The Magellanic Bridge: The Nearest Purely Tidal Stellar Population

We report on observations of the stellar populations in twelve fields spanning the region between the Magellanic Clouds, made with the Mosaic-II camera on the 4-meter telescope at the Cerro-Tololo Inter-American Observatory. The two main goals of the observations are to characterize the young stellar population (which presumably formed in situ in the Bridge and therefore represents the nearest stellar population formed from tidal debris), and to search for an older stellar component (which would have been stripped from either Cloud as stars, by the same tidal forces which formed the gaseous Bridge). We determine the star-formation history of the young inter-Cloud population, which provides a constraint on the timing of the gravitational interaction which formed the Bridge. We do not detect an older stellar population belonging to the Bridge in any of our fields, implying that the material that was stripped from the Clouds to form the Magellanic Bridge was very nearly a pure gas.Comment: 19 pages, 9 figures. Accepted to Ap

Dynamic payload estimation in four wheel drive loaders

Knowledge of the mass of the manipulated load (i.e. payload) in off-highway machines is useful information for a variety of reasons ranging from knowledge of machine stability to ensuring compliance with transportion regulations. This knowledge is difficult to ascertain however. This dissertation concerns itself with delineating the motivations for, and difficulties in development of a dynamic payload weighing algorithm. The dissertation will describe how the new type of dynamic payload weighing algorithm was developed and progressively overcame some of these difficulties. The payload mass estimate is dependent upon many different variables within the off-highway vehicle. These variables include static variability such as machining tolerances of the revolute joints in the linkage, mass of the linkage members, etc as well as dynamic variability such as whole-machine accelerations, hydraulic cylinder friction, pin joint friction, etc. Some initial effort was undertaken to understand the static variables in this problem first by studying the effects of machining tolerances on the working linkage kinematics in a four-wheel-drive loader. This effort showed that if the linkage members were machined within the tolerances prescribed by the design of the linkage components, the tolerance stack-up of the machining variability had very little impact on overall linkage kinematics. Once some of the static dependent variables were understood in greater detail significant effort was undertaken to understand and compensate for the dynamic dependent variables of the estimation problem. The first algorithm took a simple approach of using the kinematic linkage model coupled with hydraulic cylinder pressure information to calculate a payload estimate directly. This algorithm did not account for many of the aforementioned dynamic variables (joint friction, machine acceleration, etc) but was computationally expedient. This work however produced payload estimates with error far greater than the 1% full scale value being targeted. Since this initial simplistic effort met with failure, a second algorithm was needed. The second algorithm was developed upon the information known about the limitations of the first algorithm. A suitable method of compensating for the non-linear dependent dynamic variables was needed. To address this dilemma, an artificial neural network approach was taken for the second algorithm. The second algorithm’s construction was to utilise an artificial neural network to capture the kinematic linkage characteristics and all other dynamic dependent variable behaviour and estimate the payload information based upon the linkage position and hydraulic cylinder pressures. This algorithm was trained using emperically collected data and then subjected to actual use in the field. This experiment showed that that the dynamic complexity of the estimation problem was too large for a small (and computationally feasible) artificial neural network to characterize such that the error estimate was less than the 1% full scale requirement. A third algorithm was required due to the failures of the first two. The third algorithm was constructed to ii take advantage of the kinematic model developed and utilise the artificial neural network’s ability to perform nonlinear mapping. As such, the third algorithm developed uses the kinematic model output as an input to the artificial neural network. This change from the second algorithm keeps the network from having to characterize the linkage kinematics and only forces the network to compensate for the dependent dynamic variables excluded by the kinematic linkage model. This algorithm showed significant improvement over the previous two but still did not meet the required 1% full scale requirement. The promise shown by this algorithm however was convincing enough that further effort was spent in trying to refine it to improve the accuracy. The fourth algorithm developed proceeded with improving the third algorithm. This was accomplished by adding additional inputs to the artificial neural network that allowed the network to better compensate for the variables present in the problem. This effort produced an algorithm that, when subjected to actual field use, produced results very near the 1% full scale accuracy requirement. This algorithm could be improved upon slightly with better input data filtering and possibly adding additional network inputs. The final algorithm produced results very near the desired accuracy. This algorithm was also novel in that for this estimation, the artificial neural network was not used soley as the means to characterize the problem for estimation purposes. Instead, much of the responsibility for the mathematical characterization of the problem was placed upon a kinematic linkage model that then fed it’s own payload estimate into the neural network where the estimate was further refined during network training with calibration data and additional inputs. This method of nonlinear state estimation (i.e. utilising a neural network to compensate for nonlinear effects in conjunction with a first principles model) has not been seen previously in the literature

Differential Contributions of Three Parenting Dimensions to Preschool Literacy and Social Skills in a Middle-Income Sample

This study investigated parenting practices among families of preschoolers in a middle-income community, as well as the contributions of these practices to children\u27s literacy and learning-related social skills. A total of 229 families of preschoolers were recruited. Parents completed a survey describing their parenting practices, while children\u27s literacy skills were directly assessed by using standardized measures. Parents also reported on children\u27s social development. Factor analyses supported a three-dimensional structure of parenting including the home learning environment, autonomy support/expectations, and management/discipline. Path models showed that the home learning environment predicted literacy skills; specifically, parents\u27 teaching about letters and sounds was associated with alphabet knowledge, while shared book reading was marginally linked to vocabulary. Management/discipline was uniquely related to self-regulation, while cooperative/compliant skills were associated with the home learning environment, support/expectations, and management/discipline. Findings suggested that parenting could be conceptualized as three relatively independent dimensions, each of which demonstrated domain-specific contributions to early literacy and social skills

Dwindling Surface Cooling of a Rotating Jovian Planet Leads to a Convection Zone that Grows to a Finite Depth

Recent measurements of Jupiter's gravitational field (by Juno) and seismology of Saturn's rings (by Cassini) strongly suggest that both planets have a stably-stratified core that still possesses a primordial gradient in the concentration of heavy elements. The existence of such a "diffusely" stratified core has been a surprise as it was long expected that the Jovian planets should be fully convective and hence fully mixed. A vigorous zone of convection, driven by surface cooling, forms at the surface and deepens through entrainment of fluid from underneath. In fact, it was believed that this convection zone should grow so rapidly that the entire planet would be consumed in less than a million years. Here we suggest that two processes, acting in concert, present a solution to this puzzle. All of the giant planets are rapidly rotating and have a cooling rate that declines with time. Both of these effects reduce the rate of fluid entrainment into the convection zone. Through the use of an analytic prescription of entrainment in giant planets, we demonstrate that these two effects, rotation and dwindling surface cooling, result in a convection zone which initially grows but eventually stalls. The depth to which the convective interface asymptotes depends on the rotation rate and on the stratification of the stable interior. Conversely, in a nonrotating planet, or in a planet that maintains a higher level of cooling than current models suggest, the convection zone deepens forever, eventually spanning the entire planet.Comment: 7 pages, 2 figures, accepted for publication by Astrophysical Journal Letter

Hypernatural Numbers as Ultrafilters

In this paper we present a use of nonstandard methods in the theory of ultrafilters and in related applications to combinatorics of numbers

A precise determination of chlorinity of sea water using the Ag-AgCl indicator electrode

A knowledge of the relative currents of the oceans depends to a large extent upon a knowledge of the distribution of mass. Measurements of temperature and cblorinity are made to furnish the necessary data for the computations of the density

Torsional Rigidity of Rectangular Wood Composite Materials

The torsional rigidity of wood members is necessary for predicting lateral torsional buckling of laterally unsupported beams, and is useful for estimating the stiffness of two-way floor systems and the natural frequency for wood floors. Current estimations of torsional rigidity of composite wood materials are based upon elastic constant ratios of solid wood. Recently published work has found differences in the elastic constant ratios of solid wood versus structural composite lumber (SCL) materials. These differences in elastic properties may indicate differences in torsional rigidity. Rectangular sections of solid-sawn lumber and various SCL materials were tested to determine values of torsional rigidity. Torsional rigidity of solid-sawn lumber was significantly different (

Almost disjoint large subsets of semigroups

AbstractThere are several notions of largeness in a semigroup S that originated in topological dynamics. Among these are thick, central, syndetic and piecewise syndetic. Of these, central sets are especially interesting because they are partition regular and are guaranteed to contain substantial combinatorial structure. It is known that in (N,+) any central set may be partitioned into infinitely many pairwise disjoint central sets. We extend this result to a large class of semigroups (including (N,+)) by showing that if S is a semigroup in this class which has cardinality κ then any central set can be partitioned into κ many pairwise disjoint central sets. We also show that for this same class of semigroups, if there exists a collection of μ almost disjoint subsets of any member S, then any central subset of S contains a collection of μ almost disjoint central sets. The same statement applies if “central” is replaced by “thick”; and in the case that the semigroup is left cancellative, “central” may be replaced by “piecewise syndetic”. The situation with respect to syndetic sets is much more restrictive. For example, there does not exist an uncountable collection of almost disjoint syndetic subsets of N. We investigate the extent to which syndetic sets can be split into disjoint syndetic sets