Miracle-dz/UniFAR-FTUs huggingface dataset:
Problem solving and collaborative involvement among adolescents with spinal cord injury and their caregivers

Objective To determine the relationship between constructive adolescent problem solving (positive problem-solving orientation and rational problem-solving style) and caregiver problem solving and collaborative involvement with primary caregiver among adolescents with spinal cord injuries (SCIs). Positive constructive adolescent problem solving was hypothesized to be predicted by more effective caregiver problem solving and higher collaborative involvement. Methods Participants in this cross-sectional study were 79 adolescent and primary caregiver dyads recruited from a pediatric rehabilitation care system in North America. All participants completed a standardized problem-solving instrument and adolescent participants completed an adapted measure of collaborative parent involvement. Results More effective caregiver problem solving and adolescent perceptions of more collaboration with caregivers around SCI care were significantly associated with higher positive problem-solving orientation and higher rational problem-solving style among adolescents. Conclusions Results underscore the importance of caregiver problem-solving skills and their collaboration with adolescents with SCI when addressing care needs. Clinically, findings highlight opportunities for parent involvement and skill-building as an important factor of rehabilitation for adolescents with SCI.
Assessing marine heat waves in the Mediterranean Sea: a comparison of fixed and moving baseline methods

The study of marine heat waves as extreme temperature events has a wide range of applications, from a gauge for ecological and socioeconomic impact to a climate change indicator. Various definitions of marine heat waves as extreme sea temperature events exist to account for its broad applicability, with statistical definitions based on percentile based thresholds being widespread in its use. Using satellite and model data of the Mediterranean Sea, we analyze the statistical implications of choosing baseline climatological periods for threshold delineation, which are either fixed in the past or shifted in time. We show that in the context of a warming Mediterranean Sea, using a fixed baseline leads to a saturation of marine heat wave days that compromises the significance of this marine indicator, with 90% of climate models analyzed predicting an average above 189 marine heat wave days per year by 2050 even for the lowest emission scenario. We argue that only with a moving baseline, can we reach a definition for marine heat waves which yield consistently rare extreme events.
Internal Model Principle Based Nonlinear Robust Satellite Formation Control Despite Disturbances

[Abstract] The development of a new robust control system for the formation flying of satellites is the subject of this paper. The control system design is based on the internal model principle for the robust control of nonlinear systems. For simplicity, it is assumed that a leader satellite is revolving in an elliptical orbit, and a follower satellite is moving along a certain path. But the relative dynamics of the follower satellite include external periodic forces and uncertain parameters. The objective is to design control systems, which accomplish tracking of periodic relative position trajectories, with respect to the leader satellite, and are capable of rejecting periodic perturbing forces. A third-order exosystem is introduced for modelling the periodic signals and periodic perturbing forces. Then the nonlinear time-varying formation model is represented as a nonlinear autonomous system in an extended state space for the purpose of design. The control system includes an internal model of a multi-fold exosystem. The choice of the internal model can be made to eliminate harmonic components of any finite order in the relative position error of the follower satellite, caused by the periodic disturbance forces and uncertainties in the parameters. Based on the linear quadratic optimal control theory, two control systems for the trajectory control of the follower satellite using (i) state variable feedback and (ii) output feedback are developed. The closed-loop linearized system is exponentially stable; and therefore, accurate relative trajectory control is possible even in the presence of random perturbing forces. Simulation results are presented which show that formation control is accomplished by the state or output feedback, in spite of the periodic as well as random perturbing forces and uncertainties in the parameters.
No benefit of HIF prolyl hydroxylase inhibition for hypertensive renal damage in renovascular hypertensive rats

Introduction: We previously reported that malignant hypertension is associated with impaired capillary density of target organs. Here, we tested the hypothesis that stabilization of hypoxia-inducible factor (HIF) in a modified “preconditioning” approach prevents the development of malignant hypertension. To stabilize HIF, we employed pharmacological inhibition of HIF prolyl hydroxylases (PHD), that profoundly affect HIF metabolism. Methods: Two-kidney, one-clip renovascular hypertension (2K1C) was induced in rats; controls were sham operated. 2K1C rats received either intermittent injections of the PHD inhibitor ICA (2-(1-chloro-4-hydroxyisoquinoline-3-carboxamido) acetate) or placebo. Thirty-five days after clipping, the frequency of malignant hypertension was assessed (based on weight loss and the occurrence of characteristic vascular lesions). In addition, kidney injury was compared between all ICA treated versus all placebo treated 2K1C, regardless of the occurrence of malignant hypertension. HIF stabilization was evaluated by immunohistochemistry, and HIF target gene expression by RT-PCR. Results: Blood pressure was elevated to the same degree in ICA- and placebo-treated 2K1C compared to control rats. ICA treatment did not affect the frequency of malignant hypertension or the extent of kidney tissue fibrosis, inflammation, or capillary density. There was a trend towards higher mortality and worse kidney function in ICA-treated 2K1C rats. ICA increased the number of HIF-1α-positive renal tubular cell nuclei and induced several HIF-1 target genes. In contrast, expression of HIF-2α protein as well as HIF-2 target genes were markedly enhanced by 2K1C hypertension, irrespective of ICA treatment. Discussion: We conclude that intermittent PHD inhibition did not ameliorate severe renovascular hypertension in rats. We speculate that the unexpected strong renal accumulation of HIF-2α in renovascular hypertension, which could not be further augmented by ICA, may contribute to the lack of a benefit from PHD inhibition.
Caring for relations and organizational success—conceptualization of an Augustinian leadership scale

While making great strides in recent decades to connect leadership and human flourishing, the positive leadership literature has yet to focus on the aspect of the communal. Based on a close reading of Augustine’s works, this paper examines Augustinian leadership and emphasizes the importance of a view on leadership that aims at community building and contains an ethical framework characterized by veracity. This leadership style is founded on caritas (Gr.: agape, Eng.: love) as the main motive for leaders. Based on Augustine’s thinking, this kind of love is defined as a way to attain knowledge. We identify four subconstructs to constitute an Augustinian leadership scale: Centrality of the community, Veracity, Empathy and Success (through temperance). We provide theoretical grounds for the distinctiveness of this leadership construct as compared with neighboring constructs. Finally, we propose a testable framework of Augustinian leadership with a direct effect on affective commitment as well as a mediated effect, and with a sense of belonging as the mediating variable. We provide ideas for future research and present practical implications of the theoretical insights on Augustinian leadership.
Chemo-enzymatic synthesis and biological activity evaluation of propenylbenzene derivatives

Propenylbenzenes, including isosafrole, anethole, isoeugenol, and their derivatives, are natural compounds found in essential oils from various plants. Compounds of this group are important and valuable, and are used in the flavour and fragrance industries as well as the pharmaceutical and cosmetic industries. The aim of this study was to develop an efficient process for synthesising oxygenated derivatives of these compounds and evaluate their potential biological activities. In this paper, we propose a two-step chemo-enzymatic method. The first step involves the synthesis of corresponding diols 1b–5b from propenylbenzenes 1a–5avia lipase catalysed epoxidation followed by epoxide hydrolysis. The second step involves the microbial oxidation of a diasteroisomeric mixture of diols 1b–5b to yield the corresponding hydroxy ketones 1c–4c, which in this study was performed on a preparative scale using Dietzia sp. DSM44016, Rhodococcus erythropolis DSM44534, R. erythropolis PCM2150, and Rhodococcus ruber PCM2166. Application of scaled-up processes allowed to obtain hydroxy ketones 1-4c with the following yield range 36–62.5%. The propenylbenzene derivatives thus obtained and the starting compounds were tested for various biological activities, including antimicrobial, antioxidant, haemolytic, and anticancer activities, and their impact on membrane fluidity. Fungistatic activity assay against selected strains of Candida albicans results in MIC50 value varied from 37 to 124 μg/mL for compounds 1a, 3a–c, 4a,b, and 5a,b. The highest antiradical activity was shown by propenylbenzenes 1-5a with a double bond in their structure with EC50 value ranged from 19 to 31 μg/mL. Haemolytic activity assay showed no cytotoxicity of the tested compounds on human RBCs whereas, compounds 2b–4b and 2c–4c affected the fluidity of the RBCs membrane. The tested compounds depending on their concentration showed different antiproliferative activity against HepG2, Caco-2, and MG63. The results indicate the potential utility of these compounds as fungistatics, antioxidants, and proliferation inhibitors of selected cell lines.
Periodic autoregressive model identification using genetic algorithms

Periodic autoregressive (PAR) models extend the classical autoregressive models by allowing the parameters to vary with seasons. Selecting PAR time‐series models can be computationally expensive, and the results are not always satisfactory. In this article, we propose a new automatic procedure to the model selection problem by using the genetic algorithm. The Bayesian information criterion is used as a tool to identify the order of the PAR model. The success of the proposed procedure is illustrated in a small simulation study, and an application with monthly data is presented.
LRU-C: Parallelizing Database I/Os for Flash SSDs

The conventional database buffer managers have two inherent sources of I/O serialization: read stall and mutex conflict. The serialized I/O makes storage and CPU under-utilized, limiting transaction throughput and latency. Such harm stands out on flash SSDs with asymmetric read-write speed and abundant I/O parallelism. To make database I/Os parallel and thus leverage the parallelism in flash SSDs, we propose a novel approach to database buffering, the LRU-C method. It introduces the LRU-C pointer that points to the least-recently-used-clean page in the LRU list. Upon a page miss, LRU-C selects the current LRU-clean page as a victim and adjusts the pointer to the next LRU-clean one in the LRU list. This way, LRU-C can avoid the I/O serialization of read stalls. The LRU-C pointer enables two further optimizations for higher I/O throughput: dynamic-batch-write and parallel LRU-list manipulation. The former allows the background flusher to write more dirty pages at a time, while the latter mitigates mutex-induced I/O serializations. Experiment results from running OLTP workloads using MySQL-based LRU-C prototype on flash SSDs show that it improves transaction throughput compared to the Vanilla MySQL and the state-of-the-art WAR solution by 3x and 1.52x, respectively, and also cuts the tail latency drastically. Though LRU-C might compromise the hit ratio slightly, its increased I/O throughput far offsets the reduced hit ratio.
A Numerical Solution for One-dimensional Parabolic Equation Using Pseudo-spectral Integration Matrix and FDM

This study presents a numerical method for the solution of one type of PDEs equation. In this study, apply the pseudo-spectral successive integration method to approximate the solution of the one-dimensional parabolic equation. This method is based on El-Gendi pseudo-spectral method. Also the Finite Difference Method (FDM) is used as a minor method. The present numerical results are in satisfactory agreement with exact solution.
What Modern NVMe Storage Can Do, And How To Exploit It: High-Performance I/O for High-Performance Storage Engines

NVMe SSDs based on flash are cheap and offer high throughput. Combining several of these devices into a single server enables 10 million I/O operations per second or more. Our experiments show that existing out-of-memory database systems and storage engines achieve only a fraction of this performance. In this work, we demonstrate that it is possible to close the performance gap between hardware and software through an I/O optimized storage engine design. In a heavy out-of-memory setting, where the dataset is 10 times larger than main memory, our system can achieve more than 1 million TPC-C transactions per second.
Studies on BVPs for IFDEs involved with the Riemann-Liouville type fractional derivatives

Abstract In this article, we present a new method for converting the boundary value problems for impulsive fractional differential systems involved with the Riemann-Liouville type derivatives to integral systems, some existence results for solutions of a class of boundary value problems for nonlinear impulsive fractional differential systems at resonance case and non-resonance case are established respectively. Our analysis relies on the well known Schauder’s fixed point theorem and coincidence degree theory. Examples are given to illustrate main results. This paper is motivated by [Solvability of multi-point boundary value problem of nonlinear impulsive fractional differential equation at resonance, Electron. J. Qual. Theory Differ. Equ. 89(2011), 1-19], [Existence result for boundary value problem of nonlinear impulsive fractional differential equation at resonance, J, Appl, Math, Comput. 39(2012) 421-443] and [Solvability for a coupled system of fractional differential equations with impulses at resonance, Bound. Value Probl. 2013, 2013: 80].
Benchmarks for Discrete Fourier Transform (DFT) calculations in R

The DFT calculator in R,stats::fft, uses the Mixed-Radix algorithm of Singleton (1969). In this vignette we show how this calculator compares toFFT in thefftw package (Krey et al., 2011), which uses the FFTW algorithm of Frigo and Johnson (2005). For univariate DFT computations, the methods are nearly equivalent with two exceptions which are not mutually exclusive: (1) the series to be transformed is very long (10 6 terms), and especially (2) when the series length is not highly composite. In both exceptions the algorithm FFT outperformsfft. Update: I have decided that (for now)psd will not usefftw::FFT, despite its advantage overstats::fft for large-n ‘NHC’ series, simply because the binaries on CRAN have not been reliably built for some time now. If they do become reliable, I may consider using fftw::FFT instead.
Novel Ant Colony Optimization Algorithm Based on Order Optimization

To evaluate the quality of optimal solutions obtained by the ant colony optimization(ACO) algorithm in limited time,an improved ACO algorithm is presented on the basis of the ordinal optimization.An initial solution is selected using the blind picking rule,and the pheromone is initialized correspondingly.The number of iterations to achieve the optimal solution meeting the demand is then determined and is used as the termination condition of the algorithm.To make better use of the solutions obtained at each iteration,the first l solutions are employed to enhance search capability at the beginning phase of the algorithm.While the current optimal solution is used at the end phase of the algorithm to accelerate the convergence.The time complexity of the novel algorithm is not increased under the condition that ensures the convergence.Simulation results on the traveling salesman problem show that the proposed algorithm is superior to the max-min ant system in both the quality of solutions and the speed of convergence.
Algorithm to Compare the Slopes (Regression Coefficients) between the subgroups in Simple/Multiple Regression using PROC REG

Regression analysis is the most common technique used for data analysis in clinical trials. In regression analysis, a regression line is fitted for the response variable (e.g. Viral load at the end of the study) based on few explanatory variables (e.g. Baseline viral load, time since first diagnosis etc.). A regression line Y=a+bX is fitted, where Y is the response variable, X is the explanatory variable, a denotes the intercept and b is the slope (regression coefficient) of the line. The slope indicates the change in the value of Y if X is changed by one unit. Therefore slope is often useful measure of examining the rate of change in variable Y. In clinical trials, comparing slope (rate of change) for two (or more subgroups e.g. Active vs. Placebo) can be the area of interest to assess the effect of medical treatment. SAS® procedure PROC REG does not performs the desired analysis directly but some kind of data manipulation is needed. This paper will discuss the algorithm for comparing the regression coefficients for simple/multiple regression for 2 or more subgroups.
Supplemental Material: Stabilization of frequency dependent squeezing with bichromatic control of filter cavity length, alignment and incident beam pointing

Yuhang Zhao1,2,∗ Eleonora Capocasa, Marc Eisenmann, Naoki Aritomi, Michael Page, Yuefan Guo, Eleonora Polini, Koji Arai, Yoichi Aso, Martin van Beuzekom, Yao-Chin Huang, Ray-Kuang Lee, Harald Lück, Osamu Miyakawa, Pierre Prat, Ayaka Shoda, Matteo Tacca, Ryutaro Takahashi, Henning Vahlbruch, Marco Vardaro, Chien-Ming Wu, Matteo Leonardi, Matteo Barsuglia, and Raffaele Flaminio Institute for Cosmic Ray Research (ICRR), KAGRA Observatory, The University of Tokyo, Kamioka-cho, Hida City, Gifu 506-1205, Japan National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo, 181-8588, Japan Laboratoire Astroparticule et Cosmologie (APC), 10 rue Alice Domon et Léonie Duquet, 75013 Paris, France Nikhef, Science Park, 1098 XG Amsterdam, Netherlands Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université Savoie Mont Blanc, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France LIGO, California Institute of Technology, Pasadena, California 91125, USA Institute of Photonics Technologies, National Tsing-Hua University, Hsinchu 300, Taiwan Institut für Gravitationsphysik, Leibniz Universität Hannover and Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut), Callinstraße 38, 30167 Hannover, Germany Institute for High-Energy Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands and Università di Padova, Dipartimento di Fisica e Astronomia, I-35131 Padova, Italy (Dated: March 1, 2022)
Survey of single image super resolution based on deep learning

: In order to understand the development of single image super-resolution reconstruction (SISR) based on deep learning and grasp the hotspots and directions of the current research, this paper combs the existing model of single image super-resolution reconstruction based on deep learning. Firstly, the paper introduces the related deep learning algorithm, these models based on deep learning and their evaluation index. In addition, it compares the performance of existing models through experiments
Emotion recognition for color facial images

This paper introduces a tensor perceptual color framework (TPCF) for facial expression recognition (FER), which is based on information contained in color facial images. The TPCF enables multilinear image analysis in different color spaces, and demonstrates that color components provide additional information for robust FER. Using this framework, the components in either RGB, YCbCr, CIELab or CIELuv space of color images are unfolded to 2-D tensors based on multilinear algebra and tensor concepts, from which the features are extracted by Log-Gabor filters. The mutual information quotient method is employed for feature selection. These features are classified using a multiclass linear discriminant analysis classifier. The effectiveness of color information on FER using low-resolution and facial expression images with illumination variations is assessed for performance evaluation. Experimental results demonstrate that color information has significant potential to improve emotion recognition performance due to the complementary characteristics of image textures. Furthermore, the perceptual color spaces (CIELab and CIELuv) are better overall for FER than other color spaces, by providing more efficient and robust performance for FER using facial images with illumination variation.
Gradient-like dynamics in neural networks

This report presents a formalism that enables the dynamics of a broad class of neural networks to be understood A number of previous works have analyzed the Lyapunov stability of neural network models This type of analysis shows that the excursion of the solutions from a stable point is bounded The purpose of this work is to present a model of the dynamics that also describes the phase space behavior as well as the structural stability of the system This is achieved by writing the general equations of the neural network dynamics as a gradient like system In this paper some important properties of gradient like systems are developed and then it is demonstrated that a broad class of neural network models are expressible in this form y Acknowledgments This research was supported by a grant from Boeing Computer Services under Contract W
Magnetic Resonance Image Analysis by Information

Quantitative analysis of magnetic resonance (MR) images is a powerful tool for image-guided diagnosis, monitoring, and intervention. The major tasks involve tissue quantification and image segmentation where both the pixel and context images are considered. To extract clinically useful information from images that might be lacking in prior knowledge, we introduce an unsu- pervised tissue characterization algorithm that is both statistically principled and patient specific. The method uses adaptive standard finite normal mixture and inhomogeneous Markov random field models, whose parameters are estimated using expectation-max- imization and relaxation labeling algorithms under information theoretic criteria. We demonstrate the successful applications of the approach with synthetic data sets and then with real MR brain images. method is adopted, both pixel and context images should be con- sidered. In this paper, we introduce an unsupervised image anal- ysis procedure, which is both statistically principled and patient specific, a feature especially important in cases with limited or no prior knowledge (4), (10). We assume that the MR images are single valued and the anatomy of the site may contain ab- normalities. The method involves adaptive use of standard finite normal mixture (SFNM) and inhomogeneous Markov random field (MRF) models, whose parameters are estimated using the fast expectation-maximization (EM) and modified iterated con- ditional modes (MICM) algorithms under a selected informa- tion theoretic criterion (15)-(17). The major difference of our study from previous research in the area (1)-(3), (5), (10) is as follows.