On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

Managers as drivers of CSR in state-owned enterprises

Corporate social responsibility (CSR) is increasingly required not only of private sector companies, but also of state-owned enterprises (SOEs), whose main function is to provide services to society and not principally to obtain financial reward. The important role played by managers in implementing CSR has been demonstrated by prior research. The objective of this paper is to examine how managers of SOEs perceive the concept of CSR, the reasons for their involvement and how it is integrated into everyday business practices. In addition, we examine whether managers' attitudes towards CSR policies are influenced by their personal profiles and by the sector in which the SOEs work. We find that, although these managers are aware of the importance of the CSR principles underlying their activities and understand the meaning, scope and dimensions of CSR, there remains an evident need to enhance the application of CSR policies among the strategies of SOEs. The manager profile and the sector in which the SOEs work both have a strong influence on their behaviour regarding CSR issues.     

Planning,governance and rural futures in Australia and the USA: revisiting the case for rural regional planning

Rural regions in post-industrial countries confront significant new challenges, particularly in relation to climate, biodiversity, unconventional resource development and energy. Yet at a time when the contours of these challenges are still being sketched, and preliminary, planned interventions undertaken, the practice of rural planning finds itself at a low ebb. We examine two ‘critical cases’, one each from Australia and the USA, to explore the issues and options for capacity of rural regional planning to surmount these new challenges. Our examination indicates the urgent need for a renewed discourse on rural regional planning.     

The Kinetics of Interesterfication on Waste Cooking Oil (Sunflower Oil) for the Production of Fatty Acid Alkyl Esters using a Whole Cell Biocatalyst (Rhizopus oryzae) and Pure Lipase Enzyme

Recent research and technology have provided promising outcomes to rely on biodiesel as the alternative and conventional source of fuel. The use of renewable sources constitutes the main stream of research. Waste Cooking Oil (WCO) was used for biodiesel production in this study. Lipase enzyme producing fungi Rhizopus oryzae 262 and commercially available pure lipase enzyme were used for comparative study in the production of FAAE. The whole cell biocatalyst and pure enzyme were immobilized using calcium alginate beads. It was prepared by optimizing with different molar ratios of calcium chloride and different percent sodium alginate. Entrapment immobilization was done for whole cell biocatalyst. PE was also immobilized by entrapment for the transesterification reaction. Four different solvents methanol, ethanol, n-propanol, n-butanol were used as the acyl acceptors. The reaction parameters like temperature, molar ratio, reaction time, and amount of enzyme to be used were also optimized for methanol alone. The same parameters were adopted for the other acyl acceptors too. Among the different acyl acceptors, methanol whose reaction parameters were optimized showed maximum conversion of triglycerides to FAAE - 94% with PE and 84% with WCB. On the whole, PE showed better catalytic converting ability with all the acyl acceptor compared to WCB, further study, it was observed that three consecutive and reversible reactions occurred in the interesterification of triglycerides. So, a kinetic model based on Michaelis-Menten equation with competitive substrate inhibition was used to find the maximum reaction rate V_i for the four solvents using pure enzyme and WCB.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.