Identification of potential sites for deep-ocean waste isolation with a geographic site-selection model

Identification of optimal sites for the isolation of waste on the abyssal seafloor was performed with two approaches: by the traditional method of map overlays of relevant attributes, and by a specially developed, automated Site-Selection Model (SSM). Five initial, Surrogate Sites, identified with the map-overlay approach, were then compared with the more rigorously produced scores from the SSM. The SSM, a process for optimization of site locations, accepts subjective, expert-based judgments and transforms them into a quantitative, reproducible, and documented product. The SSM is adaptable to any siting scenario. Forty-one factors relevant to the isolation scenario, including 21 weightable factors having a total of 123 scorable categories, have been entered into the SSM. Factors are grouped under project definition, unique environments, anthropogenic, geologic, biologic, weather, oceanographic and distance criteria. The factor scores are linked to a georeferenced database array of all factors, corresponding to 1°×1° latitude–longitude squares. The SSM includes a total of 2241 one-degree squares within 1000 n.m. of the U.S. coasts, including the western North Atlantic, the Gulf of Mexico, and the eastern North Pacific. Under a carefully weighted and scored scenario of isolation, the most favorable sites identified with the SSM are on the Hatteras and Nares Abyssal Plains in the Atlantic. High-scoring sites are also located in the Pacific abyssal hills province between the Murray and Molokai Fracture Zones. Acceptable 1° squares in the Gulf of Mexico are few and of lower quality, with the optimum location on the northern Sigsbee Abyssal Plain. Two of the five Surrogate Site locations, on the Hatteras and Sigsbee Abyssal Plains, correspond to the best SSM sites in each ocean area. Two Pacific and a second Atlantic Surrogate Site are located in low-scoring regions or excluded by the SSM. Site-selection results from the SSM, although robust, are an initial attempt to quantify the site-selection process. The SSM database exposes a significant lack of high-quality information for many areally mappable attributes on the abyssal seafloor, particularly bottom-current speed and measures of biologic productivity and flux. Terminologies and classifications of some measures, such as sediment types, suffer from parochialism and vary by ocean. Considerable research is needed even for a broad understanding of the environmental measures required to make sound societal decisions about use of the abyssal seafloor for disposal or other purposes.     

Management of coastal eutrophication: Integration of field data, ecosystem-scale simulations and screening models

A hybrid approach for eutrophication assessment in estuarine and coastal ecosystems is presented. The ASSETS screening model (http://www.eutro.org) classifies eutrophication status into five classes: High (better), Good, Moderate, Poor and Bad (worse). This model was applied to a dataset from a shallow coastal barrier island system in southwest Europe (Ria Formosa), with a resulting score of Good. A detailed dynamic model was developed for this ecosystem, and the outputs were used to drive the screening model. Four scenarios were run on the research model: pristine, standard (simulates present loading), half and double the current nutrient loading. The Ria Formosa has a short water residence time and eutrophication symptoms are not apparent in the water column. However, benthic symptoms are expressed as excessive macroalgal growth and strong dissolved oxygen fluctuations in the tide pools. The standard simulation results showed an ASSETS grade identical to the field data application. The application of the screening model to the other scenario outputs showed the responsiveness of ASSETS to changes in pressure, state and response, scoring a grade of High under pristine conditions, Good for half the standard scenario and Moderate for double the present loadings. The use of this hybrid approach allows managers to test the outcome of measures against a set of well-defined metrics for the evaluation of state. It additionally provides a way of testing and improving the pressure component of ASSETS. Sensitivity analysis revealed that sub-sampling the output of the research model at a monthly scale, typical for the acquisition of field data, may significantly affect the outcome of the screening model, by overlooking extreme events such as occasional night-time anoxia in tide pools.     

Method for obtaining the wheel–rail contact location and its application to the normal problem calculation through ‘CONTACT’

This work presents a robust methodology for calculating inter-penetration areas between railway wheel and rail surfaces, the profiles of which are defined by a series of points. The method allows general three-dimensional displacements of the wheelset to be considered, and its characteristics make it especially suitable for dynamic simulations where the wheel–rail contact is assumed to be flexible. The technique is based on the discretisation of the geometries of the surfaces in contact, considering the wheel as a set of truncated cones and the rail as points. By means of this approach, it is possible to reduce the problem to the calculation of the intersections between cones and lines, the solution for which has a closed-form expression. The method has been used in conjunction with the CONTACT algorithm in order to solve the static normal contact problem when the lateral displacement of the wheelset, its yaw angle and the vertical force applied in the wheelset centroid are prescribed. The results consist of smooth functions when the dependent coordinates are represented as a function of the independent ones, lacking the jump discontinuities that are present when a rigid contact model is adopted. Example results are shown and assessed for the normal contact problem for different lateral and yaw positions of the wheelset on the track.     

Socio-demographic and built environment influences on the odds of being overweight or obese: The Atlanta experience

This paper explores the association of socio-demographic and built environment characteristics on the odds of being overweight and obese using data from the Atlanta SMARTRAQ travel survey. A new methodological framework based on a multinomial logit (MNL) model and an enhanced odds ratio plot is presented. The use of an MNL model overcomes limitations of many prior studies that employ a sequence of binary logit models to examine multiple weight categories. The use of an enhanced odds ratio plot provides important information into the relative importance of socio-demographic and built environment characteristics. Several new findings for the Atlanta area result from this study. Socio-demographic variables, including age and educational attainment, exhibit a non-linear relationship with the odds of being overweight or obese. Gender, age, ethnicity, and educational attainment are strongly associated with the odds of being overweight or obese, while income and number of students between 5 and 16 years old in the household have smaller effects. Built environment characteristics such as increased net residential densities and enhanced street connectivity are associated with reductions in the odds of being overweight and/or obese. Relative to socio-demographic variables, however, such built environment characteristics have a much smaller impact on describing the odds of being overweight or obese.     

License plate extraction method for identification of vehicle violations at a railway level crossing

The primary cause of most railroad accidents is vehicle entry into railway level crossings despite warning messages. To identify drivers who violate railway level crossing regulations, vehicle license plate recognition can be applied at railway level crossings. The purpose of this paper is to present an effective method for extracting the license plate region from vehicle images taken at railway level crossings. The method proposed in this paper uses the variation in the gray-level values across the image of a license plate. For license plate region extraction, the character region is first recognized by identifying the character width and the difference between the background region and the character region. The license plate region is then extracted by finding the inter-character distance in the plate region. In addition, the license plate type is identified by the difference in the gray-level value between the background region and the character region. The proposed method is effective in solving the current challenges in extracting the license plate region from the damaged frames of license plates issued for domestic use, including new types of license plates. According to the experimental results, the proposed method yields a high extraction rate of 99.5% for vehicle license plates.     

Vehicle interior noise model based on a power law

Identifying the components of a vehicle’s interior noise is important in many phases of the noise, vibration, and harshness (NVH) development process. Many test methods that have been widely used in the automobile industry to separate noise sources are based on system identification methods in the frequency domain. However, none of the frequency response function-based methods can directly estimate the wind noise component. In this article, an analytical model for the interior noise level based on a simple power law was developed. It was assumed that the mean squared acoustic pressure for the interior noise could be obtained by summing up those of the wind noise, road noise, and background noise. The wind noise and road noise were further assumed to depend only on wind speed and the vehicle’s driving speed, respectively, and to follow a simple power law. The resulting analytical model includes five parameters that can be optimized for the vehicle and the road. The validity of the model was verified by using data obtained from cruise tests performed on a proving ground for cruise speeds ranging from 40 km/h to 130 km/h. The model is applied to the overall and 1/3-octave bands of interior noise and is shown to describe the data trends fairly well. For the test vehicle used in the present work, the overall mean squared pressures for the wind and road noise components are shown to be proportional to the wind speed to the 5.8 power and to the driving speed to the 3.4 power, respectively.     

Parameter identification for a LuGre model based on steady-state tire conditions

The purpose of this study was to effectively identify parameters for a LuGre friction model based on experimental measures. In earlier work related to this study (Yang et al., 2009), which was based on the characters of polygonal wear (Sueoka and Ryu, 1997), we showed a frictional vibration model for a mass on a moving belt. This model reflected lateral vibrations caused by velocity and toe-in angle. An important aspect of the present study is the improved friction model. A previous friction model, which divided the process into four parts, expressed the sable excited vibration well but failed to reflect the hysteresis loop change when vehicles accelerated or decelerated continuously. A LuGre friction model can solve this problem, but several model parameters must be obtained experimentally. We measured contact width and length of tires as vertical pressure changed; this provided a theoretical basis for apparent stiffness of a unit of tire tread. Based on tire data from Bakker E’s article in a SAE paper from 1987, we identified the Stribeck exponent and Stribeck velocity in LuGre. Then, the results were implemented in a vibration system that verified the rationality of the data.