bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis and characterization of Fe3O4@SiO2 core-shell nanoparticles for mirror and efficient magnetic compound fluid polishing of TC4 titanium alloy
Authors: Wang Youliang , Kang Yating , Zhang Wenjuan , Yin Xincheng 
Published: September 2024

Abstract
To fully investigate the polishing performance of the Fe3O4@SiO2 core–shell abrasive for TC4 titanium alloy plate and solve the problem of uneven distribution of abrasive particles in magnetic compound fluid polishing (MCFP) and obtain ultra-smooth titanium alloy surface. In this paper, the functional Fe3O4@SiO2 core–shell abrasives were prepared and characterized by XRD, FTIR, VSM and TEM to demonstrate the successful encapsulation of SiO2 in Fe3O4 first. Second, a new polishing device was built to perform polishing experiments. Four different fractions of Magnetic compound fluid (MCF) were configured and utilized in polishing experiments for MCFP of TC4 titanium alloy to verify the polishing properties. Finally, the contact model between abrasive particles and workpiece is established and the material removal mechanism is discussed. The results show that SiO2 was successfully coated on Fe3O4. The polishing results show that the MCF containing the core–shell structure had the best polishing effect. The surface roughness Ra decreased from 0.201 μm to 0.025 μm after 20 min of polishing. The surface roughness of TC4 titanium alloy with an initial roughness Sa of 0.248 μm is reduced to 0.023 μm after polishing by Fe3O4@SiO2 core–shell abrasive. It is shown that the application of core–shell abrasive to process titanium alloys can obtain smooth and defect-free surfaces with good polishing performance. The surface quality of titanium alloy can be improved effectively by using core–shell abrasive, and a new idea for polishing titanium alloy is proposed. This study plays an important role in solving the problem of uneven particle distribution in MCFP. These results pave the way for further research into the application of core–shell abrasives in precision machining and expand the application scope of particles with core–shell structure.
Enhancing pap smear image classification: integrating transfer learning and attention mechanisms for improved detection of cervical abnormalities
Authors: Sood Tamanna , Khandnor Padmavati , Bhatia Rajesh 
Published: September 2024

Abstract
Cervical cancer remains a major global health challenge, accounting for significant morbidity and mortality among women. Early detection through screening, such as Pap smear tests, is crucial for effective treatment and improved patient outcomes. However, traditional manual analysis of Pap smear images is labor-intensive, subject to human error, and requires extensive expertise. To address these challenges, automated approaches using deep learning techniques have been increasingly explored, offering the potential for enhanced diagnostic accuracy and efficiency. This research focuses on improving cervical cancer detection from Pap smear images using advanced deep-learning techniques. Specifically, we aim to enhance classification performance by leveraging Transfer Learning (TL) combined with an attention mechanism, supplemented by effective preprocessing techniques. Our preprocessing pipeline includes image normalization, resizing, and the application of Histogram of Oriented Gradients (HOG), all of which contribute to better feature extraction and improved model performance. The dataset used in this study is the Mendeley Liquid-Based Cytology (LBC) dataset, which provides a comprehensive collection of cervical cytology images annotated by expert cytopathologists. Initial experiments with the ResNet model on raw data yielded an accuracy of 63.95%. However, by applying our preprocessing techniques and integrating an attention mechanism, the accuracy of the ResNet model increased dramatically to 96.74%. Further, the Xception model, known for its superior feature extraction capabilities, achieved the best performance with an accuracy of 98.95%, along with high precision (0.97), recall (0.99), and F1-Score (0.98) on preprocessed data with an attention mechanism. These results underscore the effectiveness of combining preprocessing techniques, TL, and attention mechanisms to significantly enhance the performance of automated cervical cancer detection systems. Our findings demonstrate the potential of these advanced techniques to provide reliable, accurate, and efficient diagnostic tools, which could greatly benefit clinical practice and improve patient outcomes in cervical cancer screening.
Nanodosimetric investigation of the track structure of therapeutic carbon ion radiation part 1: measurement of ionization cluster size distributions
Authors: Hilgers Gerhard , Schwarze Miriam , Rabus Hans 
Published: September 2024

Abstract
At the Heidelberg Ion-Beam Therapy Center, the track structure of carbon ions of therapeutic energy after penetrating layers of simulated tissue was investigated for the first time. Measurements were conducted with carbon ion beams of different energies and polymethyl methacrylate (PMMA) absorbers of different thicknesses to realize different depths in the phantom along the pristine Bragg peak. Ionization cluster size (ICS) distributions resulting from the mixed radiation field behind the PMMA absorbers were measured using an ion-counting nanodosimeter. Two different measurements were carried out: (i) variation of the PMMA absorber thickness with constant carbon ion beam energy and (ii) combined variation of PMMA absorber thickness and carbon ion beam energy such that the kinetic energy of the carbon ions in the target volume is constant. The data analysis revealed unexpectedly high mean ICS values compared to stopping power calculations and the data measured at lower energies in earlier work. This suggests that in the measurements the carbon ion kinetic energies behind the PMMA absorber may have deviated considerably from the expected values obtained by the calculations. In addition, the results indicate the presence of a marked contribution of nuclear fragments to the measured ICS distributions, especially if the carbon ion does not cross the target volume.
A linear photonic swap test circuit for quantum kernel estimation
Authors: Baldazzi Alessio , Leone Nicolò , Sanna Matteo , Azzini Stefano , Pavesi Lorenzo 
Published: September 2024

Abstract
The swap test is a quantum algorithm capable of computing the absolute value of the scalar product of two arbitrary wavefunctions. Scalar products represent a crucial ingredient to many quantum machine learning (QML) methods, but their evaluation is not straightforward at all. For this reason, many research efforts have been made without achieving an efficient and robust implementation. Here, we present an integrated photonic circuit designed to implement the swap test algorithm. Our approach relies solely on linear optical integrated components and qudits, represented by single photons from an attenuated laser beam propagating through a set of waveguides. By utilizing 23 spatial degrees of freedom for the qudits, we can configure all the necessary arrangements to set any two-qubit state and perform the swap test. This simplifies the requirements on the circuitry elements and eliminates the need for non-linearity, heralding, or post-selection to achieve multi-qubit gates. Our photonic swap test circuit successfully encodes two qubits and estimates their scalar product with a measured root mean square error smaller than 0.05. This result paves the way for the development of integrated photonic architectures capable of performing QML tasks with robust devices operating at room temperature.
The long mid-transition: tracking decades of coastal oil well decommissioning in California
Authors: Partridge Tristan , Barandiaran Javiera 
Published: September 2024

Abstract
This article presents an account of four different experiences of oil well decommissioning and abandonment along the Santa Barbara coast in California, together forming an ongoing process that began over a century ago and which has, more recently, become a component of statewide transition plans. The cases include the re-abandonment of nineteenth century wells in Summerland; offshore oil platform removal in the 1990s; and two different areas of operational closures due to infrastructure failures and bankruptcies. Informed by this history, we suggest that decommissioning must be actively managed through coordinated, well-funded, multi-agency action, grounded in historical awareness of policy trajectories. Additionally, the scale of the need together with the economics and policies in place mean that oil well decommissioning will be happening for a very long time, even if no new wells are built.
Measuring the transition to regenerative agriculture in the UK with a co-designed experiment: design, methods and expected outcomes
Authors: Berthon Katherine , Jaworski Coline C , Beacham Jonathan D , Jackson Peter , Leake Jonathan , McHugh Niamh M , Capstick Lucy , Daniell Tim , Krzywoszynska Anna , Cameron Duncan , Holland John , Hartley Sue , Desneux Nicolas , Jowett Kelly , Zhao Yu , Watt Penelope J , Dicks Lynn V 
Published: September 2024

Abstract
Regenerative agriculture is promoted as a farming system that can improve agricultural sustainability, address soil degradation, and provide ecosystem service benefits. However, there remains limited evidence for the quantifiable benefits of a widespread transition to regenerative agriculture on soil, biodiversity, and crop quality, particularly at the landscape scale, and poor integration of findings across disciplines. Social and cultural aspects of the transition, such as the positioning of regenerative agriculture as a grassroots movement, farmers’ perspectives on defining regenerative practices, and social or political barriers to implementation, are harder to quantify and often overlooked in evidence-based approaches. Here, we present the detailed methodology for our interdisciplinary, co-designed landscape-scale experiment measuring changes in soil health, biodiversity, yield, and grain quality, as well as social and political dimensions of the implementation of regenerative practices. Our unique approach, through the co-production process, the landscape-scale, and the focus on a systemic transition instead of individual practices, will bring strong evidence of the benefits of regenerative agriculture for sustained agricultural productivity, the mitigation of climate change and biodiversity depletion in agroecosystems. Our research aims to guide future studies transforming theoretical ecology into testable hypotheses in real-world systems and provide actionable evidence to inform agricultural policies in the UK and beyond.
Modal Idealism
Authors: Builes David 
Published: September 2024

Abstract
According to Idealism, every fundamental entity is conscious, and moreover every fundamental property is a phenomenal property. The goal of this chapter is to defend “Modal Idealism,” which is the thesis that Idealism is metaphysically necessary. In particular, I give four different arguments for Modal Idealism. First, I argue that, if at least some possible fundamental properties are phenomenal properties, then the denial of Modal Idealism leads to implausible brute necessities. Second, I argue that those who endorse the Eleatic Principle, according to which it is necessary that all concrete objects have causal powers, should endorse Modal Idealism. Third, I argue that Modal Idealism helps us secure our knowledge of our own conscious states in the face of influential “debunking” arguments. Fourth, I argue that Modal Idealism is theoretically fruitful: it allows us to make substantial progress on several perennial metaphysical debates beyond the philosophy of mind.
Idealism and the Best of All (Subjectively Indistinguishable) Possible Worlds
Authors: Yetter-Chappell Helen 
Published: September 2024

Abstract
The space of possible worlds is vast. Some of these possible worlds are materialist worlds, some may be worlds bottoming out in 0s and 1s, or other strange things we cannot even dream of … and some are idealist worlds. From among all of the worlds subjectively indistinguishable from our own, the idealist ones have uniquely compelling virtues. Idealism gives us a world that is just as it appears; a world that’s fit to literally enter our minds when we perceive it. If the world is an idealist world, we live in a perceptual Eden. We did not fall from Eden. Rather, we deluded ourselves into believing that we couldn’t possibly live in Eden when we committed to materialism. Reflecting on these big-picture issues gives us reason to question this commitment and embrace a radically new account of reality and our relation to it.
Naïve Realism, Incorporeal Objects, and the Time-lag Argument
Authors: Fish William 
Published: September 2024

Abstract
The chapter begins by outlining the core aspects of a naïve realist theory of successful visual perception, before introducing an important objection to this view known as the time-lag argument, which uses the fact that light moves at a finite speed to argue that we do not see everyday material objects. The chapter explores and critiques extant responses to this argument, before undertaking a phenomenological investigation of our perception of light sources, which suggests that many familiar light sources that we see are not themselves material objects yet remain objective parts of the material world: they are ‘incorporeal’ objects. Moreover, in cases where we do have a material object that is a light source, the incorporeal object created by the underlying processes in this object typically occludes the material object itself. The chapter explores how these findings provide a novel way of responding to the time-lag argument.
Relationalism, Acquaintance, and SubjectivitySome Metaphysical Implications
Authors: Debus Dorothea 
Published: September 2024

Abstract
The chapter explores one account of acquaintance and subjectivity, namely ‘Relationalism’ about perceptual experience. According to a Relationalist, a subject who perceives an object stands in a direct experiential relation to the relevant object, which gives her a subjective perspective on the object with which she is thereby acquainted. However, or so this chapter aims to show, a Relationalist is committed to a non-reductive account of perceptual experience, and thus of the mind more generally. According to a Relational Account of perceptual experience, the experiential relations which obtain between perceiving subjects and the objects perceived are special: According to Relationalism, perceptual relations are sui generis relations.
Aristotle on Thumos
Authors: Marechal Patricia 
Published: September 2024

Abstract
This chapter argues that Aristotelian thumos is a non-reducible mental phenomenon that plays a central role in Aristotle’s theory of the mind, motivation, and action. For Aristotle, thumos is not, as others have argued, a desire for the noble, social appraisal, or retaliation; rather, it is an inner drive or impulse to act. More precisely, it is an executory urge to implement or enact one’s ends or goals, whatever they are. Thumos accounts for someone’s proneness to spring into action and can contribute to bringing about rational or non-rational first-order desires. It is a second-order motivation, which both human and non-human animals have and that comes in degrees: we can be more or less thumotic, which amounts to being more or less disposed to making first-order desires effective so that they issue in actions. Aristotelian thumos, then, anticipates features of the modern notion of the will.
EmbedGEM: a framework to evaluate the utility of embeddings for genetic discovery
Authors: Mukherjee Sumit , McCaw Zachary R , Pei Jingwen , Merkoulovitch Anna , Soare Tom , Tandon Raghav , Amar David , Somineni Hari , Klein Christoph , Satapati Santhosh , Lloyd David , Probert Christopher ,   Insitro Research Team, Koller Daphne , O’Dushlaine Colm , Karaletsos Theofanis 
Published: September 2024

Abstract

Summary
Machine learning-derived embeddings are a compressed representation of high content data modalities. Embeddings can capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have a major limitation: it is unclear if genetic variants associated with embeddings are relevant to the disease or trait of interest. In this work, we describe EmbedGEM (Embedding Genetic Evaluation Methods), a framework to systematically evaluate the utility of embeddings in genetic discovery. EmbedGEM focuses on comparing embeddings along two axes: heritability and disease relevance. As measures of heritability, we consider the number of genome-wide significant associations and the mean χ2 statistic at significant loci. For disease relevance, we compute polygenic risk scores for each embedding principal component, then evaluate their association with high-confidence disease or trait labels in a held-out evaluation patient set. While our development of EmbedGEM is motivated by embeddings, the approach is generally applicable to multivariate traits and can readily be extended to accommodate additional metrics along the evaluation axes. We demonstrate EmbedGEM’s utility by evaluating embeddings and multivariate traits in two separate datasets: (i) a synthetic dataset simulated to demonstrate the ability of the framework to correctly rank traits based on their heritability and disease relevance and (ii) a real data from the UK Biobank, including metabolic and liver-related traits. Importantly, we show that greater disease relevance does not automatically follow from greater heritability.

Availability and implementation
https://github.com/insitro/EmbedGEM.
Sensitivity of CT-derived radiomic features to extraction libraries and gray-level discretization in the context of immune biomarker discovery
Authors: Ebrahimpour Leyla , Lemaréchal Yannick , Yolchuyeva Sevinj , Orain Michèle , Lamaze Fabien , Driussi Arnaud , Coulombe François , Joubert Philippe , Després Philippe , Manem Venkata S K 
Published: September 2024

Abstract

Objectives
Radiomics can predict patient outcomes by automatically extracting a large number of features from medical images. This study is aimed to investigate the sensitivity of radiomics features extracted from 2 different pipelines, namely, Pyradiomics and RaCat, as well as the impact of gray-level discretization on the discovery of immune checkpoint inhibitors (ICIs) biomarkers.

Methods
A retrospective cohort of 164 non-small cell lung cancer patients administered with ICIs was used in this study. Radiomic features were extracted from the pre-treatment CT scans. Univariate models were used to assess the association of common radiomics features between 2 libraries with progression-free survival (PFS), programmed death ligand 1 (PD-L1), and tumour infiltrating lymphocytes (CD8 counts). In addition, we also examined the impact of gray-level discretization incorporated in Pyradiomics on the robustness of features across various clinical endpoints.

Results
We extracted 1224, 441 radiomic features using Pyradiomics and RaCat, respectively. Among these, 75 features were found to be common between the 2 libraries. Our analysis revealed that the directionality of association between radiomic features and clinical endpoints is highly dependent on the library. Notably, a larger number of Pyradiomics features were statistically associated with PFS, whereas RaCat features showed a stronger association with PD-L1 expression. Furthermore, intensity-based features were found to have a consistent association with clinical endpoints regardless of the gray-level discretization parameters in Pyradiomics-extracted features.

Conclusions
This study highlights the heterogeneity of radiomics libraries and the gray-level discretization parameters that will impact the feature selection and predictive model development for biomarkers. Importantly, our work highlights the significance of standardizing radiomic features to facilitate translational studies that use imaging as an endpoint.

Advances in knowledge
Our study emphasizes the need to select stable CT-derived handcrafted features to build immunotherapy biomarkers, which is a necessary precursor for multi-institutional validation of imaging biomarkers.
External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images
Authors: van Dijk David P J , Volmer Leroy F , Brecheisen Ralph , Martens Bibi , Dolan Ross D , Bryce Adam S , Chang David K , McMillan Donald C , Stoot Jan H M B , West Malcolm A , Rensen Sander S , Dekker Andre , Wee Leonard , Olde Damink Steven W M ,   the Body Composition Collaborative, Tweed Thais T T , Tummers Stan , van der Kroft Gregory , Ligthart Marjolein A P , Aberle Merel R , Tim Lubbers , Bongers Bart C , Ubachs Jorne , Kruitwagen Roy F P M , Pugh Siân , Primrose John N , Bridgewater John A , Pucher Philip H , Curtis Nathan J , Dreyer Stephan B , Kazmierski Michael 
Published: September 2024

Abstract

Objectives
Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images.

Methods
Expert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2535 patients with abdominal cancer. DLNN performance was evaluated with (geometric) dice similarity (DS) and Lin’s concordance correlation coefficient.

Results
There was a strong concordance between automatic and manual segmentations with median DS for SM, VAT, and SAT of 0.97 (IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: SM 0.964 (0.959-0.968), VAT 0.998 (0.998-0.998), and SAT 0.992 (0.991-0.993). Bland-Altman metrics indicated only small and clinically insignificant systematic offsets; SM radiodensity: 0.23 Hounsfield units (0.5%), SM: 1.26 cm2.m−2 (2.8%), VAT: −1.02 cm2.m−2 (1.7%), and SAT: 3.24 cm2.m−2 (4.6%).

Conclusion
A robustly-performing and independently externally validated DLNN for automated body composition analysis was developed.

Advances in knowledge
This DLNN was successfully trained and externally validated on several large patient cohorts. The trained algorithm could facilitate large-scale population studies and implementation of body composition analysis into clinical practice.
Yttrium cuprates modification in linear generator application for power generation
Authors: Emetere Moses Eterigho 
Published: September 2024

Abstract
Yttrium barium copper oxide (YBCO) is used for special applications in linear generators because of its excitation loss, lower weight, and higher efficiency. These qualities enable the compound to operate better than the conventional copper wire coil in the stator unit of the linear generator. However, the continuous use of YBCO in linear generators has a fundamental challenge that affects industrial production and material stability after prolonged use. This paper seeks to sustain the adoption of YBCO by improving its quality for linear generator applications. The yttrium cuprates modification (YBYbCO) was synthesized using the solid-state reaction technique by doping YBCO with ytterbium. The crystalline structure, microstructural pattern, and stability of the new sample were adequately measured and found to be structurally stable to ensure durability. It was reported that applying YBYbCO in the linear generator would lead to a 200% increase in energy generation. The higher number of particles and lower individual particulate resistance enable it to withstand chemical pressure, thereby prolonging the lifespan of the linear generator.