bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spectral micro-CT for simultaneous gold and iodine detection, and multi-material identification
Authors: Perion P. , Arfelli F. , Menk R.H. , Brombal L. 
Published: April 2024

Abstract
Multiple energy bin spectral micro-CT (SμCT) is an advanced imaging technique that allows multi-material decomposition according to their specific absorption patterns at a sub-100 μm scale. Typically, iodine is the preferred CT contrast agent for cardiovascular imaging, while gold nanoparticles have gained attention in recent years owing to their high absorption properties, biocompatibility and ability to target tumors.

In this work, we demonstrate the potential for multi-material decomposition through SμCT imaging of a test sample at the PEPI lab of INFN Trieste. The sample, consisting of gold, iodine, calcium, and water, was imaged using a Pixirad1/PixieIII chromatic detector with multiple energy thresholds and a wide spectrum (100 kV) produced by a micro-focus X-ray tube. The results demonstrate the simultaneous detection and separation of the four materials at a spatial scale of 35 μm, suggesting the potential of this technique in improving material detectability and quantification in a range of pre-clinical applications, including cardiovascular and oncologic imaging.
Detection of gastrointestinal foreign bodies in pets using single-grid-based dark-field X-ray imaging
Authors: Lee Jonghyeok , Lim Hyunwoo , Lee Hunwoo , Cho Hyosung 
Published: April 2024

Abstract
Gastrointestinal foreign bodies (GI-FBs) occur when pets consume non-digestible items that
do not readily pass through the stomach or intestines. While some GI-FBs pass through, many become
lodged along the gastrointestinal tract and cause discomfort, often leading to
sickness. Conventional absorption-based radiography is widely used to detect GI-FBs in
pets. However, detecting low-density FBs, such as wood, plastic, clothing, and sticks, is
typically difficult using conventional radiography. This study presents a novel approach for
detecting low-density GI-FBs in pets by using single-grid-based dark-field X-ray imaging
(SG-DFXI). It obtains microstructural information from small-angle X-ray scattering (SAXS) of the
sample using a conventional X-ray source and grid. SG-DFXI requires minimal exposure and system
setup and is specifically utilized to detect low-density materials that are invisible when using
conventional radiography. To validate the efficacy of the proposed method, an experiment was
conducted on a mouse phantom attached to a wooden chopstick as a low-density GI-FB. Quantitative
evaluation was performed using image quality metrics of the contrast-to-noise ratio (CNR) and
relative contrast gain (RCG). The CNR value measured in the dark-field image obtained with an
autocorrelation length of ξG
= 294 nm was 4.59, approximately 5.4
times larger than that of the absorption image obtained in the same imaging setup. In addition,
the RCG characteristics improved as the autocorrelation length of the system increased; the RCG
value for ξG
= 294 nm was 5.4, approximately 3.2 times larger than
that for ξG
= 194 nm. Thus, increasing the autocorrelation length of
the system is critical to improve its ability to detect low-density FBs. Consequently, SG-DFXI
appears to be a promising method that can be used effectively and easily to detect GI-FBs in pets,
which are barely visible in absorption images.
Data Processing Engine (DPE): data analysis tool for particle tracking and mixed radiation field characterization with pixel detectors Timepix
Authors: Marek L. , Granja C. , Jakubek J. , Ingerle J. , Turecek D. , Vuolo M. , Oancea C. 
Published: April 2024

Abstract
Hybrid semiconductor pixelated detectors from the Timepix family are advanced detectors for online particle tracking, offering energy measurement and precise time stamping capabilities for particles of various types and energies. This inherent capability makes them highly suitable for various applications, including imaging, medical fields such as radiotherapy and particle therapy, space-based applications aboard satellites and the International Space Station, and industrial applications. The data generated by these detectors is complex, necessitating the development and deployment of various analytical techniques to extract essential information.
For this purpose, and to aid the Timepix user community, it was designed and developed the
“Data Processing Engine” (DPE) as an advanced tool for data processing designed explicitly for Timepix detectors. The functionality of the DPE is structured into three distinct processing levels: i) Pre-processing: this phase involves clusterization and the application of necessary calibrations and corrections. ii) Processing: this stage includes particle classification, employing machine learning algorithms, and the recognition of radiation fields. iii) Post-processing: involves various analyses, such as directional analysis, coincidence analysis, frame analysis, Compton directional analysis, and the generation of physics products, are performed. The core of the DPE is supported by an extensive experimental database containing calibrations and referential radiation fields of typical environments, including protons, ions, electrons, gamma rays and X rays, as well as thermal and fast neutrons. To enhance accessibility, the DPE is implemented into various user interface platforms such as a command-line tool, an application programming interface, and as a graphical user interface in the form of a web portal. The DPE's broad utility is exemplified through its integration into various applications and developments.
Worldviews more than experience predict Californians’ support for wildfire risk mitigation policies
Authors: Howe Alexander A , Blomdahl Erika M , Smith-Eskridge Ellie , Pinto Dakoeta R , Brunson Mark W , Howe Peter D , Huntly Nancy J , Klain Sarah C 
Published: April 2024

Abstract
California must adapt to increasing wildfire activity concurrent with climate change and expanding housing development in fire-prone areas. Recent decades have seen record-breaking fire activity, economic costs, and human health impacts. Residents more frequently face home evacuations, prolonged periods of unhealthy air quality, and power shut-offs. Understanding how these experiences influence support for risk mitigation policies is essential to inform action on climate and fire adaptation. To better understand linkages between experience and policy support, we surveyed California residents (n = 645) about their wildfire-related experiences, risk perceptions, and support for 18 wildfire risk mitigation policies. To assess how the relationship between policy support and wildfire experience is modulated by preexisting worldviews, we measured the extent to which respondents are motivated by individualistic or communitarian values as proposed in the cultural theory of risk. We surveyed residents across a gradient of wildfire impacts, spatially stratifying residences based on wildland-urban-interface type and proximity to large 2020 wildfires. Support was generally high for most policies, though most respondents opposed incorporating future risk into insurance rates and coverage. Policy support models showed that communitarian worldviews were more consistently associated with greater support for diverse wildfire mitigation policies than were measures of recent experience with wildfire. These results suggest that California residents within our sample regions already support many wildfire risk mitigation strategies, and preexisting societal beliefs are a stronger predictor of these views than personal experiences with wildfire. Policy-makers can utilize this understanding to focus on crafting policies and messaging that resonates with individualistic values.
Satellite-detected large CO2 release in southwestern North America during the 2020–2021 drought and associated wildfires
Authors: Chen Hui , He Wei , Liu Jinxiu , Nguyen Ngoc Tu , Chevallier Frédéric , Yang Hua , Lv Yiming , Huang Chengcheng , Rödenbeck Christian , Miller Scot M , Jiang Fei , Liu Junjie , Johnson Matthew S , Philip Sajeev , Liu Zhiqiang , Zeng Ning , Basu Sourish , Baker David F 
Published: April 2024

Abstract
Southwestern North America (SWNA) continuously experienced megadroughts and large wildfires in 2020 and 2021. Here, we quantified their impact on the terrestrial carbon budget using net biome production (NBP) estimates from an ensemble of atmospheric inversions assimilating in-situ CO2 and Carbon Observatory–2 (OCO-2) satellite XCO2 retrievals (OCO-2 v10 MIP Extension), two satellite-based gross primary production (GPP) datasets, and two fire CO2 emission datasets. We found that the 2020–2021 drought and associated wildfires in SWNA led to a large CO2 loss, an ensemble mean of 95.07 TgC estimated by the satellite inversions using both nadir and glint XCO2 retrievals (LNLG) within the OCO-2 v10 MIP, greater than 80% of SWNA’s annual total carbon sink. Moreover, the carbon loss in 2020 was mainly contributed by fire emissions while in 2021 mainly contributed by drought impacts on terrestrial carbon uptake. In addition, the satellite inversions indicated the huge carbon loss was mainly contributed by fire emissions from forests and grasslands along with carbon uptake reductions due to drought impacts on grasslands and shrublands. This study provides a process understanding of how some droughts and following wildfires affect the terrestrial carbon budget on a regional scale.
SO(2,2) representations in polar coordinates and Pöschl-Teller potentials
Authors: Blazquez M , Negro Javier 
Published: April 2024

Abstract
This work is devoted to show the interest of polar coordinates in the description of some unitary irreducible representations (or uir’s) of the

S
O
(
2
,
2
)

group where the support space are functions on the three dimensional pseudosphere

H

R

2
,
2

. We will show that the differential equations associated to such uir’s can be interpreted as quantum systems including centrifugal terms; in our case these equations lead to one-dimensional Pöschl-Teller systems. The solutions to these equations are computed and the uir’s are characterized in terms of polar coordinates. We will also discuss briefly the more standard pseudospherical coordinates on

H

R

2
,
2

in order to appreciate some of the differences. We will consider as well the (maximally superintegrable) free classical systems defined on the real pseudosphere

H

R

2
,
2

symmetric under

S
O
(
2
,
2
)

. The constants of motion are found and they are applied to find some bounded (therefore periodic) and unbounded orbits also in terms of polar coordinates.
Thickness dependent tribological and magnetic behavior of two-dimensional cobalt telluride (CoTe2)
Authors: Slathia Surbhi , Wei Cencen , Tripathi Manoj , Tromer Raphael , Negedu Solomon Demiss , Boland Conor S , Sarkar Suman , Galvao Douglas S , Dalton Alan , Tiwary Chandra Sekhar 
Published: April 2024

Abstract
Two-dimensional (2D) layered transition-metal based tellurides (chalcogens) are known to harness their surface atoms’ characteristics to enhance topographical activities for energy conversion, storage, and magnetic applications. The gradual stacking of each sheet alters the surface atoms’ subtle features such as lattice expansion, leading to several phenomena and rendering tunable properties. Here, we have evaluated thickness-dependent mechanical properties (nanoscale mechanics, tribology, potential surface distributions, interfacial interaction) of 2D CoTe2 sheets and magnetic behavior using surface probe techniques. The experimental observations are further supported and explained with theoretical investigations: density functional theory and molecular dynamics. The variation in properties observed in theoretical investigations unleashes the crucial role of crystal planes of the CoTe2. The presented results are beneficial in expanding the use of the 2D telluride family in flexible electronics, piezo sensors, tribo-generators, and next-generation memory devices.
Effect of super absorbent polymer mixing method on shrinkage performance of cast-in-place concrete confined by existing concrete
Authors: Li Dajiang , Zuo Zhaonan , Huang Shaojie , Weng Yijian , Liu Xiaoyu , Rong Hongliu 
Published: April 2024

Abstract
To study the effect of different super absorbent polymer (SAP) mixing methods on mechanical and shrinkage properties of internal curing concrete, SAP mixing mode was used as the study variable, and simulated the bridge leveling structure. The flow performance test, mechanical performance test, free shrinkage test, restrained shrinkage test and scanning electron microscope observation test were carried out on the internal curing concrete. The influence of different mixing methods on the flow properties, mechanical properties, shrinkage properties and microstructure of internal curing concrete were analyzed. The results showed that the amount of mixing water had a great influence on the fluidity when adding pre-absorbent SAP; The effect of SAP on the mechanical properties is minimal when the dosage is 0.2% of the mass of cementing material; Pre-absorbent SAP has the best effect on improving shrinkage cracks of concrete; SAP stored water at the initial stage of hydration, promoted the generation of hydrolysis products, prolong the hydration process, filled the gap inside the concrete, and improved the anti-cracking performance. It is recommended that SAP should be partially pre-absorbed.
Processing-structure-property relationships in practical thin solid-electrolyte separators for all-solid-state batteries
Authors: Li Junhao , Kim Soochan , Mezzomo Lorenzo , Chart Yvonne , Aspinall Jack , Ruffo Riccardo , Pasta Mauro 
Published: April 2024

Abstract
Scalable processing of thin and robust solid-electrolyte (SE) separators is key for the commercialization of high-energy all-solid-state batteries (ASSBs). Herein, we report the preparation of Li6PS5Cl-based thin SE separators incorporating suitable binders for potential use in ASSBs by two scalable wet processing techniques: tape-casting with nitrile-butadiene rubber (NBR) and calendering with carboxylated nitrile butadiene rubber (XNBR). By means of tensile testing and electrochemical impedance spectroscopy, the influence of processing on the mechanical as well as the electrochemical properties of the resulting thin SE separators is investigated. A trade-off between the mechanical and electrochemical properties is observed, which is due to the inextricably linked microstructures (particle size, binder content and distribution, and porosity) induced by the two different processes. Thin SE separators prepared using the tape-casting method with the more well-distributed binder network demonstrate superior tensile mechanical properties compared to the ones prepared by the calendering method. The results provide insights into the processing-structure-property relationships of the thin SE separators, which will contribute to advancing the application of practical thin solid electrolytes in ASSBs.
A novel method for studying the wind speed probability distribution and estimating the average wind energy density
Authors: Wang Lingzhi , Zhang Xinbo 
Published: April 2024

Abstract
Traditional distribution models generally have large fitting errors at low wind speeds and poor fitting effects at multi-peak wind speed distributions. In this paper, a novel approach is proposed to fit different wind speed distributions, introducing a Gumbel distribution into common hybrid distribution models. The model parameters are solved by a combination of snake optimizer and nonlinear least squares (SO-NLS), using the optimal values obtained by the nonlinear least squares method as a set of initial input vectors for the snake optimizer. Simulation experiments were conducted using multi-peak wind speed distribution datasets with varying characteristics, comparing the fitting performance of the improved hybrid models against the conventional Weibull, Normal, and Rayleigh hybrid models. The results show that the proposed approach improved the model fit effects, particularly at low wind speeds, in all five experimental datasets. In most cases, the overall fitting effects were also improved. Furthermore, the validity and superiority of the improved hybrid models were further verified by comparing the estimated average wind energy density. Meanwhile, the experimental results also verified that SO-NLS not only yielded better optimization results but also accelerated the convergence speed than the snake optimizer. The improvements presented in this study effectively address the problem of large fitting errors at the low wind speed sections of the distribution, providing a theoretical basis for wind farm planning and design.
Sensitivity improvement in gate engineered technique dielectric modulated GaN MOSHEMT with InGaN notch for label-free biosensing
Authors: Mishra Girish Shankar , Mohankumar N , Singh Sankalp Kumar 
Published: April 2024

Abstract
This paper focuses on the impact of gate-engineered dielectric-modulated GaN MOSHEMT with InGaN notch on sensitivity enhancement for label-free biosensing. The novelty of this study utilizes the charge-plasma effect induced by the dual metal gate (DMG) technology adopted to realize the effect of sensitivity on different biomolecules. Moreover, the presence of an InGaN notch enhances carrier confinement in the 2DEG, subsequently improving the threshold voltage and device sensitivity at the AlGaN/GaN interface. The maximum drain current, IDS of 4.602 A mm−1, transconductance, gm of 18 mS/mm, and sensitivity has been improved by around 61% for the Uricase biomolecule by introducing the dual metal gate technology. The work function difference of the two metal gates suppresses the Short Channel Effects (SCEs) and hot carrier effects in DMG MOSHEMT, thereby screening the drain potential variations by the gate near the drain. In addition, increased carrier transport efficiency results from a more consistent electric field along the channel. All the simulations are carried out using the Sentaurus TCAD simulator, and the results imply the feasibility of gate-engineered GaN MOSHEMT for label-free biosensing.
Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors
Authors: Knipfer Marco , Meier Stefan , Volk Tobias , Heimerl Jonas , Hommelhoff Peter , Gleyzer Sergei 
Published: April 2024

Abstract
Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.
Chinese Visceral Adiposity Index Predict Prehypertension Progression and Regression: A Prospective Cohort Study Involving Middle-Aged and Older Adults
Authors: Dai Senjie , Yang Yang , Wang Dongying 
Published: April 2024

Abstract

BACKGROUND
Limited data are published on the relationship of the Chinese visceral adiposity index (CVAI) with prehypertension progression or regression. Therefore, we investigated this association through the China Health and Retirement Longitudinal Study.

METHODS
Participants with prehypertension were assigned to two groups according to baseline CVAI, and after 4 years of follow-up, their blood pressure was analyzed for deterioration or improvement. We constructed logistic regression models for assessing the association of CVAI with the progression or regression of prehypertension. A restricted cubic spline (RCS) model was utilized for determining the dose–response association. Subgroup analysis and sensitivity analysis were also conducted.

RESULTS
The study included 2,057 participants with prehypertension. During the follow-up, 695 participants progressed to hypertension, 561 participants regressed to normotension, and 801 participants remained as prehypertensive. An association was observed between a high CVAI value and a higher incidence of progression to hypertension and between a high CVAI value and a lower incidence of regression to normotension (OR = 1.66 and 0.58, 95% CI: 1.35–2.05 and 0.47–0.73, respectively). The RCS model exhibited a linear association between CVAI and prehypertension progression and regression (all P for non-linear &gt; 0.05). The results of the subgroup and sensitivity analyses agreed with those of the primary analysis.

CONCLUSIONS
A significant association was noted between CVAI and prehypertension progression and regression. Thus, as part of the hypertension prevention strategy, monitoring CVAI is crucial in individuals with prehypertension.
VirusPredictor: XGBoost-based software to predict virus-related sequences in human data
Authors: Liu Guangchen , Chen Xun , Luan Yihui , Li Dawei 
Published: April 2024

Abstract

Motivation
Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data.

Results
We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, i.e. 0.76, 0.93, and 0.98 for 150–350 (Illumina short reads), 850–950 (Sanger sequencing data), and 2000–5000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to &gt;0.98 when query sequences increased from 150–350 to &gt;850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g. ∼1000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients’ unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions.

Availability and implementation
www.dllab.org/software/VirusPredictor.html.