Channel Position Research Articles

AccuStripes: Visual exploration and comparison of univariate data distributions using color and binning

Understanding and analyzing univariate distributions of data in terms of their shapes as well as their specific characteristics, regarding gaps, spikes, or outliers, is crucial in many scientific disciplines. In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., “accumulated stripes”) design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks’ Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes’ design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes’ design outperformed the line charts in accuracy for all study tasks.

Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.

The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial-temporal (EEG ST) feature learning to automatic emotion classification. The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG's spatial and temporal features, which are subsequently classified using Softmax. The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal-valence, arousal, and valence dimensions, respectively, for the DEAP dataset. The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies. The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.

EEG Emotion Recognition Based on Self-Distillation Convolutional Graph Attention Network

A convolution graph attention model based on self-distillation convolutional graph attention network (SDC-GAT) is proposed for multi-channel EEG emotion recognition. Firstly, two-dimensional feature matrix based on EEG time-domain features are constructed, and the matrix is fed into the graph attention neural network to learn the internal connections between electrical brain channels located in different brain regions. Meanwhile, the three-dimensional feature matrix is constructed according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract features containing electrode spatial position information from the three-dimensional feature matrix. Finally, outputs of the two networks are integrated to determine the emotional states. Experiments were performed on the DEAP dataset. The experimental results show that the spatial domain information of the electrode channel and the internal connection relationship between different channels are beneficial for emotion recognition. In addition, the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.

Numerical investigation on the exhaust gas combustion of an SOFC in a catalytic multichannel burner

The utilization of the exhaust gas of a solid oxide fuel cell is important to improve the energy efficiency and control pollutant emission. In this work, the combustion of solid oxide fuel cell exhaust gas (H2/CO) in a honeycomb ceramic catalytic burner is investigated numerically. A 2D numerical combustion model with 17 channels is built to analyze the influence of channel position on thermal performance and combustion characteristics. The high burnout of H2 and CO is obtained as 96.75% and 97.75%, respectively. The channels can be divided into three groups from the inside to the outside as follows: part 1, from the 9th channel to the 13th channel; part 2, from the 14th channel to the 16th channel; and part 3, the 17th channel. The channels in the same group presented the same results of flow, temperature, and combustion. Compared with the other channels, the outermost channel shows notable differences in depressing the temperature of the whole channel, moving the maximum temperature downstream, enlarging the temperature bias of the lower and upper walls, and enlarging the combustion zone. H2 and CO perform different combustion processes in the honeycomb ceramic catalytic burner. Compared with H2, the initial position of CO conversion is more affected by channel distribution. In the 17th channel, the CO oxidation rate is controlled mostly by the slower oxygen adsorption and the resulting low O(s) coverage. In the 9th channel, the CO oxidation rate is controlled mostly by the wall temperature and fuel-limited. The burnout rate of H2 changes from 95% to 99.9% with the channel position, but the burnout of CO varies little. The closer the channel to the outer wall, the higher the proportion of heterogeneous reaction and the more the generated heat. The generated heat by the channel can present a diversity of 4%.

Effects of baffle position in serpentine flow channel on the performance of proton exchange membrane fuel cells

This study used a three-dimensional numerical model of a proton exchange membrane fuel cell with five types of channels: a smooth channel (Case 1); eight rectangular baffles were arranged in the upstream (Case 2), midstream (Case 3), downstream (Case 4), and entire cathode flow channel (Case 5) to study the effects of baffle position on mass transport, power density, net power, etc. Moreover, the effects of back pressure and humidity on the voltage were investigated. Results showed that compared to smooth channels, the oxygen and water transport facilitation at the GDL-Channel interface were added 11.53%−20.60% and 7.81%−9.80% at 1.68 A·cm−2 by adding baffles. The closer the baffles were to upstream, the higher the total oxygen flux, but the lower the flux uniformity and the worse the water removal. The oxygen flux of upstream baffles was 8.14% higher than that of downstream baffles, but oxygen flux uniformity decreased by 18.96% at 1.68 A·cm−2. The order of water removal and voltage improvement was Case 4 > Case 5 > Case 3 > Case 2 > Case 1. Net power of Case 4 was 9.87% higher than smooth channel. To the Case 4, when the cell worked under low back pressure or high humidity, the voltage increments were higher. The potential increment for the backpressure of 0 atm was 0.9% higher than that of 2 atm. The potential increment for the humidity of 100% was 7.89% higher than that of 50%.

Лесоводственная эффективность проведения несплошной лесозаготовки древесины в сосняках после гидротехнической мелиорации в Сокольском районе Вологодской области

In the Sokolsky district of the Vologda region of Russian Federation, the influence of continuous harvesting of
 wood in drained pine forests on the taxation and macrostructural parameters of common pine trees (Pinus sylvestris L.)
 was determined. The objects of the study are represented by pine forests: the main species is Scots pine (P. sylvestris L.)
 with an admixture of common spruce (Picea abies (L.) H. KARST.) and birch (Betula pendula Roth). The drained forest
 objects were selected after the continuous harvesting of wood and a control stand (without logging), temporary test areas
 (runway) were delimited. Drainage measures in experimental forests were carried out in 1972. Continuous logging was
 carried out in 2005 when skidding technological drags were attached to the main corridor at an angle of 45 degrees. The
 distribution of the number of P. sylvestris L. trees by thickness steps within the boundaries of the runway in 83% (p <
 0.05) of cases tends to normal. Average periodical growth of late wood P. sylvestris L. on the runway after drainage
 increased in the channel position by 30 % (tfact ≥ tst; 4.51 > 2.70; p < 0.01), and after intermittent logging increased by
 19 % (tfact ≥ tst; 1.99 > 1.71; p < 0.1). Carrying out continuous complex harvesting of wood in drained pine forests has a
 positive effect on increasing the stock (by 38%), while in stands without logging – by 28 %.

Near-source effects on DAS recording: implications for tap tests

SUMMARY In the immediate vicinity of a source, there are strong gradients in the seismic wavefield that are tamed and modified in distributed acoustic sensing (DAS) recording due to combined effects of gauge-length averaging and local stacking on the local strain field. Close to a source broadside propagation effects are significant, and produce a characteristic impact on the local DAS channels. In the presence of topography, of surface or cable, additional effects are introduced that modify the expected signal. All these influences mean that the results of tap tests used to calibrate the channel positions along a DAS cable may give a distorted view of the actual geometry. Such effects can be important for detailed mapping of faulting processes and comparable features.

Submarine‐channel meandering reset by landslide filling, Taranaki Basin, New Zealand

AbstractLandslides are among the largest mass movements on Earth. As such, the deposits of landslides, also known as mass‐transport deposits, are significant architectural elements of continental margins, especially those receiving sediment from large deltas. Landslide dams have been shown to alter the courses of rivers and submarine channels. However, there are fewer examples of landslides completely filling submarine channels and examples of the subsequent stratigraphic evolution. A three‐dimensional seismic‐reflection dataset (<90 Hz) from the deep‐water (>1500 m) Taranaki Basin, offshore the North Island of New Zealand, was used to explore the response of a sequence of channel deposits to landslide filling. The basal channel system initially meandered like a river, with successive channel positions in close proximity, as it aggraded >250 ms two‐way travel time. This systematic, organised evolution is governed by the memory of early channel evolution, which sets the sea floor geomorphology that guides channel‐forming turbidity currents. Later, a channel approximately twice as wide as underlying channels cut off a number of channel bends, probably as a result of an increase in the discharge of channel‐forming turbidity currents. This last channel was filled with submarine landslides, which transported and deposited sediment as debris flows based on the presence of blocks within a matrix comprising chaotic, lower amplitude seismic facies. These debris‐flow deposits smoothed over the sea floor, effectively wiping the memory of channel evolution. As a result, the subsequent channel pattern bears no resemblance to the basal system. Submarine‐channel resetting by landslide filling is common in settings with frequent catastrophic basin‐margin collapses, like offshore New Zealand.

Research on the Mechanism of Frost Heaves Caused by Void Water Accumulation behind the Lining of High-Speed Railway Tunnels in Cold Regions

(1) Background: Lining voids and macroscopic water freezing are both prominent issues that threaten driving safety in high-speed railway tunnels. With the continuous expansion of the scale of high-speed rail tunnels in extremely cold areas in China, the issues of lining voids, water accumulation, and frost heaving have triggered heated discussions. The need to reveal the frost-heave mechanism is urgent. (2) Methods: Firstly, a simulation experiment of the frost heaving of accumulated water was carried out based on the discharge conditions of accumulated water. Secondly, a numerical model was used to study the evolution process of frost heaves caused by accumulated water in voids. Finally, a drainage coefficient was introduced to propose a method for calculating the frost-heave force of accumulated water in voids. (3) Results: The blockage of the drainage channel leads to the generation of frost-heave force; the freezing/thawing process of the void water develops from the thinnest part to the thickest part of the void edge, and the freeze/thaw cycle of the water body causes the frost-heave force to become greater and greater in the evacuated cavity. The higher the height and the closer the drainage channel is to the hollow bottom, the greater the frost-heave force when the accumulated water freezes. (4) Conclusions: When the evacuated water freezes, it develops from the thinner edge to the thicker center. The magnitude of the frost-heave force is affected by the freeze/thaw cycle, the height of the evacuated cavity, and the position of the drainage channel.

Performance evaluation of a blocked regulated lateral release channel for proton exchange membrane fuel cell based on entropy generation analysis

Oxygen-deficient regions at low voltage restrict the enhancement of proton exchange membrane fuel cell performance. To address this issue, a blocked regulated lateral release channel (BRLRC) is proposed to divert oxygen from blocked channel into oxygen-deficient areas. The implications of BRLRC on oxygen distribution, drainage capacity and output performance are investigated by adopting a three-dimensional multi-phase model. Through entropy generation analysis, multiple irreversible losses across varying locations for various flow fields are assessed. Structural optimization of BRLRC, focusing on parameters such as width, entrance, and junction positions of auxiliary channels, is conducted to boost net output power of the fuel cell. Results show that compared to the blocked channel, BRLRC further augments the output power, oxygen uniformity, drainage capacity and diminishes pump power. Analysis of cathodic entropy generation reveals that over 90% stems from mass transfer, with the catalyst layer as the main contributor, while BRLRC significantly reduces total entropy generation by mitigating mass transfer irreversibility. By accurately oxygen allocation, the structurally optimized BRLRC attains peak output power even with an oxygen concentration that is 7.5% lower than the maximal observed concentration. Building on this efficiency, when contrasted with the straight channel, BRLRC amplifies the maximum net output power by 9.04%, exceeding the 5.18% increase achieved by the conventional blocked channel.

Utilizing hiPSC-derived oligodendrocytes to study myelin pathophysiology in neuropsychiatric and neurodegenerative disorders.

Oligodendrocytes play a crucial role in our central nervous system (CNS) by myelinating axons for faster action potential conduction, protecting axons from degeneration, structuring the position of ion channels, and providing nutrients to neurons. Oligodendrocyte dysfunction and/or dysmyelination can contribute to a range of neurodegenerative diseases and neuropsychiatric disorders such as Multiple Sclerosis (MS), Leukodystrophy (LD), Schizophrenia (SCZ), and Autism Spectrum Disorder (ASD). Common characteristics identified across these disorders were either an inability of oligodendrocytes to remyelinate after degeneration or defects in oligodendrocyte development and maturation. Unfortunately, the causal mechanisms of oligodendrocyte dysfunction are still uncertain, and therapeutic targets remain elusive. Many studies rely on the use of animal models to identify the molecular and cellular mechanisms behind these disorders, however, such studies face species-specific challenges and therefore lack translatability. The use of human induced pluripotent stem cells (hiPSCs) to model neurological diseases is becoming a powerful new tool, improving our understanding of pathophysiology and capacity to explore therapeutic targets. Here, we focus on the application of hiPSC-derived oligodendrocyte model systems to model disorders caused by oligodendrocyte dysregulation.

Fight for Customers - Correlations of Trade Organizations and Sales Promotionon the Hungarian FMCG Market

Tract of time after the millennial brought well definable changes in retailers’ trade organizations and marketing activity in the Hungarian three-pole FMCG market. Operation conditions of enterprises became more complicated which is also reflected in the variability of the number and intensity of relevant influencing factors as well as in the difficulties of forecasting them. Out of these the change of consumer/purchase behaviour, the growth of competition intensity, the swell of procurement marketing, changes in channel position, the innovation of informatics and info communication tools, the dominant change of economic environment, the stiffening legislation and the unexpected influence of ecological environment can be highlighted. An important starting point for exploring the impact of the changes focusing on market competition is that the number and activity of chain stores operating in Hungary has stabilized, which has shifted the direction of research to explore the relationship between trade organization and marketing, improving competitive advantages and management efficiency.JEL Code: M31

Numerical study on cooling performance and thermal stress characteristic of an f-class gas turbine stator blade

In this study, a coupled numerical computation approach integrating aerothermal and thermomechanical effects was employed to investigate the cooling efficiency and thermal stress characteristics of gas turbine stator blades. A comprehensive analysis was conducted considering varying turbulence intensities in the coolant flow (spanning from 0.05 to 0.15) and different coolant media configurations, including pure air, dual-medium mixture of air and steam, and pure steam. The distributional traits of cooling efficiency and thermal stress on the stator blade surface under these conditions were meticulously examined. Furthermore, quantitative assessments were performed to determine the extentto which coolant turbulence intensity and coolant type affect the average cooling efficiency and maximum equivalent thermal stress of turbine stator blades, thereby revealing the influence laws. The results reveal that the minimum cooling efficiency on the stator blade surface predominantly occurs at the position of channel 4 on the pressure surface, while the highest cooling efficiency is generally found near the leading edge of the suction surface. Regions of elevated thermal stress were consistently concentrated around the stator blade tip and root areas. When the coolant turbulence intensity increased from 0.05 to 0.15, the average cooling efficiency on the stator blade surface improved by 2.06%, accompanied by a reduction of 1.12% in the maximum thermal stress. In comparison to pure air cooling, dual-medium (air and steam) cooling and pure steam cooling lead to respective enhancements in the average cooling efficiency of approximately 3.3% and 13.2%, with corresponding decreases in the maximum thermal stress of 2.18% and 10.2%.

Fight for Customers - Correlations of Trade Organizations and Sales Promotionon the Hungarian FMCG Market

Tract of time after the millennial brought well definable changes in retailers’ trade organizations and marketing activity in the Hungarian three-pole FMCG market. Operation conditions of enterprises became more complicated which is also reflected in the variability of the number and intensity of relevant influencing factors as well as in the difficulties of forecasting them. Out of these the change of consumer/purchase behaviour, the growth of competition intensity, the swell of procurement marketing, changes in channel position, the innovation of informatics and info communication tools, the dominant change of economic environment, the stiffening legislation and the unexpected influence of ecological environment can be highlighted. An important starting point for exploring the impact of the changes focusing on market competition is that the number and activity of chain stores operating in Hungary has stabilized, which has shifted the direction of research to explore the relationship between trade organization and marketing, improving competitive advantages and management efficiency.JEL Code: M31

Full-Reference Image Quality Assessment Using Self-Attention and Multiscale Features

In this paper, we construct a deep framework for full-reference image quality assessment (FR-IQA) by combining convolution and self-attention features; this approach effectively uses multiscale features to mimic the image evaluation in human eyes. We achieve the integration of information from local to global. First, convolutional neural network (CNN) and encoder of Swin transformer are used to extract multiscale paired features. Second, for each type of features, we fuse them after converting them to a fixed number of channels. For the fused features, we use the square of the difference between two pixel values at the corresponding channel positions to represent the features of the distortion degree. Then, we introduce a recurrent neural network (RNN) to capture the global features. Finally, for the two types of features, we use the full connection (FC) layer to regress two scores and then add the weights to compute the ultimate perceived score. By training and testing on publicly available FR-IQA datasets, experimental results further validate the superiority of our approach.

Automated pixel-level crack detection and quantification using deep convolutional neural networks for structural condition assessment

Crack detection is a crucial task in assessing the condition of concrete structures. Herein, a novel deep learning method based on convolutional neural networks, referred to as R-FPANet, is proposed for crack detection. The R-FPANet performs automatic segmentation and quantification of crack morphology at the pixel level. In this methodology, the modularization concept based on the following three modules is adopted: ResNet-50 is chosen as the backbone to extract features from images, the Feature Pyramid Network with Dense Block is integrated to promote the fusion of both shallow and deep features as well as enhance feature reuse, and self-attention mechanisms such as Channel Attention Module and Position Attention Module are introduced to strengthen the dependency between features. Based on the crack segmentation results, a suitably established framework is developed for quantitative analysis of the major geometric parameters, including crack area, crack length, crack mean width and crack max-width at the pixel level. To verify the effectiveness of the proposed method, a large-scale concrete crack image dataset was produced and carefully labeled at the pixel level and then utilized to train the model. Finally, our experiments reveal that the proposed approach achieves an Intersection over Union of 83.07%, further indicating that the segmentation performance of the proposed method is better than the state-of-the-art models and also confirming that the crack quantification results are close to reality. Overall, the proposed method performs well, contributing to crack detection and quantification with great potential for practical use.

The Forming China's Image in the Western Mass Media in the Context of the Belt and Road Initiative (Based on the Materials of the British News Channel BBC News)

The article offers an analysis of the creation of the image of the PRC in the context of the One Belt — One Road initiative in the Western mass media (on the example of the British news channel BBC News). The author of the study proceeds from the thesis about their important role in shaping and promoting the image of the country. In the context of information globalization, the role that the media play in international relations is multiplied many times over. A special place in such processes is occupied by global news channels, which are often of decisive importance in constructing world public opinion, setting the agenda and assessing the importance of ongoing or planned events. The Belt and Road Initiative is not only an example of global cooperation, but also a reflection of a new approach to development in the modern world as a whole, which has a serious impact on China's international prestige and image. In the course of the study, we find out that the Western mass media more often perceive the One Belt, One Road initiative in a negative aspect, ignoring the real achievements and successes of this global project. On the example of the analysis of news releases of the British television channel BBC News, it was revealed that, despite external neutrality, its editorial staff uses different methods and technologies to form a negative image of China. The preference of the editors of the TV channel to economic and political topics, specific events and positions of states as an information occasion was revealed. With the external prevailing neutral position of the TV channel towards One Belt, One Road, its real achievements and successes are ignored, the negative assessment of the Western expert community is covered, and lexemes with a negative meaning are often used.

Quasi-optical design and analysis of a remote steering launcher for CFETR ECRH system

In order to optimize the operational safety and reliability of the upper launcher for the CFETR ECRH system, a design of the launcher for NTM control based on the remote steering concept is currently being carried out for comparison with the front steering equivalent. This paper presents the layout design and analysis of the quasi-optical system in the remote steering launcher. A 3D visual quasi-optical design tool has been developed for the quasi-optical system, which can parameterize modeling, perform general astigmatic beam calculation and show the accurate beam propagation path in the upper port. Three identical sets of quasi-optical modules are arranged in the launcher, and each one consists of two fixed double-curvature focusing mirrors, which focus and reflect the steering beams (−12°–12°) from two square corrugated waveguides. The beam characteristics at the resonance layer are described, and the average beam radius is < 100 mm. The peak head loads on the surfaces of the two fixed mirrors are 1.63 MW/m2 and 1.52 MW/m2. The position and size of the beam channel in the blanket are obtained, and the opening apertures on the launcher-facing and plasma-facing sides of the blanket module are 0.54 m2 and 0.4 m2, respectively.

Thermal performance of serpentine channel immersion cooling for lithium-ion battery 18650 with HFE-7100

This paper presents Serpentine Channel Immersion Cooling (SCIC) as a new design for battery immersion cooling applications using HFE-7100 dielectric fluid. The main objective of this research is to analyze the temperature generation characteristics of Li-ion batteries with the performance of natural convection (NC) and Serpentine Channel Immersion Cooling (SCIC) with HFE-7100 liquid. The depth of discharge (DoD) is carried out at 80% of the battery capacity during the discharge process with variations in C-rates of 1 C, 1.5 C, and 2 C. The results show that the C-rate has a greater value, the temperature value released by the battery will also be greater. The negative pole, intermediate battery, and positive pole exhibit the characteristics of the battery pack’s surface temperature in order from highest to lowest throughout the discharge process. At the negative pole of the battery, the highest temperature in NC cooling is 30.1°C, 32.3°C, and 33.3°C can reduce this temperature to 28.8°C, 29.73°C, 31.8°C by SCIC with HFE-7100 liquid at DoD 1 C, 1.5 C, and 2 C respectively. Battery surface temperature characteristics based on channel position from highest to lowest using NC in the order of 6-3-2-4-5-1 and SCIC in the order of 6-5-4-3-2-1.

Geomorphic features of Lake Havasu with impacts on its water resource capacity

Wilson DC. 2024. Geomorphic features of Lake Havasu with impacts on its water resource capacity. Lake Reserv Manage. 40:93–108. Determining a reservoir’s available storage capacity and its possible rate of change over time through in-filling are 2 of many parameters needed to conduct efficient and flexible water delivery operations, particularly in cascading river-reservoir systems. This study focused on this issue through generating the first bathymetric elevation map of Lake Havasu on the Colorado River (CR) with enough detail to identify geomorphic features, including areas of deposition, to assess the current storage capacity of the reservoir. Two main areas of deposition were identified, the CR inlet delta and the Bill Williams River (BWR) delta at the reservoir’s northern and southern ends, respectively. The physical distribution of the CR inlet deposits well into the main reservoir and to some extent those of the BWR delta also may have been influenced by one episode of drastic water level lowering early in the reservoir’s history that probably re-mobilized sediments when fluvial dynamics temporarily resumed. High resolution sonar images compared with the CR channel position on a pre-reservoir topographic map, a sonar-generated reservoir bottom hardness map coupled with grain-size analyses of sediment samples, and modeling reservoir water level changes support this notion. Post-reservoir dam construction up both rivers has cut off sediment sources that historically reached Lake Havasu, effectively reducing sedimentation rates. Determined thicknesses of the sediment packages indicate that Lake Havasu has lost more than 7% of its available storage capacity since inception, which is an important parameter for adjusting management of water deliveries and storage in the Colorado River system.