10,739 research outputs found

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    An Efficient Authentication Protocol for Smart Grid Communication Based on On-Chip-Error-Correcting Physical Unclonable Function

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    Security has become a main concern for the smart grid to move from research and development to industry. The concept of security has usually referred to resistance to threats by an active or passive attacker. However, since smart meters (SMs) are often placed in unprotected areas, physical security has become one of the important security goals in the smart grid. Physical unclonable functions (PUFs) have been largely utilized for ensuring physical security in recent years, though their reliability has remained a major problem to be practically used in cryptographic applications. Although fuzzy extractors have been considered as a solution to solve the reliability problem of PUFs, they put a considerable computational cost to the resource-constrained SMs. To that end, we first propose an on-chip-error-correcting (OCEC) PUF that efficiently generates stable digits for the authentication process. Afterward, we introduce a lightweight authentication protocol between the SMs and neighborhood gateway (NG) based on the proposed PUF. The provable security analysis shows that not only the proposed protocol can stand secure in the Canetti-Krawczyk (CK) adversary model but also provides additional security features. Also, the performance evaluation demonstrates the significant improvement of the proposed scheme in comparison with the state-of-the-art

    The Effects of Salt Precipitation During CO2 Injection into Deep Saline Aquifer and Remediation Techniques

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    The by-products of combustion from the utilisation of fossil fuels for energy generation are a source of greenhouse gas emissions, mainly Carbon dioxide (CO2). This has been attributed to climate change because of global warming. Carbon capture and storage (CCS) technology has the potential to reduce anthropogenic greenhouse gas emissions by capturing CO2 from emissions sources and stored in underground formations such as depleted oil and gas reservoirs or deep saline formations. Deep saline aquifers for disposal of greenhouse gases are attracting much attention as a result of their large storage capacity. The problem encountered during CO2 trapping in the saline aquifer is the vaporisation of water along with the dissolution of CO2. This vaporisation cause salt precipitation which eventually reduces porosity and impairs the permeability of the reservoir thereby impeding the storage capacity and efficiency of the technology. Salt precipitation during CO2 storage in deep saline aquifers can have severe consequences during carbon capture and storage operations in terms of CO2 injectivity.This work investigates and assesses, experimentally, the effects of the presence of salt precipitation on the CO2 injectivity, the factors that influence them on selected core samples by core flooding experiments, and remediation of salt precipitation during CO2 injection. The investigation also covered the determination of optimum range of deep saline aquifers for CO2 storage, and the effects of different brine-saturated sandstones during CO2 sequestration in deep saline aquifers. In this investigation, three (3) different sandstone core samples (Bentheimer, Salt Wash North, and Grey Berea) with different petrophysical properties were used for the study. This is carried out in three different phases for a good presentation.• Phase I of this study involved brine preparation and measurement of brine properties such as brine salinity, viscosity, and density. The brine solutions were prepared from different salts (NaCl, CaCl2, KCl, MgCl2), which represent the salt composition of a typical deep saline aquifer. The core samples were saturated with different brine salinities (5, 10, 15, 20, 25, wt.% Salt) and testing was conducted using the three selected core samples.• Phase II entailed the cleaning and characterisation of the core samples by experimental core analyses to determine the petrophysical properties: porosity and permeability. Helium Porosimetry and saturation methods were used for porosity determination. Core flooding was used to determine the permeability of the core samples. The core flooding process was conducted at a simulated reservoir pressure of 1500 psig, the temperature of 45 °C, with injection rates of 3.0 ml/min respectively. Interfacial tension (IFT) measurements between the CO2 and various brine salinities as used in the core flooding were also conducted in this phase. Remediation scenarios of opening the pore spaces of the core samples were carried out using the same core flooding rig and the precipitated core samples were flooded with remediation fluids (low salinity brine and seawater) under the same reservoir conditions. The petrophysical properties (Porosity, Permeability) of the core samples were measured before core flooding, after core flooding and remediation test respectively.• In phase III of the study, SEM Image analyses were conducted on the core samples before core flooding, after core flooding, and remediation test respectively. This was achieved by using the FEI Quanta FEG 250 FEG high-resolution Scanning Electron Microscope (SEM) interfaced to EDAX Energy Dispersive X-ray Analysis (EDX).xivResults from Bentheimer, Salt Wash North, and Grey Berea core samples indicated a reduction in porosity, permeability impairment, as well as salt precipitation. It was also found that, at 10 to 20 wt.% brine concentrations in both monovalent and divalent brine, a substantial volume of CO2 is sequestered, which indicates the optimum concentration ranges for storage purposes. The salting-out effect was greater in divalent salt, MgCl2 and CaCl2 as compared to monovalent salt (NaCl and KCl). Porosity decreased by 0.5% to 7% while permeability was decreased by up to 50% in all the tested scenarios. CO2 solubility was evaluated in a pressure decay test, which in turn affects injectivity. Hence, the magnitude of CO2 injectivity impairment depends on both the concentration and type of salt species. The findings from this study are directly relevant to CO2 sequestration in deep saline aquifers as well as screening criteria for carbon storage with enhanced gas and oil recovery processes. Injection of remediation fluids during remediation tests effectively opened the pore spaces and pore throats of the core samples and thereby increasing the core sample's porosity in the range of 14.0% to 28.5% and 2.2% to 12.9% after using low salinity brine and seawater remediation fluids respectively. Permeability also increases in the range of 40.6% to 68.4% and 7.4% to 17.2% after using low salinity brine and seawater remediation fluids respectively. These findings provide remediation strategies useful in dissolving precipitated salt as well as decreasing the salinity of the near-well brine which causes precipitation.The SEM images of the core samples after the flooding showed that salt precipitation not only plugged the pore spaces of the core matrix but also showed significant precipitation around the rock grains thereby showing an aggregation of the salts. This clearly proved that the reduction in the capacity of the rock is associated with salt precipitation in the pore spaces as well as the pore throats. Thus, insight gained in this study could be useful in designing a better mitigation technique, CO2 injectivity scenarios, as well as an operating condition for CO2 sequestration in deep saline aquifers

    Learning-powered computer-assisted counterexample search

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    Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023, Director: Kolja Knauer[en] This thesis explores the great potential of computer-assisted proofs in the advancement of mathematical knowledge, with a special focus on using computers to refute conjectures by finding counterexamples, sometimes a humanly impossible task. In recent years, mathematicians have become more aware that machine learning techniques can be extremely helpful for finding counterexamples to conjectures in a more efficient way than by using exhaustive search methods. In this thesis we do not only present the theoretical background behind some of these methods but also implement them to try to refute some graph theory conjectures

    Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations

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    Camera-based autonomous systems that emulate human perception are increasingly being integrated into safety-critical platforms. Consequently, an established body of literature has emerged that explores adversarial attacks targeting the underlying machine learning models. Adapting adversarial attacks to the physical world is desirable for the attacker, as this removes the need to compromise digital systems. However, the real world poses challenges related to the "survivability" of adversarial manipulations given environmental noise in perception pipelines and the dynamicity of autonomous systems. In this paper, we take a sensor-first approach. We present EvilEye, a man-in-the-middle perception attack that leverages transparent displays to generate dynamic physical adversarial examples. EvilEye exploits the camera's optics to induce misclassifications under a variety of illumination conditions. To generate dynamic perturbations, we formalize the projection of a digital attack into the physical domain by modeling the transformation function of the captured image through the optical pipeline. Our extensive experiments show that EvilEye's generated adversarial perturbations are much more robust across varying environmental light conditions relative to existing physical perturbation frameworks, achieving a high attack success rate (ASR) while bypassing state-of-the-art physical adversarial detection frameworks. We demonstrate that the dynamic nature of EvilEye enables attackers to adapt adversarial examples across a variety of objects with a significantly higher ASR compared to state-of-the-art physical world attack frameworks. Finally, we discuss mitigation strategies against the EvilEye attack.Comment: 15 pages, 11 figure

    Safety analysis of pemigatinib leveraging the US Food and Drug administration adverse event reporting system

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    Background: Cholangiocarcinoma (CCA) is a highly lethal and aggressive epithelial tumor of the hepatobiliary system. A poor prognosis, propensity for relapse, low chance of cure and survival are some of its hallmarks. Pemigatinib, the first targeted treatment for CCA in the United States, has been demonstrated to have a significant response rate and encouraging survival data in early-phase trials. The adverse events (AEs) of pemigatinib must also be determined.Objective: To understand more deeply the safety of pemigatinib in the real world through data-mining of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).Methods: Disproportionality analysis was employed in a retrospective pharmacovigilance investigation to identify the AEs linked to pemigatinib use as signals. Data were collected between 1 January 2020 to 30 June 2022. Four data-mining methods (proportional reporting odds ratio; proportional reporting ratio; Bayesian confidence propagation neural networks of information components; empirical Bayes geometric means) were used to calculate disproportionality.Results: A total of 203 cases using pemigatinib as the prime-suspect medication were found in our search, which involved 99 preferred terms (PTs). Thirteen signals of pemigatinib-induced AEs in seven System Organ Classes were detected after confirming the four algorithms simultaneously. Nephrolithiasis was an unexpected significant AE not listed on the drug label found in our data-mining. Comparison of the differences between pemigatinib and platinum drugs in terms of 33 PTs revealed that 13 PTs also met the criteria of the four algorithms. Ten of these PTs were identical to those compared with all other drugs, in which (excluding a reduction in phosphorus in blood) other PT signal values were higher than those of all other drugs tested. However, comparison of the differences between pemigatinib and infigratinib in terms of the 33 PTs revealed no significant signals in each algorithm method.Conclusion: Some significant signals were detected between pemigatinib use and AEs. PTs with apparently strong signals and PTs not mentioned in the label should be taken seriously

    International Investment Law Protection of Foreign Portfolio Investments: ‘To be, or not to be’?

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    The view that foreign portfolio investments (FPI) are investments within the contemplation of the international investment law regime led to a foreign ETF holder bringing an investment arbitration claim challenging Malaysia’s foreign exchange policy to deal with the Asian Financial Crisis. This same belief led to tens of thousands of foreign holders of Argentine sovereign bond security interests bringing various investment arbitration claims against Argentina’s public expenditure policy decision to restructure its public debt during the Argentine economic crisis. Thus, the sustenance of this belief can lead foreign holders of emerging/frontier economies’ FPIs to challenge their macroeconomic measures for dealing with economic distress or full-blown economic crisis. Hence the relevance of this thesis. This thesis argues against the extension of international investment law recognition and protection of FPIs in emerging and frontier economies for policy and legal reasons. Firstly, though quite arguable, unrestricted FPI movement seems to be correlated with economic distress or crisis. Bolstering this narrative is the IMF’s recognition of the necessity for imposing some controls, even pre-emptive controls on FPI movement. Secondly, the international investment law regime is infamous for its effect of constraining regulatory autonomy. Extending investment law protection will only serve to constrain macroeconomic independence and flexibility with severe consequences during economic distress and crisis. Thirdly, FPIs are not investments within the contemplation of the ICSID Convention and ought not to be accorded jurisdictional recognition. The potential for investment law protected FPI to constrain macroeconomic policymaking can detract from economic development contrary to the objectives of ICSID. Finally, even if jurisdiction is found the substantive protection standards considered are likely to fall short. Also, a balancing of the competing rights between host States and FPI will tilt in favour of the host States, owing to the greater costs that would be incurred if it tilts otherwise

    SecureTrack- A contact tracing IoT platform for monitoring infectious diseases

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    The COVID-19 pandemic has highlighted the need for innovative solutions to monitor and control the spread of infectious diseases. With the potential for future pandemics and the risk of outbreaks particularly in academic institutions, there is a pressing need for effective approaches to monitor and manage such diseases. Contact tracing using Global Positioning Systems (GPS) has been found to be the most prevalent method to detect and tackle the extent of outbreaks during the pandemic. However, these services suffer from the inherent problems of infringement of data privacy that creates hindrance in adoption of the technology. Non-cellular wireless technologies on the other hand are well-suited to provide secure contact tracing methods. Such approaches integrated with the Internet of Things (IoT) have a great potential to aid in the fight against any type of infectious diseases. In response, we present a unique approach that utilizes an IoT based generic framework to identify individuals who may have been exposed to the virus, using contact tracing methods, without compromising the privacy aspect. We develop the architecture of our platform, including both the frontend and backend components, and demonstrate its effectiveness in identifying potential COVID-19 exposures (as a test case) through a proof-of-concept implementation. We also implement and verify a prototype of the device. Our framework is easily deployable and can be scaled up as needed with the existing infrastructure.Comment: 22 Pages, 8 figures, To be published in "The Global Interdisciplinary Green Cities Conference 2023 Business, Engineering, Art, Architecture, Design, Political Science, International Relations, Applied Science & Technology.

    A Simulation of the Impacts of Climate Change on Civil Aircraft Takeoff Performance

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    Climate change affects the near-surface environmental conditions that prevail at airports worldwide. Among these, air density and headwind speed are major determinants of takeoff performance, and their sensitivity to global warming carries potential operational and economic implications for the commercial air transport industry. Previous archival and prospective research observed a weakening in headwind strength and predicted an increase in near-surface temperatures, respectively, resulting in an increase in takeoff distances and weight restrictions. The main purpose of the present study was to update and generalize the extant prospective research using a more representative sample of worldwide airports, a wider range of climate scenarios, and next-generation climate models. The research questions included how much additional thrust and payload removal will be required to offset the centurial changes in takeoff conditions. This study relied on a quantitative method using the simulation instrument. Forecast climate data corresponding to four shared socioeconomic pathways (SSP1‒2.6, SSP2‒4.5, SSP3‒7.0, and SSP5‒8.5) over the available 2015‒2100 period were sourced from a high-resolution CMIP6 global circulation model. These data were used to characterize the six-hourly near-surface environmental conditions prevailing at all 881 airports worldwide having at least one million passengers in pre-COVID‒19 traffic. The missing air density was iii numerically derived from the air temperature, pressure, and humidity variables, while the headwind speed for each airport’s active runway configuration was triangulated from the wind vector components. Separately, a direct takeoff-dynamics simulation model was developed from first principles and calibrated against published performance data under international standard atmospheric conditions for two narrowbody and two widebody aircraft. The model was used to simulate 1.8 billion unique takeoffs, each initiated at 75% of maximum takeoff thrust and 100% of maximum takeoff mass. When the resulting takeoff distance required exceeded that available, the takeoff thrust was gradually increased to 100%, after which the takeoff mass was gradually decreased to an estimated breakeven load factor. In total, 65 billion takeoff iterations were simulated. Longitudinal changes to takeoff thrust, distance, and payload were recorded and examined by aircraft type, climate scenario, and climate zone. The results show that despite a marked centurial increase in the global mean air temperature of 9.4%‒18.0% relative to the year 2015 under SSP2‒4.5 and SSP3‒7.0, air density will only decrease by 0.6%‒1.1% due to its weak sensitivity to temperature. Likewise, mean headwinds were observed to remain almost unchanged relative to the 2015 baseline. As a result, the global mean takeoff thrust was found to increase by no more than 0.3 percentage point while payload removals did not exceed 1.1 passenger. Significant deviations from the mean were observed at climatic outlier airports, including those located around the Siberian plateau, where takeoff operations may become more difficult. This study contributes to the air transport climate adaption body of knowledge by providing contrasting results relative to earlier research that reported strong impacts of global warming on takeoff performance

    A Secure and Distributed Architecture for Vehicular Cloud and Protocols for Privacy-preserving Message Dissemination in Vehicular Ad Hoc Networks

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    Given the enormous interest in self-driving cars, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. Cloud computing is also gaining widespread deployment. Marriage between cloud computing and VANETs would help solve many of the needs of drivers, law enforcement agencies, traffic management, etc. The contributions of this dissertation are summarized as follows: A Secure and Distributed Architecture for Vehicular Cloud: Ensuring security and privacy is an important issue in the vehicular cloud; if information exchanged between entities is modified by a malicious vehicle, serious consequences such as traffic congestion and accidents can occur. In addition, sensitive data could be lost, and human lives also could be in danger. Hence, messages sent by vehicles must be authenticated and securely delivered to vehicles in the appropriate regions. In this dissertation, we present a secure and distributed architecture for the vehicular cloud which uses the capabilities of vehicles to provide various services such as parking management, accident alert, traffic updates, cooperative driving, etc. Our architecture ensures the privacy of vehicles and supports secure message dissemination using the vehicular infrastructure. A Low-Overhead Message Authentication and Secure Message Dissemination Scheme for VANETs: Efficient, authenticated message dissemination in VANETs are important for the timely delivery of authentic messages to vehicles in appropriate regions in the VANET. Many of the approaches proposed in the literature use Road Side Units (RSUs) to collect events (such as accidents, weather conditions, etc.) observed by vehicles in its region, authenticate them, and disseminate them to vehicles in appropriate regions. However, as the number of messages received by RSUs increases in the network, the computation and communication overhead for RSUs related to message authentication and dissemination also increases. We address this issue and present a low-overhead message authentication and dissemination scheme in this dissertation. On-Board Hardware Implementation in VANET: Design and Experimental Evaluation: Information collected by On Board Units (OBUs) located in vehicles can help in avoiding congestion, provide useful information to drivers, etc. However, not all drivers on the roads can benefit from OBU implementation because OBU is currently not available in all car models. Therefore, in this dissertation, we designed and built a hardware implementation for OBU that allows the dissemination of messages in VANET. This OBU implementation is simple, efficient, and low-cost. In addition, we present an On-Board hardware implementation of Ad hoc On-Demand Distance Vector (AODV) routing protocol for VANETs. Privacy-preserving approach for collection and dissemination of messages in VANETs: Several existing schemes need to consider safety message collection in areas where the density of vehicles is low and roadside infrastructure is sparse. These areas could also have hazardous road conditions and may have poor connectivity. In this dissertation, we present an improved method for securely collecting and disseminating safety messages in such areas which preserves the privacy of vehicles. We propose installing fixed OBUs along the roadside of dangerous roads (i.e., roads that are likely to have more ice, accidents, etc., but have a low density of vehicles and roadside infrastructure) to help collect data about the surrounding environment. This would help vehicles to be notified about the events on such roads (such as ice, accidents, etc.).Furthermore, to enhance the privacy of vehicles, our scheme allows vehicles to change their pseudo IDs in all traffic conditions. Therefore, regardless of whether the number of vehicles is low in the RSU or Group Leader GL region, it would be hard for an attacker to know the actual number of vehicles in the RSU/GL region
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