Turbulent flow and heat transfer in eccentric annulus

Published versio

Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation

High-resolution regional hindcasting of ocean and sea ice plays an important role in the assessment of shipping and operational risks in the Arctic Ocean. The ice-ocean model NEMO-LIM3 was modified to improve its simulation quality for appropriate spatio-temporal resolutions. A multigrid model setup with connected coarse- (14 km) and fine-resolution (5 km) model configurations was devised. These two configurations were implemented and run separately. The resulting computational cost was lower when compared to that of the built-in AGRIF nesting system. Ice and tracer boundary-condition schemes were modified to achieve the correct interaction between coarse- and fine grids through a long ice-covered open boundary. An ice-restoring scheme was implemented to reduce spin-up time. The NEMO-LIM3 configuration described in this article provides more flexible and customisable tools for high-resolution regional Arctic simulations

Revealing sub-{\mu}m inhomogeneities and {\mu}m-scale texture in H2O ice at Megabar pressures via sound velocity measurements by time-domain Brillouin scattering

Time-domain Brillouin scattering technique, also known as picosecond ultrasonic interferometry, which provides opportunity to monitor propagation of nanometers to sub-micrometers length coherent acoustic pulses in the samples of sub-micrometers to tens of micrometers dimensions, was applied to depth-profiling of polycrystalline aggregate of ice compressed in a diamond anvil cell to Megabar pressures. The technique allowed examination of characteristic dimensions of elastic inhomogeneities and texturing of polycrystalline ice in the direction normal to the diamond anvil surfaces with sub-micrometer spatial resolution via time-resolved measurements of variations in the propagation velocity of the acoustic pulse traveling in the compressed sample. The achieved two-dimensional imaging of the polycrystalline ice aggregate in-depth and in one of the lateral directions indicates the feasibility of three-dimensional imaging and quantitative characterization of acoustical, optical and acousto-optical properties of transparent polycrystalline aggregates in diamond anvil cell with tens of nanometers in-depth resolution and lateral spatial resolution controlled by pump laser pulses focusing.Comment: 32 pages, 5 figure

Hydrocortisone concentration influences time to clinically significant healing of acute inflammation of the ocular surface and adnexa – results from a double-blind randomized controlled trial

BACKGROUND: The efficacy of topical ophthalmic corticosteroids depends upon small modifications in preparations, such as drug concentration. The aim of this study was to confirm that hydrocortisone acetate (HC-ac) ophthalmic ointments of 2.5% and 1% are more effective than a 0.5% eye ointment. METHODS: In this randomized, double-blind, placebo-controlled, parallel-group clinical study, the change of signs and symptoms of acute inflammation of the ocular surface and adnexa was evaluated in 411 subjects. RESULTS: Median time to clinically relevant response as estimated by 50% reduction in clinical signs and symptoms (CSS) total score over the entire trial was similar for subjects treated with HC-ac 2.5% (73.5 h) and for subjects treated with HC-ac 1.0% (67.7 h) and was considerably and significantly longer for subjects treated with HC-ac 0.5% (111.8 h) [p < 0.001 for both dosages]. All trial medications were safe and well tolerated. CONCLUSION: Hydrocortisone acetate 2.5% and Hydrocortisone acetate 1% eye ointments are efficacious and safe treatments for acute inflammations of the ocular surface or adnexa, and showed significantly better efficacy than a control group treated with Hydrocortisone acetate 0.5% therapy. TRIAL REGISTRATION: Current Controlled Trials ISRCTN15464650

A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification

Complex systems' modeling and simulation are powerful ways to investigate a multitude of natural phenomena providing extended knowledge on their structure and behavior. However, enhanced modeling and simulation require integration of various data and knowledge sources, models of various kinds (data-driven models, numerical models, simulation models, etc.), intelligent components in one composite solution. Growing complexity of such composite model leads to the need of specific approaches for management of such model. This need extends where the model itself becomes a complex system. One of the important aspects of complex model management is dealing with the uncertainty of various kinds (context, parametric, structural, input/output) to control the model. In the situation where a system being modeled, or modeling requirements change over time, specific methods and tools are needed to make modeling and application procedures (meta-modeling operations) in an automatic manner. To support automatic building and management of complex models we propose a general evolutionary computation approach which enables managing of complexity and uncertainty of various kinds. The approach is based on an evolutionary investigation of model phase space to identify the best model's structure and parameters. Examples of different areas (healthcare, hydrometeorology, social network analysis) were elaborated with the proposed approach and solutions

Classicality concept test on neutral pseudoscalar meson qubits with Wigner inequalities

In this study, we introduce the concept of Classicality and derive Wigner inequalities that depend on two instants, with a potential extension to three instants. We explore the experimental feasibility of testing the violations of these inequalities in both pure and mixed flavor states of $K^0$-, $D^0$-, and $B_s$- meson pairs. Using the Werner noise model, we demonstrate that violations of time-dependent Wigner inequalities can be detected even when background processes constitute up to 50% of the system

Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble

The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term forecast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.Comment: 7 pages, 6 figure

Hunting, Fishing and Early Agriculture in Northern Primor'e in the Russian Far East

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Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time