Physical mechanism and numerical simulations of surface layer temperature inversion in tropical ocean

The one-dimensional Kraus-Turner mixed layer model improved by Liu is developed to consider the effect of salinity and the equations of temperature and salinity under the mixed layer. On this basis, the processes of growth and death of surface layer temperature inversion is numerically simulated under different environmental parameters. At the same time, the physical mechanism is preliminarily discussed combining the observations at the station of TOGA-COARE 0 degrees N, 156 degrees E. The results indicate that temperature inversion sensitively depends on the mixed layer depth, sea surface wind speed and solar shortwave radiation, etc., and appropriately meteorological and hydrological conditions often lead to the similarly periodical occurrence of this inversion phenomenon.

Factors influencing the climatological mixed layer depth in the South China Sea: numerical simulations

The mixed layer depth (MLD) in the upper ocean is an important physical parameter for describing the upper ocean mixed layer. We analyzed several major factors influencing the climatological mixed layer depth (CMLD), and established a numerical simulation in the South China Sea (SCS) using the Regional Ocean Model System (ROMS) with a high-resolution (1/12A degrees x1/12A degrees) grid nesting method and 50 vertical layers. Several ideal numerical experiments were tested by modifying the existing sea surface boundary conditions. Especially, we analyzed the sensitivity of the results simulated for the CMLD with factors of sea surface wind stress (SSWS), sea surface net heat flux (SSNHF), and the difference between evaporation and precipitation (DEP). The result shows that of the three factors that change the depth of the CMLD, SSWS is in the first place, when ignoring the impact of SSWS, CMLD will change by 26% on average, and its effect is always to deepen the CMLD; the next comes SSNHF (13%) for deepening the CMLD in October to January and shallowing the CMLD in February to September; and the DEP comes in the third (only 2%). Moreover, we analyzed the temporal and spatial characteristics of CMLD and compared the simulation result with the ARGO observational data. The results indicate that ROMS is applicable for studying CMLD in the SCS area.

自适应神经元实现的直线永磁同步电机伺服系统的预见前馈补偿

介绍了高精度微进给直线永磁同步电机伺服系统的 IP位置控制 ,提出了对该系统的最优预见前馈补偿 ,以提高系统的跟踪性能。为了能适应系统参数的变化 ,采用自适应神经元来实现预见前馈补偿。自适应神经元的输入向量为预见步数 ,权值为预见前馈系数 ,权值具有明确的物理意义 ,而且权的初始值不是随机数 ,而是系统在额定情况下算出的预见前馈系数 ,从而加快了神经元的调整速度 ,提高了系统的跟踪能力。仿真实验结果证明了所提出方案的有效性

Parallel Simulation of Subsonic Fluid Dynamics on a Cluster of Workstations

An effective approach of simulating fluid dynamics on a cluster of non- dedicated workstations is presented. The approach uses local interaction algorithms, small communication capacity, and automatic migration of parallel processes from busy hosts to free hosts. The approach is well- suited for simulating subsonic flow problems which involve both hydrodynamics and acoustic waves; for example, the flow of air inside wind musical instruments. Typical simulations achieve $80\\%$ parallel efficiency (speedup/processors) using 20 HP-Apollo workstations. Detailed measurements of the parallel efficiency of 2D and 3D simulations are presented, and a theoretical model of efficiency is developed which fits closely the measurements. Two numerical methods of fluid dynamics are tested: explicit finite differences, and the lattice Boltzmann method.

Parameter sensitivity in simulations of snowmelt

Essery, RLH & P, Etchevers, (2004). Parameter sensitivity in simulations of snowmelt. Journal of Geophysical Research, 109, doi:10. 1029/2004JD005036.

Simulations of Two-Dimensional Foam under Couette Shear

null Sponsorship: Financial support is acknowledged from the University of Wales Aberystwyth Senate Fund, the Ulysses France-Ireland Exchange Scheme and EPSRC (EP/D014956/1, EP/D048397/1, EP/D071127/1).

X-ray simulations for imaging applications in the agricultural and food industry

R. Zwiggelaar, C.R. Bull, and M.J. Mooney, 'X-ray simulations for imaging applications in the agricultural and food industry', Journal of Agricultural Engineering Research 63(2), 161-170 (1996)

The paradox of using tacit and explicit knowledge: Strategies to face dilemnas

Jasimuddin, Sajjad, Klein, Jonathan, and Connell, Con, 'The paradox of using tacit and explicit knowledge: Strategies to face dilemnas', Management Decision (2005) 43(1) pp.102-112 RAE2008

A trait specific model of competition in a spatially structured plant community

John Warren and Chris Topping (2004). A trait specific model of competition in a spatially structured plant community. Ecological Modelling, 180 pp.477-485 RAE2008

The Advanced Students’ Motivation for Explicit Learning of Grammar

The role of the explicit teaching and learning of grammar in the process of foreign language teaching is a very controversial issue. Language teachers and language teaching methodologists differ in their opinions as to whether grammar should be taught at all and, if so, what is the best way of presenting it. In order to find out students’ opinions on the usefulness of the explicit teaching and learning of grammar a questionnaire was given to fifty-one English majors studying in the Institute of English at Adam Mickiewicz University in Poznań. The results of the questionnaire reveal a very positive attitude on the part of the students towards the explicit learning of grammar. The deductive way of learning seems to be favoured by the majority of the respondents, who also unanimously point to translation as a very effective form of grammar exercise.

Optimized strong stability preserving IMEX Runge-Kutta methods

Esta es la versión no revisada del artículo: Inmaculada Higueras, Natalie Happenhofer, Othmar Koch, and Friedrich Kupka. 2014. Optimized strong stability preserving IMEX Runge-Kutta methods. J. Comput. Appl. Math. 272 (December 2014), 116-140. Se puede consultar la versión final en https://doi.org/10.1016/j.cam.2014.05.011

DIP: Density Inference Protocol for wireless sensor networks and its application to density-unbiased statistics

Wireless sensor networks have recently emerged as enablers of important applications such as environmental, chemical and nuclear sensing systems. Such applications have sophisticated spatial-temporal semantics that set them aside from traditional wireless networks. For example, the computation of temperature averaged over the sensor field must take into account local densities. This is crucial since otherwise the estimated average temperature can be biased by over-sampling areas where a lot more sensors exist. Thus, we envision that a fundamental service that a wireless sensor network should provide is that of estimating local densities. In this paper, we propose a lightweight probabilistic density inference protocol, we call DIP, which allows each sensor node to implicitly estimate its neighborhood size without the explicit exchange of node identifiers as in existing density discovery schemes. The theoretical basis of DIP is a probabilistic analysis which gives the relationship between the number of sensor nodes contending in the neighborhood of a node and the level of contention measured by that node. Extensive simulations confirm the premise of DIP: it can provide statistically reliable and accurate estimates of local density at a very low energy cost and constant running time. We demonstrate how applications could be built on top of our DIP-based service by computing density-unbiased statistics from estimated local densities.

Forewarding in Mobile Opportunistic Networks

Recent advances in processor speeds, mobile communications and battery life have enabled computers to evolve from completely wired to completely mobile. In the most extreme case, all nodes are mobile and communication takes place at available opportunities – using both traditional communication infrastructure as well as the mobility of intermediate nodes. These are mobile opportunistic networks. Data communication in such networks is a difficult problem, because of the dynamic underlying topology, the scarcity of network resources and the lack of global information. Establishing end-to-end routes in such networks is usually not feasible. Instead a store-and-carry forwarding paradigm is better suited for such networks. This dissertation describes and analyzes algorithms for forwarding of messages in such networks. In order to design effective forwarding algorithms for mobile opportunistic networks, we start by first building an understanding of the set of all paths between nodes, which represent the available opportunities for any forwarding algorithm. Relying on real measurements, we enumerate paths between nodes and uncover what we refer to as the path explosion effect. The term path explosion refers to the fact that the number of paths between a randomly selected pair of nodes increases exponentially with time. We draw from the theory of epidemics to model and explain the path explosion effect. This is the first contribution of the thesis, and is a key observation that underlies subsequent results. Our second contribution is the study of forwarding algorithms. For this, we rely on trace driven simulations of different algorithms that span a range of design dimensions. We compare the performance (success rate and average delay) of these algorithms. We make the surprising observation that most algorithms we consider have roughly similar performance. We explain this result in light of the path explosion phenomenon. While the performance of most algorithms we studied was roughly the same, these algorithms differed in terms of cost. This prompted us to focus on designing algorithms with the explicit intent of reducing costs. For this, we cast the problem of forwarding as an optimal stopping problem. Our third main contribution is the design of strategies based on optimal stopping principles which we refer to as Delegation schemes. Our analysis shows that using a delegation scheme reduces cost over naive forwarding by a factor of O(√N), where N is the number of nodes in the network. We further validate this result on real traces, where the cost reduction observed is even greater. Our results so far include a key assumption, which is unbounded buffers on nodes. Next, we relax this assumption, so that the problem shifts to one of prioritization of messages for transmission and dropping. Our fourth contribution is the study of message prioritization schemes, combined with forwarding. Our main result is that one achieves higher performance by assigning higher priorities to young messages in the network. We again interpret this result in light of the path explosion effect.

On the Performance and Robustness of Managing Reliable Transport Connections

We revisit the problem of connection management for reliable transport. At one extreme, a pure soft-state (SS) approach (as in Delta-t [9]) safely removes the state of a connection at the sender and receiver once the state timers expire without the need for explicit removal messages. And new connections are established without an explicit handshaking phase. On the other hand, a hybrid hard-state/soft-state (HS+SS) approach (as in TCP) uses both explicit handshaking as well as timer-based management of the connection’s state. In this paper, we consider the worst-case scenario of reliable single-message communication, and develop a common analytical model that can be instantiated to capture either the SS approach or the HS+SS approach. We compare the two approaches in terms of goodput, message and state overhead. We also use simulations to compare against other approaches, and evaluate them in terms of correctness (with respect to data loss and duplication) and robustness to bad network conditions (high message loss rate and variable channel delays). Our results show that the SS approach is more robust, and has lower message overhead. On the other hand, SS requires more memory to keep connection states, which reduces goodput. Given memories are getting bigger and cheaper, SS presents the best choice over bandwidth-constrained, error-prone networks.

CONFIGR: A Vision-Based Model for Long-Range Figure Completion

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the “early vision” stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.