On the use of combined compact scheme for numerical solution of the two-layer oceanic shallow water equations

Many types of oceanic physical phenomena have a wide range in both space and time. In general, simplified models, such as shallow water model, are used to describe these oceanic motions. The shallow water equations are widely applied in various oceanic and atmospheric extents. By using the two-layer shallow water equations, the stratification effects can be considered too. In this research, the sixth-order combined compact method is investigated and numerically implemented as a high-order method to solve the two-layer shallow water equations. The second-order centered, fourth-order compact and sixth-order super compact finite difference methods are also used to spatial differencing of the equations. The first part of the present work is devoted to accuracy assessment of the sixth-order super compact finite difference method (SCFDM) and the sixth-order combined compact finite difference method (CCFDM) for spatial differencing of the linearized two-layer shallow water equations on the Arakawa's A-E and Randall's Z numerical grids. Two general discrete dispersion relations on different numerical grids, for inertia-gravity and Rossby waves, are derived. These general relations can be used for evaluation of the performance of any desired numerical scheme. For both inertia-gravity and Rossby waves, minimum error generally occurs on Z grid using either the sixth-order SCFDM or CCFDM methods. For the Randall's Z grid, the sixth-order CCFDM exhibits a substantial improvement , for the frequency of the barotropic and baroclinic modes of the linear inertia-gravity waves of the two layer shallow water model, over the sixth-order SCFDM. For the Rossby waves, the sixth-order SCFDM shows improvement, for the barotropic and baroclinic modes, over the sixth-order CCFDM method except on Arakawa's C grid. In the second part of the present work, the sixth-order CCFDM method is used to solve the one-layer and two-layer shallow water equations in their nonlinear form. In one-layer model with periodic boundaries, the performance of the methods for mass conservation is compared. The results show high accuracy of the sixth-order CCFDM method to simulate a complex flow field. Furthermore, to evaluate the performance of the method in a non-periodic domain the sixth-order CCFDM is applied to spatial differencing of vorticity-divergence-mass representation of one-layer shallow water equations to solve a wind-driven current problem with no-slip boundary conditions. The results show good agreement with published works. Finally, the performance of different schemes for spatial differencing of two-layer shallow water equations on Z grid with periodic boundaries is investigated. Results illustrate the high accuracy of combined compact method.

未来气候变化对黄土高原黑河流域水资源的影响

气候变化对黄土高原的水资源有重要影响,对其影响进行评估可以为区域发展提供重要的决策依据。基于分布式水文模型SWAT和4种全球环流模式的各3种排放情景,评估了2010～2039年黄土高塬沟壑区黑河流域水资源对气候变化的潜在响应。结果表明,黑河流域2010～2039年的年均降水变化-2.3%～7.8%,年均最高和最低温度分别升高0.7～2.2℃和1.2～2.8℃,年均径流量变化-19.8%～37.0%,1.2m剖面年均土壤水分含量变化-5.5%～17.2%,年均蒸散量普遍增长0.1%～5.9%;水文气象变量变化趋势复杂,但T检验表明年降水、径流、土壤水分和蒸散增长的概率较大。对于季节变化,降水可能在12～7月份和9月份增长,8月份和10～11月份减少;径流在4～7月份和9～10月份增加,11～3月份和8月份减少;土壤水分在各月都增长;蒸散11～6月份普遍增长,7～10月份减少的可能性较大。未来气候将发生显著变化并对水资源有重要影响,需采取必要的措施来减缓其不利影响。

Equivalence and Reduction of Hidden Markov Models

This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.

Color Region Grouping and Shape Recognition with Deformable Models

A new deformable shape-based method for color region segmentation is described. The method includes two stages: over-segmentation using a traditional color region segmentation algorithm, followed by deformable model-based region merging via grouping and hypothesis selection. During the second stage, region merging and object identification are executed simultaneously. A statistical shape model is used to estimate the likelihood of region groupings and model hypotheses. The prior distribution on deformation parameters is precomputed using principal component analysis over a training set of region groupings. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with similarly colored adjacent objects. Furthermore, the recovered parametric shape model can be used directly in object recognition and comparison. Experiments in segmentation and image retrieval are reported.

Synergistic effects of proteasome inhibitor carfilzomib in combination with tyrosine kinase inhibitors in imatinib-sensitive and -resistant chronic myeloid leukemia models

The tyrosine kinase inhibitor (TKI) imatinib has transformed the treatment and outlook of chronic myeloid leukemia (CML); however, the development of drug resistance and the persistence of TKI-resistant stem cells remain obstacles to eradicating the disease. Inhibition of proteasome activity with bortezomib has been shown to effectively induce apoptosis in TKI-resistant cells. In this study, we show that exposure to the next generation proteasome inhibitor carfilzomib is associated with a decrease in ERK signaling and increased expression of Abelson interactor proteins 1 and 2 (ABI-1/2). We also investigate the effect of carfilzomib in models of imatinib-sensitive and -resistant CML and demonstrate a potent reduction in proliferation and induction of apoptosis in a variety of models of imatinib-resistant CML, including primitive CML stem cells. Carfilzomib acts synergistically with the TKIs imatinib and nilotinib, even in imatinib-resistant cell lines. In addition, we found that the presence of immunoproteasome subunits is associated with an increased sensitivity to carfilzomib. The present findings provide a rational basis to examine the potential of carfilzomib in combination with TKIs as a potential therapy for CML, particularly in imatinib-resistant disease.

Air-Sea Exchange of Legacy POPs in the North Sea Based on Results of Fate and Transport, and Shelf-Sea Hydrodynamic Ocean Models

The air-sea exchange of two legacy persistent organic pollutants (POPs), γ-HCH and PCB 153, in the North Sea, is presented and discussed using results of regional fate and transport and shelf-sea hydrodynamic ocean models for the period 1996–2005. Air-sea exchange occurs through gas exchange (deposition and volatilization), wet deposition and dry deposition. Atmospheric concentrations are interpolated into the model domain from results of the EMEP MSC-East multi-compartmental model (Gusev et al, 2009). The North Sea is net depositional for γ-HCH, and is dominated by gas deposition with notable seasonal variability and a downward trend over the 10 year period. Volatilization rates of γ-HCH are generally a factor of 2–3 less than gas deposition in winter, spring and summer but greater in autumn when the North Sea is net volatilizational. A downward trend in fugacity ratios is found, since gas deposition is decreasing faster than volatilization. The North Sea is net volatilizational for PCB 153, with highest rates of volatilization to deposition found in the areas surrounding polluted British and continental river sources. Large quantities of PCB 153 entering through rivers lead to very high local rates of volatilization.

Power-Capped DVFS and Thread Allocation with ANN Models on Modern NUMA Systems

Abstract—Power capping is an essential function for efficient power budgeting and cost management on modern server systems. Contemporary server processors operate under power caps by using dynamic voltage and frequency scaling (DVFS). However, these processors are often deployed in non-uniform memory
access (NUMA) architectures, where thread allocation between cores may significantly affect performance and power consumption. This paper proposes a method which maximizes performance under power caps on NUMA systems by dynamically optimizing two knobs: DVFS and thread allocation. The method selects the optimal combination of the two knobs with models based on artificial neural network (ANN) that captures the nonlinear effect of thread allocation on performance. We implement
the proposed method as a runtime system and evaluate it with twelve multithreaded benchmarks on a real AMD Opteron based NUMA system. The evaluation results show that our method outperforms a naive technique optimizing only DVFS by up to
67.1%, under a power cap.