26,772 research outputs found

    Essays on monetary policy and financial stability

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    Doutoramento em EconomiaBy focusing on the relationship between financial stability and monetary policy for the cases of Chile, Colombia, Japan, Portugal and the UK, this thesis aims to add to the existing literature on the fundamental issue of the relationship between financial stability and monetary policy, a traditional topic that gained importance in the aftermath of the GFC as Central Banks lowered policy rates in an effort to rescue their economies. As the zero-lower bound loomed and the reach of traditional monetary policy narrowed, policy makers realised that alternative frameworks were needed and hence, macroprudential policy measures aimed at targeting the financial system as a whole were introduced. The second chapter looks at the relationship between monetary policy and financial stability, which has gained importance in recent years as Central Bank policy rates neared the zero-lower bound. We use an SVAR model to study the impact of monetary policy shocks on three proxies for financial stability as well as a proxy for economic growth. Monetary policy is represented by policy rates for the EMEs and shadow rates for the AEs in our chapter. Our main results show that monetary policy may be used to correct asset mispricing, to control fluctuations in the real business cycle and also to tame credit cycles in the majority of cases. Our results also show that for the majority of cases, in line with theory, local currencies appreciate following a positive monetary policy shock. Monetary policy intervention may indeed be successful in contributing to or achieving financial stability. However, the results show that monetary policy may not have the ability to maintain or re-establish financial stability in all cases. Alternative policy choices such as macroprudential policy tool frameworks which are aimed at targeting the financial system as a whole may be implemented as a means of fortifying the economy. The third chapter looks at the institutional setting of the countries in question, the independence of the Central Bank, the political environment and the impact of these factors on financial Abstract stability. I substantiate the literature review discussion with a brief empirical analysis of the effect of Central Bank Independence on credit growth using an existing database created by Romelli (2018). The empirical results show that there is a positive relationship between credit growth and the level of Central Bank Independence (CBI) due to the positive and statistically significant coefficient on the interaction term between growth in domestic credit to the private sector and the level of CBI. When considering domestic credit by deposit money banks and other financial institutions, the interaction term is positive and statistically significant for the case of the UK for the third regression equation. A number of robustness checks show that the coefficient is positive and statistically significant for a number of cases when implementing a variety of estimation methods. Fluctuations in credit growth are larger for higher levels of CBI and hence, in periods of financial instability or ultimately financial crises, CBI would be reined back in an effort to re-establish financial stability. Based on the empirical results, and in an effort to slow down surging credit supply and to maintain financial stability, policy makers and governmental authorities should attempt to decrease the level of CBI when the economy shows signs of overheating and credit supply continues to increase. The fourth chapter looks at the interaction between macroprudential policy and financial stability. The unexpected interconnectedness of the global economy and the economic blight that occurred as a result of this, recapitulated the need to implement an alternative policy framework aimed at targeting the financial system as a whole and hence, targeting the maintenance of financial stability. In this chapter, an index of domestic macroprudential policy tools is constructed and the effectiveness of these tools in controlling credit growth, managing GDP growth and stabilising inflation growth is studied using a dynamic panel data model for the period between 2000 and 2017. The empirical analysis includes two panels namely an EU panel of 27 countries and a Latin American panel of 7 countries, the chapter also looks at a case study of Japan, Portugal and the UK. Our main results find that a tighter macroprudential policy tool stance leads to a decrease in both credit growth and GDP growth while, a tighter macroprudential policy tool stance results in higher inflation in the majority of cases. Further, we find that capital openness plays a more important role in the case of Latin America, this may be due to the region’s dependence on foreign capital flows and exchange rate movements. Lastly, we find that, in times of higher perceived market volatility, GDP growth tends to be higher and inflation growth tends to be lower in the EU. In the other cases, higher levels of perceived market volatility result in higher inflation, higher credit growth and lower GDP Abstract growth. This is in line with expectations as an increase in perceived market volatility is met with an increased flow of assets into safer markets such as the EU. This thesis establishes a relationship between financial stability and monetary policy by studying the response of Chile, Colombia, Japan, Portugal and the UK in the aftermath of the GFC as Central Banks lowered policy rates in an effort to rescue their economies. In short, the results of the work conducted in this thesis may be summarised as follows. Our results show that monetary policy contributes to the achievement of financial stability. Still, monetary policy alone is not sufficient and should be reinforced by less traditional policy choices such as macroprudential policy tools. Secondly, we find that the level of CBI should be reined in in times of surging credit supply in an effort to maintain financial stability. Finally, we conclude that macroprudential policy tools play an important role in the achievement of financial stability. These tools should complement traditional monetary policy frameworks and should be adapted for each region.info:eu-repo/semantics/publishedVersio

    Event-based tracking of human hands

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    This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space)

    Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning

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    Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also capture a scenario signature. PIPS passively monitors SoOPs containing scenario signatures through a single receiver. Moreover, PIPS leverages the Dynamic Data Driven Applications System (DDDAS) framework to devise and customize the sampling frequency, enabling the system to use the most impacted frequency band as the rated frequency band. Various regression methods within three ensemble learning strategies are used to train and predict the receiver position. The PRF spectrum of 60 positions is collected in the experimental scenario, and three criteria are applied to evaluate the performance of PIPS. Experimental results show that the proposed PIPS possesses the advantages of high accuracy, configurability, and robustness.Comment: DDDAS 202

    TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation

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    Multi-modal fusion of sensors is a commonly used approach to enhance the performance of odometry estimation, which is also a fundamental module for mobile robots. However, the question of \textit{how to perform fusion among different modalities in a supervised sensor fusion odometry estimation task?} is still one of challenging issues remains. Some simple operations, such as element-wise summation and concatenation, are not capable of assigning adaptive attentional weights to incorporate different modalities efficiently, which make it difficult to achieve competitive odometry results. Recently, the Transformer architecture has shown potential for multi-modal fusion tasks, particularly in the domains of vision with language. In this work, we propose an end-to-end supervised Transformer-based LiDAR-Inertial fusion framework (namely TransFusionOdom) for odometry estimation. The multi-attention fusion module demonstrates different fusion approaches for homogeneous and heterogeneous modalities to address the overfitting problem that can arise from blindly increasing the complexity of the model. Additionally, to interpret the learning process of the Transformer-based multi-modal interactions, a general visualization approach is introduced to illustrate the interactions between modalities. Moreover, exhaustive ablation studies evaluate different multi-modal fusion strategies to verify the performance of the proposed fusion strategy. A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities. The quantitative and qualitative odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Glomalin Arbuscular Mycorrhizal Fungal Reproduction, Lifestyle and Dynamic Role in Global Sustainable Agriculture for Future Generation

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    Glomalin, a type of glycoprotein produced by arbuscular mycorrhizal fungi in the phylum Glomeromycota, contributes to the mitigation of soil degradation. Moreover, AM fungi and glomalin are highly correlated with other soil physico-chemical parameters and are sensitive to changes in the environment; also, they have been recommended for monitoring the recovery of degraded soil or stages of soil degradation. AM fungi are commonly known as bio-fertilisers. Moreover, it is widely believed that the inoculation of AM fungi provides tolerance to host plants against various stressful situations like heat, salinity, drought, metals and extreme temperatures. AM fungi, being natural root symbionts, provide essential plant inorganic nutrients to host plants, thereby improving growth and yield under unstressed and stressed regimes. The role of AM fungi as a bio-fertiliser can potentially strengthen plants’ adaptability to changing environment. They also improve plant resilience to plant diseases and root system development, allowing for better nutrient absorption from the soil. As a result, they can be utilised as both a biofertilizer and a biocontrol agent. Present manuscript represents the potential of AM fungi as biostimulants can probably strengthen plants’ ability to change the agriculture system for green technology

    QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches

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    Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor

    Epigenetics : a catalyst of plant immunity against pathogens

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    The plant immune system protects against pests and diseases. The recognition of stress-related molecular patterns triggers localised immune responses, which are often followed by longer-lasting systemic priming and/or up-regulation of defences. In some cases, this induced resistance (IR) can be transmitted to following generations. Such transgenerational IR is gradually reversed in the absence of stress at a rate that is proportional to the severity of disease experienced in previous generations. This review outlines the mechanisms by which epigenetic responses to pathogen infection shape the plant immune system across expanding time scales. We review the cis- and trans-acting mechanisms by which stress-inducible epigenetic changes at transposable elements (TEs) regulate genome-wide defence gene expression and draw particular attention to one regulatory model that is supported by recent evidence about the function of AGO1 and H2A.Z in transcriptional control of defence genes. Additionally, we explore how stress-induced mobilisation of epigenetically controlled TEs acts as a catalyst of Darwinian evolution by generating (epi)genetic diversity at environmentally responsive genes. This raises questions about the long-term evolutionary consequences of stress-induced diversification of the plant immune system in relation to the long-held dichotomy between Darwinian and Lamarckian evolution

    Tools for Rapid Detection and Control of Foodborne Microbial Pathogens

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    Foodborne illnesses have become more common over time, posing a major threat to human health around the world. Foodborne pathogens can be present in a variety of foods, and it is critical to detect them in order to ensure a safe food supply and prevent foodborne illnesses. Traditional methods for detecting foodborne pathogens are time-consuming and labor-intensive. As a result, a range of technologies for quick detection of foodborne pathogens have been developed, as it is necessary for many food analysis. Nucleic acid-based, biosensor-based, and immunological-based approaches are the three types of rapid detection methods. The ideas and use of modern quick technologies for the detection of foodborne bacterial infections are the focus of this chapter
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