bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative x-ray scattering of free molecules
Authors: Ma Lingyu , Goff Nathan , Carrascosa Andrés Moreno , Nelson Silke , Liang Mengning , Cheng Xinxin , Yong Haiwang , Gabalski Ian , Huang Lisa , Crane Stuart W , Green Alice E , Allum Felix , Lenzen Philipp , Muvva Sri Bhavya , Heald Lauren F , Liu Yusong , Bhattacharyya Surjendu , Larsen Kirk A , Graßl Martin , Forbes Ruaridh , Centurion Martin , Wolf Thomas J A , Kirrander Adam , Minitti Michael P , Weber Peter M
Published: September 2024

Advances in x-ray free electron lasers have made ultrafast scattering a powerful method for investigating molecular reaction kinetics and dynamics. Accurate measurement of the ground-state, static scattering signals of the reacting molecules is pivotal for these pump-probe x-ray scattering experiments as they are the cornerstone for interpreting the observed structural dynamics. This article presents a data calibration procedure, designed for gas-phase x-ray scattering experiments conducted at the Linac Coherent Light Source x-ray Free-Electron Laser at SLAC National Accelerator Laboratory, that makes it possible to derive a quantitative dependence of the scattering signal on the scattering vector. A self-calibration algorithm that optimizes the detector position without reference to a computed pattern is introduced. Angle-of-scattering corrections that account for several small experimental non-idealities are reported. Their implementation leads to near quantitative agreement with theoretical scattering patterns calculated with ab-initio methods as illustrated for two x-ray photon energies and several molecular test systems.
Multi-source domain adaptation network for partial discharge severity assessment in gas-insulated switchgear
Authors: Wang Yanxin , Yan Jing , Zhang Wenjie , Geng Yingsan , Wang Jianhua , Srinivasan Dipti
Published: September 2024

Gas-insulated switchgear (GIS) partial discharge (PD) severity assessment is critical for ensuring the reliable operation of GIS systems. However, existing assessment methods often overlook the long-term dependencies of historical data and fail to adequately address challenges related to limited on-site samples and variations in sample distribution. To overcome these challenges, we propose a novel multi-source domain adaptation network (MSDAN) specifically designed for on-site GIS PD severity assessment, which is the first model developed considering distribution differences in GIS PD severity assessment for different defect types. Our approach begins with the development of a feature extractor that captures both discernible PD features and long-term dependencies. We then introduce a multi-source domain adaptation strategy to mitigate distribution disparities across PD severity samples from different defect types, ensuring effective domain alignment. Additionally, we incorporate an adaptive weighted classification mechanism to accurately assess PD severity by considering the varying contributions of different defect types to the target domain task. Experimental results demonstrate that MSDAN achieves a remarkable accuracy of 95.38% in on-site GIS PD severity assessment, outperforming other benchmark models in both accuracy and robustness. This highlights the potential of MSDAN as a robust solution for real-world GIS PD severity assessment.
AVA-YOLO: image-based multiscale feature fusion enhanced perception model for snow avalanche detection
Authors: Liu Zhiming , Zhu Xing , Pang Lu , Fu Xiaodi , Zhu Hao , Liu Xiaojing
Published: September 2024

The global climate change has led to frequent occurrences of snow avalanche disasters. However, the significant variations in scale and shape during the avalanche process, and complex background imagery pose significant challenges to automated detection efforts. There is an urgent need to combine advanced deep learning technology to research automatic detection and recognition of avalanches in the field. In this paper, a novel deep learning model based on YOLOv8 improved multi-scale detection called AVA-YOLO is proposed to solve this problem. In AVA-YOLO, a key component, AKA (AKConv Combined Attention) module was designed and developed. This module combines the deformable convolutional properties of AKConv with the state-of-the-art self-attention module Exponential Moving Average, aiming to better perceive the feature map information of different shaped avalanches and to enhance the global relevance, thus improving the utilization of the information. Secondly, a new multi-scale sensing network structure was designed by increasing the number of detection heads to four and introducing the AKA module into the key positions of the network, while the association between model layers was newly designed to enhance the fusion of shallow and deep information to improve the detection accuracy. Experimental results demonstrated the effectiveness of AVA-YOLO, achieving 95.7% mAP50 and 75.6% mAP50:95 detection accuracies, as well as an F1 score of 0.92. Finally, a number of experiments were conducted to demonstrate the superior performance of the proposed model in comparison to other versions of YOLO, which will further exploit the potential of webcams as an underutilized technical capability in snow avalanche intelligence and portable monitoring.
Application of group decomposition and 1.5-dimensional spectra to acoustic composite fault diagnosis of rolling bearings
Authors: Yao Rong-Hua , Zhou Jun
Published: September 2024

Under the background of strong noise and mutual interference coupling of each fault, the acoustic compound fault diagnosis of rolling bearing is very challenging。 For the composite fault features which are difficult to be extracted due to strong noise interference and uneven distribution of fault intensity, put forward the optimization of swarm decomposition combined with 1.5-dimensional(1.5-d) spectrum method of acoustic composite rolling bearing fault feature separation. The method firstly uses the composite index to iteratively search for the optimal group decomposition threshold value, the adaptive group decomposition of composite fault acoustic signals is realized by optimal parameter group disassembly, and then selects the sensitive components for the decomposed components, and then further analyzes the envelope signal of the sensitive components to reduce the redundancy components and the noise interference, and selects the 1.5-d spectrum to further analyze the envelope signal, thus realizes the effective separation of the composite faults of the rolling bearings acoustic faults. Rolling bearing simulations and experimental acoustic signals verify the validity of the proposed method, and this work gives a new tool for composite fault diagnosis of revolving machinery.
Multiplicity and symmetry breaking for supercritical elliptic problems in exterior domains
Authors: Boscaggin Alberto , Colasuonno Francesca , Noris Benedetta , Weth Tobias
Published: September 2024

We deal with the following semilinear equation in exterior domains

−
Δ
u
+
u
=
a

(
x
)

|

u

|

p
−
2

u
,

u
∈

H
0
1

(

A
R

)

,

where

A
R

:=
{
x
∈

R

N

:

|

x

|

>

R

}

,

N
⩾
3

, R > 0. Assuming that the weight a is positive and satisfies some symmetry and monotonicity properties, we exhibit a positive solution having the same features as a, for values of p > 2 in a suitable range that includes exponents greater than the standard Sobolev critical one. In the special case of radial weight a, our existence result ensures multiplicity of nonradial solutions. We also provide an existence result for supercritical p in nonradial exterior domains.
Linearised Fokker–Planck collision model for gyrokinetic simulations
Authors: von Boetticher A , Parra F I , Barnes M
Published: September 2024

We introduce a gyrokinetic, linearised Fokker–Planck collision model that satisfies conservation laws and is accurate at arbitrary collisionalities. The differential test-particle component of the operator is exact; the integral field-particle component is approximated using a spherical harmonic and a modified Laguerre polynomial expansion developed by Hirshman and Sigmar (1976 Phys. Fluids
19 1532). The numerical methods of the implementation in the δf-gyrokinetic code

stella

(Barnes et al 2019 J. Comput. Phys.
391 365–80) are discussed, and conservation properties of the operator are demonstrated. The collision model is then benchmarked against the collision model of the gyrokinetic solver GS2 in the limiting cases of a reduced test-particle collision operator and energy- and momentum-conserving operator. The accuracy of the full collision model is investigated by solving the parallel Spitzer-Härm problem for the transport coefficients. It is shown that retaining collisional energy flux and higher-order terms in the field-particle operator reduces errors in the transport coefficients from 10%–25% for a simple momentum- and energy-conserving model to under 1%.
Accuracy of kinetic equilibrium reconstruction of NSTX and NSTX-U plasmas and its impact on the transport and stability analysis
Authors: Avdeeva G , Thome K E , Berkery J W , Kaye S M , McClenaghan J , Meneghini O , Odstrcil T , Sabbagh S A , Smith S P , Turnbull A D
Published: September 2024

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 reconstruction depends on the uncertainty and number of constraints, as well as the choice of basis functions to represent the pressure and current density profiles. 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 of basis functions were tested. The 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 the experimental kinetic profile data have a significant impact on the trapped gyro Landau fluid 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 using the GATO code shows plasmas that are 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.
Measurement-device-independent detection of beyond-quantum state
Authors: Yu Baichu , Hayashi Masahito
Published: September 2024

In quantum theory, a quantum state on a composite system of two parties realizes a non-negative probability with any measurement element with a tensor product form. However, there also exist non-quantum states which satisfy the above condition. Such states are called beyond-quantum states, and cannot be detected by standard Bell tests. To distinguish a beyond-quantum state from quantum states, we propose a measurement-device-independent (MDI) test for beyond-quantum state detection, which is composed of quantum input states on respective parties and quantum measurements across the input system and the target system on respective parties. The performance of our protocol is independent of the forms of the tested states and the measurement operators, which provides an advantage in practical scenarios. We also discuss the importance of tomographic completeness of the input sets to the detection.
Transfer of nonclassical correlations between bosonic field and atomic ensemble through electromagnetically induced transparency
Authors: Liu Wanfang , Ding Chengyun , Yin Xunchang , Zhang Liwei
Published: September 2024

Dark state polariton, as an important concept in the mechanism of electromagnetic induced transparency (EIT), can map the state of bosonic fields to atomic ensembles. To reflect the mapping ability of dark state polariton, we choose the odd and even bosonic coherent states as the probe field in EIT process, and employ spin squeezing, entanglement, and quantum correlation to characterize nonclassical correlations of atomic ensembles during the manipulation of the driving field. It is shown that the differences between the odd and even coherent states are comprehensively reflected in the three characterizations of nonclassical correlations generated through dark state polaritons. The even bosonic coherent states can perfectly transfer bosonic squeezing into atomic ensembles, resulting in spin squeezing. Although the odd bosonic coherent states cannot induce the spin squeezing, they have an advantage over the even bosonic coherent states in generating quantum entanglement and quantum correlations. Furthermore, we demonstrate that atomic ensembles can achieve significant spin squeezing with squeezing degree ∝ 1/N
2/3 through the one-axis twisting (OAT) model and two-axis twisting (TAT) model under the large N limit with the low excitation conditions, and the EIT mechanism was used to transfer the generated spin squeezing to the bosonic field, providing a feasible strategy for obtaining significant bosonic squeezing.
Zn-MOF as a saturable absorber for thulium/holmium-doped fiber laser
Authors: Ahmad H , Chiam J W , Samion M Z , Thambiratnam K , Mutlu S , Yılmaz S S , Arsu N , Ortaç B
Published: September 2024

Metal–organic framework (MOF) is a class of material that is highly porous and modular. Due to their unique properties, MOFs have found applications in gas storage, gas separation, sensing, and supercapacitors. [Zn2(H2L)2(1,2-Bis(4-pyridyl)ethene)4]n, zinc (Zn)-based MOF was used in this work to achieve mode-locked operation in a thulium/holmium-doped fiber laser due to its excellent optical absorption at a wavelength of 1925 nm. The saturable absorber (SA) was fabricated by drop-casting a mixture of Zn-MOF and isopropanol on an arc-shaped fiber. The center wavelength of the mode-locked laser is 1906.75 nm, with a maximum average output power of 3.251 mW. The fundamental repetition rate and the pulse width were 12.89 MHz and 1.772 ps. At the same time, the pulse energy and peak power were 252 pJ and 142 W, respectively. To the authors’ knowledge, this is the first time an MOF has been used for mode-locked pulse generation in a thulium/holmium-doped all-fiber laser. This work extends the use of MOF material as a saturable absorber for mode-locking applications in near-infrared fiber lasers.
Regression modeling of Bödewadt slip flow dynamics involving Reiner-Rivlin nanofluid based on a modified Buongiorno approach
Authors: Ibrahim Tayyaba , Mustafa M , Khan Junaid Ahmad , Mushtaq Ammar
Published: September 2024

Regression models are useful in analyzing rotational flows as they enable accurate predictions of wall shear and heat transfer coefficient. In addition, Bödewadt flow is of paramount importance in fluid dynamics of rotating systems such as turbomachinery and geophysical flows. Moreover, nanofluid’s enhanced heat transfer properties can improve cooling efficiency in applications involving turbines and electronic systems. This study delves into the Bödewadt boundary layer flow of a Reiner-Rivlin fluid containing nanoparticles over a stationary porous disk under slip conditions. The two-phase Buongiorno model is employed, incorporating temperature-dependent diffusion coefficients for enhanced accuracy. To facilitate numerical simulations, the transport equations are converted into an ordinary differential system comprising four unknowns. In the present work, a highly reliable Keller-Box methodology is adopted which agrees very well with the MATLAB built-in program ‘bvp4c’. The computed 2-D and 3-D streamlines vividly capture the Bödewadt flow scenario with Reiner-Rivlin nanofluid. The principle aim to investigate the impact of non-Newtonian behaviour and slip on the flow pattern, while also examining the behavior of temperature/concentration field for nanoparticle working fluids. As thermophoretic diffusion increases, the thermal boundary layer thickens considerably, leading to a notable decrease in the cooling rate of the disk. In contrast, Brownian diffusion has only a minimal impact on the heat transport. In addition, wall suction effect is observed to significantly boost the disk’s cooling rate, though at the expanse of increasing skin friction coefficients. This study introduces linear and quadratic regression models designed to precisely predict both the surface drag and disk cooling rate, which are crucial factors in engineering processes.
Optical soliton solutions for the nonlinear Schrödinger equation with higher-order dispersion arise in nonlinear optics
Authors: Ahmed Hakima Khudher , Ismael Hajar Farhan
Published: September 2024

Optical solitons and traveling wave solutions for the higher-order dispersive extended nonlinear Schrödinger equation are studied. Ultrashort pulse propagation in optical communication networks is described by this equation. To find exact solutions to the model, the unified Riccati equation expansion method and the Jacobi elliptic function expansion method are successfully applied. The optical solutions includes various solitary wave solutions, such as dark, bright, combined dark-bright, singular, combined periodic, periodic, Jacobian elliptic, and rational functions. Three-dimensional and two-dimensional graphs of solutions are presented. Also, the dynamical behavior of waves and the impact of time on solutions by selecting appropriate parameters are illustrated.
MXene-based semi-circle with a thin wire-shaped resonator wideband polarization-insensitive solar absorber
Authors: Aliqab Khaled , Agravat Raj , Patel Shobhit K , Ali Naim Ben , Alsharari Meshari , Armghan Ammar
Published: September 2024

Fossil fuels’ supply peaks, decreases, and shortages are determined by their proven reserves, research, and consumption rates. With a large upfront cost, renewable and alternative energy sources are essential to solving the twin issues of energy and climate change. Solar absorbers are an excellent way to use renewable energy from the environment. This paper suggested an MXene-based semi-circle with a thin wire-shaped resonator (MSCWTWSR) solar absorber where the resonator layer consists of MXene material and Fe is used as substrate layer and the resonator has semi-circle and thin wire geometry which effectively absorbs the sun radiation with wideband. This proposed MSCWTWSR solar absorber works at 200–3000 (nm) wavelength and has more than 93% average absorption. The first band bandwidth of this MSCWTWSR solar absorber is 400 (nm), the second band is 530 (nm), and the third band is 470 (nm). This structure got more than 93% absorption in the AM 1.5 solar irradiation configuration. The structure gives in the Transverse electric (TE) field and Transverse magnetic (TM) field and the structure has polarization for insensitive. Furthermore, there is also investigated different incidence angles. A suggested article includes sections on testing for electric and magnetic intensities with a comparison table. The suggested solar absorber is employed in a distinct thermal heating application since MXene has a low thermal resistance and good thermal stability.
Revisiting bounds on primordial black hole as dark matter with X-ray background
Authors: Tan Xiu-Hui , Xia Jun-Qing
Published: September 2024

Within the mass range of 1016 - 5 × 1018 g, primordial black holes (PBHs) persist as plausible candidates for dark matter. Our study involves a reassessment of the constraints on PBHs through a comparative analysis of the cosmic X-ray background (CXB) and the emissions arising from their Hawking evaporation. We identify previously overlooked radiation processes across the relevant energy bands, potentially refining the bounds on PBHs. These processes encompass the direct emission from Hawking radiation, in-flight annihilation, the final state of radiation, and positronium annihilation. Thorough consideration is given to all these processes and their respective emission fractions, followed by a precise calculation of the 𝒟 factor for observations directed towards the high-latitude Galactic contribution. Furthermore, we integrate the flux originating from extragalactic sources, both of which contribute to the measured isotropic flux. Through a comparative analysis of data derived from previous CXB observations utilizing an Active Galactic Nuclei (AGN) double power-law model, we establish the most stringent constraints for PBHs, thereby excluding the possibility of PBHs constituting the entire dark matter mass within the range of 2.5 × 1017 - 3 × 1017g.
Exploring dark forces with multimessenger studies of extreme mass ratio inspirals
Authors: Bhalla Badal , Sinha Kuver , Xu Tao
Published: September 2024

The exploration of dark sector interactions via gravitational waves (GWs) from binary inspirals has been a subject of recent interest. We study dark forces using extreme mass ratio inspirals (EMRIs), pointing out two issues of interest. Firstly, the innermost stable circular orbit (ISCO) of the EMRI, which sets the characteristic length scale of the system and hence the dark force range to which it exhibits enhanced sensitivity, probes force mediator masses that complement those studied with supermassive black hole (SMBH) or neutron star binaries. The LISA mission (the proposed μAres detector) will probe mediators with masses m
V ∼ 10-16  eV (m
V ∼ 10-18  eV), corresponding to ISCOs of 106
M
⊙ (108
M
⊙) central SMBHs. Secondly, while the sensitivity to dark couplings is typically limited by the uncertainty in the binary component masses, independent mass measurements of the central SMBH through reverberation mapping campaigns or the motion of dynamical tracers enable one to break this degeneracy. Our results therefore highlight the necessity for coordinated studies, loosely referred to as “multimessenger”, between future μHz- mHz GW observatories and ongoing and forthcoming SMBH mass measurement campaigns, including OzDES-RM, SDSS-RM, and SDSS-V Black Hole Mapper.
OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy
Authors: Zhang Runkai , Rong Rong , Xu Yun , Wang Haixian , Wang Xiaoyun
Published: September 2024

Objective. Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy. Approach. We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting. Main results. OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model’s effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band. Significance. The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs.
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

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

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

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

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

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

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

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

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

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

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

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

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

In 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

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

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.