Miracle-dz/UniFAR-FTUs huggingface dataset:
A Synopsis of Current Theories on Drug-Induced Nephrotoxicity

The overriding goal of the treatment of patients is its effectiveness and safety. However, all medications currently being used also exert some adverse pharmaceutical reactions, which may be regarded as an unintended but inevitable cost of pharmacotherapy. The kidney, as the main organ that eliminates xenobiotics, is an organ especially predisposed and vulnerable to the toxic effects of drugs and their metabolites during their excretion from the body. Moreover, some drugs (e.g., aminoglycosides, cyclosporin A, cisplatin, amphotericin B, and others) have a “preferential” nephrotoxicity potential, and their use is associated with an increased risk of kidney damage. Drug nephrotoxicity is, therefore, both a significant problem and a complication of pharmacotherapy. It should be noted that, currently, there is no generally recognized definition of drug-induced nephrotoxicity and no clear criteria for its diagnosis. This review briefly describes the epidemiology and diagnosis of drug-induced nephrotoxicity and characterizes its pathomechanisms, including immunological and inflammatory disturbances, altered kidney blood flow, tubulointerstitial injury, increased lithogenesis–crystal nephropathy, rhabdomyolysis, and thrombotic microangiopathy. The study also lists the basic drugs with nephrotoxicity potential and provides a short overview of the preventive methods for reducing the risk of drug-related kidney damage developing.
Roof type classification with innovative machine learning approaches

Recently, convolutional neural network-based methods have been used extensively for roof type classification on images taken from space. The most important problem with classification processes using these methods is that it requires a large amount of training data. Usually, one or a few images are enough for a human to recognise an object. The one-shot learning approach, like the human brain, aims to effect learning about object categories with just one or a few training examples per class, rather than using huge amounts of data. In this study, roof-type classification was carried out with a few training examples using the one-time learning approach and the so-called Siamese neural network method. The images used for training were artificially produced due to the difficulty of finding roof data. A data set consisting of real roof images was used for the test. The test and training data set consisted of three different types: flat, gable and hip. Finally, a convolutional neural network-based model and a Siamese neural network model were trained with the same data set and the test results were compared with each other. When testing the Siamese neural network model, which was trained with artificially produced images, with real roof images, an average classification success of 66% was achieved.
Interfacial characteristics of a carbon nanotube–polystyrene composite system

The performance of a composite material system is critically controlled by the interfacial characteristics of the reinforcement and the matrix material. Here we report a study on the interfacial characteristics of a carbon nanotube (CNT)-reinforced polystyrene (PS) composite system through molecular mechanics simulations and elasticity calculations. In the absence of atomic bonding between the reinforcement and the matrix material, it is found that the nonbond interactions consists of electrostatic and van der Waals interaction, deformation induced by these forces, as well as stress/deformation arising from mismatch in the coefficients of thermal expansion. All of these contribute to the interfacial stress transfer ability, the critical parameter controlling material performance. Results of a CNT pullout simulation suggests that the interfacial shear stress of the CNT–PS system is about 160 MPa, significantly higher than most carbon fiber reinforced polymer composite systems.
Third-Order Optical Nonlinearities of 2D Materials at Telecommunications Wavelengths

All-optical signal processing based on nonlinear optical devices is promising for ultrafast information processing in optical communication systems. Recent advances in two-dimensional (2D) layered materials with unique structures and distinctive properties have opened up new avenues for nonlinear optics and the fabrication of related devices with high performance. This paper reviews the recent advances in research on third-order optical nonlinearities of 2D materials, focusing on all-optical processing applications in the optical telecommunications band near 1550 nm. First, we provide an overview of the material properties of different 2D materials. Next, we review different methods for characterizing the third-order optical nonlinearities of 2D materials, including the Z-scan technique, third-harmonic generation (THG) measurement, and hybrid device characterization, together with a summary of the measured n2 values in the telecommunications band. Finally, the current challenges and future perspectives are discussed.
Explicit Estimators for a Banded Covariance Matrix in a Multivariate Normal Distribution

The problem of estimating mean and covariances of a multivariate normal distributedrandom vector has been studied in many forms. This thesis focuses on the estimatorsproposed in [15] for a banded covariance structure with m-dependence. It presents theprevious results of the estimator and rewrites the estimator when m = 1, thus makingit easier to analyze. This leads to an adjustment, and a proposition for an unbiasedestimator can be presented. A new and easier proof of consistency is then presented.This theory is later generalized into a general linear model where the correspondingtheorems and propositions are made to establish unbiasedness and consistency. In thelast chapter some simulations with the previous and new estimator verifies that thetheoretical results indeed makes an impact.
The Effect of Nano Calcium Carbonate and/or Recombinant Bone Morphogenetic Protein as a Biological Orthodontic Retainer on the Body Weight of Experimental Rat

Background: Retention after orthodontic tooth movement (OTM) is essential to prevent relapse. This study examined the effects of a fixed orthodontic device and Nano Calcium Carbonate (CaCO3) nanoparticles with or without recombinant human bone morphogenetic protein (rhBMP) on rat body weight. Materials and Methods: OTM was administered for 21 days to 80 Wistar Albino rats. First molar mesialization was active then forming two 40-rat groups which were subdivided into four subgroups of 10 rats each. These subgroups received 5 µg/kg rhBMP, 75 µg/kg CaCO3, 80 µg/kg rhBMP-loaded CaCO3 and one control. The relapse rate was examined weekly over the second 21 days when the second group exhibited mechanical retention and the first did not. Group 1 rats were murdered after 21 days (day 42), whereas group 2 rats entered a third 21-day post-retention period and then murdered (day 63). BW and OTM were measured on days 1, 21, 28, 35, 42, and 63. Results: Within each group, the animal body weight was reduced significantly after the intervention and continued over time with a higher average reduction in the 9-week group than the 6-week group. However, there were no significant (P-value ˃0.05) differences in the BW between the groups of the two (6-week and 9-week) sets and the subgroups of the 6-week set across each time point. In contrast, there was a significant (P-value ˂0.05) difference between the BW of the conjugate subgroup and the other three subgroups in the 9-week set, particularly on 63rd day. Conclusion: CaCO3 nanoparticles and/or BMP with orthodontic treatment collectively or individually cause a reduction of body weight in rats.
The impact of job insecurity on job burnout among hospitality employees during COVID-19 pandemic: the moderating role of supervisor and co-worker support

PurposeThis study evaluates the moderating role of work-based social supports (i.e. supervisor support and co-worker support) in the relationship between job insecurity and job burnout among hospitality employees in Malaysia. Besides, the direct effect between job insecurity and job burnout is examined.Design/methodology/approachThe cross-sectional data of this study were based on a total of 220 self-administered questionnaires that have been completed by hospitality employees from three different states in Malaysia. Respondents were recruited based on a snowball sampling approach. The data were collected during the COVID-19 pandemic, which was from October 2020 to January 2021.FindingsPartial least square-structural equation modeling (PLS-SEM) was performed via SmartPLS software. The finding confirmed that job insecurity significantly intensifies employees' job burnout. Supervisor support and co-worker support were found to moderate the link between job insecurity and burnout. As anticipated, the relationship between job insecurity and job burnout increased when supervisor support is low. But high co-worker support was found to strengthen the impact of job insecurity on job burnout instead of the reverse.Originality/valueThis study supplements the existing literature by clarifying which sources of work-based social support (i.e. co-worker support or supervisor) is more salient in alleviating the adverse impact of job insecurity on job burnout during the COVID-19 pandemic among hospitality employees in Malaysia.
Mean Reversion Lessens Mean Blur: Evidence from the S&P Composite Index

This study makes use of a very long time series of the S&P Composite Index, checking once more that the rates of return benefit from aggregational normality. It performs unit root tests as well as elementary statistical tests that take advantage of normality. It finds that mean blur is not consistent with the hypothesis of random walk with constant parameters, because the means of the annual real rates of linear return can be estimated as usual. It gives further evidence that the rates of return on the S&P Composite Index are mean-reverting.
Turbulence-Model Transition Predictions

References 1 Melosh, R. J., "Basis for Derivation of Matrices for the Direct Stiffness Method," AIAA Journal, Vol. 1, No. 7, July 1963, pp. 16311637. 2 Oden, J. T. , Finite Elements of Nonlinear Continua, McGraw-Hill, New York, 1972, p. 146. 3 Przemieniecki, J. S. , Theory of Matrix Structural Analysis, McGraw-Hill, New York, 1968, pp. 83-106. 4 Turner, M. J, Clough, R. W, Martin, H. C, and Topp, L. J, ""Stiffness and Deflection Analysis of Complex Structures," Journal of the Aeronautical Sciences, Vol. 23, 1956, pp. 805-823. 5 Wilson, E. L. , Taylor, R. L. , Doherty, W. P., and Ghabousii, J., "'Incompatible Displacement Models," Numerical and Computer Methods in Structural Mechanics, edited by S. J. Fenves et al. Academic Press, New York, pp. 43-57. 6 Zienkiewicz, O. C. and Cheung, Y. K. , The Finite Element Method in Structural and Continuum Mechanics, McGraw-Hill, London, 1967, p. 66.
Counterexamples in importance sampling for large deviations probabilities

A guiding principle in the efficient estimation of rare-event probabilities by Monte Carlo is that importance sampling based on the change of measure suggested by a large deviations analysis can reduce variance by many orders of magnitude. In a variety of settings, this approach has led to estimators that are optimal in an asymptotic sense. We give examples, however, in which importance sampling estimators based on a large deviations change of measure have provably poor performance. The estimators can have variance that decreases at a slower rate than a naive estimator, variance that increases with the rarity of the event, and even infinite variance. For each example, we provide an alternative estimator with provably efficient performance. A common feature of our examples is that they allow more than one way for a rare event to occur; our alternative estimators give explicit weight to lower probability paths neglected by leading-term asymptotics.
Internet Financial Credit Scoring Models Based on Deep Forest and Resampling Methods

In recent years, deep learning credit scoring models have become a hot research topic in Internet finance. However, most of the existing studies are based on deep neural network models, whose structure is difficult to design. Moreover, previous research seldom considers the impact of class imbalance problems on credit scoring performance. To fill this gap, we propose a new deep learning credit scoring model based on deep forest (DF) and resampling methods. First, we combine DF with five resampling methods including random over-sampling (ROS), random under-sampling (RUS), synthetic minority over-sampling technique (SMOTE), tomek links and SMOTE+ Tomek, respectively, to build responding models. We validate that the RUS-DF model has the best credit scoring performance among the above models. Then, to further evaluate the advantages of the deep ensemble model RUS–DF, we compare it with four models building by combining RUS with multilayer perceptron, convolutional neural network, and long short-term memory and random forests, respectively. All the experiments are conducted on four Internet financial credit scoring datasets. The results show that the RUS-DF model obtains better classification performance and stability than other models and is suitable for solving the credit scoring problem with imbalanced data.
Big bang in a universe with infinite extension

How can a universe coming from a point-like big bang event have infinite spatial extension? It is shown that the relativity of simultaneity is essential in answering this question. Space is finite as defined by the simultaneity of one observer, but it may be infinite as defined by the simultaneity of all the clocks participating in the Hubble flow.
Photometrie des offenen Sternhaufens NGC 4349

Die U, B, V-Photometric von Platten, die am Boyden Observatory in Bloemfontein aufgenommen worden sind, ergab fur NGC 4349: Entfernung = 1700 pc, Durchmesser = 9′ = 4.5 pc, Zahl der Sterne bis V = 15.6 etwa 70, Sterndichte im Zentrum = 17**/pc3, Alter = 0.6 · 109 Jahre.
Lower Limits on the Temperature and Hydrogen Ionization Fraction in the Diffuse, Ionized Interstellar Gas

A search for the forbidden O I emission line from the diffuse interstellar medium was carried out with the Wisconsin 15 cm dual etalon Fabry-Perot interferometer. The scans place an upper limit of 0.02 on the forbidden O I 6300 A/H-alpha line intensity which appears to be inconsistent with the standard McKee and Ostriker (1977) photoionization model of the gas if the temperature is as high as 8000 K. These observations indicate that either the electron temperature is near 5500-6000 K or the origin and distribution of the warm, ionized gas is very different from that in the McKee and Ostriker model. 18 refs.
Activation energy analysis as a tool for extraction and investigation of p–n junction leakage current components

The origin of p–n junction reverse current is investigated by a method based on the analysis of the leakage current activation energy. Its main advantages lie in the possibility to distinguish multiple reverse-bias dependent leakage components and determine their mechanisms, which can be different than the Shockley–Read–Hall or field-enhanced generation mechanisms. This is illustrated for state-of-the-art silicided shallow junctions, exhibiting a local Schottky effect, due to small-area silicide penetrations. An estimate of the area of the Schottky (or Shannon) contacts follows from the analysis. The method may be used for various semiconductor materials and leakage current origins.
TGF-Beta Induces Activin A Production in Dermal Fibroblasts Derived from Patients with Fibrodysplasia Ossificans Progressiva

Fibrodysplasia ossificans progressiva (FOP) is a catastrophic, ultra-rare disease of heterotopic ossification caused by genetic defects in the ACVR1 gene. The mutant ACVR1 receptor, when triggered by an inflammatory process, leads to heterotopic ossification of the muscles and ligaments. Activin A has been discovered as the main osteogenic ligand of the FOP ACVR1 receptor. However, the source of Activin A itself and the trigger of its production in FOP individuals have remained elusive. We used primary dermal fibroblasts from five FOP patients to investigate Activin A production and how this is influenced by inflammatory cytokines in FOP. FOP fibroblasts showed elevated Activin A production compared to healthy controls, both in standard culture and osteogenic transdifferentiation conditions. We discovered TGFβ1 to be an FOP-specific stimulant of Activin A, shown by the upregulation of the INHBA gene and protein expression. Activin A and TGFβ1 were both induced by BMP4 in FOP and control fibroblasts. Treatment with TNFα and IL6 produced negligible levels of Activin A and TGFβ1 in both cell groups. We present for the first time TGFβ1 as a triggering factor of Activin A production in FOP. As TGFβ1 can promote the induction of the main driver of FOP, TGFβ1 could also be considered a possible therapeutic target in FOP treatment.
Fiber Bragg grating pressure sensors: a review

Abstract. Fiber Bragg grating (FBG) pressure sensors have the potential to replace conventional voltage sensors due to their compact size, resistance to electromagnetic interference, excellent safety, distributed sensing, and numerous other intrinsic benefits. It is frequently employed in the domains of civil engineering, aerospace, and medicine. Our work examines two types of sensitized FBG pressure sensors, namely FBG pressure sensors with innately improved sensitivity and FBG pressure sensors with extrinsically increased sensitivity. Our work examines microstructured fiber Bragg grating pressure sensors and polymer fiber Bragg grating pressure sensors for FBG pressure sensors with innately heightened sensitivity. Our research examines metal-mechanical FBG pressure sensors and polymer-mechanical FBG pressure sensors for extrinsically sensitized FBG pressure sensors. This report also summarizes the benefits and drawbacks of various FBG pressure sensors and temperature compensation techniques. Finally, the future of FBG pressure sensors is examined.
Initiation of a Solitary Wave Family in the Demise of a Nocturnal Thunderstorm Density Current

Abstract This paper describes the characteristics and evolving nature of a vigorous thunderstorm density current very early in the morning of 9 May 1981 in Oklahoma. Because the ambient lower atmosphere was stratified, interesting interactions between the outflow current and the ambient environment resulted. The leading portion of the current was modulated by at least three gravity wavelike perturbations of horizontal spacing 12 km which initially coexisted with it. However, as the current evolved, it initiated an undular borelike disturbance which propagated ahead into the stable boundary layer, carrying cold outflow air in large amplitude rolls. Eventually the wave family left the decelerating outflow air in its wake. This borelike disturbance resembles the Australian “morning glory” phenomenon and appears to represent an early stage in the development of a solitary wave family. The observations resemble other reported morning glories and solitary waves as well their laboratory and numerically simulated.
Research on Iterative Method in Solving Linear Equations on the Hadoop Platform

Solving linear equations is ubiquitous in many engineering problems, and iterative method is an efficient way to solve this question. In this paper, we propose a general iteration method for solving linear equations. Our general iteration method doesnt contain denominators in its iterative formula, and this relaxes the limits that traditional iteration methods require the coefficient aii to be non-zero. Moreover, as there is no division operation, this method is more efficient. We implement this method on the Hadoop platform, and compare it with the Jacobi iteration, the Guass-Seidel iteration and the SOR iteration. Experiments show that our proposed general iteration method is not only more efficient, but also has a good scalability.
Robust estimation for boundary correction in wavelet regression

For boundary problems present in wavelet regression, two common methods are usually considered: polynomial wavelet regression (PWR) and hybrid local polynomial wavelet regression (LPWR). Normality assumption played a key role for making such choices for the order of the low-order polynomial, the wavelet thresholding value and other calculations involved in LPWR. However, in practice, the normality assumption may not be valid. In this paper, for PWR, we propose three automatic robust methods based on: MM-estimator, bootstrap and robust threshold procedure. For LPWR, the use of a robust local polynomial (RLP) estimator with a robust threshold procedure has been investigated. The proposed methods do not require any knowledge of noise distribution, are easy to implement and achieve high performances when only a small amount of data is in hand. A simulation study is conducted to assess the numerical performance of the proposed methods.
Remarks on Chacon’s biting lemma

Chacon's Biting Lemma states roughly that any bounded sequence in LI possesses a subsequence converging weakly in LI outside a decreasing family Ek of measurable sets with vanishingly small measure. A simple new proof of this result is presented that makes explicit which sets Ek need to be removed. The proof extends immediately to the case when the functions take values in a reflexive Banach space. The linit function is identified via the Young measure and approximations. The desci iption of concentration provided by the lemma is discussed via a simple example.
Modeling the Performance of an Acoustic Doppler Current Profiler

Abstract A systematic examination of measurement error in acoustic Doppler current profiler (ADCP) velocity estimates, in the limit of large signal-to-noise ratio, is made using a system model and sonar signal simulations coupled into an ADCP. The model is extremely successful in predicting ADCP performance. The signal simulations provide model validation. Three main sources of error are examined: frequency tracking, measurement variance (inherent variance of pulse-to-pulse incoherent volume reverberation), and measurement bias. A theoretical lower bound on measurement variance is directly tested by coupling simulated signals into an ADCP. The observed measurement variance is approximately twice the theoretical value and varies as the inverse of the product of the pulse and averaging period (bin). Model predictions of velocity errors for back-to-back beam pairs measuring a sequence of increasing velocity-shear profiles in a medium of randomly distributed scatterers are in excellent agreement with errors m.
AlGaN/GaN HEMTs with an InGaN back‐barrier grown by ammonia‐assisted molecular beam epitaxy

We report on the study of AlGaN/GaN high electron mobility transistors (HEMTs) incorporating an InGaN back‐barrier grown by ammonia‐assisted molecular beam epitaxy (NH3‐MBE). The structural characterizations confirm the good crystalline quality of the heterostructure and the pseudomorphic growth of InGaN. This is also verified by the Hall effect mobility of about 2130 cm2 V−1 s−1. The associated two‐dimensional electron gas carrier concentration is in the range of 8 × 1012 cm−2 for heterostructures grown on GaN:Fe‐on‐sapphire templates. Normal DC transistor operation is observed. Similar results have been obtained on silicon substrates. To our knowledge, this is the first demonstration of AlGaN/GaN HEMTs with an InGaN back‐barrier grown by NH3‐MBE.
Simulations to Determine the Mean and Variance of the Point‐Object Analogue of the Clark‐Evans Statistic

Doguwa and Upton (1988) propose edge corrections for the mean and variance of the average distance X, between a sampling point and the nearest object in a spatial point pattern for the case of a regular grid of sampling points. However, their edge corrections are not applicable to circular sampling windows. This paper extends the range of application of X, by providing its revised mean and variance for use with circular regions.