bluuebunny/crossref_metadata_2025_split huggingface dataset:
I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks
Authors: Kang Taeho , Chen Yiyu , Wallraven Christian 
Published: September 2024

Abstract

Objective. In this paper, we conduct a detailed investigation on the effect of independent component (IC)-based noise rejection methods in neural network classifier-based decoding of electroencephalography (EEG) data in different task datasets. Approach. We apply a pipeline matrix of two popular different independent component (IC) decomposition methods (Infomax and Adaptive Mixture Independent Component Analysis (AMICA)) with three different component rejection strategies (none, ICLabel, and multiple artifact rejection algorithm [MARA]) on three different EEG datasets (motor imagery, long-term memory formation, and visual memory). We cross-validate processed data from each pipeline with three architectures commonly used for EEG classification (two convolutional neural networks and one long short-term memory-based model. We compare decoding performances on within-participant and within-dataset levels. Main Results. Our results show that the benefit from using IC-based noise rejection for decoding analyses is at best minor, as component-rejected data did not show consistently better performance than data without rejections—especially given the significant computational resources required for independent component analysis (ICA) computations. Significance. With ever-growing emphasis on transparency and reproducibility, as well as the obvious benefits arising from streamlined processing of large-scale datasets, there has been an increased interest in automated methods for pre-processing EEG data. One prominent part of such pre-processing pipelines consists of identifying and potentially removing artifacts arising from extraneous sources. This is typically done via IC-based correction for which numerous methods have been proposed, differing not only in the decomposition of the raw data into ICs, but also in how they reject the computed ICs. While the benefits of these methods are well established in univariate statistical analyses, it is unclear whether they help in multivariate scenarios, and specifically in neural network-based decoding studies. As computational costs for pre-processing large-scale datasets are considerable, it is important to consider whether the trade-off between model performance and available resources is worth the effort.
A Geometric Approach to Textual Augmented Data Filtering
Authors: Feng Sherry J.H , Lai Edmund M-K , Li Weihua 
Published: September 2024

Abstract
Data augmentation is necessary if the amount of training data is insufficient for supervised learning. For natural language processing tasks, obtaining good quality augmented data is not easy. This paper introduces GATFilter, a novel method for filtering out inappropriate augmented textual data for text classification (TC). Utilizing geometric concepts, more specifically the principle component and convex hull analyses, this method adeptly preserves the semantic integrity of words within augmented texts. GATFilter is versatile and applicable across various types of textual augmentation methods. Experiments using several datasets and augmentation strategies showed that classifiers trained with GATFilter-filtered augmented data sets showed improvements in key performance metrics, including accuracy, precision, recall, and F1 score. The method’s efficacy is notably influenced by the quality of the underlying augmentation techniques, indicating its potential to complement and refine various text augmentation strategies. Furthermore, our analysis showed that GATFilter is particularly able to amplify the effectiveness of methods that generate good quality augmented data. GATFilter is openly available online on Github1, and as a Python package2.
Design and implementation of dynamic balance in ultra-high speed IP network
Authors: Ge Yining , Chen Shikang 
Published: September 2024

Abstract
Nowadays, with the rapid development of IP network service demand, network construction is also accelerating, IP network is expanding at a higher rate, and gradually moving toward ultra-high speed networks above 40G. In view of the problems of inconsistent IP packet order, random IP packet length and traffic burst brought by the application environment of high traffic, high bandwidth and high concurrency in ultra-high speed IP network, this paper introduces a kind of dynamic balance processing technology of ultra-high speed IP network based on information label. By optimizing the IP processing architecture, a two-level scheduling architecture of multi-board and multi-module is established, and VPN is established in ultra-high speed IP network by using dynamic balance processing technology and order preserving technology to realize high performance IP processing, so as to achieve ultra-high speed data transmission.
Research on non-linear dynamic simulation model of feeding system
Authors: Haitao Sun , Hui Liang , Huiyong Deng , Kaibo Cui , Youcai Jiang 
Published: September 2024

Abstract
The automatic gun feeding system is an important mechanism for achieving continuous shooting, but due to its complex structure, there is a lot of contact during firing, and its reliability is lower. To improve the reliability of the feeding system and its dynamic characteristics, a multi rigid body dynamic simulation model of the feeding system considering contact nonlinearity was established based on nonlinear dynamics theory. The dynamic characteristics of the feeding system were analyzed for single and continuous firing. The research results indicate that the presence of contact nonlinearity increases the interaction forces between components and also increases the uncertainty of component motion.
Finite element analysis of grout-filled GFRP tube repaired corroded tubular offshore platforms
Authors: Lu Yaping , Zhou Yongli , Yan Xiaoyu , Meng Ziyang , Hou Xiaoyu 
Published: September 2024

Abstract
Steel tubular offshore platforms are exposed to aggressive marine conditions, which leads to inevitable corrosion and degradation of the structures after a long time of service. Repairing the corroded tubular structure is not easy, especially for the members that are beneath the sea level. The current paper aims to provide an alternative approach for the repairing of corroded steel tubular platforms, by which a novel method with grout-filled GFRP tube cladding the tubular members is proposed. The conception design of the method is introduced, and the repairing process is demonstrated in detail. Then the repairing efficiency of repaired joints and platforms with different corrosion rates is investigated with finite element analysis, which proves a satisfactory reinforcement on the strength of them. The enhanced ratio is up to 117% for the T-joint under compression, and 44% for the compression brace of the K-joint, 37% for the tension brace of the K-joint, 45% for the platform. Finite element models are developed to explore the influence of the GFRP tube thickness ratio and the cube strength of grout material on its strength and repair efficiency. Then the corresponding repair structure suggestions under each corrosion rate are put forward.
Performance and mechanism analysis of EMA-GMA modified PMMA/ASA alloy
Authors: Gu Zhaohui , Wang Hui 
Published: September 2024

Abstract
This article examines the impact of the EMA-GMA combination on the mechanical characteristics, fluidity, and glass transition temperature of PMMA/ASA alloy and uses scanning electron microscopy to characterize the modified PMMA/ASA alloy. The findings indicate that the addition of 5% EMA-GMA to PMMA/ASA significantly enhances its fracture resistance without compromising its stiffness. When the additional amount of EMA-GMA exceeds 10%, the mechanical properties and fluidity of the alloy begin to decline significantly. EMA-GMA within 15% can promote PMMA/ASA compatibility.
Research on the co-pyrolysis of biomass and coal
Authors: Zhang Jiaojiao , Lv Minghang , Li Fuli , Wang Zhe , Liu Xinyu 
Published: September 2024

Abstract
Co-pyrolysis of biomass and coal has attracted much attention as a potential energy conversion technology. In this paper, the significant effects of n-hexane insoluble substance (INS) on tar, water, and coke residue yields were analyzed in depth using statistical analysis. With INS and mixing ratio as independent variables, multivariate analysis of variance (ANOVA) was used to comprehensively investigate the effects of the interaction between the two on the yields of different pyrolysis products, revealing the significant interaction between different pyrolysis products. The genetic algorithm was used to establish an optimization model to optimize the mixing ratios of the co-pyrolysis, and the considerable differences between the experimental and theoretically calculated values of the product yields of the co-pyrolysis compounds were analyzed by statistical tests, and subgroup analyses were carried out to determine the specific differences under different mixing ratios. CNN prediction model was established, and model optimization confirmed the differences between the experimental and theoretically calculated values for different pyrolytic coupling ratio-specific ratios by data validation and subgroup analysis. The further extension and optimization of the model provide a new theoretical basis and idea for further research on the co-pyrolysis of biomass and coal, which can help to improve the energy conversion efficiency and product quality.
Comprehensive energy system planning with a focus on electric-thermal load correlations
Authors: Ding Chonglei , Zhang Xiaoming , Liang Guangzhe , Feng Jiaoyang 
Published: September 2024

Abstract
Deepening research on electrothermal integrated energy systems has heightened the coupling between electric power and thermal systems. Accurate electrothermal load scenario modeling and thorough consideration of their interdependencies are crucial for effective planning and scheduling. The traditional method of generating scenarios cannot fully reflect the full complexity of the original power load. To address this, our paper introduces an enhanced clustering approach. Employing the Frank-Copula function to express the correlation between electric and thermal loads, we optimize the clustering and scene reduction sequence, yielding correlated typical electric and thermal load datasets. These refined load profiles serve as the foundation for comprehensive planning and analysis of the integrated energy system.
Research on wind turbine blade coating erosion modeling based on Generic modeling
Authors: Xue Yuantian , Song Changcheng , Yang Chenlai 
Published: September 2024

Abstract
In order to improve the accuracy of the prediction of the erosion phenomenon of wind turbine blade coatings, this study incorporates the wind-sand erosion data of wind turbine blade coating flat specimens obtained through wind tunnel experiments on the basis of the widely used generalized erosion model. A prediction model that accurately reflects the erosion and wear characteristics of wind turbine blade coatings is constructed using the least squares method. All the function parts included in the model showed favorable fitting effects, and the error between the model prediction results and the measured data was limited to a tolerable range. Therefore, the model has proved to be effective in predicting the erosion rate of wind turbine blade coating materials, which provides a solid theoretical foundation for the numerical simulation of the erosion and wear of wind turbine blades in a two-phase wind-sand flow environment.
Increasing exposure of global croplands productivity to growing season heatwaves under climate warming
Authors: Chen Yongjun , Zhang Wenxia , Zhou Tianjun 
Published: September 2024

Abstract
Growing season heatwaves that occur simultaneously over global croplands can negatively impact global food baskets. The long-term changes of growing season heatwaves, as well as their impacts on croplands productivity, are crucial to food security, but remain unclear. Here, we investigated changes in the frequency, intensity and magnitude of growing season heatwaves from the past to the future over the global croplands, based on observations and Coupled Model Intercomparison Project Phase 6 models. We introduced an index, gross primary productivity (GPP) exposure, as a proxy of the overall impact of heatwaves on cropland productivity. The results show that the frequency and intensity of growing season heatwaves have increased since 1950 and will continue throughout the 21st century. The increase of the annual accumulated magnitude of growing season heatwaves in the future is mainly contributed by the increase of heatwave frequency. This leads to a global-scale increase in the GPP exposure to growing season heatwaves, with Asia, North America, and Europe being the most affected. The continued increase in GPP exposure is dominated by increases in heatwaves rather than GPP itself. Under the lower emission scenario SSP1-2.6, the global cropland GPP exposure will reduce by 86.11% and 330.47% relative to that under SSP2-4.5 and SSP5-8.5 scenarios, respectively, by the end of 21st century. Our results provide crucial insights into potential impacts of heatwaves on cropland productivity and hence food security.
Community engagement enhances undergraduate civil engineering capstone design projects
Authors: Good Kelly D , Smith Virginia 
Published: September 2024

Abstract
Over the past several decades numerous studies have shown the value of community engagement in global water, sanitation, and hygiene (WASH) programs. Engaging and collaborating with communities through the design process enables wider community acceptance and ownership of WASH interventions, ensures a more functional and impactful design, and opens the door for co-benefits of these systems. Accounting for a range of dimensions within a community is critical to creating WASH designs that provide the most benefits. Undergraduate engineering curriculums focus on design, but often do not explicitly account for the social, environmental, and economic elements that affect the success, efficacy, and sustainability of a design. There is increasing emphasis on educating engineers on sustainability and systems thinking so they are equipped to solve complex, multidisciplinary problems. To understand how social, environmental, and economic elements fit into engineering design and how to create sustainable WASH interventions, undergraduate engineering students benefit from participating in WASH design projects that engage and collaborate with communities. This perspective highlights the value of community engagement with partners in Uganda in teaching water resources and environmental engineering design to undergraduate civil engineering students.
Microstructural and mechanical characterizations of weld metal of S960QL ultra high strength steel joints obtained with different multi-pass laying techniques using GMAW
Authors: Mert Tolga , Gurol Ugur , Tumer Mustafa 
Published: September 2024

Abstract
15 mm thick ultra-high strength steel plates with 960 MPa yield strength were welded using different multi-pass laying techniques (i.e., stringer and weaving beads) with torch manipulation. Weld metals obtained were compared using different mechanical (i.e., micro tensile tests and Vickers hardness maps) and microstructural (i.e., optical microscope, scanning electron microscope, x-ray diffraction, electron backscatter diffraction) characterization techniques. Coarser grains and acicular ferrite were observed in weld metal obtained with the weaving pass procedure. There were hardness differences in the face and root passes of both weld metals. Yet, hardness values were 19% and 11% higher for the face and root regions of the joint obtained by stringer pass procedure, respectively. Fractographs of micro tensile test specimens revealed dimples depicting ductile network structure for both joints.
Metrological robustness of high photon number optical cat states
Authors: Stammer Philipp , Fernández Martos Tomás , Lewenstein Maciej , Rajchel-Mieldzioć Grzegorz 
Published: September 2024

AbstractIn the domain of quantum metrology, cat states have demonstrated their utility despite their inherent fragility with respect to losses. Here, we introduce noise robust optical cat states which exhibit a metrological robustness for phase estimation in the regime of high photon numbers. These cat states are obtained from the intense laser driven process of high harmonic generation (HHG), and show a resilience against photon losses. Focusing on a realistic scenario including experimental imperfections we opt for the case in which we can maximize the lower bound of the quantum Fisher information (QFI) instead of analyzing the best case scenario. We show that the decrease of the QFI in the lossy case is suppressed for the HHG-cat state compared to the even and odd counterparts. In the regime of small losses of just a single photon, the HHG-cat state remains almost pure while the even/odd cat state counterparts rapidly decohere to the maximally mixed state. More importantly, this translates to a significantly enhanced robustness for the HHG-cat against photon loss, demonstrating that high photon number optical cat states can indeed be used for metrological applications even in the presence of losses.
Responses of summer mesoscale convective systems to irrigation over the North China Plain based on convection-permitting model simulations
Authors: Wang Yixiao , Yang Ben , Yang Zhao , Feng Zhe , Qiu Bo , Dai Guoqing , Qian Yun , Zhang Yaocun 
Published: September 2024

Abstract
Extensive irrigation activities in the North China Plain (NCP) significantly influence regional weather and climate. However, previous studies focusing on the NCP were primarily based on coarse-resolution models, which are unable to explicitly resolve convection systems, causing large uncertainty in precipitation simulations. In this study, a convection-permitting model coupled with a dynamic irrigation scheme is utilized to investigate the impacts of irrigation on summertime mesoscale convective systems (MCSs) over the NCP. Sensitivity experiments with irrigation off and on are conducted for 5 summers and an MCS identification and tracking algorithm is applied to both satellite observations and model simulations. We find that incorporating irrigation in the model increases MCS precipitation, which agrees more with observations. The probability distributions of MCS lifetime, area, propagation speed, and intensity are all better simulated with irrigation. Irrigation increases the occurrence frequency of MCSs throughout the entire day. The nighttime increase is partly because of more frequent local initiation of MCS developed from isolated deep convection, while the daytime increase is mainly attributed to the changes in MCSs initiating elsewhere and then propagating to the NCP. On average, irrigation induces additional moisture that is more thermodynamically favorable for precipitation, but this effect is partially offset by the weakened ascending air motion primarily caused by irrigation surface cooling. Compared to weak MCS precipitation events, strong MCS precipitation events experience greater enhancement in precipitation intensity when including irrigation because the offset effect from the change in large-scale ascending air motion is insignificant. In addition, irrigation makes the variation of MCS precipitation intensity more correlated with the variation in ascending motion but less correlated with that in atmospheric moisture. Our results suggest the pronounced impacts of irrigation on MCSs over the NCP which should be included in numerical models to improve regional precipitation simulation and prediction.
The connection between sea ice dynamics enhancement and atmospheric circulation in the East Siberian Sea during spring
Authors: Zhang Daqian , Zhang Lujun , Shi Guorui , Xu Xiaoyang 
Published: September 2024

Abstract
The change in sea ice thickness can be divided into dynamical and thermodynamical effect. In the last four decades, the drastic changes in multi-year ice thickness in East Siberian Sea during spring have made the sea ice more susceptible to dynamical effect. On one hand, the dynamical effect on sea ice in this region is the strongest, surpassing that of other Arctic marginal seas, and has been continuously strengthening since 1996. On the other hand, this region’s dynamical effect varies with the Central Arctic Index (CAI). An increase in CAI extreme positive phase years leads to more frequent occurrences of cyclonic circulation anomalies, favoring the development of dynamical effect in spring East Siberian Sea. Furthermore, the influence of 10 m wind field on dynamical effect has shifted from being dominated by the northward component (v) to a combined effect of both northward and eastward components (v and u). This change is a result of alteration in the response pattern of wind field to CAI across the interdecadal periods.
A study on landscape feature and emotional perception evaluation of waterfront greenway
Authors: Sun Dong , Zhou Fujian , Lin Junjie , Yang Qianrui , Lyu Mei 
Published: September 2024

Abstract
Waterfront greenway is important spatial carriers for urban residents to engage in walking tours and fitness activities. Improving the quality of its walking spaces can have a positive impact on the mental well-being of pedestrians. This enhancement is a crucial aspect of building healthy cities. This study focused on urban waterfront greenway landscapes. Field surveys combined with questionnaire scales were used to assess pedestrians’ emotional perceptions, while concurrently collecting image data of waterfront greenway walking spaces. Semantic segmentation was employed to identify elements, and landscape metrics were calculated using relevant formulas. Correlation analysis was used to investigate the relationship between landscape metric features and emotional perceptions. And established a multiple regression model of pedestrian spatial perception on waterfront greenways to investigate the influence mechanisms of emotional perception and landscape metrics. The study indicates that greenness, proportion of water area, and vegetation diversity positively impact emotional perception of waterfront leisure greenway walking spaces, whereas path width negatively affects emotional perception. Specifically, the magnitude of influence is ranked as follows: greenness (0.491), proportion of water area (0.356), proportion of trails (−0.272), and vegetation diversity (0.130). The design guidelines for urban waterfront greenway spaces were proposed, guided by quantitative studies on landscape elements of waterfront greenway spaces and based on the measurement features of these elements. Moreover, the environmental experience of the waterfront green walking spaces was further enhanced, satisfying the public’s visual perception and psychological needs. The contribution of this study is to lay the foundation for the development of spatial control items for urban waterfront greenway.