Combined analysis of unique and repetitive events in quantitative risk assessment

For risk assessment to be a relevant tool in the study of any type of system or activity, it needs to be based on a framework that allows for jointly analyzing both unique and repetitive events. Separately, unique events may be handled by predictive probability assignments on the events, and repetitive events with unknown/uncertain frequencies are typically handled by the probability of frequency (or Bayesian) approach. Regardless of the nature of the events involved, there may be a problem with imprecision in the probability assignments. Several uncertainty representations with the interpretation of lower and upper probability have been developed for reflecting such imprecision. In particular, several methods exist for jointly propagating precise and imprecise probabilistic input in the probability of frequency setting. In the present position paper we outline a framework for the combined analysis of unique and repetitive events in quantitative risk assessment using both precise and imprecise probability. In particular, we extend an existing method for jointly propagating probabilistic and possibilistic input by relaxing the assumption that all events involved have frequentist probabilities; instead we assume that frequentist probabilities may be introduced for some but not all events involved, i.e. some events are assumed to be unique and require predictive – possibly imprecise – probabilistic assignments, i.e. subjective probability assignments on the unique events without introducing underlying frequentist probabilities for these. A numerical example related to environmental risk assessment of the drilling of an oil well is included to illustrate the application of the resulting method.     

Extraction of parabens by melamine sponge with determination by high-performance liquid chromatography

In the present study, we propose a novel method for the extraction of parabens in personal care products. A new, simple adsorptive material was obtained by combining metal-organic frameworks and melamine sponges using the adhesive property of polyvinylidene fluoride. This new material, metal-organic frameworks/melamine sponges, was found to be particularly suitable for solid-phase extraction. The structural characteristics of metal-organic frameworks/melamine sponges were first analyzed by scanning electron microscopy. Subsequently, solid-phase extraction was performed on sample solutions, and the extracted substances were then analyzed by high-performance liquid chromatography. Following optimization of important experimental conditions, excellent recovery rates were obtained. Our novel method was then applied to the extraction of four parabens (methylparahydroxybenzoates, ethylparahydroxybenzoates, propylparahydroxybenzoates, and butylparahydroxybenzoates) from real samples. The results yielded limits of detection of 0.26–0.41 ng/mL. The inter- and intra-day recoveries were 104.0–109.7% and 91.2–98.1%, respectively (relative standard deviation, <13.8%).     

云南东川区域重金属环境暴露健康风险评价

以云南省昆明市东川主要河流及附近土壤、植物为研究对象,对其进行采样分析,并根据美国环境保护署(U.S.EPA)推荐健康风险评价方法对东川流域多种环境因素进行了重金属的健康风险评价。结果表明,水环境中致癌物质造成的风险最大,风险值在2.35×10^(-4)~8.27×10^(-4),达到U.S.EPA推荐的最大值10^(-4),植物根部的健康风险值为0.40×10^(-1)~7.00×10^(-1),茎叶部分的风险值为2.42×10^(-2)~15.24×10^(-2),根部存在的重金属对人体的健康风险约为茎叶部分的5倍;土壤中非致癌物质的健康风险影响较大,总健康风险值区间为0.64×10^(-1)~3.83×10^(-1)。评价结果可为该区域地表水、土壤和植物资源的污染治理、保护开发提供依据。     

Residue behaviour and safety evaluation of fenaminstrobin in peanut field ecosystems

A rapid, sensitive, and selective method using a quick, easy, cheap, effective, rugged, and safe procedure in combination with high-performance liquid chromatography and tandem mass spectrometry was developed for the analysis of fenaminstrobin in peanut and soil. The average recoveries in all samples fall within 88.1%–10%, having relative standard deviations of 2.5%–14%. The limits of quantitation of fenaminstrobin in peanut shell, peanut kernels, peanut plant, and soil were 0.005, 0.004, 0.01, and 0.002 mg kg^?1, respectively. The field trial results show that the half-lives of fenaminstrobin in peanut plant and soil are 1.3–10 and 5.5–20 days, respectively. Residues in peanut kernels were found to be present at <0.004 mg kg^?1 levels, based on good agricultural practices recommended by the manufacturer. The risk posed by fenaminstrobin exposure at the recommended dosage is negligible to humans, depending on the risk quotient.     

Applying green analytical chemistry for development and validation of RP-HPLC stability indicating assay method for estimation of fenoverine in bulk and dosage form using quality by design approach

A green and robust reverse-phase liquid chromatographic method has been developed for the determination of fenoverine (FEN), by applying combined principles of green analytical chemistry and quality by design approaches on a Spherisorb C18 column (150?×?4.6?mm, 3?µm) with UV detection at 262?nm. A two level fractional factorial design (2^^7-3) Res IV was used for screening of influential chromatographic factors. The critical method parameters actively affecting critical quality attributes (CQAs) were identified and further optimized using Box–Behnken design. The predicted optimum assay conditions comprised of methanol and ammonium acetate buffer 20?mM, in an extent of 81:19% v/v individually having a flow rate of 1.0?mL/min with a column oven temperature of 33°C. The drug was stressed in hydrolytic, oxidative, reductive, thermal, and photolytic conditions. The developed method was validated successfully. The detector response was linear in the concentration of 0.5–160?µg/mL with a limit of detection (LOD) and limit of quantitation (LOQ) as 0.1 and 0.3?µg/mL, respectively. The % recovery was found to be 99.7%. The analytical method volume intensity value for developed method was 45?mL and the environment assessment tool (EAT) score was 41.07. The method is simple, environmentally benign, rapid, and robust for the determination of FEN in bulk and in its dosage form.     

Gompertz zero-inflated cure rate regression models applied to credit risk data

In the financial market, it is important to consider that there is a proportion of customers that have settled their debt in time zero, immediately recovering their ability to pay. In this context, in this paper, we propose a survival analysis methodology that allows the insertion of times equal to zero in scenarios where credit risk is observed. The proposed model addresses the survival analysis model of the zero-inflated cure rate which incorporates the heterogeneity of three subgroups (individuals having events in the initial time, and individuals not susceptible and susceptible to the event). In our proposal, all available survival data of customers are modeled considering that the number of competitive causes follows a Poisson distribution and the baseline risk function follows a Gompertz distribution. The model parameter estimation is obtained by the maximum likelihood estimation procedure and simulation studies are conducted to evaluate the estimators' performance. The studied methodology will be applied to a credit database provided by a financial institution in Brazil.     

Multivariate dynamic modeling for Bayesian forecasting of business revenue

Forecasting enterprise-wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on model developments to address these challenges for forecasting in a large-scale retail company. Focused on multivariate revenue forecasting across collections of supermarkets and product categories, hierarchical dynamic models are natural: these are able to couple revenue streams in an integrated forecasting model, while allowing conditional decoupling to enable relevant and sensitive analysis together with scalable computation. Structured models exploit multi-scale modeling to cascade information on price and promotion activities as predictors relevant across categories and groups of stores. With a context-relevant focus on forecasting revenue 12 weeks ahead, the study highlights product categories that benefit from multi-scale information, defines insights into when, how, and why multivariate models improve forecast accuracy, and shows how cross-category dependencies can relate to promotion decisions in one category impacting others. Bayesian modeling developments underlying the case study are accessible in custom code for interested readers.     

光伏太阳能电池生产过程中的污染问题

日益壮大的光伏产业正在为人类发展作出积极的贡献.采用光伏发电来代替传统的发电技术,将会减少89%的空气污染.然而,在这个倡导绿色环境的时代,光伏器件生产引起的环境、健康和安全（EHS）问题也日益引起社会各界的广泛关注.认清光伏产品在生产过程中污染的真相对维持光伏产业的可持续发展有着相当重要的意义.本文综述了常见太阳能电池的EHS问题,并提出了可以减少污染的有效途径;采用生命周期评估（LCA）方法进一步比较了各种太阳能电池生产过程中污染物的排放量数据.