bluuebunny/crossref_metadata_2025_split huggingface dataset:
Getting Started, Getting Data
Authors: Lemaître Hervé , Madan Christopher R. , Quinn Declan , Whelan Robert 
Published: December 2024

AbstractThis chapter explores the availability and accessibility of open-access neuroimaging datasets. It describes how to download datasets using command-line tools (e.g., wget, curl), data management tools such as Datalad, Amazon Web Services (i.e., AWS CLI), and graphical user interface options (e.g., CyberDuck). The chapter emphasizes the importance of accessibility and of documentation for improved research reproducibility. After reading this chapter, researchers will be equipped with the knowledge and tools to download large neuroimaging datasets, including those utilized in this book. We also demonstrate how to download data from OpenNeuro for a range of operating systems.
Structural MRI and Computational Anatomy
Authors: Hoffstaedter Felix , Antonopoulos Georgios , Gaser Christian 
Published: December 2024

AbstractStructural magnetic resonance imaging can yield highly detailed images of the human brain. In order to quantify the variability in shape and size across different brains, methods developed in the field of computational anatomy have proved exceptionally useful. For example, voxel-based morphometry is a popular method that involves segmenting magnetic resonance imaging scans into gray matter, white matter, and cerebrospinal fluid, and transforming individual brain shapes to a standard template space for comparative analysis. However, computational anatomy—when applied to brain data at scale—can be complex and computationally expensive. Furthermore, there are many possible pipelines that can be applied to structural brain data and for this reason it is important to follow best practices for reproducible neuroimaging analyses. This chapter demonstrates reproducible processing using the CAT12 (Computational Anatomy Toolbox) extension to SPM12 that focuses on voxel- and region-based morphometry. Through worked examples, we demonstrate three approaches to reproducible image analysis: “minimal”, “intermediate”, and a “comprehensive” protocol using the FAIRly big workflow based on DataLad. The comprehensive approach automatically facilitates parallel execution of whole dataset processing using container technology and also produces re-executable run records of each processing step to enable fully automatic reproducibility.
Einleitung
Authors: Scheiding Christian Thomas 
Published: December 2024

ZusammenfassungDer Klimawandel, die Überbeanspruchung der planetaren Grenzen, soziale Gerechtigkeitsaspekte und weitere nachhaltigkeitsbezogene Problemkomplexe führen zu einigen der größten gesamtgesellschaftlichen Herausforderungen der modernen Menschheit (Hauff & Kleine 2009; Meadows et al. 1972; Rockström et al. 2009; United Nations 2015). Aus diesen Herausforderungen und deren Dringlichkeit resultiert ein Zeitalter der Nachhaltigkeit (Swilling 2020) mit Nachhaltiger Entwicklung als normativer Zielperspektive für alle Bereiche des gesellschaftlichen, wirtschaftlichen und politischen Miteinanders (Renn et al. 2007).
Ergebnisse der Untersuchung
Authors: Scheiding Christian Thomas 
Published: December 2024

ZusammenfassungDie Ergebnisse der vorliegenden Untersuchung lassen sich in insgesamt drei Hauptkategorien einteilen, in denen sie in den folgenden Abschnitten dargestellt werden. Die erste Kategorie gibt einen Überblick über die untersuchten Fälle und stellt diese sowohl in ihren formalen als auch inhaltlichen Charakteristiken dar und synthetisiert sie auf der inhaltlichen Ebene.
Abschließende Reflexionen
Authors: Scheiding Christian Thomas 
Published: December 2024

ZusammenfassungNachdem die Ergebnisse der Untersuchung in dem vorangegangenen Kapitel detailliert und strukturiert dargestellt wurden, gilt es nun sie abschließend zu reflektieren. Nach einer Reflexion zu methodischen Aspekten und der Güte der vorliegenden Untersuchung werden die Ergebnisse zusammenfassend dargestellt. Daraufhin wird ein Rückbezug zu den gegenstandstheoretischen Konzepten individuellen Entscheidungsverhalten durchgeführt.
Prediction of the Lateral Ultimate Bearing Capacity of the RC Pipe Pile with Corrosion-Damaged Partial Length
Authors: Yu Hongyang , Wang Xiaohui 
Published: December 2024

AbstractFor reinforced concrete (RC) pipe pile used in the high-pile wharf in the marine environment, due to the dry–wet cycling in the splash/tidal zone, corrosion of the reinforcing bars in the pipe pile often occurs in this zone. In the present paper, the lateral ultimate bearing capacity of the RC pipe pile with corrosion-damaged partial length is predicted via the finite element method. Corrosion-induced concrete cover cracking, the reduction of effective cross section and strength of steel bars, as well as the bond degradation between steel bars and concrete are considered to predict the lateral ultimate bearing capacity of the corroded RC pipe piles under different axial compression ratios and corrosion levels. The modelling results show that, the ultimate capacity of the corroded RC pipe piles reduces due to the corrosion-damaged partial length. The failure location of the corroded RC pipe piles transfers from the bottom region of the uncorroded pile to the middle-corroded length. The increased axial compression ratio, corrosion level and corrosion-damaged length all affects the ultimate capacity and failure mode of the corroded RC pipe piles.
Enhancing the Performance of Oil Well Cement Strength with Graphene Oxide
Authors: Li Zihe , Xu Jiangtao , Miao Xia , Cao Wei , Zhang Jinqi , Lu Duyou , Yao Xiao 
Published: December 2024

AbstractThis study aims to use graphene oxide to improve the strength properties of oil well cement. The fluidity, setting time, strength properties, and hydration products of oil well cement paste with different GO dosages (0–0.05 wt%) were investigated. The fluidity of cement paste decreased linearly with increasing GO dosage was indicated. The addition of GO not only promoted cement hydration but also reduced the R-index of portlandite, which densified the microstructure of hardened paste and thus enhanced the strength properties. By using a 0.02% dosage of graphene oxide, the compressive strength of oil well cement pastes increased by 28.9% and 11.1% at 1 day and 28 days, respectively. Meanwhile, the flexural strength of cement paste increased by 72.5 and 34.1% compared to control. The enhancement of strength properties reduces the brittleness of oil well cement pastes, and improve wellbore integrity and cementing quality.
Robustness Study and Deformation Pattern Analysis of Multiple Prediction Regression Models for a Panel Rockfill Dam
Authors: Zhu Xinyuan , Zhou Zheng , Tian Jinzhang 
Published: December 2024

AbstractPanel rockfill dams, due to their cost-effectiveness, short construction periods, high safety standards, and strong adaptability to foundations, have gained rapid development and considerable attention in recent years. However, the inherent material properties and structural behavior of panel rockfill dams change continuously during operation. Particularly, long-term operation results in coordination issues between the panel concrete and the rockfill material, leading to deformations. Regular monitoring and prediction of horizontal displacements in panel rockfill dams are crucial to ensure the dam's safety. This study focuses on the robustness of two prediction regression models, namely, displacement statistical regression models and grey prediction monitoring models, applied to a panel rockfill dam in the Lower Elesai Hydropower Project in Cambodia. The research explores the accuracy, applicability, strengths, and weaknesses of these two commonly used regression models for fitting and predicting deformations, such as displacements, in panel rockfill dams. Furthermore, both prediction regression models are employed to analyze horizontal displacement monitoring data of the panel dam, conducting an in-depth study of deformation patterns. This analysis aims to determine the initial reservoir period's coordination patterns of deformation and provide recommendations based on the findings.
Mechanism Model of Inherent Defects Risks for Buildings Quality Under Inherent Defects Insurance
Authors: Ji Yingbo , Tong Wenjing , Yao Fuyi , Li Hong Xian , Liu Xinnan 
Published: December 2024

AbstractInherent defects insurance is one of the practical tools for building quality management, with the inherent defects risk assessment of buildings as its core. Unfortunately, the domestic Technical Inspection Service has failed to build a comprehensive risk system covering the project. As a result, the risk assessment mainly focuses on the construction process inspection, lacking a risk assessment model with theoretical support for the inherent defects. Therefore, the mechanism of inherent defect risks is analysed in this paper to provide a theoretical basis for the risk assessment. This paper identifies the inherent defects risks and risk factors of buildings and constructs the causal mapping and weights among building quality risks based on the Analytic Hierarchy Process of group decisions using expert interviews and questionnaires. It was found that the risk factors of survey and design, concrete pouring, roof waterproofing, and insulation engineering, and the completion review had a significant impact on the inherent defect risks.
Intelligent Analysis Research on Reservoir Dam Structure Settlement Prediction in Coal Mining Subsidence Area
Authors: Chang Qiang , Yan Hao , Zhao Daiyao , Zhang Ning 
Published: December 2024

AbstractWhen constructing a dam in a mined-out area, it is necessary to handle the mined-out area effectively. Analyzing the distribution of mined-out areas is essential to ensure the safety of dam structures. In response to this issue, a machine learning-based method for analyzing the settlement of reservoir dam structures in coal mining subsidence areas is proposed. The method includes the following steps: first, analyze the geological conditions of the mined-out area and calculate the conditions for each depth of the mined-out area. Secondly, analyze the deformation mechanism of the mined-out area and calculate the impact of the mined-out area at representative depths. Next, use machine learning methods to regressively fit the deformation displacement and obtain the deformation function. Finally, validate the predicted depth based on on-site monitoring data and drilling information. The study concludes that deeper mined-out areas lead to larger total displacement, and as the depth increases, the displacement caused by self-weight stress gradually increases. Additionally, the displacement response of the dam load decreases with the increase in depth. The predicted depths align well with the actual depths. The proposed method is reasonable and feasible, providing a basis for reinforcement schemes in mined-out areas.
Research on Cumulative Damage of Reinforced Concrete Short Columns
Authors: Zhou Hongyu , Tang Qi , Zhao Xiaohua , Liu Yanan 
Published: December 2024

AbstractReinforced concrete columns are the main load-bearing elements in many structures and are also the elements that bear seismic effects when earthquakes occur. In high-rise buildings, bridges, and other building structures column members also gradually accumulate damage due to fatigue cyclic loading and gradually reduce their load-bearing performance. The design and fabrication of reinforced concrete column members, the application of equal amplitude cyclic loading to them, so that they enter different damage stages, the study of their fatigue damage after the fatigue damage of the load-bearing capacity, deformation capacity, and damage morphology and other damage, to reveal the fatigue damage on the shear performance of the members of the law of influence. The monotonic pushover and low circumferential fatigue proposed static tests of reinforced concrete columns were carried out to study the fatigue shear damage characteristics, damage mechanism, and mechanical properties of the member such as the bearing capacity after the damage. The test process is divided into two stages. In the first stage, a certain number of equal-amplitude cyclic loads are applied to the reinforced concrete column members to bring them to different degrees of damage, and the cumulative damage development law is studied. In the second stage, the members after the accumulated damage were subjected to damage tests under monotonic loading to study their residual bearing capacity and dimensional effects in the damaged state.
Development of gene-in-plasmid DNA reference materials certified by single-molecule counting
Authors: Lee Da-Hye , Yoo Hee-Bong , Hong Kee-Suk , Park Sang-Ryoul , Jeong Sangkyun , Yang Inchul 
Published: December 2024

AbstractThe mole, the SI unit for measuring the amount of a substance, was redefined as a fixed number of entities. This definition enables straightforward quantification of substances by counting individual entities. Counting proves particularly effective for quantifying large and discrete biological entities such as DNA, proteins, viruses, and cells, which are challenging to quantify via traditional physical or chemical methods. In this study, we detail our approach to develop gene reference materials certified through single-molecule counting, which enables mole-traceable measurements. We quantified three plasmid DNA constructs, each carrying a specific gene of interest, via single-molecule counting. The resulting values were cross-validated via digital PCR and LC‒MS. Sequence impurities in the certified reference materials were quantified via single-molecule real-time sequencing, whereas fragment impurities were quantified via two-color digital PCR analysis. We precisely accounted for various sources of uncertainty, including measurement precision, weighing, homogeneity, and impurities, when estimating the total uncertainty of the reference materials. In conclusion, a practical format for gene-based DNA reference materials, a measurement method to achieve metrological traceability, and methods for quantifying fragments and sequence impurities were developed and implemented in this study. We anticipate that our gene-based DNA reference materials will serve as valuable higher-order standards for the calibration of other methods or reference materials for DNA quantification in a variety of bioanalytical applications.
Classical correspondence beyond the Ehrenfest time for open quantum systems with general Lindbladians
Authors: Hernández Felipe , Ranard Daniel , Riedel C. Jess 
Published: December 2024

Abstract
Quantum and classical systems evolving under the same formal Hamiltonian H may exhibit dramatically different behavior after the Ehrenfest timescale

$$t_E \sim \log (\hbar ^{-1})$$

t
E

∼
log

(

ħ

-
1

)

, even as

$$\hbar \rightarrow 0$$

ħ
→
0

. Coupling the system to a Markovian environment results in a Lindblad equation for the quantum evolution. Its classical counterpart is given by the Fokker–Planck equation on phase space, which describes Hamiltonian flow with friction and diffusive noise. The quantum and classical evolutions may be compared via the Wigner-Weyl representation. Due to decoherence, they are conjectured to match closely for times far beyond the Ehrenfest timescale as

$$\hbar \rightarrow 0$$

ħ
→
0

. We prove a version of this correspondence, bounding the error between the quantum and classical evolutions for any sufficiently regular Hamiltonian H(x, p) and Lindblad functions

$$L_{k}(x,p)$$

L
k

(
x
,
p
)

. The error is small when the strength of the diffusion D associated to the Lindblad functions satisfies

$$D \gg \hbar ^{4/3}$$

D
≫

ħ

4
/
3

, in particular allowing vanishing noise in the classical limit. Our method uses a time-dependent semiclassical mixture of variably squeezed Gaussian states. The states evolve according to a local harmonic approximation to the Lindblad dynamics constructed from a second-order Taylor expansion of the Lindbladian. Both the exact quantum trajectory and its classical counterpart can be expressed as perturbations of this semiclassical mixture, with the errors bounded using Duhamel’s principle. We present heuristic arguments suggesting the 4/3 exponent is optimal and defines a boundary in the sense that asymptotically weaker diffusion permits a breakdown of quantum-classical correspondence at the Ehrenfest timescale. Our presentation aims to be comprehensive and accessible to both mathematicians and physicists. In a shorter companion paper, we treat the special case of Hamiltonians that decompose into kinetic and potential energy with linear Lindblad operators, with explicit bounds that can be applied directly to physical systems.
Standard technique in Japan for measuring hepatic venous pressure gradient
Authors: Imai Yusuke , Koizumi Yohei , Hiasa Yoichi , Hirooka Masashi , Tokumoto Yoshio , Yoshida Osamu , Chikamori Fumio 
Published: December 2024

Abstract
Background
Direct measurement of portal venous pressure (PVP) is invasive, so the hepatic venous pressure gradient (HVPG) is commonly measured to evaluate portal hypertension (PH). HVPG is the gold standard for estimating PVP but few reports have covered standardized measurement techniques.

Methods
This study validated standardized techniques for PVP measurement.

Results
In Western countries, electronic transducers are commonly used to measure PVP, whereas the water column method is still frequently applied in Japan. Setting a reference point for accurate PVP measurement is important but complicated. According to Japanese guidelines, the reference point for PVP measurement is 10 cm above the dorsal surface or in the midaxillary line. For simpler determination, the anterior axillary point, defined as the point of convergence between the proximal pectoralis major muscle and arm when both arms are positioned against the trunk in a supine position, can be used as the reference point. New methods, such as endoscopic ultrasound-guided portal pressure gradient, offer less invasive alternatives. Non-invasive methods like elastography measure liver and spleen stiffness, which correlate with HVPG. The Baveno VII criteria incorporate measurements of liver and splenic stiffness for risk stratification. Biomarkers such as type IV collagen, M2BPGi, and FIB-4 score also predict HVPG. The Baveno VII consensus emphasizes the status of HVPG as the gold standard while advocating for non-invasive alternative methods to improve patient care and monitor treatment efficacy.

Conclusions
Continued development of non-invasive tests is crucial for safer, more convenient PH management.
Serum IL-6 concentration is a useful biomarker to predict the efficacy of atezolizumab plus bevacizumab in patients with hepatocellular carcinoma
Authors: Miura Ryoichi , Ono Atsushi , Nakahara Hikaru , Shirane Yuki , Yamaoka Kenji , Fujii Yasutoshi , Uchikawa Shinsuke , Fujino Hatsue , Murakami Eisuke , Kawaoka Tomokazu , Miki Daiki , Tsuge Masataka , Kishi Takeshi , Ohishi Waka , Sakamoto Naoya , Arihiro Koji , Hayes Clair Nelson , Oka Shiro 
Published: December 2024

Abstract
Background
This study aims to identify biomarkers for treatment response of atezolizumab plus bevacizumab (Atezo+Bev) in patients with hepatocellular carcinoma (HCC).

Methods
96 patients who received Atezo+Bev or lenvatinib as a first-line systemic therapy were enrolled as the training group after propensity score matching (PSM), and 42 patients treated with Atezo+Bev were enrolled as the validation group. 17 serum cytokines were measured by Luminex multiplex assay at the start of treatment. For further assessment of the association between cytokine levels and the tumor microenvironment (TME), immunohistochemistry (IHC) was performed on pre-treatment liver biopsy specimens.

Results
In the derivation set, multivariate analysis identified elevated IL-6 as an independent risk factor in the Atezo+Bev group (HR 5.80: p<0.01), but not in the lenvatinib group; in a subset analysis of patients with low IL-6, PFS was longer in the Atezo+Bev training group than in the lenvatinib group (p = 0.02). A validation study also showed a significantly longer prognosis in the low IL-6 group for both PFS (p = 0.0001) and OS (p = 0.03). Serum IL-6 had a positive correlation with tumor IL-6 expression (ρ = 0.56, p < 0.0001) and an inverse correlation with the CD8/CD163-positive cell count ratio (ρ = −0.4, p < 0.01).

Conclusion
Serum IL-6 levels are thought to be involved in the suppression of tumor immunity and are useful in predicting the therapeutic effect of Atezo+Bev treatment.
Generalized convolution quadrature for non smooth sectorial problems
Authors: Guo J. , Lopez-Fernandez M. 
Published: December 2024

AbstractWe consider the application of the generalized convolution quadrature (gCQ) to approximate the solution of an important class of sectorial problems. The gCQ is a generalization of Lubich’s convolution quadrature (CQ) that allows for variable steps. The available stability and convergence theory for the gCQ requires non realistic regularity assumptions on the data, which do not hold in many applications of interest, such as the approximation of subdiffusion equations. It is well known that for non smooth enough data the original CQ, with uniform steps, presents an order reduction close to the singularity. We generalize the analysis of the gCQ to data satisfying realistic regularity assumptions and provide sufficient conditions for stability and convergence on arbitrary sequences of time points. We consider the particular case of graded meshes and show how to choose them optimally, according to the behaviour of the data. An important advantage of the gCQ method is that it allows for a fast and memory reduced implementation. We describe how the fast and oblivious gCQ can be implemented and illustrate our theoretical results with several numerical experiments.
Machine learning-based tropospheric delay prediction for real-time precise point positioning under extreme weather conditions
Authors: Haji-Aghajany Saeid , Rohm Witold , Hadas Tomasz , Bosy Jaroslaw 
Published: December 2024

Abstract
Satellite signals from the Global Navigation Satellite System (GNSS) are refracted as they pass through the troposphere, owing to the variable density and composition of the atmosphere, causing tropospheric delay. Typically, tropospheric delay is treated as an unknown parameter in GNSS data processing. Given the growing need for real-time GNSS applications, accurate tropospheric delay predictions are crucial to improve Precise Point Positioning (PPP). In this paper, time-series of tomography data are used for wet refractivity prediction employing Machine Learning (ML) techniques in both Poland and California, under extreme weather conditions including sweeping rain bands and storms. The predicted wet refractivity is implemented for tropospheric delay determination through ray-tracing technique. PPP processing is conducted in both static and kinematic modes using different setups. These are: (1) common PPP, called Com-PPP, (2) Ray-PPP, which applies obtained tropospheric delay on GNSS observations and thus eliminates tropospheric parameters from unknowns, and (3) Dif-PPP, which applies the difference of estimated tropospheric delay from ray-tracing and GNSS measurements to compensate for the remaining tropospheric delay in the observations. The results show that Dif-PPP reduces the Mean Absolute Error (MAE) of the Three-Dimensional (3-D) component between 8 and 33% in static mode compared to the Com-PPP method. Additionally, it can improve the convergence time of the up component in the kinematic mode by between 6 and 17%.