Component- and Alloy-Specific Modeling for Evaluating Aluminum Recycling Strategies for Vehicles

Previous studies indicated that the availability of mixed shredded aluminum scrap from end-of-life vehicles (ELV) is likely to surpass the capacity of secondary castings to absorb this type of scrap, which could lead to a scrap surplus unless suitable interventions can be identified and implemented. However, there is a lack of studies analyzing potential solutions to this problem, among others, because of a lack of component- and alloy-specific information in the models. In this study, we developed a dynamic model of aluminum in the global vehicle stock (distinguishing 5 car segments, 14 components, and 7 alloy groups). The forecasts made up to the year 2050 for the demand for vehicle components and alloy groups, for the scrap supply from discarded vehicles, and for the effects of different ELV management options. Furthermore, we used a source-sink diagram to identify alloys that could potentially serve as alternative sinks for the growing scrap supply. Dismantling the relevant components could remove up to two-thirds of the aluminum from the ELV stream. However, the use of these components for alloy-specific recycling is currently limited because of the complex composition of components (mixed material design and applied joining techniques), as well as provisions that practically prevent the production of safety-relevant cast parts from scrap. In addition, dismantling is more difficult for components that are currently penetrating rapidly. Therefore, advanced alloy sorting seems to be a crucial step that needs to be developed over the coming years to avoid a future scrap surplus and prevent negative energy use and emission consequences.     

Short communication: effects of supplementation with pomegranate seed pulp on concentrations of conjugated linoleic acid and punicic acid in goat milk

The effects of feeding pomegranate seed pulp (PSP) on milk yield, milk composition, fatty acid profiles of milk fat, and blood metabolites were examined in this study. During a pretrial period, 27 multiparous southern Khorasan (Iran) cross-bred goats were fed a similar diet and dry matter intake, milk yield, and milk composition were recorded. After adaptation and based on pretrial records, the goats were randomly assigned to 1 of 3 experimental diets and were housed in individual stalls. Experimental diets included 0, 6, or 12% of PSP (dry matter basis) and were fed as total mixed rations ad libitum for a 45-d period. Diets were formulated to be isonitrogenous and isocaloric. Supplementation of PSP did not affect dry matter intake or average daily gain of goats. Milk yield was not affected by inclusion of PSP in the diet. Milk fat concentration of goats fed diets with 6 and 12% PSP increased, but milk fat yield, milk protein concentration, and milk solids-not-fat concentration of goats were not affected by diets. Feeding PSP did not affect blood glucose, cholesterol, urea N, triglyceride, or lipoproteins. Feeding goats a diet containing 12% PSP modified the milk fatty acid profile, including conjugated linoleic, punicic, and vaccenic acids.     

A Comparative Assessment of Artificial Neural Network,Generalized Regression Neural Network,Least-Square Support Vector Regression,and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R² indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.     

The role of automobiles for the future of aluminum recycling

To reach required product qualities with lowest costs, aluminum postconsumer scrap is currently recycled using strategies of downgrading and dilution, due to difficulties in refining. These strategies depend on a continuous and fast growth of the bottom reservoir of the aluminum downgrading cascade, which is formed by secondary castings, mainly used in automotive applications. A dynamic material flow model for the global vehicle system was developed to assess the likelihood, timing, and extent of a potential scrap surplus. The results demonstrate that a continuation of the above-mentioned strategies will lead to a nonrecyclable scrap surplus by around 2018 ± 5 if no additional measures are taken. The surplus could grow to reach a level of 0.4-2 kg/cap/yr in 2050, corresponding to a loss of energy saving potential of 43-240 TWh/yr electricity. Various intervention options for avoiding scrap surplus are discussed. Effective strategies need to include an immediate and rapid penetration of dramatically improved scrap sorting technologies for end-of-life vehicles and other aluminum applications.     

A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images

A new framework for model-based lung tissue segmentation in three-dimensional thoracic CT images is proposed. In the first stage, a parametric model for lung segmenting surface is created using shape representation based on level sets method. This model is constituted by the sum of a mean distance function and a number of weighted eigenshapes. Consequently, unlike the other model-based segmentation methods, there is no need to specify any marker point in this model. In the second stage, the segmenting surface is varied so as to be matched with the binarized input image. For this purpose, a region-based energy function is minimized with respect to the parameters including the weights of eigenshapes and coefficients of a three-dimensional similarity transform. Finally, the resulted segmenting surface is post-processed in order to improve its fitness with the lung borders of the input image. The experimental results demonstrated the outperformance of the proposed framework over its model-based counterparts in model matching stage. Moreover, it performed slightly better in terms of final segmentation results.     

A Comparative Assessment of Support Vector Machines,Probabilistic Neural Networks,and K-Nearest Neighbor Algorithms for Water Quality Classification

Water quality is one of the major criteria for determining the planning and operation policies of water resources systems. In order to classify the quality of a water resource such as an aquifer, it is necessary that the quality of a large number of water samples be determined, which might be a very time consuming process. The goal of this paper is to classify the water quality using classification algorithms in order to reduce the computational time. The question is whether and to what extent the results of the classification algorithms are different. Another question is what method provides the most accurate results. In this regard, this paper investigates and compares the performance of three supervised methods of classification including support vector machine (SVM), probabilistic neural network (PNN), and k-nearest neighbor (KNN) for water quality classification. Using two performance evaluation statistics including error rate and error value, the efficiency of the algorithms is investigated. Furthermore, a 5-fold cross validation is performed to assess the effect of data value on the performance of the applied algorithms. Results demonstrate that the SVM algorithm presents the best performance with no errors in calibration and validation phases. The KNN algorithm, having the most total number and total value of errors, is the weakest one for classification of water quality data.     

Reinforcement versus species-specific defense reactions as determinants of avoidance barpressing.

In three experiments, rats whose escape and avoidance barpresses were reinforced by entering a safe floor in an otherwise shuttle conditioning arrangement (a shuttle barpress task) showed better learning than the rats that did not receive such reinforcement only when the shock intensity was very weak. Barpresses were found to have mostly nonfrontal topography and appeared not to be under the control of escape or avoidance contingency of reinforcement. Preavoidance training in which only frontal barpresses were allowed to terminate shock greatly facilitated avoidance responding and nullified the inverse effect of shock intensity. The findings were interpreted as indicating that innate defensive reactions play a more prominent role in avoidance behavior than reinforcement, but they do not appear to be as fixed and unmodifiable by learning as often suggested. (PsycINFO Database Record (c) 2010 APA, all rights reserved)