Search

This paper presents a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. The novelty of this work lies in an effective fiber length evaluation based on a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. This makes it possible to use an electron microscope to image fiber samples corresponding to the sample size using microtomography. Molded samples and pellets of four polyamide-6 short-glass fiber-reinforced composites with mass fractions of 15%, 30%, and 50% were considered. The aspect ratio distribution measured by SEM for the investigated materials was 20.25 with a coefficient of variation of 5.1%. The fiber aspect ratio obtained based on mean field homogenization theory and the tensile curve approximation was underestimated at 13.698 with a coefficient of variation of 5.2%. The deviation between the micro- and macro-estimates can be represented as a mean effective aspect ratio of 68% with a coefficient of variation of 8.5%. The developed technology for preparing samples for SEM and automated image processing can be used to study other short-reinforced polymer composite materials. The obtained estimates can serve as a useful reference when calibrating other models of short-fiber-reinforced polymer materials.

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images.

This paper discusses an approach to estimating the parameters of the cohesive zone model (CZM) by mode II by extruding the bushing along the lug axis. This method of evaluation requires small samples, which is particularly relevant when investigating short fiber-reinforced polymers (SFRPs) with additively manufactured embedded elements. Adhesion is investigated on the example of 30% carbon fiber-reinforced polyamide-6 molded to Ti-6Al-4V (VT6) selective laser-melted (SLM) alloy bushing in cases of a roughness Ra = 2.66 μm (vibratory finishing), Ra = 8.79 μm (sandblasting), and Ra = 10.02 (directly from SLM). The values of the maximum equivalent tangential contact stress were in a range from 1.1 MPa to 9.5 MPa, while the critical fracture energy for tangential slip was estimated at 15 N/mm for all cases. Experimental validation of the obtained CZM mode II was carried out by evaluating the load-carrying capacity of the lugs with different bushings. In both the experiment and the calculation, greater bushing roughness provides greater lug load-bearing capacity. The ribbed bushings added significant strength in the experiments, which confirmed the importance of considering the tangential mode in the contact model. The presented models can be used for the preliminary evaluation of short fiber-reinforced polyamide-6 parts with titanium-embedded elements bearing capacity.

A fully developed turbulent flow in a concentric annulus, Re =10,000, ri/ro=0.5, with an inner rotating cylinder in the velocity range N=Uω/Ub=0÷4, is studied via a large-eddy simulation. Also, for comparison, simulations by steady-state, unstatiounary RANS k-ω SST (URANS), and Elliptic Blending Model (EBM) were made. The main focus of this study is on the effect of high rotation on the mean flow, turbulence statistics, and vortex structure. Distribution of the tangential velocity and the Reynolds stress tensor change their behaviour at N>0.5∼1. With rotation increases, the production of tangential fluctuation becomes dominant over axial ones and the position of turbulent kinetic energy maximum shifts towards the wall into the buffer zone. URANS and EBM approaches show good agreement with LES in mean flow, turbulent statistics, and integral parameters. The difference in pressure loss prediction between LES and URANS does not exceed 20%, but the average difference is about 11%. The EBM approach underestimates pressure losses up to 9% and on average not more than 5%. Vortex structures are described well by URANS.

Abstract  Location awareness is the key capability of mobile computing applications. Despite high demand, indoor location technologies have not become truly ubiquitous mainly due to their requirements of costly infrastructure and dedicated hardware components. Received signal strength (RSS) based location systems are poised to realize economical ubiquity as well as sufficient accuracy for variety of applications. Nevertheless high resolution RSS based location awareness requires tedious sensor data collection and training of classifier which lengthens location system development life cycle. We present a rapid development approach based on online and incremental learning method which significantly reduces development time while providing competitive accuracy in comparison with other methods. ConSelFAM (Context-aware, Self-scaling Fuzzy ArtMap) extends the Fuzzy ArtMap neural network system. It enables on the fly expansion and reconstruction of location systems which is not possible in previous systems. [ABSTRACT FROM AUTHOR]

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. The novelty of this work lies in an effective fiber length evaluation based on a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. This makes it possible to use an electron microscope to image fiber samples corresponding to the sample size using microtomography. Molded samples and pellets of four polyamide-6 short-glass fiber-reinforced composites with mass fractions of 15%, 30%, and 50% were considered. The aspect ratio distribution measured by SEM for the investigated materials was 20.25 with a coefficient of variation of 5.1%. The fiber aspect ratio obtained based on mean field homogenization theory and the tensile curve approximation was underestimated at 13.698 with a coefficient of variation of 5.2%. The deviation between the micro- and macro-estimates can be represented as a mean effective aspect ratio of 68% with a coefficient of variation of 8.5%. The developed technology for preparing samples for SEM and automated image processing can be used to study other short-reinforced polymer composite materials. The obtained estimates can serve as a useful reference when calibrating other models of short-fiber-reinforced polymer materials. [ABSTRACT FROM AUTHOR]

This paper discusses an approach to estimating the parameters of the cohesive zone model (CZM) by mode II by extruding the bushing along the lug axis. This method of evaluation requires small samples, which is particularly relevant when investigating short fiber-reinforced polymers (SFRPs) with additively manufactured embedded elements. Adhesion is investigated on the example of 30% carbon fiber-reinforced polyamide-6 molded to Ti-6Al-4V (VT6) selective laser-melted (SLM) alloy bushing in cases of a roughness Ra = 2.66 μm (vibratory finishing), Ra = 8.79 μm (sandblasting), and Ra = 10.02 (directly from SLM). The values of the maximum equivalent tangential contact stress were in a range from 1.1 MPa to 9.5 MPa, while the critical fracture energy for tangential slip was estimated at 15 N/mm for all cases. Experimental validation of the obtained CZM mode II was carried out by evaluating the load-carrying capacity of the lugs with different bushings. In both the experiment and the calculation, greater bushing roughness provides greater lug load-bearing capacity. The ribbed bushings added significant strength in the experiments, which confirmed the importance of considering the tangential mode in the contact model. The presented models can be used for the preliminary evaluation of short fiber-reinforced polyamide-6 parts with titanium-embedded elements bearing capacity. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the L.N. Gumilyov Eurasian National University. Bioscience Series is the property of L.N. Gumilyov Eurasian National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimating climate response to observed and projected increases in atmospheric greenhouse gases usually requires averaging among multiple independent simulations of computationally expensive global climate models to filter out the internal climate variability. Studies have shown that advanced pattern recognition methods allow one to obtain accurate estimates of the forced climate signal from just a handful of such climate realizations. The accuracy of these methods for a fixed ensemble size, however, decreases with an increasing magnitude of the low-frequency, decadal and longer internal climate variability. Here we generalize a previously developed Bayesian methodology of Linear Dynamical Mode (LDM) decomposition for spatially extended time series to enable joint identification and analysis of forced signal and internal variability in ensembles of climate simulations, a methodology dubbed here an ensemble LDM, or ELDM. The new ELDM method is shown to outperform its pattern-recognition competitors by more accurately isolating the forced signal in small ensembles of both toy- and state-of-the-art climate-model simulations. It is able to do so by explicitly recognizing a non-random structure of the internal variability, identified by the ELDM algorithm alongside the optimal forced-signal estimate, which allows one to study possible dynamical connections between the two types of variability. The optimal ELDM filtering provides a unique opportunity for objective intercomparison of decadal and longer climate variability across different global climate models—a task that proved difficult due to uncertainties associated with the noisy character and limited length of historical climate simulations combined with parameter uncertainties of alternative signal-detection methods. [ABSTRACT FROM AUTHOR]

A fully developed turbulent flow in a concentric annulus, Re = 10,000 , r i / r o = 0.5 , with an inner rotating cylinder in the velocity range N = U ω / U b = 0 ÷ 4 , is studied via a large-eddy simulation. Also, for comparison, simulations by steady-state, unstatiounary RANS k- ω SST (URANS), and Elliptic Blending Model (EBM) were made. The main focus of this study is on the effect of high rotation on the mean flow, turbulence statistics, and vortex structure. Distribution of the tangential velocity and the Reynolds stress tensor change their behaviour at N > 0.5 ∼ 1 . With rotation increases, the production of tangential fluctuation becomes dominant over axial ones and the position of turbulent kinetic energy maximum shifts towards the wall into the buffer zone. URANS and EBM approaches show good agreement with LES in mean flow, turbulent statistics, and integral parameters. The difference in pressure loss prediction between LES and URANS does not exceed 20%, but the average difference is about 11%. The EBM approach underestimates pressure losses up to 9% and on average not more than 5%. Vortex structures are described well by URANS. [ABSTRACT FROM AUTHOR]

It is well-known that the upper ocean heat content (OHC) variability in the tropical Pacific contains valuable information about dynamics of El Niño–Southern Oscillation (ENSO). Here we combine sea surface temperature (SST) and OHC indices derived from the gridded datasets to construct a phase space for data-driven ENSO models. Using a Bayesian optimization method, we construct linear as well as nonlinear models for these indices. We find that the joint SST-OHC optimal models yield significant benefits in predicting both the SST and OHC as compared with the separate SST or OHC models. It is shown that these models substantially reduce seasonal predictability barriers in each variable—the spring barrier in the SST index and the winter barrier in the OHC index. We also reveal the significant nonlinear relationships between the ENSO variables manifesting on interannual scales, which opens prospects for improving yearly ENSO forecasting. [ABSTRACT FROM AUTHOR]

Natural and social systems exhibit complex behavior reflecting their rich dynamics, whose governing laws are not fully known. This study develops a unified data-driven approach to estimate predictability of such systems when several independent realizations of the system's evolution are available. If the underlying dynamics are quasi-linear, the signal associated with the variable external factors, or forcings, can be estimated as the ensemble mean; this estimation can be optimized by filtering out the part of the variability with a low ensemble-mean-signal-to-residual-noise ratio. The dynamics of the residual internal variability is then encapsulated in an optimal, in a Bayesian sense, linear stochastic model able to predict the observed behavior. This model's self-forecast covariance matrices define a basis of patterns (directions) associated with the maximum forecast skill. Projecting the observed evolution onto these patterns produces the corresponding component time series. These ideas are illustrated by applying the proposed analysis technique to (1) ensemble data of regional sea-surface temperature evolution in the tropical Pacific generated by a state-of-the-art climate model and (2) consumer-spending records across multiple regions of the Russian Federation. These examples map out a range of possible solutions—from a solution characterized by a low-dimensional forced signal and a rich spectrum of predictable internal modes (1)—to the one in which the forced signal is extremely complex, but the number of predictable internal modes is limited (2). In each case, the proposed decompositions offer clues into the underlying dynamical processes, underscoring the usefulness of the proposed framework. [ABSTRACT FROM AUTHOR]

Worldwide populations of shorebirds are declining, associated with a complex interplay of climate change, predation, human disturbance and habitat degradation. Comprehensive information on the distribution and breeding ecology of shorebird populations is crucial to understand and mitigate these threats. Kazakhstan, the largest country in Central Asia, comprises multiple flyways and breeding habitats for shorebird species, including the Kentish Plover Charadrius alexandrinus, but information on the population size and breeding distribution of shorebird species in the region is highly limited. We conducted a wide‐scale survey of Kentish Plover across Kazakhstan during the breeding season and utilize species distribution modelling to outline key anthropogenic and environmental variables that determine Kentish Plover presence. Our results reveal widespread distribution of Kentish Plovers across Kazakhstan but indicate that breeding densities are generally low. Our distribution modelling stresses the primary importance of proximity to water bodies and climate as the main predictors of Kentish Plover presence, but reveals a weak association with indicators of human disturbance. We utilize our distribution modelling to provide the first quantitative estimate of the breeding population size of Kentish Plover in Kazakhstan, which indicates a modest number of individuals given the size of the country (between 12 000 and 32 000 individuals). Our results indicate the key routes via which climate change may impact on population‐level distributions of Kentish Plover and provide a platform for future studies investigating species distributions across similarly vast and inaccessible regions. [ABSTRACT FROM AUTHOR]

The ship's resistance and manoeuvrability in shallow waters can be adversely influenced by the presence of fluid mud layers on the seabed of ports and waterways. Fluid mud exhibits a complex non-Newtonian rheology that is often described using the Herschel–Bulkley model. The latter has been recently implemented in a maritime finite-volume CFD code to study the manoeuvrability of ships in the presence of muddy seabeds. In this paper, we explore the accuracy and robustness of the CFD code in simulating the flow of Herschel–Bulkley fluids, including power-law, Bingham and Newtonian fluids as particular cases. As a stepping stone towards the final maritime applications, the study is carried out on a classic benchmark problem in non-Newtonian fluid mechanics: the laminar flow around a sphere. The aim is to test the performance of the non-Newtonian solver before applying it to the more complex scenarios. Present results could also be used as reference data for future testing. Flow simulations are carried out at low Reynolds numbers in order to compare our results with an extensive collection of data from the literature. Results agree both qualitatively and quantitatively with literature. Difficulties in the convergence of the iterative solver emerged when simulating Bingham and Herschel–Bulkley flows. A simple change in the interpolation of the apparent viscosity has mitigated such difficulties. The results of this work, combined with our previous code verification exercises, suggest that the non-Newtonian solver works as intended and it can be thus employed on more complex applications. [ABSTRACT FROM AUTHOR]

The loss of autocorrelations of tropical sea surface temperatures (SST) during late spring, also called the spring predictability barrier (SPB), is a factor that strongly limits the predictability of El Nino Southern Oscillation (ENSO), and especially the statistical SST‐based ENSO forecasts starting from the winter‐spring season. Recent studies show that Pacific atmospheric circulation anomalies in winter‐spring may have a long‐term impact on the summer tropical climate via the SST footprint. Here, we infer an index based on sea level pressure (SLP) data from February to March in a single area surrounding Hawaii, and show that this area is the most informative part of the large SLP pattern initiating the SST footprinting mechanism. We then construct a statistically optimal linear model of the Nino 3.4 index taking this atmospheric index as a forcing. We find that this forcing efficiently lowers the SPB and provides significant improvements of interseasonal Niño 3.4 forecasts. Plain Language Summary: Interseasonal forecasting of El Niño Southern Oscillation (ENSO) is in high demand due to the impacts of ENSO on regional climatic conditions around the world as well as the global climate. Improvements in the quality of climate data in recent decades have led to the active use of statistical ENSO models, which compete with physical models in predictive power. The main disadvantage of statistical forecasts is the pronounced seasonal growth of uncertainty when predicting the upcoming summer‐fall ENSO conditions from winter‐spring months; this phenomenon is called the spring predictability barrier (SPB). A number of recent works revealed that winter‐spring atmospheric anomalies can substantially impact the ENSO system through the SPB via a complex atmosphere‐ocean interaction mechanism. Here, we introduce a reliable ENSO predictor constructed from sea level pressure data relating to this mechanism and show that the predictor significantly improves the multimonth (up to 1 year) ENSO forecast by lowering the SPB in a statistical model of the key ENSO index. Key Points: A novel early El Nino Southern Oscillation (ENSO) predictor based on the February–March sea level pressure is introducedSignificant correlations of the predictor with the upcoming summer – next spring ENSO conditions are shownThe predictor significantly improves the interseasonal forecast skills of the statistical Nino 3.4 index model [ABSTRACT FROM AUTHOR]

Annual cycle events may be interlinked, influence following annual cycle stages, and may alter performance of individuals. Such links, called carry-over effects, can explain individual variation in timing or reproductive success in migratory species. Identifying the key links affecting fitness may reveal the mechanisms of species population dynamics but the current evidence for the strongest carry-over effects is equivocal. Here, we aim to assess the carry-over effects in great reed warblers Acrocephalus arundinaceus, a long-distance migratory songbird, using 103 full-annual tracks from three European and two Asian breeding populations. Our results showed strong positive relationships within autumn and spring migration periods and buffering capacity of the non-breeding period preventing events to carry over between these periods. Moreover, we found no profound relation between the non-breeding habitat quality or seasonality (quantified using stable isotopes and remote sensing data) and the timing of spring migration. The strongest carry-over effects occurred in individuals from the southern European breeding population compared to the northern and the central European populations. A moderate relationship between the habitat seasonality during moult and the spring migration timing indicates the importance of the complete moult. The overall weak carry-over effects of non-breeding habitat conditions found in this study contrast with previous results and imply between-species differences in these crucial relationships. Moreover, the population-specific carry-over effects highlight the importance of multi-population approach and advise caution in interpretation of results from single-population studies. Finally, the carry-over effect from the moulting period indicates the significance of a so-far neglected link in the species. Significance statement: Environmental conditions vary in space and time. Therefore, migratory species adjust the timing of migration in order to maximise their fitness. However, the links between annual cycle events in multiple populations and the consequences of environmental conditions outside the breeding range are scarcely known. In this study, we used tracking data of the great reed warbler, an insectivorous bird species breeding across western Eurasia and wintering in Africa, to study a complex system of links between annual events. We found that the strength of these links differed between geographically distinct populations but not between sexes. Moreover, harsh environmental conditions during moult delayed the timing of subsequent events. Our findings could help explain large-scale differences in population size changes observed in some species and highlight the importance of energetically demanding moult period for the life of migratory species. Finally, our results demonstrate the need for multi-population approach in studies on seasonal interactions. [ABSTRACT FROM AUTHOR]

Development of vascular collaterals in a lesion area is one of the key factors that determine not only the choice of treatment for ischemic stroke (IS) patients, but also outcome and therapy effectiveness. The main method for examining the vessels’ ramification is CT angiography (CTA). CTA analysis may be improved by incorporating filters designed to extract more features about vessels and quantify their level of development. This work suggests the usage of radiomics methods in the analysis of vesselness measure calculated from CTA images. Vesselness measurement is based on the analysis of the Hessian matrix with a few modifications dictated by practical aspects of this issue. The developed algorithm was implemented as a filter that generates a new 3D image, every voxel of which has the probability of belonging to a vessel-like structure. Further analysis of the distribution of vesselness in the lesion area and in the intact contralateral area was conducted with the methods from the open library PyRadiomics. A set of radiomics features was calculated. Preliminary analysis on a sample of 30 IS patients showed the presence of significant differences between afflicted and intact hemispheres. [ABSTRACT FROM AUTHOR]

We describe the mathematical tools of the optimal estimation method for retrieval of tropospheric temperature profiles (0–10 km) from radiometric data with the desired profiles parameterized in the form of expansion over empirical orthogonal functions obtained by a singular analysis of the covariance matrix of the radiosonde measurement data. It is shown that within the framework of such an approach one can use a high-resolution altitude grid for a relatively small dimensionality of the inverse problem. This permits one to properly analyze the statistics of radiosonde measurements and, at the same time, retrieve the temperature profiles without using a large computing power. We have conducted test retrievals of temperature using a large ensemble of simulated noisy spectra of atmospheric self-radiation based on the statistics of radiosonde temperature measurements above Nizhny Novgorod. It is found that the r.m.s. error of temperature retrieval from spectra in frequency ranges 50–55 GHz, 55–59 GHz, and 50–59 GHz varies within the ranges 0.5–2.7 K (at altitudes of 0–10 km), 0.3–1 K (at altitudes of 0–2 km), and 0.3–2.8 K (at altitudes of 0–10 km), respectively. The optimal number of empirical orthogonal functions (dimensionality of the problem) is 6–8 in the case of the 50–55 GHz spectra, 8 in the case of the 55–59 GHz spectra, and 10 in the case of the 50–59 GHz spectra. [ABSTRACT FROM AUTHOR]

We suggest a new method for building data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network with specific structure, which allows for the joint reconstruction of both a low-dimensional embedding for dynamical components in the data and an evolution operator. The key link of the method is a Bayesian optimization of both model structure and the hypothesis about the data generating law, which is needed for constructing the cost function for model learning. First, the performance of the method is successfully tested in the situation when a signal from a low-dimensional dynamical system is hidden in noisy multidimensional observations. Second, the method is used for building the data-driven model of the low frequency variability (LFV) in the quasigeostrophic model of the Earth's midlatitude atmosphere—a high-dimensional chaotic system. It is demonstrated that the key regimes of the atmospheric LFV are reproduced correctly in data simulations by means of the obtained model. [ABSTRACT FROM AUTHOR]

A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system's dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models. [ABSTRACT FROM AUTHOR]

A nonlinear decomposition method is applied to the analysis of global sea surface temperature (SST) time series in different epochs related to the Pacific Decadal Oscillation (PDO) since the end of 19th century to present time. This method allows one to extract an optimal (small) number of global nonlinear teleconnection patterns associated with distinct dominant time scales from the original high-dimensional spatially extended data set. In particular, it enables us to reveal ENSO teleconnection patterns corresponding to different PDO cycles during the last 145 years, to uncover four climate shifts connected with PDO phase changes and to reconstruct the corresponding global PDO patterns. We find that SST teleconnections between the ENSO region, extra-tropical Pacific regions and the Indian ocean became fundamentally nonlinear since the second half of 20th century. [ABSTRACT FROM AUTHOR]

The main cause of non-traumatic subarachnoid haemorrhage is an intracranial aneurysm's rupture. The choice of treatment approach is exceptionally difficult in cases of aneurysms with additional branches on the aneurysm's dome or neck. The impact of the arterial branches on local hemodynamics is still unclear and controversial question. At the same time, up-to-date methods of image processing and mathematical modeling provide a way to investigate the hemodynamic environment of aneurysms. The paper discusses hemodynamic aspects of aneurysms harboring arterial branch through the use of patient-specific 3D models and computational fluid dynamics (CFD) methods. The analysis showed that the presence of the arterial branches has a great influence on flow streamlines and wall shear stress, particularly for side wall aneurysm. [ABSTRACT FROM AUTHOR]

This is a study of the development of video culture in Russia in the late Soviet and early post-Soviet eras through in-depth interviewing. There has been a cult audience in Russia, although without the discursive framework which has shaped Western cult cinema (i.e. participants didn’t self-identify as cultists): a phenomenon this article terms ‘analytical cult’. Not all movies that achieved cult status outside Russia have become cult in this national context, and vice versa: there are movies treated as cult in Russia that have never been positioned as such outside the country. Some forms of cultism in Russia also have no direct analogues in their Western cult counterparts due to nationally specific means of access to cinematic distribution and production, namely video parlours and authored voiceovers. These have developed into cult forms in their own right. Therefore, although cult cinema can possess a transnational currency, it can also be reshaped in cross-cultural transitions. This kind of transnational cult demonstrates that its participatory practices may not be self-reflexively positioned as ‘cult’ by audiences/marketers/film-makers. [ABSTRACT FROM PUBLISHER]

We present a detailed description of a new approach for the extraction of principal nonlinear dynamical modes (NDMs) from high-dimensional data. The method of NDMs allows the joint reconstruction of hidden scalar time series underlying the observational variability together with a transformation mapping these time series to the physical space. Special Bayesian prior restrictions on the solution properties provide an efficient recognition of spatial patterns evolving in time and characterized by clearly separated time scales. In particular, we focus on adaptive properties of the NDMs and demonstrate for model examples of different complexities that, depending on the data properties, the obtained NDMs may have either substantially nonlinear or linear structures. It is shown that even linear NDMs give us more information about the internal system dynamics than the traditional empirical orthogonal function decomposition. The performance of the method is demonstrated on two examples. First, this approach is successfully tested on a low-dimensional problem to decode a chaotic signal from nonlinearly entangled time series with noise. Then, it is applied to the analysis of 250-year preindustrial control run of the INMCM4.0 global climate model. There, a set of principal modes of different nonlinearities is found capturing the internal model variability on the time scales from annual to multidecadal. [ABSTRACT FROM AUTHOR]

Isolated islands and their often unique biota continue to play key roles for understanding the importance of drift, genetic variation and adaptation in the process of population differentiation and speciation. One island system that has inspired and intrigued evolutionary biologists is the blue tit complex ( Cyanistes spp.) in Europe and Africa, in particular the complex evolutionary history of the multiple genetically distinct taxa of the Canary Islands. Understanding Afrocanarian colonization events is of particular importance because of recent unconventional suggestions that these island populations acted as source of the widespread population in mainland Africa. We investigated the relationship between mainland and island blue tits using a combination of Sanger sequencing at a population level (20 loci; 12 500 nucleotides) and next-generation sequencing of single population representatives (>3 200 000 nucleotides), analysed in coalescence and phylogenetic frameworks. We found (i) that Afrocanarian blue tits are monophyletic and represent four major clades, (ii) that the blue tit complex has a continental origin and that the Canary Islands were colonized three times, (iii) that all island populations have low genetic variation, indicating low long-term effective population sizes and (iv) that populations on La Palma and in Libya represent relicts of an ancestral North African population. Further, demographic reconstructions revealed (v) that the Canary Islands, conforming to traditional views, hold sink populations, which have not served as source for back colonization of the African mainland. Our study demonstrates the importance of complete taxon sampling and an extensive multimarker study design to obtain robust phylogeographical inferences. [ABSTRACT FROM AUTHOR]

The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin-Neelin-Ghil (JNG) hybrid seasonally forced coupled ocean-atmosphere model of El Niño-Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample. [ABSTRACT FROM AUTHOR]

After Stalin's death in 1953, the Soviet Union and the United States began to seek alternatives to their military rivalry by using culture as a weapon of "soft-power" in order to accomplish their foreign policy goals using attraction rather than coercion. As the Cold War intensified, a thriving competition developed between the superpowers to determine which of them could send more cultural diplomats to the other side, in the form of soloists and performing groups. This article addresses the undercurrent of socio-economic and political processes involved in organizing violinist David Oistrakh's first concert tour of the United States in 1955. I'll discuss how Soviet organizations worked with non-governmental Western partners, such as concert firms and impresarios to bring Soviet performers to the United States. I'll outline the development of the competition between Frederick C. Schang from Columbia Artists Management, and Sol Hurok from Hurok Artists, Inc. in the organization of Oistrakh's American outreach from 1955 to 1959. I'll also discuss how Soviet and American violinists, such as Oistrakh and Yehudi Menuhin, played significant roles in both the organization of tours and the choice of which concert firms would be used in the future. While driven primarily by economic motivations at first, these cultural overtures would have increasingly political and diplomatic implications as they expanded the Soviet Union's cultural influence to the West. The ripple effect of this "back door diplomacy" very likely affected the outcome of the Cold War itself. [ABSTRACT FROM AUTHOR]