Optimal dividend and reinsurance in the presence of two reinsurers

In this paper the optimal dividend (subject to transaction costs) and reinsurance (with two reinsurers) problem is studied in the limit diffusion setting. It is assumed that transaction costs and taxes are required when dividends occur, and that the premiums charged by two reinsurers are calculated according to the exponential premium principle with different parameters, which makes the stochastic control problem nonlinear. The objective of the insurer is to determine the optimal reinsurance and dividend policy so as to maximize the expected discounted dividends until ruin. The problem is formulated as a mixed classical-impulse stochastic control problem. Explicit expressions for the value function and the corresponding optimal strategy are obtained. Finally, a numerical example is presented to illustrate the impact of the parameters associated with the two reinsurers' premium principle on the optimal reinsurance strategy.postprin

Leaky Bucket-Inspired Power Output Smoothing with Load-Adaptive Algorithm

The renewables will constitute an important part of the future smart grid. As a result, the growing portion of renewable generation in the power grid will bring challenges to the operations of the power grid because of the fluctuation and intermittency properties of renewables. In order to make the operations of power grid stable and reliable, the power outputs from renewable energy sources must be smoothed. In this paper, we propose a scheme inspired from the idea of the leaky bucket mechanism for smoothing the power output from a renewable energy system. In our proposed method, the settings of energy storage size and power output level have significant effects on the system performance and thus needs to be determined. An optimization framework is thus proposed for storage and power output planning of the renewable energy system. To operate our proposed scheme practically, a load-adaptive power smoothing algorithm is devised aiming to match the power output level with the actual load in the grid. Our simulation studies show that the proposed algorithm can reduce the operation cost comparing to other algorithms and maintain high renewable energy utilization.postprin

The maximum of randomly weighted sums with long tails in insurance and finance

In risk theory we often encounter stochastic models containing randomly weighted sums. In these sums, each primary real-valued random variable, interpreted as the net loss during a reference period, is associated with a nonnegative random weight, interpreted as the corresponding stochastic discount factor to the origin. Therefore, a weighted sum of m terms, denoted as S m (w), represents the stochastic present value of aggregate net losses during the first m periods. Suppose that the primary random variables are independent of each other with long-tailed distributions and are independent of the random weights. We show conditions on the random weights under which the tail probability of max 1≤m≤n S m (w)-the maximum of the first n weighted sums-is asymptotically equivalent to that of S n (w)-the last weighted sum. © 2011 Copyright Taylor and Francis Group, LLC.postprin

A Novel Online Scheduling Algorithm for Hierarchical Vehicle-to-Grid System

SAC-SGC.1: Smart Grid Energy Managementpostprin

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

Joint unscented kalman filter for dual estimation in a bifilar pendulum for a small UAV

It has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining convergence. In this paper, a new real time solution to the model identification problem is provided with the use of a Joint Unscented Kalman Filter for dual estimation. The identification procedures can be easily implemented using a microcontroller, a gyroscope sensor, and a simple bifilar pendulum setup. Accuracy, robustness, and convergence speed are achieved.published_or_final_versio

Metabolic signaling directs the reciprocal lineage decisions of αβ and γδ T cells

Wiring metabolic signaling circuits in thymocytes Cell differentiation is often accompanied by metabolic changes. Yang et al. report that generation of double-positive (DP) thymocytes from double-negative (DN) cells coincides with dynamic regulation of glycolytic and oxidative metabolism. Given the central role of mechanistic target of rapamycin complex 1 (mTORC1) signaling in regulating metabolic changes, they examined the role of mTORC1 pathway in thymocyte development by conditionally deleting RAPTOR, the key component of the mTORC1 complex, in thymocytes. Loss of RAPTOR impaired the DN-to-DP transition, but unexpectedly also perturbed the balance between αβ and γδ T cells and promoted the generation of γδ T cells. Their studies highlight an unappreciated role for mTORC1-dependent metabolic changes in controlling thymocyte fates. The interaction between extrinsic factors and intrinsic signal strength governs thymocyte development, but the mechanisms linking them remain elusive. We report that mechanistic target of rapamycin complex 1 (mTORC1) couples microenvironmental cues with metabolic programs to orchestrate the reciprocal development of two fundamentally distinct T cell lineages, the αβ and γδ T cells. Developing thymocytes dynamically engage metabolic programs including glycolysis and oxidative phosphorylation, as well as mTORC1 signaling. Loss of RAPTOR-mediated mTORC1 activity impairs the development of αβ T cells but promotes γδ T cell generation, associated with disrupted metabolic remodeling of oxidative and glycolytic metabolism. Mechanistically, we identify mTORC1-dependent control of reactive oxygen species production as a key metabolic signal in mediating αβ and γδ T cell development, and perturbation of redox homeostasis impinges upon thymocyte fate decisions and mTORC1-associated phenotypes. Furthermore, single-cell RNA sequencing and genetic dissection reveal that mTORC1 links developmental signals from T cell receptors and NOTCH to coordinate metabolic activity and signal strength. Our results establish mTORC1-driven metabolic signaling as a decisive factor for reciprocal αβ and γδ T cell development and provide insight into metabolic control of cell signaling and fate decisions. Development of αβ and γδ T cells requires coupling of environmental signals with metabolic and redox regulation by mTORC1. Development of αβ and γδ T cells requires coupling of environmental signals with metabolic and redox regulation by mTORC1

Evaluation of the quality of care of a haemodialysis public-private partnership programme for patients with end-stage renal disease

published_or_final_versio

Identification of amino acid substitutions in mutated peptides of nucleoprotein from avian influenza virus

Nucleoprotein (NP), the structural component of ribonucleoprotein complex of avian influenza virus, performs multiple essential functions in the regulation of viral RNA synthesis and in the control of nuclear traffic of viral proteins. Mutations have often been found in NP, some of which are relevant to viral survival strategies. In this study, we used nanospray-MS/MS to analyze tryptic digestion of nucleoprotein of avian influenza virus (H5N1) and to identify three mutated peptides. The MS/MS analyses allowed the confident determination of the three mutated amino acid residues F313Y, I194V and V408I/L in the mutated peptides of LLQNSQVYSLIRPNENPAHK, GVGTMVMELVR and ASAGQI/LSVQPTFSVQR, respectively. © 2009 Elsevier B.V. All rights reserved.postprin