bluuebunny/crossref_metadata_2025_split huggingface dataset:
Power absorption and temperature rise in deep learning based head models for local radiofrequency exposures
Authors: Kodera Sachiko , Yoshida Reina , Rashed Essam A , Diao Yinliang , Takizawa Hiroyuki , Hirata Akimasa 
Published: February 2025

Abstract
Objective: Computational uncertainty and variability of power absorption and temperature rise in humans for radiofrequency (RF) exposure is a critical factor in ensuring human protection. This aspect has been emphasized as a priority. However, accurately modeling head tissue composition and assigning tissue dielectric and thermal properties remains a challenging task. This study investigated the impact of segmentation-based versus segmentation-free models for assessing localized RF exposure. &#xD;Approach: Two computational head models were compared: one employing traditional tissue segmentation and the other leveraging deep learning to estimate tissue dielectric and thermal properties directly from magnetic resonance images. The finite-difference time-domain method and the bioheat transfer equation was solved to assess temperature rise for local exposure. Inter-subject variability and dosimetric uncertainties were analyzed across multiple frequencies.&#xD;Main Results: The comparison between the two methods for head modeling demonstrated strong consistency, with differences in peak temperature rise of 7.6±6.4%. The segmentation-free model showed reduced inter-subject variability, particularly at higher frequencies where superficial heating dominates. The maximum relative standard deviation in the inter-subject variability of heating factor was 15.0% at 3 GHz and decreased with increasing frequencies.&#xD;Significance: This study highlights the advantages of segmentation-free deep-learning models for RF dosimetry, particularly in reducing inter-subject variability and improving computational efficiency. While the differences between the two models are relatively small compared to overall dosimetric uncertainty, segmentation-free models offer a promising approach for refining individual-specific exposure assessments. These findings contribute to improving the accuracy and consistency of human protection guidelines against RF electromagnetic field exposure.
Alleviating the trade-off between coincidence time resolution and sensitivity using scalable TOF-DOI detectors
Authors: Onishi Yuya , Ota Ryosuke 
Published: February 2025

Abstract
Objective. Coincidence time resolution (CTR) in time-of-flight positron emission tomography (TOF-PET) has significantly improved with advancements in scintillators, photodetectors, and readout electronics. Achieving a CTR of 100 ps remains challenging due to the need for sufficiently thick scintillators—typically 20 mm—to ensure adequate sensitivity because the photon transit time spread within these thick scintillators impedes achieving 100 ps CTR. Therefore thinner scintillators are preferable for CTR better than 100 ps. To address the trade-off between TOF capability and sensitivity, we propose a readout scheme of PET detectors. Approach. The proposed scheme utilizes two orthogonally stacked one-dimensional PET detectors, enabling the thickness of the scintillators to be reduced to approximately 13 mm without compromising sensitivity. This is achieved by stacking the detectors along the depth-of-interaction (DOI) axis of a PET scanner. We refer to this design as the cross-stacked detector, or xDetector. Furthermore, the xDetector inherently provides DOI information using the same readout scheme. Main results. Experimental evaluations demonstrated that the xDetector achieved the best CTR of 175 ps full width at half maximum (FWHM) and an energy resolution of 11% FWHM at 511 keV with 3 × 3 × 12.8 mm3 lutetium oxyorthosilicate crystals, each coupled one-to-one with silicon photomultipliers. The CTRs are between the xDetector and reference detector with a single timing resolution of 111.2 ± 0.8 ps FWHM. In terms of xy-spatial resolution, the xDetector exhibited an asymmetric resolution due to its readout scheme: one resolution was defined by the 3.2 mm readout pitch, while the other was calculated using the center-of-gravity method. Significance. The xDetector effectively resolves the trade-off between TOF capability and sensitivity while offering scalability and DOI capability. By integrating state-of-the-art scintillators, photodetectors, and readout electronics with the xDetector scheme, achieving a CTR of 100 ps FWHM alongside high DOI resolution becomes a practical possibility.
Heralding Higher-Dimensional Bell and Greenberger-Horne-Zeilinger States Using Multiport Splitters
Authors: Bhatti Daniel , Barz Stefanie 
Published: February 2025

Abstract
One of the most important resources for quantum optical experiments and applications are on-demand highly-entangled multiphoton quantum states. A promising way of generating them is heralding entanglement generation at a high rate from letting independent photons interfere. However, such schemes often work for a specific internal degree of freedom of the interfering photons only. Going to higher numbers of entangled photons, the success probabilities decrease while the number of necessary resources, e.g., auxiliary photons and optical elements, increases. To make probabilistic schemes feasible also for larger quantum states, it is therefore important to find resource-efficient generation schemes with high success probabilities. In this work, we introduce easily implementable schemes to herald qubit Greenberger-Horne-Zeilinger (GHZ) states, higher-dimensional Bell states and higher-dimensional three-party GHZ states. Our schemes solely rely on multiphoton interference, i.e., they can be adjusted to work for arbitrary degrees of freedom. Furthermore, they demonstrate high success probabilities and need comparably few auxiliary photons.
Transparent PT-symmetric nonlinear networks
Authors: Akramov Mashrab , Yusupov Jambul , Ehrhardt Matthias , Susanto Hadi , Matrasulov Davron 
Published: February 2025

Abstract
We study reflectionless wave propagation in networks modeled by the nonlocal nonlinear Schrödinger equation on metric graphs, with transparent boundary conditions applied at the vertices. Using a "potential approach" previously developed for the nonlinear Schrödinger equation, we derive transparent boundary conditions specific to the nonlocal nonlinear Schrödinger equation on metric graphs. These conditions prevent backscattering at the vertices, which is essential for reducing losses in signal, heat, and charge transfer in applications including optical fibers, optoelectronic networks, and low-dimensional materials.&#xD;
A physics-based modeling method for DC characteristics of GaN HEMTs with conventional, dual-, and tri-gate
Authors: Wang Ashu , Zeng Lingyan 
Published: February 2025

Abstract
In this study, we have developed a physics-based modeling approach to accurately characterize the DC properties of GaN high electron mobility transistors (HEMTs) featuring conventional, dual-, and tri-gate architectures. Our methodology begins with the derivation of analytical expressions for the intrinsic drain current of these three device types, omitting the effects of source and drain access resistances. For dual-gate HEMTs, we employ an approximation of depletion within a rectangular region to depict the channel modulation achieved by the sidewall gate. Building upon this, we dissect the combined influence of the top and sidewall gates in tri-gate HEMTs into two distinct control mechanisms. This involves initially calculating the two-dimensional electron gas (2DEG) density under sole top gate control, followed by determining the 2DEG depletion width governed by the sidewall gate. Incorporating access resistances, the DC characteristics of all three devices are computed through numerical iteration. To validate the channel control model directly, we exclude the self-heating effect and conduct comparative TCAD simulations. Furthermore, our modeling results, which encompass the self-heating effect, are corroborated by experimental data, thereby ensuring the reliability and accuracy of our approach.
Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities
Authors: Phang Chun-Ren , Hirata Akimasa 
Published: February 2025

Abstract
Objective: Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilitate the tedious manual labeling process. Notably, recently proposed sleep staging algorithms lack model explainability and still require performance improvement. Approach: We implemented multiscale neurophysiology-mimicking kernels to capture sleep-related electroencephalogram (EEG) activities at varying frequencies and temporal lengths; the implemented model was named “Multiscale Temporal Convolutional Neural Network (MTCNN).” Further, we evaluated its performance using an open-source dataset (Sleep-EDF Database Expanded comprising 153 days of polysomnogram data). Main results: By investigating the learned kernel weights, we observed that MTCNN detected the EEG activities specific to each sleep stage, such as the frequencies, K-complexes, and sawtooth waves. Furthermore, regarding the characterization of these neurophysiologically significant features, MTCNN demonstrated an overall accuracy (OAcc) of 91.12% and a Cohen kappa coefficient of 0.86 in the cross-subject paradigm. Notably, it demonstrated an OAcc of 88.24% and a Cohen kappa coefficient of 0.80 in the leave-few-days-out analysis. Our MTCNN model also outperformed the existing deep learning models in sleep stage classification even when it was trained with only 16% of the total EEG data, achieving an OAcc of 85.62% and a Cohen kappa coefficient of 0.75 on the remaining 84% of testing data. Significance: The proposed MTCNN enables model explainability and it can be trained with lesser amount of data, which is beneficial to its application in the real-world because large amounts of training data are not often and readily available.
Impacts of climate change on groundwater quality: a systematic literature review of analytical models and machine learning techniques
Authors: Chowdhury Tahmida Naher Naher , Battamo Ashenafi Yohannes , Nag Rajat , Zekker Ivar , Salauddin Md 
Published: February 2025

Abstract
The increasing impact of global climate change on hydrogeological and hydrological systems presents substantial challenges to the sustainable management of groundwater quality. Changes in precipitation regimes, temperature fluctuations, and the frequency of extreme hydro-climatic events driven by climate change accelerate the deterioration of groundwater quality (GWQ), thereby threatening ecosystems and human health. In response to these challenges, recent research has increasingly focused on developing and refining analytical models (AM) and machine learning (ML) techniques to understand better and predict the impacts of climate change on GWQ. This systematic literature review critically examines the current state of knowledge on applying AM and ML models in the context of GWQ assessment under climate-induced stressors. By synthesizing findings from a comprehensive review of existing studies, this paper discusses the capabilities, limitations, and future directions of hybrid ML and traditional AM in GWQ prediction, vulnerability, and threshold estimation. The review reveals that while ML approaches significantly enhance predictive accuracy and model robustness, there remain substantial challenges in their application due to the complexity of climate-induced variables and the scarcity of high-resolution data. This paper aims to provide GWQ researchers, water resource managers, and policymakers with an advanced understanding of the interactions between climate change and GWQ and the innovative AM and ML modelling approaches available to address these challenges. By highlighting the potential and limitations of current models, this review offers insights into developing more effective and adaptive management strategies for safeguarding groundwater quality in an era of rapid climatic change.&#xD;
Massless conformal fields in ten dimensions , minimal unitary representation of E
7(-25) and exceptional supergravity
Authors: Gunaydin Murat 
Published: February 2025

Abstract
Minimal unitary representation of $SO(d,2)$ and its deformations describe all the conformally massless fields in $d$ dimensional Minkowskian spacetimes. In critical dimensions these spacetimes admit extensions with twistorial coordinates plus a dilatonic coordinate to causal spacetimes coordinatized by Jordan algebras $J_3^{\mathbb{A}}$ of degree three over the four division algebras $\mathbb{A}=\mathbb{R} , \mathbb{C} , \mathbb{H} , \mathbb{O} $. We study the minimal unitary representation (minrep) of the conformal group $E_{7(-25)}$ of the spacetime coordinatized by the exceptional Jordan algebra $J_3^{\mathbb{O}}$ defined over octonions $\mathbb{O}$. &#xD;We show that the minrep of $E_{7(-25)}$ decomposes into infinitely many massless representations of the ten dimensional conformal group $SO(10,2)$. Corresponding conformal fields transform as symmetric tensors in spinor indices of $SO(9,1)$ subject to certain constraints. Even and odd tensorial fields describe bosonic and fermionic conformal fields , respectively. Each irrep of $SO(10,2)$ comes with infinite multiplicity and fall into a unitary representation of an $SU(1,1)$ subgroup that commutes with $SO(10,2)$. &#xD;The noncompact generators transforming in spinor representation $16 $ of $SO(10)$ interpolate between the bosonic and fermionic representations and hence act like "bosonic supersymmetry" generators. These results truncate to similar results in the lower critical space-time dimensions. We also give the decomposition of the minrep of $E_{7(-25)}$ with respect to the subgroup $SO^*(12)\times SU(2)$ with $SO^*(12) $ acting as the conformal group of the spacetime coordinatized by the $J_3^{\mathbb{H}}$ defined over quaternions $\mathbb{H}$. Group $E_{7(-25)}$ is also the U-duality group of the exceptional $N=2$ Maxwell-Einstein supergravity in four dimensions. We discuss the relevance of our results to the composite scenario that was proposed for the exceptional supergravity so as to accommodate the families of quarks and leptons of the standard model as well as to the proposal that $E_{7(-25)}$ acts as spectrum generating symmetry group of the $5d$ exceptional supergravity.
Invasion of perennial sagebrush steppe by shallow-rooted exotic cheatgrass reduces stable forms of soil carbon in a warmer but not cooler ecoregion.
Authors: Katz Sydney , Maxwell Toby , de Graaff Marie Anne , Germino Matthew 
Published: February 2025

Abstract
Soil organic carbon (“SOC”) in drylands comprises nearly a third of the global SOC pool and has relatively rapid turnover and thus is a key driver of variability in the global carbon cycle. SOC is also a sensitive indicator of longer-term directional change and disturbance-responses of ecosystem C storage. Biome-scale disruption of the dryland carbon cycle by exotic annual grass invasions (mainly Bromus tectorum, “Cheatgrass”) threatens carbon storage and corresponding benefits to soil hydrology and nutrient retention. Past studies on cheatgrass impacts mainly focused on total C, and of the few that evaluated SOC, none compared the very different fractions of SOC, such as relatively unstable particulate organic carbon (POC) or relatively stable, mineral-associated organic carbon (MAOC). We measured SOC and its POC and MAOC constituents in the surface soils of sites that had sagebrush canopies but differed in whether their understories had been invaded by cheatgrass or not, in both warm and relatively colder ecoregions of the western USA. MAOC stocks were 36.1% less in the 0-10 cm depth and 46.1% less in the 10-20 cm depth in the cheatgrass-invaded stands compared to the uninvaded stands of the warmer Colorado Plateau, but not in the cooler and more carbon-rich Wyoming Basin ecoregion. In plots where cheatgrass increased SOC, it was via unstable POC. These findings indicate that cheatgrass effects on the distribution of soil carbon among POC and MAOC fractions may vary among ecoregions, and that cheatgrass can reduce forms of carbon that are otherwise considered stable and ‘secure’, i.e. sequestered.
Prediction of rotary seal leakage rate under the influence of stress relaxation based on G A-PSO-BP hybrid algorithm
Authors: Zhang Yi , He Wei , Hu Ling , Lou Zicheng , Zhang Daqin , Wang Zheng 
Published: February 2025

Abstract
Rotary combination seals of composite impactors are susceptible to failure and relaxation in high-pressure, high-temperature deep-well environments. The aim of this study is to analyze the effect of stress relaxation on the performance of the seals and to assess their performance under different operating conditions. The study adopts GA-PSO-BP algorithm to predict the leakage, simulates the contact pressure change of the sealing surface before and after stress relaxation by Abqus software, and calculates the leakage rate by using MATLAB software in combination with the thermoelastic flow lubrication model and the finite element analysis data, to establish the prediction model dataset. The study also optimised the neural network prediction model and verified the accuracy of the model experimentally. The results show that increasing pressure leads to an increase in contact pressure, but relaxation increases contact length and decreases maximum pressure, and increasing compression increases and then decreases contact pressure and increases the peak value, but relaxation decreases the peak value and increases the risk of leakage. The increase in speed and temperature both lead to an increase in leakage rate, and the GA-PSO-BP model predicts more accurately the rotary combination seals with a higher leakage rate after considering the stress relaxation.
Towards virtual painting recolouring using vision transformer on X-ray fluorescence datacubes
Authors: Bombini Alessandro , Bofı́as Fernando Garcı́a-Avello , Giambi Francesca , Ruberto Chiara 
Published: February 2025

Abstract
In this contribution, we define (and test) a pipeline to perform virtual painting recolouring using raw data of X-Ray Fluorescence (XRF) analysis on pictorial artworks. To circumvent the small dataset size, we generate a synthetic dataset, starting from a database of XRF spectra; furthermore, to ensure a better generalisation capacity (and to tackle the issue of in-memory size and inference time), we define a Deep Variational Embedding network to embed the XRF spectra into a lower dimensional, K-Means friendly, metric space. &#xD;&#xD;We thus train a set of models to assign coloured images to embedded XRF images. We report here the devised pipeline performances in terms of visual quality metrics, and we close on a discussion on the results.
Democratisation and franchise extension: the literature
Authors: Aidt Toke S , Franck Raphaël 
Published: February 2025

Abstract
This chapter surveys theories from the political economics literature on franchise extension that seeks to explain the logic of democratisation during the long nineteenth century, and it evaluates the empirical literature in economics and political science that confront the competing theories with historical and contemporaneous data. It classifies these theories into two categories. Bottom-up theories emphasise that the members of the incumbent elite share power with others to avoid losing power entirely or because they become persuaded of the merits of doing so. Conversely, top-down theories emphasise that the ruling elite, or some part thereof, can directly benefit from democratisation. Based on this survey, the chapter develops three hypotheses of suffrage reform—the threat of revolution hypothesis, the social movement hypothesis, and the political expedience hypothesis—which are tested in the subsequent chapters in the context of the Great Reform Act of 1832 using the new process-focused approach.
A Comprehensive Bioinformatic Evaluation of the NTRK Family's Potential as Prognostic Biomarkers In Breast Cancer
Authors: Mohammadi Ramtin , Ghiasi Mohsen , Mehdizadeh Saber , Mohammadi Javad , Mohammad Ganji Shahla 
Published: February 2025

Abstract

Motivation
Breast cancer (BC), with its rising prevalence and mortality rate, is one of the most significant human health issues. The family of transmembrane tyrosine kinases that promote neuronal growth includes the Neurotrophic Tyrosine Kinase Receptors (NTRK). NTRK1–3 genes encode the members of this family. Alterations of NTRK genes can induce carcinogenesis both in neurogenic and non-neurogenic cells. The prevalence of NTRK gene fusion is under 1% in solid tumours but is highly encountered in rare tumours. Since the prognostic values of NTRK families’ expression in various types of cancer are becoming increasingly evident, we aimed to conduct a comprehensive bioinformatics study evaluating the prognostic significance of the NTRK family in BC. online Bioinformatic databases including TCGA, UALCAN, Kaplan–Meier plotter, bc-GenExMiner, cBioPortal, STRING, Enrichr, and TIMER were utilized for analysis.

Results
High levels of NTRK2 and 3 demonstrated better associations with overall survival (OS) and recurrence-free survival (RFS) in BC patients (P &lt; 0.05), while high levels of NTRK1 showed an applicable correlation with RFS in BC patients (P &lt; 0.001). Our findings provide a new outlook that might aid in the field of personalized medicine and therapeutic use of NTRK as a prognostic biomarker in BC.
Investigating surgeon performance metrics as key predictors of robotic herniorrhaphy outcomes using iterative machine learning models: retrospective study
Authors: Shin Thomas H , Fanta Abeselom , Shields Mallory , Kaoukabani Georges , Gokcal Fahri , Liu Xi , Tavakkoli Ali , Kudsi O Yusef 
Published: February 2025

Abstract

Background
Robotic data streams allow for capture of objective performance indicators, providing the ability to quantify and analyse operator technique and movement in optimizing postoperative outcomes. This study provided proof-of-concept demonstration of how intraoperative surgeon-factors could influence post-robotic herniorrhaphy complications via machine learning analyses of objective performance indicators.

Study design
Data on robotic-assisted ventral hernia repair were retrospectively reviewed between February 2013 and November 2022 at a single academic centre. Machine learning modelling on systematic chart review data correlated perioperative patient factors, intraoperative objective performance indicators, and postoperative outcomes. Complications were classified with the Clavien–Dindo scale. Endpoints of interest included postoperative complications at discharge, at postoperative day 30, and at the last follow-up. Machine learning models employed included linear, k-nearest neighbours, support vector, decision tree, random forest, adaptive boosting, and extreme gradient boosting regression algorithms.

Results
Some 520 patients undergoing robotic ventral hernia were included. Median age of patients was 56 years with 52.7% male and median body mass index 31.9 kg/m2. 92.7% of patients had at least one medical comorbidity peoperatively. Complications occurred in 33 (6.3%) patients at time of discharge. Machine learning models demonstrated an accuracy 0.95, a precision 0.92, a recall 0.95, and a F1 0.92 of objective performance indicator predicting complications and an accuracy 0.95, a precision 0.95, a recall 0.95, and a F1 0.94 by Clavien–Dindo grade at time of discharge. Thematic analyses of top ranked factors included operator-specific objective performance indicators alongside patient factors canonically associated with hernia complications.

Conclusions
This study showed the novel application of machine learning modelling to bridge objective performance indicators and clinical patient factors to postoperative clinical outcomes, demonstrating the relevance of dynamic intraoperative surgeon factors on clinical outcomes.