bluuebunny/crossref_metadata_2025_split huggingface dataset:
CFD-based prediction of flow noise in offshore pipeline systems
Authors: Sun Kailang , Yin Wenhui 
Published: August 2024

Abstract
The ship’s water injection system exhibits significant vibration and noise issues, necessitating the prompt implementation of targeted control measures. In this study, the simulation technique of computational fluid dynamics (CFD) combined with the finite element method and automatic matching layer method was applied to emulate the flow field environment of the outlet pipeline of a side valve in a seawater cooling system. Pulsating pressure data on the pipeline and valve’s wall were extracted from the flow field calculation and utilized as dipole noise sources and structural vibration excitations, enabling the prediction and separation of flow noise and flow-induced vibration noise in the water injection system. Leveraging the data from the flow field simulation, an analysis was conducted on the radiated noise at the side valve’s outlet, comparing the impact of different underwater depths and outlet pipe diameters on the underwater flow noise. The findings suggest that measures such as increasing the side valve’s diameter and lowering the discharge port’s underwater position can effectively reduce system flow noise, offering valuable insights for the design of noise reduction in seawater cooling systems for silent surface ships and submarines.
Research on risk data analysis of hazardous chemical vessels based on stream computing
Authors: Lv Taizhi , Wu Enze , Tang Peiyi 
Published: August 2024

Abstract
To effectively improve the safety supervision and information management level of vessels carrying hazardous chemicals, the maritime management department is promoting the construction of hazardous chemical vessel monitoring systems based on artificial intelligent technology for hazardous chemical terminals and shipping companies in their jurisdiction. It can identify specific risks and transmit the risk data in real-time to the data center. To analyze data in real-time, a risk data analysis platform for hazardous chemical vessels based on stream computing is designed and implemented. The platform acquires vessel automatic identification system (AIS), risk, basic information, and other data. Based on the stream computing of Flink, it evaluates the risk status of vessels in real time, counts the risk data, and displays the results in the form of charts. The analysis of risk data for hazardous chemical vessels helps maritime management departments to track the real-time navigation status of key vessels, timely identify potential safety hazards, and effectively improve the level and efficiency of safety supervision.
Adaptive neural network for dynamic prediction of machine tool energy consumption
Authors: Li Chengchao , Zhang Qiongzhi , He Shuangyu , Cao Huajun 
Published: August 2024

Abstract
As the “mother machine” of manufacturing, machine tools are extensively utilized, but suffer from high energy consumption and low energy efficiency. Energy consumption prediction for machine tools holds significant importance for energy planning in the manufacturing industry. Some scholars have achieved the prediction of machine tool energy consumption using data-driven modeling methods. However, these studies are based on single processing conditions, requiring a substantial amount of data support to achieve accurate predictions under specific conditions. In practical processing, machine tools have multiple working conditions, making it impractical to obtain prediction models for each condition. To this end, this paper proposes an adaptive neural network algorithm for energy consumption prediction under different working conditions. When machine tool conditions change, the existing prediction model can be adjusted by acquiring a limited quantity of current condition data to obtain a prediction model for the current condition. It reduced data collection time and lowered model reconstruction costs. A milling experiment was conducted to validate the practicality and feasibility of the proposed algorithm. The unadapted neural networks and other algorithmic models exhibit error rates of over 15% on the training set, but the adaptive neural networks maintain an error rate of essentially below 8%.
Research on fault diagnosis algorithm of automobile noise based on deep learning
Authors: Hao Yaodong , Chen Daliang 
Published: August 2024

Abstract
This study is devoted to improving the accuracy and efficiency of automobile noise fault diagnosis by using deep learning technology. By constructing a convolutional neural network (CNN) model, we deeply analyze and learn the automobile noise data, aiming at realizing the automatic identification of different types of faults. The experimental results show that the deep learning model shows significant improvement in several performance indicators compared with the traditional methods. Different parameter configurations are used to train the deep learning model. The model structure includes a convolution layer, a maximum pooling layer, and a full connection layer. By extracting the hierarchical features of the data, the model can better adapt to the complex characteristics of noise data. Compared with traditional methods, the CNN model has achieved obvious advantages in accuracy, recall, and F1 score. The research results not only verify the effectiveness of deep learning in automobile noise fault diagnosis but also show the robustness and adaptability of the model under more complex parameter configurations. The conclusion of this study provides empirical support for the application of deep learning technology in the field of automobile noise fault diagnosis.
Exploring the vibration attenuation capabilities of fish surface mucus films: a novel biomimetic model for underwater vehicle drag reduction
Authors: Zeng Qihang , Duan Zhengyong , Chen Qing 
Published: August 2024

Abstract
This groundbreaking research delves into the ability of naturally occurring mucus films on fish surfaces to attenuate vibrations, utilizing nanomechanical systems for a comprehensive analysis. The mucus samples were collected from healthy Asian carp. We grouped the test subjects based on the varying durations of mucus adsorption on the fish scales: 1, 5, 10, and 30 minutes, with the bare scale serving as a benchmark. To evaluate the mucus film’s mechanical characteristics, we employed a nano-indentation tester, while a customized vibration excitation apparatus assessed its vibration attenuation capabilities. Our results indicate that the mucus film adheres to the fish scales via electrostatic forces, forming a smooth inner layer. As the adsorption time increases, the accumulation of additional proteins leads to a thicker outer layer. Remarkably, this outer layer exhibits significant vibration attenuation, assisting Asian carp in mitigating turbulence resistance and enhancing their swimming prowess.
A novel prediction model of total drag coefficient of an MPV vehicle based on LSA optimization
Authors: Yang Li , Jiang Shuyun , Zhang Jun , Liu Chuanbo 
Published: August 2024

Abstract
Environmental degradation and energy scarcity are caused by the ongoing rise in the number of automobiles. The most efficient way to lower air resistance while driving is to boost vehicle energy efficiency and reduce energy consumption. In this case, a specific MPV model serves as the study subject in this work, and we use CFD simulation to estimate the drag coefficient along with the selection of important factors. Furthermore, to increase prediction accuracy, a lightning search algorithm (LSA)-based BP-neural-network prediction model is created and developed. The results show that the average relative error reduction of 2.83% in the drag resistance model optimized by the lightning search method represents a significant improvement in accuracy. This phenomenon can be explained by the LSA’s ability to resolve the BP neural network’s initial weight and threshold uncertainty as well as the issue of local minima being quickly reached. The high prediction accuracy drag prediction model, obtained herein, is useful for creating and producing automobile shapes with low drag coefficients.
A novel motherboard test item yield prediction model based on parallel feature extraction
Authors: Yan Zhangpeng , Zhai Weimin , Li Chao 
Published: August 2024

Abstract
Functional testing of motherboards in Surface Mount Technology (SMT) assembly lines is crucial. Accurate yield prediction for each test item optimizes testing strategies, reduces costs, and ensures test coverage. Manual estimation of test item yields remains common, hindering accurate on-site predictions. Existing research on motherboard yield lacks predictions for individual test items and ignores temporal correlations during placement. This paper introduces a method, a convolutional bidirectional long short-term memory attention network (CBA-Net), which combines a convolutional neural network and a bidirectional long short-term memory network with an attention mechanism for parallel processing. It preprocesses historical test data, leveraging both networks to identify key features and extract temporal correlations. The attention mechanism optimizes yield predictions by assigning weights to information at different time steps. Experimental validation using actual production data demonstrates that the proposed method performs better compared to traditional models.
Improvement of YOLOv5s-Ghost model
Authors: Huang Qiuling , Li Zhizhong 
Published: August 2024

Abstract
Based on the YOLOv5s-Ghost model, the changes in the number of channels and feature scales of the YOLOv5s-Ghost model, as well as the structure and function of GhostConv convolution, were studied. It was found that the YOLOv5s-Ghost model has insufficient information extraction and a YOLOv5s-EGhost model with fewer model parameters and higher detection accuracy was designed. GhostConv convolution has the functions of compressing channels and downsampling. The YOLOv5s-Ghost model has a lower number of channels at low channels, and each channel doubling of the model is accompanied by a downsampling operation. Applying GhostConv convolution to the low channel of the YOLOv5s-Ghost model can cause insufficient information extraction. Therefore, ECRS and EC3Ghost2 modules were designed to be placed in the low channel of the model, and a regular 3×3 convolution was used to replace GhostConv convolution at the third downsampling point, improving the situation of insufficient information extraction in the low channel of the YOLOv5s-Ghost model. Gc3-Ghost and GSPPF modules were designed in the high channels of the model, which can reduce the number of model parameters while maintaining model accuracy. On the self-made defect dataset, the improved YOLOv5s-EGhost model reduces the number of parameters by 58.6% and 21.1% respectively compared to YOLOv5s and YOLOv5s Ghost, improves the R index by 2.0% and 1.8 percentage points respectively, and improves Map_0.5 by 0.7 percentage points and 1.8 percentage points respectively, which can meet the requirements of real-time detection.
PRP protocol optimization method based on Q-learning under the Linux operating system
Authors: Wang Ruini , Hu Xianlang , Kong Xiangying 
Published: August 2024

Abstract
In the rapid development of industrial networks, in the face of complex and changeable network environments, the demand for reliable and efficient communication systems is becoming increasingly prominent. In this paper, a PRP protocol optimization method based on Q-learning under the Linux operating system is proposed. By introducing a Q-learning algorithm, the adaptive learning of a dynamic network environment is realized through Q-value update rules, to adjust PRP protocol parameters. The experimental results show that the optimization of PRP protocol based on Q-learning can improve the performance of PRP protocol under various network conditions and load changes, thus improving communication reliability.
Research on low-speed unmanned vehicle diagnosis system based on fault tree
Authors: Jin Zhigang , Zhang Qingyu , Wang Yaofu 
Published: August 2024

Abstract
To reduce the safety risks of low-speed unmanned vehicles losing control and colliding with pedestrians or vehicles due to malfunctions during autonomous task execution, Fault Tree Analysis (FTA) is used to analyze the safety of low-speed unmanned vehicles. A low-speed unmanned vehicle fault diagnosis system is designed to take corresponding measures in case of vehicle faults and improve vehicle driving safety.
Research on vehicle A-pillar visualization based on A-pillar detection and decision tree model
Authors: Yuan Huan , Sun Jiaqi , Zhang Xiaoya , Zhu Yao 
Published: August 2024

Abstract
When turning or passing through intersections, the vehicle’s A-pillar blind spot may obscure road conditions. Using cameras to capture and display external images on the A-pillar screen can eliminate the A-pillar blind spot. However, this solution still faces a mismatch between the displayed screen image and the surrounding environment image. To obtain more accurate blind spot images, accurate blind spot cropping areas are calculated through vehicle A-pillar detection and image registration technology. By introducing eye position and posture as features and considering the corresponding blind spot cropping areas as target variables, a decision tree model is trained to predict the blind spot cropping areas. This model can rapidly and accurately predict the blind spot areas for display on the A-pillar screen, with an average pixel error of 12.7, a coefficient of determination of 0.85, and an image processing speed within one millisecond. Experiments show that the A-pillar blind spot visualization system based on A-pillar detection and decision tree model prediction effectively eliminates the A-pillar blind spot, enhancing driving safety.
Dynamic facial expression recognition based on attention mechanism
Authors: Bao Nengsheng , Ma Yawei , Wei Xiang , Liang Zuodong 
Published: August 2024

Abstract
In the wild, dynamic facial emotion recognition is a highly challenging task. Traditional approaches often focus on extracting discriminative features or preprocessing data to remove noisy frames. The former overlooks differences between keyframes and noise frames, while the latter can be complex and less robust. To address this issue, we propose a spatiotemporal feature extraction network based on an attention mechanism. In the spatial feature extraction stage, our method incorporates prior knowledge through an attention mechanism, allowing the model to precisely select and focus on key features. Additionally, incorporating prior knowledge reduces unnecessary search space, leading to faster convergence to optimal solutions. To suppress the impact of irrelevant frames, our study introduces a dynamic-aware temporal feature extraction mechanism, enabling the network to accurately capture key frames in video sequences, thereby enhancing model performance. Finally, at the decision stage, we design a fragment-based feature fusion filter to mitigate the influence of irrelevant frames on sequence classification. Our method achieves accuracy rates of 54.18% on DFEW, 42.37% on FERV39k, and 75.55% on CAER tests.
Towards a precise measurement of 157Tb nuclear decay data: Sample purification using resonance ionization mass spectrometry
Authors: Studer Dominik , Schumann Dorothea , Chiera Nadine Mariel , Maugeri Emilio Andrea , Kieck Tom , Kossert Karsten , Wendt Klaus 
Published: August 2024

Abstract
In nuclear physics, 157Tb emerges as a prime candidate
for experiments aimed at elucidating neutrino mass constraints and
at searching for sterile neutrinos. Despite its importance,
157Tb exhibits highly uncertain values for its nuclear decay
properties. A significant challenge in many efforts to measure such
data lies in the simultaneous undesired presence of 158Tb in
the samples, which hinders precise activity determination. Mass
separation emerges as a crucial method for obtaining pure 157Tb
specimens. This work outlines the production of an isotopically-pure
157Tb sample through mass separation and ion implantation,
using the RISIKO facility at the University of Mainz. The initial
material was obtained from proton-irradiated Ta samples through
radiochemical separation at the Paul Scherrer Institute. In total, a
sample containing 8.7(9) · 1012 atoms of 157Tb was
obtained. The efficiency of the mass separation and ion implantation
was 13(2) %. The purified material served as the basis for new
research endeavors at the Physikalisch Technische Bundesanstalt
Braunschweig aiming at the determination of nuclear data for
157Tb with significantly improved precision.
Effect of micelle concentration on degumming of sesame oil (Sesamum indicum L) with ultrafiltration membrane
Authors: Une S , Sutardi  , Hastuti P , Susanto H 
Published: August 2024

Abstract
Sesame oil (Sesamum indicum L.) is obtained from roasted and pressed sesame seeds. The existence of phospholipids and contaminants in sesame oil is unfavorable, as it induces a darkening of the oil’s hue, thereby diminishing its oxidative stability. Failure to eliminate phospholipids compounds from sesame oil through the degumming process leads to a deterioration in quality thus influencing consumer acceptability. This research aimed to investigate the impact of micelle concentrations in hexanes-sesame oil mixture on the degumming process employing ultrafiltration membranes. This investigation involved the preparation of sesame oil through a procedure that entailed roasting at 180°C for 30 minutes, followed by pressing at a pressure of 140 kN for 5 minutes. Micelles of sesame oil roasted with hexane were prepared at concentrations of 20%, 25% and 30%. Moreover, the degumming of sesame oil micelles was accomplished by utilizing PES (Polyether sulfone) and PVDF (Polyvinylidene diflouride) ultrafiltration membranes and then by analyzing the resulting sesame oil’s properties. The results indicated that using a PVDF membrane for sesame oil degumming resulted in a higher permeate flux than a PES membrane. Furthermore, treatment with a 30% micelle concentration resulted in a yield of 70.63%, with phospholipid levels of 13.96 ppm and membrane rejection value of 92.9%.
Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation
Authors: Huynh Bao Ngoc , Groendahl Aurora Rosvoll , Tomic Oliver , Liland Kristian Hovde , Knudtsen Ingerid Skjei , Hoebers Frank , van Elmpt Wouter , Dale Einar , Malinen Eirik , Futsaether Cecilia Marie 
Published: August 2024

Abstract

Objective. Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and consistent but may produce unrealistic contours or miss relevant structures. We evaluate approaches for increasing the quality and assessing the uncertainty of CNN-generated contours of head and neck cancers with PET/CT as input. Approach. Two patient cohorts with head and neck squamous cell carcinoma and baseline 18F-fluorodeoxyglucose positron emission tomography and computed tomography images (FDG-PET/CT) were collected retrospectively from two centers. The union of manual contours of the gross primary tumor and involved nodes was used to train CNN models for generating automatic contours. The impact of image preprocessing, image augmentation, transfer learning and CNN complexity, architecture, and dimension (2D or 3D) on model performance and generalizability across centers was evaluated. A Monte Carlo dropout technique was used to quantify and visualize the uncertainty of the automatic contours. Main results. CNN models provided contours with good overlap with the manually contoured ground truth (median Dice Similarity Coefficient: 0.75–0.77), consistent with reported inter-observer variations and previous auto-contouring studies. Image augmentation and model dimension, rather than model complexity, architecture, or advanced image preprocessing, had the largest impact on model performance and cross-center generalizability. Transfer learning on a limited number of patients from a separate center increased model generalizability without decreasing model performance on the original training cohort. High model uncertainty was associated with false positive and false negative voxels as well as low Dice coefficients. Significance. High quality automatic contours can be obtained using deep learning architectures that are not overly complex. Uncertainty estimation of the predicted contours shows potential for highlighting regions of the contour requiring manual revision or flagging segmentations requiring manual inspection and intervention.
Collective Victims Beyond Reach?
Authors: Nili Shmuel 
Published: August 2024

Abstract
Affluent democracies have been hosting trillions stolen by foreign kleptocrats from some of the world’s poorest people. This chapter assumes an elusive intuition: affluent democracies who have been culpably entangled in foreign kleptocrats’ larceny have weighty (albeit defeasible) moral reasons to disgorge the fruits of this larceny, even if such disgorgement has little to no prospect of improving the lot of the victims of kleptocracy whom they cannot reach. The chapter’s main effort is to trace this disgorgement intuition to a much more general idea: that a liberal democracy as a collective agent has its own moral integrity. Much as with individual persons, this collective integrity provides independent moral reasons for and against certain actions—reasons whose force is not reducible to what the democracy in question owes to others. The juxtaposition of this collective integrity with the specific problem of kleptocratic loot helps us to learn more about both.
Traumatic Legacies and Narrative Claims
Authors: Lawlor Hannie 
Published: August 2024

Abstract
Chapter 1 takes as its starting point Marianne Hirsch’s notion of postmemory and explores the potential for appropriation that Hirsch posits when the second and third generations are compelled to give voice to a trauma that is not their ‘own’. It focuses on two responses to the Holocaust that stage a genealogical search to uncover a suppressed family trauma: Colombe Schneck’s autobiographical account in La Réparation (Reparation) (2012) and Juana Salabert’s novel Velódromo de Invierno (Winter Velodrome) (2001). As it examines the ways in which the postgenerational narrators interrogate and navigate the indirect connection to the harrowing legacy that has passed down to them, the chapter questions the use of appropriation as a marker by which to evaluate the ethics of postmemorial narratives.