Efficient simulation of unsaturated flow in heterogeneous soils using an exponential integrator

- Provided a practical variable-stepsize implementation of the exponential Euler method (EEM). - Introduced a new second-order variant of the scheme that enables the local error to be estimated at the cost of a single additional function evaluation. - New EEM implementation outperformed sophisticated implementations of the backward differentiation formulae (BDF) of order 2 and was competitive with BDF of order 5 for moderate to high tolerances.

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

With the best of intentions: A critical discourse analysis of HPE curriculum materials

Much of physical education curriculum in the developed world and specifically in Australia tends to be guided in principle by syllabus documents that represent, in varying degrees, some form of government education priorities. Through the use of critical discourse analysis we analyze one such syllabus example (an official syllabus document of one of the Australian States) to explore the relationships between the emancipatory/social justice expectations presented in the rubric of and introduction to the official syllabus document, and the language details of learning outcomes that indicate how the expectations might be satisfied. Given the complexity and multilevel pathways of message systems/ideologies we question the efficacy of such documents oriented around social justice principles to genuinely deliver more radical agendas which promote social change and encourage a preparedness to engage in social action leading to a betterment of society.

3D Fe2(MoO4)3 microspheres with nanosheet constituents as high-capacity anode materials for lithium-ion batteries

Three-dimensional (3D) Fe2(MoO4)3 microspheres with ultrathin nanosheet constituents are first synthesized as anode materials for the lithium-ion battery. It is interesting that the single-crystalline nanosheets allow rapid electron/ion transport on the inside, and the high porosity ensures fast diffusion of liquid electrolyte in energy storage applications. The electrochemical properties of Fe2(MoO4)3 as anode demonstrates that 3D Fe2(MoO4)3 microspheres deliver an initial capacity of 1855 mAh/g at a current density of 100 mA/g. Particularly, when the current density is increased to 800 mA/g, the reversible capacity of Fe2(MoO4)3 anode still arrived at 456 mAh/g over 50 cycles. The large and reversible capacities and stable charge–discharge cycling performance indicate that Fe2(MoO4)3 is a promising anode material for lithium battery applications. Graphical abstract The electrochemical properties of Fe2(MoO4)3 as anode demonstrates that 3D Fe2(MoO4)3 microspheres delivered an initial capacity of 1855 mAh/g at a current density of 100 mA/g. When the current density was increased to 800 mA/g, the Fe2(MoO4)3 still behaved high reversible capacity and good cycle performance.

Data-aware task scheduling for all-to-all comparison problems in heterogeneous distributed systems

Solving large-scale all-to-all comparison problems using distributed computing is increasingly significant for various applications. Previous efforts to implement distributed all-to-all comparison frameworks have treated the two phases of data distribution and comparison task scheduling separately. This leads to high storage demands as well as poor data locality for the comparison tasks, thus creating a need to redistribute the data at runtime. Furthermore, most previous methods have been developed for homogeneous computing environments, so their overall performance is degraded even further when they are used in heterogeneous distributed systems. To tackle these challenges, this paper presents a data-aware task scheduling approach for solving all-to-all comparison problems in heterogeneous distributed systems. The approach formulates the requirements for data distribution and comparison task scheduling simultaneously as a constrained optimization problem. Then, metaheuristic data pre-scheduling and dynamic task scheduling strategies are developed along with an algorithmic implementation to solve the problem. The approach provides perfect data locality for all comparison tasks, avoiding rearrangement of data at runtime. It achieves load balancing among heterogeneous computing nodes, thus enhancing the overall computation time. It also reduces data storage requirements across the network. The effectiveness of the approach is demonstrated through experimental studies.

Abnormal levels of heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) in tumour tissue and blood samples from patients diagnosed with lung cancer

Lung cancer is the second most common type of cancer in the world and is the most common cause of cancer-related death in both men and women. Research into causes, prevention and treatment of lung cancer is ongoing and much progress has been made recently in these areas, however survival rates have not significantly improved. Therefore, it is essential to develop biomarkers for early diagnosis of lung cancer, prediction of metastasis and evaluation of treatment efficiency, as well as using these molecules to provide some understanding about tumour biology and translate highly promising findings in basic science research to clinical application. In this investigation, two-dimensional difference gel electrophoresis and mass spectrometry were initially used to analyse conditioned media from a panel of lung cancer and normal bronchial epithelial cell lines. Significant proteins were identified with heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1), pyruvate kinase M2 isoform (PKM2), Hsc-70 interacting protein and lactate dehydrogenase A (LDHA) selected for analysis in serum from healthy individuals and lung cancer patients. hnRNPA2B1, PKM2 and LDHA were found to be statistically significant in all comparisons. Tissue analysis and knockdown of hnRNPA2B1 using siRNA subsequently demonstrated both the overexpression and potential role for this molecule in lung tumorigenesis. The data presented highlights a number of in vitro derived candidate biomarkers subsequently verified in patient samples and also provides some insight into their roles in the complex intracellular mechanisms associated with tumour progression.