bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Amplification Algorithm of Small Sample Soil Composite Data Based on Probability Distribution
Authors: Zhang Shuyue , Liang Xiangyang , Song Yang 
Published: October 2024

Abstract
Analyzing and studying the data of soil compounds from 10 cities, two important features were found in this project. First, the data for all compounds in soil follow a normal distribution with different parameters. Therefore, this paper argues that small-sample soil compound amplification data should also have these two characteristics. Based on this, this paper proposes a new algorithm called PDWCC (probability distribution with correlation coefficient) for amplification of small-sample soil compound data. The algorithm combines a probability distribution with a Pearson correlation coefficient. In addition, an optimized genetic algorithm incorporating a mutation operator was introduced to improve the efficiency of data amplification. Finally, experimental comparisons were made with conventional Bootstrap and SMOTE amplification algorithms using SVM classifiers. The results show that the PDWCC amplification algorithm outperforms the other algorithms in terms of classification accuracy, recall and F1 value with mean values of 81.77%, 79.34% and 78.65%, respectively.
A Holt Trend-Enabled Attention-ConV-GRU Method for Long-Term Degradation Prediction of Fuel Cells
Authors: Xie Teng , Wang Chu , Wang Peng , Wang Xueren , Di Ruohai 
Published: October 2024

Abstract
Proton Exchange Membrane Fuel Cell (PEMFC) has attracted widespread attention as a promising energy conversion device. However, the lack of durability is one of the major reasons limiting the further development of PEMFC. Accurately predicting the Remaining Usable Life (RUL) of a PEMFC is a key prerequisite for durability enhancement. This paper is dedicated to addressing the limitations of the training data interval for RUL prediction performance. Based on the above ideas, we propose a Holt trend-enabled Attention-Convolution-Gated Recurrent Unit method for extracting trend information and making multi-step predictions. The experimental results show that this strategy improves the multi-step prediction and maintains the inherent degradation trend over a longer range of time steps. The Root Mean Square Error and Mean Absolute Error both fell by more than 45%. The prediction accuracy reach 93.76%.
Research on Surface Defect Detection Method of Wire Rope Outer Covering Layer Based on Deep Learning
Authors: Shi Enxiu , Huang Xuxiang , Zhang Pan , Hu Gangyi 
Published: October 2024

Abstract
Aiming at the requirement of a certain enterprise for the visual inspection accuracy of the surface defects of the outer sheath of the wire rope to reach 95 %, this paper adopts the existing Faster R-CNN algorithm to detect the surface defects of the outer sheath of the wire rope, and its accuracy mAP 0.5 is 8 8.7%. In order to improve the detection accuracy, the Faster R-CNN algorithm is improved. Among them, ResNeXt-101 is obtained on the basis of ResNeXt-50. It is used as the backbone network, and the attention mechanism feature pyramid network ACFPN is introduced to extract multi-scale features to enhance the network’s defect detection ability. The defect detection accuracy mAP 0.5 is improved to 93.2% In view of the diversity and large size differences of the defects of the outer sheath of the wire rope, a new loss function is constructed for the network. At the same time, the BO algorithm is used to optimize the hyperparameters of the stochastic gradient optimizer SGD of the improved network. The final designed ResNeXt101-ACFPN-Faster R-CNN algorithm is used to detect the surface defects of the outer sheath of the wire rope, and the detection accuracy mAP 0.5 reaches 95.8%. The detection goal of the enterprise is achieved. The research results show that the network optimization method proposed in this paper has a good effect on improving the detection accuracy of the surface defects of the outer sheath of the wire rope, and also verifies the effectiveness and feasibility of the algorithm improvement.
SFFN: Serial Feature Fusion Network for Visual Tracking
Authors: Kou Qi , Wang Peng , Sun Mengyu , Li Xiaoyan , Di Ruohai , Lv Zhigang 
Published: October 2024

Abstract
Recently, The Transformer tracker has excellent visual target tracking performance. The parallel arrangement of structural feature fusion networks proposed by the TransT tracker makes the Transformer learn the erroneous information. To address the issue, this paper proposes the SFFN tracker. It constructed a serial feature fusion network with the Transformer module in a serial interleaved arrangement to avoid learning erroneous information during the Transformer feature fusion process. The experiments show that SFFN performs well on the GOT-10k dataset, and it improves over TransT by 2.6%, 2.6%, and 2.3% in AO, SR0.5, and SR0.75, respectively.
Lightweight Object Detection Based on Autonomous Driving
Authors: Hua Song , Wang Peng , Li Xiaoyan , Sun Bo , Sun Mengyu , Di Ruohai , Lv Zhigang 
Published: October 2024

Abstract
Addressing the issues of low accuracy and large size for tiny objects like pedestrians, non-motor vehicles, and vehicles in traditional object detection models for autonomous driving. An advanced variant of the YOLOv7-tiny model for automatic driving object detection is proposed in this study. The VoVEGSCSP module, designed for cross-layer integration within local networks, is introduced. The SPPCSPC module was replaced with the Space Pyramid Pool Fast (SPPF). This reduces parameters and calculations while preserving the diversity of the original feature scale. CARAFE is a lightweight universal upsampling operator that replaces nearest neighbor interpolation in the upsampling module, thereby reducing feature information loss during upsampling. Results demonstrate that on the KITTI dataset, the improved YOLOv7-tiny algorithm reduces parameters by 35.7% and computation by 34.1%, with only a 0.7% decrease in mAP@0.5 accuracy.
UAV detection technology research based on deep learning
Authors: Wang Yu , Bai Xiaojun , Di Liuhua , Zhao Shifeng 
Published: October 2024

Abstract
In the process of UAV target recognition, the target is small, the feature is not obvious, and the recognition accuracy is low, a UAV target detection method called YOLOv8-UD is proposed based on YOLOv8. This method replaces downsampling layers with SPD convolutions to mitigate fine-grained feature loss issues. It employs a BiFPN structure to get information which is multi-scale, enhancing representation of global semantic message and Improve awareness of small goals. As a result, to target detection, it significantly enhances the correctness and effectiveness. Additionally, to address difficulty of detecting low-resolution small targets, the network structure was modified by adding a detection head, for small target, thereby enhancing capability to detect it. Combining an improved loss function based on Inner-IoU accelerated model convergence and improved detection accuracy of samples at high IoU thresholds. We can tell by experiments that The algorithm proposed in this paper have 0.805 and 0.334 in mAP(0.5) and mAP(0.5:0.95) metrics, respectively. Compared to YOLOv8, improvements of 4.6% and 7% are achieved in mAP(0.5) and mAP (0.5:0.95) metrics. The experiments validate that the proposed way significantly enhances accuracy to UAV small target detection.
Lightweight Detection Algorithm for Air-to-Ground Threat Object
Authors: Xu Xin , Liu Bin , Zhong Yun , Hua Song 
Published: October 2024

Abstract
In air-to-ground threat object detection, the typically strong concealment, small size, and high movement speed of threat objects often lead to challenges such as missed detections, low accuracy, and an excessive number of network parameters. An efficient lightweight detection algorithm for air-to-ground threat objects is proposed, built upon the YOLOv7-tiny framework. To address the issue of large network parameters, lightweight modules ELAN-GS and Slim-Neck are employed. Additionally, to refine the detection precision for small objects, the NWD metric and NWD loss function are introduced. Results from experiments on the DOTA dataset highlight that, compared with the original network, the proposed algorithm achieves a 33.2% reduction in parameters and a 26.3% reduction in computational cost. The small object accuracy, mAPs@0.5:0.95, is 23.7%, which is a 4.4% enhancement relative to the original network. The significant reduction in parameters and computational cost, along with the substantial improvement in small object detection accuracy, demonstrate the effectiveness of the proposed algorithm.
SPCF-YOLO: Dense Small Target Detection Algorithm for Autonomous Driving
Authors: Zhao Yan , Zhao Xiangmo , Wang Peng , Li Liangliang , Sun Bo , Di Ruohai 
Published: October 2024

Abstract
In the automatic driving scene, the pedestrian and vehicle targets are dense and block each other, and the problem of target false detection and missing detection is easy to occur. In this paper, a dense small target detection algorithm SPCF-YOLO is proposed. A new SPCSA module has been designed to strengthen the capture of edge features in occluded small targets. MPDIoU replaces the original CIOU loss function to address cases where the predicted and real frames have the same aspect ratio, but the aspect ratio difference of anchor frame is large. Experimental results show that in KITTI 2D data set, compared with the original YOLOv7 algorithm, mAP@0.5 is 93.5%, an increase of 0.5%. mAP@0.5:0.95 is 66.9%, an increase of 2%; micro-target mAP@0.5:0.95 is 50.3%, an increase of 2.6%; and the computational load and parameter number remain basically unchanged, which proves the effectiveness of the design algorithm.
Research on Polyurethane Creep Prediction Model Based on BWO-BP
Authors: Di Ruohai , Song Nan , Lv Zhigang , Li Jianheng , Gao Hui , Xu Yuntao 
Published: October 2024

Abstract
Polyurethane foam materials can creep under the influence of temperature, humidity, and pressure during use, which seriously affects their safety and state of use. Therefore, studying the creep behavior of polyurethane foam materials and developing corresponding creep mechanism models have significant scientific value for understanding their damage patterns. As a type of machine learning, Backpropagation (BP) neural network can be used for data analysis. However, there are many issues when determining the network structure. Whale Optimization (BWO) algorithm, as a type of swarm intelligence algorithm, can efficiently determine the framework of the BP model. BP which has been optimized through the BWO algorithm, has been utilized to construct a model for the prediction of polyurethane’s creep failure. The optimization process has significantly lowered the mean squared error from 0.30 in the non-optimized BP model to 0.07 in the enhanced BWO-BP model, which validates the effectiveness of the proposed algorithm.
Research on Image Inpainting Algorithm Based on Self-Attention Mechanism
Authors: Zhao Li , Jiao Yuxin , Shi Jiangrong , Cao Lei , Liang Huan 
Published: October 2024

Abstract
This paper presents a two-path image coloring network based on generative adversarial networks to address structure loss and texture blurring in image coloring. The network uses a modified residual network and adds long-term and short-term attention mechanisms in the decoding stage. The input picture is identified using a patch discriminator. The algorithm is validated using the Places2 dataset and its restoration results are evaluated using image evaluation methods. Experiments show that the larger the occluded region, the better the results. The algorithm improves psnr and ssim values by about 2% and 15% compared to traditional algorithms, and the images repaired are clearer and more realistic.
Reactive Load Pre-measurement for Large Industrial Users Based on Improved GBDT Regression Algorithm
Authors: Gao Zeming , Cheng Lun , Yang Xiaodong , Chen Linhao 
Published: October 2024

Abstract
With the development of intelligence and marketization of power system, reactive load prediction is the key technology applied to the intelligent technical means of power grid. For large industrial users, the management of reactive power is one of the key factors to maintain the voltage stability. Therefore, the accurate prediction of the reactive load of large industrial users is of great significance to optimize the operation of the power system and reduce energy waste. To this end, this paper realizes the prediction set date based on the improved gradient lifting tree (Gradient Boosting Decision Tree, GBDT) regression algorithm, selects the whole data as the historical data for training, and compares the data with the traditional prediction method. The experiments show that the improved GBDT regression algorithm is 28.9% more accurate than the time series algorithm MA, which predicts only from historical data.
Research on Target Position and Motion Parameters Based on Azimuth Wave Number of Echo Signal
Authors: Chu Lina , Song Bin , Wang Lin , Zhao Yuefei , Li Can , Zhao Song 
Published: October 2024

Abstract
The circular synthetic aperture radar (CSAR) system by a small rotor unmanned aerial vehicle, being capable of three-dimensional target imaging, is one of the research hotspots in SAR imaging. In this paper, for the frequency modulated continuous wave CSAR system, the geometric structure and wavenumber spectrum are derived, and then the relationship between the azimuth wavenumber and system parameters as well as flight motion conditions is analyzed. Through simulation analysis, it is verified that the azimuth wavenumber of the echo signal is affected by the position and motion parameters of the imaging target, laying a theoretical foundation for the development and application of the wavenumber domain imaging algorithm of CSAR.
Upper Limb Motion Intention Recognition Based on sEMG and EMD-LSSVM
Authors: Wang Xuefeng , Liao Fuyuan , Zhang Wenyue , Yao Fei , Li Jiajun 
Published: October 2024

Abstract
A method is proposed to enhance the precision of recognizing upper limb motion intentions using surface electromyography. This approach combines empirical mode decomposition with a least squares support vector machine. This method performs empirical mode decomposition on the preprocessed sEMG to obtain its intrinsic mode function and residual function, this method assesses the correlation between each intrinsic mode function and sEMG signals, and selects the component with high correlation as the effective component. Finally, the time-domain features of sEMG and the first three intrinsic mode functions are calculated and combined to obtain a new feature vector. The findings indicate that using EMD-LSSVM improves the accuracy of recognizing upper limb motion intentions by 9 % and 5 % higher than that of EMD-SVM and EMD-PSO-SVM respectively.
One-Step Molten Salt Method For Synthesis of Perovskite-Like Sr2Nb2O7 and Sr5Nb4O15 Ceramics
Authors: Xi Dongyang , Sun Lei 
Published: October 2024

Abstract
In this study, Sr2Nb2O7 and Sr5Nb4O15 layered perovskite-like ceramics were synthesized by a single process using a one-step molten salt method. By optimizing the key parameters, including the choice of molten salt, reaction temperature and residence time, two materials achieve synchronous production. The crystal structures of the synthesized materials were confirmed by X-ray diffraction. It is proved that it has typical perovskite-like structural characteristics. Scanning electron microscopy reveals the morphology of these materials. Sr2Nb2O7 presents a sheet-based structure, and Sr5Nb4O15 presents a rod-based structure. This study provides an efficient and direct method for the synthesis of perovskite materials. One-step molten salt method has great potential in the synthesis of multiphase materials. These findings are expected to promote the application of perovskite materials in the fields of electronics, optics and catalysis.
Detection of Amphibian Species based on Cytochrome b Metabarcoding using High-Throughput Sequencing Approach
Authors: Farhana Ramli Farah , Munian Kaviarasu 
Published: October 2024

Abstract
Amphibians are essential in terrestrial and freshwater ecosystems, contributing to multiple ecological functions. The presence of amphibians in a particular area usually signifies a healthy ecosystem as it indicates that the habitat meets the requirements of their life cycles. However, the amphibian population have been declining due to human activities including pollution and habitat destruction. Researchers are using new methods to monitor amphibian diversity to address this decline. Conventional sampling methods were compared with new DNA metabarcoding techniques using advanced sequencing platforms. Therefore, this study aims to analyze the early detection of amphibian species using Cytochrome b (Cytb) gene with metabarcoding methods and next generation sequencing techniques. Thirty-one samples were used in this study and underwent processes such as extraction, amplification using Cytb gene, library preparation, and sequencing. The result showed that the total number of reads was 658,655, with reads in OTUs numbered 474,061. Out of 31 samples, only three species were successfully detected: Fejervarya limnocharis with the highest abundance (20.6%), followed by Polypedates leucomystax and Pulchrana baramica. The results indicate that Cytb has low capabilities in detecting amphibian species, which might be due to its highly conserved region and the lack of databases for this gene. For future studies, an enhanced DNA metabarcoding procedure should be applied, including better choice of genetic marker and bioinformatics pipelines to minimize error and to improve the accuracy and reliability of detecting amphibian’s species using NGS and DNA metabarcoding. This would enhance efforts to protect amphibian biodiversity.
Insights into Microbial Communities and Foodborne Pathogens in Food Samples from Kinta Valley, Perak, Malaysia through Metagenomic Analysis
Authors: Quek Jia Weai Jerrald , Leong Wong Jun , Liang Tan Joon , Chieng Yeo Chew , Hoon Saw Seow 
Published: October 2024

Abstract
Foodborne illnesses pose an escalating global threat, predominantly stemming from bacterial contamination. Despite a growing incidence rate in Malaysia, there remains a dearth of comprehensive studies utilising metagenomic approaches to analyse foodborne pathogens. This study aimed to assess microbial communities and identify foodborne pathogens in 27 samples, including vegetables, meats, and fruits through whole-genome shotgun metagenomic sequencing. Our analysis revealed a prevalent bacterial presence in most samples, with Proteobacteria and Firmicutes exhibiting notable abundance. Taxonomic profiling unveiled diverse microbial profiles, with specific genera indicating potential health hazards. Detection of common foodborne pathogens, including Escherichia, Salmonella, Listeria, Shigella, Vibrio, Cronobacter, Bacillus, Yersinia, and Klebsiella, underscored contamination risks associated with these food items. Importantly, our approach facilitated species-level taxonomic resolution, crucial for pinpointing potential foodborne pathogens. The detection of these pathogens in the samples highlights concerns regarding food safety and consumption risks. However, our findings also emphasize the potential of metagenomic sequencing to enhance pathogen detection techniques.
Computational Insights into Kaempferol and Luteolin: Potential Mitigators of the Omicron B.1.1.529 SARS-CoV-2 Variant
Authors: Yu Heng Liew , Khoo Gideon , Griffiths Lyn R. , Keat Wei Loo 
Published: October 2024

Abstract
The coronavirus disease 2019 (COVID-19) has been circulating in the local and global population. Among the SARS-CoV-2 variants, the Omicron strain tends to be detected in the recent genomic surveillance studies. Despite being a manageable disease, recovered patients may experience post-COVID-19 conditions such as cough (50.6%), fatigue (45.8%), and memory loss (37.4%). While developing an updated vaccine for COVID-19 is time-consuming, the in-silico discovery of phytoconstituents that bind to the SARS-CoV-2 receptor binding domain (RBD), may provide insights on the potential of edible plants as complementary treatments. This study aimed to investigate and compare the ligand-protein interactions of phytoconstituents and COVID-19 treatment drug, remdesivir, to the SARS-CoV-2 spike protein. The molecular docking results indicated that kaempferol and luteolin could potentially mitigate COVID-19, as they exhibited higher RBD-binding affinities than remdesivir.
Long strings of consecutive composite values of polynomials
Authors: Ford Kevin , Gabdullin Mikhail 
Published: October 2024

We show that for any polynomial

f
:

Z

→

Z

f:\mathbb {Z}\to \mathbb {Z}

with positive leading coefficient and irreducible over

Q

\mathbb {Q}

, if

x
x

is large enough then there is a string of

(
log
⁡
x
)
(
log
⁡
log
⁡
x

)

1

/

835

(\log x)(\log \log x)^{1/835}

consecutive integers

n
∈
[
1
,
x
]

n\in [1,x]

for which

f
(
n
)

f(n)

is composite. This improves the result by Kevin Ford, Sergei Konyagin, James Maynard, Carl Pomerance, and Terence Tao [J. Eur. Math. Soc. (JEMS) 23 (2023), pp. 667–700], which has the exponent of

log
⁡
log
⁡
x

\log \log x

being a constant depending on

f
f

which can be exponentially small in the degree of

f
f

.
Discovery
Authors: Parslow Christopher 
Published: October 2024

Abstract
This chapter reconstructs the history of the prosciutto sundial beginning with its rediscovery and the first written descriptions. It examines the earliest discussions of it on the international scholarly network circulating clandestine information about the finds from Herculaneum and Pompeii to illustrate how these established the errors and misconceptions recycled in later studies. The controversies sparked inside the Bourbon court by its discovery and initial analysis are revealed in a rare eyewitness account excerpted here for the first time. It emphasizes the light it sheds on a variety of aspects of these early excavations.
The Luther Myth
Authors: Hayden-Roy Patrick 
Published: October 2024

Abstract
This study traces the transposition of Luther’s image from religious reformer to völkisch icon in the period from the close of the Napoleonic Wars up through the immediate post-World-War II era. Using evidence drawn from not only theological works and literary and philosophical sources, but also speeches, theatrical works, public celebrations and monuments, it traces the development of Luther’s image within the German Protestant imagination. The study demonstrates how this image evolved within the environment of German nationalist sentiment of the nineteenth and twentieth centuries from a religious reformer and advocate of freedom to a symbol of völkisch nationalist identity, such that with the seizure of power by the Nazis, Luther was used to portray a symbiosis between the new regime and the tradition of Protestant religiosity. The study looks particularly at how Protestant Germans styled Luther to affirm the essentialist racial identity politics of the Nazis, the cult of authoritarian leadership around Adolf Hitler, the drive to impose state control over all competing sources of authority, and the victimizing of German Jews.