bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short-Term Effects of Heart Rate Variability Biofeedback on Working Memory
Authors: Bahameish Mariam , Stockman Tony 
Published: February 2024

AbstractDrawing upon the well-documented impact of long-term heart rate variability biofeedback (HRVB) on psychophysiological responses, this study seeks to explore the short-term effects arising from a single HRVB session during and after paced breathing exercise. The research aligns with the neurovisceral integration model, emphasizing the link between heart rate variability (HRV) levels and cognitive performance. Therefore, a randomized controlled trial employing a between-subjects design was conducted with 38 participants. Each participant was assigned to either the paced breathing intervention group or the spontaneous breathing control group. The study assessed various parameters such as cardiac vagal tone, evaluated through vagally mediated HRV measures, and working memory, measured using the N-back task. Additionally, participants’ affective states were assessed through self-reported questionnaires, specifically targeting attentiveness, fatigue, and serenity. The results notably reveal enhancements in the working memory task and an elevated state of relaxation and attention following the HRVB session, as evidenced by higher averages of correct responses, serenity and attentiveness scores. However, the findings suggest that this observed improvement is not influenced by changes in cardiac vagal tone, as assessed using a simple mediation analysis. In conclusion, this study presents promising insights into the impact of a single HRVB session, laying the foundation for future research advancements in this domain.
Tarlov Cysts and Premature Ejaculation
Authors: Liu Yuanyuan , Sun Dalin , Gao Zhenghong , Wang Zhongjiang , Jin Baofang 
Published: February 2024

AbstractTarlov cysts adjacent to the spinal cord are usually asymptomatic and found incidentally via magnetic resonance imaging. On rare occasions, they increase in size to produce symptoms resembling disk herniation. We report a rare case of a sacral cyst resulting in premature ejaculation in a 32-year-old man who presented with pelvic pain and acquired premature ejaculation. Spinal nerve root decompression, excision of intraspinal Tarlov cyst, and spinal nerve root adhesion release surgery significantly improved his pain and premature ejaculation at a six-month follow-up.
Morphometrics predicts the differential regurgitant fraction in bilateral pulmonary arteries of patients with repaired tetralogy of fallot
Authors: Teng Hui-Chung , Chen Yi-Chun , Chen Yung-Lin , Weng Ken-Pen , Pan Jun-Yen , Chang Ming-Hua , Cheng Hsiu-Wen , Wu Ming-Ting 
Published: February 2024

AbstractIn patients with repaired tetralogy of Fallot (rTOF), the regurgitant fraction (RF) in left pulmonary artery (LPA) and right pulmonary artery (RPA) is usually unequal. The morphometrics may play a crucial role in this RF discrepancy. Cardiovascular MR of 79 rTOF patients and 20 healthy controls were retrospectively enrolled. Forty-four from the 79 patients were matched in age, sex and body surface area to the 20 controls and were investigated for: (1) phase-contrast flow of main pulmonary artery (MPA), LPA, and RPA; (2) vascular angles: the angles between the thoracic anterior–posterior line (TAPL) with MPA (θM–AP), MPA with RPA (θM–R), and MPA with LPA (θM–L); (3) cardiac angle, the angle between TAPL and the interventricular septum; (4) area ratio of bilateral lung and hemithorax regions. Compared with the 20 controls, the 44 rTOF patients exhibited wider θM–AP, sharper θM–L angle, and a smaller θM–L/θM–R ratio. In the 79 rTOF patients, LPA showed lower forward, backward, and net flow, and greater RF as compared with RPA. Multivariate analysis showed that the RF of LPA was negatively associated with the θM–L/θM–R ratio and the age at surgery (R2 = 0.255). Conversely, the RF of RPA was negatively associated with the left lung/left hemithorax area ratio and cross-sectional area (CSA) of LPA, and positively associated with CSA of RPA and MPA (R2 = 0.366). In rTOF patients, the RF of LPA is more severe than that of RPA, which may be related to the vascular morphometrics. Different morphometric parameters are independently associated with the RF of LPA or RPA, which may offer potential insights for surgical strategies.
Multi-phase-field approach to fracture demonstrating the role of solid-solid interface energy on crack propagation
Authors: Jafarzadeh Hossein , Shchyglo Oleg , Steinbach Ingo 
Published: February 2024

AbstractA multi-phase-field approach for crack propagation considering the contribution of the interface energy is presented. The interface energy is either the grain boundary energy or the energy between a pair of solid phases and is directly incorporated into to the Ginzburg–Landau equation for fracture. The finite difference method is utilized to solve the crack phase-field evolution equation and fast Fourier method is used to solve the mechanical equilibrium equation in three dimensions for a polycrystalline material. The importance of the interface (grain boundary) energy is analyzed numerically for various model problems. The results show how the interface energy variations change the crack trajectory between the intergranular and transgranular fracture.
A Novel Heterozygous Variant in AICDA Impairs Ig Class Switching and Somatic Hypermutation in Human B Cells and is Associated with Autosomal Dominant HIGM2 Syndrome
Authors: Della Mina Erika , Jackson Katherine J. L. , Crawford Alexander J. I. , Faulks Megan L. , Pathmanandavel Karrnan , Acquarola Nicolino , O’Sullivan Michael , Kerre Tessa , Naesens Leslie , Claes Karlien , Goodnow Christopher C. , Haerynck Filomeen , Kracker Sven , Meyts Isabelle , D’Orsogna Lloyd J. , Ma Cindy S. , Tangye Stuart G. 
Published: February 2024

AbstractB cells and their secreted antibodies are fundamental for host-defense against pathogens. The generation of high-affinity class switched antibodies results from both somatic hypermutation (SHM) of the immunoglobulin (Ig) variable region genes of the B-cell receptor and class switch recombination (CSR) which alters the Ig heavy chain constant region. Both of these processes are initiated by the enzyme activation-induced cytidine deaminase (AID), encoded by AICDA. Deleterious variants in AICDA are causal of hyper-IgM syndrome type 2 (HIGM2), a B-cell intrinsic primary immunodeficiency characterised by recurrent infections and low serum IgG and IgA levels. Biallelic variants affecting exons 2, 3 or 4 of AICDA have been identified that impair both CSR and SHM in patients with autosomal recessive HIGM2. Interestingly, B cells from patients with autosomal dominant HIGM2, caused by heterozygous variants (V186X, R190X) located in AICDA exon 5 encoding the nuclear export signal (NES) domain, show abolished CSR but variable SHM. We herein report the immunological and functional phenotype of two related patients presenting with common variable immunodeficiency who were found to have a novel heterozygous variant in AICDA (L189X). This variant led to a truncated AID protein lacking the last 10 amino acids of the NES at the C-terminal domain. Interestingly, patients’ B cells carrying the L189X variant exhibited not only greatly impaired CSR but also SHM in vivo, as well as CSR and production of IgG and IgA in vitro. Our findings demonstrate that the NES domain of AID can be essential for SHM, as well as for CSR, thereby refining the correlation between AICDA genotype and SHM phenotype as well as broadening our understanding of the pathophysiology of HIGM disorders.
Recoverability of quantum channels via hypothesis testing
Authors: Jenčová Anna 
Published: February 2024

AbstractA quantum channel is sufficient with respect to a set of input states if it can be reversed on this set. In the approximate version, the input states can be recovered within an error bounded by the decrease of the relative entropy under the channel. Using a new integral representation of the relative entropy in Frenkel (Integral formula for quantum relative entropy implies data processing inequality, Quantum 7, 1102 (2023)), we present an easy proof of a characterization of sufficient quantum channels and recoverability by preservation of optimal success probabilities in hypothesis testing problems, equivalently, by preservation of $$L_1$$

L
1

-distance.
Exploring biometric domain adaptation in human action recognition models for unconstrained environments
Authors: Freire-Obregón David , Barra Paola , Castrillón-Santana Modesto , De Marsico Maria 
Published: February 2024

AbstractIn conventional machine learning (ML), a fundamental assumption is that the training and test sets share identical feature distributions, a reasonable premise drawn from the same dataset. However, real-world scenarios often defy this assumption, as data may originate from diverse sources, causing disparities between training and test data distributions. This leads to a domain shift, where variations emerge between the source and target domains. This study delves into human action recognition (HAR) models within an unconstrained, real-world setting, scrutinizing the impact of input data variations related to contextual information and video encoding. The objective is to highlight the intricacies of model performance and interpretability in this context. Additionally, the study explores the domain adaptability of HAR models, specifically focusing on their potential for re-identifying individuals within uncontrolled environments. The experiments involve seven pre-trained backbone models and introduce a novel analytical approach by linking domain-related (HAR) and domain-unrelated (re-identification (re-ID)) tasks. Two key analyses addressing contextual information and encoding strategies reveal that maintaining the same encoding approach during training results in high task correlation while incorporating richer contextual information enhances performance. A notable outcome of this study is the comprehensive evaluation of a novel transformer-based architecture driven by a HAR backbone, which achieves a robust re-ID performance superior to state-of-the-art (SOTA). However, it faces challenges when other encoding schemes are applied, highlighting the role of the HAR classifier in performance variations.
Enhancing cervical cancer diagnosis with graph convolution network: AI-powered segmentation, feature analysis, and classification for early detection
Authors: Fahad Nur Mohammad , Azam Sami , Montaha Sidratul , Mukta Md. Saddam Hossain 
Published: February 2024

AbstractCervical cancer is a prevalent disease affecting the cervix cells in women and is one of the leading causes of mortality for women globally. The Pap smear test determines the risk of cervical cancer by detecting abnormal cervix cells. Early detection and diagnosis of this cancer can effectively increase the patient’s survival rate. The advent of artificial intelligence facilitates the development of automated computer-assisted cervical cancer diagnostic systems, which are widely used to enhance cancer screening. This study emphasizes the segmentation and classification of various cervical cancer cell types. An intuitive but effective segmentation technique is used to segment the nucleus and cytoplasm from histopathological cell images. Additionally, handcrafted features include different properties of the cells generated from the distinct cervical cytoplasm and nucleus area. Two feature rankings techniques are conducted to evaluate this study’s significant feature set. Feature analysis identifies the critical pathological properties of cervical cells and then divides them into 30, 40, and 50 sets of diagnostic features. Furthermore, a graph dataset is constructed using the strongest correlated features, prioritizes the relationship between the features, and a robust graph convolution network (GCN) is introduced to efficiently predict the cervical cell types. The proposed model obtains a sublime accuracy of 99.11% for the 40-feature set of the SipakMed dataset. This study outperforms the existing study, performing both segmentation and classification simultaneously, conducting an in-depth feature analysis, attaining maximum accuracy efficiently, and ensuring the interpretability of the proposed model. To validate the model’s outcome, we tested it on the Herlev dataset and highlighted its robustness by attaining an accuracy of 98.18%. The results of this proposed methodology demonstrate the dependability of this study effectively, detecting cervical cancer in its early stages and upholding the significance of the lives of women.
Neural Network Based Adaptive Inverse Optimal Control for Non-Affine Nonlinear Systems
Authors: Sancı Muhammet Emre , Öke Günel Gülay 
Published: February 2024

AbstractIn this paper, a novel methodology is introduced for the inverse optimal control of non-affine, nonlinear and discrete-time systems. Although inverse optimal control of affine systems is studied in detail in technical literature, there is no adequate research about its implementation on non-affine systems. here are two main contributions of this work. Firstly using the input–output data of the system to be controlled its NARMA-L2 model is obtained using a multi-layer feedforward neural network, this step provides a conversion from a non-affine to affine system model. After the affine system model is obtained, the inverse optimal control law is applied. The second contribution of this paper is the computation of the inverse optimal control signal. The selection of the P matrix in the control law is crucial since its value directly affects the control performance. Here a novel method is proposed where an adaptive and optimal P matrix is computed online using a recurrent neural network to minimize a predefined cost function. The performance of the proposed control method is evaluated by simulations performed on benchmark problems. The robustness of the method is also tested by additional simulations where noise and disturbance is imposed on the system. The obtained results justify the applicability of the proposed approach.
Finite-Time Stability of Inertial Neural Networks with Delayed Impulses
Authors: Wan Xuran , Li Lulu , Wang Lei 
Published: February 2024

AbstractThis paper studies the finite-time stability (FTS) of the inertial neural networks (INNs) with delayed impulses. Unlike previous related works, we consider the delayed impulses and propose a new impulsive control strategy. We extend the existing FTS results to the cases with delayed impulses. We also establish some global and local FTS criteria of INNs. Moreover, we estimate the settling-time in different cases and investigate the optimization strategy. We present three numerical examples to verify the validity of our theoretical results.
Bipartite Synchronization of Fractional Order Multiple Memristor Coupled Delayed Neural Networks with Event Triggered Pinning Control
Authors: Dhivakaran P. Babu , Gowrisankar M. , Vinodkumar A. 
Published: February 2024

AbstractThis paper investigates the leader and leaderless bipartite synchronization with the signed network utilizing the model of multiple memristor and coupled delayed neural network in an event-triggered pinning control. The usage of the descriptor method in fractional-order neural networks in case of a non-differentiable delay can be seen in this paper. Further, Lyapunov functional criteria, including Lur’e Postnikov Lyapunov functional, is established, and bipartite leader and leaderless synchronization are proved. The obtained numerical results can be seen as accurate to the theoretical results.
Rethinking Position Embedding Methods in the Transformer Architecture
Authors: Zhou Xin , Ren Zhaohui , Zhou Shihua , Jiang Zeyu , Yu TianZhuang , Luo Hengfa 
Published: February 2024

AbstractIn the transformer architecture, as self-attention reads entire image patches at once, the context of the sequence between patches is omitted. Therefore, the position embedding method is employed to assist the self-attention layers in computing the ordering information of tokens. While many papers simply add the position vector to the corresponding token vector rather than concatenating them, few papers offer a thorough explanation and comparison beyond dimension reduction. However, the addition method is not meaningful because token vectors and position vectors are different physical quantities that cannot be directly combined through addition. Hence, we investigate the disparity in learnable absolute position information between the two embedding methods (concatenation and addition) and compare their performance on models. Experiments demonstrate that the concatenation method can learn more spatial information (such as horizontal, vertical, and angle) than the addition method. Furthermore, it reduces the attention distance in the final few layers. Moreover, the concatenation method exhibits greater robustness and leads to a performance gain of 0.1–0.5% for existing models without additional computation overhead.
GNNCL: A Graph Neural Network Recommendation Model Based on Contrastive Learning
Authors: Chen Jinguang , Zhou Jiahe , Ma Lili 
Published: February 2024

AbstractIn the field of recommendation algorithms, the representation learning for users and items has evolved from using single IDs or historical interactions to utilizing higher-order neighbors. This can be achieved by modeling the user–item interaction graph to capture user preferences for items. Despite the promising results achieved by these algorithms, they still suffer from the issue of data sparsity. In order to mitigate the impact of data sparsity, contrastive learning has been adopted in graph collaborative filtering to enhance performance. However, current recommendation algorithms using contrastive learning yield uneven representations after data augmentation and do not consider the potential relationships among users (or items). To address these challenges, we propose a graph neural network-based recommendation model that integrates contrastive learning (GNNCL). This model combines data augmentation with added noise and the exploration of semantic neighbors for nodes. For the structural neighbors on the interaction graph, we introduce a novel and straightforward contrastive learning approach, abandoning previous graph augmentation methods, and introducing uniform noise into the embedding space to create contrastive views. To unearth potential semantic neighbor relationships in the semantic space, we assume that users with similar representations possess semantic neighbor relationships and merge these semantic neighbors into the prototype contrastive learning. We utilize a clustering algorithm to obtain prototypes for users and items and employ the EM algorithm for prototype contrastive learning. Experimental results validate the effectiveness of our approach. Particularly, on the Yelp2018 and Amazon-book datasets, our method exhibits significant performance improvements compared to basic graph collaborative filtering models.
Gender differences in google scholar representation and impact: an empirical analysis of political communication, journalism, health communication, and media psychology
Authors: Goyanes Manuel , Tóth Tamás , Háló Gergő 
Published: February 2024

AbstractImproving gender equality in top-tier scholars and addressing gender bias in research impact are among the significant challenges in academia. However, extant research has observed that lingering gender differences still undermine female scholars. This study examines the recognition of female scholars through Google Scholar data in four different subfields of communication, focusing on two pressing issues: (1) gender representation among the most cited scholars and (2) gender differences in citations. Our findings demonstrate significant differences in gender proportions among the most cited scholars across all subfields, but especially in Political Communication and Journalism. The regression analysis revealed significant differences in citation scores in Political Communication, Journalism, and the pooled sample. However, results revealed that gender differences in research impact were not statistically significant in Health Communication and Media Psychology. Our study advocates for shifts in the citing behavior of communication scholars, emphasizing the importance of actively recognizing and citing studies conducted by female researchers to drive advancements in communication research.
Are life forms real? Aristotelian naturalism and biological science
Authors: Lockhart Jennifer Ryan , Lott Micah 
Published: February 2024

AbstractAristotelian naturalism (AN) holds that the norms governing the human will are special instances of a broader type of normativity that is also found in other living things: natural goodness and natural defect. Both critics and defenders of AN have tended to focus on the thorny issues that are specific to human beings. But some philosophers claim that AN faces other difficulties, arguing that its broader conception of natural normativity is incompatible with current biological science. This paper has three aims. First, we consider a distinctive and nuanced critique of AN’s general understanding of natural normativity put forward by Tim Lewens. Second, after giving a defense of AN, we explore and evaluate Lewens’ proposed alternative view—Kantian projectivism about life forms. We present a problem for Kantian projectivism and suggest reasons for thinking that AN is, after all, the superior position. Finally, we clarify and explain how AN’s claims about life forms and our knowledge of them, relate to empirical observation and to contemporary biological science.
Complementary Mass Transport Investigations in Open-Cell Foams: Full-Field Computational Fluid Dynamics Simulation with Random-Walk Microscopic Particle Tracking and Methane Nuclear Magnetic Resonance Displacement Measurements
Authors: Sadeghi Mehrdad , Brix Andreas , Trunk Sebastian , Pesch Georg R. , Freund Hannsjörg , Thöming Jorg 
Published: February 2024

AbstractNumerical simulation can provide detailed understanding of mass transport within complex structures. For this purpose, numerical tools are required that can resolve the complex morphology and consider the contribution of both convection and diffusion. Solving the Navier–Stokes equations alone, however, neglects self-diffusion. This influences the simulated displacement distribution of flow especially in porous media at low Péclet numbers (Pe < 16) and in near-wall regions where diffusion is the dominant mechanism. To address this problem, this study uses μCT-based computational fluid dynamics (CFD) simulations in OpenFOAM coupled with the random-walk particle tracking (PT) module disTrackFoam and cross-validated experimentally using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) measurements of gas flow within open-cell foams (OCFs). The results of the multi-scale simulations—with a resolution of 130–190 µm—and experimental PFG NMR data are compared in terms of diffusion propagators, which are microscopic displacement distributions of gas flows in OCFs during certain observation times. Four different flow rates with Péclet numbers in the range of 0.7–16 are studied in the laminar flow regime within 10 and 20 PPI OCFs, and axial dispersion coefficients were calculated. Cross-validation of PFG NMR measurements and CFD-PT simulations revealed a very good matching with integral differences below 0.04%, underpinning the capability of both complementary methods for multi-scale transport analysis.