Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanocomposites industriels simplifiés : analyse structurale et propriétés mécaniques

Cette these propose l'etude de materiaux composites industriels simplifies constitues de caoutchouc non reticule (copolymere styrene-butadiene « SBR ») renforces par des charges nanometriques de silice hautement dispersible. Afin d'identifier les mecanismes physico-chimiques responsables de ce renforcement et etre capable de l'optimiser, nous devons comprendre les correlations existantes entre les proprietes macroscopiques du materiau et la structure des charges a differentes echelles.Pour cela, une large campagne d'experiences de diffusion de rayons-X aux petits angles (DXPA) ainsi que de nombreux cliches de microscopie electronique ont ete realises. En couplant ces donnees avec des simulations Monte-Carlo, il a ete notamment possible de mettre en avant la presence d'une organisation a trois niveaux en partant de billes elementaires d'une dizaine de nanometres formant des agregats eux-memes arranges selon un reseau tridimensionnel branche existant a travers tout l'echantillon.L'analyse du renforcement dans les nanocomposites a ete effectuee par rheometrie et analyse dynamique mecanique. D'autres techniques telles que la spectroscopie dielectrique, la resonance magnetique nucleaire, l'analyse thermogravimetrique ou la spectrometrie infrarouge ont egalement contribue a une caracterisation complete de ces materiaux, en particulier pour sonder la dynamique des chaines de SBR a l'interface avec la charge.Afin de deceler les correlations existantes entre structure et proprietes, nous nous sommes attaches a decrire systematiquement l'influence de parametres-cles tels que la fraction volumique en silice, le type de polymere employe (greffable sur la silice ou pas) ou leur masse molaire sur la morphologie des charges (taille des agregats, .) ainsi que sur le comportement mecanique (module d'elasticite, .) des composites. Ce travail a permis d'identifier la densite de greffage des chaines comme le parametre definissant la structure des composites et impactant significativement le renforcement.Cette these, resolument tournee vers la comprehension fondamentale, s'inscrit, a terme, dans la recherche d'une loi de comportement decrivant l'effet de la structure des charges sur les performances des pneumatiques. Cette derniere doit permettre de repondre a des problematiques rencontrees en ingenierie telles que la resistance a l'usure, l'adherence, ou la resistance au roulement.De plus, dans le but d'atteindre des informations supplementaires quant aux interactions entre le caoutchouc et la silice, nous avons mis au point un protocole experimental permettant de formuler des echantillons dits « modeles » renforces avec une silice colloidale. Cette derniere etant beaucoup mieux definie d'un point de vue geometrique, son analyse structurale est grandement facilitee rendant possible l'etude des potentiels mis en jeu pendant la production des nanocomposites.
3D Semantic Segmentation Algorithm for Indoor Scenes based on Long-term Memory

Deep learning has a strong ability to tackle pixel- level labeling tasks in image understanding. However, the disorder and irregularity of 3D point cloud data make it difficult to be applied. Though there are a few approaches that have acquired satisfied results on 3D data processing recently, there is no consideration of the relationships between each point, which plays an important role in indoor objects semantic segmentation and has been proved on plane images. In this paper, a spatial long-term connection of points is modeled by a fully connected conditional random field (CRF) to enhance the memory ability of the network. Since it is infeasible to compute due to the dimension explosion caused by potentially fully connected CRF, a highly efficient method in utilizing linear combination of multiple Gaussian kernels is proposed to approximate it. Finally, the CRF has been integrated into the deep learning framework, making it trainable end-to-end. The test evaluation of the experiments show that this network reaches current benchmarks and have a strong performance on public 3D indoor datasets. From these results, it is concluded that the relationships of each pair of points are conductive to semantic segmentation for indoor scenes.
Extraversion and interpersonal support as risk, resource, and protective factors in the prediction of unipolar mood and anxiety disorders.

Whereas there is extensive research on factors that contribute to vulnerability for depression and anxiety, research on how to promote mental health or offset risk effects in individuals likely to develop these disorders is lacking. Resilience models focus on risk, resource, and protective factors and their relationships. The current longitudinal study evaluated whether extraversion and interpersonal support functioned in resource or protective roles in relation to unipolar mood disorder (UMD), anxiety disorder (AD), and comorbid diagnoses. Data from 534 adolescents over a 3-year period were examined in a series of survival analyses to predict future disorder onset. The linear effect of extraversion significantly interacted with neuroticism predicting UMD diagnoses with extraversion conferring protection and introversion conferring risk at high levels of neuroticism. The quadratic effect of extraversion significantly interacted with neuroticism predicting AD and comorbid diagnoses such that extraversion escalated risk for diagnoses at high levels of neuroticism. The quadratic effect of extraversion was significant in comorbidity models, demonstrating increased risk as one progresses from slight extraversion to extreme introversion, independent of neuroticism. Interpersonal support significantly predicted UMD, AD, and comorbid diagnoses in an approximately linear fashion. Specificity tests indicated that these effects remained when including the other diagnosis in each model. Findings suggest the value of attending to extraverted traits and encouraging social connection regardless of risk status in prevention and treatment approaches. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Disturbance Observer Based Robust Adaptive Control of Maglev System*

In this paper, a robust adaptive controller based on disturbance observer (DO) is proposed to solve the tracking problem of uncertain maglev systems subjected to time-varying mismatched disturbance. Firstly, the nonlinear dynamic of maglev system is transformed into linear system by feedback linearization. The extended disturbance observer is introduced into the robust adaptive control to handle a large class of time-varying mismatched parametric uncertainty and disturbance for the maglev system. The stability of the closed-loop system is proven in the paper. The simulation results verify the effectiveness of the proposed controller at last.
Linear Discriminant Analysis via Pseudo Labels: A Unified Framework for Visual Domain Adaptation

This paper deals with the problem of visual domain adaptation in which source domain labeled data is available for training, but the target domain unlabeled data is available for testing. Many recent domain adaptation methods merely concentrate on extracting domain-invariant features via minimizing the distributional and geometrical divergence between domains simultaneously while ignoring within-class and between-class structure properties, especially for the target domain due to the unavailability of labeled data. We propose Linear Discriminant Analysis via Pseudo Labels (LDAPL), a unified framework for visual domain adaptation that can tackle these two issues together. LDAPL is to learn a domain-invariant features across both domains with preserving important properties such as minimizing the shift between domains both statistically and geometrically, retaining the original similarity of data samples, maximizing the target domain variance, and minimizing the within-class and maximizing the between-class properties of both domains. Specifically, LDAPL preserves the target domain discriminative information (or within-class and between-class properties) using pseudo labels, and these pseudo labels are refined until convergence. Extensive experiment on several visual cross-domain benchmarks, including Office+Caltech10 with all three types of features (such as Speeded Up Robust Features (SURF), Deep Convolutional Activation Feature (DeCAF6), and Visual Geometry Group-Fully Connected layer ( $\text {VGG-FC}_{6}$ ) features), COIL20 (Columbia Object Image Library), digit, and PIE (Pose, Illumination, and Expression), LDAPL achieved average accuracies of 79.11%, 99.72%, 79.0%, and 84.50%, respectively. Comparative results on several visual cross-domain classification tasks verify that LDAPL can significantly outperform the state-of-the-art primitive and domain adaptation methods. Specifically, LDAPL gains over baseline Joint Geometrical and Statistical Alignment (JGSA) method with 6.6%, 5.3%, 6.3%, and 44.93% average accuracies on Office+Caltech10 (SURF, DeCAF6, and $\text {VGG-FC}_{6}$ ), COIL20, digit, and PIE, respectively.
Markov Chain-based Modeling and Analysis of Checkpointing with Rollback Recovery for Efficient DSE in Soft Real-time Systems

Continued transistor scaling and increasing power density have led to an increase in both transient and aging-related fault-rates in silicon-based electronic systems. The use of traditional spatial redundancy-based fault-mitigation methods such as Triple Modular Redundancy (TMR) can lead to even higher power dissipation. However, in addition to accelerating the system’s rate of aging, such high power dissipation may be infeasible for resource-constrained embedded systems. Consequently, temporal redundancy-based methods are being increasingly used for satisfying embedded applications’ reliability requirements. However, such methods result in stochastic execution time and hence introduce additional complexity for soft real-time system design. A simulation-based approach for finding the quality of service (QoS)-aware optimal design points can lead to large design space exploration (DSE) time. To this end, we propose a Markov Chain-based model for checkpointing with rollback recovery, a widely used temporal redundancy-based fault-mitigation method. Specifically, a task execution using Checkpointing with intermediate validations is modeled as an absorbing Markov Chain and methods are presented for estimating the mean, variance and probability distribution of the task’s resulting execution time. Further, we propose a multilevel design space pruning approach for determining the QoS-aware configuration of Checkpointing. The presented modeling and estimation methods lead to considerable improvements in DSE time compared to a simulation-only approach.
Malignant melanoma after treatment for Merkel cell carcinoma

due to activation of melanocyte-specific autoimmunity; for these reasons, this phenomenon should be known as ‘‘druginduced vitiligo’’, rather than with the less accurate term ‘‘drug-induced depigmentation’’. Goldstein et al. reported that testosterone may interact with tyrosine hydroxylase activity; besides, it is known that agents that interact with tyrosinase activity can paradoxically disrupt melanin production, by inducing the cellular stress response with inflammation and autoimmune destruction of melanocytes. However, to date, there is no evidence of testosteroneinduced vitiligo. As far as the authors know, the present case represents the first clinical confirmation of the onset of vitiligo in a patient with hypogonadism, supporting the thesis that hypogonadism can be associated with vitiligo. At the same time, there is insufficient evidence to support a role of testosterone replacement therapy in the development of vitiligo. Indeed, no areas of depigmentation were observed at the testosterone application sites (chest and thighs). Further studies are needed to investigate the role of testosterone in pigmentation, also extending the research to possible new therapeutic implications.
Emotion Recognition Related to Stock Trading Using Machine Learning Algorithms With Feature Selection

This article proposes an emotion elicitation method to develop our Stock-Emotion dataset: a collection of the participants’ electroencephalogram (EEG) signals who paper-traded using real stock market data, virtual money, and outcomes that emotionally affected them. A system for emotion recognition using this dataset was tested. The system extracted from the EEG signals the following features: five frequency bands, Differential Entropy (DE), Differential Asymmetry (DASM), and Rational Asymmetry (RASM), for each band. Our system then carried out feature selection using a filter method (Mutual Information Matrix), combined with a wrapper process (Chi-Square statistics) and alternatively using the embedded algorithms in a Deep Learning classifier. Finally, this work classified emotions in four quadrants of the circumplex model using Random Forest and Deep Learning algorithms. Our findings show that 1) the proposed emotion elicitation method is useful to provoke affective states associated with trading, 2) the proposed feature selection process improved the classification performance of our emotion recognition system, and 3) classifier performance of the system can recognize trading related emotions and has results comparable with the state of the art research corresponding to a similar number of output classes.
Numerical Simulation of Ion Beam Evolution in Magnetized Plasma

The problem of the evolution of an ion beam in a fully ionized plasma is investigated using computer simulation. The 2D-3V axisymmetric composite numerical model is based on the particle-in-cell (PIC) method for both the ion component of the background plasma and the ion beam. The magnetohydrodynamics (MHD) approximation is used for the plasma electron component. Possibilities of using the PIC method for research non-stationary phenomena in astrophysics and plasma physics are limited due to the large difference in the masses of ions and electrons. We use a different approach, the hybrid MHD-PIC, which reduces computational costs and takes into account the final size of the Larmor radius of ions. When solving the kinetic equations by the particle-in-cell method, Cartesian coordinates are used, and then the reverse transition to cylindrical coordinates is carried out. As a result of computer simulation, the evolution of the ion beam depending on its energy and current, magnetic field and plasma density is studied. The possibility of forming regions with a low plasma density and a displaced magnetic field is shown.
Influence of Introducing the Rotor MMF Harmonic on Torque Performance of Spoke Type Permanent Magnet Motor With FSCW

This article proposes a torque optimization method that introduces the rotor magnetomotive force (MMF) harmonics, aiming to increase the average torque without increasing the cogging torque and torque ripple. A spoke type permanent magnet direct drive motor with fractional slot concentrated winding (FSCW) is designed. The FSCW can improve the utilization rate of the winding, but at the same time, it brings a wealth of MMF harmonics. First, the electromagnetic torque expression is derived using the principles of magnetic energy and virtual power. It means that the constant electromagnetic torque is generated when the stator and rotor MMF harmonics with the same space and time order are the same as the main wave speed. The analytical method is then used to analyze the influence of the stator and rotor MMF harmonics on the electromagnetic torque of the three-phase FSCW permanent magnet motor. On this basis, a rotor shape that introduces the third rotor MMF harmonics is proposed. Finally, the finite element software is used to compare and analyze the torque performance of motor before and after optimization. The finite element simulation and experiment results prove the correctness and effectiveness of the method.
Distributed Control of Islanded Series PV-Battery-Hybrid Systems with Low Communication Burden

To better integrate distributed energy sources, the series photovoltaic-battery-hybrid system has been proposed. However, the state-of-the-art control highly relies on the communication, by which real-time control variables should be transmitted among all converters. To overcome this, a novel distributed control method is proposed in this paper. Firstly, a PQ decoupling control is introduced, which enables the control of individual converters with only local measurements. Then, a reactive power distribution method is developed to achieve approximately equal power sharing among the converters. Additionally, two anti-over-modulation loops are developed to address the over-modulation issue. With the proposed method, only the total power of the series system should be transmitted, and the communication burden can be significantly reduced. Simulation and experimental results have validated the effectiveness of the proposed method.
Multifunctional active reconfigurable frequency selective surface

This paper presents a multi-functional frequency selective surface (FSS)based on varactor diode and PIN diode loading. It can be realized that when the varactor diode is not loaded with voltage and the PIN is in the on state, the frequency selective surface(FSS) exhibits strong transmittance at 4 GHz, and when the PIN is in the off state, the frequency selective surface(FSS) exhibits strong reflectivity at 4 GHz. In addition, when the PIN is turned on, stable transmission of electromagnetic waves in a wide frequency range from 1.37GHZ-5.66GHZ can be achieved by changing the capacitance of the loaded varactor diode.
Receiver Assisted LBT Mechanism Design for Beam-based Transmission in Unlicensed Bands

In the unlicensed spectrum of 5GHz, Listen-Before-Talk (LBT) technology is used to ensure the fair and friendly coexistence of NR-U (New Radio Unlicensed) and WiFi. However, when signals are transmitted in the mmWave band, the propagation distance is short. Thus, the beamforming technology is used to form a directional narrow beam with high antenna gain, which changes the interference layout, resulting in the limited range of LBT. In this case, to better evaluate the potential interference, the receiver utilizes the useful information received through beam transmission to better manage the interference in the unlicensed mmWave spectrum, which is called receiver-assisted LBT. The performance of the system based on receiver-assisted LBT will be significantly affected by the Energy Detection (ED) threshold of the receiver. Therefore, this paper aims to find the optimal ED threshold of the receiver. We propose the inner approximation algorithm, which can improve the spectral efficiency and the system throughput when multi Radio Access Technologies (RATs) work in the unlicensed mmWave spectrum. The simulation results show the effectiveness of the method.
Sharing patient-generated data with healthcare providers: findings from a 2019 national survey

OBJECTIVE Our study estimates the prevalence and predictors of wearable device adoption and data sharing with healthcare providers in a nationally representative sample. MATERIALS AND METHODS Data were obtained from the 2019 Health Information National Trend Survey. We conducted multivariable logistic regression to examine predictors of device adoption and data sharing. RESULTS The sample contained 4159 individuals, 29.9% of whom had adopted a wearable device in 2019. Among adopters, 46.3% had shared data with their provider. Individuals with diabetes (odds ratio [OR], 2.39; 95% CI, 1.66-3.45; P < .0001), hypertension (OR, 2.80; 95% CI, 2.12-3.70; P < .0001), and multiple chronic conditions (OR, 1.55; 95% CI, 1.03-2.32; P < .0001) had significantly higher odds of wearable device adoption. Individuals with a usual source of care (OR, 2.44; 95% CI, 1.95-3.04; P < .0001), diabetes (OR, 1.66; 95% CI, 1.32-2.08; P < .0001), and hypertension (OR, 1.78; 95% CI, 1.44-2.20; P < .0001) had significantly higher odds of sharing data with providers. DISCUSSION A third of individuals adopted a wearable medical device and nearly 50% of individuals who owned a device shared data with a provider in 2019. Patients with certain conditions, such as diabetes and hypertension, were more likely to adopt devices and share data with providers. Social determinants of health, such as income and usual source of care, negatively affected wearable device adoption and data sharing, similarly to other consumer health technologies. CONCLUSIONS Wearable device adoption and data sharing with providers may be more common than prior studies have reported; however, digital disparities were noted. Studies are needed that test implementation strategies to expand wearable device use and data sharing into care delivery.
Compact and Broadband Stacked Patch Antenna Loaded with Mushroom Metamaterials

A stacked patch antenna is proposed which is loaded with mushroom metamaterials. The metamaterials enable the antenna to resonate at a very low frequency while the stacked patches broaden the impedance bandwidth. A particular metamaterial antenna is demonstrated in full wave calculations. A broad bandwidth of 24.6% is achieved for such a compact antenna, of which the driven and parasitic patch lengths are kept very small at the order of 0. 21λ and 0. 25λ respectively (λ is the wavelength of center frequency in the working bandwidth). The antenna gain is observed to be 7.26dBi while the peak efficiency is over 96% in the numerical example. The proposed antenna is suitable to be used in the portable wideband communications.
Miniaturized Wideband Low-Profile Antenna Based on Capacitive loading Metasurface

A novel metasurface antenna is proposed with capacitive loading and dual-layer metasurface array. The antenna consists of dual-layer array using different period and four strips are inserted into unit of the upper array. This structure increases surface capacitance, thus reduces operating frequency and make the antenna miniaturized. The proposed antenna with a low profile of 0. 064λ0 and a metasurface size of 0. 4λ0×0. 4λ0 (λ0 is the center operating wavelength in free space) attains 26.2% simulated bandwidth (|S11| < -10dB) with 6.19-7dBi gain at 3.78-4.92GHz. Compared to the traditional metasurface antenna, the antenna size reduced to 30%.
Building Structure Imaging Algorithm with an Envelope of Ellipses based Multipath

On the basis of the propagation characteristics of electromagnetic wave attenuation, reflection, and diffraction, a imaging algorithm with an envelope of ellipses is proposed to obtain the image of the building geometry structure. The ellipse model regarding a target is established by analyzing the geometry relationship between the target and the coordinates of transceiver. With this model, it can get better imaging results using the echo delay.
CYP2C8 regulated by GAS5/miR-382-3p exerts anti-cancerous properties in liver cancer

ABSTRACT A cornucopia of literatures has characterized the involvement of a host of functional molecules in liver cancer. Herein, according to online datasets, we found that cytochrome P450 family 2 subfamily C member 8 (CYP2C8) was downregulated in liver cancer, and high CYP2C8 expression was associated with favorable overall survival. Lower levels of CYP2C8 were confirmed in liver cancer cells. CYP2C8 overexpression efficiently attenuated liver cancer cell proliferation and promoted apoptosis. We then discovered that miR-382-3p directly targeted CYP2C8 to inhibit its expression in liver cancer cells based on bioinformatic prediction and experimental confirmation. Moreover, a cytoplasmic long noncoding RNA (lncRNA), growth arrest-specific 5 (GAS5), sponged and down-regulated miR-382-3p, thus positively modulating CYP2C8 expression. Rescue assays indicated that GAS5 overexpression gave rise to decreased proliferation and increased apoptosis of liver cancer cells, while CYP2C8 knockdown counteracted GAS5-mediated anti-carcinogenic effects. In summary, our work offered a solid experimental foundation for understanding the functional role of CYP2C8 and the mechanism of GAS5/miR-382-3p/CYP2C8 axis in cell proliferation and apoptosis of liver cancer.