bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weight, weight gain and behavioural risk factors in women attending a breast cancer family history, risk and prevention clinic: an observational study
Authors: Pegington Mary , Belcher John , Barrett Emma , Virpal Pawandeep , Howell Anthony , Evans D. Gareth , Harvie Michelle 
Published: March 2024

Abstract
Background
Weight and health behaviours impact on breast cancer risk. We describe trends in weight and health behaviours in women at entry to a specialist breast cancer family history clinic in Manchester, UK, and changes after clinic entry.

Methods
Questionnaires were completed at clinic entry (1987–2019, n = 10,920), and updated in 2010–11 (n = 3283). Clinic entry characteristics were compared between joining periods 1989–98, 1999–2008 and 2009–18. Partial Least Squares analysis characterised trends in weight, smoking and alcohol intake by age at entry, year of entry and birth year. Weight changes were compared over time, between joining periods.

Results
Obesity at clinic entry increased from 10.6% in 1989–98 to 20.5% in 2009–18. Alcohol intake above recommendations and smoking prevalence decreased from 20.1% to 13.8% and 33.5% to 16.1% respectively. Weight gain was median 9.7 (IQR 1.4–20.6) % between age 20 and clinic entry (mean duration 11.9 ± 5.6 years) and a further 4.5 (0.0–12.5) % between clinic entry and 2010–11 (31.1 ± 10.4 years). Weight gain between age 20 and clinic entry was highest in the most recent joining period.

Conclusions
Obesity and weight gain are common in women attending a breast cancer family history clinic suggesting a need for weight management advice and support.
Research on surface defect detection model of steel strip based on MFFA‐YOLOv5
Authors: Chen Hao , Qiu Jianlin , Gao Depeng , Qian Lanmei , Li Xiujing 
Published: March 2024

AbstractThe surface quality of steel strip is a critical indicator of the quality of hot‐rolled strip, so accurate inspection of its surface is essential. However, the complex texture of steel strip surface defects makes the detection challenging. Here, a multi‐scale feature fusion and attention based YOLOv5 (MFFA‐YOLOv5) model is proposed. Specifically, the bottom layer features are up‐sampled and fused not only with the middle layer features, but also with the top layer features, so that the model better captures the surface texture information of the steel strip. Secondly, an improved attention mechanism module is introduced to deal with the global and local information of the steel strip surface by introducing down‐sampling and up‐sampling paths based on Convolutional Block Attention Module (CBAM). Meanwhile, a self‐attention mechanism path is added to improve the capability of feature representation. Experimental results on the NEU‐DET dataset show that the MFFA‐YOLOv5 model significantly outperforms other state‐of‐the‐art methods.
Neuroradiological diagnosis and therapy of cerebral vasospasm after subarachnoid hemorrhage
Authors: Neumann Alexander , Schacht Hannes , Schramm Peter 
Published: March 2024

Background Cerebral damage after aneurysmal subarachnoid hemorrhage (SAH) results from various, sometimes unrelated causes. After the initial hemorrhage trauma with an increase in intracranial pressure, induced vasoconstriction, but also microcirculatory disturbances, inflammation and pathological electrophysiological processes (cortical spreading depolarization) can occur in the course of the disease, resulting in delayed cerebral ischemia (DCI). In the neuroradiological context, cerebral vasospasm (CVS) remains the focus of diagnostic imaging and endovascular therapy as a frequent component of the genesis of DCI.
Methods The amount of blood leaked during aneurysm rupture (which can be detected by CT, for example) correlates with the occurrence and severity of CVS. CT perfusion is then an important component in determining the indication for endovascular spasm therapies (EST). These include intra-arterial drug administration (also as long-term microcatheter treatment) and mechanical procedures (balloon angioplasty, vasodilatation using other instruments such as stent retrievers, stenting).
Conclusion This review summarizes the current findings on the diagnosis and treatment of CVS after aneurysmal SAH from a neuroradiological perspective, taking into account the complex and up-to-date international literature.
Key Points

Citation Format
Return to Play Following Concussion: Role for Imaging?
Authors: Sanclemente Drew , Belair Jeffrey A. , Talekar Kiran S. , Roedl Johannes B. , Stache Stephen 
Published: March 2024

AbstractThis review surveys concussion management, focusing on the use of neuroimaging techniques in return to play (RTP) decisions. Clinical assessments traditionally were the foundation of concussion diagnoses. However, their subjective nature prompted an exploration of neuroimaging modalities to enhance diagnosis and management. Magnetic resonance spectroscopy provides information about metabolic changes and alterations in the absence of structural abnormalities. Diffusion tensor imaging uncovers microstructural changes in white matter. Functional magnetic resonance imaging assesses neuronal activity to reveal changes in cognitive and sensorimotor functions. Positron emission tomography can assess metabolic disturbances using radiotracers, offering insight into the long-term effects of concussions. Vestibulo-ocular dysfunction screening and eye tracking assess vestibular and oculomotor function. Although these neuroimaging techniques demonstrate promise, continued research and standardization are needed before they can be integrated into the clinical setting. This review emphasizes the potential for neuroimaging in enhancing the accuracy of concussion diagnosis and guiding RTP decisions.
Traducir para preservar la comunidad
Authors: Marroquín Paitán Diana Isabel , Santos Gonzales Natalia Isabel 
Published: March 2024

Abstracta
Esta investigación analiza el proceso de traducción comunitaria realizada por un grupo de fans hispanoamericanos de un libro de la saga de literatura fantástica juvenil Cazadores de sombras (CDS). El fandom de CDS criticó la traducción al español de una editorial transnacional por alterar el contenido y demorar al traducir. Frente a la mala recepción de esta traducción, algunos miembros del fandom decidieron organizarse en Facebook para generar una traducción que los satisfaga. Este proceso se reconstruye mediante la etnografía digital. Se describe cómo el colectivo de traductores generó espacios de coordinación para el equipo y de encuentro con la comunidad, que funcionarían como espacios de literacidad. En estos espacios, se construyó un método de traducción coherente con los objetivos sociales del fandom al realizar una traducción rápida mediante un proceso con etapas simultáneas, fomentar el aprendizaje de los miembros, y mantener la motivación y el compromiso mediante comunicación afectiva.
What is iconicity?
Authors: Bross Fabian 
Published: March 2024

Abstract
Iconicity has been defined in three majors ways in the sign language literature. Some authors describe iconicity
as a similarity mapping between a signifier (the mental representation of the form side of a linguistic sign) and its referent,
while others state that iconicity is to be understood as a similarity mapping between a signifier and its meaning. Other scholars
have defined iconicity as a similarity mapping between a signifier and some other mental representation. The goal of this paper is
to give an overview of the consequences entailed by defining iconicity as a mapping between a signifier and its referent, a
signifier and its meaning, or a signifier and some mental concept. These consequences will be discussed from different theoretical
perspectives. It will be argued that definitions viewing iconicity as a mapping between a signifier and some associated mental
concept work best, while definitions based on reference and meaning run into several theoretical problems or are, at least, rather
theory-specific.
Knowledge and risky behaviors of Kabul city butchers regarding Crimean-Congo hemorrhagic fever
Authors: Samadi Assadullah , Safi Mohammad Asif 
Published: March 2024

Abstract

Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne zoonotic disease with high morbidity and case fatality rates (CFR) in humans, especially among the high-risk groups, including butchers. Therefore, a cross-sectional study was conducted in Kabul City to determine the knowledge gaps and risky behaviors of Kabul City’s butchers about CCHF. Data were collected from 123 butchers by structured interview using a pre-designed questionnaire. The finding of the present study revealed that of the total CCHF cases in the butchers, their family members, and co-workers, 37.9% died. Nearly half of the butchers (45.0%) experienced splashing of animal blood on their faces, while 70.1% of them had cut their hands with a knife during their occupation life. In addition, 27.0% of the respondents held the knife in their mouth while dressing the animal carcasses, and 27.3% of the butchers consumed raw liver. Hand washing and PPE application were practiced by 94.3% and 22.8% of the respondents, respectively. About 92% of the respondents had heard about CCHF, and 67.4% of the participants had good knowledge about CCHF. The knowledge level of the respondents (poor or good) was statistically related to their age, history of blood splash in the face and eyes of the butchers, holding work knives in the mouth, blowing the skin of animals, hand washing with soap and water, and slaughter places (
p
< 0.05). A medium correlation between education and total knowledge of the butchers was observed (r = 0.3;
p
< 0.05). It is necessary to change such high-risk behaviors by continually coordinating awareness campaigns through mass media, workshops, and informative leaflet distribution to improve knowledge.

One Health impact statement
CCHF is a multifaceted zoonotic disease where tick bites, direct contact with the tissue, secretions, and body fluids of the infected animals and humans, and consumption of raw milk, meat, and liver of infected animals are the main transmission methods of the disease to humans. Although livestock including cattle, sheep, goats, camels, and ostrich are the main reservoirs of the virus, they are infected in subclinical form, and humans are the only hosts where fatal clinical disease develops. Since the CCHF virus circulates in nature between tick-vertebrates-ticks, the only method that can effectively control and prevent the disease in humans is by using a targeted One Health approach between the human, veterinary, and environmental health sectors. Meanwhile, it has been proven that public awareness campaigns through the use of mass media, community leaders, and integrated workshops are the most effective tools to prevent the transmission of the CCHF virus from infected animals and ticks to humans.
Estimation of off-the grid sparse spikes with over-parametrized projected gradient descent: theory and application
Authors: Bénard Pierre-Jean , Traonmilin Yann , Aujol Jean-François , Soubies Emmanuel 
Published: March 2024

Abstract
In this article, we study the problem of recovering sparse spikes with over-parametrized projected descent. We first provide a theoretical study of approximate recovery with our chosen initialization method: Continuous Orthogonal Matching Pursuit without Sliding. Then we study the effect of over-parametrization on the gradient descent which highlights the benefits of the projection step. Finally, we show the improved calculation times of our algorithm compared to state-of-the-art model-based methods on realistic simulated microscopy data.
On the coupling of three-level quantum refrigerators in the weak coupling limit
Authors: Abdou Chakour M H Ben , El Allati A , Hassouni Y 
Published: March 2024

Abstract
We propose a model of two identical autonomous quantum refrigerators that interact through their qubits coupled to cold thermal baths. We consider the same specific disposition for each refrigerator related to a three-level system coupled to different thermal baths and, in turn, to a qubit as the object to be cooled. By coupling the two systems, we show that under certain conditions on the model parameters, the devices can operate in the regime of an independent autonomous quantum refrigerator or be subject to an adjustment of the thermodynamic performances. We further investigate the correlations induced by the interaction between the two refrigerators, and illustrate their behavior with respect to their thermodynamic performances. We have found that adjusting the different interaction constants of the model can lead to an evolution of the quantum correlations of the two machines that is inversely related to the behavior of the thermodynamic quantities of both refrigerators. However, this is not necessarily the case when the temperatures of the model are considered as the reference parameters.
Ferroelectric tunnel junctions: promise, achievements and challenges
Authors: Park Sung Hyuk , Lee Hyeon Ji , Park Min Hyuk , Kim Jihyun , Jang Ho Won 
Published: March 2024

Abstract
Ferroelectric tunnel junctions (FTJs) have been the subject of ongoing research interest due to its fast operation based on the spontaneous polarization direction of ultrathin ferroelectrics and its simple two-terminal structure. Due to the advantages of FTJs, such as non-destructive readout, fast operation speed, low energy consumption, and high-density integration, they have recently been considered a promising candidate for non-volatile next-generation memory. These characteristics are essential to meet the increasing demand for high-performance memory in modern computing systems. In this review, we explore the basic principles and structures of FTJs and clarify the elements necessary for the successful fabrication and operation of FTJs. Then, we focus on the recent progress in perovskite oxide, fluorite, 2-dimensional van der Waals, and polymer-based FTJs and discuss ferroelectric materials expected to be available for FTJs use in the future. We highlight various functional device applications, including non-volatile memories, crossbar arrays, and synapses, utilizing the advantageous properties of ferroelectrics. Lastly, we address the challenges that FTJ devices currently face and propose a direction for moving forward.
Design of patch-shaped lens with thickness variations for elastic wave focusing in thin-plate structures
Authors: Park Hyun-Su , Han Jae-Hung 
Published: March 2024

Abstract
In mechanical engineering, focusing on elastic waves is pivotal for applications, such as energy harvesting, shock mitigation, and wave manipulation. While phononic crystals have historically been a key method for managing wave propagation, this study explores a novel technique. This method introduces gradient refractive-index (GRIN) lenses by altering the plate thickness and creating localized high-refractive-index zones. Unlike traditional methods, this localized GRIN approach aims to overcome the fabrication and structural limitations, particularly in thin structures. The patch-shaped lenses offer the potential for elastic wave focusing in thinner structures without any degradation of structural performance. Through numerical analysis, we established design principles and examined the elastic wave propagation and focusing characteristics across various thickness variation profiles. This study conducts a thorough analytical and experimental evaluation of these lenses to confirm their effectiveness, structural robustness, and suitability for optimizing wave concentration in various mechanical engineering applications. The research represents an alternative, innovative, and promising pathway in the field of wave focusing, transcending the traditional constraints of thin plate structures.
A small sample bearing fault diagnosis method based on novel Zernike moment feature attention convolutional neural network
Authors: Zhao Yunji , Xu Jun 
Published: March 2024

Abstract
Bearings are one of the core components of rotating machine machinery. Monitoring their health status can ensure the safe and stable operation of rotating machine equipment. The limited nature of bearing fault samples makes it difficult to meet the demand for sufficient samples based on deep learning methods. Therefore, how to solve the problem of small- samples is the key to achieving intelligent fault diagnosis. In bearing failures based on vibration signals, the complex operating environment causes the vibration signals to inevitably mix with noise. The mixing of fault signature features and noise intensifies the strong spatial coupling of different types of fault features. In addition, diagnosing bearing failures under different loads is challenging because of the complex working conditions of bearings. Given the above problems, a small sample-bearing fault diagnosis method based on a high and low-frequency layered algorithm (HLFLA) and a novel Zernike moment feature attention convolutional neural network (ZMFA-CNN) is proposed. First, the proposed HLFLA converts one-dimensional time series signals into two-dimensional signals distributed rectangularly according to different frequency bands, and is used to simplify network feature screening, reduce the impact of noise, and retain adjacent signal constraint information. In addition, a new ZMFA-CNN is proposed to further extract multi-order moment features and attention weights, and can significantly improve the model generalization ability without increasing model parameters. At the same time, it is combined with FilterResponseNorm2d and thresholded linear unit to further improve model performance. Finally, sufficient experiments verified that the algorithm proposed in this paper can solve the above problems and has excellent transfer generalization ability and noise robustness. In addition, the experimental results of applying the algorithm proposed in this article to gas turbine main bearing fault diagnosis prove the reliability of the algorithm proposed in this article.
Photocatalytic activity enhancement of nanostructured metal-oxides photocatalyst: a review
Authors: Mohd Raub Aini Ayunni , Bahru Raihana , Mohamed Mohd Ambri , Latif Rhonira , Mohammad Haniff Muhammad Aniq Shazni , Simarani Khanom , Yunas Jumril 
Published: March 2024

Abstract
Nanostructured metal oxide semiconductors have emerged as promising nanoscale photocatalysts due to their excellent photosensitivity, chemical stability, non-toxicity, and biocompatibility. Enhancing the photocatalytic activity of metal oxide is critical in improving their efficiency in radical ion production upon optical exposure for various applications. Therefore, this review paper provides an in-depth analysis of the photocatalytic activity of nanostructured metal oxides, including the photocatalytic mechanism, factors affecting the photocatalytic efficiency, and approaches taken to boost the photocatalytic performance through structure or material modifications. This paper also highlights an overview of the recent applications and discusses the recent advancement of ZnO-based nanocomposite as a promising photocatalytic material for environmental remediation, energy conversion, and biomedical applications.
Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy
Authors: Berumen Francisco , Ouellet Samuel , Enger Shirin , Beaulieu Luc 
Published: March 2024

Abstract

Objective. In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability. Approach. Data from 200 prostate patients, treated with 125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input data. The dataset was randomly split into training (150), validation (25) and test (25) sets. The aleatoric (associated with the input data) and epistemic (associated with the model) uncertainties of the DL models were assessed. Main results. For the full test set, with respect to the MC reference, the predicted prostate D
90 metric had mean differences of −0.64% and 0.08% for the UNet and ResUNet TSE models, respectively. In voxel-by-voxel comparisons, the average global dose difference ratio in the [−1%, 1%] range included 91.0% and 93.0% of voxels for the UNet and the ResUNet TSE, respectively. One forward pass or prediction took 4 ms for a 3D dose volume of 2.56 M voxels (128 × 160 × 128). The ResUNet TSE model closely encoded the well-known physics of the problem as seen in a set of uncertainty maps. The ResUNet TSE rectum D2cc had the largest uncertainty metric of 0.0042. Significance. The proposed DL models serve as rapid dose predictors that consider the patient anatomy and interseed attenuation effects. The derived uncertainty is interpretable, highlighting areas where DL models may struggle to provide accurate estimations. The uncertainty analysis offers a comprehensive evaluation tool for dose predictor model assessment.
2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation
Authors: Xu Kai , Zhang Feixiang , Huang Yong , Huang Xiaoyu 
Published: March 2024

Abstract
Segmenting esophageal tumor from computed tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy required for practical applications in current methods. To address this problem, we propose a 2.5D context-aware feature sequence fusion UNet (2.5D CFSF-UNet) model for esophageal tumor segmentation in CT sequence images. Specifically, we embed intra-slice multiscale attention feature fusion (Intra-slice MAFF) in each skip connection of UNet to improve feature learning capabilities, better expressing the differences between anatomical structures within CT sequence images. Additionally, the inter-slice context fusion block (Inter-slice CFB) is utilized in the center bridge of UNet to enhance the depiction of context features between CT slices, thereby preventing the loss of structural information between slices. Experiments are conducted on a dataset of 430 esophageal tumor patients. The results show an 87.13% dice similarity coefficient, a 79.71% intersection over union and a 2.4758 mm Hausdorff distance, which demonstrates that our approach can improve contouring consistency and can be applied to clinical applications.