bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring alternative manufacturing techniques for high-quality solar cell interconnectors: a comparative study of laser cutting and electric discharge machining
Authors: Nasresfahani Mohamad Reza , Asghari Saeed , Ashani Hossein Nazemi , Gorabi Hadi , Joharestani Mahdi Zamani 
Published: November 2024

Abstract
Solar cell interconnectors are typically crafted from thin foils to establish an electrical connection between two components. For limited production of interconnectors with non-standard dimensions, common production methods (such as stamping and sheet metal processes) cannot be utilized. This article examines alternative techniques, such as Fiber Laser Cutting Machine and Electric Discharge Machining (wire cutting). The laser cutting method did not achieve the desired quality standards, leading to the rejection of the interconnectors. Issues such as burning, local melting, and damage to the silver coating were noted at the edges. The melting of the coating layers formed brittle intermetallic compounds, which can cause cracking and reduce weldability and solderability. In contrast, the Electric Discharge Machining method successfully produces interconnectors with complex shapes by stacking layers of foil. This approach results in high-quality interconnectors that showed no signs of burning or local melting on their edges.
Mathematical and Buongiorno nanofluid modelling to predict the thermal and solutal performance of magneto-bioconvective Casson nanofluid flowing in a rotatory disk
Authors: Salawu S O , Shamshuddin MD , Bin Mizan Md Rezwan , Bari Sajjadul 
Published: November 2024

Abstract
This study aims to improve how heat and mass move in systems that use viscoelastic nanofluids under magnetic fields. These systems are commonly used in biotechnology, energy, and medical devices. The significance of this work lies in exploring the steady flow of magnetohydrodynamic (MHD) Casson nanofluids, incorporating the Buongiorno nanofluid model and swimming microorganisms. This research seeks to deepen the understanding of complex fluid behaviours by examining the effects of thermal radiation and chemical diffusion under thermal and solutal convective boundary conditions. The governing equations, which are inherently nonlinear due to multiple physical effects, are converted from two-dimensional partial differential equations (PDEs) to ordinary differential equations (ODEs) using a similarity transformation. A semi-analytical solution is derived using the collocation pseudo-spectral method within the MAPLE computational software. The study investigates how factors like Casson and magnetic parameters, Eckert number, Brownian motion, and thermophoresis affect the flow rate, temperature distribution, species concentration, and microorganism motility. These results are validated by comparing them with established benchmarks. The key findings reveal a pronounced oscillatory behaviour in the temperature profile at higher Eckert number values, while increased Brownian motion and thermophoresis lead to greater nanoparticle dispersion near the disk surface. Higher Lewis and Peclet numbers lead to increased microorganism concentration, demonstrating stronger convective and advective effects. These insights are vital for optimizing drag force, thermal gradients, and mass transfer in engineering applications that involve rotating disks and magnetic fields.
Dynamic modelling and predictive position/force control of a plant-inspired growing robot
Authors: Kalibala Abdonoor , Nada Ayman A , Ishii Hiroyuki , El-Hussieny Haitham 
Published: November 2024

Abstract
This paper presents the development and control of a dynamic model for a plant-inspired growing robot, termed the ‘vine-robot’, using the Euler–Lagrangian method. The unique growth mechanism of the vine-robot enables it to navigate complex environments by extending its body. We derive the dynamic equations of motion and employ model predictive control to regulate the task space position, orientation, and interaction forces. Simulation experiments are conducted to evaluate the performance of the proposed model and control strategy. The results demonstrate that the model effectively achieves sub-millimeter precision in the position control in both static and time varying refrence trajectroies, and sub micronewton in force control.
Long-term patterns of post-fire harvest diverge among ownerships in the Pacific West, U.S.A
Authors: Zuspan Aaron , Reilly Matthew J , Lee E Henry 
Published: November 2024

Abstract
Post-fire harvest (PFH) is a forest management practice designed to salvage value from burned timber, mitigate safety hazards from dead trees, reduce long-term fuels, and prepare sites for replanting. Despite public controversy and extensive ecological research, little is known about how much PFH occurs on private and public lands in the U.S. Pacific West, or how practices changed with shifting forest policy and increasing area burned over the last three decades. We mapped PFH across 2.2 M burned hectares in California, Oregon, and Washington between 1986–2017 and used time series intervention analysis to compare trends in area, rate (% of burned area harvested), and mean patch size between private (0.5 M ha) and federal (1.6 M ha) forest land and across a gradient of burn severity. Harvest rates varied by ownership (4.9% federal, 18.6% private, 8.0% overall), and practices evolved and diverged over the study period. PFH area and rate declined across all ownerships in the mid-1990s during a period of reduced fire activity. As area burned increased between the early 2000s and late 2010s, PFH area rebounded and surpassed late-1980s levels, while rates remained relatively low. On federal lands, PFH practices shifted in the early-to-mid 1990s towards lower rates (10.3%–3.8%) and smaller patches (6.0–3.3 ha), following policy changes and increased litigation. PFH rates on federal lands decreased at all levels of burn severity, with the largest decreases (6.2%–1.2%) in forests with low tree mortality (i.e. fire refugia). Conversely, private PFH rates and mean patch sizes more than doubled in forests burned at very low-to-moderate severity. Our results highlight how PFH practices have shifted with policy, socio-economic pressure, and increasing area burned over 31 years in the Pacific West. A similar area of PFH is now dispersed over larger fires, with practices diverging substantially between ownerships.
Thermodynamics and generalized hydrodynamics of simple integrable QFT in finite volume
Authors: Pawełczyk Jacek 
Published: November 2024

Abstract
We derive thermodynamic (TBA) and general hydrodynamic (GHD) equations corrected by virtual processes for integrable QFT on large but finite size space circle. Obtained TBA’s are solved numerically for the sinh-Gordon model. Complicated Euler scale GHD equations are expanded explicitly for small occupation ratio of virtual quasiparticles. The spectrum of velocities for the linear approximation to GHD is numerically calculated.
Therapeutic efficacy of a probiotic preparation on idiopathic halitosis: a retrospective observational study
Authors: Li 李 Ye Kan 烨侃 , Mou 牟 Fu Yuan 福元 , Qian 钱 Xiao Xian 孝先 
Published: November 2024

Abstract
Idiopathic halitosis is an unusual condition of unclear causes, which has never been thoroughly investigated. We aimed to explore the role of small intestinal bacterial overgrowth (SIBO) in the pathogenesis of idiopathic halitosis, and to evaluate the therapeutic efficacy of a probiotic preparation on this condition. This retrospective observational study included 162 idiopathic halitosis patients and 198 healthy controls (HCs). Halitosis was diagnosed using the organoleptic test, and idiopathic halitosis was diagnosed by excluding known causes. SIBO was identified through the hydrogen/methane lactulose breath test, and accordingly, patients were identified as SIBO-positive or SIBO-negative. Idiopathic halitosis patients were treated with the probiotic preparation Bifidobacterium triple viable capsule for two months, followed by re-evaluation of halitosis and SIBO. This study found that all cases of idiopathic halitosis were extra-oral. The SIBO positivity rate in idiopathic halitosis patients was significantly higher than that in HCs (74.69% [121/162] vs 3.03% [6/198], P < 0.001), with an odds ratio of 94.44% (95% CI: 39.99%–211.35%). After treatment, 80.17% (97/121) of the SIBO-positive patients became SIBO-negative. Moreover, 87.60% (106/121) of the SIBO-positive patients experienced improved halitosis, a rate significantly higher than that observed in SIBO-negative patients (2.75%, 3/41) (P < 0.001). In addition, 98.97% (96/97) of the post-treatment SIBO-negative patients experienced improved halitosis, a rate significantly higher than that of post-treatment sustained SIBO-positive patients (41.67%, 10/24) (P < 0.001). Our findings suggest that idiopathic halitosis is an extra-oral condition which mostly originates from the small intestine. SIBO is one of its underlying causes. The probiotic preparation can effectively improve idiopathic halitosis, probably through its impact on SIBO.
Morphological and structural analysis of monodispersive silica nanoparticles fabricated by stober method for self-cleaning applications
Authors: Pradhan K , Kar J P , Badapanda T , Ghosh S P 
Published: November 2024

Abstract
In this work, silica nanoparticles have been synthesized via the Stober process on a p-type silicon substrate by spin coating method. Structural and morphological studies have been carried out by x-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM). Microstructural analysis by Raman spectroscopy displays a prominent peak at 524 cm−1 which corresponds to crystalline silicon. Further, the elemental study by using energy dispersive x-ray spectroscopy (EDX) confirms the presence of silica with no other impurities. Besides, the wettability property has been studied by contact angle measurement and found to be around 130° for the 6-layer coated thin film which is quasi-super-hydrophobic. The wettability results obtained signify promising properties for the development of self-cleaning devices and applications.
Stochastic black-box optimization using multi-fidelity score function estimator
Authors: Agrawal Atul , Ravi Kislaya , Koutsourelakis Phaedon-Stelios , Bungartz Hans-Joachim 
Published: November 2024

Abstract
Optimizing parameters of physics-based simulators is crucial in the design process of engineering and scientific systems. This becomes particularly challenging when the simulator is stochastic, computationally expensive, black-box and when a high-dimensional vector of parameters needs to be optimized, as e.g. is the case in complex climate models that involve numerous interdependent variables and uncertain parameters. Many traditional optimization methods rely on gradient information, which is frequently unavailable in legacy black-box codes. To address these challenges, we present SCOUT-Nd (Stochastic Constrained Optimization for N dimensions), a gradient-based algorithm that can be used on non-differentiable objectives. It can be combined with natural gradients in order to further enhance convergence properties. and it also incorporates multi-fidelity schemes and an adaptive selection of samples in order to minimize computational effort. We validate our approach using standard, benchmark problems, demonstrating its superior performance in parameter optimization compared to existing methods. Additionally, we showcase the algorithm’s efficacy in a complex real-world application, i.e. the optimization of a wind farm layout.
CSS-Net: a collaborative framework for synthesis and segmentation of missing contrast-enhanced image with error-prediction consistency
Authors: Huang Xiaoyu , Zhang Feixiang , Huang Yong , Xu Kai 
Published: November 2024

Abstract
Automated tumor segmentation plays a critical role in facilitating the diagnosis and assessment of disease progression. Within the realm of tumor segmentation, Contrast-Enhanced (CE) scans are an effective imaging tool that allows for more intuitive observation of tumor characteristics and generally provide better segmentation results compared with Non-CE scans alone. However, CE images are not available in most cases due to the time-consuming and costly need for contrast and repeat scans. To solve this issue, this paper proposes a Collaborative framework for the Synthesis and Segmentation of missing CE images in medical imaging with error-prediction consistency (CSS-Net). CSS-Net simultaneously addresses synthesis and segmentation tasks, generating both the synthesized CE-like images and coarse segmentation results. Subsequently, a multi-layer adaptive feature fusion strategy is utilized to effectively leverage the correlation between these tasks, resulting in refined segmentation results. Additionally, the proposed method incorporates a multi-layer feature fusion block, which adaptively selects features pertinent to segmentation. Furthermore, error-prediction consistency is also introduced between coarse and refined segmentation for regularization, leading to high-performance segmentation results. What’s more, we constructed a multimodal esophageal tumor segmentation dataset with 902 patients and validated it on this dataset and two publicly available multimodal brain tumor datasets. The results indicate that our method achieved Dice scores of 89.04% in esophageal tumor segmentation, 77.01% in whole glioma segmentation, and 91.14% in Vestibular Schwannoma segmentation. This performance surpasses that of segmentation using only available modalities and other image synthesis-based segmentation methods, demonstrating the superior robustness of CSS-Net.
Spatial and demographic heterogeneity in excess mortality in the United States, 2020-2023: a multi-model approach
Authors: Kandula Sasikiran , Kristoffersen Anja Bråthen , Rø Gunnar , LeBlanc Marissa , de Blasio Birgitte Freiesleben 
Published: November 2024

Abstract
In this study, we assessed the overall impact of the Covid-19 pandemic in the United States between 2020 and 2023 through estimates of excess all-cause mortality. Monthly mortality rates over a 19-year period, stratified by age, sex and state of residence were used to forecast expected mortality for the pandemic years. A combination of models – two timeseries, a spatial random effects and a generalized additive -- was used to better capture uncertainty.
Results indicate that US national excess mortality decreased in 2023 to 157 thousand (95% prediction interval: 35K-282K) from 502K (436K-567K), 574K(484K-666K) and 377K (264K-484K) during the years 2020-2022, respectively. Unlike in previous years, deaths with Covid-19 as the underlying-cause-of-death possibly accounted for all excess deaths during 2023. While for the older age groups (75+ years) the year 2020, before vaccines were available, had the highest excess mortality rate, the two younger age groups had the highest excess mortality in 2021. In each age group, women were estimated to have consistently lower excess mortality than men. West Virginia had the highest age-standardized excess mortality among all states in 2021 and 2022.
Our findings demonstrate the value of a multi-model approach in capturing heterogeneity in excess mortality.
Can GPT-4 learn to analyse moves in research article abstracts?
Authors: Yu Danni , Bondi Marina , Hyland Ken 
Published: November 2024

Abstract
One of the most powerful and enduring ideas in written discourse analysis is that genres can be described in terms of the moves which structure a writer’s purpose. Considerable research has sought to identify these distinct communicative acts, but analyses have been beset by problems of subjectivity, reliability, and the time-consuming need for multiple coders to confirm analyses. In this article, we employ the affordances of Generative Pre-trained Transformer 4 (GPT-4) to automate the annotation process by using natural language prompts. Focusing on abstracts from articles in four applied linguistics journals, we devise prompts which enable the model to identify moves effectively. The annotated outputs of these prompts were evaluated by two assessors with a third addressing disagreements. The results show that an eight-shot prompt was more effective than one using two, confirming that the inclusion of examples illustrating areas of variability can enhance GPT-4’s ability to recognize multiple moves in a single sentence and reduce bias related to textual position. We suggest that GPT-4 offers considerable potential in automating this annotation process, when human actors with domain-specific linguistic expertise inform the prompting process.
Smartphone Evaluation of Dynamic and Static Naturalness After Double-Eyelid Blepharoplasty
Authors: Zhang Ce , Gu Qinhao , Chen Lulu , Wang Ji , Shi Hangyan , Li Jingyu , Wu Sufan 
Published: November 2024

Abstract

Background
Recent studies on postoperative measurements for upper blepharoplasty utilized static photographs, which fail to accurately reflect the postsurgical dynamic naturalness of the eyelids.

Objectives
In this study we aimed to analyze dynamic naturalness after double-eyelid blepharoplasty with a simple smartphone method involving slow-motion videos.

Methods
In this prospective observational study we enrolled patients who underwent double-eyelid blepharoplasty with the flexible suspension technique (FST) and rigid fixation technique (RFT), and individuals with congenital double eyelids. Demographic information was collected, and ultra-slow-motion videos of the eye-opening processes were recorded with a smartphone. Four keyframes were selected from each video, from which several parameters were measured. A third party evaluated the degree of naturalness of the photographs and eye-opening videos.

Results
Sixty females (20 per group) were enrolled. The fold-emerging delay (FED) score showed a linear correlation with the mean third-party dynamic naturalness rating (DNR) (R2 = 0.71, P &lt; .0001). The puffy score showed a linear correlation with the mean third-party static naturalness rating (SNR) (R2 = 0.53, P &lt; .0001). For the congenital, FST, and RFT groups, respectively, the FED scores were 62.9 ± 18.9, 52.2 ± 17.7, and 26.3 ± 18.3 (all P &lt; .05); the puffy scores were 49.8 ± 11.9, 55.9 ± 11.0, and 62.6 ± 12.4 (congenital vs FST, P = .055; others P &lt; .05); the mean third-party DNRs were 3.21 ± 0.67, 2.47 ± 0.62, and 1.78 ± 0.74 (all P &lt; .0001); and the mean third-party SNRs were 3.01 ± 0.78, 2.61 ± 0.55, and 2.14 ± 0.69 (all P &lt; .05).

Conclusions
The 2 new indices obtained from the analysis of smartphone-captured ultra-slow-motion videos are good indicators of the dynamic naturalness of double eyelids created with upper blepharoplasty.

Level of Evidence: 4 (Therapeutic)
Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis
Authors: Zhang Zhenhao , Liu Yuxi , Xiao Meichen , Wang Kun , Huang Yu , Bian Jiang , Yang Ruolin , Li Fuyi 
Published: November 2024

Abstract
Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.
Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity
Authors: De Landtsheer Sébastien , Badkas Apurva , Kulms Dagmar , Sauter Thomas 
Published: November 2024

Abstract
Stratification of patients diagnosed with cancer has become a major goal in personalized oncology. One important aspect is the accurate prediction of the response to various drugs. It is expected that the molecular characteristics of the cancer cells contain enough information to retrieve specific signatures, allowing for accurate predictions based solely on these multi-omic data. Ideally, these predictions should be explainable to clinicians, in order to be integrated in the patients care. We propose a machine-learning framework based on ensemble learning to integrate multi-omic data and predict sensitivity to an array of commonly used and experimental compounds, including chemotoxic compounds and targeted kinase inhibitors. We trained a set of classifiers on the different parts of our dataset to produce omic-specific signatures, then trained a random forest classifier on these signatures to predict drug responsiveness. We used the Cancer Cell Line Encyclopedia dataset, comprising multi-omic and drug sensitivity measurements for hundreds of cell lines, to build the predictive models, and validated the results using nested cross-validation. Our results show good performance for several compounds (Area under the Receiver-Operating Curve &gt;79%) across the most frequent cancer types. Furthermore, the simplicity of our approach allows to examine which omic layers have a greater importance in the models and identify new putative markers of drug responsiveness. We propose several models based on small subsets of transcriptional markers with the potential to become useful tools in personalized oncology, paving the way for clinicians to use the molecular characteristics of the tumors to predict sensitivity to therapeutic compounds.