Shocks in sand flowing in a silo

We study the formation of shocks on the surface of a granular material draining through an orifice at the bottom of a quasi-two dimensional silo. At high flow rates, the surface is observed to deviate strongly from a smooth linear inclined profile giving way to a sharp discontinuity in the height of the surface near the bottom of the incline, the typical response of a choking flow such as encountered in a hydraulic jump in a Newtonian fluid like water. We present experimental results that characterize the conditions for the existence of such a jump, describe its structure and give an explanation for its occurrence.Comment: 5 pages, 7 figure

Forecasting real estate prices in Portugal based on a data science approach

Mestrado Bolonha em Economia Monetária e FinanceiraThe evolution of the European residential market is notorious over the last ten years. House prices in the E.U. rose by 30.9 per cent between 2010 and the first quarter of 2021. The prices of homes in Portugal has risen almost 50 per cent during 11 years. Considering this previous argument, I propose the following research question: How to predict real estate prices. In this context, my research aims to analyze the prices’ evolution and understand the main components impacting the price of real estate. First, using the time series analysis, I use ARIMA to analyze the prices of real estate and the number of buildings sold since the first quarter of 2009, almost one year after the great recession in Portugal, until the third quarter of 2020 which was during the COVID-19 pandemic. The model was fitted and the prediction line was accurized with an upward trend. The second approach consists of analyzing the impact of five independent variables on real estate prices. To understand the most relevant components, regression analysis has been performed. I used OLS to analyze the impact of independent variables (crime rate, selected waste rate, tax rate, purchasing power and tourism rate) on real estate prices. Crime rate and tourism are negatively correlated while purchasing power, selected waste rate and tax rate are positively correlated with real estate prices. Then, I compared the accuracy of the result with neural networks and other types of regression analysis. Results were not much better than with linear regression. It is also essential to consider that this approach has some limitations, especially regarding the analysis’s granularity. The data has been collected from INE and PORDATA, the databases of contemporary Portugal, to construct the models and forecast house prices in Portugal.info:eu-repo/semantics/publishedVersio

Maximum Angle of Stability of a Wet Granular Pile

Anyone who has built a sandcastle recognizes that the addition of liquid to granular materials increases their stability. However, measurements of this increased stability often conflict with theory and with each other [1-7]. A friction-based Mohr-Coulomb model has been developed [3,8]. However, it distinguishes between granular friction and inter-particle friction, and uses the former without providing a physical mechanism. Albert, {\em et al.} [2] analyzed the geometric stability of grains on a pile's surface. The frictionless model for dry particles is in excellent agreement with experiment. But, their model for wet grains overestimates stability and predicts no dependence on system size. Using the frictionless model and performing stability analysis within the pile, we reproduce the dependence of the stability angle on system size, particle size, and surface tension observed in our experiments. Additionally, we account for past discrepancies in experimental reports by showing that sidewalls can significantly increase the stability of granular material.Comment: 4 pages, 4 figure

Computer assisted analysis of auroral images obtained from high altitude polar satellites

Automatic techniques that allow the extraction of physically significant parameters from auroral images were developed. This allows the processing of a much larger number of images than is currently possible with manual techniques. Our techniques were applied to diverse auroral image datasets. These results were made available to geophysicists at NASA and at universities in the form of a software system that performs the analysis. After some feedback from users, an upgraded system was transferred to NASA and to two universities. The feasibility of user-trained search and retrieval of large amounts of data using our automatically derived parameter indices was demonstrated. Techniques based on classification and regression trees (CART) were developed and applied to broaden the types of images to which the automated search and retrieval may be applied. Our techniques were tested with DE-1 auroral images

Angle of repose and segregation in cohesive granular matter

We study the effect of fluids on the angle of repose and the segregation of granular matter poured into a silo. The experiments are conducted in two regimes where: (i) the volume fraction of the fluid is small and it forms liquid bridges between particles, and (ii) the particles are completely immersed in the fluid. The data is obtained by imaging the pile formed inside a quasi-two dimensional silo through the transparent glass side walls. In the first series of experiments, the angle of repose is observed to increase sharply with the volume fraction of the fluid and then saturates at a value that depends on the size of the particles. We systematically study the effect of viscosity by using water-glycerol mixtures to vary it over at least three orders of magnitude while keeping the surface tension almost constant. Besides surface tension, the viscosity of the fluid is observed to have an effect on the angle of repose and the extent of segregation. In case of bidisperse particles, segregation is observed to decrease and finally saturate depending on the size ratio of the particles and the viscosity of the fluid. The sharp initial change and the subsequent saturation in the extent of segregation and angle of repose occurs over similar volume fraction of the fluid. In the second series of experiments, particles are poured into a container filled with a fluid. Although the angle of repose is observed to be unchanged, segregation is observed to decrease with an increase in the viscosity of the fluid.Comment: 9 pages, 12 figure

Potential use of natural silk for bio-dental applications

AbstractObjectivesSilks are protein polymers that are spun into fibres by silkworms and spiders under ambient conditions. Silk has been used as a biomaterial in a variety of biological applications for many years, whereas there are few applications in dentistry. The aim of this study was to explore the potential properties of natural silk for dental applications by determining the structure and features that make natural silk a biocompatible candidate.MethodsWe conducted a literature search through the recognized databases of medline, ISI web of science, SCOPUS, and EBASE to elucidate the natural properties of silk, its processing for biomedical applications and its use in dental applications.ResultsSilk has excellent natural properties, such as strength, resistance to light, temperature and humidity and biocompatibility. Once silk has been dissolved, it can be used to produce a variety of materials, such as films, gels, fibres, nanofibres, granules, foams, spheres and electrospun mats, on a micro or nano scale. Applications in dentistry include biomineralization, tissue engineering for scaffold applications and drug delivery.ConclusionsThere has been renewed research on silk-based materials for various biomedical applications, including dentistry

Oculogica: An Eye-Catching Innovation in Health Care and The Privacy Implications of Artificial Intelligence and Machine Learning in Diagnostics For The Human Brain

This article explores the use of Artificial Intelligence (AI) in emerging eye-tracking diagnostic technology, with a focus on both the patient data privacy and security regulations that firms, specifically device inventors and manufacturers, may face and how such firms can address the developing privacy and regulatory legal challenges. In addition, we discuss the ethical considerations of algorithmic bias, the impact such biases have on society and emerging technology, along with specific actions companies should take to maximize patient outcomes. Lastly, we offer a case study of Oculogica, an emerging digital health technology company—and its medical device (EyeBOX) – to illustrate how digital health firms can enhance patient outcomes, while ensuring data security and privacy, while simultaneously promoting responsible development of advanced algorithms for diagnostic AI

Modeling of Lithium-ion Battery Performance and Thermal Behavior in Electrified Vehicles

Electric vehicles (EVs) have received significant attention over the past few years as a sustainable and efficient green transportation alternative. However, severe challenges, such as range anxiety, battery cost, and safety, hinder EV market expansion. A practical means to reduce these barriers is to improve the design of the battery management system (BMS) to accurately estimate the battery state of charge (SOC) and state of health (SOH) in addition to communicating with other powertrain components. Along with a robust estimation strategy, a critical requirement in developing an efficient BMS is a high fidelity battery model to predict the battery voltage, SOC, and heat generation profile at various temperature and power demands. Such a model should also be able to capture battery degradation, which is a path-dependent parameter that affects the battery performance in terms of output voltage, power capability and heat generation. In this thesis, the Li-ion battery, a proven technology for electrified vehicles, is studied under different operation scenarios on a plug-in hybrid vehicle (PHEV). The following steps have been accomplished: 1- Development of a data-driven battery thermal model: A set of thermal characterization tests are conducted on Li-ion cells. Heat generation profiles of each battery are driven for a set of operating points including various ambient temperatures, states of charge (SOCs) and load profiles. A regression model is developed accordingly which is able to accurately predict the battery temperature during a driving or charging event. The model shows an average error of 4% in temperature predictions. 2- Development of a data-driven battery performance model for real-time on-board applications: An equivalent circuit model is developed based on the electrochemical impedance spectroscopy (EIS) tests. This model can precisely predict the battery operating voltage under various operating conditions. An overall 6% improvement is observed in voltage prediction compared to common models in the literature. Results also show, depending on the powertrain designer expected accuracy, that this model can be used to predict the battery internal resistance obtained from hybrid pulse power characterization (HPPC) tests. 3- Battery degradation studies through field tests: An electrified Ford Escape vehicle is tested through random and controlled driving and charging events and battery data is collected and analyzed to identify trends of degradation including capacity fade and power fade. A battery life model is recalibrated based on the measured battery capacities over the field test period. Although, data shortage and technical issues prevented this study from meeting its targeted scope, the presented analysis provides a pathway for future research. 4- Battery lifetime modeling: fuel consumption, all-electric range and battery capacity loss are simulated under various scenarios including different climate control loads, ambient conditions, powertrain architectures and battery preconditioning. To simulate the climate control loads impact, a vehicle cabin thermal model is developed that incorporates the ambient conditions to predict the temperature profile of the cabin and the cooling/heating load required to regulate the temperature. Accordingly, this load is translated into additional load on the battery, which enables assessment of its impacts on the battery life, fuel consumption and vehicle range