Probabilistic Crash Prediction and Prevention of Vehicle Crash

Transportation brings immense benefits to society, but it also has its costs. Costs include the cost of infrastructure, personnel, and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. This research aims to predict the probabilistic crash prediction of vehicles using Machine learning due to Natural and Structural reasons by excluding spontaneous reasons, like overspeeding, etc., in the United States. These factors range from weather factors, like Weather Conditions, Precipitation, Visibility, Wind Speed, Wind Direction, Temperature, Pressure and Humidity, to human-made structures, like Road structure factors like Bumps, Roundabouts, No Exit, Turning Loops, Give Away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes in all states collected by the US government. To calculate the probability Multinomial Expected value was used and assigned a classification label as the crash probability. We applied three classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by Natural and structural reasons for the crash. The paper has provided in-depth insights through exploratory data analysis

The urothelium: a multi-faceted barrier against a harsh environment

All mucosal surfaces must deal with the challenge of exposure to the outside world. The urothelium is a highly specialized layer of stratified epithelial cells lining the inner surface of the urinary bladder, a gruelling environment involving significant stretch forces, osmotic and hydrostatic pressures, toxic substances, and microbial invasion. The urinary bladder plays an important barrier role and allows the accommodation and expulsion of large volumes of urine without permitting urine components to diffuse across. The urothelium is made up of three cell types, basal, intermediate, and umbrella cells, whose specialized functions aid in the bladder's mission. In this review, we summarize the recent insights into urothelial structure, function, development, regeneration, and in particular the role of umbrella cells in barrier formation and maintenance. We briefly review diseases which involve the bladder and discuss current human urothelial in vitro models as a complement to traditional animal studies

An immunoresponsive three-dimensional urine-tolerant human urothelial model to study urinary tract infection

Introduction: Murine models of urinary tract infection (UTI) have improved our understanding of host-pathogen interactions. However, given differences between rodent and human bladders which may modulate host and bacterial response, including certain biomarkers, urothelial thickness and the concentration of urine, the development of new human-based models is important to complement mouse studies and to provide a more complete picture of UTI in patients. Methods: We originally developed a human urothelial three-dimensional (3D) model which was urine tolerant and demonstrated several urothelial biomarkers, but it only achieved human thickness in heterogenous, multi-layered zones and did not demonstrate the comprehensive differentiation status needed to achieve barrier function. We optimised this model by altering a variety of conditions and validated it with microscopy, flow cytometry, transepithelial electrical resistance and FITC-dextran permeability assays to confirm tissue architecture, barrier integrity and response to bacterial infection. Results: We achieved an improved 3D urine-tolerant human urothelial model (3D-UHU), which after 18-20 days of growth, stratified uniformly to 7-8 layers comprised of the three expected, distinct human cell types. The apical surface differentiated into large, CD227+ umbrella-like cells expressing uroplakin-1A, II, III, and cytokeratin 20, all of which are important terminal differentiation markers, and a glycosaminoglycan layer. Below this layer, several layers of intermediate cells were present, with a single underlying layer of CD271+ basal cells. The apical surface also expressed E-cadherin, ZO-1, claudin-1 and -3, and the model possessed good barrier function. Infection with both Gram-negative and Gram-positive bacterial classes elicited elevated levels of pro-inflammatory cytokines and chemokines characteristic of urinary tract infection in humans and caused a decrease in barrier function. Discussion: Taken together, 3D-UHU holds promise for studying host-pathogen interactions and host urothelial immune response

Performance analysis of full bridge, boost half bridge and half bridge topologies for application in phase shift converters

In this paper, performances of three topologies of bidirectional ports applicable to phase shift converters are compared. The proposed topologies include full bridge, half bridge and boost-half bridge that are commonly used as bi-directional port in various topologies of DC converters. The proposed analyses based on several indicators and characteristics of the topologies including reliability factor, switching loss, current ripple, cost, size, efficiency, range of power flow versus phase shift angle and control complexity. A phase shift converter based on proposed topologies was simulated using P-SIM. The analysis shows that considering all effective factors, full bridge topology provides better characteristics compared with others and can be selected for a phase shift converter. Also in some applications other topologies still remain a favorite choice. © 2013 IEEE

Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

Image classi cation is an important task in many medical applications, in order to achieve an adequate diagnostic of di erent le- sions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important di culties to overcome in order to obtain a good classi cation performance. In this work, a deep learning based method for accurate classi cation of wound regions is proposed. Raw images are fed into a Convolutional Neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known e ectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean ac- curacy with very few data and without any preprocessing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Reconfigurable Color Reflector by Selective Phase Change of GeTe in a Multilayer Structure

It is shown that a phase change material (PCM), germanium telluride (GeTe), when integrated into a subwavelength layered optical cavity, can produce widely tunable reflective colors. It is shown that the crystallization temperature (Tx) of GeTe is dependent on the film thickness for thin films of less than â 20 nm, which is exploited for color tuning. Four colors from the same physical structure are demonstrated by electrical heating, through novel optical and thermal engineering of a thin film stack that includes two GeTe layers with only a single integrated joule heater element. The selective sensitivity to incident light angle and low polarization dependence, as well as the low static power consumption of this device make it a good candidate for potential consumer electronics applications.A subâ wavelength optical cavity consisting of multiple layers of germanium telluride (GeTe) is shown here to produce widely tunable reflective colors. The dependence of GeTe crystallization temperature on its film thickness is exploited to achieve four colors from the same physical multiâ layer structure. An integrated electrical heating approach is used to switch between different colors.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148263/1/adom201801214-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148263/2/adom201801214_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148263/3/adom201801214.pd

Electric Bus Charging Infrastructures: Technologies, Standards, and Configurations

Rapid growth in the electrification of bus fleets, driven by substantial environmental benefits, is facing challenges such as range anxiety, prolonged charging durations, and reduced flexibility compared to combustion engine buses. This study first conducts a comprehensive bibliometric analysis of diverse publications to identify key research trends in electric buses (E-buses). It then offers a thorough comparison of charging technologies, encompassing topologies, power flow capabilities, costs, grid impacts, and efficiency, along with an examination of existing standards, norms, and challenges. With a classification of nearly 150 references, the study aims to illuminate the strengths and weaknesses of each charging technology, providing a solid background for selecting optimal topologies and strategies for specific applications. Emphasizing the importance of a nuanced trade-off between the quantity and type of chargers and E-bus battery capacity in each scenario, the research goes beyond technical considerations to explore potential future trends in the field. The information gathered in this review is a helpful guide for policymakers, industry experts, and researchers dealing with the complexities of E-bus charging infrastructure