bluuebunny/crossref_metadata_2025_split huggingface dataset:
Illicit Fentanyl in the Prenatal Period: A Significant Emerging Risk for Neonatal Opioid Withdrawal Syndrome
Authors: Rana Divya , Gaston Kan P. , DeBaer Linda , Pourcyrous Massroor 
Published: October 2024

Objective This study aimed to evaluate the impact of in-utero illicit fentanyl exposure on neonatal outcomes, including neonatal opioid withdrawal syndrome (NOWS), length of stay (LOS), and treatment requirements.
Study Design This study was conducted from March 2020 to December 2022, and focused on neonates born to mothers with opioid use or opioid use disorder (OUD). Maternal opioid use was identified through self-report or umbilical cord tissue (UCT) testing. Severe NOWS was defined as cases requiring pharmacological treatment. Statistical analyses included univariate comparisons, logistic regression, and generalized linear models to assess the associations between fentanyl exposure and neonatal outcomes.
Results Forty-seven percent (75/159) of infants had in-utero fentanyl exposure. Fentanyl-positive mothers were older, 31 ± 5 years, compared to non-fentanyl mothers, 29 ± 5, p = 0.01. They were also less likely to receive prenatal care (p < 0.01) and had a higher number of polysubstance used, 5 ± 1 compared to non-fentanyl mothers, 3 ± 1, p < 0.01. Overall, infants exposed to fentanyl had a higher incidence of severe NOWS (odds ratio = 5.8, 95% confidence interval [CI]: 2.49–12.95, p < 0.01) and required earlier NOWS treatment initiation, 1 ± 1 day compared to non-exposed infants 3 ± 2 days, p < 0.01. In adjusted analysis, fentanyl exposure was associated with a nearly three-fold increased risk of NOWS (Mantel–Haenszel combined relative risk = 2.98, 95% CI: 1.94–4.57). Furthermore, fentanyl exposure led to longer LOS, with a 40% increase for preterm neonates (p < 0.01) and a 63% increase for full-term neonates (p < 0.01). Additionally, there was a significant correlation between log fentanyl concentration in umbilical cord tissue and cumulative morphine dose required for NOWS treatment, p = 0.001.
Conclusion Prenatal illicit fentanyl exposure is an independent and strong risk factor for severe NOWS presentation in newborns requiring extended hospital stays.
Key Points
Lewis Acid Catalyzed Cycloaddition Reaction of Bicyclo[1.1.0]butanes
Authors: Chen Jie , Zhou Ling , Yang Hui 
Published: October 2024

AbstractIn recent years, formal cycloaddition reactions involving bicyclo[1.1.0]butanes (BCBs) have furnished an array of innovative methodologies and strategies for the efficient synthesis of bicyclo[2.1.1]hexanes (BCHs). Most methods can be broadly classified into two main modes: the radical pathway and the two-electron pathway. This Synpacts article will summarize the recent advancements in Lewis acid catalyzed formal cycloaddition reactions involving BCBs with alkenes, dipolar molecules, and alkynes, spanning the period from 2022 to 2024. Additionally, we introduce the formal cycloaddition reaction of BCBs with ynamides, catalyzed by Sc(OTf)3, which has been recently developed by our group. This approach offers a novel and efficient method for the synthesis of polysubstituted 2-amino-bicyclo[2.1.1]hexenes.1	Introduction2	Lewis Acid Catalyzed Formal Cycloaddition of BCBs with Alkenes and Dipoles3	Lewis Acid Catalyzed Formal Cycloaddition of BCBs with Alkynes4	Conclusion
Visible-Light-Induced Three-Component Reactions of α-Diazoesters, Indazoles, and Cyclic Ethers
Authors: Wang Wei , Liu Yan , Fu Yang , Peng Yiyuan , Yang Qin , Ding Qiuping 
Published: October 2024

AbstractAn efficient method was developed for the construction of N-short-chain ether-attached carbonyl group-substituted indazoles. The visible-light-induced three-component reactions of α-diazoesters, indazoles, and cyclic ethers, under extremely mild conditions, led to the corresponding mixture of N
1 and N
2 short-chain ether-substituted indazoles in good to excellent yields.
Tl2XY (X, Y = S, Se) monolayers with low lattice thermal conductivity and high thermoelectric performance by full-band approach with four scattering mechanisms
Authors: Yu Meng-Yuan , Yang Chuan-Lu , Li Xiaohu , Liu Yuliang , Zhao Wenkai 
Published: October 2024

Abstract
The low lattice thermal conductivity and high thermoelectric performance of the Janus Tl2SSe monolayer in the temperature region of 300–700 K are identified based on the thermoelectric properties of the Tl2S2, Tl2SSe, and Tl2Se2 monolayers. The transport coefficients for carrier concentrations and temperatures are obtained by solving the linearized Boltzmann transport equation in a full-band electronic structure. Four scattering mechanisms of acoustic deformation potential, optical deformation potential, polar optical phonon, and ionized impurity scatterings are considered. The ionized impurity scattering is recognized as the most important. The lattice thermal conductivity of the Janus Tl2SSe monolayer is substantially smaller than those of the Tl2S2, Tl2Se2 monolayers with higher symmetry. Moreover, we find that the Janus structures of the Tl2SSe monolayer increase the dielectronic constants and enhance the polar optical phonon scattering, then reduce the power factor to some extent. Therefore, the lattice thermal conductivity actually couples with the transport coefficient and cannot be individually regulated as is usually assumed. However, the ZT value of the Tl2SSe monolayer can still reach 1.77 at 700 K even if the intrinsic concentration and the bipolar effect are included. Therefore, the Tl2SSe monolayer is expected to be a promising candidate for thermoelectric materials.
Effect of cold atmospheric plasma (CAP) on the energy metabolism in HeLa cells through miRNA regulation
Authors: Jing Xumiao , Shao Changsheng , Zhu Ligang , Zhang Ying , Zhao Wei , Yang Qiyu , Zhang Fengqiu , Huang Qing 
Published: October 2024

Abstract
Cold atmospheric plasma (CAP) is an emerging tool for tumor treatment because it can inhibit cancer cell proliferation primarily through oxidative stress due to CAP-induced reactive oxygen species (ROS). Among various ROS targeting molecules in cancer cells, microRNAs (miRNAs) are one kind of important targets that can be stimulated by ROS, and many studies have shown that miRNAs are involved in the metabolism regulation of cancer cells. In this study, we applied helium-CAP (He-CAP) to HeLa cells, and observed that the ROS induced by He-CAP could modulate the miRNAs related to energy metabolism, leading to the changes of proliferation, glycolysis, tricarboxylic acid (TCA) cycling and oxidative phosphorylation (OXPHOS) in the HeLa cells, and affected the related hypoxia-inducible factor 1, p53, phosphoinositide 3-kinase signaling pathways. In addition, the analysis of miRNAs in the metabolic network revealed that the expressions of the miRNAs responsible for the promotion of energy metabolism increased, and correspondingly, the involved mRNA and protein expression decreased. As such, this study has not only demonstrated that CAP treatment could significantly change the miRNAs expression of cancer cells, but also provided a more in-depth understanding of the CAP effects on glycolysis, TCA cycling and OXPHOS processes in the cells through the comprehensive analysis of the miRNAs regulation.
Probing anomalous Zγγγ couplings at a future muon collider
Authors: Amarkhail H , İnan S C , Kisselev A V 
Published: October 2024

Abstract
The sensitivity to anomalous quartic gauge couplings (AQGCs) of the γ
γ
γ
Z interaction is studied in the μ
+
μ
− → μ
+
γ
γ
μ
− scattering at a future muon collider with unpolarized beams. The anomalous γ
γ
γ
Z vertex is described by two couplings, ζ
1 and ζ
2. The differential and total cross sections are calculated for the center-of-mass energies of 3 TeV, 14 TeV, and 100 TeV. For these values of the collision energy the 95% C.L. exclusion regions for AQGCs are obtained depending on the systematic error. In particular, for the 14 TeV muon collider with the integrated luminosity L = 20 ab−1 the best sensitivities are derived to be ζ
1 = 3.1 × 10−5 TeV−4 and ζ
2 = 6.5 ×10−5 TeV−4. These constraints are three orders of magnitude stronger than the bounds obtained for the 27 TeV HE-LHC with L = 15 ab−1. At the 100 TeV muon collider with L = 1000 ab−1 AQGCs can be probed up to 1.64 × 10−8 TeV−4 and 3.4 × 10−8 TeV−4 for ζ
1 and ζ
2, respectively. The partial-wave unitarity constraints on couplings ζ
1, ζ
2 are evaluated. It is shown that the unitarity is not violated in the region of the AQGCs examined in the present paper.
Superconducting energy gap structure of CsV3Sb5 from magnetic penetration depth measurements
Authors: Grant Morgan J , Liu Yi , Cao Guang-Han , Wilcox Joseph A , Guo Yanfeng , Xu Xiaofeng , Carrington Antony 
Published: October 2024

Abstract
Experimental determination of the structure of the superconducting order parameter in the kagome lattice compound CsV3Sb5 is an essential step towards understanding the nature of the superconducting pairing in this material. Here we report measurements of the temperature dependence of the in-plane magnetic penetration depth,

λ
(
T
)

, in crystals of CsV3Sb5 down to

∼

60

mK

. We find that

λ
(
T
)

is consistent with a fully-gapped state but with significant gap anisotropy. The magnitude of the gap minima are in the range

∼

0.2

–0.3

T

c

for the measured samples, markedly smaller than previous estimates. We discuss different forms of potential anisotropy and how these can be linked to the V and Sb Fermi surface sheets. We highlight a significant discrepancy between the calculated and measured values of

λ
(
T
=
0
)

which we suggest is caused by spatially suppressed superconductivity.
Machine learning based mechanical fault diagnosis and detection methods: a systematic review
Authors: Xin Yuechuan , Zhu Jianuo , Cai Mingyang , Zhao Pengyan , Zuo Quanzhi 
Published: October 2024

Abstract
Mechanical fault diagnosis and detection (FDD) are crucial for enhancing equipment reliability, economic efficiency, production safety, and energy conservation. In the era of Industry 4.0, artificial intelligence (AI) has emerged as a significant tool for mechanical FDD, attracting considerable attention from both academia and industry. This review focuses on the application of AI techniques in mechanical FDD using artificial intelligence techniques based on the existing research. It examines various AI algorithms including k-nearest neighbors, support vector machine, artificial neural network, deep learning, reinforcement learning, computer vision, and transformer algorithm integrating theoretical foundations with practical applications in industrial production. Furthermore, a comprehensive overview of these algorithms applications in mechanical FDD is provided. Finally, a critical assessment highlights the advantages and limitations of these techniques, while forecasting the developmental trajectories of future intelligent diagnostic technologies based on machine learning. This review serves to bridge the gap between researchers in AI and fault diagnosis, contributing significantly to the field.
Study on enhancing the quantitative performance of LIBS for Cu element in liquids based on the chitosan-parafilm enrichment method
Authors: Wang Hongbao , Li Honglian , Yao Zhichao , Sun Jiaxing , Pang Lili 
Published: October 2024

Abstract
To enhance the accuracy of laser-induced breakdown spectroscopy (LIBS) in detecting heavy metal elements in solutions, a chitosan (CS)-Parafilm (PM) enrichment method is proposed. This method involves drying chitosan-heavy metal complexes on a Parafilm substrate. During the drying of droplets, electrostatic attraction and chelation by CS effectively concentrate analytes. Additionally, the hydrophobic effect of the PM substrate induces Marangoni flow, which drags analytes from the bottom edge to the central top of the droplet surface, effectively suppressing the coffee ring effect (CRE). Using LIBS technology, spectra were uniformly collected in an array on the surface of sediments. Through the analysis of cumulative excitation spectra and measurement repeatability, the results showed that the RSD of the emission lines Cu I 324.754 nm and Cu I 327.396 nm was reduced to 3.85% and 3.78%, respectively. This indicates that the CS-PM enrichment method allows for uniform deposition of analytes within the samples, effectively suppressing the CRE. Quantitative analysis of Cu elements using the CS-PM enrichment method was conducted using PSO-SVM, PSO-BPNN, and random forest (RF) algorithms. The RF algorithm demonstrated the best predictive performance with Rp
2
of 0.977, root mean square error of prediction of 3.86 mg l−1, mean absolute error of 3.0 mg l−1, and RPD of 5.52. Thus, it can be seen that CS-PM effectively improves the repeatability of spectral measurements and the accuracy of quantitative analysis predictions, providing a technical reference for enhancing the stability and quantitative performance of element analysis in liquids using LIBS.
Dual-feature enhanced hybrid convolutional network for imbalanced fault diagnosis of rolling bearings
Authors: Zhao Yingjie , Yan Changfeng , Liu Bin , Kang Jianxiong , Li Shengqiang 
Published: October 2024

Abstract
Deep learning has been extensively applied in the rolling bearing fault diagnosis domain due to its superior data analysis and feature extraction capabilities. However, in practical applications, the normal operating state occupies most of the service life of equipment, and the occurrence probability of each kind of fault is different, leading to imbalanced data that significantly degrades the performance of the neural network. In order to solve this problem, a dual-feature enhanced hybrid convolutional network (DEHCNet) is proposed. Firstly, an impulse segment enhancement module is constructed to enhance impulse segment features in the raw data, helping the network to learn fault features more accurately. Then, a hybrid convolutional module is designed to fully mine discriminant fault features of minority classes from imbalanced data. In addition, a feature-enhanced combinational pooling module is devised to guide the network to focus more on the critical features and maximize the retention of key features in dimensionality reduction operations, thereby reducing the influence of data imbalance on the classifier. Finally, three distinct datasets are used to verify the DEHCNet. Experimental results show that this network has better diagnostic accuracy and robustness under conditions of data imbalance.
Stochastic Cahn–Hilliard and conserved Allen–Cahn equations with logarithmic potential and conservative noise*
Authors: Primio Andrea Di , Grasselli Maurizio , Scarpa Luca 
Published: October 2024

Abstract
We investigate the Cahn–Hilliard and the conserved Allen–Cahn equations with logarithmic type potential and conservative noise in a periodic domain. These features ensure that the order parameter takes its values in the physical range and, albeit the stochastic nature of the problems, that the total mass is conserved almost surely in time. For the Cahn–Hilliard equation, existence and uniqueness of probabilistically-strong solutions is shown up to the three-dimensional case. For the conserved Allen–Cahn equation, under a restriction on the noise magnitude, existence of martingale solutions is proved even in dimension three, while existence and uniqueness of probabilistically-strong solutions holds in dimension one and two. The analysis is carried out by studying the Cahn–Hilliard/conserved Allen–Cahn equations jointly, that is a linear combination of both the equations, which has an independent interest.
Stage-IV cosmic shear with Modified Gravity and model-independent screening
Authors: Tsedrik M. , Bose B. , Carrilho P. , Pourtsidou A. , Pamuk S. , Casas S. , Lesgourgues J. 
Published: October 2024

Abstract
We forecast constraints on minimal model-independent parametrisations of several Modified
Gravity theories using mock Stage-IV cosmic shear data. We include nonlinear effects and
screening, which ensures recovery of General Relativity on small scales. We introduce a power
spectrum emulator to accelerate our analysis and evaluate the robustness of the growth index
parametrisation with respect to two cosmologies: ΛCDM and the normal branch of the DGP
model. We forecast the uncertainties on the growth index γ to be of the order ∼
10%. We find that our halo-model based screening approach demonstrates excellent performance,
meeting the precision requirements of Stage-IV surveys. However, neglecting the screening
transition results in biased predictions for cosmological parameters. We find that the screening
transition shows significant degeneracy with baryonic feedback, requiring a much better
understanding of baryonic physics for its detection. Massive neutrinos effects are less prominent
and challenging to detect solely with cosmic shear data.
A model-independent precision test of General Relativity using LISA bright standard sirens
Authors: Afroz Samsuzzaman , Mukherjee Suvodip 
Published: October 2024

Abstract
The upcoming Laser Interferometer Space Antenna (LISA), set for launch in the mid-2030s, will enhance our capability to probe the universe through gravitational waves (GWs) emitted from binary black holes (BBHs) across a broad range of cosmological distances. LISA is projected to observe three classes of BBHs: massive BBHs (MBBHs), extreme mass-ratio inspirals (EMRIs), and stellar mass BBHs. This study focuses on MBBHs, which are anticipated to occur in gas-rich environments conducive to producing powerful electromagnetic (EM) counterparts, positioning them as excellent candidates for bright sirens. By combining GW luminosity distance measurements from these bright sirens with Baryon Acoustic Oscillation (BAO) measurements derived from galaxy clustering and sound horizon measurements from the Cosmic Microwave Background (CMB), and spectroscopic redshift measurements from observations of the electromagnetic (EM) counterpart, we propose a data-driven model-independent method to reconstruct deviations in the variation of the effective Planck mass (in conjunction with the Hubble constant) as a function of cosmic redshift. Using this multi-messenger technique, we achieve precise measurements of deviations in the effective Planck mass variation with redshift (z), with a precision ranging from approximately 2.4% to 7.2% from redshift z=1 to z=6 with a single event. Additionally, we achieved a measurement of the Hubble constant with a precision of about 1.3%, accounting for variations in the effective Planck mass over 4 years of observation time (T
obs). This assumes that EM counterparts are detected for 75% of the events. This precision improves with observation time as T
obs
-1/2. This approach not only has the potential to reveal deviations from General Relativity but also to significantly expand our understanding of the universe's fundamental physical properties.
A gentle introduction to gradient-based optimization and variational inequalities for machine learning
Authors: Wadia Neha S , Dandi Yatin , Jordan Michael I 
Published: October 2024

Abstract
The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems. In these broader settings, new mathematical challenges emerge that involve equilibria and game theory instead of optima. Gradient-based methods remain essential—given the high dimensionality and large scale of machine-learning problems—but simple gradient descent is no longer the point of departure for algorithm design. We provide a gentle introduction to a broader framework for gradient-based algorithms in machine learning, beginning with saddle points and monotone games, and proceeding to general variational inequalities. While we provide convergence proofs for several of the algorithms that we present, our main focus is that of providing motivation and intuition.
Real Time Prediction and Modelling of Power Grid System Stability and Load Balancing
Authors: Wu Xinhua , Jiang Shuting , Li Zuxin , Liao Ruifeng 
Published: October 2024

Abstract
The extensive geographical distribution of the power system determines its highly complex structure. The system includes key components such as generators, substations, transmission and distribution grids, and users. This system has characteristics such as nonlinearity, time-varying, and parameter uncertainty. Power system stability is inherently linked to its regular operational performance. This article uses Simulink models to simulate the working state of generators and circuit breakers during faults. Under the conditions of a synchronous generator with a rated power of 350MW, a voltage of 10.5KV, a three-phase parallel RLC load rated voltage of 115KV, and a power consumption of 300MW, when a short occurs on line, the speed and relative angle of the generator tend to a fixed value, and the system remains stable. When a fault occurs in the circuit within 0.1 seconds, the circuit breaker will cut off the faulty line within 0.6 seconds. Avoiding the occurrence of oscillations and unstable states in typical dynamic systems.
Enhanced Vehicle Recognition and Collision Alert System Using Yolo+DeepSORT Algorithm
Authors: Wang Xinchi , Wei Huimei , Wang Lijuan , Zhao Chenrui , Wu Siheng 
Published: October 2024

Abstract
This paper sets out to develop and deploy a deep learning-driven vehicle collision avoidance warning system. Initially, it delves into the foundational principles of traditional collision avoidance warning systems, alongside key safety driving concepts. It then elaborates on leveraging deep learning technology to enhance the precision and reliability of target tracking. This includes detailed discussions on techniques such as appearance feature extraction, Kalman filters, the Hungarian algorithm, and more. In the technical section, the paper outlines the specific implementation of utilizing the YOLO algorithm for target detection and the DeepSORT algorithm for target tracking within the system architecture. The experimental section showcases the system’s performance and stability through formatted experimental results, followed by a comprehensive analysis of the data obtained. Concluding, the paper summarizes its research findings, underscoring the significance and feasibility of a deep learning-based vehicle collision avoidance warning system.
Adaptive Style Normalization and Similarity Grouping for Unsupervised Cross-Domain Person Re-Identification
Authors: Wang Mingxuan , Hui Yan , Hu Xiuhua , Yang Ruiying 
Published: October 2024

Abstract
In unsupervised cross-domain person re-identification, feature extraction often includes identity-irrelevant information, reducing model generalization. Additionally, ignoring potential similarities within the target domain leads to noisy pseudo-labels. To address these issues, this paper introduces a method for cross-domain person re-identification that combines adaptive style normalization with similarity grouping. First, adaptive style normalization is applied on the backbone network to mitigate loss of information caused by instance normalization, thus improving the generalization ability and identity recognition accuracy of the model. Second, similarity grouping is employed to uncover potential similarities in target domain, overcoming the problem of loud pseudo-labels and further improve model performance. Using this method, mAP accuracy increased by 2.27% on the Market-1501 dataset and by 4.26% on the DukeMTMC-ReID dataset, enhancing its application in the real world.