基于数字图像比色法测定水中氟化物

在酸性溶液中,氟能破坏二甲酚橙〖CD*2〗锆显色体系,使溶液颜色从紫红色变为橙色或黄色,通过采集图像颜色信息构建3个特征颜色分量的多元线性回归模型,用以定量测定水中氟。该方法在0 mg/L~1.5 mg/L范围内线性良好,方法检出限为0.03 mg/L。两个质量浓度水平的氟化物标准溶液6次测定结果的RSD分别为1.4%和1.3%,相对误差分别为0.3%和1.7%。     

An extension of Multiple Greek Letter method for common cause failures modelling

Common cause failure describes a condition where several components share the same source of failure that causes them to fail or become unavailable simultaneously. The objective of this paper is to present an improved approach to common cause failure modelling within reliability analyses. The currently used methods allow one component to share common characteristics with only one group of components, which may be affected by the same source of failure. Therefore, an improved method was developed, where components can be assigned to several groups of components that are susceptible to faulty operation with respect to their similar characteristics. A mathematical derivation of the method is presented and the theory is applied to smaller theoretical samples and to a simplified real example. The results show that the new method enables a more detailed reliability analysis. The results prove that consideration of common cause failures using the improved method may decrease the system reliability compared to traditional common cause failure consideration. The system reliability decreases more, if the redundant components have more similarities and are therefore assigned to several common cause failure groups.     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

The evolution of Müllerian mimicry

It is now 130 years since Fritz Müller proposed an evolutionary explanation for the close similarity of co-existing unpalatable prey species, a phenomenon now known as Müllerian mimicry. Müller's hypothesis was that unpalatable species evolve a similar appearance to reduce the mortality involved in training predators to avoid them, and he backed up his arguments with a mathematical model in which predators attack a fixed number (n) of each distinct unpalatable type in a given season before avoiding them. Here, I review what has since been discovered about Müllerian mimicry and consider in particular its relationship to other forms of mimicry. Müller's specific model of associative learning involving a "fixed n" in a given season has not been supported, and several experiments now suggest that two distinct unpalatable prey types may be just as easy to learn to avoid as one. Nevertheless, Müller's general insight that novel unpalatable forms have higher mortality than common unpalatable forms as a result of predation has been well supported by field experiments. From its inception, there has been a heated debate over the nature of the relationship between Müllerian co-mimics that differ in their level of defence. There is now a growing awareness that this relationship can be mediated by many factors, including synergistic effects between co-mimics that differ in their mode of defence, rates of generalisation among warning signals and concomitant changes in prey density as mimicry evolves. I highlight areas for future enquiry, including the possibility of Müllerian mimicry systems based on profitability rather than unprofitability and the co-evolution of defence.     

A resilience-based approach for comparing expert preferences across two large-scale coastal management programs

This report proposes a method for assessing resilience-building components in coastal social–ecological systems. Using the proposed model, the preferences of experts in Masan Bay (South Korea) and Puget Sound (USA) are compared. A total of 30 management objectives were determined and used to build a hierarchic tree designed using the principles of the Analytic Hierarchy Process (AHP). Surveys were performed with 35 Puget Sound experts using face-to-face interviews and with 28 Masan Bay experts by mail. The results demonstrate that the legal objective, which enables legislation, was the highest preferred component in both regions. The knowledge translation variable was also given a high preference score in both regions. An analysis of variance (ANOVA) showed that the Puget Sound experts significantly favored attention to education, habitat restoration and species protection objectives in comparison to the Masan Bay experts. The Masan Bay experts placed greater emphasis on legislation and the type of institutional design than did the Puget Sound experts. Using cluster analysis, four distinct groups of respondents were independently identified in Puget Sound and three groups were identified in Masan Bay. One unique subgroup in the Puget Sound experts group, which was characterized by its high preferences for habitat restoration and species protection, was not observed in Masan Bay. Demographic variables (length of career and role in coastal issue) failed to account for the differences in groupings and preferences in either region, except for the variable ‘favoring information source’ in the Puget Sound group. This finding implies that the demographic information was not related to differences in group opinions in both regions. The analysis framework presented here was effective in identifying expert preferences regarding the overall structure and emphasis in coastal management programs. Thus, this framework can be applied towards coastal policy development.     

阳澄湖水源中典型嗅味物质测定与来源分析

以阳澄湖水源为研究对象,采用固相微萃取-气质联用法测定水中2-甲基异莰醇(2-MIB)、土臭素(GSM)、2,4,6-三氯苯甲醚(2,4,6-TAC)、2,3,4-三氯苯甲醚(2,3,4-TAC)、2,3,6-三氯苯甲醚(2,3,6-TAC)、2-异丙基-3甲氧基吡嗪(IPMP)、2-异丁基-3甲氧基吡嗪(IBMP)、...     

Effects of mobile phone use on driving performance in a multiresource workload scenario

Objective: This study explores the influence of mobile phone secondary tasks on driving from the perspective of visual, auditory, cognitive, and psychomotor (VACP) multiple resource theory, and it is anticipated to benefit the human-centered design of mobile phone use while driving.

Methods: The present study investigated 6 typical phone use scenarios while driving and analyzed the effects of phone use distractions on driving performance. Thirty-six participants were recruited to participate in this experiment. We abandoned traditional secondary tasks such as conversations or dialing, in which cognitive resources can become interference. Instead, we adopted an arrow secondary task and an n-back delayed digit recall task.

Results: The results show that all mobile phone use scenarios have a significant influence on driving performance, especially on lateral vehicle control. The visual plus psychomotor resource occupation scenario demonstrated the greatest deterioration of driving performance, and there was a significant deterioration of driving speed and steering wheel angle once the psychomotor resource was occupied.

Conclusions: Phone use distraction leads to visual, cognitive, and/or motor resource functional limitations and thus causes lane violations and traffic accidents.     

Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS technology

IntroductionAnalyzing key factors of motorcycle accidents is an effective method to reduce fatalities and improve road safety. Association Rule Mining (ARM) is an efficient data mining method to identify critical factors associated with injury severity. However, the existing studies have some limitations in applying ARM: (a) Most studies determined parameter thresholds of ARM subjectively, which lacks objectiveness and efficiency; (b) Most studies only listed rules with high parameter thresholds, while lacking in-depth analysis of multiple-item rules. Besides, the existing studies seldom conducted a spatial analysis of motorcycle accidents, which can provide intuitive suggestions for policymakers. Method: To address these limitations, this study proposes an ARM-based framework to identify critical factors related to motorcycle injury severity. A method for parameter optimization is proposed to objectively determine parameter thresholds in ARM. A method of factor extraction is proposed to identify individual key factors from 2-item rules and boosting factors from multiple-item rules. Geographic information system (GIS) is adopted to explore the spatial relationship between key factors and motorcycle injury severity. Results and conclusions: The framework is applied to a case study of motorcycle accidents in Victoria, Australia. Fifteen attributes are selected after data preprocessing. 0.03 and 0.7 are determined as the best thresholds of support and confidence in ARM. Five individual key factors and four boosting factors are identified to be related to fatal injury. Spatial analysis is conducted by GIS to present hot spots of motorcycle accidents. The proposed framework has been validated to have better performance on parameter optimization and rule analysis in ARM. Practical applications: The hot spots of motorcycle accidents related to fatal factors are presented in GIS maps. Policymakers can refer to those maps straightforwardly when decision making. This framework can be applied to various kinds of traffic accidents to improve the performance of severity analysis.     

Genetic amniocentesis in multiple gestations

Thirty-one genetic amniocenteses involving multiple gestations were performed in the genetics unit between 1976 and 1982. Three sets of triplets were included. Precise locations of the sacs were determined using real-time ultrasonography and successful sampling of all sacs was accomplished. Spontaneous abortions occurred in two normal twins and one normal triplet gestation. Two therapeutic abortions were performed for fetal abnormalities. Two cases of discordance for trisomy 21 (one twin and one triplet) were allowed to continue; the twin case terminated at 25 weeks' gestation with neonatal deaths and the triplets are alive and well.     

Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing, China

Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of ? 0.0043 ppb, mean absolute error of 0.4836?ppb, root mean squared error of 0.5320?ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.