bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is Serum Uric Acid to Creatinine Ratio Associated with Hypertension
and Metabolic Syndrome in Children with Obesity?
Authors: Ozdemir Emine Gulsah , Bulus Ayse Derya 
Published: February 2025

AbstractChildhood obesity is a global health problem with increasing prevalence,
leading to long-term cardiovascular complications. Research conducted on
adults has established a correlation between serum uric acid to creatinine
ratio (SUA/Cr) and metabolic syndrome (MetS) components. The study
investigates the relationship between SUA/Cr and hypertension (HT) and MetS
components in children with obesity.A total of 103 children with obesity who underwent ambulatory blood pressure
measurement (ABPM) were included the study and patients were divided into
two groups “HT” (n=60) and “Normal” (n=43). Demographic, anthropometric, and
laboratory characteristics were retrospectively analyzed.The study included 103 children (42 female, 61 male) with a mean age of
13.7±2.9 years. HT prevalence was significantly higher in patients with
severe obesity and dyslipidemia (p=0.045, p=0.01). Males exhibited
significantly higher SUA/Cr than females (p<0.001). However, SUA/Cr
showed no significant differences between patients with and without HT,
MetS, dyslipidemia, or hyperglycemia (p=0.69, p=0.64, p=0.90, p=0.37).
Furthermore, linear regression analysis did not establish a significant
effect of SUA/Cr on ABPM parameters (p>0.05).In our cohort, no significant association was found between SUA/Cr and HT, as
well as MetS components in children with obesity. These findings highlight
the need for further investigation into the complex mechanisms regulating
uric acid metabolism, obesity, and cardiovascular risk in children.
Monte Carlo toolkit for designing and validating step-range-filter spectrometer designs
Authors: Johnson T. M. , Lahmann B. , Russell L. , Vanderloo N. L. , Cufari M. J. , Reichelt B. L. , Chang C. W. , Birkel A. , Kabadi N. V. , Sutcliffe G. D. , Adrian P. J. , Pearcy J. A. , Kunimune J. H. , Dannhoff S. G. , Evans T. E. , Johnson M. Gatu , Séguin F. H. , Petrasso R. D. , Li C. K. , Frenje J. A. 
Published: February 2025

Here, we present a Monte Carlo toolkit for validating step range filter (SRF) spectrometer designs. Geant4 is used to transport charged particles through the SRF filters to generate synthetic SRF data that include realistic CR-39 effects. Synthetic SRF spectra generated by this method inherently account for instrument response and allow for the quantification of SRF performance before shots. The usefulness of this toolkit is demonstrated through its application to a number of problems. A new broadband SRF for the ∼10 MeV wide 3He3He proton spectrum is validated, and an analysis method for analyzing 3He3He-p SRF data that accounts for instrument response is put forth. In addition, an SRF design for the compact recoil-proton spectrometer (CRS) on the Z-machine is validated. Finally, a new calibration technique for the DD-p SRF is proposed and validated.
A possum’s cautionary tale: targeted surveys in north-eastern Victoria fail to detect Leadbeater’s possum in remnant forest within its historic range
Authors: Eyre Arabella C. , Harley Dan K. P. , Briscoe Natalie J. , Lumsden Lindy F. , McComb Leo B. , Lentini Pia E. 
Published: February 2025

Endemic to Victoria, the range-restricted, Critically Endangered Leadbeater’s possum (Gymnobelideus leadbeateri) historically had a much broader distribution from south-east of Melbourne, through the wet forests of eastern Victoria, and extending into New South Wales. Undetected populations of this cryptic species may persist within forested sections of this historic range, despite these landscapes having been subject to significant disturbance through clearing, logging, and bushfires. The adoption of arboreal camera trapping has permitted targeted surveys for Leadbeater’s possum to be conducted with greater efficiency and reliability. Between 2017 and 2019, we conducted arboreal camera-trapping surveys for Leadbeater’s possum in seven patches of potentially suitable habitat, that were 13–145 km outside the species’ known range in Victoria’s Central Highlands. Site selection was informed by habitat suitability modelling. The species was detected at only one of seven areas surveyed, the patch closest to the possum’s current known range (~13 km). Notable occurrences of other cryptic arboreal mammals were recorded in rarely surveyed habitat types and at high elevations (>1000 m). Despite north-eastern Victoria’s significant remnant forest cover, the apparent loss of Leadbeater’s possum from its historic range in this region provides a cautionary tale regarding declining forest condition owing to major disturbance from fire and logging.
CMADRL: cross-modal attention based deep reinforcement learning for mobile robot’s obstacle avoidance
Authors: Lu Zhaoqing , He Li , Wang Hongwei , Yuan Liang , Xiao Wendong , Liu Zhening , Chen Yaohua 
Published: February 2025

Abstract
The multimodal perception is crucial for mobile robots to achieve safe autonomous steering in complex and changing environments without colliding with obstacles. In spite of the fact that rich environmental information can be provided by the multimodal data, how to integrate the complementary features from different sources effectively remains an open issue. In this paper, a simple yet effective multimodal deep reinforcement learning (DRL) method, concise and effective multimodal DRL, by fusing two input modalities such as depth images and pseudo-LiDAR data generated by RGB-D cameras, is proposed to improve the performance of single sensor in environmental perception tasks. And the convolutional LSTM network layers are also adopted to extract spatiotemporal features and capture temporal relationships in consecutive time steps, which allow the robot to predict dynamic changes of the environment. In addition, A cross-modal fusion module is designed to optimize the multimodal data fusion scheme, with dynamic weighting and the merging of features from different modalities, allowing the agent to focus more reasonably on the current state and improving the accuracy and efficiency of the obstacle avoidance strategy. The experimental results show that the proposed approach achieved significant performance in cumulative rewards, convergence speed, and success rate. Additionally, tests in both simulated and real-world environments further verify that collisions are effectively avoided using only a low-cost RGB-D camera, demonstrating the method’s strong generalization capabilities.
Enhancing gas–liquid two-phase stratified flow imaging with EIGEN-NET: a fusion of deep learning and measurement matrices
Authors: Yang Xuanxuan , Chen Haofeng , Xu Gengchen , Ma Gang , Wang Xiaojie 
Published: February 2025

Abstract
Gas–liquid two-phase stratified flow is a common occurrence in various industries, demanding real-time monitoring and precise measurement for safety and operational efficiency. Current methods primarily rely on sensors or limited imaging technologies, leaving room for improvement. In this paper, we introduce the EIGEN-Net framework, specifically designed for real-time monitoring of gas–liquid stratified flow within pipelines, utilizing electrical impedance tomography (EIT). EIT, a noninvasive imaging method, shows potential despite challenges from gas affecting voltage stability in image reconstruction. EIGEN-Net capitalizes on measurement matrices and deep learning to enhance measurement accuracy. It extracts crucial physical properties characterizing liquid fractions and employs acquired voltage data to pre-reconstruct a tensor. By fusing these tensors with prior information, it significantly improves imaging outcomes, effectively addressing data insufficiency issues. The incorporation of adaptive information into deep learning networks eliminates the necessity for data classification. Moreover, this approach accommodates scenarios of continuous flow variation, enhancing imaging resolution. EIGEN-Net’s versatility extends to other two-phase flow scenarios and sensor measurement matrices, holding promise for applications in various industrial inspection domains.
EIGEN-Net:Deep Learning Fusion Network Guided by Leading Eigenvalues of Measurement Matrix
Prediction of both diffusive and hydraulic conductance in the pore network model extracted from 3D images using deep learning
Authors: Huang Xiang , Lu Bin , Yuan Zigang , Zhou Wei , Liu Bin , Jiang Kaiyong 
Published: February 2025

Abstract
Pore network modeling (PNM), with network extracted from 3D images, is widely used to study mass transport in porous media. Its accuracy heavily depends on the precise determination of hydraulic and diffusive conductance values of local pore pairs. Deep learning (DL), leveraging true pore geometry, offers a promising approach for conductance estimation. However, due to the arbitrary complexity of 3D pore pair geometries, most studies have relied exclusively on 2D cross sectional images for hydraulic conductance prediction. Therefore, in this work, a convolutional neural network was trained to estimate diffusive and hydraulic conductance values, with reference ground truth values obtained by applying the lattice Boltzmann method (LBM) on 3D pore pair geometries. The main contributions include: (1) 3D image preprocessing to align pore pair geometries with LBM fluid flow, (2) high prediction accuracy with a 3D residual neural network-18 model (R
2 = 0.95 for diffusive and R
2 = 0.94 for hydraulic conductance), and (3) insights on the importance of full 3D geometry for accurate hydraulic conductance evaluation. To evaluate the generality of our method, we calculated the permeability and diffusivity of 3D porous media across a wide range of porosities using PNM, with DL predicted local conductance values. Results show accurate diffusivity predictions, but permeability was underestimated due to over-segmentation of pore spaces, which reduced flow rate estimates.
Multi-task learning for automated contouring and dose prediction in radiotherapy
Authors: Kim Sangwook , Khalifa Aly , Purdie Thomas G , McIntosh Chris 
Published: February 2025

Abstract

Objective. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in DL, the contouring and dose prediction tasks for automated treatment planning are done independently. Approach. In this study, we applied the multi-task learning (MTL) approach in order to seamlessly integrate automated contouring and voxel-based dose prediction tasks, as MTL can leverage common information between the two tasks and be able to increase the efficiency of the automated tasks. We developed our MTL framework using the two datasets: in-house prostate cancer dataset and the publicly available head and neck cancer dataset, OpenKBP. Main results. Compared to the sequential DL contouring and treatment planning tasks, our proposed method using MTL improved the mean absolute difference of dose volume histogram metrics of prostate and head and neck sites by 19.82% and 16.33%, respectively. Our MTL model for automated contouring and dose prediction tasks demonstrated enhanced dose prediction performance while maintaining or sometimes even improving the contouring accuracy. Compared to the baseline automated contouring model with the Dice score coefficients of 0.818 for prostate and 0.674 for head and neck datasets, our MTL approach achieved average scores of 0.824 and 0.716 for these datasets, respectively. Significance. Our study highlights the potential of the proposed automated contouring and planning using MTL to support the development of efficient and accurate automated treatment planning for radiotherapy.
Transport resistance strikes back: unveiling its impact on fill factor losses in organic solar cells
Authors: Saladina Maria , Deibel Carsten 
Published: February 2025

Abstract
The fill factor (

F
F

) is a critical parameter for solar cell efficiency, but its analytical description is challenging due to the interplay between recombination and charge extraction processes. A significant factor contributing to

F
F

losses, beyond recombination, that has not received much attention is the influence of charge transport. In most state-of-the-art organic solar cells, the primary limitations of the

F
F

do not just arise from non-radiative recombination, but also from low conductivity of the organic semiconductors. A closer look reveals that even in the highest efficiency cells, performance losses due to transport resistance are significant. This finding highlights the need for refined models to predict the

F
F

accurately. Here, we extend the analytical model for transport resistance to a more general case by incorporating energetic disorder. We introduce a straightforward set of equations to predict the

F
F

of a solar cell, enabling the differentiation of losses attributed to recombination and transport resistance. Our analytical model is validated with a large set of experimental current–voltage and light intensity-dependent open-circuit voltage data for a wide range of temperatures. Based on our findings, we provide valuable insights into strategies for mitigating

F
F

losses, guiding the development of more efficient solar cell designs and optimisation strategies.
Phase field modeling of sub-parabolic oxidation kinetics and in-plane stress in zircaloy under neutron/proton irradiation
Authors: Xin Tianyuan , Kharchenko Dmitrii O , Kharchenko Vasyl O , Lu Wu 
Published: February 2025

Abstract
A detailed study of the sub-parabolic oxidation kinetics in Zircaloy system has been carried out by using phase field modeling under conditions related to oxidation in autoclave and during neutron/proton irradiation. The exploited phase field model is factored in temperature gradient and voltage change between metal and oxide surface. It is shown that the irradiation accelerates oxidation rate and induces development of conjugated in-plane stress. It is found that both in-plane and tensile stresses result in a power-law decrease in a critical time for fragmentation versus dose rate. It is shown that a critical crystallite size for the related tetragonal to monoclinic phase transformation at the oxide/metal interface decreases with the dose rate. Obtained results correlate well with experimental observations in the reference points and can be exploited to estimate corrosion resistance linked to transformation to monoclinic structure and fragmentation in zirconium-based cladding materials. This study provides a deep insight into the details of oxidation kinetics at irradiation in zirconium alloys.
Comprehensive study of highly efficient Rb2AgInX6 (X = Cl, Br, I) based lead-free double perovskite solar cells
Authors: Deswal Vishal , Baghel Sarita 
Published: February 2025

Abstract
Lead-free Double Perovskite Solar Cells (DPSCs) have generated great study interest recently as a potential perovskite absorber layer, due to their cost-effectiveness, impressive stability, and superior performance. In this study, we investigated Rb2AgInX6(Cl, Br, I) based DPSCs using SCAPS-1D simulation software. Various properties, such as the density of states for conduction and valance bands, carrier concentration, and carrier mobility (electrons and holes), were calculated for simulation. Furthermore, we have optimized defect density, electron affinity, and thickness of absorber layers for Rb2AgInX6-based DPSCs. We investigated how DPSC parameters were impacted by operating temperature and then performed band gap tuning for Rb2AgInBr6-based DPSC along with their temperature stability. Tandem solar cells (TSC) have had an enormous influence on the field of photovoltaics (PV) because of their ability to effectively use a wider range of solar spectrum and reduce unwanted energy loss. In this context, we have simulated Rb2AgInBr6/Rb2AgInI6 based an all double perovskite tandem solar cells and optimized the same for future experimental applications. The optimized PCE of 12.14%, 26.49%, and 13.95% were obtained for Rb2AgInX6(X = Cl, Br, I respectively) based DPSCs. In comparison, the PCE of 38.70% was obtained for tandem structure.
Deterministic sub-Heisenberg limit phase sensitivity with the squeezed coherent state via balanced homodyne detection
Authors: Li Chenlu , Ke Shaoqiu , Ruan Pengxiang , Jing Qingli , Zhang Mingming , Wang Houlian , Wang Yuanxiang , Liu Jun 
Published: February 2025

Abstract
We investigate the phase sensitivities of three schemes with the input of the squeezed coherent states based on the Mach–Zehnder interferometer (MZI). With employing the balanced homodyne detection, the phase sensitivities of these schemes can surpass the shot noise limit (SNL) and reach the sub-Heisenberg limit. By adjusting the combination of the squeezing parameter and the photon number of the coherent state, both the phase sensitivities with using the balanced homodyne detection and the quantum Cramer–Rao bound can be improved. In the regime of low squeezing parameters, the scheme with the two-mode squeezed coherent state outperforms the other two schemes. The phase sensitivity can only reach the sub-SNL when the photon number is much larger. These schemes exhibit strong robustness against internal losses of the MZI and photon detector losses. This work will play an important role in the field of quantum precision measurement.
Enhancing the prediction of flow characteristics in an inventive plate heat exchanger using deep learning techniques
Authors: Bakır Rezan , Aboul Khail Ahmad , Bakır Halit 
Published: February 2025

Abstract
In modern energy research, minimizing fuel usage and harmful gas emissions are critical priorities. The application of advanced deep learning (DL) models to predict thermohydraulic characteristics of an innovative plate heat exchanger (IPHE) is investigated in this study. Building upon our prior work utilizing machine learning (ML) models, the focus is placed on predicting the Nusselt Number (Nu), friction factor (f), and performance (P) within a Reynolds number range of 500 to 5000. Advanced DL architectures-GRU, LSTM, and CNN-are utilized, resulting in substantial improvements in prediction accuracy and robustness. The LSTM model demonstrates superior performance, achieving R2 scores of 0.9986, 0.9985, and 0.9968 for Nu, f, and P, respectively, significantly surpassing prior ML model results of 0.98, 0.979, and 0.9628. The findings highlight the capacity of DL models to capture complex, nonlinear relationships in thermohydraulic data, offering an enhanced approach to optimizing plate heat exchanger (PHE) performance. This work contributes to energy-efficient technological advancements, supporting global efforts to reduce environmental impacts while addressing rising energy demands.
First-principles study of the impact of rhenium and osmium on the energetics of helium clusters in tungsten
Authors: Zhou Yanyao , Huang Gui-Yang , Hu Xunxiang 
Published: February 2025

Abstract
Understanding interaction between transmutation elements and helium is critically important for precisely predicting the performance of tungsten plasma facing materials in fusion reactors. The energetics of He interstitial clusters (He
n
), He interstitial- rhenium (Re)/osmium (Os) substitutional clusters (He
n
Resub/He
n
Ossub), He interstitial-tungsten(W)/Re/Os interstitial clusters (He
n
IW/Re/Os), He-vacancy-Re/Os substitutional clusters (He
n
V1/2Resub/He
n
V1/2Ossub) and He-vacancy-W/Re/Os interstitial clusters (He
n
V1/2 IW/Re/Os) in bulk body-centered cubic tungsten were investigated by using first-principles calculations. The critical numbers of He required for the self-trapping and trap mutation reactions were calculated with/without a neighboring Re/Os substitutional as well as for the case of a neighboring W/Re/Os interstitial present, based on both dissociation and transformation mechanisms. The trapping effects of Re/Os substitutional or W/Re/Os interstitial on the mobile small He
n
clusters were discussed.
The quantum XY chain with boundary fields: finite-size gap and phase behavior
Authors: Coraggio Aldo , Pelissetto Andrea 
Published: February 2025

Abstract
We present a detailed study of the finite-size one-dimensional quantum XY chain in a transverse field in the presence of boundary fields coupled with the order-parameter spin operator. We consider fields located at the chain boundaries that have the same strength and that are oppositely aligned. We derive exact expressions for the gap Δ as a function of the model parameters for large values of the chain length L. These results allow us to characterize the nature of the ordered phases of the model. We find a magnetic (M) phase (

Δ
∼

e

−
a
L

), a magnetic-incommensurate (MI) phase (

Δ
∼

e

−
a
L

f

MI

(
L
)

), a kink (K) phase (

Δ
∼

L

−
2

) and a kink-incommensurate (KI) phase (

Δ
∼

L

−
2

f

KI

(
L
)

);

f

MI

(
L
)

and

f

KI

(
L
)

are bounded oscillating functions of L. We also analyze the behavior along the phase boundaries. In particular, we characterize the universal crossover behavior across the K-KI phase boundary. On this boundary, the dynamic critical exponent is z = 4, i.e.

Δ
∼

L

−
4

for large values of L.
Information storage and transfer in autoregulated cascades
Authors: Biswas Ayan 
Published: February 2025

Abstract
We present a Gaussian framework to investigate the role of autoregulation in information storage and transfer in one-step cascades by considering activation and inhibition of the autoregulated transcription factor and the output gene it drives. With an increasing autoregulatory synthesis rate of the transcription factor, the information stored by the output gene product and the information transferred from the transcription factor to the output increase when both biochemical species are activated or inhibited. If they are regulated differently, an increasing autoregulatory synthesis rate decreases information storage and transfer. An autoactivated transcription factor activating the output is the best architecture for storing and transferring information. Interestingly, this network is also the noisiest in the family of autoregulated cascades. To unravel this nontrivial information-noise correspondence, we provide analytical results that tie correlated and uncorrelated noise factors to information storage and transfer, respectively.
Exact solution of dynamical mean-field theory for a linear system with annealed disorder
Authors: Ferraro Francesco , Grilletta Christian , Maritan Amos , Suweis Samir , Azaele Sandro 
Published: February 2025

Abstract
We investigate a disordered multi-dimensional linear system in which the interaction parameters are colored noises, varying stochastically in time with defined temporal correlations. We refer to this type of disorder as ‘annealed’, in contrast to quenched disorder in which couplings are fixed over time. Using generating functional methods, we extend dynamical mean-field theory to accommodate annealed disorder and employ it to find the exact solution of the linear model in the limit of a large number of degrees of freedom. Our analysis yields analytical results for the non-stationary autocorrelation, the stationary variance, the power spectral density, and the phase diagram of the model. Some unexpected features emerge upon changing the correlation time of the interactions. The stationary variance of the system and the critical variance of the disorder are generally found to be non-monotonic functions of the correlation time of the interactions. We also find that a re-entrant phase transition can take place when this correlation time is varied.