bluuebunny/crossref_metadata_2025_split huggingface dataset:
Olfactory Dysfunction in Adults from Rio Grande do Norte: A Cross-Sectional Study
Authors: Bedaque Henrique de Paula , Andrade Filho Emerson Kennedy Ribeiro de , Rabelo Caio de Oliveira , Moura José Eduardo Nóbrega , Moura Kelvin Leite , Diniz Junior José , Guimarães Maria Luisa Nobre Medeiros e Silva 
Published: February 2024

Abstract
Introduction Smell is one of the senses of the human body, and it can be affected by several factors, such as viral infections, traumatic brain injury, iatrogenesis, smoking, and neurodegenerative and systemic diseases.
Objectives The main goal of the present study is to describe the epidemiology of olfactory disorders in Rio Grande do Norte (RN). More specifically, to determine the prevalence of olfactory dysfunction and to identify the main risk factors related to these dysfunctions in the state's population.
Methods A total of 180 volunteers living in the RN underwent the Connecticut Clinical Research Center (CCCRC) smell test and a clinical and demographic questionnaire.
Results A total of 58.89% of the patients presented normosmia and 87.78% were classified as having between normal and mild hyposmia. A statistically significant relationship was found between worse performance in the test and nasal surgery (p = 0.041) and the subjective feeling of not having an accurate sense of smell (p = 0.006 on the right nostril). There was no statistical relationship between the olfactory status and the report of coronavirus disease 2019 (COVID-19) infection (p = 0.254).
Conclusion The occurrence of altered sense of smell in our study was different from that reported in other studies that used the same test. The relationship with COVID-19 was not clear.
Effect of Antenatal Magnesium Sulfate Exposure on Patent Ductus Arteriosus in Premature Infants
Authors: Okulu Emel , Kraja Elvis , Kostekci Yasemin Ezgi , Seker Erdal , Ozisik Mehmet Seckin , Sarısoy Doğacan , Aslan Batuhan , Çakır Maide Selin , Demirtaş Ferhan , Ramoğlu Mehmet Gökhan , Uçar Tayfun , Erdeve Omer , Atasay Begum , Koc Acar , Arsan Saadet 
Published: February 2024

Objective Magnesium sulfate (MgSO4) provides effective fetal neuroprotection. However, there is conflicting evidence regarding the association between antenatal MgSO4 exposure and patent ductus arteriosus (PDA). Thus, herein, we aimed to evaluate the association between antenatal MgSO4 exposure and PDA.
Study Design Preterm infants born between 240/7 and 316/7 weeks of gestation were included in this retrospective study. Infants who died within the first 72 hours of life and those with significant congenital anomalies were excluded from the study. Echocardiographic and clinical assessment parameters were used to define PDA and hemodynamically significant PDA (hsPDA). Treatments were planned according to the standard protocols of the unit. The following data were collected from hospital medical records: perinatal characteristics, neonatal outcomes, detailed PDA follow-up findings, and maternal characteristics including MgSO4 exposure and doses.
Results Of the 300 included infants, 98 (32.6%) were exposed to antenatal MgSO4. hsPDA rates were similar in the infants exposed and not exposed to antenatal MgSO4, when adjusted for antenatal steroid administration, gestational age, and birth weight (OR: 1.6, 95% CI: 0.849–3.118, p = 0.146). The rates of PDA ligation and open PDA at discharge were similar between the groups. A cumulative MgSO4 dose of >20 g was associated with an increased risk of hsPDA (crude OR: 2.476, 95% CI: 0.893–6.864, p = 0.076; adjusted OR: 3.829, 95% CI: 1.068–13.728, p = 0.039). However, the cumulative dose had no effect on the rates of PDA ligation or open PDA at discharge. Rates of prematurity-related morbidities and mortality were similar between the groups.
Conclusion Although antenatal MgSO4 exposure may increase the incidence of hsPDA, it may not affect the rates of PDA ligation or open PDA at discharge. Further studies are required to better evaluate the dose-dependent outcomes and identify the MgSO4 dose that not only provides neuroprotection but also has the lowest risk of adverse effects.
Key Points
Addressing Parental Gatekeeping in Early-Intervention Services: Engaging all Caregivers
Authors: Puhlman Daniel J. , Puhlman Jane 
Published: February 2024

AbstractThe successful implementation of early interventions (EIs) within families is crucial in mitigating language and developmental delays in young children. Considering that language acquisition primarily occurs through interactions between caregivers and children, parental involvement plays a significant role in EI services. Working within families where one parent interferes with the involvement of the child's other parent or caregiver can be challenging for service providers. This article examines parental gatekeeping—a concept from the family science literature that can have a significant impact on services provided by speech language pathologists (SLPs). We outline and provide examples of family dynamics that may pose challenges to SLPs working with young children and their families as well as provide strategies for effectively navigating and working within these dynamics.
Advanced age predicts increased susceptibility to attribute, goal, and risky-choice framing in negative frame valences
Authors: Xu Lance 
Published: February 2024

AbstractThe framing effect has been extensively studied in the context of attribute, goal, and risky-choice framing, revealing its significant impact on decision-making. However, the interplay between age and the framing effect remains relatively unexplored, and existing findings are inconclusive and conflicting. Addressing this gap, this study investigates the influence of age on all three types of framing using a large cohort of participants (N = 696). The Johnson-Neyman technique was employed to examine the effect across a continuous range of ages. The analysis demonstrated that advanced age significantly increased susceptibility to the framing effect. Moreover, it was found that this susceptibility primarily manifests in response to negative frames across all framing types. These findings not only align with the socioemotional selectivity theory and dual-process model but also underscore the importance of affect heuristics in decision-making among older adults. By offering a robust examination of all three types of framing and their interactions with age, this study provides a theoretical foundation for the role of age as a potential moderator in framing contexts. These findings may therefore inform the development of targeted strategies to mitigate the impact of the framing effect on elderly populations.
Populism and the quest for political power: the pitfalls to populist electoral success in Canada
Authors: Karimi Sirvan 
Published: February 2024

AbstractRecent electoral inroads by anti-establishment political parties in Europe and around the world have led to the resurgence of the debate on populism. Within the burgeoning theoretical and analytic interpretations of the surge of populism, competing arguments have been deployed. Economic dislocation and the demographic shift within liberal democratic societies have provided fertile ground for the rise of populism. However, the success of these populist political parties, particularly the radical right populist parties, in utilizing prevailing societal resentment is to a great extent conditional upon a perceived threat to national identity. While the vestiges of political distrust and social and economic indignation can be found in Canadian society, the absence of a historically ingrained strong sense of nationhood, consolidation of multiculturalism, the eclipse of class from national political discourse, and the implausibility of resorting to Anti-Americanism as a mobilizing tactic has made it difficult for both Canadian right and left-populist forces to replicate the success of their international counterparts at the national level.
Assembly and comparative analysis of the complete mitochondrial genome of Pinellia ternata
Authors: Liu Xiao , You Qian , Liu Mengmeng , Bo Chen , Zhu Yanfang , Duan Yongbo , Xue Jianping , Wang Dexin , Xue Tao 
Published: February 2024

Pinellia ternata is an important natural medicinal herb in China. However, it is susceptible to withering when exposed to high temperatures during growth, which limits its tuber production. Mitochondria usually function in stress response. The P. ternata mitochondrial (mt) genome has yet to be explored. Therefore, we integrated PacBio and Illumina sequencing reads to assemble and annotate the mt genome of P. ternata. The circular mt genome of P. ternata is 876 608 bp in length and contains 38 protein-coding genes (PCGs), 20 tRNA genes and three rRNA genes. Codon usage, sequence repeats, RNA editing and gene migration from chloroplast (cp) to mt were also examined. Phylogenetic analysis based on the mt genomes of P. ternata and 36 other taxa revealed the taxonomic and evolutionary status of P. ternata. Furthermore, we investigated the mt genome size and GC content by comparing P. ternata with the other 35 species. An evaluation of non-synonymous substitutions and synonymous substitutions indicated that most PCGs in the mt genome underwent negative selection. Our results provide comprehensive information on the P. ternata mt genome, which may facilitate future research on the high-temperature response of P. ternata and provide new molecular insights on the Araceae family.
Towards a usage-based characterisation of the English Superlative Object Construction
Authors: Bouso Tamara 
Published: February 2024

Abstract
Little attention has been paid to the English Superlative Object Construction (SOC), as in She worked her
hardest. The historical grammarians Jespersen (1909–1949) and Poutsma (1904–1929) are the only ones who do touch on the SOC, and they do so in passing
relying on what seem to be the prototypical examples of the construction. This empirical evidence, though valuable for a first
characterisation of the pattern, is insufficient to provide a detailed analysis of the form, function, frequency, and distribution
of the SOC in Present Day English from the perspective of Construction Grammar. Based on usage-based data from COCA, this paper
argues that the SOC qualifies as an intensifying comparative construction. Despite being low frequent and showing a set of highly
entrenched, lexicalised units (e.g., smile [X] prettiest, work [X] hardest),
the SOC is relatively productive, especially in informal registers where the construction can be easily accommodated to serve
emotive, phatic, and conative functions.
The semantics of Mandarin futurates
Authors: He Yuyin 
Published: February 2024

Abstract
This paper investigates future interpretations in Mandarin declarative root clauses without overt future modals,
i.e., Mandarin futurates. Mandarin futurates require future time adverbs and schedulable eventualities, which denote future
readings relative to the utterance time or a context-determined past time. Moreover, Mandarin futurates presuppose the existence
of a plan in the context and are incompatible with a single perfective aspect marker le1
. To account
for these facts, I argue that a covert future modal m-plan is present in futurates and extend the formal analysis for
English simple futurates to Mandarin with necessary modifications.
Logarithmic pinpricks in wavefunctions
Authors: Berry M V 
Published: February 2024

Abstract
Waves in the plane, punctured by excision of a small disk with radius much smaller than the wavelength, can be modified by being forced to vanish on the boundary of the disk. Such waves exhibit a logarithmically thin ‘pinprick’, and logarithmically weak oscillations persisting far away. As the radius vanishes, these modifications become asymptotically invisible. Examples are punctured plane waves, and a punctured unit disk; in the latter case, the pinprick causes a logarithmic shift in the eigenvalues. It is conjectured that the plane can be densely covered with asymptotically invisible pinpricks, and that there are analogous phenomena in higher dimensions. The curious phenomenon of pinpricks is not hard to understand, and would be worth presenting in graduate courses on waves.
Nonconvex weighted variational metal artifacts removal via convergent primal-dual algorithms
Authors: Wang Lianfang , Chen Zhangling , Liu Zhifang , Li Yutong , Zhao Yunsong , Li Hongwei , Chang Huibin 
Published: February 2024

Abstract
Direct reconstruction through filtered back projection engenders metal artifacts in polychromatic computed tomography images, attributed to highly attenuating implants, which further poses great challenges for subsequent image analysis. Inpainting the metal trace directly in the Radon domain for the extant variational method leads to strong edge diffusion and potential inherent artifacts. With normalization based on pre-segmentation, the inpainted outcome can be notably ameliorated. However, its reconstructive fidelity is heavily contingent on the precision of the pre-segmentation, and highly accurate segmentation of images with metal artifacts is non-trivial in actuality. In this paper, we propose a nonconvex weighted variational approach for metal artifact reduction. Specifically, in lieu of employing a binary function with zeros in the metal trace, an adaptive weight function is designed in the Radon domain, with zeros in the overlapping regions of multiple disjoint metals as well as areas of highly attenuated projections, and the inverse square root of the measured projection in other regions. A nonconvex

L
1

−
α

L
2

regularization term is incorporated to further enhance edge contrast, alongside a box-constraint in the image domain. Efficient first-order primal-dual algorithms, proven to be globally convergent and of low computational cost owing to the closed-form solution of all subproblems, are devised to resolve such a constrained nonconvex model. Both simulated and real experiments are conducted with comparisons to other variational algorithms, validating the superiority of the presented method. Especially in comparison to Reweighted JSR, our proposed algorithm can curtail the total computational cost to at most one-third, and for the case of inaccurate pre-segmentation, the recovery outcomes by the proposed algorithms are notably enhanced.
Image reconstruction based on nonlinear diffusion model for limited-angle computed tomography
Authors: Zhao Xuying , Jiang Wenjin , Zhang Xinting , Guo Wenxiu , Zhao Yunsong , Zhao Xing 
Published: February 2024

Abstract
The problem of limited-angle computed tomography (CT) imaging reconstruction has a wide range of practical applications. Due to various factors such as high x-ray absorption, structural characteristics of the scanned object, and equipment limitations, it is often impractical to obtain a complete angular scan, resulting in limited-angle scan data. In this paper, we propose an iterative image reconstruction algorithm for limited-angle CT. The algorithm carries out a traditional CT reconstruction and a nonlinear diffusion process alternatively. Specifically, a subtle partial differential equation is constructed to guide the nonlinear diffusion process to eliminate limited-angle artifacts in the reconstructed image. Numerical experiments on both analytic data and real data validate the efficacy of the proposed nonlinear diffusion reconstruction algorithm. Furthermore, a linear diffusion reconstruction algorithm which combines a traditional CT reconstruction algorithm and a linear diffusion process is also presented in the paper.
Adaptive anisotropic Bayesian meshing for inverse problems
Authors: Bocchinfuso A , Calvetti D , Somersalo E 
Published: February 2024

Abstract
We consider inverse problems estimating distributed parameters from indirect noisy observations through discretization of continuum models described by partial differential or integral equations. It is well understood that errors arising from the discretization can be detrimental for ill-posed inverse problems, as discretization error behaves as correlated noise. While this problem can be avoided with a discretization fine enough to decrease the modeling error level below that of the exogenous noise that is addressed, e.g. by regularization, the computational resources needed to deal with the additional degrees of freedom may increase so much as to require high performance computing environments. Following an earlier idea, we advocate the notion of the discretization as one of the unknowns of the inverse problem, which is updated iteratively together with the solution. In this approach, the discretization, defined in terms of an underlying metric, is refined selectively only where the representation power of the current mesh is insufficient. In this paper we allow the metrics and meshes to be anisotropic, and we show that this leads to significant reduction of memory allocation and computing time.
Laser-assisted two-proton radioactivity
Authors: Zou You-Tian , Cheng Jun-Hao , Xu Yang-Yang , Xiao Qiong , Liu Si-Man , Shao Fu-Qiu , Yu Tong-Pu 
Published: February 2024

Abstract
In this work, we systematically investigate the two-proton (2p) radioactivity assisted by an ultra-intense laser field within a deformed one-parameter model (OPM). The results show that the ultra-intense laser hardly affects the preformation probability of the emitted 2p-pair, but it can change the 2p radioactive half-lives to a finite extent by affecting the penetrability probability. Moreover, we compare the responses of different 2p radioactivity nuclei to the laser field, and it is found that the 2p radioactivity of nuclei with a low decay width are more easily influenced by the intense laser field. Furthermore, the effects of different physical parameters of a laser pulse on the 2p radioactivity were also discussed. The calculations indicate that the lasers characterized by shorter wavelengths and higher intensities exert a more significant influence on the rate of the average change in pulse duration. Finally, we extended the deformed OPM to predict the half-lives of 2p radioactivity candidates and identified the possible experimental objects of the laser-assisted 2p radioactivity. This study may promote the possibility of future experimental investigations on extensive facilities like GSI and ELI-NP, etc.
Dy-GNet: a lightweight and efficient 1DCNN-based network for leakage aperture identification
Authors: Zhang Lihao , Li Peng , Liu Xuanyu , Yu Junze , Hu Guole , Yu Tao 
Published: February 2024

Abstract
The deep neural network’s gas leakage aperture recognition method has high accuracy, but its low computational efficiency due to its complex structure greatly limit its application in resource-limited industrial environments and real-time processing. In this paper, we proposed an efficient recognition method based on 1D convolutional neural network. First, wavelet scattering coefficients with time-frequency information are obtained using the wavelet scattering transform. Second, the dynamic convolution is used to deepen the feature extraction and partial convolution to speed up the inference time, and the efficient Dy-G module is constructed. Finally, the Dy-G module is stacked to construct Dy-GNet to achieve the classification task. The performance of the model is verified in a noisy environment, and the results show that the model floating-point operations are 17.24 M and 83.94% accuracy is achieved at signal to noise ratio = 0 dB, which guarantees the accuracy while speeding up the inference speed with high efficiency.
ISRM: introspective self-supervised reconstruction model for rail surface defect detection and segmentation
Authors: Li Yaxing , Min Yongzhi , Yue Biao 
Published: February 2024

Abstract
The problems of intrinsic imbalance of the sample and interference from complex backgrounds limit the performance of existing deep learning methods when applied to the detection and segmentation of rail surface defects. To address these issues, an introspective self-supervised reconstruction model (ISRM) is proposed, which only requires normal samples in the training phase and incorporates the concept of self-supervised learning into an introspective autoencoder. The training framework of ISRM first extracts general features using a pretrained Feature Extractor. Subsequently, a Feature Transformer transfers the features to the target domain. Next, a synthetic defect embedder embeds Bessel-Gaussian random defects into the feature space. Finally, the asymmetric autoencoder reconstructs the rail surface features back into image space. The transformation of pretrained features into target-oriented features helps mitigate domain bias. Since defects exhibit higher commonality in the feature space relative to the image space, embedding synthetic defects into the feature space effectively improves training efficiency. Moreover, the adversarial training architecture enhances the clarity of reconstructed images. The impact of core parameters on the model performance is analyzed through ablation experiments. The results from comparative experiments demonstrate that ISRM achieves 98.5% and 97.2% accuracy on defect detection and segmentation tasks, respectively, reducing the error rate by 11.8% and 3.4% compared to the current state-of-the-art model.
An efficient and accurate surface defect detection method for quality supervision of wood panels
Authors: Yi Zhihao , Luo Lufeng , Lu Qinghua , Chen Mingyou , Zhu Wenbo , Zhang Yunzhi 
Published: February 2024

Abstract
The wood panel processing sector is integral to the landscape of industrial manufacturing, and automated detection of wood panel surface defects has become an important guarantee for improving the efficiency and quality of processing production. However, due to the diverse scales and shapes of wood panel surface defects, as well as their complex and varied colors and texture characteristics, the efforts to efficiently and accurately detect surface defects in wood panels through existing methods have fallen short. Therefore, the paper proposes an enhanced YOLOx-tiny deep learning network for wood panel surface defect detection. We introduce new modules multi-pooling feature fusion module and comprehensive feature extraction module, instead of the original SPP and Bottleneck modules to enhance key feature extraction and reduce the number of computational parameters. The experimental results conducted on the self-constructed wood panel surface defects dataset show that the mAP of our proposed method is 95.01%, which is 9.58% higher than the original YOLOx-tiny network model, and the defects recall is 91.46%, which is 13.21% higher compared to the original network. Meanwhile, the method is able to reduce 12.22% of computational parameters, which effectively improves the efficiency of the detection of surface defects on wood panels. In summary, the proposed intelligent surface defect detection approach for wood panels, which utilizes an enhanced YOLOx-tiny deep learning network, has yielded notable outcomes in enhancing both accuracy and efficiency. This method holds significant practical relevance for the wood panel manufacturing sector, offering the potential to enhance both production efficiency and quality. It also explores the automation and intelligent technology in the process of man-made board processing, which provides a valuable reference for the research in related fields.