bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes
Authors: Pluta Dustin , Hadj-Amar Beniamino , Li Meng , Zhao Yongxiang , Versace Francesco , Vannucci Marina 
Published: April 2024

AbstractStudies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.
Prognostic implication of 1-year decline in diffusing capacity in newly diagnosed idiopathic pulmonary fibrosis
Authors: Lee Hyeonsu , Kim So Yeon , Park Young Sik , Choi Sun Mi , Lee Jong Hyuk , Park Jimyung 
Published: April 2024

AbstractThe progression of idiopathic pulmonary fibrosis (IPF) is assessed through serial monitoring of forced vital capacity (FVC). Currently, data regarding the clinical significance of longitudinal changes in diffusing capacity for carbon monoxide (DLCO) is lacking. We investigated the prognostic implications of a 1-year decline in DLCO in 319 patients newly diagnosed with IPF at a tertiary hospital between January 2010 and December 2020. Changes in FVC and DLCO over the first year after the initial diagnosis were reviewed; a decline in FVC ≥ 5% and DLCO ≥ 10% predicted were considered significant changes. During the first year after diagnosis, a significant decline in FVC and DLCO was observed in 101 (31.7%) and 64 (20.1%) patients, respectively. Multivariable analysis showed that a 1-year decline in FVC ≥ 5% predicted (aHR 2.74, 95% CI 1.88–4.00) and 1-year decline in DLCO ≥ 10% predicted (aHR 2.31, 95% CI 1.47–3.62) were independently associated with a higher risk of subsequent mortality. The prognostic impact of a decline in DLCO remained significant regardless of changes in FVC, presence of emphysema, or radiographic indications of pulmonary hypertension. Therefore, serial monitoring of DLCO should be recommended because it may offer additional prognostic information compared with monitoring of FVC alone.
Investigation of morpho-physiolgical traits and gene expression in barley under nitrogen deficiency
Authors: Hajibarat Zohreh , Saidi Abbas , Ghazvini Habibollah , Hajibarat Zahra 
Published: April 2024

AbstractNitrogen (N) is an essential element for plant growth, and its deficiency influences plants at several physiological and gene expression levels. Barley (Hordeum vulgare) is one of the most important food grains from the Poaceae family and one of the most important staple food crops. However, the seed yield is limited by a number of stresses, the most important of which is the insufficient use of N. Thus, there is a need to develop N-use effective cultivars. In this study, comparative physiological and molecular analyses were performed using leaf and root tissues from 10 locally grown barley cultivars. The expression levels of nitrate transporters, HvNRT2 genes, were analyzed in the leaf and root tissues of N-deficient (ND) treatments of barley cultivars after 7 and 14 days following ND treatment as compared to the normal condition. Based on the correlation between the traits, root length (RL) had a positive and highly significant correlation with fresh leaf weight (FLW) and ascorbate peroxidase (APX) concentration in roots, indicating a direct root and leaf relationship with the plant development under ND. From the physiological aspects, ND enhanced carotenoids, chlorophylls a/b (Chla/b), total chlorophyll (TCH), leaf antioxidant enzymes such as ascorbate peroxidase (APX), peroxidase (POD), and catalase (CAT), and root antioxidant enzymes (APX and POD) in the Sahra cultivar. The expression levels of HvNRT2.1, HvNRT2.2, and HvNRT2.4 genes were up-regulated under ND conditions. For the morphological traits, ND maintained root dry weight among the cultivars, except for Sahra. Among the studied cultivars, Sahra responded well to ND stress, making it a suitable candidate for barely improvement programs. These findings may help to better understand the mechanism of ND tolerance and thus lead to the development of cultivars with improved nitrogen use efficiency (NUE) in barley.
Non-animal models for blood–brain barrier permeability evaluation of drug-like compounds
Authors: Dehnbostel Frederic O. , Dixit Vaibhav A. , Preissner Robert , Banerjee Priyanka 
Published: April 2024

AbstractDiseases related to the central nervous system (CNS) are major health concerns and have serious social and economic impacts. Developing new drugs for CNS-related disorders presents a major challenge as it actively involves delivering drugs into the CNS. Therefore, it is imperative to develop in silico methodologies to reliably identify potential lead compounds that can penetrate the blood–brain barrier (BBB) and help to thoroughly understand the role of different physicochemical properties fundamental to the BBB permeation of molecules. In this study, we have analysed the chemical space of the CNS drugs and compared it to the non-CNS-approved drugs. Additionally, we have collected a feature selection dataset from Muehlbacher et al. (J Comput Aided Mol Des 25(12):1095–1106, 2011. 10.1007/s10822-011-9478-1) and an in-house dataset. This information was utilised to design a molecular fingerprint that was used to train machine learning (ML) models. The best-performing models reported in this study achieved accuracies of 0.997 and 0.98, sensitivities of 1.0 and 0.992, specificities of 0.971 and 0.962, MCCs of 0.984 and 0.958, and ROC-AUCs of 0.997 and 0.999 on an imbalanced and a balanced dataset, respectively. They demonstrated overall good accuracies and sensitivities in the blind validation dataset. The reported models can be applied for fast and early screening drug-like molecules with BBB potential. Furthermore, the bbbPythoN package can be used by the research community to both produce the BBB-specific molecular fingerprints and employ the models mentioned earlier for BBB-permeability prediction.
Approche pédagogique pour l’étude d’autoconsommation photovoltaïque au niveau Master avec utilisation de l’API de SolarIO en langage Python
Authors: Graillet Olivia , Alicalapa Frédéric , de Peslouan Pierre-Olivier Lucas , Genon-Catalot Denis , Chabriat Jean-Pierre 
Published: April 2024

L’île de La Réunion, qui fait partie des Zones Non Interconnectées (ZNI), dépend actuellement à 62% des importations d’énergies fossiles pour la production d’électricité. Afin de contribuer à son autonomie énergétique, il est nécessaire de développer les sources d’énergies renouvelables locales. Dans ce contexte, l’unité de recherche ENERGY-Lab et la Faculté des Sciences et Technologies de l’Université de La Réunion proposent le cursus « Master Energie ». L’un des objectifs du Master est de permettre aux étudiants d’acquérir des compétences pouvant répondre aux problématiques énergétiques actuelles. A La Réunion, le secteur des installations photovoltaïques est particulièrement actif en raison d’un fort potentiel solaire, lié à son climat subtropical. La démarche pédagogique présentée dans ce document est ainsi composée d’activités en lien avec l’optimisation de l’autoconsommation d’une centrale photovoltaïque : établir un plan de sobriété énergétique, affiner la précision du dimensionnement de la centrale PV et simuler des flux de puissances. Les différents outils et technologies utilisés (tableurs, programmation Python et G LabVIEW, API, bases de données) ont été choisis pour s’adapter à la fois à un contexte scientifique et industriel.
Magnetically driven molecular orientational ordering in plastic crystals: The case of d-camphor
Authors: Zhao Miao Miao , Du Na , Zhu Yu Ying , Yen Fei 
Published: April 2024

Many solid crystals exhibit a structural phase transition where a subset of its ions or entire molecules become orientationally ordered. As to why such ordering occurs remains mostly unresolved. We consider the extremely weak magnetic elements arising from the reorientations of the molecules experiencing mutual resonance to play the chief role. Two new features are identified in d-camphor: (1) the magnetic susceptibility abruptly changes when crossing the order–disorder phase transition at TII–III = 239.8 K during cooling and at TIII–II = 245.2 K during warming and (2) the complex dielectric constant exhibits two successive discontinuities only 0.2 K apart near the critical temperatures when the sweeping rate is only 0.1 K/min. We discuss how the change in entropy associated with order–disorder transitions in plastic crystals represents temporal changes rather than spatial changes in the system. Our findings may be extended to study why many other crystalline solids exhibit orientational ordering and irreversibility.
Microscopic dynamics of gas molecules confined in porous channel-like ice structure
Authors: del Rosso L. , Colognesi D. , Donati A. , Rudić S. , Celli M. 
Published: April 2024

In the rich ice polymorphism landscape, ice XVII, metastable at ambient pressure and at temperatures below 130 K, is surely one of the most interesting from both fundamental and technological perspectives due to its porosity, i.e., its capability to repeatedly absorb and desorb molecular hydrogen by dosing the gas at pressures even below the ambient one. Here, owing to this exceptional key feature, we investigate the roto-vibrational dynamics of the H2 molecules trapped in the fully deuterated ice XVII structure. Making use of the high-resolution and brilliance of the TOSCA neutron vibrational spectrometer, combined with high-resolution Raman data, we are able to efficiently distinguish the center-of-mass translational bands from the rotational ones and to study them as a function of the guest filling of the ice structure, unraveling a peculiar behavior for the confined particle in a low-dimensional system. Moreover, we also report the study of the microscopic dynamics of confined nitrogen and oxygen, which are the most abundant molecular species in the atmosphere and are of paramount interest for technological applications. Finally, we show that the ice XVII porosity is a unique feature, especially in the low pressure regime, within the emptied-hydrate phases discovered to date.
On the covariant formulation of gauge theories with boundaries
Authors: Assanioussi Mehdi , Kowalski-Glikman Jerzy , Mäkinen Ilkka , Varrin Ludovic 
Published: April 2024

AbstractIn the present article, we review the classical covariant formulation of Yang–Mills theory and general relativity in the presence of spacetime boundaries, focusing mainly on the derivation of the presymplectic forms and their properties. We further revisit the introduction of the edge modes and the conditions which justify them, in the context where only field-independent gauge transformations are considered. We particularly show that the presence of edge modes is not justified by gauge invariance of the presymplectic form, but rather by the condition that the presymplectic form is degenerate on the initial field space, which allows to relate this presymplectic form to the symplectic form on the gauge reduced field space via pullback.
The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system
Authors: Chen Ying , Dong Zhiyang , Xu Chongxuan , Liu Junfei , Si Yu , Chen Ting 
Published: April 2024

Abstract
In recent years, there have been frequent outbreaks of harmful algal blooms (HAB) in coastal areas, which have caused serious economic losses to the local community. Therefore, accurate and rapid prediction of microalgal concentrations is necessary for early warning and countermeasures before the occurrence of HAB. This paper presents a model for predicting microalgae concentration based on unthresholded recurrence plots (UTRPs) combined with an improved broad learning system (BLS). Spectral data acquisition of algae species at different concentrations using l-induced fluorescence spectroscopy. Then, the 1D spectral data are dimensionally lifted by UTRPs transformation, recurrence plots (RPs) transformation can fully extract the internal information of 1D sequence data, and at the same time, UTRPs avoids the influence of artificially selected thresholds on the feature transformation results of traditional (RPs). Finally, a lightweight flat network BLS was used for microalgae concentration regression prediction, at the same time the BLS regularization method was improved. When comparing the two most commonly used deep learning regression models and integrated learning models, UTRPs and elastic net (UTRPs-ENBLS) achieves convincing results.
Physics-informed deep-learning applications to experimental fluid mechanics
Authors: Eivazi Hamidreza , Wang Yuning , Vinuesa Ricardo 
Published: April 2024

Abstract
High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.
Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularization
Authors: Li Zipeng , Liu Xuan , Zhang Kaiyu , Li Chao , Chen Jinglong 
Published: April 2024

Abstract
Utilizing unsupervised domain adaptation for intelligent fault diagnosis (IFD) has demonstrated significant potential for ensuring the security of machinery systems. Nonetheless, the inherent imbalance attribute of collected data affects the performance of diagnostic model. Especially, for machines working under varied conditions, the acquired unlabeled data frequently exhibits diverse degrees of distributional deviations, thus further undermining the transferable model’s generalization capability. To address this challenge, we introduce a method termed Dynamic Unsupervised Imbalanced Domain Adaptation (DUIDA) for IFD. Employment of class rebalancing and label-dependent margin regularization strategies optimizes the selection of decision boundaries which counteract the distributional deviations introduced by the imbalance. In addition, by integrating a dynamic weighting mechanism, encompassing both adversarial-based and MMD-based domain adaptation, our model becomes versatile across varied UIDA tasks, assigning higher weights to fundamental faulty features. Finally, our empirical analyses on two faulty bearing datasets substantiate the efficacy and superior performance of the proposed framework across diverse operational scenarios.
Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning
Authors: Liu Xuefeng , Li Longhao , Zhang Fan , Li Naiqing 
Published: April 2024

Abstract
To address the adverse effects of nonlinearity and dynamic time-varying in complex chemical processes on the accuracy of the soft sensor model, a local-semi-supervised ensemble learning for soft sensor modeling (local semi-supervised-selective ensemble learning-long short term memory, LS-SEL-LSTM) method is proposed in this article. Firstly, a hierarchical clustering method incorporating spatiotemporal criteria is proposed to reduce the influence of nonlinearity in global model prediction accuracy. The method considers the dynamic time-varying characteristics of temporal data and generates multiple local datasets. Then, to address the issue of multi-rate between auxiliary variables and dominant variables, a semi-supervised weight fusion mechanism based on temporal correlation is proposed, which effectively utilizes auxiliary variables to reconstruct local semi-supervised datasets and establishes local soft sensing models using LSTM. Concurrently, the parameters of the established model were optimized using the flower pollination algorithm. Subsequently, a selective ensemble learning method based on sub-model prediction accuracy and an adaptive combination weight calculation method for sub-models were proposed to improve the prediction accuracy. Finally, the effectiveness of the proposed method was verified through the actual dataset of the sulfur recovery process. The results indicate that LS-SEL-LSTM performs well in handling complex chemical processes with nonlinear and dynamic time-varying characteristics.
DA-VICReg: a data augmentation-free self-supervised learning approach for diesel engine fault diagnosis
Authors: Chen Tianyou , Xiang Yang , Wang Jiaxing 
Published: April 2024

Abstract
Self-supervised learning (SSL) aims to extract useful representations from unlabeled data by maximizing the agreement between positive pairs. However, traditional SSL relies on carefully designed data augmentation methods to generate positive pairs. When dealing with 1D vibration signals, data augmentation prone to potentially compromise the fault information in the original signals. Therefore, this paper proposes a data augmentation-free SSL framework for diesel engine fault diagnosis called Domain Adaptation Variance Invariance Covariance Regularization (DA-VICReg). The DA-VICReg uses cyclic angular vibrations collected within the same time period as positive pairs and extracts useful features from unlabeled data using a loss function composed of three terms: Variance, Invariance, and Covariance. We found that when positive pairs originate from different operating conditions, such as varying speeds and torques, the model can develop feature extraction capabilities that remain unaffected by changes in operating conditions. In addition, a spatial pyramid pooling layer and a trilinear attention module are used to extract vibration features at different scales and focus on critical spatial locations and channels. Finally, the proposed approach was validated through experiments on two types of diesel engines, and a comparison with prominent SSL methods confirms the superiority of the proposed approach. In engineering practice, this method can utilize a large amount of signals stored in different time periods for self-supervised training and learn useful features for downstream fault diagnosis tasks.
On rationally integrable planar dual multibilliards and piecewise smooth projective billiards
Authors: Glutsyuk Alexey 
Published: April 2024

Abstract
The billiard flow in a planar domain Ω acts on the tangent bundle

T

R

2

|
Ω

as geodesic flow with reflections from the boundary. It has the trivial first integral: squared modulus of the velocity. Bolotin’s conjecture, now a joint theorem of Bialy, Mironov and the author, deals with those billiards whose flow admits an additional integral that is polynomial in the velocity and whose restriction to the unit tangent bundle is non-constant. It states that (1) if the boundary of such a billiard is C
2-smooth, nonlinear and connected, then it is a conic; (2) if it is piecewise C
2-smooth and contains a nonlinear arc, then it consists of arcs of conics from a confocal pencil and segments of ‘admissible lines’ for the pencil; (3) the minimal degree of the additional integral is either 2, or 4. In 1997 Sergei Tabachnikov introduced projective billiards: planar curves equipped with a transversal line field, which defines a reflection acting on oriented lines and the projective billiard flow. They are common generalization of billiards on surfaces of constant curvature, but in general may have no canonical conserved quantity. In a previous paper the author classified those C
4-smooth connected nonlinear planar projective billiards whose flow admits a non-constant integral that is a rational 0-homogeneous function of the velocity (with coefficients depending on the position): these billiards are called rationally 0-homogeneously integrable. It was shown that: (1) the underlying curve is a conic; (2) the minimal degree of integral is equal to two, if the billiard is defined by a dual pencil of conics; (3) otherwise it can be arbitrary even number. In the present paper we classify piecewise C4-smooth rationally 0-homogeneously integrable projective billiards. Unexpectedly, we show that such a billiard associated to a dual pencil of conics may have integral of minimal degree 2, 4, or 12. For the proof of main results we prove dual results for the so-called dual multibilliards introduced in the present paper.
Thin film sensing near exceptional point utilizing terahertz plasmonic metasurfaces
Authors: K N Indu Krishna , Roy Chowdhury Dibakar 
Published: April 2024

Abstract
Non-Hermitian quantum systems along with engineered metasurfaces enable a versatile podium for sensor designs from industrial to medical sectors. The singularity points known as exceptional points (EPs) can be realized in such non-Hermitian systems. EP demonstrates a square root topology on minute perturbations, hence promising to be a potential candidate to sense external parameters, such as temperature, thermal fluctuations, refractive index, and biomolecules. Hence, in this work, through numerical and analytical investigations, we explore the sensing capabilities in the vicinity of EP utilizing suitably designed terahertz metasurfaces. Here, we propose a non-Hermitian metasystem comprising two orthogonally twisted square split ring resonators coupled by near-field Electromagnetic interactions that can exhibit dark-bright modes. In such a system, the presence of an active (photo-doped) material in the split gap of one of the resonators opens up an effective avenue to introduce controllable asymmetric losses, ultimately leading to the emergence of EPs in the polarization space. Hence, thin film sensing at the proximity of the emerged EP is investigated for different refractive indices by coating with an overlayer atop the metasurface. In such a configuration, the sensitivities of the eigenstates are calculated in terms of the refractive index unit, which turns out to be −0.044 THz RIU−1 and −0.063 THz RIU−1 when the system is perturbed near EP. Our proposed metasurface-inspired EP-based sensing strategy can open up novel ways to sense the refractive index of unknown materials besides other physical parameters.