Calculating the ocean's mean dynamic topography from a mean sea surface and a geoid

In principle the global mean geostrophic surface circulation of the ocean can be diagnosed by subtracting a geoid from a mean sea surface (MSS). However, because the resulting mean dynamic topography (MDT) is approximately two orders of magnitude smaller than either of the constituent surfaces, and because the geoid is most naturally expressed as a spectral model while the MSS is a gridded product, in practice complications arise. Two algorithms for combining MSS and satellite-derived geoid data to determine the ocean’s mean dynamic topography (MDT) are considered in this paper: a pointwise approach, whereby the gridded geoid height field is subtracted from the gridded MSS; and a spectral approach, whereby the spherical harmonic coefficients of the geoid are subtracted from an equivalent set of coefficients representing the MSS, from which the gridded MDT is then obtained. The essential difference is that with the latter approach the MSS is truncated, a form of filtering, just as with the geoid. This ensures that errors of omission resulting from the truncation of the geoid, which are small in comparison to the geoid but large in comparison to the MDT, are matched, and therefore negated, by similar errors of omission in the MSS. The MDTs produced by both methods require additional filtering. However, the spectral MDT requires less filtering to remove noise, and therefore it retains more oceanographic information than its pointwise equivalent. The spectral method also results in a more realistic MDT at coastlines. 1. Introduction An important challenge in oceanography is the accurate determination of the ocean’s time-mean dynamic topography (MDT). If this can be achieved with sufficient accuracy for combination with the timedependent component of the dynamic topography, obtainable from altimetric data, then the resulting sum (i.e., the absolute dynamic topography) will give an accurate picture of surface geostrophic currents and ocean transports.

The Dubawnt Lake palaeo-ice stream: evidence for dynamic ics sheet behaviour on the Canadian Shield and insights regarding the controls on ice-stream location and vigour

We report evidence for a major ice stream that operated over the northwestern Canadian Shield in the Keewatin Sector of the Laurentide Ice Sheet during the last deglaciation 9000-8200 (uncalibrated) yr BP. It is reconstructed at 450 km in length, 140 km in width, and had an estimated catchment area of 190000 km. Mapping from satellite imagery reveals a suite of bedforms ('flow-set') characterized by a highly convergent onset zone, abrupt lateral margins, and where flow was presumed to have been fastest, a remarkably coherent pattern of mega-scale glacial lineations with lengths approaching 13 km and elongation ratios in excess of 40:1. Spatial variations in bedform elongation within the flow-set match the expected velocity field of a terrestrial ice stream. The flow pattern does not appear to be steered by topography and its location on the hard bedrock of the Canadian Shield is surprising. A soft sedimentary basin may have influenced ice-stream activity by lubricating the bed over the downstream crystalline bedrock, but it is unlikely that it operated over a pervasively deforming till layer. The location of the ice stream challenges the view that they only arise in deep bedrock troughs or over thick deposits of 'soft' fine-grained sediments. We speculate that fast ice flow may have been triggered when a steep ice sheet surface gradient with high driving stresses contacted a proglacial lake. An increase in velocity through calving could have propagated fast ice flow upstream (in the vicinity of the Keewatin Ice Divide) through a series of thermomechanical feedback mechanisms. It exerted a considerable impact on the Laurentide Ice Sheet, forcing the demise of one of the last major ice centres.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Combined bias and outlier identification in dynamic data reconciliation

Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).

A new approach for the solution of the optimal set-point tracking problem for nonlinear systems

In this paper, a discrete time dynamic integrated system optimisation and parameter estimation algorithm is applied to the solution of the nonlinear tracking optimal control problem. A version of the algorithm with a linear-quadratic model-based problem is developed and implemented in software. The algorithm implemented is tested with simulation examples.

An integrated dynamic whole life costing analysis for air distribution systems

Air distribution systems are one of the major electrical energy consumers in air-conditioned commercial buildings which maintain comfortable indoor thermal environment and air quality by supplying specified amounts of treated air into different zones. The sizes of air distribution lines affect energy efficiency of the distribution systems. Equal friction and static regain are two well-known approaches for sizing the air distribution lines. Concerns to life cycle cost of the air distribution systems, T and IPS methods have been developed. Hitherto, all these methods are based on static design conditions. Therefore, dynamic performance of the system has not been yet addressed; whereas, the air distribution systems are mostly performed in dynamic rather than static conditions. Besides, none of the existing methods consider any aspects of thermal comfort and environmental impacts. This study attempts to investigate the existing methods for sizing of the air distribution systems and proposes a dynamic approach for size optimisation of the air distribution lines by taking into account optimisation criteria such as economic aspects, environmental impacts and technical performance. These criteria have been respectively addressed through whole life costing analysis, life cycle assessment and deviation from set-point temperature of different zones. Integration of these criteria into the TRNSYS software produces a novel dynamic optimisation approach for duct sizing. Due to the integration of different criteria into a well- known performance evaluation software, this approach could be easily adopted by designers in busy nature of design. Comparison of this integrated approach with the existing methods reveals that under the defined criteria, system performance is improved up to 15% compared to the existing methods. This approach is interpreted as a significant step forward reaching to the net zero emission building in future.

Centrality and spectral radius in dynamic communication networks

We explore the influence of the choice of attenuation factor on Katz centrality indices for evolving communication networks. For given snapshots of a network observed over a period of time, recently developed communicability indices aim to identify best broadcasters and listeners in the network. In this article, we looked into the sensitivity of communicability indices on the attenuation factor constraint, in relation to spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We proposed relaxed communicability measures where the spectral radius bound on attenuation factor is relaxed and the adjacency matrix is normalised in order to maintain the convergence of the measure. Using a vitality based measure of both standard and relaxed communicability indices we looked at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We illustrated our findings with two examples of real-life networks, MIT reality mining data set of daily communications between 106 individuals during one year and UK Twitter mentions network, direct messages on Twitter between 12.4k individuals during one week.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

Arable crop rotations under abiotic stress: a dynamic economic model

Break crops and multi-crop rotations are common in arable farm management, and the soil quality inherited from a previous crop is one of the parameters that determine the gross margin that is achieved with a given crop from a given parcel of land. In previous work we developed a dynamic economic model to calculate the potential yield and gross margin of a set of crops grown in a selection of typical rotation scenarios, and we reported use of the model to calculate coexistence costs for GM maize grown in a crop rotation. The model predicts economic effects of pest and weed pressures in monthly time steps. Validation of the model in respect of specific traits is proceeding as data from trials with novel crop varieties is published. Alongside this aspect of the validation process, we are able to incorporate data representing the economic impact of abiotic stresses on conventional crops, and then use the model to predict the cumulative gross margin achievable from a sequence of conventional crops grown at varying levels of abiotic stress. We report new progress with this aspect of model validation. In this paper, we report the further development of the model to take account of abiotic stress arising from drought, flood, heat or frost; such stresses being introduced in addition to variable pest and weed pressure. The main purpose is to assess the economic incentive for arable farmers to adopt novel crop varieties having multiple ‘stacked’ traits introduced by means of various biotechnological tools available to crop breeders.

Acceleration and visualization of Dynamic Network Optimization

With the emerging prevalence of smart phones and 4G LTE networks, the demand for faster-better-cheaper mobile services anytime and anywhere is ever growing. The Dynamic Network Optimization (DNO) concept emerged as a solution that optimally and continuously tunes the network settings, in response to varying network conditions and subscriber needs. Yet, the DNO realization is still at infancy, largely hindered by the bottleneck of the lengthy optimization runtime. This paper presents the design and prototype of a novel cloud based parallel solution that further enhances the scalability of our prior work on various parallel solutions that accelerate network optimization algorithms. The solution aims to satisfy the high performance required by DNO, preliminarily on a sub-hourly basis. The paper subsequently visualizes a design and a full cycle of a DNO system. A set of potential solutions to large network and real-time DNO are also proposed. Overall, this work creates a breakthrough towards the realization of DNO.

An incremental space to visualize dynamic data sets

In Information Visualization, adding and removing data elements can strongly impact the underlying visual space. We have developed an inherently incremental technique (incBoard) that maintains a coherent disposition of elements from a dynamic multidimensional data set on a 2D grid as the set changes. Here, we introduce a novel layout that uses pairwise similarity from grid neighbors, as defined in incBoard, to reposition elements on the visual space, free from constraints imposed by the grid. The board continues to be updated and can be displayed alongside the new space. As similar items are placed together, while dissimilar neighbors are moved apart, it supports users in the identification of clusters and subsets of related elements. Densely populated areas identified in the incSpace can be efficiently explored with the corresponding incBoard visualization, which is not susceptible to occlusion. The solution remains inherently incremental and maintains a coherent disposition of elements, even for fully renewed sets. The algorithm considers relative positions for the initial placement of elements, and raw dissimilarity to fine tune the visualization. It has low computational cost, with complexity depending only on the size of the currently viewed subset, V. Thus, a data set of size N can be sequentially displayed in O(N) time, reaching O(N (2)) only if the complete set is simultaneously displayed.

A Robust, Fully Adaptive Hybrid Level-Set/Front-Tracking Method for Two-Phase Flows with an Accurate Surface Tension Computation

We present a variable time step, fully adaptive in space, hybrid method for the accurate simulation of incompressible two-phase flows in the presence of surface tension in two dimensions. The method is based on the hybrid level set/front-tracking approach proposed in [H. D. Ceniceros and A. M. Roma, J. Comput. Phys., 205, 391400, 2005]. Geometric, interfacial quantities are computed from front-tracking via the immersed-boundary setting while the signed distance (level set) function, which is evaluated fast and to machine precision, is used as a fluid indicator. The surface tension force is obtained by employing the mixed Eulerian/Lagrangian representation introduced in [S. Shin, S. I. Abdel-Khalik, V. Daru and D. Juric, J. Comput. Phys., 203, 493-516, 2005] whose success for greatly reducing parasitic currents has been demonstrated. The use of our accurate fluid indicator together with effective Lagrangian marker control enhance this parasitic current reduction by several orders of magnitude. To resolve accurately and efficiently sharp gradients and salient flow features we employ dynamic, adaptive mesh refinements. This spatial adaption is used in concert with a dynamic control of the distribution of the Lagrangian nodes along the fluid interface and a variable time step, linearly implicit time integration scheme. We present numerical examples designed to test the capabilities and performance of the proposed approach as well as three applications: the long-time evolution of a fluid interface undergoing Rayleigh-Taylor instability, an example of bubble ascending dynamics, and a drop impacting on a free interface whose dynamics we compare with both existing numerical and experimental data.

Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude.