bluuebunny/crossref_metadata_2025_split huggingface dataset:
Serpentirhabdias orientalis sp. nov. (nematoda: rhabdiasidae), a new lungworm species in naja kaouthia from Thailand: the first record of the genus from the oriental region and an elapid snake
Authors: Charoennitiwat Vachirapong , Tongpon Supakit , Suksuwan Phatthariya , Chaisiri Kittipong , Laoungbua Panithi , Tawan Tanapong , Thaenkham Urusa , Ratnarathorn Napat 
Published: February 2025

Abstract

A new lungworm species, Serpentirhabdias orientalis sp. nov., was discovered in the respiratory organs of the monocled cobra (Naja kaouthia) in Thailand, marking the first record of a Serpentirhabdias species in both the Oriental Region – Southeast Asia – and in an elapid snake. Morphological analysis revealed distinctive features that clearly differentiate it from all 22 previously known species, such as an elongated body (up to 5858 µm), 6 onchia, a triangular oral opening, and a thin cuticle. The species also exhibited significant differences in several characteristics, such as tail length, distance from the anterior end to the excretory pore and the number of eggs in the uteri, when compared to closely related species such as Serpentirhabdias moi and Serpentirhabdias mussuranae. Specimens were analysed using light microscopy, and genetic sequencing of COI and 28S rRNA confirmed its distinctiveness. Phylogenetic analysis, reinforced by morphological data, revealed a close relationship between S. orientalis sp. nov. and Neotropical species, raising intriguing questions about the biogeography and evolutionary history of lungworms. The high prevalence and intensity of infection in the monocled cobras could have ecological consequences, potentially impacting the health of wild snake populations. Increasing wildlife–human interactions highlight the need to monitor parasitic infections for ecological and veterinary insights. These findings contribute to the expanding taxonomy of Serpentirhabdias and emphasize the importance of further research into parasitic infections in both wild and captive reptiles, with potential implications for conservation and veterinary management.
Foreign financial shocks and domestic credit policy in the small open economy
Authors: Shim Jae Hun 
Published: February 2025

Abstract
This paper introduces a global banking system in a small open economy DSGE model and features global relative price adjustments with incomplete asset market to investigate the role of international financial imperfections. We show that credit policy could be more powerful than monetary policy to alleviate foreign financial shocks since an expansionary monetary policy and alternative policy rules are not a sufficient tool in the global financial crisis. In particular, credit policy based on international credit spread outperforms credit policy based on domestic credit spread since the former attempts to remove distortions from international financial imperfections and reduces real costs of foreign loans. Accordingly, the lower costs of external finance further boost investment and effectively stabilize the economy without substantial asset purchases.
Kant as Comprehensive Liberal
Authors: Krasnoff Larry 
Published: February 2025

Abstract
In a well-known essay, Thomas Pogge argues that Kant’s political philosophy is not comprehensive in Rawls’ sense, since it is independent of his moral philosophy. However, Pogge understands Kant’s comprehensive liberalism as the view that his moral philosophy entails his political philosophy. I question whether this is the best way to understand comprehensive liberalism, in Kant or in general. I argue that Kant’s comprehensive moral philosophy is not an independent argument for the moral truth of liberal ideals, but a liberal way of justifying an independent commitment to politically liberal values, given that we all have values that are not political.
The safe withdrawal rate: evidence from a broad sample of developed markets
Authors: Anarkulova Aizhan , Cederburg Scott , O’Doherty Michael S. , Sias Richard 
Published: February 2025

Abstract
We use a comprehensive new dataset of asset-class returns in 38 developed countries to examine a popular class of retirement spending rules that prescribe annual withdrawals as a constant percentage of the retirement account balance. A 65-year-old couple willing to bear a 5 percent chance of financial ruin can withdraw just 2.31 percent per year, a rate materially lower than conventional advice (e.g., the 4% rule). Our estimates of failure rates under conventional withdrawal policies have important implications for individuals (e.g., savings rates, retirement timing, and retirement consumption), public policy (e.g., participation rates in means-tested programs), and society (e.g., elderly poverty rates).
PyReX: A Recursion Based Crossover Detection Algorithm in Python for Along‐Track Geophysical Measurements
Authors: Vikram K. V. N. G. , Krishna D. V. P. , Sreejith K. M. 
Published: February 2025

AbstractA crossover point is the location of intersection of any two ground tracks charted by multiple platforms (ships, satellite radar and laser altimeters etc.). Detection of crossovers is of prime importance to estimate the discrepancies in the geophysical measurements at the crossover points. Usual approach of crossover detection considers consecutive data points in tracks as segments and checks for intersections between all combinations of these segments. We present a Recursion based crossover detection algorithm in Python (PyReX) for rapid detection of crossovers by avoiding redundant intersection checks. We test the performance of this algorithm using along‐track sea surface height measurements from satellite altimeters. We observe that the time taken for flagging a crossover between pair of tracks with N segments each varies as vis‐a‐vis the dependency associated with the traditional methods. We further demonstrate that PyReX significantly improves the computation speed for high frequency along‐track measurements from satellite altimeters and ship‐borne gravity data compared to existing algorithms. PyReX is a flexible, open‐source code that could be easily customized for variety of applications involving large‐scale track‐line data sets.
Calibrating Strain Measurements: A Comparative Study of DAS, Strainmeter, and Seismic Data
Authors: Chien Chih‐Chieh , Gerstoft Peter , Hatfield William , Hollberg Leo , Lipovsky Bradley P. , Manos John‐Morgan , Mellors Robert J. , Winebrenner Dale P. , Zumberge Mark A. 
Published: February 2025

AbstractSignificant interest has developed in using optical fibers for seismology through Distributed Acoustic Sensing (DAS). However, converting DAS strain measurements to actual ground motions can result in errors and uncertainties due to imperfect coupling of the fiber to the earth and instrument response functions. To address this, we conducted a comparative analysis of strain data recorded by DAS, Optical Fiber Strainmeters (OFSs), and estimates derived from seismic data. This study used dark fibers in a commercial cable connecting two islands in Puget Sound, Washington, USA. The cable extends from a telecommunication substation on Whidbey Island, through an underground conduit, and across Saratoga Passage to Camano Island. The strain along the cable was recorded using OFS Michelson interferometers and a DAS interrogator, with a broadband seismometer positioned at one end. Comparing a teleseismic earthquake recording showed that summed DAS channels agreed well with OFS recordings. The amplitude discrepancies between the measurements and the seismometer's estimated strain indicated poor coupling between the cable and the earth. We also evaluated DAS amplitude response using a piezoelectric cylinder (PZT) to generate ground truth strain. The findings revealed a notable amplitude decrease in DAS recordings at lower frequencies, highlighting the need for amplitude calibration. Moreover, some underwater signals in the study area were strongly correlated with the velocity of the tidal current. These signals can be localized through coherence calculations between the DAS and OFS recordings.
Cross‐Attractor Transforms: Improving Forecasts by Learning Optimal Maps Between Dynamical Systems and Imperfect Models
Authors: Agarwal N. , Amrhein D. E. , Grooms I. 
Published: February 2025

AbstractBiased, incomplete numerical models are often used for forecasting states of complex dynamical systems by mapping an estimate of a “true” initial state into model phase space, making a forecast, and then mapping back to the “true” space. While advances have been made to reduce errors associated with model initialization and model forecasts, we lack a general framework for discovering optimal mappings between “true” dynamical systems and model phase spaces. Here, we propose using a data‐driven approach to infer these maps. Our approach consistently reduces errors in the Lorenz‐96 system with an imperfect model constructed to produce significant model errors compared to a reference configuration. Optimal pre‐ and post‐processing transforms leverage “shocks” and “drifts” in the imperfect model to make more skillful forecasts of the reference system. The implemented machine learning architecture using neural networks constructed with a custom analog‐adjoint layer makes the approach generalizable across applications.
Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory
Authors: Wang Yu , Cao Yong , Shen Yue , Zhao Ruixia , Zeng Xiaoqing , Yao Li , Dai Kan 
Published: February 2025

AbstractIn this study, we modeled the multi‐model blending process using random variables and explicitly derived the distribution of the blended forecast error under the assumption of normally distributed errors. Utilizing this error distribution, we regained the scalar version of the Best Linear Unbiased Estimator. Notably, the model yielded negative weights, which contradict traditional assumptions that weights should be positive. To address this, we modified the algorithm to set negative weights to zero and compared the performance with the original algorithm. Our findings indicate that setting negative weights to zero results in a slight improvement in blending performance compared to using negative weights directly. This improvement may be attributed to modeling the actual forecast error as a normally distributed unified parameter and applying a consistent correlation coefficient across the annual data set.
PEGSGraph: A Graph Neural Network for Fast Earthquake Characterization Based on Prompt ElastoGravity Signals
Authors: Hourcade Céline , Juhel Kévin , Bletery Quentin 
Published: February 2025

AbstractState‐of‐the‐art earthquake early warning systems use the early records of seismic waves to estimate the magnitude and location of the seismic source before the shaking and the tsunami strike. Because of the inherent properties of early seismic records, those systems systematically underestimate the magnitude of large events, which results in catastrophic underestimation of the subsequent tsunamis. Prompt elastogravity signals (PEGS) are low‐amplitude, light‐speed signals emitted by earthquakes, which are highly sensitive to both their magnitude and focal mechanism. Detected before traditional seismic waves, PEGS have the potential to produce unsaturated magnitude estimates faster than state‐of‐the‐art systems. Accurate instantaneous tracking of large earthquake magnitude using PEGS has been proven possible through the use of a Convolutional Neural Network (CNN). However, the CNN architecture is sub‐optimal as it does not allow to capture the geometry of the problem. To address this limitation, we design PEGSGraph, a novel deep learning model relying on a Graph Neural Network (GNN) architecture. PEGSGraph accurately estimates the magnitude of synthetic earthquakes down to 7.6–7.7 and determines their focal mechanisms (thrust, strike‐slip or normal faulting) within 70 s of the event's onset, offering crucial information for predicting potential tsunami wave amplitudes. Our comparative analysis on Alaska and Western Canada data shows that PEGSGraph outperforms PEGSNet, providing more reliable rapid magnitude estimates and enhancing tsunami warning reliability.
Induction chemotherapy followed by camrelizumab plus apatinib and chemotherapy as first-line treatment for extensive-stage small-cell lung cancer: a multicenter, single-arm trial
Authors: Liu Ming , Qiu Guihuan , Guan Wenhui , Xie Xiaohong , Lin Xinqing , Xie Zhanhong , Zhang Jiexia , Qin Yinyin , Du Haijian , Chen Xin , Deng Yu , Li Shiyue , Zhong Nanshan , Zhou Chengzhi 
Published: February 2025

Abstract
Chemo-immunotherapy is the current first-line treatment for patients with extensive-stage small cell lung cancer (ES-SCLC), but survival benefits are modest. We aimed to evaluate the safety, antitumor activity and biomarkers of first-line camrelizumab and apatinib plus chemotherapy in untreated ES-SCLC patients. In this single-arm trial (ClinicalTrials.gov NCT05001412), eligible patients received 2 cycles of etoposide and carboplatin (EC) as induction treatment followed by 2–4 cycles of camrelizumab, apatinib plus EC, then maintenance camrelizumab plus apatinib. Primary endpoint was safety. Secondary endpoints included objective response rate (ORR), duration of response, progression-free survival (PFS), and overall survival (OS). Targeted sequencing and whole transcriptome sequencing were performed to explore biomarkers. All enrolled 40 patients were treated and analyzed for safety. During the entire treatment, treatment-emergent adverse events (TEAEs) occurred in 40 patients (100%), and 30 (75.0%) were grade ≥3. The most common grade ≥3 TEAEs were neutropenia (35.0%), anemia (15.0%) and increased alanine aminotransferase (15.0%). No treatment-related deaths occurred. Among 36 evaluable patients, ORR was 88.9% (95% CI: 73.9%–96.9%), median PFS was 7.3 months (95% CI: 6.6–9.2) and median OS was 17.3 months (11.8-not reached). Mutations in RB1, high levels of tumor mutation burden, natural killer cells, and interferons, and low levels of cancer-associated fibroblasts, correlated with prolonged PFS. Induction chemotherapy followed by camrelizumab, apatinib plus EC demonstrated acceptable safety and promising antitumor activity in untreated ES-SCLC patients. The identified biomarkers need further validation.

Trial Registration ClinicalTrials.gov Identifier: NCT05001412.
Updated MDSGene review on the clinical and genetic spectrum of LRRK2 variants in Parkinson´s disease
Authors: Krüger Clara , Lim Shen-Yang , Buhrmann Alissa , Fahrig Fenja L. , Gabbert Carolin , Bahr Natascha , Madoev Harutyun , Marras Connie , Klein Christine , Lohmann Katja 
Published: February 2025

Abstract
Pathogenic variants in the LRRK2 gene are one of the most commonly identifiable monogenic causes of Parkinson´s disease (PD, PARK-LRRK2). This systematic MDSGene literature review comprehensively summarizes published demographic, clinical, and genetic findings related to LRRK2 variants (https://www.mdsgene.org/). Data on 4660 individuals with 283 different variants were curated. The median age at onset in the PD patients with available information was 56 years, notably, with approximately one-third having PD onset <50 years. Tremor was the most frequently reported initial symptom and more common than reported in other dominantly inherited forms of PD. Of the 211 potentially PD-causing variants, 25 were classified as pathogenic or likely pathogenic, and the remaining 186 (88.2%) were variants of uncertain significance. p.G2019S was the most frequently reported pathogenic variant, followed by p.R1441G and p.R1441C. This systematic review represents the most extensive database on PARK-LRRK2 to date and provides a vital resource to improve precision medicine.
Contrastive learning for nested Chinese Named Entity Recognition via template words
Authors: Wang Yuke , Liu Qiao , Dai Tingting , Lang Junjie , Lu Ling , Chen Yinong 
Published: February 2025

AbstractExisting Chinese named entity recognition (NER) research utilises 1D lexicon‐based sequence labelling frameworks, which can only recognise flat entities. While lexicons serve as prior knowledge and enhance semantic information, they also pose completeness and resource requirements limitations. This paper proposes a template‐based classification (TC) model to avoid lexicon issues and to identify nested entities. Template‐based classification provides a template word for each entity type, which utilises contrastive learning to integrate the common characteristics among entities with the same category. Contrastive learning makes template words the centre points of their category in the vector space, thus improving generalisation ability. Additionally, TC presents a 2D table‐filling label scheme that classifies entities based on the attention distribution of template words. The proposed novel decoder algorithm enables TC recognition of both flat and nested entities simultaneously. Experimental results show that TC achieves the state‐of‐the‐art performance on five Chinese datasets.
Co‐simulations of induction machines coupled with a radial ball bearing for mechanical defects analysis
Authors: El Bouharrouti Nada , Sitnikov Maksim , Hemeida Ahmed , Martin Floran , Kudelina Karolina , Naseer Muhammad U. , Belahcen Anouar 
Published: February 2025

AbstractIn this paper, two co‐simulations are introduced to study the behaviour of a 2‐D finite‐element model and a magnetic equivalent model of an induction machine, both coupled with a mechanical radial ball bearing model. This comprehensive approach allows for the generation of synthetic vibration and current signals of a ball bearing mounted on a shaft, taking into account the entire machinery. The study investigates the misalignment effect caused by an inner ring defect in a ball bearing, analysing its impact on the current of the IM models. Likewise, the effects of eccentricity in the induction machine models on rotor vibration are studied through their coupling with the mechanical ball bearing model. This multiphysics co‐simulation provides a novel numerical application to generate data relative to mechanical defects in induction machines. By using this coupled method, an inner ring defect of 1 mm depth and 3 mm length in the bearing model is accurately identified in the frequency spectrum of the stator current, whereas eccentricity faults for levels ranging between 5% and 40% are detected in the vibration spectrum of the ball bearing. The findings are accurately supported by related theory, and further validated through experimental verification for the case of the inner ring fault.
A Stackelberg game‐based model for low‐carbon scheduling of commercial building loads considering lifecycle unit carbon‐emission factors
Authors: Huang Qifeng , Zhuang Zhong , Duan Meimei , Yang Shihai , Sheng Ju , Huang Yixuan 
Published: February 2025

AbstractThe accelerated growth of smart cities and the intensifying impact of climate change have introduced new demands for low‐carbon commercial buildings. The majority of existing low‐carbon scheduling methods for commercial buildings focus on operational carbon emissions embedded in consumed electricity from the electricity network without a lifecycle perspective, resulting in the underestimation of the carbon emissions of consumed electricity. This article proposes a Stackelberg game model for low‐carbon scheduling of commercial building loads. In this model, the lifecycle unit carbon‐emission factors are calculated and then transferred to commercial buildings employing the carbon‐emission flow method. Subsequently, a low‐carbon scheduling model considering the carbon transaction, demand response, and thermal comfort is established for commercial building loads. Finally, the Stackelberg game model is implemented to determine the interaction between commercial buildings and the electricity network. The case study indicates that approximately 23% of indirect carbon emissions from electricity used in commercial buildings originate from the extraction, construction, transportation, demolition, and recycling stage, while approximately 77% occur during the operation stage.
A methodology for estimating radiofrequency signal attenuation from rainfall and atmospheric gases in 5G‐and‐beyond networks
Authors: Batista Andreza P. , Ayub Muhammad S. , Adasme Pablo , Begazo Dante C. , Shad Muhammad R. , Saadi Muhammad , Rosa Renata L. , Rodríguez Demóstenes Z. 
Published: February 2025

AbstractIndustrial applications demand ultra‐reliable and low latency communications, especially in Industrial Internet of Things (IIoT) environments. To meet these expectations, telecommunication network technologies have evolved, culminating in the deployment of fifth‐generation (5G) networks by telecom operators worldwide. However, 5G and beyond networks, operating in millimetre‐wave frequency bands, can suffer signal degradation caused by atmospheric phenomena, such as rainfall and atmospheric gases. This article presents a significant contribution to scientific research by proposing a methodology that leverages ITU‐R recommendations (P.530, P.837, P.838, P.618‐13, P.839‐4, P.1511‐2) for rain‐induced attenuation and incorporates local meteorological data using the Gumbel Distribution. Also, the methodology considers ITU‐R Recommendations (P.676, P.836, P.835) for atmospheric gas‐induced attenuation. A comprehensive flowchart demonstrates the practical application of these recommendations, enabling accurate assessment of signal attenuation for various cities around the world with distinct geographical coordinates. The dataset is obtained from local Meteorological Stations or using digital maps from ITU recommendations. The results obtained with the approximate values of precipitation rates using the proposed method were compared with values from the ITU's study group and verified the effectiveness of the proposed approach, with relative errors not exceeding 1.07%. These analyses indicated the potential impact of atmospheric conditions on 5G network performance, offering valuable insights for optimising network design and improving communication reliability that is relevant to IIoT applications.
Análisis de errores de estudiantes francófonos en español con fines específicos (EFE)
Authors: Montero Rafael Cuevas 
Published: February 2025

Resumen
En el presente estudio se analizan situaciones comunicativas simuladas en parejas en las que participan estudiantes
francófonos de ELE con sus profesores. Esta actividad pedagógica se ha llevado a cabo a través de llamadas telefónicas con el fin de
evaluar tanto la interacción oral como la capacidad de improvisación y anticipación de los discentes en el contexto profesional turístico.
Nuestro instrumento de investigación es un corpus de lengua oral en el que se han compilado los errores lingüísticos cometidos. Por
tanto, nuestro objetivo principal consiste en clasificar e identificar el origen de dichos errores a partir de su lengua materna, el
francés.