bluuebunny/crossref_metadata_2025_split huggingface dataset:
Testing for the Functional Form of a Continuous Covariate in the Shared‐Parameter Joint Model
Authors: Piulachs Xavier , El Ghouch Anouar , Van Keilegom Ingrid 
Published: February 2025

ABSTRACTShared‐parameter joint modeling is a useful technique for properly associating longitudinal and time‐to‐event data. When the interest is in the survival outcome, the conditional logarithm of the hazard function for an event is conventionally presumed to be linearly related over time to a set of explanatory covariates, among other terms. However, this hypothesis is quite restrictive and may yield misleading results. Our objective here is to easily check such a modeling assumption for any continuous fixed covariate. For this purpose, we examine the appropriateness of a nonparametric test criterion based on a penalty‐modified version of the Akaike information criterion. An extensive numerical study is conducted to check the validity of the test within the joint modeling framework, while determining the extent to which the function embedding the continuous covariate deviates from linearity. Furthermore, once a deviation from linearity is detected, the improvement in the model's predictive performance is examined. The usefulness of the testing procedure is illustrated using a clinical trial with HIV‐infected subjects. Specifically, our example focuses on properly accounting for the effect of nadir CD4 cell count within a predictive joint model for the time to immune recovery.
Near‐infrared Emission Carbon Dots Derived from Bromo‐Substituted Perylene Derivatives with Simultaneously High Type I/II ROS Generation for Effective Bacterial Elimination and Tumor Ablation
Authors: Jiang Shichao , Li Gaowei , Yang Mei , Su Borui , Xiao Jiamei , Ding Jie , Wei Dan , Sun Jing , Wu Chengheng , Fan Hongsong 
Published: February 2025

AbstractBacterial infections and tumor tissues are characterized by complex microenvironments with uneven oxygen availability. Effective photodynamic therapy for these conditions requires photosensitizers that can perform optimally within such environments, specifically by generating both type I and II reactive oxygen species (ROS) simultaneously. Carbon dots (CDs), a type of fluorescent nanomaterial smaller than 10 nm, are commonly used to treat bacterial infections and tumors. However, their current limitations, such as short maximum absorption and emission wavelengths, significantly restrict their therapeutic efficacy in deep tissues. In response to these challenges, a new type of fluorescent carbon dots with near‐infrared (NIR) absorption and emission properties is reported, featuring a maximum emission peak beyond 700 nm (NIR‐I region). These CDs offer strong tissue penetration and reduced tissue absorption advantages. Additionally, bromine atom doping significantly enhances the generation of type I and II ROS through efficient photodynamic processes. In vitro studies demonstrated their high photodynamic efficacy in antibacterial and antitumor applications. Ultimately, these findings translate into significant therapeutic effectiveness for treating skin infections and tumors in vivo. This study employs bromine‐doped CDs nanomaterials, which demonstrate maximum fluorescence emission in the NIR region, to achieve efficient photodynamic treatment of bacterial infections and tumor ablation in complex microenvironments.
Ultrafast Formation of Microdroplet Arrays with Chemical Gradients for Label‐Free Determination of Enzymatic Reaction Kinetics
Authors: Breitfeld M. , Dietsche C. L. , Saucedo‐Espinosa M. A. , Berlanda S. F. , Dittrich P. S. 
Published: February 2025

AbstractHigh throughput assays including enzymatic reactions are usually conducted in multiwell plates and analyzed in plate readers. This approach has limitations in i) upscaling and ii) the choice of reactions, as labeled compounds are required. A technique is introduced to rapidly generate dense microdroplet arrays by stream shearing (MASS). The fluid is delivered via a capillary onto a glass plate that carries a pattern of hydrophilic spots surrounded by a hydrophobic coating. Moving the glass plate shears off droplets from the fluid stream that are retained on the hydrophilic spot. Arrays of up to 24 192 homogenous droplets (coefficient of variation: 2.9 %) with defined volumes from 280 to 980 pL are generated in less than 22 min. Thereby, the droplet content is varied, and fine chemical gradients are obtained across the plate, which are analyzed with both fluorescence microscopy and matrix‐assisted laser desorption/ionization mass spectrometry (MALDI‐MS). This method is employed for label‐free kinetic studies of an enzymatic reaction. For the cleavage of angiotensin II‐fluorescein by the angiotensin II‐converting enzyme, a maximum reaction velocity (vmax) of 5.7 µm min−1 is determined and the Michaelis‐Menten constant (KM) of 84.8 µm is found. The platform can be further upscaled for biochemical assays as required for drug discovery and protein engineering.
Sensitive and Anisotropic Room‐Temperature Terahertz Photodetection in Quasi‐1D Nodal‐Line Semimetal NbNiTe5
Authors: Tang Keqin , Pan Xiaokai , Dong Zhuo , Yang Liu , Wang Dong , Hou Xingang , Wang Pengdong , Fang Yong , Wang Junyong , Wang Lin , Zhang Kai 
Published: February 2025

AbstractLow‐dimensional topological materials merge the benefits of reduced dimensionality with the nontrivial topological phases, garnering significant attention as promising candidates for next‐generation optoelectronic devices. The quasi‐1D nodal‐line semimetal NbNiTe5 showcases distinct in‐plane anisotropy alongside robust Dirac nodal‐line points, rendering it a fascinating platform for exploring the intricate interplay between novel quantum states of matter and low‐energy radiation. Here, sensitive and anisotropic terahertz photodetection driven by Dirac fermions and the intrinsic anisotropic properties of NbNiTe5 are presented. Leveraging the enhanced carrier transport characteristics derived from nontrivial band topology, room‐temperature responsivity of 1.36 A W−1, noise equivalent power of 5.31 × 10−11 W Hz−1/2 as well as fast photoresponse speed of 4.5 µs are achieved. The exceptionally high anisotropic photoresponse ratio of 84 highlights the potential for improving the performance of polarization‐sensitive photodetectors. This work is crucial for advancing the understanding of nontrivial topology and the development of terahertz photodetector technologies.
Federated Learning Architectures: A Performance Evaluation With Crop Yield Prediction Application
Authors: Mukherjee Anwesha , Buyya Rajkumar 
Published: February 2025

ABSTRACTIntroductionFederated learning has become an emerging technology in data analysis for IoT applications.MethodsThis paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short‐Term Memory Network and Gated Recurrent Unit. For centralized federated learning, multiple clients and one server are considered, where the clients exchange their model updates with the server that works as the aggregator to build the global model. For the decentralized framework, a collaborative network is formed among the devices either using ring topology or using mesh topology. In this network, each device receives model updates from the neighboring devices and performs aggregation to build the upgraded model.ResultsThe performance of the centralized and decentralized federated learning frameworks is evaluated in terms of prediction accuracy, precision, recall, F1‐Score, and training time. The experimental results show that 93% prediction accuracy is achieved using the centralized and decentralized federated learning‐based frameworks. The results also show that using centralized federated learning, the response time can be reduced by 75% than the cloud‐only framework.ConclusionCentralized and decentralized federated learning architectures show good performance in terms of prediction accuracy and loss. The training time, including communication for both case studies, is also not very high, as observed from the results. Further, as no raw data is shared, the data privacy is protected.Finally, the future research directions of the use of federated learning in crop yield prediction are proposed.
Investigation of Main Bearing Fatigue Estimate Sensitivity to Synthetic Turbulence Models Using a Novel Drivetrain Model Implemented in OpenFAST
Authors: Liverud Krathe Veronica , Jonkman Jason , Gebel Jakob , Rivera‐Arreba Irene , Nejad Amir R. , Bachynski‐Polić Erin E. 
Published: February 2025

ABSTRACTA coupled medium‐fidelity drivetrain model is developed and implemented in OpenFAST for a 10‐MW land‐based reference turbine. The implementation is verified against a fully coupled multibody wind turbine model, including a detailed drivetrain. The new model can simultaneously and accurately estimate main bearing loads and represent elastic bending of the drivetrain. It has low computational cost and is useful for early design phases, sensitivity analyses and complex systems like wind farms (where computational expense must be expended elsewhere). Here, the model is implemented for a monopile offshore wind turbine and used to investigate the sensitivity of main bearing basic rating life to different synthetic turbulence models. Large‐eddy simulations (LES) targeting stable, neutral, and unstable atmospheric conditions at below‐, near‐ and above‐rated wind speeds are used as a reference. The turbulence models recommended by the International Electrotechnical Commission, the Mann spectral tensor model, and the Kaimal spectral model with exponential coherence are fitted to the LES data. Additionally, a constrained turbulence generator, PyConTurb (short for Python Constrained Turbulence), based on LES data, is applied in the aero‐hydro‐servo‐elastic simulations. Taking PyConTurb as the baseline, the Kaimal model significantly underestimates fatigue of the downwind main bearing, with between 10% and 40% less damage. The Mann model also underestimates the downwind main bearing fatigue by up to 30%. The upwind main bearing damage is driven by mean loads, and differences between models are less significant, although the trends are similar. Reasons for these discrepancies are investigated and attributed to differences in spatial and temporal variations among the turbulence models.
Invisible Youths? Future Health and Social Care Challenges of Unaccompanied Minor Migrants in Spain
Authors: Cristóbal-Narváez Paula , Febas Fernandez Marcos , Rueda Maria José , Aparicio Pareja Lola 
Published: February 2025

Abstract
Research has shown that traveling alone, suffering adverse stressful experiences, and living separately from family are risk factors for mental health problems in youth migrants. Against this background, unaccompanied minor migrants (UMMs) are one of the most vulnerable migrant populations. However, further investigation is needed to identify the factors related to the migration process that can impact the health of this population in Spain. This chapter aims to identify the relevant social determinants of health among newly arrived UMMs, encompassing factors before, during, and after migration. This aim is accomplished using a qualitative methodology to identify the voices of UMMs. Specifically, 15 semi-structured interviews were conducted as part of the national action-research Migrasalud project (II IN 190517 EN 162 FA 01). The interviews were transcribed, translated (from Arabic to Spanish), and analyzed through content analysis. The results showed that UMMs perceived themselves as having good health before the migration process. They often had unrealistic expectations before migrating, experienced adverse experiences during the journey, and, after arrival, had to cope with post-migration risk stressors such as socio-labor difficulties and discrimination, which affected their well-being and mental health. The findings also underline some relevant health and social challenges, such as the need to assess mental health care, considering the specific stages of the migratory cycle in the UMM population from a person-centered approach, the promotion of cross-cultural mental healthcare models, and the development of policies and services to address the effects of discrimination in Spain.
Uncertainty propagation through integral inversion of satellite gradient data in regional gravity field recovery
Authors: Eshagh Mehdi , Pitoňák Martin , Novák Pavel 
Published: February 2025

Abstract
The Gravity field and steady-state Ocean Circulation Explorer (GOCE) mission, launched by the European Space Agency, provided high-quality gravitational gradient data with near-global coverage, excluding polar regions. These data have been instrumental in regional gravity field modelling through various methods. One approach involves a mathematical model based on Fredholm’s integral equation of the first kind, which relates surface gravity anomalies to satellite gradient data. Solving this equation requires discretising a surface integral and applying further regularisation techniques to stabilise the numerical solution of a resulting system of linear equations. This study examines four methods for modifying the system of linear equations derived by discretising the Fredholm integral equation. The methods include direct inversion, remove-compute-restore, truncation reduction of the integral formula, and inversion of a modified integral for estimating surface gravity anomalies from satellite gradient data over a test area in Central Europe. Since the system of linear equations is ill-conditioned, the Tikhonov regularisation is applied to stabilise its numerical solution. To assess the precision and reliability of the estimated gravity anomalies, the study introduces mathematical models for estimation of biased and de-biased noise variance–covariance matrices of estimated surface gravity anomalies. The results indicate that the signal-to-noise ratio of reduced satellite gradient data in the remove-compute-restore method is smaller compared to other methods in the study, necessitating stronger stabilisation of the model to recover surface gravity anomalies. This, in turn, leads to a more optimistic uncertainty propagation than the other considered methods.
Spectral Bounds on Hyperbolic 3-Manifolds: Associativity and the Trace Formula
Authors: Bonifacio James , Mazáč Dalimil , Pal Sridip 
Published: February 2025

Abstract
We constrain the low-energy spectra of Laplace operators on closed hyperbolic manifolds and orbifolds in three dimensions, including the standard Laplace--Beltrami operator on functions and the Laplacian on powers of the cotangent bundle. Our approach employs linear programming techniques to derive rigorous bounds by leveraging two types of spectral identities. The first type, inspired by the conformal bootstrap, arises from the consistency of the spectral decomposition of the product of Laplace eigensections, and involves the Laplacian spectra as well as integrals of triple products of eigensections. We formulate these conditions in the language of representation theory of

$$\textrm{PSL}_2(\mathbb {C})$$

PSL
2

(
C
)

and use them to prove upper bounds on the first and second Laplacian eigenvalues. The second type of spectral identities follows from the Selberg trace formula. We use them to find upper bounds on the spectral gap of the Laplace--Beltrami operator on hyperbolic 3-orbifolds, as well as on the systole length of hyperbolic 3-manifolds, as a function of the volume. Further, we prove that the spectral gap

$$\lambda _1$$

λ
1

of the Laplace--Beltrami operator on all closed hyperbolic 3-manifolds satisfies

$$\lambda _1 < 47.32$$

λ
1

<
47.32

. Along the way, we use the trace formula to estimate the low-energy spectra of a large set of example orbifolds and compare them with our general bounds, finding that the bounds are nearly sharp in several cases.
Scaling of the elastic energy of small balls for maps between manifolds with different curvature tensors
Authors: Krömer Milan , Müller Stefan 
Published: February 2025

Abstract
Motivated by experiments and formal asymptotic expansions in the physics literature, Maor and Shachar (J. Elasticity 134 (2019), 149–173) studied the behaviour of a model elastic energy of maps between manifolds with incompatible metrics. For thin objects they analysed the scaling of the minimal elastic energy as a function of the thickness. In particular they showed that for maps from a ball of radius h in an oriented Riemannian manifold to Euclidean space, the infimum of a model elastic energy per unit volume scales like the fourth power of h and after rescaling one gets convergence to a quadratic expression in the curvature tensor R(p), where p denotes the centre of the ball. In this paper we show the same result for general compact oriented Riemannian targets with R(p) replaced by a suitable difference of the curvature tensors in the target and the domain, thus answering Open Question 1 in the paper by Maor and Shachar. The result extends to noncompact targets provided they satisfy a uniform regularity condition. A key idea in the proof is to use Lipschitz approximations to define a suitable notion of convergence.
Metabolic dysfunction-associated steatotic liver disease and cardiovascular risk factors in rheumatoid arthritis
Authors: Saidi A. N. , Theel W. B. , Burggraaf B. , van der Lelij A. J. , Grobbee D. E. , van Zeben J. D. , van der Zwan-van Beek E. , Rauh S. P. , Cabezas M. Castro 
Published: February 2025

Abstract

Objectives
Rheumatoid arthritis (RA) is a chronic autoimmune disease linked with metabolic dysfunction-associated steatotic liver disease (MASLD), which may increase cardiovascular (CV) risk. This study explores the association between liver fibrosis, assessed by the Fibrosis-4 (FIB-4) index, and CV risk factors in RA patients.

Methods
Cross-sectional data from the Franciscus Rheumatoid Arthritis and Cardiovascular Intervention Study (FRANCIS), a randomized, cardiovascular single center, intervention study involving RA patients without cardiovascular disease (CVD) or type 2 diabetes (T2DM), were analyzed. Liver fibrosis was assessed using FIB-4, with a cut-off point of ≥ 1.3 to define high fibrosis risk, and its relationship with CV risk factors, medication use, and subclinical atherosclerosis, measured by carotid intima-media thickness (cIMT), was evaluated.

Results
Among 326 patients (68.4% female, age 53 ± 11 years, BMI 26.5 ± 4.5 kg/m2), those with high FIB-4 (n = 49) had higher cIMT (p = 0.002), apolipoprotein B48 (p = 0.04), systolic blood pressure (p = 0.007), alkaline phosphatase (p = 0.002), and anti-CCP levels (p = 0.02). High FIB-4 was associated with lower leukocyte count and complement component 3. Statin use was linked to higher FIB-4 (OR = 4.49, p = 0.014), while hydroxychloroquine use was associated with lower FIB-4 (OR = 0.11, p = 0.004). Disease activity scores did not differ between low and high FIB-4 groups.

Conclusions
Elevated FIB-4 in RA patients is associated with increased cIMT, higher blood pressure, and elevated atherogenic remnants. Incorporating FIB-4 measurements into routine clinical care for RA populations could effectively identify individuals at the highest CV risk, enabling the implementation of more intensive CV risk management strategies.

Key Points
• RA patients with liver fibrosis have higher cIMT, indicating greater risk of atherosclerosis.
• RA patients with liver fibrosis show accumulation of circulating atherogenic chylomicron remnants, contributing to atherogenesis.
• HCQ may provide a protective effect against liver fibrosis in RA patients.
Multiphoton tomographic analysis of hyaluronic acid delivery: comparison of carbon dioxide laser and 1927 nm thulium laser over time
Authors: Nguyen Lynhda , Mess Christian , Schneider Stefan W. , Huck Volker , Herberger Katharina 
Published: February 2025

Abstract
Hyaluronic acid injections, known as skin quality boosters (SQB), improve skin hydration and quality. Laser-assisted delivery using ablative (AFL) and non-ablative fractional lasers (NAFL) enhances the uptake of topical drugs and cosmeceuticals. This study aims to compare the biodistribution of SQB as well as the morphologic and metabolic cellular changes following CO2 and thulium (Tm) laser treatments. Healthy male and female volunteers were assigned to either the CO2 laser or the Tm laser treatment group. Areas on their forearm were marked for following treatments: laser with SQB application, laser monotherapy, topical SQB monotherapy, and untreated control. Measurements were carried out 30 min and 30 days post-treatment using multiphoton-tomography equipped with fluorescence lifetime imaging (MPT-FLIM). Eleven volunteers were included in each group. SQB penetration reached the upper dermal layer in both groups, with larger aggregates after CO2 treatment. The CO2 laser induced a higher level of inflammation, which persisted as subclinical inflammation even 30 days post-treatment. In contrast, skin treated with the Tm laser showed a metabolic state similar to the untreated control after 30 days. Both CO2 laser and Tm laser enhance cutaneous uptake of the SQB, potentially improving skin hydration and quality. While the CO2 laser may be more effective in delivering SQB, the Tm laser may be a treatment option with minimal downtime. MPT-FLIM proved to be a suitable tool for monitoring treatment outcomes.
Tierschutzethische Forderungen und rechtliche Gestaltung im Bereich der landwirtschaftlichen
Tierhaltung
Authors: Caspar Johannes 
Published: February 2025

Zusammenfassung
Die landwirtschaftliche Tierhaltung steht im Spannungsfeld zwischen ökonomischer Praxis und ethischen
Anforderungen. Der Beitrag untersucht, inwieweit Tierrechtstheorien zur Verbesserung des gesetzlichen Tierschutzes
in der Nutztierhaltung beitragen können und welche Forderungen sich daraus ableiten lassen. Zudem
wird die aktuelle Tierschutznovelle der Bundesregierung betrachtet, die jedoch daran scheitert, den ethischen
Anforderungen an eine faire Behandlung von Nutztieren gerecht zu werden.
A novel financial performance metric to minimize misclassification costs in model selection
Authors: Martin John , Abdollahian Mali , Taheri Sona , Akman David 
Published: February 2025

Abstract
A novel financial performance metric (FPM) is introduced seeking to minimise the misclassification cost arising from false positives and false negatives in credit risk assessment. Using the German Credit Dataset (GCD), important financial variables are simulated according to four different asset classes to enable a more accurate and reliable, multidimensional model selection. The misclassification cost arising from FPM is compared with commonly used statistical metrics and the credit scoring example dependent cost matrix (CSEDCM) metric. The results show that CSEDCM underestimates false prediction costs by as much as 99% compared to the FPM. A range of high- performance machine learning methods was compared using FPM and statistical metrics. The Multi-Layer Perceptron outperformed other methods on statistical metrics and overall on financial costs, while a mix of algorithms worked best on either side of the decision threshold. The results confirmed that the proposed FPM would provide a significant financial benefit to organisations.