bluuebunny/crossref_metadata_2025_split huggingface dataset:
Separability probability of two-qubit states
Authors: Thanh Huong Ha , The Khoi Vu 
Published: October 2024

Abstract
Many works have been devoted to determining the volume of the set of separable states and the separability probability both analytically and numerically. However, computing the exact separability probability remains a highly nontrivial open problem. In these notes, we examine this problem in the simplest context of two-qubit states. A novel aspect of our method is the use of the Duistermaat-Heckman measure, which is the push-forward of Liouville measure on the coadjoint orbit along the moment map. On each coadjoint orbit, the Hilbert-Schmidt measure differs from the Duistermaat-Heckman measure by a constant. The latter measure can be explicitly computed by using tools from symplectic geometry. We confirm the conjecture that the separability probability of two-qubit states with Hilbert-Schmidt measure is

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Exploring uncertainty of small-scale blue swimming crab fishery (Portunus pelagicus) toward local blue economy: The case of Pati Coastal Waters, Central Java, Indonesia
Authors: Nurhijayat A , Adrianto L , Zairion Z , Salim M G , Zulfikar A 
Published: October 2024

Abstract
Blue swimming crab (BSC) fisheries are characterized as small-scale, with limited biological and socio-economic data. Information limitations, such as those related to administrative inputs, have become a pressing issue in understanding the sustainability of BSC fisheries in coastal Pati Regency. This study aimed to analyze fishery uncertainty. The research was conducted from December 2020 to March 2021 in Alasdowo Village, Dukuhseti District, Pati Regency. Biological data was collected from the daily logbooks of BSC middlemen, encompassing the catch of 368 fishermen from January 2019 to March 2021. Socio-economic data was obtained through structured interviews and focus group discussions. The production value of BSC in zone 2 was approximately 724.6 ± 889.7 kg/day, with a coefficient of variability of 1.23. The number of fishing trips was 188 ± 137, with a coefficient of variability of 0.73. The productivity value was approximately 3.2 ± 3.7 kg/trip, with a coefficient of variability of 1.16. The BSC fishery using trap-net gear in zone 2 exhibits high uncertainty, as evidenced by the high coefficients of variability. Exploring fishery uncertainty can be a valuable component of fisheries management through a social-ecological systems approach.
Metal-free ultrathin terahertz absorber with independently tunable dual narrow bands
Authors: Verma Vikrant , Varshney Gaurav 
Published: October 2024

Abstract
A technique is introduced to precisely control the resonance behaviour of a metal-free graphene-based terahertz absorber by independently tuning the dual resonant peaks. The proposed ultrathin absorber features a multilayer configuration, with two isolated resonator layers of patterned graphene on dielectric (SiO2) substrates and a thin graphite sheet at the bottom serving as a reflector. The stacked arrangement enables independent tunability of the high-absorptivity resonant peaks at 7.33 and 9.34 THz. The structure, with a thickness of just λ/15 of the free-space wavelength, offers a compact design suitable for space-constrained applications. Its symmetrical geometry ensures polarization insensitivity and stable performance for incident angles up to 60°. Simulated results, analyzed via CST Studio and validated with an equivalent circuit model, demonstrate excellent thermal stability. Furthermore, the narrowband response of the proposed absorber improves its sensitivity to refractive index (RI) variations induced by biomolecular interactions, validating its suitability in biosensing applications. The absorber demonstrates peak absorption across both the resonant frequencies with an analyte optimized at 1.5 μm thickness. Sensitivity levels of 1.1 and 1.05 THz/RIU along with figure-of-merit values of 2.11 and 2.23 are recorded for the lower and upper bands, respectively. The absorber offers enhanced selectivity due to low values of full width at half maximum. High Q-factors of 12.85 and 19.3 confirm its strong potential for RI sensing.
Computed tomography employing sensing of high energy particle current
Authors: Zygmanski Piotr , Brivio Davide , Hoegele Wolfgang 
Published: October 2024

Abstract

Objective. We demonstrate High Energy Current Computed Tomography (HEC-CT) employing megavoltage linac x-rays. Approach. Using deterministic radiation transport we simulate two-parameter HEC-CT projections and using inverse Fourier transform we reconstruct two distinct material parameters for water phantoms with ICRP tissue inserts and materials of different atomic number Z. The HEC-CT projections are obtained by beam scanning and rotating the object. Main Results. The first HEC-CT material parameter is alike the standard attenuation coefficient with dependence on atomic number and material density similar to the Hounsfield Units. The second material parameter has opposite trends and does not find any analogy in the standard CT framework. Significance. New CT method has been invented for medical imaging or non-destructive testing. The key feature of the technique is a two-value CT reconstruction based on particle current instead of transmitted dose.
Development of a distributed hydrological model combined with sediment transport calculations and its application to a medium-scale basin in northeast China
Authors: Huang Jinbai , Wang Qingming , Feng Shaoyuan , Wang Bin , Wen Jiawei , Zhang Gengxi 
Published: October 2024

Abstract
An accurate assessment of sediment transport can assist land and resource managers in any region. The primary objective of this study was to develop a distributed hydrological model for calculating rainfall-runoff coupled with sediment transport to provide a practical method for estimating sediment transport in western Heilongjiang Province, China. The Alun River Basin, which has typical meteorological and hydrological characteristics of western Heilongjiang Province, was chosen as the study area. A sediment transport calculation module was developed based on physically based formulas for the suspended load and bedload, which was coupled with a numerical model of the physical distributed rainfall-runoff process. The model’s ability was validated by comparing the sediment sampling survey results of the particle size group distribution (PSGD) in the flow and the corresponding value of the model calculation results for a control case. The root mean square error (RMSE) and the mean absolute error (MAE) were mostly <10%. The model was used to calculate the rainfall-runoff combined with sediment transport in 2016, and the results indicate that the primary form of sediment transport was as suspended load. The suspended load accounted for over 90% of the total sediment transport caused by the maximum flood peak, and the suspended load accounted for over 80% during the period of normal water level across the selected sections. The sediment transport process was dominated by the runoff process. The contribution of the monthly sediment transport in winter to the annual total value was mostly <1%; while in the rainy season, the contribution of the monthly sediment transport to the annual total value was >15%. The results provide a basis for the assessment of sediment transport and the development of a numerical platform for modeling hydrological processes coupled with land surface processes in western Heilongjiang Province, China.
Cyclic loading effects on the heat-generation performance of carbon nanotube-embedded cement composites
Authors: Bang Jinho , Jang Daeik , Yang Beomjoo 
Published: October 2024

Abstract
This study investigates the heat-generation stability of carbon nanotube (CNT)/cement composites after exposing to cyclic loading conditions. The specimens were fabricated with varying CNT contents and levels of fly ash replacement. Results showed that increasing CNT content reduced electrical resistivity, while the impact on the electrical characteristics was found to be insubstantial, even though a considerable portion of fly ash was replaced. In addition, the electrical resistivity of the specimens after exposed to cyclic loading increased. Electrical heating tests revealed both negative and positive temperature coefficient effects depending on the applied voltages. Higher CNT contents improved the heat-generation capability, but the heating capability decreased after exposed to the cyclic loadings which is deduced from the damage of CNT networks during cyclic loadings. In this regard, the authors concluded that the heat-generation stability can be significantly affected by the applied loadings. Thus, the future research will be conducted to improve the heat-generation stability of the cement-based electrical heating systems as exposed to artificial deteriorations.
Unpaired image translation to mitigate domain shift in liquid argon time projection chamber detector responses
Authors: Huang Yi , Torbunov Dmitrii , Viren Brett , Yu Haiwang , Huang Jin , Lin Meifeng , Ren Yihui 
Published: October 2024

Abstract
Deep learning algorithms often are developed and trained on a training dataset and deployed on test datasets. Any systematic difference between the training and a test dataset may severely degrade the final algorithm performance on the test dataset—what is known as the domain shift problem. This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. Typically, the domain shift problem is solved through various domain adaptation (DA) methods. However, these methods are often tailored for a specific downstream task, such as classification or semantic segmentation, and may not easily generalize to different tasks. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image (UI2I) translation techniques, designed to find translations between different image domains in a fully unsupervised fashion. In this study, the approach is applied to a domain shift problem commonly encountered in Liquid Argon Time Projection Chamber (LArTPC) detector research when seeking a way to translate samples between two differently distributed LArTPC detector datasets deterministically. This translation allows for mapping real-world data into the simulated data domain where the downstream algorithms can be run with much less domain-shift-related performance degradation. Conversely, using the translation from the simulated data to a real-world domain can increase the realism of the simulated dataset and reduce the magnitude of any systematic uncertainties. To evaluate the quality of the translations, we use both pixel-wise metrics and a downstream task to measure the effectiveness of UI2I methods for mitigating the domain shift problem. We adapted several popular UI2I translation algorithms to work on scientific data and demonstrated the viability of these techniques for solving the domain shift problem with LArTPC detector data. To facilitate further development of DA techniques for scientific datasets, the ‘Simple Liquid-Argon Track Samples’ dataset used in this study is also published.
Efficient mapping of phase diagrams with conditional Boltzmann Generators
Authors: Schebek Maximilian , Invernizzi Michele , Noé Frank , Rogal Jutta 
Published: October 2024

Abstract
The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences. However, the computational prediction of the relative stability between phases based on their free energy is a daunting task, as traditional free energy estimators require a large amount of simulation data to obtain uncorrelated equilibrium samples over a grid of thermodynamic states. In this work, we develop deep generative machine learning models based on the Boltzmann Generator approach for entire phase diagrams, employing normalizing flows conditioned on the thermodynamic states, e.g. temperature and pressure, that they map to. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples across the entire phase diagram. Using a permutation-equivariant architecture allows us, thereby, to treat solid and liquid phases on the same footing. We demonstrate our approach by predicting the solid–liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods while significantly reducing the number of energy evaluations needed.
A facile approach for growing Ag nano-dendrites employing towards E. coli cells monitoring using SERS: a proof-of-concept study
Authors: Murugan Dhatchayani , Diwan Aarti , Chithravel Akila , Shekhawat Abhishek S , Singh Neetika , Lakkakula Satish , Gowrishankar Shanmugaraj , Srivastava Tulika , Saxena Shailendra K , Shrivastav Anand M 
Published: October 2024

Abstract
Here, a highly efficient and inexpensive technique to fabricate silver dendritic nanostructures with unique numerous lateral branches has been demonstrated. The study utilized the metal-assisted growth (MAG) technique, which is a simple, one-step technique that allows exact control over the size and morphology of the generated dendrites. The prepared dendrites are characterized using various techniques and the fabrication mechanism is well explored. The final synthesized Ag dendrites are then utilized to detect in-house cultured E. coli. cells using surface-enhanced Raman scattering (SERS) activity. The proposed reason for their superior SERS performance is due to the contribution from the large amount of plasmon-active ‘hotspots’ present in dendritic structures at the sharp edges enhancing the nearby local electromagnetic fields. The proposed work suggests Ag dendritic nanostructures as a potential candidate for SERS-based applications.
Effect of Cd-doping on optoelectronic properties of ZnO thin films and their application for MAPbI3 photodetector
Authors: Rana Ankur , Rana Megha , Malik Riya , Khanna Suraj P , Srivastava R , Suman C K 
Published: October 2024

Abstract
The Sol–gel method was used to synthesize Cd-doped ZnO nanoparticles at different doping concentrations. The nanomaterials crystal structure and microstructure were explained by XRD and SEM analysis of the materials. The absorption and transmission spectra were analyzed to explore the optical properties of Cd-doped ZnO thin films. The band gap of nanomaterials decreases from 3.26 to 3.12 eV with the increase of Cd doping concentration in ZnO. The Cd-doped ZnO shows an increasing trend of electrical conductivity and mobility with the increase of Cd concentration in ZnO. The Cd-doped ZnO-based MAPbI3 photodetectors show substantial responsivity in the wavelength range of 365 to 635 nm. The highest responsivity for devices FTO/ Zn1-xCdxO/MAPbI3/Al, having x = 0.05, 0.10 and 0.15, upon 465 nm wavelength (4 mW cm−2) illumination are ∼0.192, 0.272, and 2.1 μA W−1, respectively. The LDR of the x = 0.15 Cd-doped ZnO photodetector is two times higher than the x = 0.05 concentration of Cd doped ZnO photodetectors. Our studies confirm that the Cd-doped ZnO creates band narrowing and may be used for suitable perovskite photodetector.
Multi-omics integration analysis reveals the role of N6-methyladenosine in lncRNA translation during glioma stem cell differentiation
Authors: Zhang Meng , Cai Runqiu , Liu Jingjing , Wang Yulan , He Shan , Wang Quan , Song Xiaofeng , Wu Jing , Zhao Jian 
Published: October 2024

Abstract
Glioblastoma is one of the most lethal brain diseases in humans. Although recent studies have shown reciprocal interactions between N6-methyladenosine (m6A) modifications and long noncoding RNAs (lncRNAs) in gliomagenesis and malignant progression, the mechanism of m6A-mediated lncRNA translational regulation in glioblastoma remains unclear. Herein, we profiled the transcriptomes, translatomes, and epitranscriptomics of glioma stem cells and differentiated glioma cells to investigate the role of m6A in lncRNA translation comprehensively. We found that lncRNAs with numerous m6A peaks exhibit reduced translation efficiency. Transcript-level expression analysis demonstrates an enrichment of m6A around short open reading frames (sORFs) of translatable lncRNA transcripts. Further comparison analysis of m6A modifications in different RNA regions indicates that m6A peaks downstream of sORFs inhibit lncRNA translation more than those upstream. Observations in glioma-associated lncRNAs H19, LINC00467, and GAS5 further confirm the negative effect of m6A methylation on lncRNA translation. Overall, these findings elucidate the dynamic profiles of the m6A methylome and enhance the understanding of the complexity of lncRNA translational regulation.
mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development
Authors: Zhang Heming , Cao Dekang , Chen Zirui , Zhang Xiuyuan , Chen Yixin , Sessions Cole , Cruchaga Carlos , Payne Philip , Li Guangfu , Province Michael , Li Fuhai 
Published: October 2024

Abstract

Motivation
Multi-omics data, i.e. genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining critical biomarkers. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. Nevertheless, it is nontrivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models.

Results
To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of The Cancer Genome Atlas (TCGA) and Alzheimer’s disease (AD) samples.

Availability and implementation
The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.
An adaptive null proportion estimator for false discovery rate control
Authors: Gao Zijun 
Published: October 2024

Abstract
The false discovery rate is a commonly used criterion in multiple testing, and the Benjamini–Hochberg procedure is a standard approach to false discovery rate control. To increase its power, adaptive Benjamini–Hochberg procedures, that use estimates of the null proportion, have been proposed. A particularly popular approach being that based on Storey’s estimator. The performance of Storey’s estimator hinges on a critical hyperparameter, such that a pre-fixed configuration may lack power and existing data-driven hyperparameters may compromise false discovery rate control. In this work, we propose a novel class of adaptive hyperparameters and establish the false discovery rate control of the associated adaptive Benjamini–Hochberg procedure using a martingale argument. Within this class of data-driven hyperparameters, we further present a specific configuration designed to maximize the number of rejections and characterize its convergence to the optimal hyperparameter under a mixture model. The proposed method exhibits significant power gains, particularly in cases with a conservative null distribution, which are common in composite null testing, or with a moderate proportion of weak nonnulls, as is typically observed in biological experiments with enrichment processes.
Bayesian network-guided sparse regression with flexible varying effects
Authors: Ren Yangfan , Peterson Christine B , Vannucci Marina 
Published: October 2024

ABSTRACT
In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genomics or imaging studies. We distinguish between the predictors, which are the features utilized in the outcome prediction model, and the subject-level covariates, which modulate the effects of the predictors on the outcome. We construct a varying coefficients modeling framework where we infer a network among the predictor variables and utilize this network information to encourage the selection of related predictors. We employ variable selection spike-and-slab priors that enable the selection of both network-linked predictor variables and covariates that modify the predictor effects. We demonstrate through simulation studies that our method outperforms existing alternative methods in terms of both feature selection and predictive accuracy. We illustrate VERGE with an application to characterizing the influence of gut microbiome features on obesity, where we identify a set of microbial taxa and their ecological dependence relations. We allow subject-level covariates, including sex and dietary intake variables to modify the coefficients of the microbiome predictors, providing additional insight into the interplay between these factors.