bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anomaly Identification Model of Power Battery Self-discharge Based on Deep Belief Networks
Authors: Wang Peng , Yan Wen , Shen Bin , Liang Xinmiao 
Published: December 2023

Abstract
Electric vehicles will receive signal data such as voltage and current while the vehicle is running, which can reflect the safety status of the vehicle. Therefore, a quantitative description of the safety status of power batteries during operation can be achieved by analyzing and mining the safety features contained in the historical data of the vehicle that is running. However, considering the physical characteristics of the battery system and factors such as sensor design and acquisition accuracy, there is inevitably information coupling, redundancy, and error between different signals, making it difficult to extract and quantify safety features accurately. To solve this problem, this paper combines the identification method of abnormal risk characteristics of self-discharge of power battery, constructs abnormal risk characteristics of self-discharge of power battery, and completes the calculation and safety quantification of self-discharge risk characteristics. On this basis, by combining multiple safety features with the vehicle safety status obtained from the above analysis, a supervised learning sample data set is constructed, and the deep confidence network (DBN) is used to map and describe the vehicle safety feature data and the vehicle safety status, so as to achieve accurate judgment and fault identification of the vehicle comprehensive status.
Research on Lean Improvement of a Powder Production Line Based on Contradiction Analysis
Authors: Guo Jidong , Chen Jie , Long Lijian , Huang Jie , Yang Yiben , Zhang Yongyang 
Published: December 2023

Abstract
The intelligent improvement of production and manufacturing plays an important role in providing automated production for a company. In response to the problems of low production efficiency, high defect rate, and slow mold change in a certain company’s powder production site, this study combines the ideas of quality management and lean improvement, and analyzes the equipment utilization and production capacity through methods such as contradiction analysis, process function analysis, and fishbone diagram, By using Flexsim simulation and analytic hierarchy process, an optimization plan was obtained, which shortened the processing time and mold change time, improved the production capacity of the production line, and provided specific solutions and new ideas for the company’s production and operation. This helps to improve the technical performance of process oriented manufacturing production.
Low-light Image Enhancement Method Based on Illumination Curve Adjustment
Authors: Cheng Fensen , Zhang Jun 
Published: December 2023

Abstract
This paper presents an advanced low-light image enhancement approach based on Zero-Reference Deep Curve Estimation. The technique enhances images through: (1) the integration of the Dynamic Stochastic Resonance equation for enhanced contrast and brightness, leveraging adjustable parameters for optimal image quality, and (2) application of an Anisotropic Diffusion function for image denoising and smoothing, preserving edges and texture details while effectively removing noise. This adaptive algorithm tailors diffusion intensity to local image features, further refining the resulting image quality. Experimental results confirm the efficacy of our approach in improving visual quality and noise removal in low-light images.
Can Explainable Artificial Intelligence Optimize the Data Quality of Machine Learning Model? Taking Meibomian Gland Dysfunction Detections as a Case Study
Authors: Wang Mini Han , Zhou Ruoyu , Lin Zhiyuan , Yu Yang , Zeng Peijin , Fang Xiaoxiao , yang Jie , Hou Guanghui , Li Yonghao , Yu Xiangrong , Chong Kelvin Kam-Lung 
Published: December 2023

Abstract
Data quality plays a crucial role in computer-aided diagnosis (CAD) for ophthalmic disease detection. Various methodologies for data enhancement and preprocessing exist, with varying effectiveness and impact on model performance. However, the process of identifying the most effective approach usually involves time-consuming and resource-intensive experiments to determine optimal parameters. To address this issue, this study introduces a novel guidance framework that utilizes Explainable Artificial Intelligence (XAI) to enhance data quality. This method provides evidence of the significant contribution of XAI in classifying meibomian gland dysfunction (MGD) by aiding in feature selection, improving model transparency, mitigating data biases, providing interpretability, enabling error analysis, and establishing trust in machine learning (ML) models using multi-source meibomian datasets. The experimental results demonstrate substantial performance improvements in ML models when utilizing enhanced datasets compared to original images, as indicated by increased accuracy (0.67 vs. 0.86), recall (0.46 vs. 0.89), F1 score (0.48 vs. 0.84), XAI indicator (0.51 vs. 0.81), and IOU score (0.44 vs. 0.79). These findings highlight the significant potential of XAI in ML model MGD classification, particularly in advancing interpretability, standardization, fairness, domain integration, and clinical adoption. Consequently, the proposed framework not only saves valuable resources but also provides interpretable evidence for decision-making in data enhancement strategies. This study contributes to the understanding of XAI’s role in ML model MGD classification and its potential for driving advancements in key areas such as interpretability, standardization, fairness, domain integration, and clinical adoption.
The Optimal Layout Combination of Radar Stations in Multi-station Passive Time Difference Positioning System
Authors: He Jian , Song Shuling , Pu Renwei 
Published: December 2023

Abstract
This paper investigates the problem of optimizing the selection of radar site in three-dimensional terrain for multi-site passive time-difference positioning system. First, the directness of the radar to the surveillance airspace is investigated. A judgment method based on planar convex closure is given to determine whether a terrain point obscures the radar site to be selected, based on it, a screening algorithm is given to find the region where the received signals are not blocked. Second, the problem of optimal position combination of multiple radar stations is investigated. Taking a convex polygonal surveillance airspace as the object of study, a planning model with the objective of selecting the optimal combination of radar positions is given, which takes into account the constraints on the radar’s directness to the surveillance airspace, as well as the constraints on the far bounds of the detections, and proposes a genetic algorithm for solving the model, which uses an individual coding of the real type and introduces the penalty coefficients, which transforms the constrained planning problem to an unconstrained planning problem.
Research on the Numerical Method of Pollutant Emission during Gas Turbine Start-up
Authors: Wang Gugofeng , Chen Kun , Fan Hongtao , Huang Sijia , Xie Zhiyuan , Shen Huixia 
Published: December 2023

Abstract
It was of great research value and significance to predict the pollutant emission from the gas turbine start-up process by numerical method, which could improve the operation and regulation of gas turbines. In the paper, the computational fluid dynamics(CFD)-chemical reaction network (CRN) method was used to study the pollutant emission of gas turbines. According to the control process of the start-up control module, the start-up process had been divided into 12 states, and the CFD numerical analysis was carried out. Based on the data of Damkohler number (Da), temperature and velocity obtained by CFD, the chemical reaction network model of each state point was established. The research showed that the design parameter value of the state point was the basis of compiling the calculation process and source code; The basic data and partition basis for the chemical reaction network could been provided by the results of CFD analysis, which was an important guarantee for accurate prediction of NOx; The method of CFD-CRN could accurately predict NOx emission of gas engine during start-up, and provided reasonable and reliable research means and mathematical methods.
Automatic Matching of Optical and SAR Images Based on Attention Structure Features
Authors: Deng Jiwei , Yang Chao , Gong Guoqin , Zhao Hai , Wang Lei , Ye Yuanxin 
Published: December 2023

Abstract
Due to the disparity in imaging techniques, significant radiometric and geometric variances exist among optical and Synthetic Aperture Radar (SAR) images, making it a challenging task for achieving automatic and accurate matching in contemporary international academic research. Handcrafted structural features have shown some success in heterogeneous image matching in recent years. However, improving its matching performance manually proves to be difficult. As a result, this work presents a matching strategy based on attention-enhanced structural feature representation to improve optical and SAR images matching accuracy. In this research, a novel multi-branch global attention module is built using handmade structural feature extraction. This module can focus on the common information of structural feature descriptors in space and channel, extracting finer and more robust image features. Then, the proposed method utilizes the sum of squared difference (SSD) learning metric, which is based on the fast Fourier transform, to develop a loss function. This loss function is then used to train positive and negative samples in order to enhance the discriminative ability of the model. Experimental results obtained from training and testing on numerous optical and SAR datasets demonstrate that the proposed method significantly improves the accuracy of matching optical and SAR images compared to both current structural feature matching methods and advanced deep learning matching models.
Design of Fully Differential Amplification Circuit and Analysis of Related Parameters Based on Feedback Mechanism
Authors: Zhu Liyong , Wang Hangyan 
Published: December 2023

Abstract
In this paper, a new feedback-based mismatch reduction method is proposed for CMOS process-based differential circuit design. The method combines an active feedback circuit and a differential amplifier circuit to reduce the error caused by device mismatch by utilizing the feedback signal, which is calculated to achieve higher circuit performance and accuracy. Meanwhile, based on the actual circuit simulation in Candence, it is concluded that under the conditions of input voltage of 5V, operating current of 100mA, and the same transistor parameters, the circuit with the introduction of the feedback mechanism is able to achieve a maximum gain of about 10dB and a lower common mode rejection ratio at the same frequency compared with the circuit without feedback.
Gaseous argon time projection chamber with electroluminescence enhanced optical readout
Authors: Amarinei R.M. , Sánchez F. , Bordoni S. , Lux T. , Giannessi L. , Roe E. , Radicioni E. 
Published: December 2023

Abstract
Systematic uncertainties in accelerator oscillation neutrino
experiments arise from nuclear models describing neutrino-nucleus
interactions. To mitigate these uncertainties, we can study
neutrino-nuclei interactions with detectors possessing enhanced
hadron detection capabilities at energies below the nuclear Fermi
level. Gaseous detectors not only lower the particle detection
threshold but also enable the investigation of nuclear effects on
various nuclei by allowing for changes in the gas composition. This
approach provides valuable insights into the modelling of
neutrino-nucleus interactions and significantly reduces associated
uncertainties. Here, we discuss the design and first operation of a
gaseous argon time projection chamber optically read. The detector
operates at atmospheric pressure and features a single stage of
electron amplification based on a thick GEM. Here, photons are
produced with wavelengths in the vacuum ultraviolet regime. In an
optical detector, the primary constraint is the light yield. This
study explores the possibility of increasing the light by applying a
low electric field downstream of the ThGEM. In this region, called
the electroluminescence gap, electrons propagate and excite the
argon atoms, leading to the subsequent emission of photons. This
process occurs without any further electron amplification, and it is
demonstrated that the total light yield increases up to three times
by applying moderate electric fields of the order of
3 kV/cm. Finally, an indirect method is discussed for determining
the photon yield/charge gain of a ThGEM, giving a value of
18.3 photons detected per secondary electron.
Impact of T
i/T
e ratio on ion transport based on EAST H-mode plasmas
Authors: LIU 刘 Jianwen 建文 , ZANG 臧 Qing 庆 , LIANG 梁 Yunfeng 云峰 , CHEN 陈 Jiale 佳乐 , WU 邬 Xiaohe 潇河 , KNIEPS Alexander , HU 扈 Jiahui 嘉辉 , JIN 金 Yifei 仡飞 , ZHANG 张 Bin 斌 , CHU 储 Yuqi 宇奇 , LIU 刘 Haiqing 海庆 , LYU 吕 Bo 波 , DUAN 段 Yanmin 艳敏 , LI 李 Miaohui 妙辉 , CHEN 陈 Yingjie 颖杰 , GONG 龚 Xianzu 先祖 , Team the EAST 
Published: December 2023

Abstract
At the EAST tokamak, the ion temperature (T
i) is observed to be clamped around 1.25 keV in electron cyclotron resonance (ECR)-heated plasmas, even at core electron temperatures up to 10 keV (depending on the ECR heating power and the plasma density). This clamping results from the lack of direct ion heating and high levels of turbulence-driven transport. Turbulent transport analysis shows that trapped electron mode and electron temperature gradient-driven modes are the most unstable modes in the core of ECR-heated H-mode plasmas. Nevertheless, recently it was found that the T
i/T
e ratio can increase further with the fraction of the neutral beam injection (NBI) power, which leads to a higher core ion temperature (T
i0). In NBI heating-dominant H-mode plasmas, the ion temperature gradient-driven modes become the most unstable modes. Furthermore, a strong and broad internal transport barrier (ITB) can form at the plasma core in high-power NBI-heated H-mode plasmas when the T
i/T
e ratio approaches ~1, which results in steep core T
e and T
i profiles, as well as a peaked n
e profile. Power balance analysis shows a weaker T
e profile stiffness after the formation of ITBs in the core plasma region, where T
i clamping is broken, and the core T
i can increase further above 2 keV, which is 80% higher than the value of T
i clamping in ECR-heated plasmas. This finding proposes a possible solution to the problem of T
i clamping on EAST and demonstrates an advanced operational regime with the formation of a strong and broad ITB for future fusion plasmas dominated by electron heating.
Solon and the Demos in his Poetry
Authors: Edwards Anthony T 
Published: December 2023

Abstract
I aim in this study to extract from the fragments of Solon’s poetry evidence for what the Athenian demos expected from Solon’s archonship. I assume that where Solon discusses the demos, he necessarily alludes to demotic demands. Solon’s testimony shows the demos to have been concerned primarily with three issues: the enslavement of Athenians, redistribution of land, and establishment of a tyranny. I conclude that the demos expected, vainly, Solon to end the enslavement of their numbers, to redistribute land, and to achieve these goals by establishing himself as a tyrant if necessary.
Outcomes following cardiac resynchronisation therapy in older people
Authors: Safdar Nawaz Z , Kamalathasan Stephe , Gupta Ankit , Wren Joshua , Bird Rory , Papp Dorothy , Latto Rebecca , Ahmed Ali , Palin Victoria , Gierula John , Witte Klaus K , Straw Sam 
Published: December 2023

Abstract

Introduction
Older patients may be less likely to receive cardiac resynchronisation therapy (CRT) for the management of heart failure. We aimed to describe the differences in clinical response, complications, and subsequent outcomes following CRT implantation compared to younger patients.

Methods
We conducted a retrospective cohort study of unselected, consecutive patients implanted with CRT devices between March 2008 and July 2017. We recorded complications, symptomatic and echocardiographic response, hospitalisation for heart failure, and all-cause mortality comparing patients aged &lt;70, 70–79 and ≥ 80 years.

Results
Five hundred and seventy-four patients (median age 76 years [interquartile range 68–81], 73.3% male) received CRT. At baseline, patients aged ≥80 years had worse symptoms, were more likely to have co-morbidities, and less likely to be receiving comprehensive medical therapy, although left ventricular function was similar. Older patients were less likely to receive CRT-defibrillators compared to CRT-pacemakers. Complications were infrequent and not more common in older patients. Age was not a predictor of symptomatic or echocardiographic response to CRT (67.2%, 71.2% and 62.6% responders in patients aged &lt;70, 70–79 and ≥ 80 years, respectively; P = 0.43), and time to first heart failure hospitalisation was similar across age groups (P = 0.28). Ten-year survival was lower for older patients (49.9%, 23.9% and 6.8% in patients aged &lt;70, 70–79 and ≥ 80 years, respectively; P &lt; 0.001).

Conclusions
The benefits of CRT on symptoms and left ventricular function were not different in older patients despite a greater burden of co-morbidities and less optimal medical therapy. These findings support the use of CRT in an ageing population.
Re-analysis and meta-analysis of summary statistics from gene–environment interaction studies
Authors: Pham Duy T , Westerman Kenneth E , Pan Cong , Chen Ling , Srinivasan Shylaja , Isganaitis Elvira , Vajravelu Mary Ellen , Bacha Fida , Chernausek Steve , Gubitosi-Klug Rose , Divers Jasmin , Pihoker Catherine , Marcovina Santica M , Manning Alisa K , Chen Han 
Published: December 2023

Abstract

Motivation
Summary statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene–environment interactions, there is a need for gene–environment interaction-specific methods that manipulate and use summary statistics.

Results
We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene–exposure and/or gene–covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene–environment interaction studies.

Availability and implementation
REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.
TsImpute: an accurate two-step imputation method for single-cell RNA-seq data
Authors: Zheng Weihua , Min Wenwen , Wang Shunfang 
Published: December 2023

AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) technology has enabled discovering gene expression patterns at single cell resolution. However, due to technical limitations, there are usually excessive zeros, called “dropouts,” in scRNA-seq data, which may mislead the downstream analysis. Therefore, it is crucial to impute these dropouts to recover the biological information.ResultsWe propose a two-step imputation method called tsImpute to impute scRNA-seq data. At the first step, tsImpute adopts zero-inflated negative binomial distribution to discriminate dropouts from true zeros and performs initial imputation by calculating the expected expression level. At the second step, it conducts clustering with this modified expression matrix, based on which the final distance weighted imputation is performed. Numerical results based on both simulated and real data show that tsImpute achieves favorable performance in terms of gene expression recovery, cell clustering, and differential expression analysis.Availability and implementationThe R package of tsImpute is available at https://github.com/ZhengWeihuaYNU/tsImpute.
Adenosylhomocysteinase plays multiple roles in maintaining the identity and pluripotency of mouse embryonic stem cells
Authors: Jiang Qi , Lan Shubing , Tan Fancheng , Liang Yiping , Guo Zhencheng , Hou Yanlin , Zhang Hui , Wu Guangming , Liu Zhonghua 
Published: December 2023

Abstract
Adenosylhomocysteinase (AHCY), a key enzyme in the methionine cycle, is essential for the development of embryos and the maintenance of mouse embryonic stem cells (mESCs). However, the precise underlying mechanism of Ahcy in regulating pluripotency remains unclear. As the only enzyme that can hydrolyze S-adenosylhomocysteine in mammals, AHCY plays a critical role in the metabolic homeostasis, epigenetic remodeling, and transcriptional regulation. Here, we identified Ahcy as a direct target of OCT4 and unveiled that AHCY regulates the self-renewal and differentiation potency of mESCs through multiple mechanisms. Our study demonstrated that AHCY is required for the metabolic homeostasis of mESCs. We revealed the dual role of Ahcy in both transcriptional activation and inhibition, which is accomplished via the maintenance of H3K4me3 and H3K27me3, respectively. We found that Ahcy is required for H3K4me3-dependent transcriptional activation in mESCs. We also demonstrated that AHCY interacts with polycomb repressive complex 2 (PRC2), thereby maintaining the pluripotency of mESCs by sustaining the H3K27me3-regulated transcriptional repression of related genes. These results reveal a previously unrecognized OCT4–AHCY–PRC2 axis in the regulation of mESCs’ pluripotency and provide insights into the interplay between transcriptional factors, cellular metabolism, chromatin dynamics and pluripotency regulation.
Phylogenetic association analysis with conditional rank correlation
Authors: Wang Shulei , Yuan Bo , Tony Cai T , Li Hongzhe 
Published: December 2023

Summary
Phylogenetic association analysis plays a crucial role in investigating the correlation between microbial compositions and specific outcomes of interest in microbiome studies. However, existing methods for testing such associations have limitations related to the assumption of a linear association in high-dimensional settings and the handling of confounding effects. Hence, there is a need for methods capable of characterizing complex associations, including nonmonotonic relationships. This article introduces a novel phylogenetic association analysis framework and associated tests to address these challenges by employing conditional rank correlation as a measure of association. The proposed tests account for confounders in a fully nonparametric manner, ensuring robustness against outliers and the ability to detect diverse dependencies. The proposed framework aggregates conditional rank correlations for subtrees using weighted sum and maximum approaches to capture both dense and sparse signals. The significance level of the test statistics is determined by calibration through a nearest-neighbour bootstrapping method, which is straightforward to implement and can accommodate additional datasets when these are available. The practical advantages of the proposed framework are demonstrated through numerical experiments using both simulated and real microbiome datasets.