bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bio-inspired Herringbone Grooved surface topographies and their effect on gas foil journal bearing performance
Authors: Priyadarshini Srusti , Behera Suraj K 
Published: February 2025

Abstract
This paper investigates the performance of bio-inspired Herringbone Grooved-Gas Foil Journal Bearings (HG-GFJBs) using numerical simulations based on the Reynolds equation. Drawing inspiration from the natural arrangement of feather riblets of birds, the study explores how biologically inspired groove geometries can improve the efficiency and functionality of Gas Foil Journal Bearings (GFJBs). These natural patterns, which have evolved to enhance pressure distribution, are applied to the surface topographies of gas foil bearings to examine their impact on static performance. The results indicate that the air-lubricated HG-GFJBs are capable of operating at high speeds and sustaining significant radial loads. Furthermore, an Artificial Neural Network (ANN) was trained using numerical data in order to assess performance using the regression coefficient. Decision surface plots of the Adaptive Neuro-Fuzzy Inference System (ANFIS) were then produced in order to determine the optimal bearing parameters. By leveraging principles derived from nature, the study opens up new possibilities for enhancing the performance and longevity of GFBs, making them more efficient and reliable for a variety of engineering applications.
Deep learning-based video-level view classification of two-dimensional transthoracic echocardiography
Authors: Cheng Hanlin , Shi Zhongqing , Qi Zhanru , Wang Xiaoxian , Guo Guanjun , Fang Aijuan , Jin Zhibin , Shan Chunjie , Chen Ruiyang , Du Yue , Qian Sunnan , Luo Shouhua , Yao Jing 
Published: February 2025

Abstract
In recent years, deep learning (DL)-based automatic view classification of 2D transthoracic echocardiography (TTE) has demonstrated strong performance, but has not fully addressed key clinical requirements such as view coverage, classification accuracy, inference delay, and the need for thorough exploration of performance in real-world clinical settings. We proposed a clinical requirement-driven DL framework, TTESlowFast, for accurate and efficient video-level TTE view classification. This framework is based on the SlowFast architecture and incorporates both a sampling balance strategy and a data augmentation strategy to address class imbalance and the limited availability of labeled TTE videos, respectively. TTESlowFast achieved an overall accuracy of 0.9881, precision of 0.9870, recall of 0.9867, and F1 score of 0.9867 on the test set. After field deployment, the model’s overall accuracy, precision, recall, and F1 score for view classification were 0.9607, 0.9586, 0.9499, and 0.9530, respectively. The inference time for processing a single TTE video was 105.0 ± 50.1 ms on a desktop GPU (NVIDIA RTX 3060) and 186.0 ± 5.2 ms on an edge computing device (Jetson Orin Nano), which basically meets the clinical demand for immediate processing following image acquisition. The TTESlowFast framework proposed in this study demonstrates effective performance in TTE view classification with low inference delay, making it well-suited for various medical scenarios and showing significant potential for practical application.
RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI
Authors: Tang Xiaowen , Zhu Yinsu 
Published: February 2025

Abstract

Objectives Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (EDLA) to improve breast cancer subtype prediction using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods A dataset of 344 patients with histologically confirmed breast cancer was divided into training (n = 200), validation (n = 60), and testing (n = 62) cohorts. RAE-Net, built on ResNet-50 with Multi-Head Attention (MHA) fusion and Multi-Layer Perceptron (MLP) mechanisms, combines radiomic and deep learning features for subtype prediction. The EDLA module adds uncertainty estimation to enhance classification reliability. Results The RAE-Net model incorporating the MFF module demonstrated superior performance, achieving a mean accuracy of 0.83 and a Macro-F1 score of 0.78, surpassing traditional radiomics models (accuracy: 0.79, Macro-F1: 0.75) and standalone deep learning models (accuracy: 0.80, Macro-F1: 0.76). When an EDLA uncertainty threshold of 0.2 was applied, the performance significantly improved, with accuracy reaching 0.97 and Macro-F1 increasing to 0.92. Additionally, RAE-Net outperformed two recent deep learning networks, ResGANet and HIFUSE. Specifically, RAE-Net showed a 0.5% improvement in accuracy and a higher AUC compared to ResGANet. In comparison to HIFUSE, RAE-Net reduced both the number of parameters and computational cost by 90% while only increasing computation time by 5.7%. Conclusions RAE-Net integrates feature fusion and uncertainty estimation to predict breast cancer subtypes from DCE-MRI. The model achieves high accuracy while maintaining computational efficiency, demonstrating its potential for clinical use as a reliable and resource-efficient diagnostic tool.
In-situ analysis on drop in thermal performance of wind turbine gearbox system: global challenges and sustainable ideas
Authors: Sunil Kumar K , Muniamuthu Sumathy , Salunkhe Sachin , Cep Robert , Abouel Nasr Emad 
Published: February 2025

Abstract
The key thrust of this research work is to in-situ Analysis of the drop in Thermal Performance of wind turbine gearbox systems and global challenges and sustainable ideas detection of several failures that occur in the wind turbine gearbox with the help of the latest emerging sensors, and the source of the slave gearbox is a significant input for the master gearbox setup running at 1100 to 1150 rpm at generator side from Jan 2020 to Dec 2020. From the Experimental results, it is clearly understood that the majority of failures occurred due to irregular velocities ranging from 9 to 10 m s−1 at average velocities, and the kinematic viscosities of the oil exceed 400 μ resulting in heavy vibrations resulting in higher maintenance costs. The Master gearbox gives more Predominant results than the Slave gearbox; oil sump temperatures exceeding 85 °C result in poor active power generation falling below 100 Kw, and slave Gearbox oil sump temperatures are achieved at 100 °C; the Maximum temperature attained from the IMS Drive end-stage rate of 85 °C to 90 °C at lower speeds for master gearbox; Whereas slave gearbox temperatures are attained at 110 °C. The average temperature attained from the HSS stage of the Gearbox coupled with the generator is 90 °C, causing heavy vibration frequencies ranging from decibels causing tooth pitting failures for the master gearbox and for the slave gearbox. Average temperatures were attained by 120 °C.
LW-PV DETR: lightweight model for photovoltaic panel surface defect detection
Authors: Han Tao , Bao Meiping , He Tao , Zhang Rui , Feng Xi , Huang Yourui 
Published: February 2025

Abstract
The photovoltaic industry is developing rapidly, and efficiently completing the operation and maintenance of photovoltaic systems has become a research hotspot, with photovoltaic panel defect detection being particularly critical. Due to factors such as the complex background of infrared images of photovoltaic panels taken by drones, the small proportion of defect areas, and equipment limitations, existing models face challenges in detection accuracy and deployment. Aiming at the three typical defects commonly found on the surface of photovoltaic (PV) panels, namely, shading, glass breakage and hot spots, a surface defect detection model (LW-PV DETR) for photovoltaic panels is proposed based on the Real-Time DEtection TRansformer (RT-DETR-R18) object detection model. In the backbone network, a lightweight and efficient attention feature extraction module (Faster-Rep-EMA Block) is designed to enhance the model feature extraction ability. In the Encoder, the lightweight convolution (GSConv) module is introduced to achieve model lightweighting. The feature focusing diffusion pyramid network (FFDPN) is proposed to enhance the model’s feature fusion capability. Simultaneously, to avoid the loss of small object features, a multi-level feature selective fusion (MLFSF) module is designed for feature focusing. For the loss function, Inner-IoU is introduced to improve the localization accuracy of bounding box regression. Experimental results on the public photovoltaic panel infrared image dataset GB_HSP_modified show that, compared to the baseline model, LW-PV DETR improves precision, recall, and mean Average Precision (mAP50, mAP50-95) by 3.9%, 18.6%, 18.5% and 10.9%, respectively, while the model’s parameter count is reduced by 11.83%. Compared to other mainstream object detection models, LW-PV DETR also demonstrates excellent detection performance, providing an important reference for research on intelligent detection of photovoltaic panel surface defects.
Party autonomy and the applicable law to the merits in intellectual property arbitration
Authors: Rivoire Maxence 
Published: February 2025

Abstract
The freedom of the parties to choose the applicable law to the merits is often presented as an important benefit of arbitrating disputes involving intellectual property (IP). Yet, the reality is more uncertain and controversial than is commonly assumed. Is party autonomy really permitted in IP arbitration? Should it be? This article answers these questions with regard to patents, trademarks, and copyrights, using recent examples drawn from arbitral practice. It first examines the situation where the parties only made a choice of contract law, and considers in this regard whether that law can and/or should be extended to infringement claims and/or the remedies to infringement, either directly or through the technique of characterization. After discussing the impact of overriding mandatory rules in contractual IP cases, the article then examines the situation where the parties chose a law to govern IP questions, including by way of a broad choice-of-law agreement covering non-contractual problems. It seeks to provide as much guidance as possible to arbitral tribunals, using mainly the distinction among infringement, ownership, and validity issues, and by distinguishing among different scenarios in which party autonomy is more or less acceptable.
Patient Factors That Impact FACE-Q Aesthetics Outcomes: An Exploratory Cross-Sectional Regression Analysis
Authors: Gallo Lucas , Churchill Isabella , Kim Patrick , Rae Charlene , Voineskos Sophocles H , Thoma Achilles , Pusic Andrea L , Cano Stefan J , Klassen Anne F 
Published: February 2025

Abstract

Background
The FACE-Q Aesthetics is a validated tool for assessing patient-reported outcomes related to surgical and non-surgical facial aesthetic treatments. Recognizing patient-specific variables that may influence FACE-Q scores is essential to control for potential confounders in research.

Objectives
This study aimed to identify factors that predict FACE-Q Aesthetic scale scores.

Methods
A cross-sectional survey was conducted among an international cohort of participants recruited through the Prolific platform. Participants aged 20 years or older who had undergone non-invasive facial aesthetic procedures within the past year were included. Demographic and clinical information was collected, and univariable and multivariable linear regression analyses were employed to assess predictors of FACE-Q Face Overall, Psychological, and Social scale scores.

Results
A total of 1,259 participants were analyzed, with an average age of 42.6 years (±11.9). The mean scores were 52.4 (±18.3) for the Face Overall scale, 56.5 (±23.7) for the Psychological scale, and 62.7 (±24.0) for the Social scale. Several factors were significantly associated (p &lt; 0.05) with higher scores, including lower body mass index (BMI), African American ethnicity, male gender, Fitzpatrick skin type V, residence in the United States, financial stability, and residual effects of prior aesthetic treatments. Younger participants were more likely to report higher Face Overall scores (p&lt;0.05).

Conclusions
This study identified several patient characteristics that predict Face Overall, Psychological, and Social scale scores. These findings offer valuable insights into how patient-specific factors influence outcomes following facial aesthetic procedures and underscore the need to account for these variables in future research using the FACE-Q Aesthetics tool.
Single-nucleus transcriptome atlas of the basal forebrain reveals diverse ageing-related pathways
Authors: Chen Jiale , Li Qianqian , Ji Bingqing , Zhang Ke , Ren Miao , Li Anan , Gong Hui , Wang Jian , Cao Gang , Luo Qingming , Li Xiangning 
Published: February 2025

Abstract
The basal forebrain is a critical brain region involved in various neurobiological processes, including learning, memory, and attention. Basal forebrain cells undergo structural and functional changes during ageing, increasing their vulnerability to neurodegenerative diseases.
To reveal the molecular landscape of distinct cell types during developmental and early ageing, we constructed a comprehensive single-nucleus transcriptomic atlas spanning postnatal day 4 (P4), postnatal day 14 (P14), 3 month (3M), 9 months (9M) and 15 months (15M). Distinct molecular regulatory patterns of basal forebrain subclasses and ageing-related pathways were unveiled. The transcriptional regulation analysis and pseudo-time analysis revealed the dynamic regulatory network of cholinergic neurons during postnatal development. To gain insights into the relationship between ageing and Alzheimer's disease (AD), we conducted an integrative analysis. We identified four potential regulatory networks involved in cholesterol/lipid metabolism, DNA damage repair, and death receptor signal in 5×FAD mice. The cholinergic subtype CN-2 exhibited heightened activity of regulons associated with Srebf2 and Zmiz1, which, in turn, target hub genes implicated in these networks. Meanwhile, CN-2 is the main cholinergic subtype contributing to the alteration of the NRXN signalling pathway.
This study offers a significant data source to elucidate the molecular underpinnings of ageing-related and pathological changes in the basal forebrain, paving the way for future research and therapeutic interventions that target specific basal forebrain subclasses and signalling pathways.
Frontotemporal dementia subtyping using machine learning, multivariate statistics and neuroimaging
Authors: Metz Amelie , Zeighami Yashar , Ducharme Simon , Villeneuve Sylvia , Dadar Mahsa 
Published: February 2025

Abstract
Frontotemporal dementia (FTD) is a prevalent form of early-onset dementia characterized by progressive neurodegeneration and encompasses a group of heterogeneous disorders. Due to overlapping symptoms, diagnosis of FTD and its subtypes still poses a challenge. Magnetic resonance imaging (MRI) is commonly used to support the diagnosis of FTD. Using machine learning and multivariate statistics, we tested whether brain atrophy patterns are associated with severity of cognitive impairment, whether this relationship differs between the phenotypic subtypes and whether we could use these brain patterns to classify patients according to their FTD variant. A total of 136 patients (70 behavioural variant FTD, 36 semantic variant primary progressive aphasia and 30 non-fluent variant primary progressive aphasia) from the frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) database underwent brain MRI and clinical and neuropsychological examination. Deformation-based morphometry, which offers increased sensitivity to subtle local differences in structural image contrasts, was used to estimate regional cortical and subcortical atrophy. Atlas-based associations between atrophy values and performance across different cognitive tests were assessed using partial least squares. We then applied linear regression models to discern the group differences regarding the relationship between atrophy and cognitive decline in the three FTD phenotypes. Lastly, we assessed whether the combination of atrophy and cognition patterns in the latent variables identified in the partial least squares analysis could be used as features in a machine learning model to predict FTD subtypes in patients. Results revealed four significant latent variables that combined accounted for 86% of the shared covariance between cognitive and brain atrophy measures. Partial least squares-based atrophy and cognitive patterns predicted the FTD phenotypes with a cross-validated accuracy of 89.12%, with high specificity (91.46–97.15%) and sensitivity (84.19–93.56%). When using only MRI measures and two behavioural tests in the partial least squares and classification algorithms, ensuring clinical feasibility, our model was equally precise in the same participant sample (87.18%, specificity 76.14–92.00%, sensitivity 86.93–98.26%). Here, including only atrophy or behaviour patterns in the analysis led to prediction accuracies of 69.76% and 76.54%, respectively, highlighting the increased value of combining MRI and clinical measures in subtype classification. We demonstrate that the combination of brain atrophy and clinical characteristics and multivariate statistical methods can serve as a biomarker for disease phenotyping in FTD, whereby the inclusion of deformation-based morphometry measures adds to the classification accuracy in the absence of extensive clinical testing.
Developmental changes in connectivity-based parcellation in typical cortical areas during early life
Authors: Cao Zuozhen , Zhao Zhiyong , Zhu Qinfeng , Shen Yao , Shen Yiqi , Li Mingyang , Wu Dan 
Published: February 2025

Abstract
The human brain undergoes profound developmental changes in the cortex during early life. Functional parcellation of the cortex may also be altered, but there was a limited methodology for quantification. Here, we employed connectivity-based parcellation of sub-regions as a marker to characterize the heterogeneous development of the cerebral cortex. Particularly, we investigated the parcellation of primary motor (M1), sensory cortex (S1), and insula based on tractography-based connectivity among 3 stages during first 2 yr of life and also adulthood. We identified 4 and 3 sub-regions along superior-to-inferior axis in M1 and S1, respectively, and 2 sub-regions along anterior-to-posterior axis in insula. Connectivity profiles and volume fractions of different sub-regions showed distinct development changes. Specifically, measures of each sub-region in M1 and S1 showed a steady development during first 2 yr of life and gradually became similar to adulthood patterns. In contrast, sub-regions in insula exhibited nearly no change in this stage but tremendous differences in adult group. These findings may suggest primary cortices may experience early development in early life, while the high-order cortex may change at a later stage. These findings provided insights into the heterogeneous development during early life and indicated the necessity of using an age-specific cortical parcellation in studying baby brains.
Therapeutic left-to-right shunting in heart failure
Authors: Fioretti Francesco , Nair Ajith P , Anker Stefan D , Borlaug Barry A , Kereiakes Dean J , Lindenfeld JoAnn , Stone Gregg W , Butler Javed 
Published: February 2025

Abstract
Heart failure with reduced or preserved ejection fraction are both associated with elevated left atrial pressure at rest due to fluid overload or during exercise, leading to pulmonary venous congestion. Even with available treatments, heart failure hospitalizations remain high, and improvements in quality-of-life scores and functional capacity are modest. Thus, there is growing interest in non-pharmacological methods to decompress the left atrium and improve heart failure symptoms and outcomes. Left-to-right shunts have emerged as a potential therapeutic option to reduce left atrial hypertension, improve quality of life, and impact long-term outcomes. This nascent field carries both potential therapeutic promise as well as many unanswered questions. Recent data have questioned whether the effects of this therapy vary based on the left ventricular ejection fraction, pulmonary vascular resistance, and/or right ventricular structure and function. This review discusses the basis for left-to-right shunt therapies, synthesizes past and ongoing clinical trials, and offers future directions.
May Measurement Month 2022: an analysis of blood pressure screening results from Republic of the Congo
Authors: Ellenga Mbolla Bertrand , Kouala Landa Christian Michel , Bakekolo Rog Paterne , Makani Bassakouahou Jospin Karel , Ebenguela Ebatetou , Eyeni Sinomono Tony , Ngolo Letomo Kivie , Gankama Thibaut , Moualengué Bijou , Kwealeu Jean-Pierre , Massinsa Kibongui Rode Vaclaire , Gamboulou Cherdan , Gerard Mampouya Ondaye , Ngolo Onanga Victoire , Boungou Nkoueyi Lavelle , Mabongo D’assise , Ondzotto Gontran , Beaney Thomas , Kerr Gabriele , Poulter Neil R , Gombet Thierry Raoul 
Published: February 2025

Abstract
The May Measurement Month (MMM) campaign was carried out in Republic of the Congo in 2022 with the aim of raising awareness of raised blood pressure (BP). Here, we report on the findings of the campaign.
Adults aged ≥ 18 years were recruited opportunistically in rural areas and some cities in Congo. Three seated BP readings were taken for each participant, along with completion of a questionnaire on demographics, lifestyle factors, and comorbidities. Hypertension was defined as a systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg or being on antihypertensive medication. Controlled BP was defined as being on antihypertensive medication with a BP &lt;140/90 mmHg. Multiple imputation was used to estimate any missing BP readings. In total, 8619 were screened, with a mean age of 44.8 years and 49.8% were female. Of those screened, 2596 (30.1%) reported previous COVID-19 vaccination, 476 (5.5%) reported diabetes, 422 (4.9%) previous myocardial infarction, 124 (1.4%) previous stroke, 1211 (14.1%) were current smokers and 1237 (14.4%) reported daily alcohol intake. Of all participants, 3054 (35.4%) had hypertension, of whom 1331 (43.6%) were aware, and 944 (30.9%) were on antihypertensive medication, of whom 801 (84.9%) were on monotherapy and 116 (12.3%) on dual therapy. Of those on antihypertensive medication, 235 (24.9%) had controlled BP, and of all participants with hypertension, 7.7% had controlled BP.
The MMM campaign in Republic of the Congo identified significant numbers of participants with either untreated or inadequately treated hypertension. Therapeutic education must be more active, in addition to enhanced screening, facilitating access to care and making medication available.