bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating brain activity at rest in patients with persistent genital arousal disorder (PGAD) using functional magnetic resonance imaging
Authors: Dalkeranidis Eleni , Kümpers Franziska M L M , Sinke Christopher , Krüger Tillmann H C 
Published: February 2025

Abstract
Persistent genital arousal disorder (PGAD) is a rare disease causing high emotional distress eminently impacting the individual’s quality of life. Experts in this field assume that the disease is caused by a multifaceted interplay of different etiologies which may share a common neurobiological basis. However, only one functional neuroimaging investigation exist, and a more in-depth comprehension of the neurobiological foundation is required. Therefore, this study aims to provide new insights into how the functional integration of brain regions may relate to PGAD. By using the functional magnetic resonance imaging (fMRI) technique, functional connectivity at rest (rs-FC) was compared between patients suffering PGAD (n = 26) and healthy controls (n = 26). Patients with PGAD showed different pattern in connectivity within brain structures putatively associated with the psychological and somatic dimensions of the disease including the right amygdala, left anterior cingulate cortex, right insula cortex, thalamic nuclei and prefrontal regions as seeds. The majority of these showed differences in brain connectivity pattern to the precuneus and prefrontal regions. The study offers preliminary insights into the characteristics and relevant neural mechanisms of PGAD. Nevertheless, since this study did not identify any peripheral correlates that would corroborate the interpretation of these findings, they were interpreted from a more theoretical perspective, thereby offering potential areas of focus for future research.
Synergistic combination of ceftazidime and avibactam with Aztreonam against MDR Klebsiella pneumoniae in ICU patients
Authors: Khattab Sally , Askar Aya Mohamed , Abdellatif Hidi A. A. , Othman Amira A. A. , Rayan Amal H. , Azab Hasnaa 
Published: February 2025

Abstract
The proliferation of multidrug-resistant, metallo-beta-lactamase-producing Klebsiella pneumoniae (MBL-producing K. pneumoniae) poses a major threat to public health resulting in increasing treatment costs, prolonged hospitalization, and mortality rate. Treating such bacteria presents substantial hurdles for clinicians. The combination of Aztreonam (ATM) and ceftazidime/avibactam (CAZ/AVI) is likely the most successful approach. The study evaluated the in vitro activity of CAZ/AVI in combination with ATM against MBL-producing K. pneumoniae clinical isolates collected from Suez Canal University Hospital patients. Carbapenem-resistant K. pneumoniae were isolated and identified from different specimens. The presence of metallo-β-lactamases was detected phenotypically by modified carbapenem inactivation method (mCIM) and EDTA-CIM (eCIM) testing, and genotypically for the three metallo-β-lactamase genes: blaNDM, blaIMP, and blaVIM by conventional PCR method. The synergistic effect of CAZ/AVI with ATM against MBL-producing K. pneumoniae was detected by ceftazidime-avibactam combination disks and E-test for antimicrobial susceptibility testing. Out of the 65 K. pneumoniae isolates recovered, 60% (39/65) were carbapenem-resistant (CRKP). According to the mCIM and eCIM tests, 89.7% (35/39) of CRKP isolates were carbapenemase-positive, and 68.6% (24/35) were metallo-β-lactamase (MBL)-positive. By using the conventional PCR, at least one of the MBL genes was present in each metallo-bata-lactamase-producing isolate: 8.3% carried the blaVIM gene, 66.7% the blaNDM, and 91.7% the blaIMP gene. After doing the disk combination method for ceftazidime-avibactam plus Aztreonam, 62.5% of the isolates shifted from resistance to sensitivity. Also, ceftazidime/avibactam plus Aztreonam resistance was reduced markedly among CRKP using the E-test. The addition of Aztreonam to ceftazidime/avibactam is an effective therapeutic option against MBL-producing K. pneumoniae.

Clinical Trials Registry: Pan African Clinical Trials Registry. Trial No.: PACTR202410744344899. Trial URL: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=32000
Impact of truck electrification on air pollution disparities in the United States
Authors: McNeil Wilson H. , Porzio Jason , Tong Fan , Harley Robert A. , Auffhammer Maximilian , Scown Corinne D. 
Published: February 2025

Abstract
Electrifying heavy-duty trucks reduces on-road diesel emissions but shifts the burden of supplying energy to power-generation facilities. The combined effect of Inflation Reduction Act investments in grid decarbonization and truck electrification will alter the magnitude and distribution of air pollution burdens across the United States. These investments are intended to facilitate a just energy transition, with 40% of the benefits flowing to disadvantaged communities per the Justice40 Initiative. Here we evaluate the combined effects of Inflation Reduction Act grid decarbonization and truck electrification investments on a national scale to determine whether the air pollution benefits would meet this 40% goal for both disadvantaged communities and the most exposed racial–ethnic groups. We find that truck electrification and decarbonization reduce air-pollution-related premature mortality in disadvantaged communities. However, the relative disparity between disadvantaged and non-disadvantaged communities increases, suggesting that a disproportionate share of benefits accrue to non-disadvantaged communities. Whereas absolute disparity in grid emissions decreases over time for all racial–ethnic groups, relative disparity remains largely unchanged, with Black populations being the most exposed. Electrifying drayage corridors would result in comparatively large health benefits for disadvantaged communities, suggesting that increasing targeted electrification investments in short-haul routes near urban areas (for example, ports) could be promising.
Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions
Authors: Callaghan Max , Banisch Lucy , Doebbeling-Hildebrandt Niklas , Edmondson Duncan , Flachsland Christian , Lamb William F. , Levi Sebastian , Müller-Hansen Finn , Posada Eduardo , Vasudevan Shraddha , Minx Jan C. 
Published: February 2025

Abstract
Current climate mitigation policies are not sufficient to meet the Paris temperature target, and ramping up efforts will require rapid learning from the scientific literature on climate policies. This literature is vast and widely dispersed, as well as hard to define and categorise, hampering systematic efforts to learn from it. We use a machine learning pipeline using transformer-based language models to systematically map the relevant scientific literature on climate policies at scale and in real-time. Our “living systematic map” of climate policy research features a set of 84,990 papers, and classifies each of them by policy instrument type, sector, and geography. We explore how the distribution of these papers varies across countries, and compare this to the distribution of emissions and enacted climate policies. Results suggests a potential stark under-representation of industry sector policies, as well as diverging attention between science and policy with respect to economic and regulatory instruments.
Appearance consistency and motion coherence learning for internal video inpainting
Authors: Liu Ruixin , Zhu Yuesheng , Luo GuiBo 
Published: February 2025

AbstractInternal learning‐based video inpainting methods have shown promising results by exploiting the intrinsic properties of the video to fill in the missing region without external dataset supervision. However, existing internal learning‐based video inpainting methods would produce inconsistent structures or blurry textures due to the insufficient utilisation of motion priors within the video sequence. In this paper, the authors propose a new internal learning‐based video inpainting model called appearance consistency and motion coherence network (ACMC‐Net), which can not only learn the recurrence of appearance prior but can also capture motion coherence prior to improve the quality of the inpainting results. In ACMC‐Net, a transformer‐based appearance network is developed to capture global context information within the video frame for representing appearance consistency accurately. Additionally, a novel motion coherence learning scheme is proposed to learn the motion prior in a video sequence effectively. Finally, the learnt internal appearance consistency and motion coherence are implicitly propagated to the missing regions to achieve inpainting well. Extensive experiments conducted on the DAVIS dataset show that the proposed model obtains the superior performance in terms of quantitative measurements and produces more visually plausible results compared with the state‐of‐the‐art methods.
A miniaturized microstrip suppressing cell as an integrated HCC in class F/F−1 power amplifiers for 5G applications
Authors: Shama Farzin 
Published: February 2025

AbstractThis study presents the design and implementation of a miniaturized suppressing cell (MSC) functioning as a low‐pass filter (LPF) utilizing an interdigital shaped resonator. The MSC is engineered to effectively control harmonics in power amplifiers (PAs), particularly in applications for next‐generation 5G systems. The LPF achieves a cut‐off frequency of 2.21 GHz, demonstrating a sharp transition region spanning 0.6 GHz, where the attenuation transitions from −3 to −40 dB. Notably, the filter exhibits an ultra‐extended rejection bandwidth, maintaining at least −20 dB from 2.8 to 29.17 GHz. The measured flat group delay further confirms the excellent passband performance of this LPF, indicating minimal signal distortion. Given the critical role of harmonic control circuits (HCCs) in enhancing the efficiency and linearity of various PA classes, including class‐F and class‐F−1 configuration, the proposed MSC serves as a vital component in HCC designs. This advancement not only contributes to improved performance in PAs but also supports the demands of modern wireless communication systems, reinforcing the MSC's significance in high‐frequency applications.
Improved Gaussian based rapid quantification of scheduling uncertainty considering source‐load extreme scenario enhancement
Authors: Qian Haiya , Chen Shupei , Xu Qingshan , Zang Haixiang , Li Feng 
Published: February 2025

AbstractExtreme events are typically low‐probability occurrences with limited historical data and a high degree of unpredictability. The inherent conflict between the dispatch rationality in extreme and conventional scenarios, makes it hard for traditional methods to consider both performances. This article introduces a rapid quantification method for evaluating dispatch uncertainty in extreme scenarios within integrated energy systems. The method enhances the speed and precision of energy dispatch predictions by establishing a direct correlation between meteorological data and energy dispatch. The process begins with the collection of extreme scenarios sets. The Maximal Information Coefficient (MIC) is then employed to identify distinctive meteorological characteristics across different sets of extreme scenarios. To compensate for the lack of historical data in these scenarios, the Synthetic Minority Over‐Sampling Technique (SMOTE) is utilized to augment the scenario dataset. Subsequently, the outcomes of the integrated energy system (IES) are calculated as output. Finally, Gaussian Process Quantile Regression (GPR‐Q) is applied to predict dispatch uncertainty in these extreme scenarios. After comparing with existing approaches, this method can innovatively avoid the prediction error of new energy to a certain extent and quickly provide the interval probability distribution of scheduling predictions with richer information. Such results better align with the needs of real dispatch scenarios.
Dynamical modelling of rubble pile asteroids using data-driven techniques
Authors: Fodde I. , Ferrari F. 
Published: February 2025

Context. Most asteroids are likely rubble pile asteroids (i.e. aggregates of loosely consolidated material). Their dynamics are hard to model due to their granular nature leading to a complex multi-scale and multi-regime system.
Aims. This paper proposes a new data-driven methodology that aims to provide an analytical dynamical model of rubble pile asteroids using data coming from different numerical simulations. This analytical model will allow for the use of tools from the dynamical systems theory in order to investigate the results of the simulations while also providing more general results outside of what is contained within the training data.
Methods. We use a novel technique called sparse identification of nonlinear dynamics (SINDy) to obtain an analytical model. The data used originate from high-fidelity and numerically complex discrete element simulations that take into account the contact interactions of non-spherical particles and their gravitational forces.
Results. We find a simple analytical model that is accurate across various dynamical regimes. The dynamical model was used to characterise the phase space of the system, and we found that the stable region shrinks as the angular momentum increases. This allows for the determination of the critical angular momentum limit for asteroids with different shapes and bulk densities, and what the dynamical fate is of the asteroid after it reaches this limit.
Conclusions. This work shows that SINDy is capable of modelling the dynamics of rubble pile asteroids, thus allowing for the characterisation of the phase space using techniques from dynamical systems theory such as finite time Lyapunov exponents.
Broadcasting in random recursive dags
Authors: Lugosi Gabor , Devroye Luc , Briend Simon 
Published: February 2025

A uniform $k$-{\sc dag} generalizes the uniform random recursive
tree by picking $k$ parents uniformly at random from the existing
nodes. It starts with $k$ ''roots''. Each of the $k$ roots is
assigned a bit. These bits are propagated by a noisy channel. The
parents' bits are flipped with probability $p$, and a majority vote
is taken. When all nodes have received their bits, the $k$-{\sc dag}
is shown without identifying the roots. The goal is to estimate the
majority bit among the roots. We identify the threshold for $p$ as a
function of $k$ below which the majority rule among all nodes yields
an error $c+o(1)$ with $c<1/2$.
Above the threshold the majority rule errs with
probability $1/2+o(1)$.
Calibration of the Solar Position Sensor on GOES-R as a proxy for Total Solar Irradiance I: modeling the SPS bandpass
Authors: Snow Martin , Penton Steven , Woodraska Don , Beland Stephane , Coddington Odele 
Published: February 2025

The Geostationary Environmental Operational Satellites R series (GOES-R) includes
an instrument that measures visible light from the Sun at high cadence:
the Solar Position Sensor (SPS).
SPS is part of the Extreme ultraviolet and X-ray Irradiance Sensors (EXIS)
instrument package.
The visible wavelength range observed by SPS
includes the peak power of the solar spectrum. Tracking the solar input to the
climate system is important at all timescales.
This article
is the first in a series that will describe using the SPS data as a high-cadence proxy
for Total Solar Irradiance (TSI), and as an input to the spectral model used in
the NOAA Climate Data Record for solar irradiance.

We describe the design of the SPS instrument
and create a model of its output using the solar spectrum measured by the
Total and Spectral Irradiance Sensor-1 (TSIS-1) on the International Space Station.
We apply the bandpasses of the SPS components to the daily TSIS-1
Solar Irradiance Monitor (SIM) spectrum and integrate over wavelength
to simulate the SPS measurement.
After applying the appropriate SPS filter transmittances and diode responsivity to the
TSIS-1/SIM spectrum, we compare the integrated irradiances
from the full SIM spectrum and the SPS model to the TSI measurement from TSIS-1.
These comparisons of daily
averages show that the integrated SPS model reproduces TSI with an uncertainty
of 53 parts per million. We also show a preliminary comparison of the SPS data to the high-cadence TSI measurements from the Digital Absolute RAdiometer
(DARA) on the FY-3E satellite.

The modeled SPS spectrum shows excellent agreement with TSI on a daily cadence.
Once all the instrument calibrations have been established, the SPS data will
be a viable operational high-cadence proxy for TSI.
Two-Flap Technique, Superior Pedicle and Central Pedicle Retro Glandular Flap: Innovative Technique to Ensure Upper Pole Fullness after Breast Reduction Surgery
Authors: Gupta Rajat , Bansal Priya , Chaudhary Gautam , Sharma Shubham 
Published: February 2025

Abstract
Introduction The two-flap technique, superior pedicle flap and central pedicle retro glandular (RG) flap, represents a novel advancement in breast reduction surgery, addressing issues such as upper pole deflation, asymmetry, and overall breast contour irregularities seen with traditional methods. This study evaluates the outcomes, efficacy, and safety of the two-flap technique, emphasizing its ability to provide enhanced aesthetic results and high patient satisfaction.
Materials and Methods A retrospective study was conducted on 165 patients who underwent the two-flap technique between February 2022 and February 2024. The technique involved preoperative skin markings for precise flap creation, using superior and central pedicles to ensure optimal vascularity and contour. Tissue reduction was performed based on individual patient needs, and post-surgical follow-ups ranged from 6 months to 1 year. Data collected included demographic details, tissue reduction volumes, postoperative complications, and patient satisfaction. Statistical analysis was conducted using SPSS software.
Results The majority of patients (66.67%) were aged 31 to 40 years, with a mean age of 34.59 years. Tissue reduction ranged from 200 to 800 g, with 48.49% of patients having 200 to 400 g removed. Complications included seroma in 7.27% and wound dehiscence in 3.64%. Patient satisfaction was high, with 89.09% reporting being “highly satisfied.” The technique demonstrated significant improvements in upper pole fullness and breast contour.
Conclusion The two-flap technique offers a safe, effective, and aesthetically pleasing solution for breast reduction surgery. Its innovative approach minimizes complications and maximizes patient satisfaction, though further studies are recommended to validate its efficacy in larger breast sizes.
Identifying existing research challenges and enhancing outcomes through the development of standardized methodologies
Authors: Jilcha Kassu 
Published: February 2025

Abstract
The aim of this study is to identify the challenges faced by postgraduate students in their research endeavors and to develop a standardized research process model. To achieve this objective, web-based surveys were distributed to 91 postgraduate students, and a case study was conducted to evaluate the application of research methodologies at Addis Ababa University. The findings revealed several challenges, including difficulties in selecting a research topic, choosing appropriate methodologies, addressing biases, conducting reliability and validity analyses, and managing time constraints. Additionally, the study has developed a comprehensive research design framework to address these challenges, offering a standardized and holistic approach that is suitable for researchers across diverse disciplines.
Origin of the change in giant magnetoresistance effects of Fe/V multilayer by hydrogen absorption
Authors: Akamaru Satoshi , Miyake Kyosuke 
Published: February 2025

This study investigated the giant magnetoresistance (GMR) effect of a Fe/V(001) multilayer under different hydrogen concentrations in a hydrogen–nitrogen gas mixture to elucidate the effect of hydrogen absorption in the V layer on the exchange interactions between each Fe layer. The resistance against hydrogen concentration in the gas mixture revealed a phase boundary that was dependent on the V thickness in Fe/V, between hydrogen dissolved in the V metal and V hydride phases. The magnetoresistance in Fe/V with a V thickness of 1.7–2.0 nm demonstrated a GMR effect, which was reduced under low hydrogen concentration in the gas mixture, corresponding to the hydrogen dissolved phase in the V layer. However, Fe/V samples with V thicknesses within the range of 2.2–2.7 nm exhibited the GMR effect during the formation of the V hydride phase, although these samples did not display any GMR behavior under nitrogen gas. These behaviors were reversible to hydrogen concentration in the gas mixture. The dependence of the exchange coupling coefficient on the V layer thickness was estimated from the GMR behavior, revealing that the exchange coupling coefficient was governed by the change in the crystalline phase from the V metal to the hydride and not by the thickness of the V layer. In the V hydride phase, the GMR effect was gradually reduced following hydrogen absorption, suggesting that the induced structural disorder and/or stress in the V layer due to excess hydrogen absorption inhibited the exchange interactions between each Fe layer.
On projective stochastic-gradient type methods for solving large scale systems of nonlinear ill-posed equations: applications to machine learning
Authors: Rabelo J C , Saporito Y , Leitão A , Madureira A L 
Published: February 2025

Abstract
We propose and analyze a stochastic-gradient type method for solving systems of nonlinear ill-posed equations. The method considered here extends the stochastic gradient descent (SGD) type iteration introduced in Rabelo et al (2022 Inverse Problems
38 025003) for solving linear ill-posed systems. A distinctive feature of our method resides in the adaptive choice of the stepsize, which promotes a relaxed orthogonal projection of the current iterate onto a conveniently chosen convex set. This characteristic distinguish our method from other SGD type methods in the literature (where the stepsize is typically chosen a priori) and accounts for the faster convergence observed in the numerical experiments conducted in this manuscript. The convergence analysis discussed here includes: monotonicity and mean square convergence of the iteration error (exact data case), stability and semi-convergence (noisy data case). In the later case, our method is coupled with an a priori stopping rule. Numerical experiments are presented for two large scale nonlinear inverse problems in machine learning (both with real data): (i) we address, using neural networks, the big data problem of CO-concentration prediction considered in the above cited article; (ii) we tackle the classification problem for the MNIST database (http://yann.lecun.com/exdb/mnist/). Additionally, a parameter identification problem in a 3D elliptic PDE system is considered.