Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Degradation Modeling and Its Prediction Algorithm of an IGBT Gate Oxide Layer Based on a CNN-LSTM Network

The problem of health status prediction of insulated-gate bipolar transistors (IGBTs) has gained significant attention in the field of health management of power electronic equipment. The performance degradation of the IGBT gate oxide layer is one of the most important failure modes. In view of failure mechanism analysis and the easy implementation of monitoring circuits, this paper selects the gate leakage current of an IGBT as the precursor parameter of gate oxide degradation, and uses time domain characteristic analysis, gray correlation degree, Mahalanobis distance, Kalman filter, and other methods to carry out feature selection and fusion. Finally, it obtains a health indicator, characterizing the degradation of IGBT gate oxide. The degradation prediction model of the IGBT gate oxide layer is constructed by the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) Network, which show the highest fitting accuracy compared with Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and CNN-LSTM models in our experiment. The extraction of the health indicator and the construction and verification of the degradation prediction model are carried out on the dataset released by the NASA-Ames Laboratory, and the average absolute error of performance degradation prediction is as low as 0.0216. These results show the feasibility of the gate leakage current as a precursor parameter of IGBT gate oxide layer failure, as well as the accuracy and reliability of the CNN-LSTM prediction model.
Glutamate, Humic Acids and Their Combination Modulate the Phenolic Profile, Antioxidant Traits, and Enzyme-Inhibition Properties in Lettuce

Lettuce (Lactuca sativa L., Asteraceae) is a popular vegetable leafy crop playing a relevant role in human nutrition. Nowadays, novel strategies are required to sustainably support plant growth and elicit the biosynthesis of bioactive molecules with functional roles in crops including lettuce. In this work, the polyphenolic profile of lettuce treated with glutamic acid (GA), humic acid (HA), and their combination (GA + HA) was investigated using an untargeted metabolomics phenolic profiling approach based on high-resolution mass spectrometry. Both aerial and root organ parts were considered, and a broad and diverse phenolic profile could be highlighted. The phenolic profile included flavonoids (anthocyanins, flavones, flavanols, and flavonols), phenolic acids (both hydroxycinnamics and hydroxybenzoics), low molecular weight phenolics (tyrosol equivalents), lignans and stilbenes. Overall, GA and HA treatments significantly modulated the biosynthesis of flavanols, lignans, low molecular weight phenolics, phenolic acids, and stilbene. Thereafter, antioxidant capacity was evaluated in vitro with 2,2-diphenyln-1-picrylhydrazyl (DPPH), 2,2′-Azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), ferric reducing antioxidant power (FRAP), and cupric ion reducing antioxidant capacity (CUPRAC) assays. In addition, this study examined the inhibitory properties of enzymes, including acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), tyrosinase, alpha-amylase, and alpha-glucosidase. Compared to individual treatments, the combination of GA + HA showed stronger antioxidant abilities in free radical scavenging and reducing power assays in root samples. Moreover, this combination positively influenced the inhibitory effects of root samples on AChE and BChE and the tyrosinase inhibitory effect of leaf samples. Concerning Pearson’s correlations, antioxidant and enzyme inhibition activities were related to phenolic compounds, and lignans in particular correlated with radical scavenging activities. Overall, the tested elicitors could offer promising insights for enhancing the functional properties of lettuce in agricultural treatments.
Informative Drop‐Out in Longitudinal Data Analysis

A model is proposed for continuous longitudinal data with non-ignorable or informative drop-out (ID). The model combines a multivariate linear model for the underlying response with a logistic regression model for the drop-out process. The latter incorporates dependence of the probability of drop-out on unobserved, or missing, observations. Parameters in the model are estimated by using maximum likelihood (ML) and inferences drawn through conventional likelihood procedures. In particular, likelihood ratio tests can be used to assess the informativeness of the drop-out process through comparison of the full model with reduced models corresponding to random drop-out (RD) and completely random processes
PROBABILITÉS ET STATISTIQUES

Thèmes abordés : Introduction au calcul des probabilités Variables aléatoires discrètes et continues: fonction de probabilités et de densité, fonction de répartition, espérance, variance et autres caractéristiques Principales distributions statistiques Couples de variables aléatoires et vecteurs aléatoires: distribution conjointe, distributions marginales et conditionnelles, indépendance, covariance et corrélation, espérance et variance conditionnelle Introduction à la statistique Statistiques descriptives Notions d'estimateurs et qualités des estimateurs Inférence concernant une moyenne et une variance: estimateurs, distributions d'échantillonnage Notion d'intervalle de confiance à une moyenne.
Statistical–Dynamical Seasonal Forecast of North Atlantic and U.S. Landfalling Tropical Cyclones Using the High-Resolution GFDL FLOR Coupled Model

AbstractRetrospective seasonal forecasts of North Atlantic tropical cyclone (TC) activity over the period 1980–2014 are conducted using a GFDL high-resolution coupled climate model [Forecast-Oriented Low Ocean Resolution (FLOR)]. The focus is on basin-total TC and U.S. landfall frequency. The correlations between observed and model predicted basin-total TC counts range from 0.4 to 0.6 depending on the month of the initial forecast. The correlation values for U.S. landfalling activity based on individual TCs tracked from the model are smaller and between 0.1 and 0.4. Given the limited skill from the model, statistical methods are used to complement the dynamical seasonal TC prediction from the FLOR model. Observed and predicted TC tracks were classified into four groups using fuzzy c-mean clustering to evaluate the model’s predictability in observed classification of TC tracks. Analyses revealed that the FLOR model has the highest skill in predicting TC frequency for the cluster of TCs that tracks through .
The effects of betaine and nano-emulsified plant-oil supplementation on growth performance and serum biochemistry indices of heat-stressed broiler chickens

Abstract This study aimed to investigate the effects of water-added betaine (BET) and Magic oil, (MAGO) on growth performance and blood biochemistry of broilers exposed to constant heat stress (32 ± 1 °C) during 21–35 d. On day 21, 320 chicks (Ross 308) were straight run to one of four groups based supplements with drinking water (DW): control, BET (1 g/L DW), and/or MAGO (1 ml/L DW), respectively). Each treatment consisted of eight replicates with ten birds each. The performance data were obtained at 21, 28, and 35 d. Serum biochemical data of the birds were estimated at day 35. The results showed that body weight gain (BWG) and feed intake did not differ significantly (p > .05) between the treatments. However, Bet alone or in combination with MAGO has improved feed conversion ratio (FCR) during the 28–35 d and 21–35 d periods; also, BET lowered (p < .05) creatinine compared to control. The addition of MAGO alone or in combination with BET increased (p < .05) total protein, albumin, globulin, and low-density lipoprotein-cholesterol. In addition, MAGO decreased total lipids, and creatinine, whereas it increased (p < .05) cholesterol, especially good cholesterol, and triglycerides compared with the control. In conclusion, water-BET-supplements alone or in combination with MAGO has improved the FCR under heat stress conditions. Therefore, it is recommended to use Bet alone or both supplements under high-temperature conditions in broiler houses. However, further studies on the use of MAGO and/or BET at different doses are needed to maximise BWG and maintain the productivity of the birds. Highlights Water-enriched magic oil and betaine had higher added value than feeding alone by improving the feed conversion ratio during 21–35 d. Compared with the other groups, water-supplemented magic oil produced some benefits, such as lower serum glucose, higher serum high-density lipoprotein ‘good cholesterol’ and lower creatinine levels. The added value of the combination of magic oil and betaine for growth performance and serum health biomarkers needs further investigation at various levels.
Transfer operator for piecewise affine approximations of interval maps

Nous considerons un algorithme naturel d'approximation par des transformations affines par morceaux d'applications de l'intervalle f dilatantes et C 1+Lipschitz par morceaux, melangeantes et markoviennes. Nous montrons que les densites des mesures de probabilite invariantes absolument continues des approximations convergent uniformement vers la densite de la mesure de probabilite invariante absolument continue de f, avec une vitesse exponentielle. Nous utilisons pour cela des techniques perturbatives recemment introduites qui nous permettent de comparer les operateurs de transfert des approximations avec celui de f
Automatic one-hand gesture (mudra) identification in bharatanatyam using eigenmudra projections and convolutional neural networks

Abstract. Mudras in traditional Indian dance forms convey meaningful information when performed by an artist. The subtle changes between the different mudras in a dance form render automatic identification challenging as compared to conventional hand gesture identification, where the gestures are uniquely distinct from each other. Therefore, the objective of this study is to build a classifier model for the identification of the asamyukta mudra of bharatanatyam, one of the most popular classical dance forms in India. The first part of the paper provides a comprehensive review of the issues present in bharatanatyam mudra identification and the various studies conducted on the automatic classification of mudras. Based on this review, we observe that the unavailability of a large mudra corpus is a major challenge in mudra identification. Therefore, the second part of the paper focuses on the development of a relatively large database of mudra images consisting of 29 asamyukta mudras prevalent in bharatanatyam, which is obtained by incorporating different variabilities, such as subject, artist type (amateur or professional), and orientation. The mudra image database so developed is made available for academic research purposes. The final part of this paper describes the development of a convolutional neural network (CNN)-based automatic mudra identification system. Multistyle training of mudra classes on a conventional CNN showed a 92% correct identification rate. Based on the “eigenface” projection used in face recognition, “eigenmudras” projections of mudra images are proposed for improving the CNN-based mudra identification. Although the CNNs trained on the eigenmudra-projected images provide nearly equal identification rates as that obtained using the CNNs trained on raw mudra grayscale images, both models provide complementary mudra class information. The presence of complementary class information is confirmed by the improvement in the mudra identification performance when the CNN models trained from the raw mudra and eigenmudra-projected images are combined by computing the average of the scores obtained in the final softmax layers of both models. The same trend of improved mudra identification is observed upon combination of the average score level of VGG19 CNN models of the raw mudra images and corresponding eigenmudra-projected images.
The two-space homogenization method

In this thesis, we consider the two-space homogenization method, which produces macroscopic expressions out of descriptions of the behaviour of the microstructure. Specifically, we focus on its application to poroelastic media. After describing the method, we provide examples to demonstrate that the resultant expressions are equivalent to an explicit derivation, which might not always be possible, and to outline the method for proving that the expressions converge to their macroscopic equivalents. Upon providing the basis for this method, we follow Burridge and Keller’s work for using this to prove the existence of Biot’s consolidation equations for poroelastic media and to provide expressions for the derivation of the parameters of these equations from the microstructure [5]. We then discuss the benefits and challenges that arise from this formulation of Biot’s consolidation equations.
Passive scalars in turbulent channel flow at high Reynolds number

We study passive scalars in turbulent plane channels at computationally high Reynolds number, thus allowing us to observe previously unnoticed effects. The mean scalar profiles are found to obey a generalized logarithmic law which includes a linear correction term in the whole lower half-channel, and they follow a universal parabolic defect profile in the core region. This is consistent with recent findings regarding the mean velocity profiles in channel flow. The scalar variances also exhibit a near universal parabolic distribution in the core flow and hints of a sizeable log layer, unlike the velocity variances. The energy spectra highlight the formation of large scalar-bearing eddies with size proportional to the channel height which are caused by a local production excess over dissipation, and which are clearly visible in the flow visualizations. Close correspondence of the momentum and scalar eddies is observed, with the main difference being that the latter tend to form sharper gradients, which translates into higher scalar dissipation. Another notable Reynolds number effect is the decreased correlation of the passive scalar field with the vertical velocity field, which is traced to the reduced effectiveness of ejection events.
Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations

As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions.
L-Tyrosine Limits Mycobacterial Survival in Tuberculous Granuloma

Caused by the intracellular pathogen Mycobacterium tuberculosis (Mtb), tuberculosis (TB) remains a massive global public health issue. A well-known and key TB trait is caseous necrotic granuloma, which allows mycobacteria to reactivate and disseminate, thus confounding TB eradication programs. Amino acid (AA) metabolism is key to regulating immune responses in Mtb infections; however, it is currently unclear if AAs can be used to treat tuberculous granulomas. Here, we screened 20 proteinogenic AAs using a Mycobacterium marinum-infected zebrafish granuloma model. Only L-tyrosine simultaneously reduced Mycobacterium marinum (M. marinum) levels in zebrafish larvae and adults and inhibited intracellular pathogen survival levels. Mechanistically, L-tyrosine significantly upregulated interferon-γ (IFN-γ) expression in M. marinum -infected zebrafish adults but not in larvae. Using N-acetylcysteine (NAC) to inhibit reactive oxygen species (ROS), L-tyrosine appeared to inhibit Mtb intracellular survival by promoting ROS production. Thus, L-tyrosine as a non-essential AA may reduce mycobacterial survival in both macrophages and tuberculous granulomas. Our research provides a platform for the clinical development of AAs for active or latent TB patients infected with drug-sensitive or drug-resistant Mtb.
Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both, the correct associations among the observations and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. The number S corresponds to the number of fences involved in the problem. Each fence consists of a set of observations where each observation belongs to a different object. The S ≥ 3 MTT problem is an NP-hard combinatorial optimization problem. There are two general ways to solve this. One way is to seek the optimum solution, this can be achieved by applying a branch-and- bound algorithm. When using these algorithms the problem has to be greatly simplified to keep the computational cost at a reasonable level. Another option is to approximate the solution by using meta-heuristic methods. These methods aim to efficiently explore the different possible combinations so that a reasonable result can be obtained with a reasonable computational effort. To this end several population-based meta-heuristic methods are implemented and tested on simulated optical measurements. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.
Fiber-Type-Specific Hypertrophy with the Use of Low-Load Blood Flow Restriction Resistance Training: A Systematic Review

Emerging evidence indicates that the use of low-load resistance training in combination with blood flow restriction (LL-BFR) can be an effective method to elicit increases in muscle size, with most research showing similar whole muscle development of the extremities compared to high-load (HL) training. It is conceivable that properties unique to LL-BFR such as greater ischemia, reperfusion, and metabolite accumulation may enhance the stress on type I fibers during training compared to the use of LLs without occlusion. Accordingly, the purpose of this paper was to systematically review the relevant literature on the fiber-type-specific response to LL-BFR and provide insights into future directions for research. A total of 11 studies met inclusion criteria. Results of the review suggest that the magnitude of type I fiber hypertrophy is at least as great, and sometimes greater, than type II hypertrophy when performing LL-BFR. This finding is in contrast to HL training, where the magnitude of type II fiber hypertrophy tends to be substantially greater than that of type I myofibers. However, limited data directly compare training with LL-BFR to nonoccluded LL or HL conditions, thus precluding the ability to draw strong inferences as to whether the absolute magnitude of type I hypertrophy is indeed greater in LL-BFR vs. traditional HL training. Moreover, it remains unclear as to whether combining LL-BFR with traditional HL training may enhance whole muscle hypertrophy via greater increases in type I myofiber cross-sectional area.
A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.
NAMPT and PARylation Are Involved in the Pathogenesis of Atopic Dermatitis

Atopic dermatitis (AD) is a chronic inflammatory skin disease of very high prevalence, especially in childhood, with no specific treatment or cure. As its pathogenesis is complex, multifactorial and not fully understood, further research is needed to increase knowledge and develop new targeted therapies. We have recently demonstrated the critical role of NAD+ and poly (ADP-ribose) (PAR) metabolism in oxidative stress and skin inflammation. Specifically, we found that hyperactivation of PARP1 in response to DNA damage induced by reactive oxygen species, and fueled by NAMPT-derived NAD+, mediated inflammation through parthanatos cell death in zebrafish and human organotypic 3D skin models of psoriasis. Furthermore, the aberrant induction of NAMPT and PARP activity was observed in the lesional skin of psoriasis patients, supporting the role of these signaling pathways in psoriasis and pointing to NAMPT and PARP1 as potential novel therapeutic targets in treating skin inflammatory disorders. In the present work, we report, for the first time, altered NAD+ and PAR metabolism in the skin of AD patients and a strong correlation between NAMPT and PARP1 expression and the lesional status of AD. Furthermore, using a human 3D organotypic skin model of AD, we demonstrate that the pharmacological inhibition of NAMPT and PARP reduces pathology-associated biomarkers. These results help to understand the complexity of AD and reveal new potential treatments for AD patients.