Identification methods of key contributing factors in crashes with high numbers of fatalities and injuries in China

Objectives: In China, serious road traffic crashes (SRTCs) are those in which there are 10–30 fatalities, 50–100 serious injuries, or a total cost of 50–100 million RMB (U.S.$8–16 M), and particularly serious road traffic crashes (PSRTCs) are those that are more severe or costly. Due to the large number of fatalities and injuries as well as the negative public reaction they elicit, SRTCs and PSRTCs have become of great concern to China during recent years. The aim of this study is to identify the main factors contributing to these road traffic crashes and to propose preventive measures to reduce their number.

Methods: 49 contributing factors of the SRTCs and PSRTCs that occurred from 2007 to 2013 were collected from the database “In-depth investigation and analysis system for major road traffic crashes” (IIASMRTC) and were analyzed through the integrated use of principal component analysis and hierarchical clustering to determine the primary and secondary groups of contributing factors.

Results: Speeding and overloading of passengers were the primary contributing factors, featuring in up to 66.3 and 32.6% of accidents, respectively. Two secondary contributing factors were road related: lack of or nonstandard roadside safety infrastructure and slippery roads due to rain, snow, or ice.

Conclusions: The current approach to SRTCs and PSRTCs is focused on the attribution of responsibility and the enforcement of regulations considered relevant to particular SRTCs and PSRTCs. It would be more effective to investigate contributing factors and characteristics of SRTCs and PSRTCs as a whole to provide adequate information for safety interventions in regions where SRTCs and PSRTCs are more common. In addition to mandating a driver training program and publicization of the hazards associated with traffic violations, implementation of speed cameras, speed signs, markings, and vehicle-mounted Global Positioning Systems (GPS) are suggested to reduce speeding of passenger vehicles, while increasing regular checks by traffic police and passenger station staff and improving transportation management to increase income of contractors and drivers are feasible measures to prevent overloading of people. Other promising measures include regular inspection of roadside safety infrastructure and improving skid resistance on dangerous road sections in mountainous areas.     

Effects of predators on sea urchin density and habitat use in a southern California kelp forest

Grazing sea urchins can reduce kelp abundance and therefore strongly affect kelp forest community structure. Despite the ecological importance of sea urchins, direct field studies on the role that urchin predators play in shaping urchin populations are rare for southern California. We conducted surveys and manipulative experiments within kelp forests near San Diego, CA, (32–51′28″N, 117–16′00″W) from 2006 to 2009 to determine whether predators such as sheephead (Semicossyphus pulcher) and spiny lobsters (Panulirus interruptus) may be linked to purple urchin (Strongylocentrotus purpuratus) and red urchin (Strongylocentrotus franciscanus) distribution and habitat use, as well as purple urchin density-dependent mortality. Purple urchins were less dense and more cryptic inside a local marine protected area (MPA) that contained high predator abundance than in nearby heavily fished areas, whereas red urchins rarely were found outside the MPA. Urchin proportional mortality was inversely density dependent during the day when sheephead were active, despite fish aggregations in plots of high urchin density, but was density independent during the night when lobsters were active. Urchin mortality was reduced under understory algal cover during the day, but not during the night. Examining whether urchin mortality from predation is density dependent and how habitat complexity influences this relationship is imperative because behavioral changes and increases in urchin populations can have vast ecological and economic consequences in kelp forest communities.     

Determination of polycyclic aromatic hydrocarbons in dry tea

Twenty-eight different tea samples sold in the United States were evaluated using high-performance liquid chromatography (HPLC) with fluorescence detection (FLD) for their contamination with polycyclic aromatic hydrocarbons (PAHs). Many PAHs exhibit carcinogenic, mutagenic, and teratogenic properties and have been related to several kinds of cancer in man and experimental animals. The presence of PAHs in environmental samples such as water, sediments, and particulate air has been extensively studied, but food samples have received little attention. Eighteen PAHs congeners were analyzed, with percentage recovery higher than 85%. Contamination expressed as the sum of the 18 analyzed PAHs was between 101 and 1337 μg/kg on dry mass and the average contents in all of the 28 examined samples was 300 μg/kg on dry mass. Seven of the congeners were found in all samples with wide ranges of concentrations as follows: fluorene (7–48 μg/kg), anthracene (1–31 μg/kg), pyrene (1–970 μg/kg), benzo(a)anthracene (1–18 μg/kg) chrysene (17–365 μg/kg), benzo(a)pyrene (1–29 μg/kg), and indeno(1,2,3-cd)pyrene (4–119 μg/kg). The two most toxic congeners benzo(a)pyrene and dibenzo(a,h)anthracene were found at high concentrations only in Earl Grey Twinnings, Earl Grey Harney& Sons Fine Teas, and Chai Ultra Spice Black Tea Twinnings. Six PAH congeners are considered as suspected carcinogens (U.S.EPA), formed the basis of the estimation of the toxic equivalent (TEQ), Chai Ultra-Spice Black Tea Twinnings had the highest TEQ (110.9) followed by two grey tea samples, Earl Grey Harney & Sons Fine Tea (57.7) and Earl Grey Twinnings (54.5). Decaffeinated grey teas had the lowest TEQs, decaffeinated Earl Grey Bigelow (9.4) and Green Tea Honey Lemon Decaffeinated Lipton (9.6).     

Improving situational awareness through the design of offshore installations

Maintaining the situational awareness of control room operators on offshore installations contributes to the timely diagnosis of conditions and making appropriate decisions. This is particularly important when dealing with events and incidents. Recent initiatives aimed at reducing operators’ exposure to the hazards of working on offshore installations may have a negative impact upon situational awareness within the control room environment.This paper discusses mitigation of the negative impact through the design and operation of the installation and control system; either by improving the general level of situational awareness or by specifically targeting the areas affected by these initiatives.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.