bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between chronic pain and attrition: a prospective analysis of a national sample of midlife adults in the USA, 2004–2014
Authors: Liang Yunlong
Published: March 2024

Background

Health conditions of participants can significantly affect longitudinal drop-out in population-based epidemiological surveys, yet few studies have examined the association between chronic pain (CP) and follow-up attrition.

Methods

The Midlife in the United States study (MIDUS) was used to explore the longitudinal association between CP and survey attrition. CP was assessed by three measures: the presence of CP, CP interference and the number of pain sites at MIDUS 2. The types of sample attrition at MIDUS 3 encompassed several categories: complete, refusal to participate, inability to participate due to physical or mental constraints, deceased, non-working numbers, participants consistently unavailable for interviews, global refusal or withdrew from the study and not fielded. Multinomial logistic regression was employed to examine these relationships and to explore the moderation effects of sociodemographic variables and multiple chronic conditions on these associations.

Results

High-interference pain was associated with a 162% increased risk (RR 2.62, 95% CI 1.12 to 6.16, p=0.026) of being physically and mentally unable to participate in MIDUS 3. Individuals reporting the presence of CP (RR 0.65, 95% CI 0.45 to 0.95, p=0.028) and those with three or more CP sites (RR 0.48, 95% CI 0.27 to 0.87, p=0.016) were less likely to refuse participation in MIDUS 3. However, no further significant associations or moderating effects were identified.

Conclusion

Population-based epidemiological surveys may be susceptible to attrition bias from participants with CP, necessitating the adoption of adaptive survey methodologies.
Distribution of case volumes in surgery: an analysis of the British Spine Registry
Authors: Koh Chan Hee , Muirhead William , Khan Danyal Zaman , Layard Horsfall Hugo , Prezerakos George , Sayal Parag , Marcus Hani J
Published: March 2024

Objectives

To characterize the distribution of case volumes within a surgical field.

Design

An analysis of British Spine Registry.

Setting

295 centers in England that conducted at least one spinal operation either within the NHS or private settings between 1 May 2016 and 27 February 2021.

Participants

644 surgeons.

Main outcome measures

Mathematical descriptions of distributions of cases among surgeons and the extent of workforce-level case-volume concentration as a surrogate marker.

Results

There were wide variations in monthly caseloads between surgeons, ranging from 0 to average monthly high of 81.8 cases. The curves showed that 37.7% of surgeons were required to perform 80% of all spinal operations, which is substantially less than in fields outside of healthcare.

With the COVID-19 pandemic, the case volumes of surgeons with the highest volumes dropped dramatically, whereas those with the lowest case numbers remained nearly unchanged. This, along with the relatively low level of case-volume concentration within spinal surgery, may indicate an inevitability of at least some level of surgical care being provided by the relatively lower volume surgeons.

Conclusions

While there is a reasonable degree of workforce-level case volume concentration within spinal surgery, with high volume spinal surgeons providing a large proportion of care, it is not clear whether a further concentration of case volumes into those few hands is possible or desirable.
Targeting refractory/recurrent neuroblastoma and osteosarcoma with anti-CD3×anti-GD2 bispecific antibody armed T cells
Authors: Yankelevich Maxim , Thakur Archana , Modak Shakeel , Chu Roland , Taub Jeffrey , Martin Alissa , Schalk Dana , Schienshang Amy , Whitaker Sarah , Rea Katie , Lee Daniel W , Liu Qin , Shields Anthony F , Cheung Nai-Kong V , Lum Lawrence G
Published: March 2024

Background

The survival benefit observed in children with neuroblastoma (NB) and minimal residual disease who received treatment with anti-GD2 monoclonal antibodies prompted our investigation into the safety and potential clinical benefits of anti-CD3×anti-GD2 bispecific antibody (GD2Bi) armed T cells (GD2BATs). Preclinical studies demonstrated the high cytotoxicity of GD2BATs against GD2+cell lines, leading to the initiation of a phase I/II study in recurrent/refractory patients.

Methods

The 3+3 dose escalation phase I study (NCT02173093) encompassed nine evaluable patients with NB (n=5), osteosarcoma (n=3), and desmoplastic small round cell tumors (n=1). Patients received twice-weekly infusions of GD2BATs at 40, 80, or 160×106GD2BATs/kg/infusion complemented by daily interleukin-2 (300,000 IU/m2) and twice-weekly granulocyte macrophage colony-stimulating factor (250 µg/m2). The phase II segment focused on patients with NB at the dose 3 level of 160×106GD2BATs/kg/infusion.

Results

Of the 12 patients enrolled, 9 completed therapy in phase I with no dose-limiting toxicities. Mild and manageable cytokine release syndrome occurred in all patients, presenting as grade 2–3 fevers/chills, headaches, and occasional hypotension up to 72 hours after GD2BAT infusions. GD2-antibody-associated pain was minimal. Median overall survival (OS) for phase I and the limited phase II was 18.0 and 31.2 months, respectively, with a combined OS of 21.1 months. A phase I NB patient had a complete bone marrow response with overall stable disease. In phase II, 10 of 12 patients were evaluable: 1 achieved partial response, and 3 showed clinical benefit with prolonged stable disease. Over 50% of evaluable patients exhibited augmented immune responses to GD2+targets post-GD2BATs, as indicated by interferon-gamma (IFN-γ) EliSpots, Th1 cytokines, and/or chemokines.

Conclusions

This study demonstrated the safety of GD2BATs up to 160×106 cells/kg/infusion. Coupled with evidence of post-treatment endogenous immune responses, our findings support further investigation of GD2BATs in larger phase II clinical trials.
Prediction of progressive fibrosing interstitial lung disease in patients with systemic sclerosis: insight from the CRDC cohort study
Authors: Hui Min , Duan Xinwang , Zhou Jiaxin , Li Mengtao , Wang Qian , Zhao Jiuliang , Hou Yong , Xu Dong , Zeng Xiaofeng
Published: March 2024

Background

This study aims to establish a reliable prediction model of progressive fibrosing interstitial lung disease (PF-ILD) in patients with systemic sclerosis (SSc)-ILD, to achieve early risk stratification and to help better in preventing disease progression.

Methods

304 SSc-ILD patients with no less than three pulmonary function tests within 6–24 months were included. We collected data at baseline and compared differences between SSc patients with and without PF-ILD. Least absolute shrinkage and selection operator regularisation regression and multivariable Cox regression were used to construct the prediction model, which were presented as nomogram and forest plot.

Results

Among the 304 patients with SSc-ILD included, 92.1% were women, with a baseline average age of 46.7 years. Based on the 28 variables preselected by comparison between SSc patients without PF-ILD group (n=150) and patients with SSc PF-ILD group (n=154), a 9-variable prediction model was constructed, including age≥50 years (HR 1.8221, p=0.001), hyperlipidemia (HR 4.0516, p<0.001), smoking history (HR 3.8130, p<0.001), diffused cutaneous SSc subtype (HR 1.9753, p<0.001), arthritis (HR 2.0008, p<0.001), shortness of breath (HR 2.0487, p=0.012), decreased serum immunoglobulin A level (HR 2.3900, p=0.002), positive anti-Scl-70 antibody (HR 1.9573, p=0.016) and usage of cyclophosphamide/mycophenolate mofetil (HR 0.4267, p<0.001). The concordance index after enhanced bootstrap resampling adjustment was 0.874, while the optimism-corrected Brier Score was 0.144 in internal validation.

Conclusion

This study developed the first prediction model for PF-ILD in patients with SSc-ILD, and internal validation showed favourable accuracy and stability of the model.
Medication utilisation trends during pregnancy and factors influencing adverse pregnancy outcomes in patients with rheumatoid arthritis
Authors: Song Yeo-Jin , Cho Soo-Kyung , Jung Yu-Seon , Jung Sun-Young , Keum Jihyun , Nam Eunwoo , Sung Yoon-Kyoung , Yoo Dae Hyun
Published: March 2024

Objectives

We aimed to investigate medication utilisation trends during pregnancy and identify factors associated with adverse pregnancy outcomes (APOs) in patients with rheumatoid arthritis (RA).

Methods

Female patients with RA aged 20–50 years were identified from the Korean national health insurance database between 2010 and 2020. Pregnancy episodes were divided into two groups according to pregnancy outcome: the delivery group and the APO group (abortion and stillbirth). The characteristics and medication utilisation patterns were compared between the two groups, and multivariable logistic regression analysis was conducted to identify the factors associated with APOs.

Results

A total of 5728 pregnancy episodes were included, comprising 4576 delivery episodes and 1152 APO episodes. The mean maternal age for all pregnancy episodes was 33.7 years; 33.3 years in the delivery group and 33.7 years in the APO group. Hydroxychloroquine was the most commonly used conventional synthetic disease-modifying antirheumatic drug (DMARD) during the preconception period and pregnancy in both groups. The prescription rate of all DMARDs decreased rapidly during pregnancy. In the multivariable analysis, use of methotrexate (adjusted OR (aOR): 2.14, 95% CI 1.57 to 2.92) and leflunomide (aOR: 2.68, 95% CI 1.39 to 5.15) within 3 months before conception was associated with APOs.

Conclusion

Methotrexate and leflunomide are associated with an increased possibility of APOs, emphasising the importance of appropriate medication adjustment when planning for pregnancy.
Role of practice standardization in outcome optimization for CDH
Authors: Dimmer Alexandra , Baird Robert , Puligandla Pramod
Published: March 2024

Standardization of care seeks to improve patient outcomes and healthcare delivery by reducing unwanted variations in care as well as promoting the efficient and effective use of healthcare resources. There are many types of standardization, with clinical practice guidelines (CPGs), based on a stringent assessment of evidence and expert consensus, being the hallmark of high-quality care. This article outlines the history of CPGs, their benefits and shortcomings, with a specific focus on standardization efforts as it relates to congenital diaphragmatic hernia management.
High-rate tests on resistive plate chambers operated with eco-friendly gas mixtures
Authors: Abbrescia M. , Aielli G. , Aly R. , Arena M. C. , Barroso M. , Benussi L. , Bianco S. , Bordon F. , Boscherini D. , Bruni A. , Buontempo S. , Busato M. , Camarri P. , Cardarelli R. , Congedo L. , De Jesus Damiao D. , De Serio M. , Di Ciaccio A. , Di Stante L. , Dupieux P. , Eysermans J. , Ferretti A. , Galati G. , Gagliardi M. , Guida R. , Iaselli G. , Joly B. , Lee K. S. , Liberti B. , Lucero Ramirez D. , Mandelli B. , Manen S. P. , Massa L. , Pastore A. , Pastori E. , Piccolo D. , Pizzimento L. , Polini A. , Proto G. , Pugliese G. , Quaglia L. , Ramos D. , Rigoletti G. , Rocchi A. , Romano M. , Samalan A. , Salvini P. , Santonico R. , Saviano G. , Simone S. , Terlizzi L. , Tytgat M. , Vercellin E. , Verzeroli M. , Zaganidis N.
Published: March 2024

Results obtained by the RPC ECOgas@GIF++ Collaboration, using Resistive Plate Chambers operated with new, eco-friendly gas mixtures, based on tetrafluoropropene and carbon dioxide, are shown and discussed in this paper. Tests aimed to assess the performance of this kind of detectors in high-irradiation conditions, analogous to the ones foreseen for the coming years at the Large Hadron Collider experiments, were performed, and demonstrate a performance basically similar to the one obtained with the gas mixtures currently in use, based on tetrafluoroethane, which is being progressively phased out for its possible contribution to the greenhouse effect. Long term aging tests are also being carried out, with the goal to demonstrate the possibility of using these eco-friendly gas mixtures during the whole High Luminosity phase of the Large Hadron Collider.
Mass splitting and spin alignment for $$\phi $$ mesons in a magnetic field in NJL model
Authors: Sheng Xin-Li , Yang Shu-Yun , Zou Yao-Lin , Hou Defu
Published: March 2024

Based on the Nambu–Jona–Lasinio (NJL) model, we develop a framework for calculating the spin alignment of vector mesons and applied it to study $$\phi $$
ϕ
mesons in a magnetic field. We calculate mass spectra for $$\phi $$
ϕ
mesons and observe mass splitting between the longitudinally polarized state and transversely polarized states. The $$\phi $$
ϕ
meson in a thermal equilibrium system is preferred to occupy the state with spin $$\lambda =0$$

λ
=
0

than those with spin $$\lambda =\pm 1$$

λ
=
±
1

, because the former state has a smaller energy. As a consequence, we conclude that the spin alignment will be larger than 1/3 if one measures along the direction of the magnetic field, which is qualitatively consistent with the recent STAR data. Around the critical temperature $$T_{C}=150~\hbox {MeV}$$

T
C

=
150

MeV

, the positive deviation from 1/3 is proportional to the square of the magnetic field strength, which agrees with the result from the non-relativistic coalescence model. Including the anomalous magnetic moments for quarks will modify the dynamical masses of quarks and thus affect the mass spectra and spin alignment of $$\phi $$
ϕ
mesons. The discussion of spin alignment in the NJL model may help us better understand the formation of hadron’s spin structure during the chiral phase transition.
Testing beyond-Kerr spacetimes with GWTC-3
Authors: Santos Rafael M. , Nunes Rafael C. , de Araujo Jose C. N.
Published: March 2024

The Kerr spacetime is a fundamental solution of general relativity (GR), describing the gravitational field around a rotating, uncharged black hole (BH). Kerr spacetime has been crucial in modern astrophysics and it serves as a foundation for the study of gravitational waves (GWs). Possible deviations in Kerr geometry may indicate deviations from GR predictions. In this work, we consider the Johannsen–Psaltis metric, which is a beyond-Kerr metric characterized by a single free parameter, and then we probe this theory framework using several GWs observations from the third Gravitational-wave Transient Catalog (GWTC-3). We find that, for most of the events analyzed, there are no significant deviations from the null hypothesis, i.e. the Kerr metric. Our main findings demonstrate alignment and certain enhancements when compared to previous estimates documented in the literature.
Generic spherically symmetric thin-shell wormholes with equilibrium throat at the innermost photonsphere are unstable
Authors: Mazharimousavi S. Habib
Published: March 2024

In this research, we prove analytically that a generic spherically symmetric thin-shell wormhole (TSW) with its throat located at the innermost photonsphere of the bulk asymptotically flat black hole and supported by a generic surface barotropic perfect fluid is unstable against a radial linear perturbation. This is the generalization of the instability of the Schwarzschild TSW (STSW) with the throat’s radius located at $$a_{0}=3M$$

a
0

=
3
M

that was revealed by Poisson and Visser in their seminal work (Poisson and Visser in Phys Rev D 52, 7318, 1995). where they studied the mechanical stability of STSW. Our proof provides a link between the instability of the null circular geodesics on the innermost photonsphere of a generic static spherically symmetric asymptotically black hole and the TSW constructed in the same bulk with $$a_{0}=r_{c}$$

a
0

=

r
c

where $$ a_{0} $$

a
0

and $$r_{c}$$

r
c

are the radius of the TSW and the innermost photonsphere, respectively. For asymptotically flat spherically symmetric black holes possessing more than one photonspheres, the number of the photonspheres is odd and at least one photonsphere is stable which implies the corresponding TSW with its throat identical with the stable photonsphere is also stable.
Autoencoders for real-time SUEP detection
Authors: Chhibra Simranjit Singh , Chernyavskaya Nadezda , Maier Benedikt , Pierini Maurzio , Hasan Syed
Published: March 2024

Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns (SUEP), at the Large Hadron Collider: the production of dark quarks in proton–proton collisions leading to a dark shower and the high-multiplicity production of dark hadrons. The final experimental signature is spherically symmetric energy deposits by an anomalously large number of soft Standard Model particles with a transverse energy of O(100)$$\,\text {MeV}$$

MeV

. Assuming Yukawa-like couplings of the scalar portal state, the dominant production mode is gluon fusion, and the dominant background comes from multi-jet QCD events. We have developed a deep learning-based Anomaly Detection technique to reject QCD jets and identify any anomalous signature, including SUEP, in real-time in the High-Level Trigger system of experiments like the Compact Muon Solenoid at the Large Hadron Collider. A deep convolutional neural autoencoder network has been trained using QCD events by taking transverse energy deposits in the inner tracker, electromagnetic calorimeter, and hadron calorimeter sub-detectors as 3-channel image data. Due to the sparse nature of the data, only $$\sim $$
∼
0.5% of the total $$\sim $$
∼
$${300}\,\textrm{k}$$

300

k

image pixels have nonzero values. To tackle this challenge, a nonstandard loss function, the inverse of the so-called Dice Loss, is exploited. The trained autoencoder with learned spatial features of QCD jets can detect 40% of the SUEP events, with a QCD event mistagging rate as low as 2%. The model inference time has been measured using the $$^{\texttt {TM}}$$

TM

  processor and found to be $$\sim $$
∼
$${20}\textrm{ms}$$

20
ms

, which perfectly satisfies the High-Level Trigger system’s latency of $$\mathcal {O}(10^2)~\textrm{ms}$$

O
(

10
2

)

ms

. Given the virtue of the unsupervised learning of the autoencoders, the trained model can be applied to any new physics model that predicts an experimental signature anomalous to QCD jets.
A Stage-Wise Residual Attention Generation Adversarial Network for Mandibular Defect Repairing and Reconstruction
Authors: Zhong Chenglan , Xiong Yutao , Tang Wei , Guo Jixiang
Published: March 2024

Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The repairing and reconstruction of mandible defects is a significant yet challenging task in oral–maxillofacial surgery. Currently, the mainly available methods are traditional digitalized design methods that suffer from substantial artificial operations, limited applicability and high reconstruction error rates. An automated, precise, and individualized method is imperative for maxillofacial surgeons. In this paper, we propose a Stage-wise Residual Attention Generative Adversarial Network (SRA-GAN) for mandibular defect reconstruction. Specifically, we design a stage-wise residual attention mechanism for generator to enhance the extraction capability of mandibular remote spatial information, making it adaptable to various defects. For the discriminator, we propose a multi-field perceptual network, consisting of two parallel discriminators with different perceptual fields, to reduce the cumulative reconstruction errors. Furthermore, we design a self-encoder perceptual loss function to ensure the correctness of mandibular anatomical structures. The experimental results on a novel custom-built mandibular defect dataset demonstrate that our method has a promising prospect in clinical application, achieving the best Dice Similarity Coefficient (DSC) of 94.238% and 95% Hausdorff Distance (HD95) of 4.787.
Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers
Authors: Jiang Zhengyuan , Sun Siyan , Peng Hong , Liu Zhicai , Wang Jun
Published: March 2024

Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid Networks (FPN) is a significant architecture for object detection that utilizes these multi-level feature maps. However, the use of FPN also increases the detector’s complexity. For object detection methods that only use a single-level feature map, the detection performance is limited to some extent because the single-level feature map cannot balance deep semantic information and shallow detail information. We introduce a novel detector — the Spiking Neural P Multiple-in-Single-out (SNPMiSo) detector to address these challenges. The SNPMiSo detector is constructed based on SNP-like neurons. In SNPMiSo, we employ two kinds of Transformers to boost the important features across different-level feature maps separately. After enhancing the features, we use an incremental upsampling module to upsample and merge the two feature maps. This combined feature map is input into the NAF dilated residual module and the NAF dual-branch detection head. This process allows us to extract multi-scale features and carry out detection tasks. Our tests show promising results: On the COCO dataset, SNPMiSo attains an Average Precision (AP) of 38.7, an improvement of 1.0 AP over YOLOF. In addition, SNPMiSo demonstrates a quicker detection speed, outperforming some advanced multi-level and single-level object detectors.
ASSOCIATING HIGHWAY DENSITY WITH ASTHMA: UNRAVELING THE LINK BETWEEN TRAFFIC-RELATED POLLUTION AND HEALTH IN U.S. NON-CALIFORNIA METROPOLITAN AREAS
Authors: GAO JING , SHEN Z. Y. , ŠTREIMIKIENĖ DALIA , BALEŽENTIS TOMAS , ZHOU YOU
Published: March 2024

This study delves into the influence of traffic-related pollution on respiratory diseases within the United States. While prior research has established a connection between air pollution and mortality or morbidity in sensitive age groups, this association has been predominantly observed in highly polluted areas, particularly in California. In this paper, we extend this line of investigation by examining adult patients with asthma symptoms across various metropolitan areas throughout the country. To address potential endogeneity concerns in our empirical framework, we adopt highway density as an instrumental variable (IV) for NO2 concentrations. This approach allows us to gain a more robust understanding of the relationship between traffic-related pollution and respiratory health outcomes. Our findings indicate that NO2 does not have a significant impact on patients with asthma symptoms in the overall sample. However, we observed that it can exacerbate asthma conditions in patients residing in warm areas. A back-of-the-envelope calculation estimates a $6.758 billion economic loss due to increasing asthma attack on adults in the selected study area. This regional disparity outside of California underscores the necessity for adjusting current regulations on vehicle emissions based on unique regional characteristics. Such adjustments could help mitigate adverse health effects associated with traffic-related pollution in different parts of the United States.
PUBLICITY, FACILITIES DIVERSITY AND WILLINGNESS TO PAY FOR HOUSEHOLD WASTE SORTING: NEW EVIDENCE FROM HETEROGENEITY REGRESSION
Authors: LONG XINGLE , WANG QINGLIN
Published: March 2024

Household waste sorting is important to actively promote the construction of beautiful countryside and beautiful city via household waste reduction or recycling. The Law of China on the Prevention and Control of Solid Waste Environmental Pollution indicates that household waste treatment fee system follows the principle of “who produces, who pays”, reflecting differentiated management such as classified pricing and metering fees. This research mainly investigates the determinants of willingness to pay (WTP) for household waste sorting in China. This study mainly compares different effects of the waste sorting information publicity, waste sorting facilities diversity, perceived convenience and reference group behavior on WTP via multiple and logistic regression. It also explores gender and regional heterogeneity for WTP of household waste sorting in China. This study also analyzes how individuals respond to different levels of WTP fees of household waste sorting via logistic regression. The empirical results reveal that the facilities diversity of waste sorting has the highest effect on WTP for household waste sorting, followed by perceived convenience, publicity and reference group behaviors. Females have relatively lower WTP than males. Education has significant positive effects on males’ waste sorting WTP. In logistic regression, reference group has larger effect on WTP compared with other antecedents. It is important to enhance facilities and publicity of household waste sorting. This research is of great significance to increase the household waste sorting behavior, enhance the governance system of household waste sorting via the collaboration of government, medias, communities and individuals.
Geometry from D-branes in nonrelativistic string theory
Authors: Güijosa Alberto , Rosas-López Igmar C.
Published: March 2024

Nonrelativistic (NR) string theory was discovered as a framework that underlies and unifies the various noncommutative open string (NCOS) theories, which were originally envisioned as surprising exceptions to the maxim that all string theories are gravitational in nature. In that view, the fact that NCOS has a gravitational dual was believed to be directly analogous to the AdS/CFT correspondence. When NCOS theories were understood to be simply the particular classes of states of the underlying NR theory that include longitudinal D-branes, it was suggested that the duality between NCOS and the corresponding gravitational theory is not an instance of gauge/gravity-type duality, but of open-string/closed-string duality between D-branes and black branes. This paper provides direct evidence in support of this perspective, by starting from a stack of D-branes in NR string theory and deriving the long-distance profile of the curved geometry in the corresponding black brane.
Heat and mass transfer analysis of a Maxwell-power-law fluid in a laminar planar jet flow with Cattaneo–Christov double diffusion theory
Authors: Yang Zhennan , Wei Chunqiu , Zhang Yan , Bai Yu , Wang Qiao , Liu Chunyan , Ramzan Muhammad
Published: March 2024

This paper focuses on the heat and mass transfer characteristics of a steady laminar planar jet flow of a Maxwell-power-law (MPL) fluid. MPL is a renowned fluid model with shear thinning and viscoelastic characteristics that can accurately exhibit the rheological features of polymer liquids. In the envisioned study, the MPL constitutive equation is introduced based on an experiment utilizing the Xanthan gum solution. The heat and mass equations are supported by the Cattaneo–Christov double diffusion (CCDD) concept with unique characteristics of thermal conductivity, mass diffusion, and chemical reaction. Thermal conductivity and mass diffusion coefficient are considered to be velocity gradient-dependent. The problem is addressed numerically by the bvp4c method in Matlab after applying the similarity transformations to the governing equations. The impacts of related parameters on velocity, temperature, and concentration are analyzed. Results show that an upsurge in the power-law index hinders momentum, energy, and mass transmission. An interesting phenomenon is that the variation in relaxation time can hinder or promote the heat and mass transfer of a Maxwell-power-law fluid jet before or after the intersection point, respectively. The study can provide a theoretical reference for engineering applications of jets.
Multimodal Sentiment Analysis for Movie Scenes Based on a Few-Shot Learning Approach
Authors: Yang Hao , Li Bo
Published: March 2024

Multimodal sentiment analysis involves discerning the emotions of speakers through diverse features such as sound, text, and images. However, current research in this field heavily relies on extensive supervised datasets, demanding substantial labor and computational resources. This study introduces a meta-learning-based approach for multimodal sentiment analysis, aiming to delve into emotional information within movie scenes. Leveraging meta-learning techniques, this approach seeks to accurately capture emotions in movie scenes using a limited number of annotated samples, achieving model generalization under constrained labeled data. Specifically, we introduce an optimization-based meta-learning approach for the multimodal sentiment analysis tasks in text and vision, enhancing the model’s ability to generalize to new tasks with limited annotations. Additionally, an innovative strategy based on Meta-Prompting is proposed to handle multimodal data. The merits of the proposed method are validated across different datasets.
An Ensemble Keyword Extraction Model for News Texts with Statistical and Graphical Features
Authors: Abibullayeva Aiman , Kılıç Hüma , Cetin Aydin
Published: March 2024

Keyword extraction is an essential tool for many text mining applications such as automatic indexing, summarizing, classification, clustering and automatic filtering. Automated keyword extraction is essential as the daily text data to be reached and processed have increased tremendously over the Internet, e.g. millions of news articles are published daily online. In this paper, a novel ensemble model for automatic extraction of keywords from news articles is proposed. The proposed model handles keyword extraction as a sequence labeling task. Two sub-modules representing the statistical and graphical features by their calculated scores for each input token were combined in the token classification module. The Ensemble Token Classification module was trained and tested separately with the ensemble algorithms Random Forest, XgBoost, Decision Tree and Voting Classification. For training, we collected two news datasets from Kazakh and Russian news sites published in Cyrillic alphabet. We also collected an Arabic news dataset, ArabianNews. The performance of the model was also compared with the widely used 500N-KPCrowd dataset in the literature, which consists of English news content in Latin alphabet. The proposed model achieved the best performance with an [Formula: see text]-score of 0.71 and 0.86 on the 500N-KPCrowd and Russian datasets, respectively. We attained the best [Formula: see text]-score (0.97) with the KazakhNews and ArabianNews datasets.
Consistency Checking for Refactoring from Coarse-Grained Locks to Fine-Grained Locks
Authors: Zhang Yang , Liu Jingjing , Qi Lin , Meredith Grant
Published: March 2024

Refactoring for locks is widely used to improve the scalability and performance of concurrent programs. However, when refactoring from coarse-grained locks to fine-grained locks, the behavior of concurrent programs may be changed. To this end, we present LockCheck, a consistency-checking approach based on the parallel extended finite automaton for fine-grained locks. First, we model the critical sections of concurrent programs through control flow analysis and dependency analysis. Second, we sequentialize the concurrent programs to get all the possible transition paths. Furthermore, it reduces the exploration of the redundant paths using partial order theory to obtain the compared transition paths. Finally, we combine consistency rules to check the consistency of the program before and after refactoring. We evaluated LockCheck in five open-source projects. A total of 1528 refactoring operations have been evaluated and 93 inconsistent refactoring operations have been detected. The results show that LockCheck can effectively detect inconsistent behavior when coarse-grained locks are refactored into fine-grained locks.
On the continuum limit of epidemiological models on graphs: Convergence and approximation results
Authors: Ayuso de Dios Blanca , Dovetta Simone , Spinolo Laura V.
Published: March 2024

We focus on an epidemiological model (the archetypical SIR system) defined on graphs and study the asymptotic behavior of the solutions as the number of vertices in the graph diverges. By relying on the theory of graphons we provide a characterization of the limit and establish convergence results. We also provide approximation results for both deterministic and random discretizations.
Knot quandle decomposition along a torus
Authors: Bonatto Marco , Cattabriga Alessia , Horvat Eva
Published: March 2024

We study the structure of the augmented fundamental quandle of a knot whose complement contains an incompressible torus. We obtain the relationship between the fundamental quandle of a satellite knot and the fundamental quandles/groups of its companion and pattern knots. General presentations of the fundamental quandles of a link in a solid torus, a link in a lens space and a satellite knot are described. In the last part of this paper, an algebraic approach to the study of affine quandles is presented and some known results about the Alexander module and quandle colorings are obtained.
Generalized Lagrangian for canonical and non-canonical scalar field
Authors: Joshi Tanisha , Pathak S. D. , Khlopov Maxim
Published: March 2024

In this paper, we proposed a generalized Lagrangian for three different classes of scalar fields namely quintessence ([Formula: see text]), phantom ([Formula: see text]) and tachyonic ([Formula: see text]) parameterized by [Formula: see text]. These three scalar fields can be described by a common single Lagrangian called generalized scalar field Lagrangian (GSL). We obtain mathematically consistent forms of generalized equations of motion parameterized by [Formula: see text]. It can recover the individual equation of motion of quintessence, phantom and tachyon scalar fields from generalized equation of motion of the unified scalar field.
INVESTIGATION ON HYBRID FIBER METAL LAMINATE USING ULTRASONIC VIBRATION-COUPLED MICROWIRE ELECTRICAL DISCHARGE MACHINING
Authors: KARTHIKEYAN M. , MUTHU VIJAYA PANDIAN S. , VIJAYAKUMAR R.
Published: March 2024

This study examines the machinability of hybrid fiber metal laminates (HFML), which are made by nickel–chromium alloy (IN-625) metal-cored carbon (Ca)/aramid (Ar) fiber laminate using ultrasonic vibration-coupled microwire electrical discharge machining (UV-[Formula: see text]WEDM). Since UV-[Formula: see text]WEDM parameters significantly impact the erosion rate ([Formula: see text]) and surface undulation ([Formula: see text]), the main objective was to identify the optimal machining parameters. The input variables include the pulse on ([Formula: see text]), pulse off ([Formula: see text]), current ([Formula: see text]), cutting inclination ([Formula: see text]), and servo voltage ([Formula: see text]) coupled with ultrasonic vibration (UV). The empirical findings show that the servo voltage ([Formula: see text]) significantly impacts [Formula: see text] (73.93%) and [Formula: see text] (70.02%). The performance categorization order of significant influencing variable is [Formula: see text]. The desirability interpretation generated the optimum setting for minimizing [Formula: see text] and maximizing [Formula: see text] is [Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text]V, [Formula: see text][Formula: see text]A, and [Formula: see text]. Scanning electron microscopic (SEM) images were used to perform the micro-interlayer analysis on the machined surface. Moreover, creating an appropriate HFML is necessary to cut various shapes and sizes to satisfy the demands of diverse applications. 60% of components in the aerospace sector are reportedly rejected in real time due to dimension departure, poor surface finish, and damage found in the final assembly. Investigating the viability of cutting-edge machining techniques like UV-[Formula: see text]WEDM is crucial to minimize damage and improve the quality of HFMLs.
Dynamic Analysis and Optimization on Passive/Active Vibration Reduction of a Beam Structure with Distributed Smart Foams
Authors: Zhang Wen-Yong , Niu Mu-Qing , Deng Lan-Feng , Fan Yimin , Chen Li-Qun
Published: March 2024

A smart foam is a vibration absorption structure integrated with piezoelectric material and shows promise for a highly efficient structural vibration reduction both passively and actively. The arrangement of the smart foams on the primary structure is a key issue in practical engineering. In this work, the vibration reduction performances of distributed smart foams attached to a simply supported beam are investigated, and the distribution scheme is optimized. The dynamic equations of the vibration system are established for passive and active vibration reduction, respectively. The frequency responses are analyzed based on a Newmark-[Formula: see text] method, and the accuracy is verified by a finite element analysis. An optimization method based on the genetic algorithm is proposed for the smart foams’ quantity and locations. The study reveals that, with the same total attached mass (1% of the beam mass), a limited distribution of smart foams achieves a larger vibration reduction ratio than a single smart foam or uniformly distributed smart foams. For the passive reduction mode, the optimal scheme is to arrange four smart foams at the middle of the beam, and a reduction ratio of 38% is achieved. It is related to both the first-order modal shape of the beam and the mass distribution of smart foams. For the active reduction mode, the optimal scheme is to arrange two smart foams separately, and 98% of the vibration is reduced. The optimal location is no longer at the middle of the beam, because the active moment of the piezoelectric beam makes the main contribution, rather than the passive vibration absorption. This research provides an optimization method and instructions for both passive and active vibration reductions of smart foams.
Damage Identification of Simple Supported Bridges Under Moving Loads Based on Variational Mode Decomposition and Deep Learning
Authors: Wang Chao , Pan Xiang , Qi Tian-Yu , Han Gui-Ning , Ren Wei-Xin
Published: March 2024

Aiming at rapid and economical damage detection of a large number of simple supported bridges, a new structural damage identification method under moving load based on variational mode decomposition (VMD) and deep learning is proposed. Firstly, a moving vehicle is used as an exciting load to invoke structural damage feature and enhance the signal-to-noise ratio, and the structural vertical acceleration response is extracted by a finite element simulating analysis under various damage cases. In order to simulate the influence of noise and expand the samples, Gaussian white noise is added to the extracted data, and then the response signal is decomposed into a series of intrinsic mode functions (IMFs) using VMD, and the optimal IMF component is selected as the damage sample of the structure. Then, a one-dimensional convolutional neural network (CNN) model is built and trained by the various samples of damage. The vibration response of the practical bridge is processed and inputted by the trained CNN model to identify the location of the damage and degree of the structure. Finally, the effectiveness and anti-noise performance of the proposed method are verified through numerical analysis and a simply supported beam bridge model experiment. The results show that the average identification accuracy of the numerical simulations and experimental is 93.4% and 86.8% with 20% Gaussian white noise, respectively. Sensors at different locations have almost the same identification effect for various cases of damage, so it is possible to identify structural damage only using a small amount of accelerometer.
Iterative kernel regression with preconditioning
Authors: Shi Lei , Zhang Zihan
Published: March 2024

Kernel methods are popular in nonlinear and nonparametric regression due to their solid mathematical foundations and optimal statistical properties. However, scalability remains the primary bottleneck in applying kernel methods to large-scale data regression analysis. This paper aims to improve the scalability of kernel methods. We combine Nyström subsampling and the preconditioned conjugate gradient method to solve regularized kernel regression. Our theoretical analysis indicates that achieving optimal convergence rates requires only [Formula: see text] memory and [Formula: see text] time (up to logarithmic factors). Numerical experiments show that our algorithm outperforms existing methods in time efficiency and prediction accuracy on large-scale datasets. Notably, compared to the FALKON algorithm [A. Rudi, L. Carratino and L. Rosasco, Falkon: An optimal large scale kernel method, in Advances in Neural Information Processing Systems (Curran Associates, 2017), pp. 3891–3901], which is known as the optimal large-scale kernel method, our method is more flexible (applicable to non-positive definite kernel functions) and has a lower algorithmic complexity. Additionally, our established theoretical analysis further relaxes the restrictive conditions on hyperparameters previously imposed in convergence analyses.
Operational Risk Measurement: An Optimal Timescale Selection Method Based on Information Entropy
Authors: Chang Yanpeng , Wang Yinghui , Li Jianping , Zhu Xiaoqian
Published: March 2024

Operational risk capital is typically allocated on an annual timescale in extant research, while ignoring that the dependence between operational risk events may differ on different timescales, such as semi-annual and quarterly. This study examines whether operational risk dependence structures differ on multiple timescales and proposes an optimal timescale selection method based on information entropy theory to obtain more reasonable operational risk measurement results. Different dependence modeling methods, including linear correlation and copula functions, are employed to analyze the dependence structures on multiple timescales. The information changes between different timescales are calculated to select the optimal timescale. The empirical analysis is based on 2,024 operational risk events from 1994 to 2017 in the Chinese Operational Loss Database (COLD), which is one of the largest external operational risk datasets for the Chinese banking industry. The results reveal significant differences in operational risk dependence at different timescales which affect the operational risk measurement results. Crucially, the quarterly timescale should be considered for more reasonable measurements. The findings provide important insights for considering a more reasonable allocation of operational risk capital under multiple timescale dependence.
A novel method of image denoising based on 2D dual-tree DWT and SWT
Authors: Talbi Mourad , Baazaoui Riadh , Nasraoui Brahim
Published: March 2024

In this paper, we propose a new image denoising technique which consists in applying a stationary wavelet transform (SWT)-based image denoising technique, in the domain of 2D dual-tree discrete wavelet transform (DWT). In fact, this proposed technique consists first of applying the 2D dual-tree DWT to the noisy image. Then, the noisy wavelet coefficients obtained from this application are denoised by applying to each of them, a SWT-based image denoising technique. Finally, the denoised image is reconstructed by applying the inverse of the 2D dual-tree DWT to the obtained denoised wavelet coefficients. For applying this SWT-based image denoising technique, we use the soft thresholding, the Daubechies 4 as the mother wavelet, and the decomposition level is equal to 5. The performance of this image denoising technique proposed in this work is proven by its comparison to three other denoising techniques existing in the literature. These three techniques are the denoising technique based on the soft thresholding in the SWT domain, the image denoising technique based on soft thresholding in the domain of 2D dual-tree DWT and the image denoising approach using deep neural network. All the previously mentioned techniques, including our proposed denoising approach, are applied to a number of noisy images, and the obtained results are in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Those results show that this proposed denoising technique outperforms the other three denoising techniques used in this evaluation.
Examining the Implications of Knowledge Boundaries for a Large-Scale Agile Transformation Initiative of a Manufacturing Company
Authors: Orejuela Silvia , Johansson Glenn , Motte Damien
Published: March 2024

To stay competitive in today’s environment, characterized by changing customer requirements, digitalization, new technologies, etc., companies must deliver fast and continuous innovation. Manufacturing companies have therefore started to launch large-scale agile transformation initiatives. A specificity of manufacturing companies is their need for deeply specialized organizational units. Knowledge boundaries resulting from knowledge specialization tend to impact transformation initiatives. Therefore, this work examines the implications of knowledge boundaries between organizational units for a large-scale agile transformation initiative of a manufacturing company. The findings show that these knowledge boundaries imply a need for knowledge integration based on flexibility and dynamism as key aspects. A conceptual model that illustrates these aspects during the initial phase of implementation of these transformation initiatives is presented.
Dark energy stars in f(R,G) gravity
Authors: Das Krishna Pada , Debnath Ujjal
Published: March 2024

In this paper, we have provided a discussion regarding the structural properties of a spherical compact stellar object within the background of [Formula: see text] modified gravity. We have considered that the interior region of the compact stellar body is filled by a composition of anisotropic dark energy and isotropic normal matter which are assumed to be non-interacting. To relate the two stated fluids, we have considered the dark energy density directly proportional to the matter density. In particular, Krori–Barua (K–B) matric potentials are used to explore the solutions of modified field equations corresponding to [Formula: see text]. Next, we have delivered a detailed 3-dimensional graphical analysis to design physically well-behavior and stability of different parameters like energy density, individual pressure components for both dark energy and normal matter and anisotropy. Also, we have explored the stability and physical acceptability criterion by causality conditions, adiabatic index, TOV equation, and Harrison–Zeldovich–Novikov criteria. Consequently, we have discussed the violation of energy conditions for dark energy and hold the same for the normal matter. Here is an important note we have taken for three compact objects like, SAX JI808.4-3658, PSR J1614-2230, and 4U1820-30, for graphical analysis. Furthermore, we have calculated the predicted values of central density, central pressure, and surface pressure for both dark and normal matter. Finally, we concluded that our proposed dark energy stellar configuration maintains all required stability and physical acceptability criteria.