An LMI projection approach for H-inf optimal model reduction and invariance of robust stability margin

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The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

Comparative study on corpus development for Malay investment fraud detection in website

In the online world, fraudsterscaneasily manipulate people to gain something and usually formonetary gain. Corpus development research can be use identify keywords used by fraudsters online to prevent the crime. The aim of this research is to develop a corpus for Malay investment fraud so that it can be used in detection and classification of investment fraud in Malay website and compare the most suitable technique. In this research, Part-of-Speech tagger (POS) and Named Entity Recognition (NER) tagger are selected. Proposedmethodology that are used in this research is corpus development, training and development of dataset using Naïve Bayes and performance evaluation. The dataset used in this research is online news archive and discussion forums. This research able to help the law enforcements agencies in collecting and notifying the keyword used by fraudsters so that they can take anylegal actions.Keywords: corpus development; information extraction; part recognition; fraud detection

On H-inf optimal model reduction and invariance of robust stability margin for descriptor systems

The H8 optimal model reduction problem is that of given a complex transfer function Gdes of order n and finding a stable reduced order transfer function Gr of order r with r <n such that the approximation error || Gdes - Gr ||H8 is as small as possible

Joint order and dependency reduction for LPV state-space models

In the reduction of Linear Parameter-Varying (LPV) models, decreasing model complexity is not only limited to model order reduction (state reduction), but also to the simplification of the dependency of the model on the scheduling variable. This is due to the fact that the concept of minimality is strongly connected to both types of complexities. While order reduction of LPV models has been deeply studied in the literature resulting in the extension of various reduction approaches of the LTI system theory, reduction of the scheduling dependency still remains to be a largely open problem. In this paper, a model reduction method for LPV state-space models is proposed which achieves both state-order and scheduling dependency reduction. The introduced approach is based on an LPV Ho-Kalman algorithm via imposing a sparsity expectation on the extended Hankel matrix of the model to be reduced. This sparsity is realized by an L1-norm based minimization of a priori selected set of dependencies associated sub-Markov parameters. The performance of the proposed method is demonstrated via a representative simulation exampl

Handling risk of uncertainty in model-based production optimization: a robust hierarchical approach

Model-based economic optimization of oil production suffers from high levels of uncertainty. The limited knowledge of reservoir model parameters and varying economic conditions are the main contributors of uncertainty. The negative impact of these uncertainties on production strategy increases and becomes profound with time. In this work, a multi-objective optimization problem is formulated which considers both economic and model uncertainties and aims to mitigate the negative effects i.e., risk of these uncertainties on the production strategy. The improved robustness is achieved without heavily compromising the primary objective of economic life-cycle performance. An ensemble of varying oil price scenarios and geological model realizations are used to characterize the economic and geological uncertainty space respectively. The primary objective is an average NPV over these ensembles. As the risk of uncertainty increases with time, the secondary objective is aimed at maximizing the speed of oil production to mitigate risk. This multi-objective optimization is implemented separately with both forms of uncertainty in a hierarchical or lexicographic way

Model and economic uncertainties in balancing short-term and long-term objectives in water-flooding optimization.

Model-based optimization of oil production has a significant scope to increase ultimate recovery or financial life-cycle performance. The Net Present Value (NPV) objective in such an optimization framework, because of its nature, focuses on the long-term gains while the short-term production is not explicitly addressed. At the same time the achievable NPV is highly uncertain due to the limited knowledge of reservoir model parameters and varying economic conditions. Different (ad-hoc) methods have been proposed to introduce short-term considerations to balance short-term and long-term objectives in a model-based approach. In this work, we address the question whether through an explicit handling of model and economic uncertainties in NPV (robust) optimization, an appropriate balance between these economic objectives is naturally obtained. A set (ensemble) of possible realizations of the reservoir models is considered as a discretized approximation of the uncertainty space, while different oil price scenarios are considered to characterize the economic uncertainty. A gradient-based optimization procedure is used where the gradient information is computed by solving adjoint equations. A robust optimization framework with an average NPV with respect to the ensemble of models and the oil price scenarios is formulated and the NPV build-up over time is studied. As robust optimization (RO) does not attempt to reduce the sensitivity of the solution to uncertainty, a mean-variance optimization (MVO) approach is implemented which maximizes the average NPV and minimizes the variance of the NPV distribution. It is shown by simulation examples that with RO, the average NPV is increased compared to the reactive strategy, with both forms of uncertainty. However, an NPV build-up over time that is considerably slower than for a reactive strategy is obtained. A faster NPV build-up compared to RO is achieved in MVO by choosing different weightings on variance in the mean-variance objective, at the price of slightly compromising on the long-term gains