Analysis of risk bounds in partially specified additive factor models

The study of worst case scenarios for risk measures (e.g. the Value at Risk) when the underlying risk vector (or portfolio of risks) is not completely specified is a central topic in the literature on robust risk measurement. In this paper we discuss partially specified factor models as introduced in Bernard et al. (2017) in more detail for the class of additive factor models which admit more explicit results. These results allow to describe in more detail the reduction of risk bounds obtainable by this method in dependence on the degree of positive resp. negative dependence induced by the systematic risk factors. The insight may help in applications of this reduction method to get a better qualitative impression on the range of influence of the partially specified factor structure.     

How vector feeding preference through an infectious host relates to the seasonal transmission rates in a mathematical vector‐host model

Vector‐borne diseases, such as leishmaniasis, dengue, malaria, and yellow fever, transmitted by microparasites show periodic fluctuations in their prevalence. The novelty of this research is to assess the relationship between the vector feeding preference for an infectious host and the annual seasonal transmission through a vector‐host mathematical model. For the first time, numerical simulations illustrate that by increasing the vector feeding preference value in the transmission dynamics, periodic fluctuations accentuate and the endemic equilibrium average increases in vector and host populations. Moreover, increasing the vector feeding preference value, the amplitude strengthens for the infectious host and vector populations. This periodic behavior shows a similar pattern with the Peruvian incidence data from 2000 to 2016 for Andean cutaneous leishmaniasis provided by the Ministry of Health of Peru (MINSA). In addition, using the Floquet theory, the time average method and the linear operator method provides for the first time that the basic reproduction number for a nonautonomous system depends explicitly on the vector feeding preference for the infectious host. The nonautonomous model system shows that is a threshold parameter for the local stability of the disease‐free periodic solution. Therefore, the vector feeding preference is an important factor that should be considered and attended to for future research. Public and veterinary health in Peru and other countries should consider the vector feeding preference for specific host to vector control.     

Allostatic load as a complex clinical construct: A case‐based computational modeling approach

Allostatic load (AL) is a complex clinical construct, providing a unique window into the cumulative impact of stress. However, due to its inherent complexity, AL presents two major measurement challenges to conventional statistical modeling (the field's dominant methodology): it is comprised of a complex causal network of bioallostatic systems, represented by an even larger set of dynamic biomarkers; and, it is situated within a web of antecedent socioecological systems, linking AL to differences in health outcomes and disparities. To address these challenges, we employed case‐based computational modeling (CBM), which allowed us to make four advances: (1) we developed a multisystem, 7‐factor (20 biomarker) model of AL's network of allostatic systems; (2) used it to create a catalog of nine different clinical AL profiles (causal pathways); (3) linked each clinical profile to a typology of 23 health outcomes; and (4) explored our results (post hoc) as a function of gender, a key socioecological factor. In terms of highlights, (a) the Healthy clinical profile had few health risks; (b) the pro‐inflammatory profile linked to high blood pressure and diabetes; (c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems; and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%); while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles. © 2015 Wiley Periodicals, Inc. Complexity 21: 291–306, 2016     

微量元素与脑卒中(2)

脑卒中是目前中国最重要的公共卫生问题,正以其高发病率、高死亡率对我国造成巨大的社会、经济困难。从脑卒中的流行病学特征、脑卒中发病的危险因素、脑卒中患者的微量元素失衡及微量元素检测的临床意义四个方面阐述了微量元素与脑卒中的关系。     

A non-Gaussian Ornstein–Uhlenbeck model for pricing wind power futures

The recent introduction of wind power futures written on the German wind power production index has brought with it new interesting challenges in terms of modelling and pricing. Some particularities of this product are the strong seasonal component embedded in the underlying, the fact that the wind index is bounded from both above and below and also that the futures are settled against a synthetically generated spot index. Here, we consider the non-Gaussian Ornstein–Uhlenbeck type processes proposed by Barndorff-Nielsen and Shephard in the context of modelling the wind power production index. We discuss the properties of the model and estimation of the model parameters. Further, the model allows for an analytical formula for pricing wind power futures. We provide an empirical study, where the model is calibrated to 37 years of German wind power production index that is synthetically generated assuming a constant level of installed capacity. Also, based on 1 year of observed prices for wind power futures with different delivery periods, we study the market price of risk. Generally, we find a negative risk premium whose magnitude decreases as the length of the delivery period increases. To further demonstrate the benefits of our proposed model, we address the pricing of European options written on wind power futures, which can be achieved through Fourier techniques.     

Large‐scale Experimental Study on the Effects of Flooring Materials on Combustion Behavior of Thermoplastics

The effects of flooring materials on the combustion behavior of thermoplastics is investigated. Based on the ISO 9705 fire test setup, an experimental rig was designed. Full‐scale experiments of PP combustion were carried out using five flooring boards, namely gypsum, steel, wood, ceramic tile and PVC. The experimental results indicate that the flooring boards play an important role in the heat release rates of typical thermoplastics combustion. Specifically, the time for the sharp increase of heat release rate is generally later for the flooring boards with larger thermal conductivity, except for the case of PVC. Preliminary analyses suggest that the reason for the exception of PVC is the expansion and carbonization of PVC at high temperature. In addition, experimental results also show that the corresponding peak heat release rate of thermoplastics combustion would be generally smaller for the flooring board with a larger thermal mass, except for the case of gypsum. The primary cause for the exception of gypsum may be the heat absorption by the crystal water released from the gypsum during the burning of hot pool oil.     

Mathematical Modeling and Simulation of Corrosion Processes in Nigerian Crude Oil Pipelines

In this work, a corrosion prediction mathematical model for risk assessment in oil and gas production and transportation facilities has been created. This work focuses on partial pressure of carbon (iv) oxide, CO₂ and the operating temperature in process equipment and transportation facility pipes as a function of corrosion rate. The model equation formulated was based on the principle of multiple linear regressions of data. The final model representing the corrosion rate of crude oil equipment was obtained CR = b _o + b ₁ T + b ₂ P (CO ₂). The model was simulated using polymath software. The correlation between the experimental and simulated resulted obtained using root mean square deviation (coefficient of determination) was 99.74% which is high, suggesting that the relationship between the predictor and response variables is linear. The variation in the model equation is 0.0066374. This low value of the variance shows that the model is accurate.     

Levels of Pesticide Residues in Food: Evaluation of Data from Total Diet Studies in Italy

Abstract

Total Diet Studies on pesticide residues in foods carried out in Italy in the last two decades are briefly summarized and data are discussed. Health risk assessment is expressed by the ratio total intake/ADI (%ADI ingested) for each compound and by the sum of the percentages of ADI for each compound within the same class of pesticides.

The total dietary intake of chlorinated pesticides, that was almost 100% of ADI in the years 1970-74, decreased down to 10% in the period 1978-84. This trend was confirmed for DDT in recent years, while data on Lindane and Heptachlor seem to be constant.

As regards the organophosphorus pesticides the sum of the percentages of ADI ingested for each compound, extrapolated from recent data (1990-1991) is about 20% and can be regarded as reasonably acceptable because the study included practically all the mainly used compounds.

Only few data are available for some pesticides like dithiocarbamates, especially EBDCs and their derivatives (e.g. ETU), other carbamates (e.g. aldicarb), paraquat etc. Moreover, analytical methods for these compounds should be improved.

The need for a considerable improvement in the number and organization of monitoring structures, in the use of standardized analytical procedures, in good laboratory practice standards and in the realibility of “monitoring protocols” and their homogeneity is evidenced.     

The long‐term extreme price risk measure of portfolio in inventory financing: An application to dynamic impawn rate interval

Different from the short‐term risk measure for traditional financial assets (stocks, bonds, etc.), the key to illiquid inventory portfolio traded in the over‐the‐counter markets is to estimate the long‐term extreme price risk with time varying volatility. In this article, a new long‐term extreme price risk (value at risk and conditional value at risk) measure method for inventory portfolio and an application to dynamic impawn rate interval are proposed. To realize this, we first establish AutoRegressive Moving Average‐Exponential Generalized Autoregressive Conditional Heteroskedasticity‐Extreme Value Theory model and multivariatet‐Copula to depict the autocorrelation, fat tails, and volatility clustering of returns of inventories and the nonlinear dependence structure of inventories. Furthermore, we obtain the long‐term extreme price risk with time varying volatility via Monte Carlo simulation instead of square‐root‐of time rule. The results show that, first, benefits from risk diversification is significant; second, long‐term extreme price risk measure of inventory portfolio via Monte Carlo method outperforms the square‐root‐of time rule; the last is that the dynamic rate interval based on the long‐term price risk is superior to the crude rules of thumb in terms of reducing efficiency loss and improving risk coverage. In summary, this article provides a new quantitative framework for managing the risk of portfolio in inventory financing practice for banks constrained by risk limitation. © 2014 Wiley Periodicals, Inc. Complexity 20: 17–34, 2015