Turbulent properties of internal waves in the South China Sea

Author Posting. © The Oceanography Society, 2011. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 24 no. 4 (2011): 78–87, doi:10.5670/oceanog.2011.96.Luzon Strait and South China Sea waters are among the most energetic internal wave environments in the global ocean. Strong tides and stratification in Luzon Strait give rise to internal waves that propagate west into the South China Sea. The energy carried by the waves is dissipated via turbulent processes. Here, we present and contrast the relatively few direct observations of turbulent dissipation in South China Sea internal waves. Frictional processes active in the bottom boundary layer dissipate some of the energy along China's continental shelf. It appears that more energy is lost in Taiwanese waters of the Dongsha Plateau, where the waves reach their maximum amplitudes, and where the bottom topography abruptly shoals from 3,000 m in the deep basin to 1,000 m and shallower on the plateau. There, energy dissipation by turbulence reaches 1 W m–2, on par with the conversion rates of Luzon Strait.Support for this work was provided by the US Office of Naval Research and the National Science Council of Taiwan

Experiment on the promoting-inhibiting effects on methane explosion by using haloalkanes

Methane explosion is one of the major disasters that seriously threaten the safety of coal mine production, the development of efficient methane explosion suppression technology can effectively improve the prevention and control level of methane explosion accidents, and its focus is on the function of explosion suppression materials. In order to systematically study the effect of typical haloalkanes extinguishing agents on methane explosion, the effects of typical haloalkanes such as heptafluoropropane (C3HF7), hexafluoropropane (C3H2F6) and trifluoromethane (CHF3) on the ignition and explosion characteristics of methane were systematically studied by combining experimental tests and theoretical analysis. The effects of haloalkanes on methane explosion pressure parameters and laminar burning velocity were tested by a 20 L spherical explosive vessel and a self-developed Bunsen burner laminar flame propagation velocity system. The variation laws of peak explosion pressure, maximum pressure rise rate, laminar burning velocity, and laminar flame morphology evolution were obtained. The results show that with the increase of the added volume fraction, the haloalkanes had a double effect of promoting and inhibiting the methane explosion process. Under the chemical equivalent condition, only C3HF7 can first promote and then inhibit the peak explosion pressure and maximum pressure rise rate of methane, while CHF3 and C3H2F6 can inhibit the effect. The three haloalkanes all showed inhibition on the combustion rate of methane laminar flow. In the oxygen-poor condition, the three haloalkanes inhibited the peak explosion pressure, the maximum pressure boost rate, and the laminar burning velocity of methane. In general, C3H2F6 and C3HF7 have better inhibition effects on methane explosion pressure characteristic parameters and laminar burning velocity than CHF3. The theoretical analysis results show that the double effect of promoting and inhibiting the haloalkanes with the increase of the mixture volume fraction can be attributed to the competition between the improvement of the heat release characteristics of the system reaction and the inhibition of the key free radicals such as H, O, and OH by the main intermediates containing F. The results of this paper provide a theoretical basis for the theoretical research and technical development of methane explosion prevention and control

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

A Novel Algorithm for Thickness Prediction in Incremental Sheet Metal Forming

Incremental sheet metal forming characterized as increased flexibility and local plastic deformation is well suitable for low-production-run manufacturing and a new sample trial production of complex shapes. Thickness thinning is still an obstacle to the application of incremental forming. In this study, a novel mathematical algorithm based on a non-uniform rational B-spline (NURBS) surface was proposed and implemented which focuses on predicting and calculating the final thickness for arbitrary parts in incremental forming. In order to evaluate the validity of the proposed model, the finite element simulation and forming experiments of three kinds of parts, such as truncated cones, truncated pyramids and ellipsoid parts, were conducted. The thickness of theoretical prediction was compared with that of finite element simulation and experiment, and good agreements were obtained. The results show that the proposed model and the method are effective and robust for predicting the thickness of the formed parts in incremental sheet metal forming

The first principle calculation of improving p-type characteristics of B x Al1-x N

Abstract AlN is one of the third-generation semiconductor materials with wide application prospects due to its 6.2 eV band gap. In the application of semiconductor deep ultraviolet lasers, progress is slow due to the difficulty in obtaining p-type AlN with good performance. In this paper, the commonly used way of Mg directly as AlN dopant is abandoned, the inhibition effect of the B component on self-compensation of AlN crystal was studied. The improvement of self-compensation performance of AlN crystal by B component is studied by first principles calculation. The results show that the addition of B component can increase the hole concentration of AlN, which is conducive to the formation of p-type AlN

A Novel Algorithm for Thickness Prediction in Incremental Sheet Metal Forming

Incremental sheet metal forming characterized as increased flexibility and local plastic deformation is well suitable for low-production-run manufacturing and a new sample trial production of complex shapes. Thickness thinning is still an obstacle to the application of incremental forming. In this study, a novel mathematical algorithm based on a non-uniform rational B-spline (NURBS) surface was proposed and implemented which focuses on predicting and calculating the final thickness for arbitrary parts in incremental forming. In order to evaluate the validity of the proposed model, the finite element simulation and forming experiments of three kinds of parts, such as truncated cones, truncated pyramids and ellipsoid parts, were conducted. The thickness of theoretical prediction was compared with that of finite element simulation and experiment, and good agreements were obtained. The results show that the proposed model and the method are effective and robust for predicting the thickness of the formed parts in incremental sheet metal forming

Innate Immune Memory in Monocytes and Macrophages: The Potential Therapeutic Strategies for Atherosclerosis

Atherosclerosis is a complex metabolic disease characterized by the dysfunction of lipid metabolism and chronic inflammation in the intimal space of the vessel. As the most abundant innate immune cells, monocyte-derived macrophages play a pivotal role in the inflammatory response, cholesterol metabolism, and foam cell formation. In recent decades, it has been demonstrated that monocytes and macrophages can establish innate immune memory (also termed trained immunity) via endogenous and exogenous atherogenic stimuli and exhibit a long-lasting proinflammatory phenotype. The important cellular metabolism processes, including glycolysis, oxidative phosphorylation (OXPHOS), the tricarboxylic acid (TCA) cycle, fatty acid synthesis, and cholesterol synthesis, are reprogrammed. Trained monocytes/macrophages with innate immune memory can be persistently hyperactivated and can undergo extensive epigenetic rewiring, which contributes to the pathophysiological development of atherosclerosis via increased proinflammatory cytokine production and lipid accumulation. Here, we provide an overview of the regulation of cellular metabolic processes and epigenetic modifications of innate immune memory in monocytes/macrophages as well as the potential endogenous and exogenous stimulations involved in the progression of atherosclerosis that have been reported recently. These elucidations might be beneficial for further understanding innate immune memory and the development of therapeutic strategies for inflammatory diseases and atherosclerosis

A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities

People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme