bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Diseases in Embankment–Bridge Transition Section With Methodological Detection Along the Qinghai‐Tibet Railway in Permafrost Regions
Authors: He Peifeng , Niu Fujun , Dong Tianchun , Luo Jing , Jiao Chenglong , Huang Yunhui , Ding Zekun , Su Wenji 
Published: February 2025

ABSTRACTEmbankment–bridge transition sections (EBTSs) suffer from diverse engineering diseases that have escalated into one of the most severe issues along the Qinghai‐Tibet Railway (QTR). Nevertheless, the causes and mechanisms of engineering diseases in EBTSs remain limited. This study employed a methodological approach to conduct field surveys in the Tuotuo River Basin in the hinterland of the Qinghai‐Tibet Plateau (QTP). Borehole investigations and nuclear magnetic resonance (NMR) techniques accurately determined the permafrost characteristics, enabling the correction of electromagnetic wave velocity and acquisition of resistivity threshold. Ground‐penetrating radar (GPR) and quasi‐3D electrical resistivity tomography (ERT) were combined to indicate permafrost resistivity above 200 Ω‐m. It reveals that the permafrost is relatively stable across a large area on the shaded side, whereas the permafrost degradation is more pronounced on the sunny side, where the maximum active layer thickness (ALT) reaches 5.2 m. Notable permafrost degradation and substantial increases in ALT were observed near the EBTS resulting from heat absorption and thermal erosion of the groundwater. Terrestrial laser scanning (TLS) captured time‐series deformation highlights the specific displacements of the EBTS, demonstrating that the displacement is the rotational behavior of wing walls. The severe heat absorption and groundwater thermal erosion around the EBTS result in permafrost degradation and the expansion of the thawing bulbs to increased structural deformation and even failure. It was shown that permafrost degradation, moisture influence, and heat transfer characteristics are the primary contributing factors to the disease's continued deterioration, and thus reinforcement measures for existing structures need to address these three issues. The mechanisms of disease development revealed in this paper provide new insights into EBTS dynamics for the EBTS design and maintenance in permafrost regions.
Tailored Synthesis of Biogenic Gold and Silver Nanoparticles Using Camellia sinensis Extracts for Enhanced Catalytic Reduction of 4‐Nitrophenol
Authors: Ávila‐Avilés Rodolfo D. , López‐Téllez Gustavo , Vilchis‐Nestor Alfredo R. 
Published: February 2025

AbstractNoble metal nanoparticles, such as gold (AuNPs) and silver (AgNPs) nanoparticles, are valued for their optical, antimicrobial, and catalytic properties, which depend on their size and shape. This study synthesizes gold and silver nanoparticles using aqueous extracts of Camellia sinensis from four tea types (white, black, green, and red) and evaluates their catalytic efficiency in the reduction of 4‐nitrophenol to 4‐aminophenol. UV–vis spectroscopy confirmed nanoparticle formation, with surface plasmon resonance peaks at 530–534 nm for gold nanoparticles and 427–442 nm for silver nanoparticles, indicating mostly spherical morphologies. Transmission electron microscopy revealed that gold nanoparticles synthesized with white tea had an average diameter of 29.46 ± 53.92 nm, while silver nanoparticles had an average diameter of 33.11 ± 18.50 nm. The catalytic evaluation using sodium borohydride as a reducing agent showed a significant correlation between extract composition, nanoparticle size, and catalytic activity (p < 0.05). White tea favored the fastest nanoparticle formation (0.5 hours), while red tea showed the slowest synthesis. Pearson correlation analysis suggests that tea type influences nanoparticle size, morphology, and catalytic performance. This study highlights the potential of Camellia sinensis extracts for the eco‐friendly synthesis of nanoparticles with tunable properties for catalytic applications.
Combined Activity‐Based Therapy and Cervical Spinal Cord Stimulation: Active Ingredients, Targets and Mechanisms of Actions to Optimize Neurorestoration of Upper Limb Function After Cervical Spinal Cord Injury
Authors: Gopaul Urvashy , Bayley Mark Theodore , Kalsi‐Ryan Sukhvinder 
Published: February 2025

ABSTRACTBackgroundImproving hand and arm function is an important goal for individuals with cervical spinal cord injury (cSCI). Activity Based Therapy (ABT) is a neurorestorative approach that incorporates a high intensity, long duration and effortful engagement to garner sensory‐motor improvements. Spinal cord stimulation is a neuromodulation modality that can restore sensory‐motor function. Spinal cord stimulation can elevate the excitability of the spinal neural network and potentially enhance the neurorestorative benefits of ABT. However, there is scarce evidence on the combined effects of ABT and spinal cord stimulation on UL recovery after cSCI.ObjectiveThis report aims to describe how theory informed the design and development of a Phase 1 study on a new UL intervention combining ABT and transcutaneous cervical spinal cord stimulation (tCSCS) (short form:ABT‐tCSCS) delivered simultaneously for individuals with cSCI.MethodThe design of the ABT‐tCSCS was guided by theory‐based frameworks such as the Rehabilitation treatment specification system and the Template for Intervention Description and Replication guide. The ABT‐TCSCS aimed to improve somatosensory‐motor deficits and function in the UL after cSCI. The ABT‐tCSCS intervention was developed through the following stages: (a) Description of the active ingredients, mechanism of action, and targets of the ABT‐tCSCS; (b) Tailoring of ABT‐tCSCS; and (c) Development of treatment regimen guidelines for the delivery of the ABT‐tCSCS.ResultsABT constitutes 4 types of exercises, including cardio‐fitness, resistance, postural/weightbearing, and functional exercises, for activation of the neuromuscular system below the level of lesion to target somatosensory‐motor impairments. In tCSCS, electrical stimulation is delivered at a frequency of 30–50 Hz at 500–1000 μs between C3‐C7. The spinal neural networks of the cervical region are stimulated to neuromodulate the descending motor commands which control the muscles. ABT‐tCSCS will be delivered simultaneously over 28 sessions (1 h/session, 3x/week over 9–10 weeks).ConclusionsCombined ABT‐tCSCS is a new intervention for neurorestoration of the upper limbs after cSCI.Trail RegistrationClinicalTrials.gov ID: NCT06472986
Risk Factors of CVD in Different Ethnic Groups in Kyrgyzstan
Authors: Azfar Hossain Syed , Ibrahim Muiz , Dzhusupov Kenesh , Orru Hans , Villa Inga , Orru Kati 
Published: February 2025

ABSTRACTIntroductionCardiovascular disease (CVD) is the leading cause of mortality in Kyrgyzstan. The study aimed to explore the CVD prevalence depending on various risk factors across diverse ethnic groups in Kyrgyzstan.Materials and MethodsA cross‐sectional study was carried out among six ethnic groups in Kyrgyzstan, aged 18 years and above. The sample was stratified for age, education, family status, and income. We used a questionnaire with 47 questions to explore the health status, behavior and lifestyle determinants, and prevalence of CVD. We used the chi‐square test to investigate differences between groups, and ANCOVA to determine differences between mean scores on analyzed variables. Logistic regression was used to analyze the relationship between independent and dependent variables. Confounding variables were only included if they correlated with both the independent and dependent variables. Interaction analyses were conducted with logistic regression to investigate if there were any differences between the nationalities in the relationships between the independent and dependent variables.ResultsOur study confirmed that the CVD prevalence across diverse ethnic groups can be significantly different: In Kyrgyzstan, the most disadvantaged groups in this context were East European and the least—Western Asian. There was no difference between studied groups found in the ethnicity‐stratified prevalence of such CVD risk factors as low fruit and vegetable consumption, alcohol intake, smoking, and body mass index (BMI). There was no statistically significant association between educational attainment and CVD risk markers within the studied ethnic groups. The analysis of ethnicity‐stratified prevalence of CVD risk markers resulted in a significant difference in physical activity across ethnicity groups.ConclusionThe study results provided an understanding of the ethnicity‐stratified prevalence of CVD risk markers in the population in Kyrgyzstan. They could serve as instrumental in tailoring targeted public health interventions to address the burden of CVDs in specific subpopulations.
Molecular Structure and Mass Spectral Data Quality–Driven Processing of High‐Resolution Mass Spectrometry for Quantitative Analysis
Authors: Fontaine Fabien , Morettoni Luca , Anderson Ken , Choi Bernard , Zamora Ismael , Bateman Kevin P. 
Published: February 2025

ABSTRACTRationaleLC‐MS‐based quantification is traditionally performed using selected or multiple reaction monitoring (SRM/MRM) acquisition functions on triple quadrupole (QQQ) instruments resulting in both high sensitivity and selectivity. This workflow requires a previously identified reaction or transition from a precursor ion to a fragment ion to be monitored to obtain the needed selectivity for the compound of interest. High‐resolution mass spectrometry (HRMS) has long sought to be a viable alternative for quantitatipve workflows but has been unable to broadly compete, mainly due to the lack of suitable data processing software.MethodsThe approach we developed agnostically and automatically identifies all ions related to the compound being analyzed in both the MS and MSMS data, acquired with data‐dependent or data‐independent methods. The algorithm automatically selects optimal parameters (ion extraction window, ions to sum, etc.) to provide the best overall method to meet the acceptance criteria defined by the user (accuracy/precision).ResultsThe results obtained are directly compared to QQQ data collected from the same set of samples and show that the automated HRMS approach is as good as and, in some cases, better than the traditional QQQ approach in terms of selectivity, sensitivity, and dynamic range.ConclusionsThis new methodology enables the use of generic methods for data collection for quantitative analysis using high‐resolution mass spectrometry. With this approach, data collection is faster, and the processing algorithm provides quality equal to or better than the current QQQ methodology. This enables an overall reduction in cycle time and improved assay performance versus current HRMS‐based quantitative analysis as well as traditional QQQ workflows.
Neuroadaptive Impulsive Control of a Class of Uncertain Nonlinear Systems
Authors: Lin Ming , Luo Zhen‐Fa , Tao Jie , Li Jun‐Yi , Liu Yong‐Hua 
Published: February 2025

ABSTRACTThis paper addresses the tracking control problem for a class of uncertain nonlinear systems, offering a neuroadaptive impulsive control solution with fast weight convergence. The proposed control architecture uniquely employs radial basis function neural networks (RBFNNs) to approximate unknown system dynamics, where the neural network (NN) weight estimator is updated at each impulsive instant. Unlike existing neuroadaptive controllers, this method quickly identifies unknown dynamics without inducing high‐frequency oscillations due to the impulse update of the NN weight estimator, thereby improving the transient performance of the closed‐loop system. Leveraging the Lyapunov stability theory for impulsive dynamical systems, this paper rigorously establishes the semi‐global uniform ultimate boundedness (SGUUB) of all closed‐loop system signals. Finally, validation through simulation studies substantiates the efficacy of the proposed neuroadaptive impulsive controller.
Dynamic Event‐Triggered Adaptive Neural Network Control for Stochastic Nonholonomic Uncertain Systems
Authors: Du Qinghui , Wang Sitian , Zhu Quanxin 
Published: February 2025

ABSTRACTIn this article, we construct a dynamic event‐triggered adaptive control strategy for a class of stochastic nonholonomic uncertain systems. The dynamic event‐triggered mechanism can make the threshold adjustable, which is firstly considered for stochastic nonholonomic uncertain systems to conserve communication resources. We propose a state‐input scaling transformation that converts the stochastic nonholonomic uncertain systems into a new form that facilitates controller design. By introducing a novel auxiliary dynamic variable to design a dynamic event‐triggered mechanism (DETM) and defining a suitable parameter, we propose a new dynamic event‐triggered adaptive neural network controller, which contains only one adaptive law. It is shown that the proposed control strategy can greatly reduce the computational complexity and communication burden, and the input‐to‐state stability (ISS) assumption is no longer needed. Simultaneously, all signals in the closed‐loop system are ensured to be uniformly ultimately bounded (UUB) in probability. Then, the uncontrollability phenomenon is eliminated by constructing an adaptive event‐triggered control‐based switching strategy. In addition, the efficacy of the proposed controller is demonstrated through simulation results.
“I Want Them to Feel Alegría”: Rhetorical Contextual Languaging as Critical Translingual Assemblages
Authors: Zapata Angie 
Published: February 2025

AbstractIn this article, the author explores rhetorical contextual languaging (as it relates to writing) as critical translingual assemblages of material ecologies↔affectual solidarities. Situated in the scholarship on critical translingual literacies, affect, and assemblage theory, two data‐theory intervals are featured as exemplars. Data are drawn from global sociopolitical movements and from one Midwest U.S. classroom of adolescent youth enacting multilingual, multimodal, and material literacies. For each interval presented, the author thinks with theory‐data to animate the critical translingual assemblages that are produced among writers of diverse racial and linguistic backgrounds, their shared affects and solidarities, and the material landscapes in which they are immersed. She concludes by discussing critical translingual assemblages as an ontological shift in multilingual writing research and as liberatory pedagogies that resist separating writers from the rhetorical contextual languaging and sensations demanded of translingual writing/processes.
Instability of the AUROC of Clinical Prediction Models
Authors: van Leeuwen Florian D. , Steyerberg Ewout W. , van Klaveren David , Wessler Ben , Kent David M. , van Zwet Erik W. 
Published: February 2025

ABSTRACTBackgroundExternal validations are essential to assess the performance of a clinical prediction model (CPM) before deployment. Apart from model misspecification, also differences in patient population, the standard of care, predictor definitions, and other factors influence a model's discriminative ability, as commonly quantified by the AUC (or c‐statistic). We aimed to quantify the variation in AUCs across sets of external validation studies and propose ways to adjust expectations of a model's performance in a new setting.MethodsThe Tufts‐PACE CPM Registry holds a collection of CPMs for prognosis in cardiovascular disease. We analyzed the AUC estimates of 469 CPMs with at least one external validation. Combined, these CPMs had a total of 1603 external validations reported in the literature. For each CPM and its associated set of validation studies, we performed a random‐effects meta‐analysis to estimate the between‐study standard deviation among the AUCs. Since the majority of these meta‐analyses have only a handful of validations, this leads to very poor estimates of . So, instead of focusing on a single CPM, we estimated a log‐normal distribution of across all 469 CPMs. We then used this distribution as an empirical prior. We used cross‐validation to compare this empirical Bayesian approach with frequentist fixed and random‐effects meta‐analyses.ResultsThe 469 CPMs included in our study had a median of 2 external validations with an IQR of [1–3]. The estimated distribution of had a mean of 0.055 and a standard deviation of 0.015. If = 0.05, then the 95% prediction interval for the AUC in a new setting has a width of at least 0.1, no matter how many validations have been done. When there are fewer than 5 validations, which is typically the case, the usual frequentist methods grossly underestimate the uncertainty about the AUC in a new setting. Accounting for in a Bayesian approach achieved near nominal coverage.ConclusionDue to large heterogeneity among the validated AUC values of a CPM, there is great irreducible uncertainty in predicting the AUC in a new setting. This uncertainty is underestimated by existing methods. The proposed empirical Bayes approach addresses this problem which merits wide application in judging the validity of prediction models.
Cognitive Emotion‐Regulation and Depressive Symptoms in a Group of Healthcare Workers During the COVID‐19 Pandemic: A Person‐Based Approach
Authors: Zhou Ting , Lin Zihe , Sha Sha , Tian Tengfei , Liang Sixiang , Zhou Li , Wang Qian 
Published: February 2025

ABSTRACTThis study aimed to identify profiles of cognitive emotion regulation strategies and to examine the association between emotion regulation profiles and depressive symptoms using a sample of healthcare workers under the impact of COVID‐19. A total of 3477 Chinese healthcare workers participated in the online survey. Latent profile analysis was used to identify cognitive regulation patterns using nine strategies. Depressive symptoms were compared among cognitive regulation patterns. The results yielded three cognitive emotion regulation patterns: regulators with low controllability of emotions and events (RLCEE, 65.5%), regulators with high controllability of emotions and events (RHCEE, 33.0%) and high regulators (HR, 5.4%). The HRs reported the highest level of depression symptoms, followed by the RHCEEs and RLCEEs. The results illustrated a significant association between depressive symptoms and emotion regulation profiles in healthcare workers under the impact of the COVID‐19 pandemic.
Defending knowledge abroad
Authors: Berry Heather , Donnelly Róisín 
Published: February 2025

AbstractResearch SummaryThis article analyzes how innovation country‐of‐origin impacts the decisions of multinational corporations (MNCs) to defend their patents against infringement in foreign countries. We argue that MNCs have stronger incentives to defend their locally generated innovations (in foreign countries) than their home‐ or third‐country generated innovations (that have been transferred to foreign countries) because of the higher risk for knowledge spillovers for locally generated innovations. We also explore whether a higher level of multimarket overlap in knowledge activities with industry rivals increases MNC decisions to defend their best innovations. We examine data on infringement cases brought by foreign firms operating in the United States and find results consistent with our expectations. Additionally, when split by patents' country‐of‐origin, our findings reveal several interesting differences.Managerial SummaryThis article examines how several characteristics of firm innovations influence knowledge spillover risks and the decisions of MNCs to defend their patents against infringement in foreign countries. We argue that locally generated patents and those patents that are more important (in terms of value and usage) or more exposed to rivals are more likely to be defended against infringement in foreign countries. Using data on firms in high tech industries and their patent infringement cases, we show support for our arguments while also revealing several interesting differences across the litigation decisions of MNCs when considering the patents' country‐of‐orign.
Carbon Nanocoils‐Assisted Formation of Tunable Pore Graphene Aerogels for Lightweight Broadband Microwave Absorption, Thermal Insulation, and Antifreeze Devices
Authors: Guo Yuan , Duan Yuping , Gu Shude , Liu Xiaoji , Fan Zeng , Pang Huifang , Pan Lujun 
Published: February 2025

AbstractThe rational design of the aerogel pore structure facilitates the maximum excitation of the materials physicochemical properties, which enables the modulation of their electromagnetic performance. However, the controllable adjustment of the aerogel pore structure remains a significant challenge. Here, the freeze‐thawing process and thermal annealing treatment are introduced to prepare reduced graphene oxide (rGO)/iron (Fe)/carbon nanocoil (CNC) aerogels. The composite aerogels with tunable pore structure are obtained by adjusting the content of CNC. Both experiments and simulations confirm that the pore structure with the addition of CNC presents a continuous 3D conductive network, which improves the conductivity loss and polarization loss. Meanwhile, the amorphous carbon structure within the CNC causes structural defects, which further enhance the polarization loss. Therefore, the rGO/Fe/CNC aerogel with optimized pore structure has lightweight and efficient electromagnetic wave absorption. At an ultra‐low filling ratio of 0.8 wt%, the effective absorption bandwidth reaches 7.9 GHz and the optimal reflection loss is −43.5 dB. In addition, due to the 3D continuous network interwoven of aerogels and the temperature stability of carbon nanomaterials, composite aerogels have excellent thermal insulation, antifreeze performance, and hydrophobicity. This multifunctional absorber has great potential for application in complex and changing electromagnetic environments.
Computational Approaches for Designing Heterostructured Electrocatalysts
Authors: Kim Miyeon , Shim Kyu In , Han Jeong Woo 
Published: February 2025

Electrocatalysts for oxidation and reduction reactions are crucial for sustainable energy production and carbon reduction. While precious metal catalysts exhibit superior activity, reducing reliance on them is necessary for large‐scale applications. To address this, transition metal‐based catalysts are studied with strategies to enhance catalytic performance. One promising strategy is heterostructures, which integrate multiple materials to harness synergistic effects. Developing efficient heterostructured electrocatalysts requires understanding their intricate characteristics, which poses challenges. While in situ and operando spectroscopy provides insights, computational materials science is essential for capturing reaction mechanisms, analyzing the origins at the atomic scale, and efficiently exploring innovative heterostructures. Despite growing recognition of computational materials science, standardized criteria for these systems remain lacking. This review consolidates case studies to propose approaches for modeling and analyzing heterostructures. It categorizes heterostructure types into vertical, semivertical, and lateral, defines their characteristics, and propose insights into minimizing or exploiting strain effects from lattice mismatches. Furthermore, it summarizes computational analyses of heterostructure stability and activity across reactions, including oxygen evolution, hydrogen evolution, oxygen reduction, carbon dioxide reduction, nitrogen reduction, and urea oxidation. This review provides an overview to refine heterostructure designs and establish a framework for systematic modeling and analysis to develop efficient electrocatalysts.