Miracle-dz/UniFAR-FTUs huggingface dataset:
On the asymmetry in the volatility of financial time series: a buffered transition approach

ABSTRACT In this paper, we propose a new threshold model to deal with the asymmetry and leverage effect in the volatility of financial time series. Our model generalizes the classical threshold stochastic volatility model. By introducing a new flexible and smooth regime-switching mechanism, we avoid the sudden jump in the log-volatility imposed by the classical model. More precisely, we propose a new threshold model that we call Buffered Threshold Stochastic Volatility (BTSV) model. After the definition of our model, we derive a sufficient condition for the strict stationarity of the model. Then, we develop a Sequential Monte Carlo method to estimate the parameters. Next, we analyse the performance of the proposed method through a simulation study. Finally, we apply our new methodology to model the Honeywell International Inc and Standard and Poor Composite 500 stock market indices. This empirical analysis shows that BTSV model is a competitive alternative for the asymmetry modelling in financial returns analysis.
Experimental parametric study and design of Piano Key Weirs

ABSTRACT Piano Key Weirs are an effective solution for dam rehabilitation as well as new dam projects with a high level of hydraulic constraints. In order to improve the efficiency of their design, an experimental study of the influence of the main geometric parameters has been performed. Thirty one configurations were tested for a wide range of discharges. The results of the study show the influence of the weir height, the keys widths and the overhangs lengths on the discharge capacity and flow characteristics. Based on hydraulic considerations, optimum values of the main geometric ratios are provided. An analytical formulation is developed to predict the discharge capacity of the weir as a function of its geometry. It shows an accuracy of 10% compared to the experimental results of this study and from other sources.
Détection automatique des points de fuite et calcul de leur incertitude à l'aide de la géomét rie projective Automatic detection of vanishing points and their uncertainty based on projective geometry

An entirely automatic algorithm of detection of van ishing points in images of urban stages is presented. This approach leans on a classic theorem of projective geometry (theorem of Chasles-Steiner), that permits to transform the problem of detection of the vanishing points from segments and their uncertainty into a problem of detection of circles in a cloud of points (every po int corresponds to a segment). The extraction of circle s uses a robust method of RanSac type, modified to be very fast in relation to techniques of accumulation (Hough ty pe or any other). This robust evaluation is refined then by an error propagation by least squares exploiting the individual variances of every segment. The develope d algorithm is robust, its precision is satisfactory and besides it is entirely automatic. Its proper workin g has been tested on a large number of images from varied urban landscapes.
The strong uniform convergence of multivariate variable kernel estimates

We show that sup, completely as, where f is a uniformly continuous density on are independent random vectors with common density f, and fn is the variable kernel estimate Here Hni is the distance between Xi and its kth nearest neighbour, K is a given density satisfying some regularity conditions, and k is a sequence of integers with the property that log asn Nous demontrons que completement lorsque ou represente une fonction de densite uniformement continue sur sont des vecteurs aleatoires independants ayant f pour densite commune, et ou fn denote l'estimateur du noyau variable, a savoir Ici, Hni repreAsente la distance entre X, et son kK-ieme voisin le plus proche, K est une fonction de densite donnee obeissant a quelques conditions de regularite et k est une suite d'entiers telle que quand.
Boundary conditions for the parabolic equation in a range‐dependent duct

The parabolic wave equation in a range‐dependent duct with a rigid bottom is considered. The range dependence will introduce amplitude errors in addition to the usual phase error of the local modes. It is shown that the amplitude error increases by local mode angle and slope of the bottom, and is inversely proportional to the depth. Imposing the vanishing of the normal derivative of the pressure at the bottom, which is the physically correct boundary condition, might also lead to ill conditioning of the parabolic model. Various ways to replace the boundary condition ∂u/∂n=0 are discussed, and their accuracy is assessed both numerically and theoretically.
A Mixed Feature Extractor for Intelligent Fault Detection and Diagnosis of Tunnel Diode Circuit Systems

This brief presents a mixed feature extractor (MFE) for the fault detection and diagnosis of tunnel diode circuit systems described by Takagi-Sugeno (T-S) fuzzy model-based Markov jump systems (MJSs). A novel neural network model is constructed, which is composed of the 1-D convolutional neural network (CNN) and Transformer. In order to make full use of feature information, the 1-D CNN model is utilized to extract the local features, and Transformer is established to obtain the global features. Then, the features taken from the MFE are concatenated and fed into a classification layer for fault detection and diagnosis. Finally, through experimental results, the proposed MFE is validated to be effective and outperform the commonly used diagnosis methods.
Optical parametric amplification

This paper presents a detailed theoretical and experimental study of optical parametric amplification. Experimental measurements in KD*P pumped at 354.7 nm and LiNbO 3 pumped at 1064 nm by a Q -switched Nd:YAG source are in excellent agreement with theory. The large gains, high conversion efficiency, and wide tuning range make optical parametric amplification an important method for power generation in the optical and infrared spectral range.
Existence and Stability of Limit Cycles in a Two-delays Model of Hematopoiesis Including Asymmetric Division

A two dimensional two-delays differential system modeling the dynamics of stem-like cells and white-blood cells in Chronic Myelogenous Leukemia is considered. All three types of stem cell division (asymmetric division, symmetric renewal and symmetric differentiation) are present in the model. Stability of equilibria is investigated and emergence of periodic solutions of limit cycle type, as a result of a Hopf bifurcation, is eventually shown. The stability of these limit cycles is studied using the first Lyapunov coefficient.
TANGENTIAL FORCE DISTRIBUTION AND FORCE PULSATION IN A LINEAR RELUCTANCE MOTOR

ABSTRACT A study of normal flux density, tangential force distribution and force pulsation is made on a segmented secondary linear reluctance motor. Appropriate analysis is included in a form convenient for numerical study. Results are obtained numerically for two different linear reluctance motor geometries corresponding to laboratory models. Some experimental results are compared with numerical results. The numerical results show that the normal flux density and the tangential force distribution vary with linear displacement but in a less severe manner than the same variables in a linear induction motor. The tangential force pulsation is significantly large and the normal force pulsation is less severe.
Assimilation of Reflectivity Data in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification

AbstractThe impact of assimilating radar reflectivity and radial velocity data with an intermittent, cycled three-dimensional variational assimilation (3DVAR) system is explored using an idealized thunderstorm case and a real data case on 8 May 2003. A new forward operator for radar reflectivity is developed that uses a background temperature field provided by a numerical weather prediction model for automatic hydrometeor classification. Three types of experiments are performed on both the idealized and real data cases. The first experiment uses radial velocity data only, the second experiment uses both radial velocity and reflectivity data without hydrometeor classification, and the final experiment uses both radial velocity and reflectivity data with hydrometeor classification. All experiments advance the analysis state to the next observation time using a numerical model prediction, which is then used as the background for the next analysis. Results from both the idealized and real data cases show that.
Drainage pipe defect identification based on convolutional neural network

At present, the detection of drainage pipe defects adopts manual frame-by-frame naked eye discrimination, which has low detection efficiency and high cost, so a two-path multi-receptive convolutional neural network is designed, which also takes into account a certain small volume on the basis of obtaining the highest classification index. The experimental results show that the volume accuracy of the designed model is 92.3%, the recall rate is 91.1%, the F1 score is 91.7%, the model volume is 30.7M, the parameter quantity is 8.97M, and the calculation amount is 2.25G. Compared with other networks, this model is more suitable for automatic identification of drainage pipes.
High-Performance Position Control for Repetitive Tasks of Motor-Driven Servo Systems Based on Periodic Disturbance Observer

Repetitive position control of the motor-driven servo system (MDSS) plays an important role in numerous industrial processing and production. In this article, based on the Euler–Lagrange equation, a dynamics model was established by analyzing the relationship of the energy conversion to describe the motion of an $n$-degree-of-freedom MDSS. For repetitive position control tasks of the considered system, a robust controller was designed to improve the performance of the closed-loop system, where the disturbance observer was employed to approximate the periodic dynamics of the system to improve both the transient and steady-state performances. The stability of the closed-loop system was analyzed based on the Lyapunov method. Moreover, to validate the control effect of the proposed method, the coordinated contouring task was considered for a dual-linear-motor-driven gantry system, where a B-spline wavelet series was employed as the periodic disturbance observer. The results of the comparative experiment show not only the effectiveness of the designed control scheme but also the superiority of the B-spline wavelet series on the approximation of periodic functions.
OSCILLATION RESULTS FOR SECOND ORDER NONLINEAR NEUTRAL DIFFERENTIAL EQUATIONS WITH DELAY

In this paper, oscillatory and asymptotic behavior of solutions of a class of nonlinear second order neutral differential equations with positive and negative coefficients of the form \begin{eqnarray*} (r_{1}(t)(x(t)+p_{1}(t)x(\tau(t)))^{\prime})^{\prime}+r_{2}(t)(x(t)+p_{2}(t)x(\sigma(t)))^{\prime} \\+p(t)G(x(\alpha(t)))-q(t)H(x(\beta(t)))=0 \end{eqnarray*} and \begin{eqnarray*} (r_{1}(t)(x(t)+p_{1}(t)x(\tau(t)))^{\prime})^{\prime}+r_{2}(t)(x(t)+p_{2}(t)x(\sigma(t)))^{\prime} \\+p(t)G(x(\alpha(t)))-q(t)H(x(\beta(t)))=f(t) \end{eqnarray*} are studied for various ranges of $p_{1}(t), p_{2}(t)$.
The effect of melatonin on cognitive functions following coronary artery bypass grafting: A triple-blind randomized-controlled trial

Background: Cognitive dysfunction presents one of the chief causes of postoperative morbidity. Melatonin as a neurohormone can improve neurocognitive functioning and sleep disorders. We evaluated the effect of melatonin on the postoperative cognitive function of patients undergoing coronary artery bypass grafting (CABG). Materials and Methods: A triple-blind randomized-controlled trial was conducted on 66 CABG candidates in Namazee Hospital (Shiraz, Iran). Patients were assigned equally into two groups receiving melatonin 10 mg or a placebo daily for 4 weeks before surgery and 2 days after surgery in the intensive care unit. The Mini-Mental State Examination (MMSE), Tower of London (ToL), and Wechsler Adults Intelligence Scale-Revised (WAIS-R) cognitive function tests were performed in both groups 4 weeks before surgery (time point 1), 2 days after surgery (time point 2), and 6 weeks after initial administration of melatonin (time point 3). Results: The mean change score (time point 3-time point 1) differed significantly between the two groups in the MMSE (P ≤ 0.001), ToL total score (P = 0.001), and WAIS-R general IQ (P ≤ 0.001), picture completion (P ≤ 0.001), vocabulary (P = 0.024), and digit span (P = 0.01). On the other hand, no significant differences were detected in the WAIS-R block design, ToL total time delay, ToL total lab, and ToL total result scores. Conclusion: The MMSE and WAIS-R tests revealed that melatonin might have prophylactic effects against postoperative cognitive disturbance in patients undergoing elective CABG.
Biostatistical evaluation of the effectiveness of fetal ultrasound diagnostics with application of new uncertainty factor and difficulty factor in cases of craniofacial malformations—gray zone in biostatistics for imaging procedures

Background The craniofacial malformations occur less frequently, with a prevalence rate of approximately 0.1%. Our aim is to investigate the effectiveness of prenatal ultrasound in the detection of the craniofacial abnormalities. Methods In our study, we have processed the prenatal sonographic and postnatal clinical and fetopathological data of 242 anatomical deviations of 218 fetuses with craniofacial malformations over a 12-year period. The patients were divided into three groups: Group I, Totally Recognized; Group II, Partially Recognized; Group III, Not Recognized. To characterize the diagnostics of disorders we developed the Uncertainty Factor F (U) = P (Partially Recognized)/[P (Partially Recognized) + T (Totally Recognized)] and Difficulty factor F (D) = N (Not Recognized)/[P (Partially Recognized) + T (Totally Recognized)]. Results Prenatal ultrasound diagnosis of fetuses with facial and neck malformations completely coincided in 71/218 cases (32.6%) with postnatal/fetopathological findings. In 31/218 cases (14.2%) the detection was only partial, while in 116/218 cases no craniofacial malformations were diagnosed prenatally (53.2%). The Difficulty Factor was high or very high in almost each disorder group, with a cumulative score of 1.28. The Uncertainty Factor cumulative score was 0.32. Conclusions The effectiveness of the detection of the facial and neck malformations was low (29.75%). The Uncertainty Factor F (U) and Difficulty Factor F (D) parameters, which characterized the difficulties of the prenatal ultrasound examination well.
Bi-level energy management strategy for power-split plug-in hybrid electric vehicles: A reinforcement learning approach for prediction and control

The implementation of an energy management strategy plays a key role in improving the fuel economy of plug-in hybrid electric vehicles (PHEVs). In this article, a bi-level energy management strategy with a novel speed prediction method leveraged by reinforcement learning is proposed to construct the optimization scheme for the inner energy allocation of PHEVs. First, the powertrain transmission model of the PHEV in a power-split type is analyzed in detail to obtain the energy routing and its crucial characteristics. Second, a Q-learning (QL) algorithm is applied to establish the speed predictor. Third, the double QL algorithm is introduced to train an effective controller offline that realizes the optimal power distribution. Finally, given a reference battery's state of charge (SOC), a model predictive control framework solved by the reinforcement learning agent with a novel speed predictor is proposed to build the bi-level energy management strategy. The simulation results show that the proposed method performs with a satisfying fuel economy in different driving scenarios while tracking the corresponding SOC references. Moreover, the calculation performance also implies the potential online capability of the proposed method.
Heterogeneity-driven hybrid denoising

A filter aimed at denoising should strongly smooth uniform regions, while preserving edges. On textured areas, the filter should attain a compromise to achieve some enhancement without destroying useful information. Filtering performances, however, locally depend on the statistical characteristics of the imaged signal, which can be embodied by the concept of local heterogeneity. It is shown that statistically homogeneous regions originate clusters in the scatterplot of standard deviation to mean. Textured regions yield scatterpoints spread apart to a larger extent. Edges produce outliers. Thus, the homogeneity may be locally measured from the joint PDF of estimated local standard deviation to estimated local mean. For each pixel having a measured local mean and a measured standard deviation, a point is detected in the PDF plane: the corresponding density is taken as a measure of homogeneity of that pixel. In this work a hybrid filter, i.e., a set of filters is considered, the denoising capability of each of which depends on the degree of local homogeneity. Images are individually processed by each filter, and the filtered image is obtained by switching among such channels at each pixel position, based on thresholding a heterogeneity feature in order to identify a number of classes, for each of which the noise-free image signal is best estimated by one of the filters. Visual judgments on simulated noisy images agree with this tendency.
A Systematic Review of Progress toward Unlocking the Power of Epigenetics in NSCLC: Latest Updates and Perspectives

Epigenetic research has the potential to improve our understanding of the pathogenesis of cancer, specifically non-small-cell lung cancer, and support our efforts to personalize the management of the disease. Epigenetic alterations are expected to have relevance for early detection, diagnosis, outcome prediction, and tumor response to therapy. Additionally, epi-drugs as therapeutic modalities may lead to the recovery of genes delaying tumor growth, thus increasing survival rates, and may be effective against tumors without druggable mutations. Epigenetic changes involve DNA methylation, histone modifications, and the activity of non-coding RNAs, causing gene expression changes and their mutual interactions. This systematic review, based on 110 studies, gives a comprehensive overview of new perspectives on diagnostic (28 studies) and prognostic (25 studies) epigenetic biomarkers, as well as epigenetic treatment options (57 studies) for non-small-cell lung cancer. This paper outlines the crosstalk between epigenetic and genetic factors as well as elucidates clinical contexts including epigenetic treatments, such as dietary supplements and food additives, which serve as anti-carcinogenic compounds and regulators of cellular epigenetics and which are used to reduce toxicity. Furthermore, a future-oriented exploration of epigenetic studies in NSCLC is presented. The findings suggest that additional studies are necessary to comprehend the mechanisms of epigenetic changes and investigate biomarkers, response rates, and tailored combinations of treatments. In the future, epigenetics could have the potential to become an integral part of diagnostics, prognostics, and personalized treatment in NSCLC.
A finite-difference simulation of ultrasonic Lamb waves in metal sheets with experimental verification

This paper describes a simple finite-difference method to simulate and Lamb wave modes in plane metal sheets. The simulation is used to predict time-domain histories of field displacements vertical to the sheet at groups of points on the sheet. The time - space data so obtained are transformed by two-dimensional FFT to yield frequency - wavenumber representations for comparison with the Lamb mode dispersion waves. An experimental rig is developed for transmission and reception of Lamb waves in sheet metal, with adjustable transducer positions on the Lamb wave axis. The rig enables two-dimensional time - space, and by transformation, frequency - wavenumber, data to be obtained experimentally. An electronic system to provide appropriate band-limited signals for single-mode Lamb wave excitation is outlined and used in conjunction with the physical rig. Experimental results show good agreement with simulation.
Analyticity and Borel summability of the $\phi ^4$ models. I. The dimension $d = 1$

We prove that the Schwinger functions of the φ 4 models, in dimension d = 1, are analytic functions of the coupling constant in a common domain which extends to |Arg λ| < 3π/2, and in which each of them is the Borel sum of its Taylor series at λ = 0; these series are uniformly Borel summable of level 1 in all directions except that of negative real numbers
Neuroprotective Effects of the Neural-Induced Adipose-Derived Stem Cell Secretome against Rotenone-Induced Mitochondrial and Endoplasmic Reticulum Dysfunction

Mesenchymal stem cells (MSCs) have therapeutic effects on neurodegenerative diseases (NDDs) known by their secreted molecules, referred to as the “secretome”. The mitochondrial complex I inhibitor, rotenone (ROT), reproduces α-synuclein (α-syn) aggregation seen in Parkinson’s disease (PD). In this present study, we examined the neuroprotective effects of the secretome from neural-induced human adipose tissue-derived stem cells (NI-ADSC-SM) during ROT toxicity in SH-SY5Y cells. Exposure to ROT significantly impaired the mitophagy by increased LRRK2, mitochondrial fission, and endoplasmic reticulum (ER) stress (ERS). ROT also increased the levels of calcium (Ca2+), VDAC, and GRP75, and decreased phosphorylated (p)-IP3R Ser1756/total (t)-IP3R1. However, NI-ADSC-SM treatment decreased Ca2+ levels along with LRRK2, insoluble ubiquitin, mitochondrial fission by halting p-DRP1 Ser616, ERS by reducing p-PERK Thr981, p-/t-IRE1α, p-SAPK, ATF4, and CHOP. In addition, NI-ADSC-SM restored the mitophagy, mitochondrial fusion, and tethering to the ER. These data suggest that NI-ADSC-SM decreases ROT-induced dysfunction in mitochondria and the ER, which subsequently stabilized tethering in mitochondria-associated membranes in SH-SY5Y cells.