og beslutningstaking antyder at vi har to systemer som tas i bruk når vi tenker: et raskt, intuitivt system (system 1) og et langsommere, mer overveiende system (system 2), referert til som toprosessteorien. Gjennom resonneringsoppgaver har denne forskningen vist at system 2 produserer normativt korrekte svar, og system 1 produserer feilaktige intuitive svar, og at system 2 tar lengre tid enn system 1. Nyere forskning utfordrer dette, da raske svar kan være normative, og tregere svar feilaktige. Følelsen av korrekthet (FoR) anses som en måling av konfliktdeteksjon, og lav FoR skal utløse bruk av system 2. Hvis system 2 brukes kan vi forvente mer bevisst overveielse, og dermed bedre tilbakekalling av informasjonen. Vi testet denne teorien ved å rekruttere 107 deltakere, hovedsakelig studenter fra UiT – Norges Arktiske Universitet. Vi brukte tre oppgaver for å teste hypotesen om tilbakekalling for Dual Process Theory: base-rate oppgaver, syllogismeoppgaver og teleologiske uttalelsesoppgaver. Oppgavene ble utført i et mellom-deltaker-design. Korrekthet, FoR og tilbakekalling ble målt. Konsistent gjennom alle oppgavene fant vi ingen signifikant sammenheng mellom FoR og tilbakekalling. Det vil si at lav FoR ikke forutsa bedre tilbakekalling. Dette antyder at lav FoR ikke utløser bruk av system 2, eller at FoR ikke er et mål på konfliktdeteksjon. Fremtidig forskning bør vurdere alternative teorier for toprosessteorien, eller endre egenskapene til hvert av systemene for å gi et mer korrekt bilde av hvordan vi bedømmer og tar beslutninger. Nøkkelord: toprosessteorien, følelsen av riktighet, tilbakekalling, konfliktdeteksjon, overveiels

Analyzing Behavioral Biometrics of Handwriting Using Myo Gesture Control Armband

Through the last few decades, computer technology has gradually merged into our everyday lives. Computers and sensors are embedded in an increasing amount of household items, enabling us to monitor and remotely control our connected devices from apps on our smartphones. The technology interfaces are also evolving along with new technologies. Among the up and coming digital interfaces are wearable technology. The Myo gesture control armband (GCA) is an example of tools which aims to make the communication from computer to human more seamless and intuitive. The Myo GCA is a multi sensor armband containing 8 surface electromyography sensors which measure electrical activity originating from skeletal muscles in the upper forearm. It is also equipped with a 9-axis inertial measurement unit which can provide information on spatial arm movements of the users. Together these sensors enable its user to pass 6 configurable commands to a smart phone or Blue-tooth connected computer. In this thesis we explore the Myo armbands potential as a multi sensor for handwriting recognition. Data are sampled and manually extracted through a cumbersome time consuming process, using recorded video as a reference to the sampled Myo data. The subjects are given the task of writing 10 repetitions each, of the four capital letters: E, L, O, and R. A strong positive correlation between same class letters within subjects has been proven in all of the four sensor types, where the orientation data yields the highest correlation coefficient values, while the sEMG data yields the lowest. Statistical similarity between same class letters has been found through singular value decomposition, where again orientation data yields the highest values, while sEMG scores the lowest of all sensor types. In an attempt to cross subject classification though k-NN, with k = 1, k = 3, and k = 5, the 1-NN classifier yields a minimum success rate of 58\% across the four letters. This is considerably better that what we would expect from a random assignment of letter classes. In the last part of the results, a similarity search by DTW is attempted. This yield poor results, with a classification success rate of around 10$\%$ on average across letters