bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geometry transition in spinfoams
Authors: Christodoulou Marios , D’Ambrosio Fabio , Theofilis Charalampos 
Published: July 2024

Abstract
We show how the fixed-spin asymptotics of the EPRL model can be used to perform the spin-sum for spin foam amplitudes defined on fixed two-complexes without interior faces and contracted with coherent spin-network states peaked on a discrete simplicial geometry with macroscopic areas. We work in the representation given in (Han and Krajewski 2014 Class. Quantum Grav.
31 01500). We first rederive the latter in a different way suitable for our purposes. We then extend this representation to 2-complexes with a boundary and derive its relation to the coherent state representation. We give the measure providing the resolution of the identity for Thiemann’s state in the twisted geometry parametrization. We then piece together these with other results in the literature and show how the spin sum can be performed analytically using the model asymptotics. These results are relevant to analytic investigations regarding the transition of a black hole to a white hole geometry. In particular, this work was the basis of the calculation presented in Christodoulou and D’Ambrosio (2018 (arXiv:1801.03027)).
Design, development, and testing of active valve piezo-hydraulic pump
Authors: Nayak Sandesh , Rao Muralidhara 
Published: July 2024

Abstract
Valves play a major role in rectifying the fluid flow through piezo-hydraulic pumps, which may be active or passive. Passive valves operate due to changes in the fluid pressure whereas active valves are operated through control signals. This paper presents a novel design of an active valve chamber assembly for a piezo-hydraulic pump. It is a novel design since none of the research publications has addressed the application of a Flexurally amplified piezoelectric actuator (FAPA) for valve actuation in active valve chamber assembly. The piezo-hydraulic pump design is done using solid edge design software. FAPA’s are used for pumping and valve actuation. Structural analysis with experimental verification for the valve chamber component is presented. The valve opening and the pumping displacement are mathematically modeled in MATLAB Simulink software. Also, mathematical simulations for pump flow rates at different actuation frequencies and voltage are discussed. The piezo-hydraulic pump components are fabricated and assembled with the actuators. Experimental investigations on the flow rate performance of the active valve piezo-hydraulic pump are carried out with changing frequency and voltage amplitude of actuators. The interpretation of results is done by comparing the experimental and simulation results. During experiments, the pump delivered a maximum flow rate of 4.5 ml min−1 at 140 V when actuated at 0.25 Hz actuation frequency.
Immobilizing antibody biorecognition layers on Au–TiO2 thin films: direct (physisorption) vs. DSP-crosslinking (chemisorption) surface functionalization
Authors: Meira Diana I , Barbosa Ana I , Proença Manuela , Pereira-Silva Patrícia , Borges Joel , Correlo Vitor M , Reis Rui L , Vaz Filipe 
Published: July 2024

Abstract
The immobilization design strategy plays an important role in biosensor development and its sensing performance. Both adsorption (physisorption) and cross-linker functionalization (chemisorption) are common approaches for immobilizing a bioreceptor layer. In this work, these two approaches were studied and compared, envisaging a functional and strongly attached bioreceptor layer onto sputtered Au–TiO2 thin films. DSP cross-linker (Lomant’s reagent) was used in the thin film’s functionalization, and the effect of different concentrations on the development of an adhesion layer was investigated. Surface morphology analysis of functionalized thin films suggested the development of uniformly coated self-assembled layers. However, DSP islands with a fractal structure were found for a concentration of 4 mg ml−1. Infrared spectroscopy confirmed the cross-linker functionalization at the thin film’s surface. Confocal microscopy of immobilized fluorescent antibodies revealed that DSP islands improve the chemical surface area available for bioreceptor immobilization. Moreover, an immunoassay using mouse IgG interaction with fluorescent anti-mouse IgG (Fab specific), working as capture and detection antibody, respectively, showed that DSP functionalization favors antibodies orientation and adhesion strength to the surface, when compared to physisorption.
Cooling of hadronic stars with dark matter components
Authors: Fibger Matheus , Negreiros Rodrigo , Lourenço Odilon , Dutra Mariana 
Published: July 2024

Abstract
Neutron stars, due to their extreme densities and pressures, act as unique laboratories for the study of dense matter. Recent research has introduced the notion of dark matter (DM) particles being part of neutron stars’ composition, thus providing a novel path for investigating this mysterious universe component. This research builds on previous studies and shifts the focus towards exploring the effects of DM mixed with hadrons on the thermal evolution of stars. It involves analyzing the cooling curves of these stars and matching them with observed data from thermally emitting compact objects. This study will demonstrate that, despite being thermally inert, DM as postulated in this model can indirectly influence the thermal evolution of neutron stars. It will be illustrated that DM has the potential to modify the thermal relaxation time and expand the range of temperatures that neutron stars of various masses can exhibit. Through this comparative analysis, the model’s precision will be evaluated, and the properties of DM particles will be further delineated. The goal of this study is to deepen our comprehension of neutron stars and the influence of DM on their thermal evolution.
Exploring charge transport dynamics in a cryogenic P-type germanium detector
Authors: Acharya P , Fritts M , Mei D-M , Wang G-J , Mahapatra R , Platt M 
Published: July 2024

Abstract
This study explores the dynamics of charge transport within a cryogenic P-type Ge particle detector, fabricated from a crystal cultivated at the University of South Dakota. By subjecting the detector to cryogenic temperatures and an Am-241 source, we observe evolving charge dynamics and the emergence of cluster dipole states, leading to the impact ionization process at 40 mK. Our analysis focuses on crucial parameters: the zero-field cross-section of cluster dipole states and the binding energy of these states. For the Ge detector in our investigation, the zero-field cross-section of cluster dipole states is determined to be 8.45 × 10−11 ± 4.22 × 10−12 cm2. Examination of the binding energy associated with cluster dipole states, formed by charge trapping onto dipole states during the freeze-out process, reveals a value of 0.034 ± 0.0017 meV. These findings shed light on the intricate charge states influenced by the interplay of temperature and electric field, with potential implications for the sensitivity in detecting low-mass dark matter.
Multi-weighted symbolic sequence entropy: a novel approach to fault diagnosis and degradation monitoring of rotary machinery
Authors: Wu Hongan , Yuan Rui , Lv Yong , Stein Dalton L , Zhu Weihang 
Published: July 2024

Abstract
Structural health monitoring relies heavily on measurements. Entropy theory is emerging as a critical quantitative analysis technique for interpreting measured data for both health monitoring and fault identification of structural systems. This paper introduces the algorithms of weighted symbolic sequence entropy (WSSE) and its multi-weighted derivation, termed multi-weighted symbolic sequence entropy (MWSSE). WSSE optimizes the existing algorithms, including symbolic sequence entropy and improved symbolic sequence entropy, by introducing a weighting factor in the computation process of the algorithm. WSSE significantly contributes to enlarging the difference between normal and fault sequences of signals in rotary machinery. The multi-weighted derivation of WSSE, namely MWSSE, more effectively depicts the dynamical characteristics of rotary machinery by utilizing entropy values of multiple weighting factors as health indicators. The MWSSE-based degradation monitoring and fault identification approaches developed by the authors are validated through application research and comparative analysis. These novel entropy algorithms offer innovative quantitative analysis techniques for fault diagnosis in rotary machinery and structural health monitoring and hold potential for application in broader research fields.
REDef-DETR: real-time and efficient DETR for industrial surface defect detection
Authors: Li Dejian , Jiang Changhong , Liang Tielin 
Published: July 2024

AbstractIndustrial surface defect detection is an important part of industrial production, which aims to identify and detecting various defects on the surface of product to ensure quality and meet customer requirements. With the development of deep learning and image processing technologies, the surface defect detection methods based on computer vision has become the mainstream method. However, the prevalent convolutional neural network-based defect detection methods also have many problems. For example, these methods rely on post-processing of Non-Maximum Suppression and have poor detection ability for small targets, which affects the speed and accuracy of surface defect detection in industrial scenarios. Therefore, we propose a novel DEtection TRansformer-based surface defect detection method. Firstly, we propose a Multi-scale Contextual Information Dilated module and fuse it into the backbone. The module is mainly composed of large kernel convolutions, which aims to expand the receptive field of the model, thus reducing the leakage rate of the model. Moreover, we design an efficient encoder which mainly contains two important modules, namely feature enhancement based on cascaded group attention module and efficient feature fusion module based on content-aware. The former module effectively enhances the high-level semantic information extracted by the backbone, thus enabling the model to better interpret features, and it can improve the problem of high computational cost of transformer encoder, thus increasing the detection speed. The latter module performs multi-scale feature fusion across the feature information of various scales, thus improving the detection accuracy of the model for small-size defects. Experimental results show that the proposed method achieves 80.6%mAP and 80.3FPS on NEU-DET, and 98.0%mAP and 79.4FPS on PCB-DET. Our proposed method exhibits excellent detection performance and achieves real-time and efficient surface defect detection capability to meet the needs of industrial surface defect detection.
A route for tunable C-VO doped perovskite SrTiO3 functionalities through epitaxial strain engineering
Authors: AL-Hamadany Raied A S , Goss J P , Briddon P R 
Published: July 2024

Abstract
Using density functional theory calculations, the interaction between oxygen vacancies and carbon in bi-axially strained SrTiO3 has been investigated. CTi-Vo binding energy and reorientation of CTi have been examined for ±1%, ±2%, ±3% and ±4% compressive and tensile strains. The results show that compressive strain is an effective route to restrict the diffusion of electrically active Vo in the presence of CTi dopants. According to our results the reorientation barrier volumetric and bi-axial strain dependences differ in the compression and tension regimes and is expected to affect dielectric characteristics of SrTiO3 under an alternating electric field.
Atomic layer deposition of Al-doped ZnO nanomembrane with in situ monitoring
Authors: Wang Jinlong , Gu Zilong , Zhao Zhe , Mei Yu , Ke Xinyi , Chen Yihao , Huang Gaoshan , Mei Yongfeng 
Published: July 2024

Abstract
Due to shortcomings such as poor homogeneity of Al doping, precisely controlling the thickness, inability to conformally deposit on high aspect ratio devices and high pinhole rate, the applications of Al-doped ZnO (AZO) nanomembrane in integrated optoelectronic devices are remarkably influenced. Here, we report in situ monitoring during the atomic layer deposition (ALD) of AZO nanomembrane by using an integrated spectroscopic ellipsometer. AZO nanomembranes with different compositions were deposited with real-time and precise atomic level monitoring of the deposition process. We specifically investigate the half-reaction and thickness evolution during the ALD processes and the influence of the chamber temperature is also disclosed. Structural characterizations demonstrate that the obtained AZO nanomembranes without any post-treatment are uniform, dense and pinhole-free. The transmittances of the nanomembranes in visible range are >94%, and the optimal conductivity can reach up to 1210 S cm−1. The output of current research may pave the way for AZO nanomembrane to become promising in integrated optoelectronic devices.
DeCoGAN: MVCT image denoising via coupled generative adversarial network
Authors: Zhang Kunpeng , Niu Tianye , Xu Lei 
Published: July 2024

Abstract

Objective. In helical tomotherapy, image-guided radiotherapy employs megavoltage computed tomography (MVCT) for precise targeting. However, the high voltage of megavoltage radiation introduces substantial noise, significantly compromising MVCT image clarity. This study aims to enhance MVCT image quality using a deep learning-based denoising method. Approach. We propose an unpaired MVCT denoising network using a coupled generative adversarial network framework (DeCoGAN). Our approach assumes that a universal latent code within a shared latent space can reconstruct any given pair of images. By employing an encoder, we enforce this shared-latent space constraint, facilitating the conversion of low-quality (noisy) MVCT images into high-quality (denoised) counterparts. The network learns the joint distribution of images from both domains by leveraging samples from their respective marginal distributions, enhanced by adversarial training for effective denoising. Main Results. Compared to an analytical algorithm (BM3D) and three deep learning-based methods (RED-CNN, WGAN-VGG and CycleGAN), the proposed method excels in preserving image details and enhancing human visual perception by removing most noise and retaining structural features. Quantitative analysis demonstrates that our method achieves the highest peak signal-to-noise ratio and Structural Similarity Index Measurement values, indicating superior denoising performance. Significance. The proposed DeCoGAN method shows remarkable MVCT denoising performance, making it a promising tool in the field of radiation therapy.
GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer
Authors: Duan Chunyan , Liu Qiantuo , Wang Jiajie , Tong Qianqian , Bai Fangyun , Han Jie , Wang Shouyi , Hippe Daniel S , Zeng Jing , Bowen Stephen R 
Published: July 2024

Abstract

Objective. Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer. Approach. Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression. Main results. GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58–0.86 vs. 0.52–0.78, p = 0.170–0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162–0.192) performed better numerically for low-dimensional models (p = 0.097–0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189–0.219, p < 0.004). Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.
Uncertainty quantification via localized gradients for deep learning-based medical image assessments
Authors: Schott Brayden , Pinchuk Dmitry , Santoro-Fernandes Victor , Klaneček Žan , Rivetti Luciano , Deatsch Alison , Perlman Scott , Li Yixuan , Jeraj Robert 
Published: July 2024

Abstract

Objective. Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model’s output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termed Local Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation. Approach. This method leverages a trained model’s localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood. Main results. (1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g. 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p < 0.05 vs p > 0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%. Significance. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.
Mathematical analysis of FLASH effect models based on theoretical hypotheses
Authors: Hu Ankang , Zhou Wanyi , Qiu Rui , Li Junli 
Published: July 2024

Abstract

Objective. Clinical applications of FLASH radiotherapy require formulas to describe how the FLASH radiation features and other related factors determine the FLASH effect. Mathematical analysis of the models can connect the theoretical hypotheses with the radiobiological effect, which provides the foundation for establishing clinical application models. Moreover, experimental and clinical data can be used to explore the key factors through mathematical analysis. Approach. We abstract the complex models of the oxygen depletion hypothesis and radical recombination-antioxidants hypothesis into concise mathematical equations. The equations are solved to analyze how the radiation features and other factors influence the FLASH effect. Then we propose methodologies for determining the parameters in the models and utilizing the models to predict the FLASH effect. Main results. The formulas linking the physical, chemical and biological factors to the FLASH effect are obtained through mathematical derivation of the equation. The analysis indicates that the initial oxygen concentration, radiolytic oxygen consumption and oxygen recovery are key factors for the oxygen depletion hypothesis and that the level of antioxidants is the key factor for the radical recombination-antioxidants hypothesis. According to the model derivations and analysis, the methodologies for determining parameters and predicting the FLASH effect are proposed: (1) the criteria for data filtration, (2) the strategy of hybrid FLASH and conventional dose rate (CONV) irradiation to ensure the acquisition of effective experimental data across a wide dose range, (3) the pipelines of fitting parameters and predicting the FLASH effect. Significance. This study establishes the quantitative relationship between the FLASH effect and key factors. The derived formulas can be used to calculate the FLASH effect in future clinical FLASH radiotherapy. The proposed methodologies guide to obtain sufficient high-quality datasets and utilize them to predict the FLASH effect. Furthermore, this study indicates the key factors of the FLASH effect and offers clues to further explore the FLASH mechanism.
Interference-induced suppression of particle emission from a Bose–Einstein condensate in lattice with time-periodic modulations
Authors: Lai 赖 Long-Quan 龙泉 , Li 李 Zhao 照 
Published: July 2024

Abstract
Emission of matter-wave jets from a parametrically driven condensate has attracted significant experimental and theoretical attention due to the appealing visual effects and potential metrological applications. In this work, we investigate the collective particle emission from a Bose–Einstein condensate confined in a one-dimensional lattice with periodically modulated interparticle interactions. We give the regimes for discrete modes, and find that the emission can be distinctly suppressed. The configuration induces a broad band, but few particles are ejected due to the interference of the matter waves. We further qualitatively model the emission process and demonstrate the short-time behaviors. This engineering provides a way to manipulate the propagation of particles and the corresponding dynamics of condensates in lattices, and may find application in the dynamical excitation control of other nonequilibrium problems with time-periodic driving.