bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes
Authors: Ren Junting , Loughnan Robert , Xu Bohan , Thompson Wesley K. , Fan Chun Chieh 
Published: July 2024

AbstractThere is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.
A Consensus to Revise the Minimum Speech Test Battery–Version 3
Authors: Dunn Camille C. , Zwolan Teresa A. , Balkany Thomas J. , Strader Heather L. , Biever Allison , Gifford René H. , Hall Melissa W. , Holcomb Meredith A. , Hill Heidi , King English R. , Larky Jannine , Presley Regina , Reed Meaghan , Shapiro William H. , Sydlowski Sarah A. , Wolfe Jace 
Published: July 2024

Purpose:
The Minimum Speech Test Battery (MSTB) for adults was introduced in 1996 (Nilsson et al., 1996) and subsequently updated in 2011 (Advanced-Bionics et al., 2011). The MSTB has been widely used by clinicians as a guide for cochlear implant (CI) candidacy evaluations and to document post-operative speech recognition performance. Due to changes in candidacy over the past 10 years, a revision to the MSTB was needed.

Method:
In 2022, the Institute for Cochlear Implant Training (ICIT) recruited a panel of expert CI audiologists to update and revise the MSTB. This panel utilized a modified Delphi consensus process to revise the test battery and to improve its applicability considering recent changes in CI care.

Results:

This resulted in the MTSB–Version 3 (MSTB-3), which includes test protocols for identifying not only traditional CI candidates but also possible candidates for electric–acoustic stimulation and patients with single-sided deafness and asymmetric hearing loss. The MSTB-3 provides information that supplements the earlier versions of the MSTB, such as recommendations of when to refer patients for a CI, recommended patient-reported outcome measures, considerations regarding the use of cognitive screeners, and sample report templates for clinical documentation of pre- and post-operative care. Electronic versions of test stimuli, along with all the materials described above, will be available to clinicians via the ICIT website (
https://www.cochlearimplanttraining.com
).

Conclusion:
The goal of the MSTB-3 is to be an evidence-based test battery that will facilitate a streamlined standard of care for adult CI candidates and recipients that will be widely used by CI clinicians.
What is the cure for absolute infertility? Biomedicalisation and routinisation of surrogacy and uterus transplantation in Nordic medical journals
Authors: Eriksson Lise 
Published: July 2024

AbstractThis article investigates 20 years of discursive struggles in Nordic medical journals around the process of legitimating and routinising gestational surrogacy and uterus transplantation in Finland and Sweden. The comparative analysis through critical discourse analysis suggests that influential health care professionals have contributed to different levels of legal and cultural adaptation of the methods, prioritising non-commercial gestational surrogacy in Finland and uterus transplantation in Sweden. The article identifies central discursive turning points in the medical journal discussions by interpreting them against the background of medical and policy developments in Finland and Sweden during the analysed twenty-year period. Legitimation and routinisation of surrogacy and uterus transplantation were developed through biomedicalisation by representing them as infertility treatments and emphasising the relational dynamics between donors and recipients—a connection that in the Nordic context is often based on kinship or close relationships. The diagnosis of absolute uterine factor infertility was central to representing women as on the boundary between fertile and infertile, as they may have functioning ovaries. Through the biomedicalised rhetoric of equal opportunities for biogenetic motherhood, the diagnosed women’s ambiguous reproductive status was used to legitimise the two methods as cures for absolute infertility, thereby reinforcing hegemonic family and kinship norms.
From brain “scar” to “bat shit crazy”: negotiating the madness of sexual violence discourse
Authors: Yapp Emma 
Published: July 2024

AbstractThis article analyses how people who identify with psychiatric diagnoses in England and Wales make sense of and talk about their experiences of sexual violence. I examine how interview participants engaged with the hegemonic trauma discourse, as well as the consequences of this for meaning-making, affective pain, and the feminist imperative to ‘speak out’. The hegemonic trauma discourse is characterised by leaving a psychological ‘scar’; is premised on a sudden interruption to a ‘good life’; and is considered pathologically unspeakable without intervention. This discourse was both validating and affectively painful for participants, and interventions targeting dissociation were helpful for assuaging distress. However, it was additionally normative and exclusionary, and did not fulfil the political promise of ‘speaking out’, as all participants faced myriad socio-political denial.
The discourse functions of simple copulas in Dzongkha
Authors: Watters Stephen 
Published: July 2024

Abstract
The marking on copular verbs in Tibetic languages is regarded as an exemplar of egophoricity, although the extent
to which it has been grammaticalized varies between languages. Dzongkha, a southern Tibetic language, is somewhat atypical of the
egophoric pattern in the sense that the basic opposition in copulas exhibits a mirative pattern, wherein the non-mirative
(egophoric) copula occurs with all grammatical persons in declaratives and interrogatives, and the mirative (non-egophoric) occurs
with the 3rd person and rarely with 1st and 2nd persons. The conversational data studied for this paper also show that the speaker
need not take knowledge stances that bifurcate the world between objectively ‘old’ and ‘new’ knowledge and the attendant
associations of knowledge with a particular grammatical person. Rather, the speaker’s representation of events is subjective, and
dependent, in part, on the knowledge stances between speaker and respondent. What is in view in conversational interaction are the
social goals of the conversation — assertions, face-saving strategies, and arriving at mutually shared knowledge — and the
Dzongkha copulas are a manipulable linguistic resource in achieving these.
Influence of discrete laser surface melting on scuffing resistance of W6Mo5Cr4V2 steel gear
Authors: Lv You , Cui Bo , Sun Zhaolong , Xiao Xinlei 
Published: July 2024

Abstract
The gear transmission system is advancing towards high-speed and heavy-duty applications. Among the main failure modes of the system, tooth surface scuffing due to increased tooth surface temperature has emerged as a prominent concern in mechanical transmission. Addressing the enhancement of gear scuffing resistance has thus become an urgent challenge in this field. This paper utilized discrete laser surface melting (DLSM) treatment to create discrete laser surface melted (DLSMed) units on the surface of W6Mo5Cr4V2 steel gears, resembling the radial ribs found on the surface of Limaria basilica. The paper investigated the size, hardness, residual austenite content, and residual stress of the DLSMed units at varying current intensities and laser frequencies. Microstructural observations were conducted on the DLSMed units, followed by gear scuffing experiments performed on the Forschungsstelle für Zahnräder und Getriebebau (FZG) testing machine. The experimental findings revealed that the change in laser frequency had a clearly weaker impact on the size of the DLSMed unit compared to current intensity. The DLSMed unit consisted of two parts: the melting zone (MZ) and the heat-affected zone (HAZ), with equiaxed and dendritic microstructures, respectively. Both zones exhibited refinement with increasing current intensity and laser frequency. Moreover, the microhardness of the DLSMed unit showed significant improvement compared to that of as-received gears. The scuffing resistance of DLSMed gears was found to be closely linked to their initial surface roughness. Residual stress formation in DLSMed gears was attributed to thermal stress and microstructural stress. The distribution pattern of DLSMed units had varying effects on the scuffing load-carrying capacity of DLSMed gears. Specifically, DLSMed gears with transverse distribution of DLSMed units demonstrated a 12.5% improvement in anti-scuffing performance compared to those with longitudinal distribution. Finally, this paper elucidated the mechanism through which DLSM enhances the scuffing resistance of W6Mo5Cr4V2 steel gears.
Densification mechanism and microstructural evolution of Si3N4 composites with CeO2 as additives during spark plasma sintering
Authors: Zhan Lina , Zhang Yao , Zhang Xuan , Xia Yuzhibiao , Liu Yao 
Published: July 2024

Abstract
The densification mechanism of CeO2 doped Si3N4 ceramics during spark plasma sintering (SPS) was investigated. The sintering process was observed to undergo significant densification at the intermediate stage of sintering (1435 °C–1816 °C), and the density and grain size of CeO2 doped Si3N4 ceramic were found to be greater than those of pure Si3N4 ceramic under the same sintering conditions. To determine the densification mechanism, a creep model was utilized to determine the densification mechanism which could be interpreted based on the values of the stress exponent (n) and the apparent activation energy (Q
d). The results revealed that the Q
d of pure Si3N4 (381.15 kJ mol−1) is higher than that of Si3N4-CeO2 powder (265.61 kJ mol−1). Moreover, the stress exponent n of Si3N4-CeO2 powder (1.12–1.33) was higher than that of pure Si3N4 powder (1.00–1.18). This can be attributed to the fact that the addition of CeO2 resulted in the formation of a liquid phase at the grain boundary which leading to a shift in the controlling mechanism from grain boundary diffusion to grain boundary sliding.
Beyond quantum annealing: optimal control solutions to maxcut problems
Authors: Pecci Giovanni , Wang Ruiyi , Torta Pietro , Mbeng Glen Bigan , Santoro Giuseppe 
Published: July 2024

Abstract
Quantum Annealing (QA) relies on mixing two Hamiltonian terms, a simple driver and a complex problem Hamiltonian, in a linear combination. The time-dependent schedule for this mixing is often taken to be linear in time: improving on this linear choice is known to be essential and has proven to be difficult. Here, we present different techniques for improving on the linear-schedule QA along two directions, conceptually distinct but leading to similar outcomes: 1) the first approach consists of constructing a Trotter-digitized QA (dQA) with schedules parameterized in terms of Fourier modes or Chebyshev polynomials, inspired by the Chopped Random Basis algorithm for optimal control in continuous time; 2) the second approach is technically a Quantum Approximate Optimization Algorithm (QAOA), whose solutions are found iteratively using linear interpolation or expansion in Fourier modes. Both approaches emphasize finding smooth optimal schedule parameters, ultimately leading to hybrid quantum–classical variational algorithms of the alternating Hamiltonian Ansatz type. We apply these techniques to MaxCut problems on weighted 3-regular graphs with N = 14 sites, focusing on hard instances that exhibit a small spectral gap, for which a standard linear-schedule QA performs poorly. We characterize the physics behind the optimal protocols for both the dQA and QAOA approaches, discovering shortcuts to adiabaticity-like dynamics. Furthermore, we study the transferability of such smooth solutions among hard instances of MaxCut at different circuit depths. Finally, we show that the smoothness pattern of these protocols obtained in a digital setting enables us to adapt them to continuous-time evolution, contrarily to generic non-smooth solutions. This procedure results in an optimized QA schedule that is implementable on analog devices.
Physical properties of InxSey and CuInSe2 thin films with potential application in radiation detectors
Authors: Molina-Mil T , Sastré-Hernández J , Aguilar-Hernández J , Hernández-Pérez M A , Vázquez-López C , Conteras-Puente G 
Published: July 2024

Abstract
A study of InxSey and CuInSe2 thin films processed by the thermal co-evaporation technique by evaluating its physical properties is carried out. Both InxSey as well as CuInSe2 thin films were synthesized by multi-source thermal co-evaporation technique, using Knudsen-type effusion cells. The optical, structural, electrical, and morphological properties of each film were analyzed in order to determine the feasibility at the formation of a p-CuInSe2/n-InxSey heterojunction. InxSey films exhibited bandgap values in the range of 2.4–2.7 eV which was determined by UV–vis spectroscopic analysis. Vibrational modes associated to In2Se3 as well as Se are presented in the InxSey films according to Raman studies. The irregular morphology of the grains on the InxSey surface, with an average size of 380 nm are related to both In2Se3 and Se materials according to Raman spectroscopy and Scanning Electron Microscopy analysis. The InxSey thin films showed resistivity vales around 10–3 Ω·cm. The Hackee´s figure of Merit (FOMH) analysis supported the physical assumption that InxSey thin films are feasible to be used as window layer in a photovoltaic device because of the high values of FOMH obtained for samples processed at 300 °C. On the other hand, an influence on the morphology of the CuInSe2 films was observed when the films were synthetized at different substrate temperature. Uniform CuInSe2 films with agglomerated cauliflower-like grains with an average size of 1.8 μm were observed by SEM. The presence of binary phases within the CuInSe2 compound were detected through Raman and x-ray characterization. Average crystallite size of 61 nm and microstrain around 2.5 × 10–3 were estimated for the CuInSe2 films through x-ray analysis. According to the physical properties analyzed the InxSey/CuInSe2 semiconductor bilayer can be a suitable candidate for application in photovoltaic devices as well as charged particle detector.
Graph constrained empirical wavelet transform and its application in bearing fault diagnosis
Authors: Tan Yuan , Zhao Shui , Lv Xiaorong , Shao Shifen , Chen Bingyan , Fan Xiyan 
Published: July 2024

Abstract
The signal decomposition based on frequency domain distribution is a fundamental methodology for mechanical component fault diagnosis. However, existing methods face challenges such as susceptibility to noise interference and limited adaptability. Therefore, this paper proposes the graph constrained empirical wavelet transform (GCEWT) method. This method introduces structured information, such as the interrelationships among different parts of the frequency domain distribution of vibration signals, into the boundary detection process of empirical wavelet transform. The high-dimensional connectivity among different parts of the time-frequency distribution is utilized to construct an adjacency matrix. By constructing an adjacent graph, the proposed method encodes the adjacency relationships among frequency bands to constrain the low-dimensional spatial relationships between them. In conjunction with spectral clustering algorithms, the GCEWT method determines the boundaries for empirical wavelet transformation in the frequency domain. This approach achieves structured and adaptive decomposition of vibration signals from components of critical equipment, facilitating the structured and adaptive extraction of fault features. The effectiveness of the proposed method is validated using vibration data from both wind turbine drivetrain systems and aircraft engines. The experimental results demonstrate that the proposed method yields more reasonable signal decomposition results compared to traditional algorithms. Additionally, the proposed method proves to be more effective in extracting weak fault features of bearings in the presence of noise.
Quantum extreme learning of molecular potential energy surfaces and force fields
Authors: Lo Monaco Gabriele , Bertini Marco , Lorenzo Salvatore , Massimo Palma G 
Published: July 2024

Abstract
Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force field of molecular systems. We address this task by using the quantum extreme learning machine paradigm. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices with a limited number of native gates. We have applied this setup to three case studies: lithium hydride, water, and formamide, carrying out both noiseless simulations and actual implementation on IBM quantum hardware. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms, while still achieving a high level of predictive accuracy compared to simulations. Our encouraging results pave the way towards the future application to more complex molecules, being the proposed setup scalable.
Self-supervised representations and node embedding graph neural networks for accurate and multi-scale analysis of materials
Authors: Kong Jian-Gang , Zhao Ke-Lin , Li Jian , Li Qing-Xu , Liu Yu , Zhang Rui , Zhu Jia-Ji , Chang Kai 
Published: July 2024

Abstract
Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose the physical insight of multi-scale information about materials. And the process of labeling data consumes many resources and inevitably introduces errors, which constrains the accuracy of prediction. We propose to train the GNN model by self-supervised learning on the node and edge information of the crystal graph. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can reach better prediction performance on material properties. Furthermore, it may provide physical insights by tuning the range information. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. To incorporate rich physical information into the GNN model, we develop the node embedding graph neural networks (NEGNN) framework and show significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy.
ctGAN: combined transformation of gene expression and survival data with generative adversarial network
Authors: Kim Jaeyoon , Seok Junhee 
Published: July 2024

Abstract
Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.