25,854 research outputs found

    Fully-Autonomous, Vision-based Traffic Signal Control: from Simulation to Reality

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    Ineffective traffic signal control is one of the major causes of congestion in urban road networks. Dynamically changing traffic conditions and live traffic state estimation are fundamental challenges that limit the ability of the existing signal infrastructure in rendering individualized signal control in real-time. We use deep reinforcement learning (DRL) to address these challenges. Due to economic and safety constraints associated training such agents in the real world, a practical approach is to do so in simulation before deployment. Domain randomisation is an effective technique for bridging the reality gap and ensuring effective transfer of simulation-trained agents to the real world. In this paper, we develop a fully-autonomous, vision-based DRL agent that achieve adaptive signal control in the face of complex, imprecise, and dynamic traffic environments. Our agent uses live visual data (i.e. a stream of real-time RGB footage) from an intersection to extensively perceive and subsequently act upon the traffic environment. Employing domain randomisation, we examine our agent’s generalisation capabilities under varying traffic conditions in both the simulation and the real-world environments. In a diverse validation set independent of training data, our traffic control agent reliably adapted to novel traffic situations and demonstrated a positive transfer to previously unseen real intersections despite being trained entirely in simulation

    Onset, comorbidity, and predictors of nicotine, alcohol, and marijuana use disorders among North American Indigenous adolescents

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    North American Indigenous (i.e., American Indian and Canadian First Nations) youth experience inequities in rates of substance abuse and dependence. Despite this, few longitudinal studies examine the developmental course of substance use disorders (SUD) among community-based samples of Indigenous youth. The purpose of the study was to examine onset and predictors of nicotine dependence, alcohol use disorders, marijuana use disorders, any SUD, and multiple SUDs across the entire span of adolescence among a longitudinal sample (N = 744) of reservation/reserve Indigenous youth in the upper-Midwest of the United States and Ontario, Canada. Using discrete time survival analysis, the results show that rates of meeting criteria for SUDs by late adolescence were 22% for nicotine, 43% for alcohol, and 35% for marijuana. Peak periods of risk for new nicotine dependence and marijuana use disorder cases occurred around 14 years of age, whereas peak periods of risk for new alcohol use disorder cases emerged slightly later around 16 years of age. We found high rates of SUD comorbidity, and the cumulative probability of developing two or more SUDs during adolescence was 31%. Internalizing disorders increased the odds of nicotine dependence and multiple SUDs, while externalizing disorders increased the odds of all outcomes except nicotine dependence. Gender, age, and per capita family income were inconsistently associated with SUD onset. The findings are embedded within broader substance use patterns identified among Indigenous youth, and prevention, intervention, and treatment implications are discussed.Peer reviewedSociolog

    Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

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    In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

    Coverage measurements of NB-IoT technology

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    Abstract. The narrowband internet of things (NB-IoT) is a cellular radio access technology that provides seamless connectivity to wireless IoT devices with low latency, low power consumption, and long-range coverage. For long-range coverage, NB-IoT offers a coverage enhancement (CE) mechanism that is achieved by repeating the transmission of signals. Good network coverage is essential to reduce the battery usage and power consumption of IoT devices, while poor network coverage increases the number of repetitions in transmission, which causes high power consumption of IoT devices. The primary objective of this work is to determine the network coverage of NB-IoT technology under the University of Oulu’s 5G test network (5GTN) base station. In this thesis work, measurement results on key performance indicators such as reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), and signal to noise plus interference (SINR) have been reported. The goal of the measurement is to find out the NB-IoT signal strength at different locations, which are served by the 5GTN cells configured with different parameters, e.g., Tx power levels, antenna tilt angles. The signal strength of NB-IoT technology has been measured at different places under the 5GTN base station in Oulu, Finland. Drive tests have been conducted to measure the signal strength of NB-IoT technology by using the Quectel BG96 module, Qualcomm kDC-5737 dongle and Keysight Nemo Outdoor software. The results have shown the values of RSRP, RSRQ, RSSI, and SINR at different locations within several kilometres of the 5GTN base stations. These values indicate the performance of the network and are used to assess the performance of network services to the end-users. In this work, the overall performance of the network has been checked to verify if network performance meets good signal levels and good network coverage. Relevant details of the NB-IoT technology, the theory behind the signal coverage and comparisons with the measurement results have also been discussed to check the relevance of the measurement results

    Unraveling the effect of sex on human genetic architecture

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    Sex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes, amongst other factors. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides the genome with a distinct hormonal milieu, differential gene expression, and environmental pressures arising from gender societal roles. This thus poses the possibility of observing gene by sex (GxS) interactions between the sexes that may contribute to some of the phenotypic differences observed. In recent years, there has been growing evidence of GxS, with common genetic variation presenting different effects on males and females. These studies have however been limited in regards to the number of traits studied and/or statistical power. Understanding sex differences in genetic architecture is of great importance as this could lead to improved understanding of potential differences in underlying biological pathways and disease etiology between the sexes and in turn help inform personalised treatments and precision medicine. In this thesis we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We further investigated whether sex-agnostic (non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we studied the potential functional role of sex differences in genetic architecture through sex biased expression quantitative trait loci (eQTL) and gene-level analyses. Overall, this study marks a broad examination of the genetics of sex differences. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Furthermore, our results suggest the need to consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms

    Structure and adsorption properties of gas-ionic liquid interfaces

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    Supported ionic liquids are a diverse class of materials that have been considered as a promising approach to design new surface properties within solids for gas adsorption and separation applications. In these materials, the surface morphology and composition of a porous solid are modified by depositing ionic liquid. The resulting materials exhibit a unique combination of structural and gas adsorption properties arising from both components, the support, and the liquid. Naturally, theoretical and experimental studies devoted to understanding the underlying principles of exhibited interfacial properties have been an intense area of research. However, a complete understanding of the interplay between interfacial gas-liquid and liquid-solid interactions as well as molecular details of these processes remains elusive. The proposed problem is challenging and in this thesis, it is approached from two different perspectives applying computational and experimental techniques. In particular, molecular dynamics simulations are used to model gas adsorption in films of ionic liquids on a molecular level. A detailed description of the modeled systems is possible if the interfacial and bulk properties of ionic liquid films are separated. In this study, we use a unique method that recognizes the interfacial and bulk structures of ionic liquids and distinguishes gas adsorption from gas solubility. By combining classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous support. The developed approach was applied to a range of ionic liquids that feature different interaction behavior with gas and porous support. Using molecular simulations with interfacial analysis, it was discovered that gas adsorption capacity can be directly related to gas solubility data, allowing the development of a predictive model for the gas adsorption performance of ionic liquid films. Furthermore, it was found that this CO2 adsorption on the surface of ionic liquid films is determined by the specific arrangement of cations and anions on the surface. A particularly important result is that, for the first time, a quantitative relation between these structural and adsorption properties of different ionic liquid films has been established. This link between two types of properties determines design principles for supported ionic liquids. However, the proposed predictive model and design principles rely on the assumption that the ionic liquid is uniformly distributed on the surface of the porous support. To test how ionic liquids behave under confinement, nitrogen physisorption experiments were conducted for micro‐ and mesopore analysis of supported ionic liquid materials. In conjunction with mean-field density functional theory applied to the lattice gas and pore models, we revealed different scenarios for the pore-filling mechanism depending on the strength of the liquid-solid interactions. In this thesis, a combination of computational and experimental studies provides a framework for the characterization of complex interfacial gas-liquid and liquid-solid processes. It is shown that interfacial analysis is a powerful tool for studying molecular-level interactions between different phases. Finally, nitrogen sorption experiments were effectively used to obtain information on the structure of supported ionic liquids

    Probing the Intergalactic medium properties using X-ray absorption from multiple tracers

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    Based on the Lambda Cold Dark Matter concordance cosmological model (ΛCDM), the majority of baryons exist in the Intergalactic medium (IGM). It is extremely challenging to observationally trace the IGM, especially at higher temperatures and low densities. Post reionisation, the vast majority of hydrogen and helium is ionized in the IGM and therefore, the observation of metals is essential for parametrising the IGM properties. My hypothesis is that there is significant absorption in the diffuse highly ionisied IGM and that this IGM column density increases with redshift. I use X-ray absorption in multiple tracers which yields information on the total absorbing column density of the matter between the observer and the source. Clear IGM detections require tracer sources that are bright, distant, and common enough to provide a good statistical sample of IGM lines of sight (LOS). To more accurately isolate any IGM contribution to spectral absorption, I examine each tracer host type to realistically model it, in addition to using appropriate intrinsic continuum curvature models. I test the robustness of the result from a number of perspectives. I examine the impact of the key underlying assumptions that affect the column density calculations including metallicity, ionisation and location of absorption. I look for any evidence of evolution in the parameters. In Chapters 2, 3, 4 and 5, I use gamma-ray bursts (GRBs), blazars and quasars (QSOs) to estimate IGM baryon column densities, metallicity, temperature, ionisation parameters and redshift distributions. My results for each tracer are presented in each of the respective chapters and collectively in Chapter 5 which includes comparative analysis. In conclusion, through the work in this thesis I demonstrate a consistent case for strong X-ray absorption in the IGM on the LOS to three different tracer types and that it is related to redshift. The results are consistent with the ΛCDM model for density, temperature and metallicity. Given these results, I would recommend that studies of distant objects should not follow the convention of assuming all X-ray absorption in excess of our Galaxy is attributed to the host galaxy, that the host is neutral and has solar metallicity. Instead, particularly at higher redshift, absorption in the IGM should be accounted for to give more accurate results for the tracer host properties
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