bluuebunny/crossref_metadata_2025_split huggingface dataset:
NPreciSe - An Automated Satellite Precipitation Product Assessment Tool
Authors: Arulraj Malarvizhi , Petković Veljko , Wen Susan , Ferraro Ralph R. , Meng Huan 
Published: September 2024

AbstractSatellite-based Quantitative Precipitation Estimates (QPE) are indirect estimates of precipitation rates and as such are often prone to errors, warranting a need for characterizing the associated uncertainties before being used in application-specific studies. Moreover, multiple satellite-based QPE products are offered through different agencies, each with their own specifications, formats and requirements, posing a challenge to understanding the products uncertainties. This manuscript presents a standardized validation system named NPreciSe – NOAA Satellite-based Precipitation Validation System, which assesses the performance of satellite-based precipitation products in near real-time over the continental United States. NPreciSe is coupled with a user-interactive web platform and built using an open-source software, Python. It is structured to help (1) the end-users determine the best satellite QPE for their specific application, and (2) the algorithm developers identify systematic biases in QPE retrievals. This manuscript presents the capabilities of the NPreciSe, discusses the methodology adopted in developing the standardized validation system, and introduces the web portal.
An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition
Authors: Gouverneur Philip , Badura Aleksandra , Li Frédéric , Bieńkowska Maria , Luebke Luisa , Adamczyk Wacław M. , Szikszay Tibor M. , Myśliwiec Andrzej , Luedtke Kerstin , Grzegorzek Marcin , Piętka Ewa 
Published: September 2024

AbstractAccess to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.
G protein coupled receptor transcripts in human immune cells and platelets
Authors: Hansen Arne , Martin Daniel , Langer Florian , Harrison Kathleen , Kehrl John , Cicala Claudia , Martinelli Elena , Brownstein Michael J. , Mezey Eva 
Published: September 2024

AbstractG-protein coupled receptors (GPCRs) are encoded by nonabundant mRNAs, and it is difficult to detect them reliably with the highly parallel methods that are in general use. Because of this, we developed and validated a sensitive, specific, semi-quantitative method for detecting these transcripts. We have used the method to profile GPCR transcripts in white blood cells (WBCs)–B, CD4, CD8, NK, and dendritic cells; monocytes, and macrophage-like monocytes treated with granulocyte-macrophage colony-stimulating factor–as well as platelets. On average, the white cells studied expressed 160 receptor mRNAs (range, 123–206). Platelets made 69. Some, but far from all, of the receptors we found have been detected earlier. We believe our data should stimulate studies of receptor function and contribute to drug development.
Temporal dynamics in psychological assessments: a novel dataset with scales and response times
Authors: Su Zhao , Liu Rongxun , Wei Yange , Zhang Ran , Xu Xiao , Wang Yang , Zhu Yue , Wang Lifei , Liang Lijuan , Wang Fei , Zhang Xizhe 
Published: September 2024

AbstractThe growing prevalence of mental health issues underscores the need for innovative screening methods. Large-scale, internet-based psychological screening has emerged as a vital tool to accurately determine morbidity rates and facilitate early diagnosis of mental disorders. However, conventional psychological screening methods often struggle with non-genuine responses and lack objective metrics. To bridge this gap, we have compiled a novel dataset derived from an expansive screening initiative at Xinxiang Medical University. The study, conducted from February 27 to March 17, 2021, yielded a dataset comprising responses from 24,292 students to four well-established psychological scales—PHQ-9, GAD-7, ISI, and PSS. A distinctive feature of this dataset is the inclusion of response time data, which captures the temporal dynamics of participants’ interactions with the scales, offering valuable insights into their response behaviour. The release of this dataset offers a substantial opportunity for researchers in the domains of psychology and public health to explore new insights into mental health, scale reliability, and the dynamics of psychological assessment.
A novel isothermal whole genome sequencing approach for Monkeypox Virus
Authors: Licheri Matthias , Licheri Manon Flore , Probst Lukas , Sägesser Cora , Bittel Pascal , Suter-Riniker Franziska , Dijkman Ronald 
Published: September 2024

Abstract
Monkeypox virus (MPXV) is the zoonotic agent responsible for mpox, an often-self-limiting pox-like disease. Since May 2022, an outbreak characterized by increased human-to-human transmission was detected outside the endemic regions. Whole genome sequencing (WGS) has been successfully used to keep track of viral evolution during outbreaks or for surveillance of multiple pathogens of public health significance. Current WGS protocols for MPXV are either based on metagenomic sequencing or tiled-PCR amplification. The latter allows multiplexing due to the efficient enrichment of the viral DNA, however, mutations or the presence of different clades can negatively influence genome coverage yield. Here, we present the establishment of a novel isothermal WGS method for MPXV based on Phi29 DNA polymerase-based multiple displacement amplification (MDA) properties making use of only 6 primers. This approach yielded from 88% up to 100% genome coverage using either alkaline denatured extracted DNA or clinical material as starting material, with the highest coverage generated by clinical material. We demonstrate that this novel isothermal WGS protocol is suitable for monitoring viral evolution during MPXV outbreaks and surveillance in any conventional laboratory setting.
Universal neural networks for real-time earthquake early warning trained with generalized earthquakes
Authors: Zhang Xiong , Zhang Miao 
Published: September 2024

AbstractDeep learning enhances earthquake monitoring capabilities by mining seismic waveforms directly. However, current neural networks, trained within specific areas, face challenges in generalizing to diverse regions. Here, we employ a data recombination method to create generalized earthquakes occurring at any location with arbitrary station distributions for neural network training. The trained models can then be applied universally with different monitoring setups for earthquake detection and parameter evaluation from continuous seismic waveform streams. This allows real-time Earthquake Early Warning (EEW) to be initiated at the very early stages of an occurring earthquake. When applied to substantial earthquake sequences across Japan and California (US), our models reliably report most earthquake locations and magnitudes within 4 seconds of the initial P-wave arrival, with mean errors of 2.6-7.3 km and 0.05-0.32, respectively. The generalized neural networks facilitate global applications of real-time EEW, eliminating complex empirical configurations typically required by traditional methods.
The TrkC-PTPσ complex governs synapse maturation and anxiogenic avoidance via synaptic protein phosphorylation
Authors: Khaled Husam , Ghasemi Zahra , Inagaki Mai , Patel Kyle , Naito Yusuke , Feller Benjamin , Yi Nayoung , Bourojeni Farin B , Lee Alfred Kihoon , Chofflet Nicolas , Kania Artur , Kosako Hidetaka , Tachikawa Masanori , Connor Steven , Takahashi Hideto 
Published: September 2024

AbstractThe precise organization of pre- and postsynaptic terminals is crucial for normal synaptic function in the brain. In addition to its canonical role as a neurotrophin-3 receptor tyrosine kinase, postsynaptic TrkC promotes excitatory synapse organization through interaction with presynaptic receptor-type tyrosine phosphatase PTPσ. To isolate the synaptic organizer function of TrkC from its role as a neurotrophin-3 receptor, we generated mice carrying TrkC point mutations that selectively abolish PTPσ binding. The excitatory synapses in mutant mice had abnormal synaptic vesicle clustering and postsynaptic density elongation, more silent synapses, and fewer active synapses, which additionally exhibited enhanced basal transmission with impaired release probability. Alongside these phenotypes, we observed aberrant synaptic protein phosphorylation, but no differences in the neurotrophin signaling pathway. Consistent with reports linking these aberrantly phosphorylated proteins to neuropsychiatric disorders, mutant TrkC knock-in mice displayed impaired social responses and increased avoidance behavior. Thus, through its regulation of synaptic protein phosphorylation, the TrkC–PTPσ complex is crucial for the maturation, but not formation, of excitatory synapses in vivo.
Laminator trust in human–robot collaboration for manufacturing fibre‐reinforced composites
Authors: Pickard Laura Rhian , Elkington Michael 
Published: September 2024

AbstractFibre‐reinforced composites manufacturing is a large and growing industry, with much of the work carried out manually by skilled human laminators. The physical nature of the work can be significantly deleterious to these workers' health, while growing demand requires increased rates of manufacture. Human–robot collaborative manufacturing offers a potential solution but requires the human to feel confident working with the robot and trust that they will be safe. Successful human trials of two different approaches to collaborative lay‐up of fibre‐reinforced plastic composites are presented, with tasks representative of manufacturing challenges in industry. Volunteer responses are measured by questionnaires, with users reporting the processes to be safe, simple to use and allowing greater ease of manufacturing than manual‐only lay‐up.
Research on interface charge behaviour and electrical threshold in layered epoxy/paper composites: From experiment to simulation
Authors: Xu Tianlei , Pang Xi , Xie Zongliang , Liu Peng , Peng Zongren 
Published: September 2024

AbstractLayered epoxy/paper composites that exhibit excellent insulating characteristics under elevated electrification and temperature conditions are essential components for power system insulation. Yet, inevitable charge accumulation occurs at hetero interfaces between layers due to interfacial polarisation and interface barrier effect, bringing about local electric field distortion and potential risk of partial discharge. A fundamental challenge is either to obtain accurate interface charge behaviours of in‐service multi‐layer insulating composites, or to construct verified simulating models for replacing experiments. Herein, a modified bipolar charge transport model to simulate interface charge behaviours in layered composites is proposed. With model parameters directly originated from equivalent experiments (e.g. conduction current measurement, thermally stimulated depolarisation current testing, and ultraviolet‐visible spectroscopy) of epoxy/paper composites, the simulated temperature‐dependent interface charge characteristics match well with pulsed electro‐acoustic results. Furthermore, electrical thresholds can also be accurately calculated using such models (maximum deviation of 8.44% from experimental results), providing references for optimised insulation structural design.
Research on temperature distribution characteristics of oil‐immersed power transformers based on fluid network decoupling
Authors: Xu Yongming , Xu Ziyi , Ren Congrui , Wang Yaodong 
Published: September 2024

AbstractDue to the complex structure and large size of large‐capacity oil‐immersed power transformers, it is difficult to predict the winding temperature distribution directly by numerical analysis. A 180 MVA, 220 kV oil‐immersed self‐cooling power transformer is used as the research object. The authors decouple the internal fluid domain of the power transformer into four regions: high voltage windings, medium voltage windings, low voltage windings, and radiators through fluid networks and establish the 3D fluid‐temperature field numerical analysis model of the four regions, respectively. The results of the fluid network model are used as the inlet boundary conditions for the 3D fluid‐temperature numerical analysis model. In turn, the fluid resistance of the fluid network model is corrected according to the results of the 3D fluid‐temperature field numerical analysis model. The prediction of the temperature distribution of windings is realised by the coupling calculation between the fluid network model and the 3D fluid‐temperature field numerical analysis model. Based on this, the effect of the loading method of the heat source is also investigated using the proposed method. The hotspot temperatures of the high‐voltage, medium‐voltage, and low‐voltage windings are 89.43, 86.33, and 80.96°C, respectively. Finally, an experimental platform is built to verify the results. The maximum relative error between calculated and measured values is 4.42%, which meets the engineering accuracy requirement.
Design and analysis of parallel quantum transfer fractal priority replay with dynamic memory algorithm in quantum reinforcement learning for robotics
Authors: Palanivel R. , Muthulakshmi P. 
Published: September 2024

AbstractThis paper introduces the parallel quantum transfer fractal priority reply with dynamic memory (P‐QTFPR‐DM) algorithm, an innovative approach that combines quantum computing and reinforcement learning (RL) to enhance decision‐making in autonomous vehicles. Leveraging quantum principles such as superposition and entanglement, P‐QTFPR‐DM optimises Q‐value approximation through a custom quantum circuit (UQC), facilitating efficient exploration and exploitation in high‐dimensional state‐action spaces. This algorithm utilises a quantum neural network (QNN) with 4 qubits to encode and process Q‐values. The autonomous vehicle, equipped with GPS for real‐time navigation, uses P‐QTFPR‐DM to reach a predefined destination with coordinates 12.82,514,234,148 latitude and 80.0,451,311,962,242 longitude. Through extensive numerical simulations, P‐QTFPR‐DM demonstrates a 30% reduction in decision‐making time and a 25% improvement in navigation accuracy compared to classical RL methods. The QNN‐based approach achieves a 95% success rate in reaching the destination within a 5‐m accuracy threshold, whereas traditional RL methods achieve only an 85% success rate. Dynamic memory management in P‐QTFPR‐DM optimises computational resources, enhancing the vehicle's adaptability to environmental changes. These results highlight the potential of quantum computing to significantly advance autonomous vehicle technology by improving both efficiency and effectiveness in complex navigation tasks. Future research will focus on refining the algorithm and exploring its real‐world applications to enhance autonomous vehicle performance.
Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures
Authors: Nair A. K. , Da Silva C. M. , Amon C. H. 
Published: September 2024

Predicting the thermal conductivity of two-dimensional (2D) heterostructures is challenging and cannot be adequately resolved using conventional computational approaches. To address this challenge, we propose a new and efficient approach that combines first-principles density functional theory (DFT) calculations with a machine-learning interatomic potential (MLIP) methodology to determine the thermal conductivity of a novel 2D van der Waals TiS2/MoS2 heterostructure. We leverage the proposed approach to estimate the thermal conductivities of TiS2/MoS2 heterostructures as well as bilayer-TiS2 and bilayer-MoS2. A unique aspect of this approach is the combined implementation of the moment tensor potential for short-range (intralayer) interactions and the D3-dispersion correction scheme for long-range (interlayer) van der Waals interactions. This approach employs relatively inexpensive computational DFT-based datasets generated from ab initio molecular dynamics simulations to accurately describe the interatomic interactions in the bilayers. The thermal conductivities of the bilayers exhibit the following trend: bilayer-TiS2 &gt; bilayer-MoS2 &gt; the TiS2/MoS2 heterostructure. In addition, this work makes the case that the 2D bilayers exhibit considerably higher thermal conductivities than bulk graphite, a common battery anode material, indicating the potential to utilize 2D heterostructures in thermal management applications and energy storage devices. Furthermore, the MLIP-based methodology provides a reliable approach for estimating the thermal conductivity of bilayers and heterostructures.
Analytical solutions to the cylindrically symmetric compressible isothermal Euler–Poisson equations of galaxies
Authors: Zhang Qiao , Dong Jianwei 
Published: September 2024

Abstract
In this paper, we study the cylindrically symmetric compressible isothermal Euler–Poisson equations of galaxies with a negative cosmological constant. By using some ansatzes, we construct a family of analytical periodic solutions to the system, which partially complements the result in Yuen (2009 Class. Quantum Grav.
26 235011), where a family of analytical periodic solutions to the system with spherical symmetry were given for the adiabatic exponent

γ
=

4
3

. Our result may provide an explanation about the dynamical evolution of the cylindrically symmetric galaxies or interstellar clouds.
Accurate determination of matrix composition in

Cd

x

Zn

1
−
x

O

semiconductor material using MS-SIMS and ToF-SIMS methods
Authors: Khosravizadeh Z , Trzyna-Sowa M , Lysak A , Przeździecka E , Jakiela R 
Published: September 2024

Abstract
This study focuses on the method for determining the exact composition for

Cd

x

Zn

1
−
x

O

semiconducting material using secondary ion mass spectrometry. The calibration curve method is employed to establish a quantitative relationship between the intensity of secondary ions and the concentrations of elements in

Cd

x

Zn

1
−
x

O

thin films. Additionally, the study compares the relative sensitivity factors with the calibration curve method for determining the value of x in

Cd

x

Zn

1
−
x

O

. A comparison between the performances of Time of Flight and Magnetic Sector SIMS in analyzing

Cd

x

Zn

1
−
x

O

thin films is also presented. This approach aims to enhance the accuracy and reliability of quantitative analysis in SIMS for

Cd

x

Zn

1
−
x

O

thin films.
Pressure tuning of HgCdTe epitaxial layers—the role of the highly disordered buffer layer
Authors: Yavorskiy D , Ivonyak Y , But D , Karpierz K , Krajewska A , Haras M , Sai P , Dub M , Kazakov A , Cywiński G , Knap W , Łusakowski J 
Published: September 2024

Abstract
Hg

1
−
x

Cd
x
Te alloys are unique because by increasing the Cd content x, one modifies the band structure from inverted to normal, which fundamentally modifies the dispersion of bulk, surface or edge (in the case of quantum wells) energy states. Using alloys with x close to the concentration xc
at which the band inversion transition is observed and with additional application of hydrostatic pressure (p), one creates a favorable playground for studying the evolution of Dirac matter and its topological properties. In this work, cryogenic magnetospectroscopy in quantizing magnetic fields (B) in the far-infrared is used to study inter-Landau-level transitions in high-quality Hg

1
−
x

Cd
x
Te MBE-grown epitaxial layers with

x
≈

x
c

as a function of p up to 4.2 kbar. Special attention is paid to elucidate the role of the substrate and buffer layers, which usually modify the pressure coefficients of epitaxial layers. For this purpose, comparative measurements were carried out on as-grown epilayers with a GaAs substrate and on free-standing layers obtained by etching off the substrate. Spectra registered as a function of B (at given p) were analyzed with the help of the Kane model modified to include magnetic field. The pressure coefficient as well as the difference between conduction and valence band deformation potentials of the free-standing layer were determined at 2 K. Surprisingly, the deformation potentials and pressure coefficients of the epitaxial layer and those of the free-standing layer differed by no more than 10% in the pressure range up to 4.2 kbar. This finding questions the common belief of a dominant influence of the substrate on the pressure coefficients of epitaxial layers. We attribute the smallness of this difference to the presence of a highly disordered CdTe buffer separating the substrate from the epitaxial layer, which relaxes the transmission of strain from the substrate to the layer. Our results contribute to a better understanding of pressure experiments carried out on epitaxial layers on a substrate.
Unveiling the boson peaks in amorphous phase-change materials
Authors: Moesgaard Jens , Fujita Tomoki , Wei Shuai 
Published: September 2024

Abstract
The boson peak is a universal phenomenon in amorphous solids. It can be observed as an anomalous contribution to the low-temperature heat capacity over the Debye model. Amorphous phase-change materials (PCMs) such as Ge–Sb–Te are a family of poor glass formers with fast crystallization kinetics, being of interest for phase-change memory applications. So far, whether boson peaks exist in PCMs is unknown and, if they do, their relevance to PCM properties is unclear. Here, we investigate the thermodynamic properties of the pseudo-binary compositions on the tie-line between Ge15Te85 and Ge15Sb85 from a few Kelvins to the liquidus temperatures. Our results demonstrate the evidence of the pronounced boson peaks in heat capacity below 10 K in the amorphous phase of all compositions. By fitting the data using the Debye model combined with a modification of the Einstein model, we can extract the characteristic parameters of the boson peaks and attribute their origin to the excess vibrational modes of dynamic defects in the amorphous solids. We find that these parameters correlate almost linearly with the Sb-content of the alloys, despite the nonmonotonic behaviors in glass forming abilities and thermal stabilities. In a broader context, we show that the correlations of the characteristic parameters of the boson peaks with T
g and kinetic fragility, vary according to the type of bonding. Specifically, metallic glasses and conventional covalent glasses exhibit distinct patterns of dependence, whereas PCMs manifest characteristics that lie in between. A deeper understanding of the boson peaks in PCMs holds the promise to enable predictions of material properties at higher temperatures based on features observed in low-temperature heat capacity.