bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semi‐Parametric Least‐Area Linear‐Circular Regression Through Möbius Transformation
Authors: Biswas Surojit , Banerjee Buddhananda 
Published: January 2025

ABSTRACTThis paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized Möbius transformation to define the regression curve. By mapping the real axis to the circle, the model effectively captures the relationship between linear and angular components. A key innovation is the introduction of an area‐based loss function, inspired by the geometry of a curved torus, for efficient parameter estimation. The semi‐parametric nature of the model eliminates the need for specific distributional assumptions about the angular error, enhancing its versatility. Extensive simulation studies, incorporating von Mises and wrapped Cauchy distributions, highlight the robustness of the framework. The model's practical utility is demonstrated through real‐world data analysis of Bitcoin and Ethereum, showcasing its ability to derive meaningful insights from complex data structures.
Reference‐Based Multiple Imputation for Longitudinal Binary Data
Authors: Cro Suzie , Quartagno Matteo , White Ian R. , Carpenter James R. 
Published: January 2025

ABSTRACTIntroductionIn clinical trials, a treatment policy strategy is often used to handle treatment nonadherence. However, estimation in this context is complicated when data are missing after treatment deviation. Reference‐based multiple imputation has been developed for the analysis of a longitudinal continuous outcome in this setting. It has been shown that Rubin's variance estimator ensures that the proportional loss of information due to missing data is approximately the same as that seen in analysis under the missing‐at‐random assumption for a broad range of commonly used reference‐based alternatives; that is it is information anchored. However, the best way to implement reference‐based multiple imputation for longitudinal binary data is unclear.MethodsWe formulate and describe two algorithms for implementing reference‐based multiple imputation for longitudinal binary outcome data using: (i) joint modeling with the multivariate normal distribution and an adaptive rounding algorithm and (ii) joint modeling with a latent multivariate normal model. A simulation study was performed to compare the properties of the two methods.ResultsAcross the broad range of scenarios evaluated, the latent normal approach typically gave slightly less bias; both methods provided approximately information anchored inference. The advantage of the latent normal approach was more marked with a rarer outcome. However, both approaches may not perform satisfactorily if the outcome prevalence is very rare, that is, .DiscussionReference‐based multiple imputation provides a practical information anchored tool for inferences about the treatment effect for a treatment policy estimand with a longitudinal binary outcome. The latent multivariate normal model is the preferred implementation.
A Varying‐Coefficient Additive Hazard Model for Recurrent Events Data
Authors: Da Zhao , Xiaochao Xia , Jialiang Li 
Published: January 2025

ABSTRACTThe additive hazard model, which focuses on risk differences rather than risk ratios, has been widely applied in practice. In this paper, we consider an additive hazard model with varying coefficients to analyze recurrent events data. The model allows for both varying and constant coefficients. We first propose an estimating equation‐based approach with spline basis smoothing for all functional coefficients. Then, we provide theoretical justifications for the resulting estimates, including consistency, rate of convergence, and asymptotic distribution. Furthermore, we construct a Cramér–von Mises test procedure to investigate whether the functional coefficients should be treated as constant, and its asymptotic null distribution is also derived. Extensive simulation experiments are conducted to evaluate the finite‐sample performance of the proposed approaches. A Chronic Granulotamous Disease data set was analyzed to illustrate our methodology.
Bioequivalence Design With Sampling Distribution Segments
Authors: Hagar Luke , Stevens Nathaniel T. 
Published: January 2025

ABSTRACTIn bioequivalence design, power analyses dictate how much data must be collected to detect the absence of clinically important effects. Power is computed as a tail probability in the sampling distribution of the pertinent test statistics. When these test statistics cannot be constructed from pivotal quantities, their sampling distributions are approximated via repetitive, time‐intensive computer simulation. We propose a novel simulation‐based method to quickly approximate the power curve for many such bioequivalence tests by efficiently exploring segments (as opposed to the entirety) of the relevant sampling distributions. Despite not estimating the entire sampling distribution, this approach prompts unbiased sample size recommendations. We illustrate this method using two‐group bioequivalence tests with unequal variances and overview its broader applicability in clinical design. All methods proposed in this work can be implemented using the developed dent package in R.
Supervised Functional Principal Component Analysis Under the Mixture Cure Rate Model: An Application to Alzheimer'S Disease
Authors: Feng Jiahui , Shi Haolun , Ma Da , Faisal Beg Mirza , Cao Jiguo 
Published: January 2025

ABSTRACTBrain imaging data is one of the primary predictors for assessing the risk of Alzheimer's disease (AD). This study aims to extract image‐based features associated with the possibly right‐censored time‐to‐event outcomes and to improve predictive performance. While the functional proportional hazards model is well‐studied in the literature, these studies often do not consider the existence of patients who have a very low risk and are approximately insusceptible to AD. We introduce a functional mixture cure rate model that extends the proportional hazards model by allowing a proportion of event‐free patients. We propose a novel supervised functional principal component analysis (sFPCA) method to extract image features associated with AD risk while accounting for the complexity arising from right censoring. The proposed method accommodates the irregular boundary issue inherent in brain images with bivariate splines over triangulations. We demonstrate the advantages of the proposed method through extensive simulation studies and provide an application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
Semiconducting Overoxidized Polypyrrole Nano‐Particles for Photocatalytic Water Splitting
Authors: Yuan Xiaojiao , Lopez Girlie Eunice , Duong Viet‐Dung , Remita Samy , Dragoe Diana , Ihiawakrim Dris , Ersen Ovidiu , Dappe Yannick , Leibl Winfried , Remita Hynd , Aukauloo Ally 
Published: January 2025

AbstractCapturing sunlight to fuel the water splitting reaction (WSR) into O2 and H2 is the leitmotif of the research around artificial photosynthesis. Organic semiconductors have now joined the quorum of materials currently dominated by inorganic oxides, where for both families of compounds the bandgaps and energies can be adjusted synthetically to perform the Water Splitting Reaction. However, elaborated and tedious synthetic pathways are necessary to optimize the photophysical properties of organic semiconductors. This study reports here, that when pyrrole dissolved distilled water is exposed to high energy radiation, this leads to the formation of nanostructured spherical polypyrrole (Nano‐PPy) particles that are characterized as overoxidized polypyrrole. Electrochemical studies and Tauc's plot highlight the production of a semiconducting material with a bandgap of ≈1.8 eV with the conduction band at ≈−0.5 V and a valence band at ≈+1.3 V vs NHE. When suspended in water and under irradiation at wavelengths higher than 420 nm, Nano‐PPy materials lead to O2 evolution, while electrons and protons can be recovered in the form of reduced quinone. Interestingly, upon intermittent visible irradiation and dark phases, a consumption of the evolved O2 from oxidation of water is observed. This concomitant O2 reduction is found to produce H2O2.
Systematic Study of the Synthesis of Monodisperse CsPbI3 Perovskite Nanoplatelets for Efficient Color‐Pure Light Emitting Diodes
Authors: Litvin Aleksandr P. , Guo Jie , Wang Jianxun , Zhang Xiaoyu , Zheng Weitao , Rogach Andrey L. 
Published: January 2025

AbstractMetal halide perovskite nanoplatelets (NPls) possess ultra‐narrow photoluminescence (PL) bands tunable over the entire visible spectral range, which makes them promising for utilization in light‐emitting diodes (LEDs) with spectrally pure emission colors. This calls for development of synthetic methods toward perovskite NPls with a high degree of control over both their thickness and lateral dimensions. A general strategy is developed to obtain such monodisperse CsPbI3 NPls through the control over the halide‐to‐lead ratio during heating‐up reaction. The excess of iodine precursor changes the chemical equilibrium, thus yielding monodisperse (3 monolayers in thickness) CsPbI3 NPls whose PL width constitutes ≈22 nm, while the lateral dimensions of NPls are determined by choice of precursor and by the reaction temperature. Postsynthetic cation exchange on the A‐site of the perovskite lattice allows for the tuning of the PL peak position, while simultaneous removal of the excess ligands and the surface passivation allows for improvement of the PL quantum yield to 96% and ensures superior stability of optical properties upon storage. Electroluminescent LEDs with the peak values are fabricated for the external quantum efficiency and luminance being 9.45% and 29800 cd m−2, respectively, and a narrow (≈26 nm) electroluminescence peak at 601 nm.
Dissecting Key Factors Influencing Electrochemical Conversion of Carbon Solutions over Nickel Single Atoms
Authors: Feng Lan‐Hui , Wang Ningru , Zou Ye‐Bin , Li Yanwei , Hu Xin‐Ming 
Published: January 2025

AbstractThe direct electrochemical conversion of bicarbonate solutions (i.e., captured CO2) has emerged as a sustainable approach for integrating CO2 capture and utilization compared to the traditional independent and sequential route. However, the process of bicarbonate conversion is poorly understood, impeding the development of this new technology. Here, the study explores the critical factors influencing bicarbonate solution conversion over Ni single‐atom catalysts, including catalyst structure, carbon source, electrolysis temperature, and electrolyzer type. The catalyst with better exposure of Ni sites and smaller charge transfer resistance exhibits higher activity for the conversion of both gaseous CO2 and bicarbonate solutions. In situ Raman spectroscopy and DFT calculations reveal that bicarbonate conversion follows a two‐step pathway, that is, bicarbonate dissociation to generate CO2 followed by CO2 reduction to form CO over Ni sites. Increasing the electrolysis temperature promotes the dissociation of HCO3− and boosts the CO production. More interestingly, switching the Ar/CO2 atmosphere affects the efficiency of CO production in H‐cell but has no influence in membrane electrode assembly cell. Such a phenomenon is attributed to different CO2 sources. This work sheds light on the electrochemical conversion of various carbon solutions and establishes the connection between the conversion of gaseous CO2 and captured CO2.
A Versatile Dual‐Responsive Shape‐Memory Gripper via Additive Manufacturing Toward High‐Performance Cross‐Scale Objects Maneuvering
Authors: Wu Sizhu , Fang Jinpeng , Gao Xueli , Liu Ruixiang , Pei Feng , Li Chuanzong , Chen Chao 
Published: January 2025

AbstractSmart grippers serving as soft robotics have garnered extensive attentions owing to their great potentials in medical, biomedical, and industrial fields. Though a diversity of grippers that account for manipulating the small objects (e.g., tiny micrometer‐scale droplets) or the big ones (e.g., centimeter‐scale screw) has been proposed, however, cross‐scale maneuvering of these two species leveraging an all‐in‐one intelligent gripper is still challenging. Here, a magnet/light dual‐responsive shape‐memory gripper (DR‐SMG) is reported, based on the hybrid of Fe‐nanoparticles and shape‐memory polymers. Thanks to its photothermal effect, the closed‐state DR‐SMG switches to the open state under the synergetic cooperation of near‐infrared‐ray (NIR) and a circinate magnetic field, referring to the temporary state. On the other hand, once the NIR is loaded, the temporary opened DR‐SMG would reconfigure to its permanent closed state owing to shape‐memory effect. Leveraging this principle, DR‐SMG can grasp and release diverse cross‐scale objects ranging from micrometers to centimeters including metals, glass balls, polymers, and small liquids. Significantly, this versatile DR‐SMG is capable of spatially delivering multifunctional chemical droplets and conductive liquid metals, thereby enabling lab‐on‐chip and electrical switch applications. This work provides new insights into intelligent grippers and further advances the field of soft robotics.
Interface Engineering and Modulation of Nickel Oxide for High Air‐Stable p‐Type Crystalline Silicon Solar Cells
Authors: Li Le , Xu Jiahui , Wang Jilei , Bao Shaojuan 
Published: January 2025

AbstractDopant‐free passivating contact crystalline silicon solar cells hold the potential of higher efficiency and cost down. In the hole‐transport terminal, one still faces the challenge of trade‐off between efficiency and stability. In this work, a H‐Al2O3/NiOx/Ni stacked hole‐transport layer is proposed, where the H‐Al2O3 standing for H‐rich Al2O3 film can effectively reduce the interfacial defects and the high work function Ni metal results in a low contact resistance of 47.12 mΩ cm2. Consequently, the solar cell achieves an efficiency of 20.51%, with a fill factor of 84.83%, demonstrating satisfactory stability. This work provides a strategy for reducing interfacial defects and highlights the potential of stacked structure design for enhancing passivated contact solar cell performance.
Large AI Models and Their Applications: Classification, Limitations, and Potential Solutions
Authors: Bi Jing , Wang Ziqi , Yuan Haitao , Shi Xiankun , Wang Ziyue , Zhang Jia , Zhou MengChu , Buyya Rajkumar 
Published: January 2025

ABSTRACTBackgroundIn recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation models, and multimodal LMs. They are updated and iterated at a very fast pace. These LMs can accomplish many tasks, e.g., daily work assistant, intelligent customer service, and intelligent factory scheduling. Their development has contributed to various industries in human society.AimsThe architectural flaws of LMs lead to several problems, including illusions and difficulty in locating errors, limiting their performance. Solving these problems properly can facilitate their further development.MethodsThis work first introduces the development of LMs and identifies their current problems, including data and energy consumption, catastrophic forgetting, reasoning ability, localization fault, and ethical problems. Then, potential solutions to these problems are provided, including increase data and computation capability, neural‐symbolic synergy, and data orientation to human pattern.DiscussionThis work discusses developing vertical domain LMs on top of some base LMs. In addition, this work introduces three typical real‐world applications of LMs, including autonomous driving, smart industrial productions, and intelligent medical assistance.ConclusionBy embracing the advantages of LMs and solving their fundamental problems, many industries are expected to achieve promising prospects in the future.
The Ocean Incubator Network (OIN) Project and the Toolkit
Authors: Vita Laura , Poto Margherita Paola , Panieri Giuliana 
Published: January 2025

Abstract
This chapter serves as the foundational entry point into understanding the Ocean Incubator Network (OIN) Project and this Toolkit as its milestone. Section 1 provides a comprehensive overview of the OIN Project, detailing its vision, objectives, key project results, and milestones. Following this, Sect. 2 explains the purpose of this Toolkit, which stands as a principal milestone of the project. It outlines the structure of the Toolkit and offers a preliminary summary of its contents, setting the stage for detailed exploration in subsequent chapters. In Sect. 3, the chapter concludes with reflections on potential future applications and developments of both the network and the Toolkit.
As an extensive guide endorsed by UNESCO to advance ocean literacy, the publication directly addresses Challenges 9 and 10 of the Ocean Decade.
Semantic-Enhanced Digital Twin for Industrial Working Environments
Authors: Yang Chao , Guo Qize , Yu Hao , Chen Yan , Taleb Tarik , Tammi Kari 
Published: January 2025

Abstract
Real-time data from diverse Internet of Things (IoT) sensors (such as cameras, temperature, light, and air quality sensors) is essential for monitoring smart manufacturing environments. However, efficiently perceiving, integrating, and interpreting this data remains a challenge, as it involves dealing with heterogeneous data formats, ensuring data accuracy, and providing real-time analytics. This paper proposes a semantic-enhanced Digital Twin (DT) to address these complexities and aims to offer a comprehensive view of industrial working environments. The paper first presents a conceptual overview of the semantic-enhanced DT architecture, followed by a detailed description of the system architecture, encompassing edge, cloud, and interface modules. Additionally, the implementation of the entire system is presented. The results demonstrate the feasibility of the proposed DT, showing its potential for deployment in real-world scenarios.
Identity and Trust Architecture for Device Lifecycle Management
Authors: Marín Frutos Agustín , García Rodríguez Jesús , Skarmeta Antonio , Loupos Konstantinos , Vavilis Sokratis 
Published: January 2025

Abstract
The Internet of Things (IoT) paradigm has become widespread, and only expected to increase in magnitude. The need for security and privacy measures adapted to this kind of environments has become apparent. This paper presents a comprehensive identity and trust architecture for managing the lifecycle of IoT devices, developed within the H2020 project ERATOSTHENES. The architecture focuses on secure device identity management, trust evaluations, and seamless interactions within multi-domain IoT environments. As part of the innovation, the solution includes the integration of self-sovereign identity principles and technologies such as Decentralized Identifiers, Verifiable Credentials and distributed ledger technologies. The proposed architecture leverages advanced cryptographic techniques and Physical Unclonable Functions to enhance security and privacy. The identity solution is complemented with a trust framework that continuously evaluates the domain’s participants, establishing a zero-trust approach. This is enhanced with cyberthreat monitoring, sharing and mitigating tools to provide security across different planes. Thus, the framework addresses critical security and privacy requirements withing the lifecycle of devices, ensuring robust and scalable IoT device management from their initial bootstrapping to their decommissioning.
Bakry-Émery curvature sharpness and curvature flow in finite weighted graphs: theory
Authors: Cushing David , Kamtue Supanat , Liu Shiping , Münch Florentin , Peyerimhoff Norbert , Snodgrass Ben 
Published: January 2025

Abstract
In this sequence of two papers, we introduce a curvature flow on (mixed) weighted graphs which is based on the Bakry-Émery calculus. The flow is described via a time-continuous evolution through the weighting schemes. By adapting this flow to preserve the Markovian property, its limits turn out to be curvature sharp. Our aim is to present the flow in the most general case of not necessarily reversible random walks allowing laziness, including vanishing transition probabilities along some edges (“degenerate” edges). This approach requires to extend all concepts (in particular, the Bakry-Émery curvature related notions) to this general case and it leads to a distinction between the underlying topology (a mixed combinatorial graph) and the weighting scheme (given by transition rates). We present various results about curvature sharp vertices and weighted graphs as well as some fundamental properties of this new curvature flow. This paper is accompanied by another paper discussing the curvature flow implementation in Python for practical use, where we present various examples and exhibit further properties of the flow, like stability properties of curvature flow equilibria.
Scanner-based real-time three-dimensional brain + body slice-to-volume reconstruction for T2-weighted 0.55-T low-field fetal magnetic resonance imaging
Authors: Uus Alena , Neves Silva Sara , Aviles Verdera Jordina , Payette Kelly , Hall Megan , Colford Kathleen , Luis Aysha , Sousa Helena , Ning Zihan , Roberts Thomas , McElroy Sarah , Deprez Maria , Hajnal Joseph , Rutherford Mary , Story Lisa , Hutter Jana 
Published: January 2025

Abstract

Background
Motion correction methods based on slice-to-volume registration (SVR) for fetal magnetic resonance imaging (MRI) allow reconstruction of three-dimensional (3-D) isotropic images of the fetal brain and body. However, all existing SVR methods are confined to research settings, which limits clinical integration. Furthermore, there have been no reported SVR solutions for low-field 0.55-T MRI.

Objective
Integration of automated SVR motion correction methods directly into fetal MRI scanning process via the Gadgetron framework to enable automated T2-weighted (T2W) 3-D fetal brain and body reconstruction in the low-field 0.55-T MRI scanner within the duration of the scan.

Materials and methods
A deep learning fully automated pipeline was developed for T2W 3-D rigid and deformable (D/SVR) reconstruction of the fetal brain and body of 0.55-T T2W datasets. Next, it was integrated into 0.55-T low-field MRI scanner environment via a Gadgetron workflow that enables launching of the reconstruction process directly during scanning in real-time.

Results
During prospective testing on 12 cases (22–40 weeks gestational age), the fetal brain and body reconstructions were available on average 6:42 ± 3:13 min after the acquisition of the final stack and could be assessed and archived on the scanner console during the ongoing fetal MRI scan. The output image data quality was rated as good to acceptable for interpretation. The retrospective testing of the pipeline on 83 0.55-T datasets demonstrated stable reconstruction quality for low-field MRI.

Conclusion
The proposed pipeline allows scanner-based prospective T2W 3-D motion correction for low-field 0.55-T fetal MRI via direct online integration into the scanner environment.

Graphical Abstract