‘Tipping points’ in the climate system are characterised by a nonlinear response to gradual forcing, and may have severe and wide-ranging impacts. One of the best ways to identify and potentially predict threshold behaviour in the climate system is through analysis of palaeoclimate records. It has been suggested that early warning signals occur on the approach to a tipping point, generated from characteristic fluctuations in a time series as a system loses stability. Although early warning signals have been found in climate models and high-resolution marine and ice core palaeodata, studies from terrestrial records are lacking. In this study, a number of Pleistocene terrestrial records were selected to represent a range of regions strongly influenced by different climate modes which are thought to be capable of displaying threshold behaviour. These records included lake sediments from the North Atlantic, tree-rings from the South Pacific, a Chinese speleothem and were complemented by a new Greenland ice core chronology. Recently developed methods to detect signals of ‘critical slowing down’, ‘flickering’, and stability changes on the approach to a tipping point were utilised. Specific methodological issues arising from analysing palaeoclimate data were also investigated using a simple bifurcation model. A number of key criteria were found to be necessary for the reliable identification of early warning signals in palaeoclimate records, most crucially, the need for a low-noise record of sufficient data length, resolution and accuracy. Analysis of a Chinese speleothem identified the East Asian Summer Monsoon as an important climate ‘tipping element’, which may display a cascade of impacts. However, in some cases where early warning signals may fail, a deeper understanding of the underlying system dynamics is required to inform the development of more robust system-specific indicators. This was exemplified by the analysis of an abrupt, centennial-duration shutdown recorded during the Younger Dryas Chronozone in New Zealand, which demonstrated no slowing down, consistent with a freshwater pulse into the Southern Ocean. This study demonstrates that time series precursors from palaeoclimate archives provide a means of useful forewarning of many potential climate tipping points.

<p>This paper presents a method for recognition of flame types coming from different kinds of fuel through analysis of flame images. Accurate detection of fire alarm and achievement of early warning is positive development for cities fire safety. Image-based fire flame detection technology is a new effective way to achieve early warning through the early fire flame detection. Different fuel combustion in air it the basic of basis to recognize the type of flame. The application built up by using generic color model and the techniques of image analysis.</p>

This thesis involves the design of an early warning source water monitoring station for a riverine source of drinking water. These stations provide downstream water utilities with advanced notification of contamination events so they have time in which to implement a response. Many threats facing riverine water supplies, such as accidental spills, are uncertain in nature. Therefore, designing a monitoring station for the detection of these events requires a probabilistic modelling approach. Sources of uncertainty considered in this research include the location, mass and duration of a spill event as well as the flow at the time of the spill and the water quality model parameters. Probability distributions for each of these uncertainties were defined and a Monte Carlo experiment was conducted. The design objectives include maximizing the probability of detection and maximizing the probability of having a threshold amount of warning time. These objectives are in conflict with each other because the probability of detection improves as the station moves closer to the intake and the amount of warning time increases as the station is located further upstream. Values for the competing objectives were calculated for a number of potential monitoring station locations at multiple sample intervals and the tradeoff solutions were analyzed. This methodology was applied to the Hidden Valley Intake which services the Regional Municipality of Waterloo’s Mannheim Water Treatment Plant. The Hidden Valley Intake is located in Kitchener, Ontario and withdraws up to 72 ML of water per day from the Grand River. Based on an analysis of the Monte Carlo simulation results for the case study application, it was found that locating the monitoring station near the Victoria Street Bridge, approximately 11 km upstream of the intake, represents the best tradeoff in the design objectives. Sampling at least once per hour is recommended to increase the amount of warning time. The impact of various sources of uncertainty was also explored in this thesis. It was found that the flow at the time of a spill and the spill location are the only sources of uncertainty that significantly impact the probability distributions of relevant model results.

This thesis involves the design of an early warning source water monitoring station for a riverine source of drinking water. These stations provide downstream water utilities with advanced notification of contamination events so they have time in which to implement a response. Many threats facing riverine water supplies, such as accidental spills, are uncertain in nature. Therefore, designing a monitoring station for the detection of these events requires a probabilistic modelling approach. Sources of uncertainty considered in this research include the location, mass and duration of a spill event as well as the flow at the time of the spill and the water quality model parameters. Probability distributions for each of these uncertainties were defined and a Monte Carlo experiment was conducted. The design objectives include maximizing the probability of detection and maximizing the probability of having a threshold amount of warning time. These objectives are in conflict with each other because the probability of detection improves as the station moves closer to the intake and the amount of warning time increases as the station is located further upstream. Values for the competing objectives were calculated for a number of potential monitoring station locations at multiple sample intervals and the tradeoff solutions were analyzed. This methodology was applied to the Hidden Valley Intake which services the Regional Municipality of Waterloo’s Mannheim Water Treatment Plant. The Hidden Valley Intake is located in Kitchener, Ontario and withdraws up to 72 ML of water per day from the Grand River. Based on an analysis of the Monte Carlo simulation results for the case study application, it was found that locating the monitoring station near the Victoria Street Bridge, approximately 11 km upstream of the intake, represents the best tradeoff in the design objectives. Sampling at least once per hour is recommended to increase the amount of warning time. The impact of various sources of uncertainty was also explored in this thesis. It was found that the flow at the time of a spill and the spill location are the only sources of uncertainty that significantly impact the probability distributions of relevant model results.

There are several methods to predict financial crises. There are also several types of indicators used by financial institutions. These indicators, which are estimated in different ways, often show various developments, although it is not possible to directly assess which is the most suitable. Here, we still try to find what characteristics that industry group has and forecast financial crises In this paper, our data started from monthly of 1977 January to 2008 December in S&P100. We consider Fama-French and Cluster Analysis to process data to make data with same characteristic within a group. Then, we use GARCH type models and apply it to VaR predicting stock turmoil. In conclusion, we found that the group which has high kurtosis value is the key factor for predicting stock crises instead of volatility. Moreover, the characteristics of this industry which can predict stock crises is a great scale. On the other hand, we can through this model to double check the reaction for anticipating. Therefore, people can do some actions to control risk to reduce the loss.

Due to the financial turmoil in recent years, risk management has become an important issue, investors would like to be fully-prepared to cope with financial crisis before it happen. This research uses the Fama and French three-factor and the U.S. Dollar Index (USDX) as an exchange rate variations indicator to capture the international relations. It constitutes a four-factor model to analyze the S&P100 stock returns changes, and we introduce the skewed-t distribution to simulate the distribution of stock returns and capture the characteristics of skewness and kurtosis. We use cluster analysis to cluster the sample companies by their risk characteristics. And then we observe the explanatory power of each risk factor. The study shows that the S&P100 stocks are subjected to the market premium, and the scale effect is smaller than others. ¡@¡@ At last, in accordance with the GARCH-Skewed-t model to simulate the average, variance, skewness and kurtosis of each cluster. We track the long-term performance of each parameter which are used to observe the unusual changes before financial crisis. The empirical results show that the skewness parameter has perfect warning for financial turmoil. The cluster with warning ability is affected by B/M ratio effect and exchange rate changes. Among the case, the cluster has the best early warning effect when it's influenced by the exchange rate indicator. It displays that by adding an exchange rate risk indicator into the multi-factor model, we will have a better clustering result. It means that the skewness parameter of cluster with influence of exchange rate indicator can be used to observe financial turmoil, which can in turns, be used as an early warning system to determine the occurrence of extreme events.

In recent year, the securities firms had suffered form the turmoil of the financial crisis in Taiwan. Although the Taiwan Stock Exchange Corporation and the Securities and Futures Commission have their own early warning systems (EWS), the EWS based on financial statements and the "capital adequacy ratio", respectively for the risks that the brokers and dealers assume, still have some defects: (1) EWS based on financial statements are static and time-lagged in the rapid-moving market, and (2) the calculation rules in the capital adequacy ratio are inelastic and inefficient. This research emphasizes on the stock positions of the dealers, and calculate the "Value at Risk" (VaR) for these positions. In this way, we hope to know whether the EWS based on VaR can detect the risks of the dealers in time, and improve the drawbacks of the EWS based on financial statements and capital adequacy ratio. We found that: (1) the EWS based on VaR can effectively reflect the market risk of the dealers, and (2) the "historical simulation" method might distort the real portfolio risk, thus we suggest that "delta-normal" is a better method, and (3) the EWS based on VaR can discriminate the risk level of different securities dealers. In conclusion, we have the suggestion of the EWS for securities firms in the future. For firm-wide operation, the EWS based on financial statements is suitable; for the credit risks the securities firms may assume, the capital adequacy ratio is better; as for the market risk of the positions, VaR, undoubtedly, is a good alternative.

Intrastate conflicts in Sub-Saharan Africa are a development tragedy and a security dilemma that requires more prevention and better intervention from the international community. Such engagement necessitates a robust early warning system, which can determine, with a sufficient degree of accuracy, the countries most at risk of experiencing intrastate conflict. This research summarizes and critiques current efforts to conceptualize intrastate conflict in Sub-Saharan Africa and determine what factors best explain the likelihood of intrastate conflict onset. The research examines the challenges of empirically modelling the human behaviour that underlies intrastate conflict, as well as some promising avenues for overcoming challenges posed by data issues and existing methodological shortcomings. The research concludes that with improved data and research design, and more attention being paid to how statistical significance reflects pathway(s) to violence, the development of an intrastate conflict early warning system is possible.

The term ‘tipping event’ is used to describe a certain class of phenomena as observed in many different fields of science. It refers to an event where a gradual change of external forcing causes a sudden, large, often unwanted, transition to the state of the system. Some examples of known tipping events in science include: Arctic sea ice melting (climate), epileptic seizures (biology), collapse of ecosystems and populations (ecology) and market crashes (finance). Three mathematical mechanisms for tipping events have been proposed in the literature: bifurcation-, noise- or rate-induced tipping. Recent research has focused on developing early-warning indicators to potentially offer forewarning, which can extract from output time series whether the external forcing approaches a critical level at which tipping occurs. Two commonly used early-warning indicators are an increase of autocorrelation and variance in the time series data for the system’s output. The theory behind the presence of these indicators is the loss of stability of the system’s current state known as ‘critical slowing down’ for the approach of a bifurcation-induced tipping. Rate-induced tipping occurs when the external forcing reaches a critical rate instead of level. For rate-induced tipping there is no loss of stability of the system’s current state and therefore it is not clear if the early-warning indicators should exist. In this thesis we investigate the presence of early-warning indicators for models that show rate-induced tipping with additive noise. We also explore a technique for determining the most likely time of tipping using optimal paths for escape. Research has mainly focussed on testing the early-warning indicators for examples of known tipping events in the past. The ultimate aim of early-warning indicators would be to have the ability to predict future tipping events. Using the early-warning indicators in isolation is susceptible to incurring false alarms and missed alarms. We present a method for approximating the probability of experiencing rate-induced tipping with noise for slow to moderate drift speeds.

In the tumultuous political climate following Brexit, the European integration lives on borrowed time. With the ever-increasing need for democratic legitimacy on the EU stage, the ‘Early Warning Mechanism’ is viewed by many as the last salvation. Since its introduction to the EU with the Treaty of Lisbon of 2007, it has been used to trigger a subsidiarity test three times, issuing so called ‘yellow cards’ to the Commission, and forcing it to review its proposal on grounds of subsidiarity. However, the Commission has ruled in favor of itself at every instance, making both the EU and the Member States question its efficiency. By presenting the three yellow cards, this study aims to scrutinize the Commission’s and the Member States’ involvement in the EWM while discussing their views on subsidiarity as a whole.