Location Of Boundary Research Articles

Two methods to analyze radial diffusion ensembles: The perils of space- and time-dependent diffusion

Particle dynamics in Earth's outer radiation belt can be modeled using a diffusion framework, where large-scale electron movements are captured by a diffusion equation across a single adiabatic invariant, L*(L). While ensemble models are promoted to represent physical uncertainty, as yet there is no validated method to analyze radiation belt ensembles. Comparisons are complicated by the domain dependent diffusion, since diffusion coefficient DLL is dependent on L. We derive two tools to analyze ensemble members: time to monotonicity tm and mass/energy moment quantities N,E. We find that the Jacobian (1/L2) is necessary for radiation belt error metrics. Components of ∂E/∂t are explicitly calculated to compare the effects of outer and inner boundary conditions, and loss, on the ongoing diffusion. Using tm, N, and E, we find that: (a) different physically motivated choices of outer boundary condition and location result in different final states and different rates of evolution; (b) the gradients of the particle distribution affect evolution more significantly than DLL; (c) the enhancement location, and the amount of initial background particles, are both significant factors determining system evolution; (d) loss from pitch-angle scattering is generally dominant; it mitigates but does not remove the influence of both initial conditions and outer boundary settings, which are due to the L-dependence of DLL. We anticipate that this study will promote renewed focus on the distribution gradients, on the location and nature of the outer boundary in radiation belt modeling, and provide a foundation for systematic ensemble modeling.

Simulation and experimental investigation of grain structure, residual stress,γ′ phases in single crystal blade

Ni-based superalloy turbine blades have been required components in contemporary aero-engine. Knowing the solidification behavior, residual stress at grain defects, and microstructure of directionally solidified turbine blades is a required condition to improve the service performance of directionally solidified turbine blades. Firstly, the temperature field evolution of the blade under the withdrawal rate of 3 mm/min was studied. The deviations in temperature distribution in the high-rate solidification (HRS) procedure, particularly near the platform, can lead to transformations in the mushy zone, potentially resulting in solidification defects. Secondly, the grain growth of hollow turbine blades was calculated using the cellular automaton-finite factor method. The simulated grain framework was essentially consistent with experimental results. A method of process bar addition based on physical field distribution is also proposed. This method involves designing a combination of one Y-shaped and two I-shaped rods to decrease the cooling rate of blade edges and eliminate stray grains (SG). Then, the residual stress distribution at the locations of stray grains and low-angle grain boundaries (LAGBs) was analyzed before and after the addition of process bars. Finally, discussions were held regarding the distribution of γ′ phases in grain defects and blades.

Quantitative assessment of cracks in concrete structures using active-learning-integrated transformer and unmanned robotic platform

Quantitative assessment of cracks in concrete bridges is crucial for structural health monitoring and digital twin. However, the training of crack segmentation models relies heavily on annotation resources, and their segmentation capabilities are often unsatisfactory in terms of the accuracy of boundary location of thin cracks encountered in practice. In this paper, an active-learning-integrated crack segmentation transformer (ACS-Former) framework is proposed to maximize segmentation performance with limited annotation resources. The two-branch ACS-Former includes (1) a feature pyramid transformer (FPT) for multi-scale crack segmentation and (2) boundary difficulty-aware active learning (BDAL) to select informative images for labeling and incorporation into FPT training. Additionally, an adhesive climbing robot is proposed for image collection of hard-to-access components of large bridges. The on-site operational feasibility and practicability of the proposed ACS-Former and climbing robot are demonstrated by field experiments performed on in-service bridges, including the quantification of cracks narrower than 0.2 mm, as required by engineering codes.

Nonlinear mechanics of phase-change-induced accretion

In this paper, we formulate a continuum theory of solidification within the context of finite-strain coupled thermoelasticity. We aim to fill a gap in the existing literature, as the existing studies on solidification typically decouple the thermal problem (the classical Stefan’s problem) from the elasticity problem, and often limit themselves to linear elasticity with small strains. Treating solidification as an accretion problem, with the growth velocity correlated with the jump in the heat flux across the boundary, it presents an initial boundary-value problem (IBVP) over a domain whose boundary location is a priori unknown. This IBVP is solved numerically for the specific example of radially inward solidification in a spherical container. Several parametric studies are conducted to compare the numerical results with the rigid cases in the literature and gain insights into the role of elastic deformations in solidification.

Solving Multi-Objective Optimal Control Problems Using a Multiresolution Approach

This paper presents an adaptive multiresolution strategy for multi-objective optimal control problems. The optimal control problem is solved using a direct approach, with individualistic grid adaptation facilitated by a local error analysis at element boundaries. Multiple objectives are considered using a dominance-based approach applying both local and global search methods to a collaborative population of unique solutions. These aspects are simultaneously incorporated via a novel application of evolutionary algorithms for adaptive optimal control problems. Together, this avoids the need for a priori specification of the quantity and temporal location of element boundaries and the set of scalarization weights defining the multi-objective descent directions. Solution fidelity can thus increase concurrently with the exploration of the design space, which leads to increased numerical efficiency while propagating and maintaining population diversity. The benefits of the proposed approach over traditional uniform-grid implementations are demonstrated. Results show that the multiresolution approach is capable of striking an effective balance between solution fidelity, population diversity, and computational cost unachievable using uniform grids.

Accuracy of kinetic equilibrium reconstruction of NSTX and NSTX-U plasmas and its impact on the transport and stability analysis

Abstract An accurate magnetohydrodynamic (MHD) equilibrium reconstruction is an essential starting point for stability and transport plasma analysis. This work describes an approach for obtaining kinetic equilibrium reconstructions using the OMFIT framework, which has been applied for the first time to spherical tokamak data from NSTX and NSTX-U. The EFIT equilibrium solver is integrated with experimental data analysis procedures and subsequent TRANSP transport simulations to enhance the accuracy of the reconstruction, in particular at the edge region, by adding constraints on the total pressure and current density profiles, based on the transport code solution. The accuracy of the equilibrium depends on the uncertainty and number of constraints, as well as the choice of basis functions to represent the pressure and current density profiles. An improved fidelity of the equilibrium reconstruction is demonstrated by reducing the variability of the magnetic axis and boundary locations from several centimeters for reconstructions based on magnetic and experimental pressure constraints to only several millimeters for kinetic reconstructions based on transport code constraints, when different representations for basis functions were tested. Variability of the safety factor on-axis was reduced ten times in the same sensitivity study. The accuracy of the equilibrium reconstruction and subsequent mapping of experimental kinetic profile data have a significant impact on TGLF and linear CGYRO turbulence simulations, which predict different spectra of unstable modes and turbulent fluxes for cases with different numbers of constraints in the equilibrium reconstruction. Conversely, the stability analysis performed with the GATO code shows plasmas stable to n = 1 MHD modes in both equilibria using magnetic and experimental pressure constraints as well as the transport code constrained equilibrium. However, a scan of parameters away from these conditions shows considerable deviation in the threshold of unstable modes between these reconstructions. Therefore, for reliable plasma analysis and use in turbulence and stability calculations, a high fidelity equilibrium reconstruction with accurate kinetic constraints based on transport code solutions is necessary.

Enhancing analysis of diadochokinetic speech using deep neural networks

Diadochokinetic speech tasks (DDK) involve the repetitive production of consonant-vowel syllables. These tasks are useful in detecting impairments, differential diagnosis, and monitoring progress in speech-motor impairments. However, manual analysis of those tasks is time-consuming, subjective, and provides only a rough picture of speech. This paper presents several deep neural network models working on the raw waveform for the automatic segmentation of stop consonants and vowels from unannotated and untranscribed speech. A deep encoder serves as a features extractor module, replacing conventional signal processing features. In this context, diverse deep learning architectures, such as convolutional neural networks (CNNs) and large self-supervised models like HuBERT, are applied for the extraction process. A decoder model uses derived embeddings to identify frame types. Consequently, the paper studies diverse deep architectures, ranging from linear layers, LSTM, CNN, and transformers. These architectures are assessed for their ability to detect speech rate, sound duration, and boundary locations on a dataset of healthy individuals and an unseen dataset of older individuals with Parkinson’s Disease. The results reveal that an LSTM model performs better than all other models on both datasets and is comparable to trained human annotators.

Репрезентация констант лиминального пространства «гостиница» в акцентных номинациях малой поэтической формы

The research aims to identify the representative constants of the liminal space “hotel” in accentual nominations of small poetic form, to classify the thematic complexes-correlates “boundary location / mental representation” that transmit the conceptual idea of the poem. The paper examines the projection of the concept of “threshold” onto the existence of the lyrical “self”, highlighting and analyzing the quintessential constants: “the actual location of the hotel” (with a branched paradigm), “the window-boundary as a significant attribute of the hotel room”, “the material (objective) plane of the image”, “desolateness / presence of people (guests)” in connection with the transition of the lyrical subject from one volitional, emotional, motivational state to another. The scientific novelty of the research lies in its focus on the little-studied aspect of the concept of “space” as the liminal space, the interpretation of the corpus of presented accentual texts, the primary description and systematization of the thematic complexes-correlates “boundary location / mental representation”. As a result, it has been found that the “hotel” as one of the main threshold topoi reveals the polar states of the subject, rapidly brings the lyrical “self” to a new level of perception of the world, and upon apperception moves it from the everyday space to the existential space.

Attention to event segmentation improves memory in young adults: A lifespan study.

People spontaneously segment an observed everyday activity into discrete, meaningful events, but segmentation can be modified by task goals. Asking young adults to attend to event segmentation while watching movies of everyday actions improved their memory up to 1 month later (Flores et al., 2017). Does attending to event segmentation improve memory across the lifespan? Participants between the ages of 20 and 79 watched movies of actors performing everyday activities while intentionally encoding them for a recall and a recognition memory test 1 week (Experiment 1) or 1 month (Experiment 2) later. In addition to intentionally encoding the movies, half of the participants segmented the movies into fine-grained events. Young adults who segmented recalled more words in their recall responses than those who intentionally encoded 1 week and 1 month later. Middle-aged adults benefited from the intervention after a 1-week delay but not after a 1-month delay. Older adults over the age of 70 did not benefit from attending to segmentation. Of those who segmented, young and older adults showed similar agreement about the locations of event boundaries. Together, the results suggest that older adults are less able, compared to young adults, to maintain or retrieve well-encoded event memories after a delay. In addition, individual differences in segmentation agreement predicted memory up to 1 month later, regardless of age. These results suggest a practical and easy-to-implement intervention for improving recall of everyday events in young and middle-aged adults that is ineffective in older adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Climatology of the open–closed boundary using ULF wave observations from South Pole, McMurdo, and distributed Antarctic AGOs

It has been shown that a proxy determination of the magnetospheric open–closed magnetic field line boundary (OCB) location can be made by examining the ultra-low-frequency (ULF) wave power in magnetometer data, with particular interest in the Pc5 ULF waves with periods of 3–10 min. In this study, we present a climatology of such Pc5 ULF waves using ground-based magnetometer data from the South Pole Station (SPA), McMurdo (MCM) station, and the Automatic Geophysical Observatories (AGOs) located across the Antarctic continent, to infer OCB behavior and variability during geomagnetically quiet times (i.e., Ap < 30 nT). For each season [i.e., austral fall (20 February 2017–20 April 2017), austral winter (20 May 2017–20 July 2017), austral spring (20 August 2017–20 October 2017), and austral summer (20 November 2017–20 January 2018)], north–south (i.e., H-component) magnetic field line residual power–spectral density (PSD) measurements taken during geomagnetically quiet periods within a 60-day window centered at the austral solstice/equinox are averaged in 10-min temporal bins to form the climatology at each station. These residual PSDs thus enable the analysis of Pc5 activity (and lower period “long-band” oscillations) and, thus, OCB location/variability as a function of season and magnetic latitude. The dawn and dusk transitions across the OCB are analyzed, with a discussion of dawn and dusk variability during nominally quiet geomagnetic periods. In addition, latitudinal dependencies of the OCB and peak Pc5 periods at each station are discussed, along with the empirical Tsyganenko model comparisons to our site measurements.

ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association

ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association

A deep learning method for reflective boundary estimation

Environment estimation is a challenging task in reverberant settings such as the underwater and indoor acoustic domains. The locations of reflective boundaries, for example, can be estimated using acoustic echoes and leveraged for subsequent, more accurate localization and mapping. Current boundary estimation methods are constrained to high signal-to-noise ratios or are customized to specific environments. Existing methods also often require a correct assignment of echoes to boundaries, which is difficult if spurious echoes are detected. To evade these limitations, a convolutional neural network (NN) method is developed for robust two-dimensional boundary estimation, given known emitter and receiver locations. A Hough transform-inspired algorithm is leveraged to transform echo times of arrival into images, which are amenable to multi-resolution regression by NNs. The same architecture is trained on transform images of different resolutions to obtain diverse NNs, deployed sequentially for increasingly refined boundary estimation. A correct echo labeling solution is not required, and the method is robust to reverberation. The proposed method is tested in simulation and for real data from a water tank, where it outperforms state-of-the-art alternatives. These results are encouraging for the future development of data-driven three-dimensional environment estimation with high practical value in underwater acoustic detection and tracking.

The effects of graphene intrinsic defects on the formation of extrinsic defects by plasma treatment

Two-dimensional materials, like graphene, are expected to make highly efficient permeation membranes for separation and sensing applications; however, defects of a specific size and location must be added to the film to make them selectively permeable. Though techniques like TEM drilling give precise information on size and location, this method is not scalable for eventual commercial use. Plasma treatments are a scalable alternative, but there is less information on where the defect formation begins and how intrinsic defects affect pore formation. This work seeks to understand these effects by using Raman spectroscopy at the same location before and after plasma treatments to map how the intrinsic defects affect the formation of extrinsic defects to create nanopores. Defect density increases with increasing RF power, independent of the gas used during the plasma treatment, suggesting that the mechanism is due to ion bombardment. Coalesced and arrested graphene syntheses are used to study the relative trends of defect formation and map existing graphene grain boundary locations. The results show that regions starting with high densities of intrinsic defects, potentially due to strain, were found to degrade into nanocrystalline graphene first on the amorphization trajectory, even before grain boundaries.

Мониторинг изменений сельскохозяйственных угодий с использованием ГИС-технологий (на примере Зерноградского района Ростовской области)

An approach of correcting agricultural land boundaries based on the use of multi-source agrarian soil data from various sources with GIS technology was used to provide a more accurate representation of the status and location of the actual agricultural land boundaries. Archival materials and remote sensing data were the object of the study. The result of the work is the digitization with correction of agricultural land boundaries and assessment of their dynamics. Keywords: GEOINFORMATION SYSTEMS, AGRICULTURAL LANDS, DATA OF REMOTE SENSING DATA, ARABLE LAND, PASTE, PASTURE

Воспроизведение приливов в z- и σ-моделях циркуляции морских вод в Охотском море

The study considers the reproduction of tides by two marine circulation models implemented for the Sea of Okhotsk: the z-model with a spatial resolution of ~3.7 km implemented directly for the Sea of Okhotsk, and the σ-model with a spatial resolution of ~3.5 km implemented for the Sea of Okhotsk and the Sea of Japan. The z-model is based on the HyRMoS (Hydrometcenter of Russia Model of Sea) model developed by S. K. Popov at the Hydrometeorological Center of Russia, the σ-model is based on the INMOM (Institute of Numerical Mathematics Ocean Model). The results of model simulations were compared with the data of harmonic constants of the main tidal harmonics presented at 98 coastal stations located in the Sea of Okhotsk and the Tatar Strait. An analysis of the calculation results showed that the accuracy scores are comparable for both models. The difference in the values of tidal harmonics revealed in the study and obtained from the z and σ models is primarily due to the accuracy and features of the bottom topography used and is also related to the location of the open boundaries and the configuration of the grid domains.

Encoding signal propagation on topology-programmed DNA origami.

Biological systems often rely on topological transformation to reconfigure connectivity between nodes to guide the flux of molecular information. Here we develop a topology-programmed DNA origami system that encodes signal propagation at the nanoscale, analogous to topologically efficient information processing in cellular systems. We present a systematic molecular implementation of topological operations involving 'glue-cut' processes that can prompt global conformational change of DNA origami structures, with demonstrated major topological properties including genus, number of boundary components and orientability. By spatially arranging reactive DNA hairpins, we demonstrate signal propagation across transmission paths of varying lengths and orientations, and curvatures on the curved surfaces of three-dimensional origamis. These DNA origamis can also form dynamic scaffolds for regulating the spatial and temporal signal propagations whereby topological transformations spontaneously alter the location of nodes and boundary of signal propagation network. We anticipate that our strategy for topological operations will provide a general route to manufacture dynamic DNA origami nanostructures capable of performing global structural transformations under programmable control.

Site effect on seismic response of buried nuclear power plant structure

The development of multifunctional, multipurpose small modular next generation nuclear reactors has become a trend in designing small-scale nuclear power stations. In this study, taking a new next-generation buried nuclear power structure as the target structure, a 3D finite element model of the site-nuclear power plant structure system was established, and its response at different sites was studied. Using the internal substructure method, this study initially explored the impact of earthquake input and artificial boundary locations on the structural response. The results indicate that the internal substructure method enhances computational efficiency without compromising accuracy. For this model, it is recommended to input earthquake motion close to the structure, set the lateral boundary location to three times the width of the structure, and set the bottom boundary location to two times the depth of the structure. Subsequently, an investigation into the response of the structure at varying site wave velocities is conducted. The results show that as the wave velocity increases, the acceleration response intensifies, while the displacement response diminishes. However, the relationship is nonlinear; the response exhibits less variation with increasing site wave velocity. The seismic response of a structure is profoundly influenced by site conditions, emphasizing the imperative need for careful consideration of specific site characteristics in seismic analyses. In addition, the response pattern of the overall structure is significantly different from that of conventional large surface-sited nuclear power plants, and further research should be conducted on the seismic response characteristics of buried nuclear power plants.

Accurate Delimitation of Mine Goaves Using Multi-Attribute Comprehensive Identification and Data Fusion Technologies in 3D Seismic Exploration

Existing goaves (e.g., shafts and roadways) in mines represent important hidden dangers during the production of underlying coal seams. In this view, the accurate identification, analysis, and delimitation of the scope of goaves have become important in the 3D seismic exploration of mines. In particular, an accurate identification of the boundary swing position of goaves for 3D seismic data volumes within a certain depth interval is key and difficult at the same time. Here, a wide-band and wide-azimuth observation system was used to obtain high-resolution 3D seismic data. The complex structure of a mine was analyzed, and a seismic double processing system was applied to verify the fine processing effect of a goaf and improve the resolution of the 3D seismic data. Based on the seismic attribute identification characteristics of the goaf structure, we decided to adopt multi-attribute comprehensive identification and data fusion technologies to accurately determine the position of the goaf and of its boundary. Combining this information with the mine roadway engineering layout, we verified the accurateness and correctness of the goaf boundary location. Our study provides a good example of the accurate identification of the 3D seismic data of a roadway goaf.

Assessing the Influence of Redlining on Intergenerational Wealth and Body Mass Index Through a Quasi-experimental Framework.

Higher levels of body mass index (BMI), particularly for those who have obesity defined as class II and III, are correlated with excess risk of all-cause mortality in the USA, and these risks disproportionately affects marginalized communities impacted by systemic racism. Redlining, a form of structural racism, is a practice by which federal agencies and banks disincentivized mortgage investments in predominantly racialized minority neighborhoods, contributing to residential segregation. The extent to which redlining contributes to current-day wealth and health inequities, including obesity, through wealth pathways or limited access to health-promoting resources, remains unclear. Our quasi-experimental study aimed to investigate the generational impacts of redlining on wealth and body mass index (BMI) outcomes. We leveraged the Panel Study of Income Dynamics (PSID) and Home Owners' Loan Corporation (HOLC) maps to implement a geographical regression discontinuity design, where treatment assignment is randomly based on the boundary location of PSID grandparents in yellowlined vs. redlined areas and used outcome measures of wealth and mean BMI of grandchildren. To estimate our effects, we used a continuity-based approach and applied data-driven procedures to identify the most appropriate bandwidths for a valid estimation and inference. In our fully adjusted model, grandchildren with grandparents living in redlined areas had lower average household wealth (β = - $35,419; 95% CIrbc - $37,423, - $7615) and a notably elevated mean BMI (β = 7.47; 95% CIrbc - 4.00, 16.60), when compared to grandchildren whose grandparents resided in yellowlined regions. Our research supports the idea that redlining, a historical policy rooted in structural racism, is a key factor contributing to disparities in wealth accumulation and, conceivably, body mass index across racial groups.

Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning.

This paper aims to simplify the application of optical coherence tomography (OCT) for the examination of subsurface morphology in the oral cavity and reduce barriers towards the adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for the simplified analysis of the large volume of data collected during OCT. Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male and 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the epithelial-stromal boundary (network 4). The area under the curve of the image and artifact detection networks was 1.00 and 0.94, respectively. The Dice similarity score for the surface and epithelial-stromal boundary segmentation networks was 0.98 and 0.83, respectively. Deep learning (DL) techniques can identify the location and variations in the epithelial surface and epithelial-stromal boundary in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes.