Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition for Multifault Diagnosis of a Multistage Reducer

Multistage reducer vibration signals have complicated spectral structures owing to the amplitude and frequency modulations of gear damage-induced vibrations and the multiplicative amplitude modulation effect caused by time-varying vibration transfer paths (in the case of local gear damage) when the multistage reducer contains both planetary and spur gears. Moreover, the difference between the vibration energies of these gears increases the difficulty of fault feature extraction when multiple failures occur in the reducer. As the meshing frequency of each gear group often varies significantly, variational mode decomposition can be performed to decompose the vibration signal according to frequency, enabling separation of the vibration signals of the spur and planetary gears. The common fault features of these gears can be extracted from the spectrum of the amplitude demodulation envelope. To verify the effectiveness of this method, we first analyzed a simulation signal, and then utilized the experimental signals from a laboratory multistage reducer for verification. In the multistage reducer simulation, we considered the amplitude and frequency modulation of the gear damage and transfer paths. In the experimental verification, we processed local faults (broken teeth) and uniform faults (uniform wear) on the sun gear and the spur gear of the planetary gear separately.
More on the cardinality of a topological space

In this paper we continue to investigate the impact that various separation axioms and covering properties have onto the cardinality of topological spaces. Many authors have been working in that field. To mention a few, let us refer to results by Arhangel’skii, Alas, Hajnal-Juhász, Bell-Gisburg-Woods, Dissanayake-Willard, Schröder and to the excellent survey by Hodel “Arhangel’skii’s Solution to Alexandroff’s problem: A survey”. Here we provide improvements and analogues of some of the results obtained by the above authors in the settings of more general separation axioms and cardinal invariants related to them. We also provide partial answer to Arhangel’skii’s question concerning whether the continuum is an upper bound for the cardinality of a Hausdorff Lindelöf space having countable pseudo-character (i.e., points are Gδ). Shelah in 1978 was the first to give a consistent negative answer to Arhangel’skii’s question; in 1993 Gorelic established an improved result; and further results were obtained by Tall in 1995. The question of whether or not there is a consistent bound on the cardinality of Hausdorff Lindelöf spaces with countable pseudo-character is still open. In this paper we introduce the Hausdorff point separating weight Hpw(X), and prove that (1) |X| ≤ Hpsw(X)aLc(X)ψ(X), for Hausdorff spaces and (2) |X| ≤ Hpsw(X)ωLc(X)ψ(X), where X is a Hausdorff space with a π-base consisting of compact sets with non-empty interior. In 1993 Schröder proved an analogue of Hajnal and Juhasz inequality |X| ≤ 2c(X)χ(X) for Hausdorff spaces, for Urysohn spaces by considering weaker invariant - Urysohn cellularity Uc(X) instead of cellularity c(X). We introduce the n-Urysohn cellularity n-Uc(X) (where n≥2) and prove that the previous inequality is true in the class of n-Urysohn spaces replacing Uc(X) by the weaker n-Uc(X). We also show that |X| ≤ 2Uc(X)πχ(X) if X is a power homogeneous Urysohn space.
Pipeline leakage aperture identification method based on pseudolabel learning

Aiming at the problem of insufficient label data in the pipeline leak detection field, this paper proposes a pseudolabel (PL) adaptive learning method based on multiscale convolutional neural network (MSCNN) with the idea of transfer learning for pipeline leak aperture identification. First, the convolutional and pooling layers for transfer learning feature extraction are improved by using a dual-channel MSCNN. Second, the KL divergence function after dimensionality reduction is used to calculate the distribution distance between the source domain and the target domain to improve the robustness of distribution alignment in high-noise environments. In addition, considering the interference of PL noise, this paper develops a pseudolabel (PL) dynamic threshold to achieve the purpose of PL adaptive updating. Compared with the fixed threshold, the improved PL learning (PLL) can effectively improve the prediction accuracy of the model. The effectiveness of the method proposed in this paper is verified by predicting pipeline leakage conditions at different distances and under different pressures. The comparative analysis results show that the method in this paper is superior to other transfer learning methods in terms of prediction accuracy, stability, and convergence speed.
Privacy-preserving Transfer Learning for Knowledge Sharing

In many practical machine-learning applications, it is critical to allow knowledge to be transferred from external domains while preserving user privacy. Unfortunately, existing transfer-learning works do not have a privacy guarantee. In this paper, for the first time, we propose a method that can simultaneously transfer knowledge from external datasets while offering an $\epsilon$-differential privacy guarantee. First, we show that a simple combination of the hypothesis transfer learning and the privacy preserving logistic regression can address the problem. However, the performance of this approach can be poor as the sample size in the target domain may be small. To address this problem, we propose a new method which splits the feature set in source and target data into several subsets, and trains models on these subsets before finally aggregating the predictions by a stacked generalization. Feature importance can also be incorporated into the proposed method to further improve performance. We prove that the proposed method has an $\epsilon$-differential privacy guarantee, and further analysis shows that its performance is better than above simple combination given the same privacy budget. Finally, experiments on MINST and real-world RUIJIN datasets show that our proposed method achieves the start-of-the-art performance.
Microbubble-Mediated Delivery for Cancer Therapy

Despite an overall improvement in survival rates for cancer, certain resistant forms of the disease still impose a significant burden on patients and healthcare systems. Standard chemotherapy in these cases is often ineffective and/or gives rise to severe side effects. Targeted delivery of chemotherapeutics could improve both tumour response and patient experience. Hence, there is an urgent need to develop effective methods for this. Ultrasound is an established technique in both diagnosis and therapy. Its use in conjunction with microbubbles is being actively researched for the targeted delivery of small-molecule drugs. In this review, we cover the methods by which ultrasound and microbubbles can be used to overcome tumour barriers to cancer therapy.
Internal additive noise effects in stochastic resonance using organic field effect transistor

Stochastic resonance phenomenon was observed in organic field effect transistor using poly(3-hexylthiophene), which enhances performance of signal transmission with application of noise. The enhancement of correlation coefficient between the input and output signals was low, and the variation of correlation coefficient was not remarkable with respect to the intensity of external noise, which was due to the existence of internal additive noise following the nonlinear threshold response. In other words, internal additive noise plays a positive role on the capability of approximately constant signal transmission regardless of noise intensity, which can be said “homeostatic” behavior or “noise robustness” against external noise. Furthermore, internal additive noise causes emergence of the stochastic resonance effect even on the threshold unit without internal additive noise on which the correlation coefficient usually decreases monotonically.
The Effect of Anxiety, Mental Toughness, Critical Thinking on Decision Making of Badminton Umpire

The purpose of this study is to determine the mediating role of mental toughness influencing the direct and indirect anxiety, critical thinking on decision making of badminton umpire. The research used a descriptive method with the analysis used path analysis. The sample consisted of 37 umpires from West Java Province who were selected using an incidental sampling method from the population of PBSI West Java umpire. The research hypothesis was tested using path analysis techniques. The research findings are: 1) Anxiety has a significant negative contribution to badminton umpire decision making. 2) Thinking critically does not contribute significantly to badminton umpire decision making. 3) Anxiety through mental toughness is significant for the decision making of the umpire. 4) Critical thinking through mental toughness contributes significantly to umpire in decision making. This conclusion is an indication that the issue of mental toughness related to decision making among umpires is a very complex problem.
Analyse d'images d’expressions faciales et orientation de la tête basée sur la profondeur

Au cours des deux dernieres decennies, le systeme de la reconnaissance automatique de visage est devenu l'une des tâches les plus interessantes dans le domaine de la vision par ordinateur. Ces systemes ont attires beaucoup d'interet en raison de leurs diverses applications dans la vie humaine. La reconnaissance de visage est une approche biometrique basee sur des methodes automatiques pour verifier l'identite des personnes a base de modalites. Cette these est consacree a la verification automatique de visage basee sur les images de profondeurs 3D. Malheureusement, les conditions non controlees telles que les variations d'illuminations, les expressions faciales et les variations de poses affectent negativement les performances de ces systemes surtout les systemes bases sur les images 2D. L'obtention d'une performance elevee dans le monde reel reste donc un probleme ouvert. Recemment, les travaux de recherche sont tournes vers la reconnaissance de visage utilisant l’information faciale 3D qui represente des caracteristiques plus discriminantes. Dans un premier temps, nous menons une etude approfondie des techniques recentes de la reconnaissance faciale 3D ainsi qu’algorithmes qui traitent les difficultes mentionnees. Ensuite, une nouvelle methode de detection de visage est proposee : la segmentation de la zone du visage avec les courbes de projection integrale IPC (Integral Projection Curves) suivi par la localisation de bout du nez. Les resultats obtenus sur les bases de donnees CASIA3DV4 et GavabDB et leurs comparaisons avec les travaux de l’etat de l’art confirment tout l’interet de la bonne precision de la detection pour un systeme de reconnaissance efficace. Enfin, nous proposons un nouveau cadre pour la verification 3D du visage en utilisant la reduction de la dimensionnalite basee sur les histogrammes des descripteurs locaux multi-blocs. A cet effet, cinq descripteurs locaux sont evalues, LBP (Local Binary Patterns), TPLBP (Three-Patch Local Binary Patterns), FPLBP (Four-Patch Local Binary Patterns), BSIF (Binarized Statistical Image Features) et LPQ (Local Phase Quantization). Des experiences sur les combinaisons de ces descripteurs au niveau des caracteristiques a l'aide d’une concatenation des histogrammes sont effectuees. La performance de l'approche proposee est evaluee avec differents algorithmes de reduction de la dimension: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) et PCA (Principal Composant Analyses) + EFM (Enhanced Fisher linear discriminant Model). La multi-classe machine a vecteurs de support (SVM) est utilisee comme un classificateur pour effectuer la verification entre les imposteurs et les clients. Nous proposons aussi une nouvelle strategie de verification de visage 3D robuste et efficace aux grandes variations des poses en utilisant, l'apprentissage par les angles d'Euler (poses : ± 60° selon l’axe Y) et la modelisation tensorielle basee sur la methode proposee HOSVD (Higher Order Singular Value Decomposition) + PCA+EFM en utilisant l’algebre multilineaire.
Structural, Spectroscopic, and Dynamic Properties of Li2+(X2∑g+) in Interaction with Krypton Atom

We report a computational study of the potential energy surface (PES) and vibrational bound states for the ground electronic state of Li2+Kr. The PES was calculated in Jacobi coordinates at the Restricted Coupled Cluster method RCCSD(T) level of calculation and using aug-cc-pVnZ (n = 4 and 5) basis sets. Afterward, this PES is extrapolated to the complete basis set (CBS) limit for correction. The obtained interaction energies were, then, interpolated numerically using the reproducing kernel Hilbert space polynomial (RKHS) approach to produce analytic expressions for the 2D-PES. The analytical PES is used to solve the nuclear Schrodinger equation to determine the bound states’ eigenvalues of Li2+Kr for a J = 0 total angular momentum configuration and to understand the effects of orientational anisotropy of the forces and the interplay between the repulsive and attractive interaction within the potential surface. In addition, the radial and angular distributions of some selected bound state levels, which lie below, around, and above the T-shaped 90° barrier well, are calculated and discussed. We note that the radial distributions clearly acquire a more complicated nodal structure and correspond to bending and stretching vibrational motions “mode” of the Kr atom along the radial coordinate, and the situation becomes very different at the highest bound states levels with energies higher than the T-shaped 90° barrier well. The shape of the distributions becomes even more complicated, with extended angular distributions and prominent differences between even and odd states.
A numerical method based on extended Raviart–Thomas (ER‐T) mixed finite element method for solving damped Boussinesq equation

In this paper, we present the approximate solution of damped Boussinesq equation using extended Raviart–Thomas mixed finite element method. In this method, the numerical solution of this equation is obtained using triangular meshes. Also, for discretization in time direction, we use an implicit finite difference scheme. In addition, error estimation and stability analysis of both methods are shown. Finally, some numerical examples are considered to confirm the theoretical results. Copyright © 2017 John Wiley & Sons, Ltd.
Modeling orientation perception adaptation to altered gravity environments with memory of past sensorimotor states

Transitioning between gravitational environments results in a central reinterpretation of sensory information, producing an adapted sensorimotor state suitable for motor actions and perceptions in the new environment. Critically, this central adaptation is not instantaneous, and complete adaptation may require weeks of prolonged exposure to novel environments. To mitigate risks associated with the lagging time course of adaptation (e.g., spatial orientation misperceptions, alterations in locomotor and postural control, and motion sickness), it is critical that we better understand sensorimotor states during adaptation. Recently, efforts have emerged to model human perception of orientation and self-motion during sensorimotor adaptation to new gravity stimuli. While these nascent computational frameworks are well suited for modeling exposure to novel gravitational stimuli, they have yet to distinguish how the central nervous system (CNS) reinterprets sensory information from familiar environmental stimuli (i.e., readaptation). Here, we present a theoretical framework and resulting computational model of vestibular adaptation to gravity transitions which captures the role of implicit memory. This advancement enables faster readaptation to familiar gravitational stimuli, which has been observed in repeat flyers, by considering vestibular signals dependent on the new gravity environment, through Bayesian inference. The evolution and weighting of hypotheses considered by the CNS is modeled via a Rao-Blackwellized particle filter algorithm. Sensorimotor adaptation learning is facilitated by retaining a memory of past harmonious states, represented by a conditional state transition probability density function, which allows the model to consider previously experienced gravity levels (while also dynamically learning new states) when formulating new alternative hypotheses of gravity. In order to demonstrate our theoretical framework and motivate future experiments, we perform a variety of simulations. These simulations demonstrate the effectiveness of this model and its potential to advance our understanding of transitory states during which central reinterpretation occurs, ultimately mitigating the risks associated with the lagging time course of adaptation to gravitational environments.
The Impact of Persistent Noise Exposure under Inflammatory Conditions

The aim of this study was to investigate the impact of noise exposure in an intensive care unit (ICU) environment on the development of postoperative delirium in a mouse model that mimics the ICU environment. Additionally, we aimed to identify the underlying mechanisms contributing to delirium and provide evidence for reducing the risk of delirium. In this study, to mimic an ICU environment, lipopolysaccharide (LPS)-injected sepsis mouse models were exposed to a 75 dB noise condition. Furthermore, we assessed neurobehavioral function and observed the level of neuroinflammatory response and blood–brain barrier (BBB) integrity in the hippocampal region. The LPS-injected sepsis mouse model exposed to noise exhibited increased anxiety-like behavior and cognitive impairment. Moreover, severe neuroinflammation and BBB disruption were detected in the hippocampal region. This study provides insights suggesting that persistent noise exposure under systemic inflammatory conditions may cause cognitive dysfunction and anxiety- like behavior via the mediation of BBB disruption and neuroinflammation. As a result, we suggest that the detailed regulation of noise exposure may be required to prevent the development of postoperative delirium.
Interferon-Alpha Decreases Cancer Stem Cell Properties and Modulates Exosomes in Malignant Melanoma

Simple Summary Malignant melanoma spreads to other organs and is resistant in part due to the presence of cancer stem cell subpopulations (CSCs). To combat these aggressive subpopulations, different therapies are being studied. Our study aimed to evaluate the anti-tumor effect of interferon-alpha (IFN-α) treatments on melanoma CSCs and explore potential biomarkers. We found that even low doses of IFN-α reduced CSC formation and stemness properties, and led to a significant decrease in the ability to form tumors in mice xenotransplants. IFN-α also modulated the expression of genes and microRNAs involved in several cancer processes and the metabolomics of released exosomes. Our data suggest further investigations of new dose and combination approaches with IFN-α in malignant melanoma. Abstract Malignant melanoma (MM) can spread to other organs and is resistant in part due to the presence of cancer stem cell subpopulations (CSCs). While a controversial high dose of interferon-alpha (IFN-α) has been used to treat non-metastatic high-risk melanoma, it comes with undesirable side effects. In this study, we evaluated the effect of low and high doses of IFN-α on CSCs by analyzing ALDH activity, side population and specific surface markers in established and patient-derived primary cell lines. We also assessed the clonogenicity, migration and tumor initiation capacities of IFN-α treated CSCs. Additionally, we investigated genomic modulations related to stemness properties using microRNA sequencing and microarrays. The effect of IFN-α on CSCs-derived exosomes was also analyzed using NanoSight and liquid chromatography (LC-HRMS)-based metabolomic analysis, among others. Our results showed that even low doses of IFN-α reduced CSC formation and stemness properties, and led to a significant decrease in the ability to form tumors in mice xenotransplants. IFN-α also modulated the expression of genes and microRNAs involved in several cancer processes and metabolomics of released exosomes. Our work suggests the utility of low doses of interferon, combined with the analysis of metabolic biomarkers, as a potential clinical approach against the aggressiveness of CSCs in melanoma.
Outward foreign direct investment from Indian manufacturing firms - does transaction cost theory explains early choices?

The objective of the paper is to see whether transaction cost theory explains early choices of Indian multinationals, when outward foreign direct investment (OFDI) policy was still not liberal. This is done through three research questions: 1) what determines choosing between joint venture (JV) and wholly-owned subsidiary (WOS) for Indian manufacturing firms; 2) whether factors are different from the one for the developed country firms; 3) do they differ if investment is made in another developing country vis-a-vis a developed country setting. The analysis is carried out on a sample of 88 WOS and 162 JVs made by 142 firms. Results indicate that different transaction cost variables influenced entry choice in developed countries vis-a-vis developing countries.
Adherence to an Anti-Inflammatory Diet and Atopic Diseases’ Prevalence in Adolescence: The Greek Global Asthma Network Study

Objective: Atopic diseases are among the most common morbidities in children and adolescents. The association between adherence to an anti-inflammatory dietary pattern and the prevalence of atopic diseases among adolescents was examined. Methods: A total of 1934 adolescents (boys: 47.5%, mean age (standard deviation): 12.7 (0.6) years) were voluntarily enrolled. Participants completed a validated questionnaire on atopic disease status as well as one assessing dietary habits and other sociodemographic and lifestyle characteristics. A special Diet Anti-inflammatory Index (DAI) score was calculated for the evaluation of adherence to an anti-inflammatory dietary pattern. Results: A total of 6.9% of the participants reported current asthma symptoms, while 25.3% reported rhinitis symptoms and 8.9% reported eczema. Adolescents with high adherence to an anti-inflammatory diet were 58% less likely to have asthma symptoms compared with those with low adherence when adjusted for multiple confounders (p < 0.01). No significant associations were observed between the level of adherence to an anti-inflammatory diet and the prevalence of allergic rhinitis and eczema. Conclusion: An anti-inflammatory diet seems to be independently associated with a lower prevalence of asthma in adolescents. Thus, pediatricians and other healthcare providers should promote anti-inflammatory dietary patterns as a preventive measure for atopic diseases from early stages of life.