QuPARA: Query-Driven Large-Scale Portfolio Aggregate Risk Analysis on MapReduce

Stochastic simulation techniques are used for portfolio risk analysis. Risk portfolios may consist of thousands of reinsurance contracts covering millions of insured locations. To quantify risk each portfolio must be evaluated in up to a million simulation trials, each capturing a different possible sequence of catastrophic events over the course of a contractual year. In this paper, we explore the design of a flexible framework for portfolio risk analysis that facilitates answering a rich variety of catastrophic risk queries. Rather than aggregating simulation data in order to produce a small set of high-level risk metrics efficiently (as is often done in production risk management systems), the focus here is on allowing the user to pose queries on unaggregated or partially aggregated data. The goal is to provide a flexible framework that can be used by analysts to answer a wide variety of unanticipated but natural ad hoc queries. Such detailed queries can help actuaries or underwriters to better understand the multiple dimensions (e.g., spatial correlation, seasonality, peril features, construction features, and financial terms) that can impact portfolio risk. We implemented a prototype system, called QuPARA (Query-Driven Large-Scale Portfolio Aggregate Risk Analysis), using Hadoop, which is Apache's implementation of the MapReduce paradigm. This allows the user to take advantage of large parallel compute servers in order to answer ad hoc risk analysis queries efficiently even on very large data sets typically encountered in practice. We describe the design and implementation of QuPARA and present experimental results that demonstrate its feasibility. A full portfolio risk analysis run consisting of a 1,000,000 trial simulation, with 1,000 events per trial, and 3,200 risk transfer contracts can be completed on a 16-node Hadoop cluster in just over 20 minutes.Comment: 9 pages, IEEE International Conference on Big Data (BigData), Santa Clara, USA, 201

Development and Validation of a HPLC-UV Method with Pre-column Derivatization for Determination of Cinnabar in Jufang Zhibao Pills

In this work, a reliable and accurate high-performance liquid chromatography method with pre-column derivatization was established and validated for determination of cinnabar in Jufang Zhibao pills. Scanning electron microscope (SEM) image was used to identify the types of cinnabar crude drug in Jufang Zhibao pills. The chromatography separation was performed on a Welch XB-C18 column (250 mm × 4.6 mm, 5 μm). The mobile phase consists of water spiked with 0.022 mmol/L sodium diethyldithiocarbamate (A, pH adjusted to 8–9 by ammonia water) and methanol (B, 80:20, v/v) at flow rate of 1.0 ml/min with the detected wavelength was 272 nm. The oven temperature was set at 35°C. The calibration for cinnabar content has good linearity (R2 =0.9999) over the range of 2.43–300 μg/ml and the average recovery was less then 1.90%. The limits of detection and quantification were 0.1127 μg and 0.2065 μg/ml. The results indicated that the proposed method has advantages of high accuracy, good repeatability and stability and can be successfully used for determination of cinnabar in Jufang Zhibao pills. It provides a basis for drug manufacture quality control and proves the feasibility of the pre-column derivatization method during the determination of cinnabar in Jufang Zhibao pills

Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance

Strong [O III] {\lambda}5007 Compact Galaxies Identified from SDSS DR16 and Their Scaling Relations

Green pea galaxies are a special class of star-forming compact galaxies with strong [O III]{\lambda}5007 and considered as analogs of high-redshift Ly{\alpha}-emitting galaxies and potential sources for cosmic reionization. In this paper, we identify 76 strong [O III]{\lambda}5007 compact galaxies at z < 0.35 from DR1613 of the Sloan Digital Sky Survey. These galaxies present relatively low stellar mass, high star formation rate, and low metallicity. Both star-forming main sequence relation (SFMS) and mass-metallicity relation (MZR) are investigated and compared with green pea and blueberry galaxies collected from literature. It is found that our strong [O III] {\lambda}5007 compact galaxies share common properties with those compact galaxies with extreme star formation and show distinct scaling relations in respect to those of normal star-forming galaxies at the same redshift. The slope of SFMS is higher, indicates that strong [O III]{\lambda}5007 compact galaxies might grow faster in stellar mass. The lower MZR implies that they may be less chemically evolved and hence on the early stage of star formation. A further environmental investigation confirms that they inhabit relatively low-density regions. Future largescale spectroscopic surveys will provide more details on their physical origin and evolution.Comment: 12 pages, 8 figures, 1 table. Published in A