Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametric Study of Ballistic Lander Deployment to Small Bodies

This paper investigates the sensitivities of spherical lander deployment to small bodies. Previous work established methods to simulate the motion of such landers in the gravitationally and topographically complex small-body environment. These methods are extended, primarily through the inclusion of a “persistent” rock model that accounts for the observed distribution of rocks and boulders on the small-body surface. The resulting framework is applied to investigate the effects of relevant environmental and hardware parameters on trends in the deployment of a lander. In the dynamically favorable environment of asteroid Itokawa, variations in the surface interaction, rock distribution, and lander mass distribution scale the settling time, slope, and surface dispersion of a given deployment scenario. In the more challenging environment of asteroid 1999 KW4 Alpha, these parameters determine the success rate with which landers are delivered to the surface, and they should be carefully considered during deploym.
Bootstrap Methods for Heavy-Tail or Autocorrelated Distributions with an Empirical Application

Chapter One: The Truncated Wild Bootstrap for the Asymmetric Infinite Variance Case * Advisers Dr Adriana Cornea-Madeira and Professor James Davidson The wild bootstrap method proposed by Cavaliere et al. (2013) to perform hypothesis testing for the location parameter in the location model, with errors in the domain of attraction of asymmetric stable law, is inappropriate. Hence, we are introducing a new bootstrap test procedure that overcomes the failure of Efron’s (1979) resampling bootstrap. This bootstrap test exploits the Wild Bootstrap of Cavaliere et al. (2013) and the central limit theorem of trimmed variables of Berkes et al. (2012) to deliver confidence sets with correct asymptotic coverage probabilities for asymmetric heavy-tailed data. The methodology of this bootstrap method entails locating cut-off values such that all data between these two values satisfy the central limit theorem conditions. Therefore, the proposed bootstrap will be termed the Truncated Wild Bootstrap (TWB) since it takes advantage of both findings. Simulation evidence to assess the quality of inference of available bootstrap tests for this particular model reveals that, on most occasions, the TWB performs better than the Parametric bootstrap (PB) of Cornea-Madeira & Davidson (2015). In addition, TWB test scheme is superior to the PB because this procedure can test the location parameter when the index of stability is below one, whereas the PB has no power in such a case. Moreover, the TWB is also superior to the PB when the tail index is close to 1 and the distribution is heavily skewed, unless the tail index is exactly 1 and the scale parameter is very high. Chapter Two: A frequency domain wild bootstrap for dependent data * Co-authored and jointly written with Professor James Davidson1 In this chapter a resampling method is proposed for a stationary dependent time series, based on Rademacher wild bootstrap draws from the Fourier transform of the data. The main distinguishing feature of our method is that the bootstrap draws share their periodogram identically with the sample, implying sound properties under dependence of arbitrary form. 1Professor Davidson has also done most of the coding using Ox programming language and his TSM software.
Abnormal recognition of the ETC gantry license plate data based on an improved DBSCAN

Electronic non-stop toll collection system (ETC) is the most popular highway toll collection system at home and abroad. Based on ETC gantry license plate data, this paper systematically describes the highway toll collection process and points out that abnormal data in the license plate data will significantly reduce the accuracy of subsequent data fusion and toll collection. And summarizes the abnormal situation of ETC gantry license plate data. Aiming at the spatial-temporal characteristics of such data, the ETC gantry license plate data abnormality recognition method based on an improved DBSCAN is proposed to realize the adaptive clustering separation of data, which could extract the average travel speed feature set and traffic volume feature set to recognize the abnormal data. The results show that the recognition rates of average speed and traffic volume reaches 96.47% and 88.47%, and the false recognize rate decreases to 0.71% and 0.91%, which could consider data spatial-temporal correlation for abnormal data recognition and solve the problems existing on the current application of ETC gantry data.
Stable Noncommutative Polynomials and Their Determinantal Representations

A noncommutative polynomial is stable if it is nonsingular on all tuples of matrices whose imaginary parts are positive definite. In this paper a characterization of stable polynomials is given in terms of strongly stable linear matrix pencils, i.e., pencils of the form $H+iP_0+P_1x_1+\cdots+P_dx_d$, where $H$ is hermitian and $P_j$ are positive semidefinite matrices. Namely, a noncommutative polynomial is stable if and only if it admits a determinantal representation with a strongly stable pencil. More generally, structure certificates for noncommutative stability are given for linear matrix pencils and noncommutative rational functions.
Diabetic retinopathy stage detection using convolutional fine-tuned transfer Learning model

Diabetic Retinopathy (DR) is a prevalent eye condition that occurs as a frequent complication among individuals with diabetes, particularly those who have been living with the disease for an extended period of time. This study uses fundus images to diagnose DR at five stages from early to late with No DR, Mild, Moderate, Severe, and Proliferative DR, commonly known as Stage 0 to Stage 4, respectively. This will aid in the timely treatment of diabetic patients preventing them from developing DR as early as possible. We used two most popular open-source datasets, the DR Detection database, namely APTOS 2019 and EyePACS, and combined them to create a larger dataset to trade off the data-centric obstacle and shortfall for any Deep Learning-based prediction models. Data augmentation and preprocessing techniques are applied to the images before feeding them to the proposed model to get a more accurate and efficient one. In the modern age oriented to Artificial Intelligence (AI), it is necessary to thoroughly analyze the identification of DR based on the existing Deep Learning (DL) models. After learning about the limitations of existing models, we have fine-tuned the ResNet50, DenseNet201 and InceptionV3 to enhance the model performance of the detection and categorization of DR. We have since proposed three Deep Convolutional Neural Networks (DCNN) models with better outcome based on accuracy than the existing state-of-the-art (SOTA) models. The fine-tuned DenseNet201 model, among the other two, performed significantly better with a validation accuracy of 90.04% and a negligible amount of loss, irrespective of each class, under the best configurable test conditions.
Effects of vitamin D supplementation on ovulation and pregnancy in women with polycystic ovary syndrome: a systematic review and meta-analysis

Objective To evaluate the effect of vitamin D supplementation on pregnancy and ovulation in patients with polycystic ovary syndrome. Method We searched Pubmed, Medline (via Ovid, 1974 to 2020), EMBASE (via Ovid, 1974 to 2020), Cochrane Central Register of Controlled Trials (via Ovid), Web of Science, CNKI, WangFang and the Vip database from inception until April 2021. Two researchers independently screened articles, collected data and evaluated the quality, with Review manager 5.3 for meta-analysis. Results Totally 20 randomized controlled studies with 1961 subjects were included. Meta analysis showed that pregnancy rate [RR=1.44 (1.28, 1.62), p<0.00,001], ovulation rate [RR=1.42 (1.14, 1.78), p=0.002] and matured oocytes rate [RR=1.08 (1.03, 1.13), p=0.002] of vitamin D supplementation group were significantly higher than those of control group. Meanwhile, early miscarriage rate [RR=0.44 (0.30, 0.66), p<0.00,001], androgen level [MD=-2.31 (-3.51, -1.11), p=0.0002], luteinizing hormone [MD=-1.47 (-2.57, -0.36), p=0.009], follicle stimulating hormone [MD=-0.15 (-0.24, -0.05), p=0.002], and premature delivery rate [RR=0.38, 95% CI (0.21, 0.70), p=0.002] were declined significantly than the controls. However, only one article suggested that the progesterone [MD=6.52 (4.52, 8.52), p<0.05] in the vitamin D intervention group was increased. There was no notable difference in the biochemical pregnancy rate [RR=0.95 (0.55, 1.63), p=0.84], gestational hypertension rate [RR=0.40, 95% CI (0.15, 1.11), p=0.08], gestational diabetes mellitus rate [RR=0.27, 95% CI (0.05, 1.39), p=0.11], fertilization rate [RR=1.05 (1.00, 1.10), p=0.04], cleavage rate [RR=1.03 (0.99, 1.06), p=0.17], high-quality embryo rate [RR=1.08 (0.98, 1.20), p=0.10], endometrial thickness [MD=0.10], 77 (-0.23, 1.77), p=0.13], estrogen level [MD=-0.34 (-1.55, 0.87), p=0.59], LH/FSH [MD=-0.14, 95% CI (-0.48, 0.20), p=1.00] and anti-Mullerian hormone [MD=-0.22 (-0.65, 0.21), p=0.32]. Conclusion Vitamin D supplementation contribute to the higher pregnancy and ovulation rates, and lower androgen, LH, FSH and early miscarriage rates in women with PCOS, regardless of the use of ovulation induction drugs or assisted reproductive technologies. However, no significant improvement was observed in fertilization rate or cleavage rate. Due to the limitation in quality of involved studies, more high-quality RCTs are needed for further validation. Systematic review registration https://www.crd. york.ac. uk/PROSPERO, identifier CRD42021250284.
Terahertz biosensing metamaterial absorber for virus detection based on spoof surface plasmon polaritons

As a promising candidate for rapid and ultrasensitive sensing of biomolecules, terahertz (THz) time‐domain spectroscopy is widely used in label‐free, noncontact, noninvasive, and nondestructive detection. In this article, planar Jerusalem cross metamaterial absorber for different virus detection based on spoof surface plasmon polaritons (SSPPs) in THz band is investigated. Strongly confined SSPPs modes are extracted from the absorption spectra of biosensing metamaterial absorber associated with local field enhancement. Meanwhile, equivalent complex refractive index (N) of different virus subtypes and protein concentrations is numerically investigated for virus detection. Besides, simulated detections for different virus subtypes (different N) are probed with the proposed biosensing metamaterial absorber. Especially for the simulated detections of three representative Avian Influenza viruses (H5N2, H1N1, H9N2), the proposed THz biosensing metamaterial absorber chip performed ultrasensitive sensitivity and high resolution by extracting the shifted resonance frequencies (ΔF) and the changed values at maximum absorptions (ΔA). All these findings show great significance on a rapid real‐time procedure for diseases diagnosis especially for the contagious and time‐sensitive target virus. The proposed ultrasensitive and selective THz metamaterial absorber opens up a new way for planar biosensing chip to be developed into practical applications in THz regime.
THE SEVEN CASES UNSTRUCTURED TRIANGULATION TECHNIQUE FOR RADIATIVE HEAT TRANSFER APPROXIMATION IN AN ETHYLENE FURNACE CRACKER

Radiative heat distribution inside an ethylene cracker furnace is often modelled using the finite volume and finite element methods. In both cases, meshes in the form of rectangles and triangles are needed to serve as the approximating points in the domain. In this paper, a new method called the Seven Cases Unstructured Triangulation Technique (7CUTT) is proposed for meshing the domain inside the cracker furnace, integrated with the deployment of sensors on the wall to obtain the boundary value. 7CUTT is an enhanced version of the Standard Advancing Front Technique (SAFT) which two normal cases in SAFT are extended to the total of seven cases for consideration during the element creation procedure for initial mesh generation. The focus of this method is to construct the initial triangular meshes with the requirement of (1) having the location of sensors deployed along the wall as boundary nodes as well as forming boundary elements, (2) generating nodes at a certain boundary with linearly different lengths of boundary edges as interior gradation controls and (3) constructing the triangular element directly in every iteration without having to re-order the Front or delete the existing elements. There are three contributions from this paper, the first one is the introduction of seven extended cases for consideration for the element creation procedure, the second is the layer concept to generate edges with linearly different lengths and the third is the post-processing mesh procedure to improve the quality of the mesh that is suitable for 7CUTT. The final mesh is obtained once the post-processing procedure of improving the mesh quality is applied to the initial mesh. 7CUTT provides the framework for the heat to be approximated using the discrete ordinate method, which is a variant of the finite volume method. Simulation results produced using FLUENT support the findings for effectively approximating the flue gas temperature distribution, the circumferential radiative heat flux incident at the reactor coils as well as the circumferential reactor coil temperature in the furnace at the end of the study.
Computational problems in linear algebra

In this thesis we consider the problems that arise in computational linear algebra when the matrix involved is vary large and sparse. This necessarily results in the emphasis throughout being mostly on iterative methods, but we begin with a chapter in which the usual direct methods for the solution of linear equations are summarized with the very large problem in mind. Methods for band-matrices and block tridiagonal matrices are considered and compared. Kron's method of tearing is described briefly. The basic and well-established techniques of iterative solution of linear equations are described. Sufficient conditions for convergence are stated and the convergence is established under these conditions. A section is devoted to the problem of deciding when the iterations may be terminated. The method of Kaczmarz is given more emphasis than is usual in the literature, a symetric version is introduced and rigorous proofs of convergence are given. Matrices with "property A" are defined and the usual associated results are given. The methods of Carre and Kularud for finding the optimum relaxation parameter in SOR are described and compared with a new technique. We also consider methods for finding the best relaxation parameter in symmetric SOR . Block iterative methods are described briefly, including Varga'a result on the comparison of different splittings. Gradient and semi-iterative methods are considered in detail, including the cases of non-symmetric and symmetric non-definite matrices, and are compared for numerical stability. For the eigenvalue problem we again summarize some well-known methods with an emphasis on the very large matrix. We give an example of the accurate results that can be expected from Lanezos' method of minimised iterations without re-orthogonalisation and show how such results can be guaranteed. Inverse iteration, the Rayleigh quotient iteration and Rutishauser's L-R algorithm are considered. We also devote a chapter to iterative methods that yield continually improving approximations to an eigenvector and corresponding eigenvalue. These are very economical in storage but have received little attention in the literature. We introduce the concept of "numerical rank", since rank really has no meaning for a matrix given by rounded numbers. We give means of finding bounds for this numerical rank. The extension of the methods to complex matrices is considered briefly and we conclude with a description of some problems that may give rise to large sparse matrices. We consider in particular the so-called "L-membrane" problem and use a conformal transformation. In this way we also illustrate the problem of the solution of an elliptic equation over an area with a curved boundary. We consider the iteration of Federenko, which has its most obvious application in the field of elliptic equations. The thesis is concluded with two further examples, from linear analysis of variance and front surveying, each of which is likely to involve large sparse matrices.
Deep Learning for Signal and Background Discrimination in Liquid based Neutrino Experiment

In high energy physics experiments, efficient data analysis tools are required to extract interesting information from the massive data. For large-scale liquid-based neutrino experiments, neutrino signals are usually overwhelmed in huge backgrounds. By constructing a liquid neutrino detector toy model, we generate simulation data in Geant4 [1] and run reconstruction for signal background discrimination. The low-level Photo Multipliers (PMT) hits are also projected to a 2D plane to create visualization outputs for classification. With the 2D images as input, we use the Convolutional Neural Network (CNN) as a specific application, which has shown remarkable performance in signal and background discrimination and outperforms those with high-level reconstruction outputs. The method is expected to be used in the neutrino experiments such as JUNO with further study.
Chinese open information extraction based on DBMCSS in the field of national information resources

Abstract Binary entity relationship tuples can be applied in many fields such as knowledge base construction, data mining, pattern extraction, and so on. The purpose of entity relationship mining is discovering and identifying the semantic relationship. As the relationship between entities are different from the general domain, using supervise learning methods to extract entity relationships in the field of ethnicity is difficult. After research, we find that some words can be used in the context of a sentence to describe the semantic relationship. In order to salve the existing difficulties of building tagged corpus and the predefined entities-relationships model, this paper proposes a method of density-based multi-clustering clustering of semantic similarity (DBMCSS) to mine the binary entity relationship tuples from the Chinese national information corpus, which can extract entity relationships without a training corpus.
From Illness to Resilience: Mediating Factors of Quality of Life in Patients with Congenital Heart Disease

Background : Congenital heart disease (CHD) is a leading cause of childhood morbidity, with an estimated prevalence of 0.8–1%. However, advances in diagnosis and treatment now allow 90% of childhood CHD patients to survive to adulthood, leading to increased interest in their quality of life (QoL). In this study, we examine the impact of clinical and psychosocial variables, including the number of surgical interventions (NSI), age at surgery, school achievement
Length-weight relationship and condition factor for Prochilodus lineatus, an important commercial fish, in contrasting water-quality environments of the middle Tietê River basin, Southeast Brazil

Abstract This work was carried out in order to provide the length-weight relationship (LWR) and the allometric condition factor (Ka), as well as its correlation with limnological variables, for Prochilodus lineatus from the middle Tietê River basin. Fish were collected using gill nets in two rivers with contrasting environmental conditions, totaling 46 specimens in the highly polluted Tietê River, and 37 in the Peixe River, a relatively well-conserved tributary. Environmental measures were obtained concomitantly to the fish capture. The results showed an isometric growth (b = 3.00) for the fish from Tietê River and a positive allometric growth (b = 3.23) for the fish from Peixe River. The mean value of Ka was unexpectedly higher for the main river (Ka = 2.63) when compared to its tributary (Ka = 2.42), being statistically different from each other. This can be explained by the much higher availability of organic sediments in the main river, resulting from a long-term eutrophication process. Nevertheless, the positive and statistically significant correlations with dissolved oxygen, for both rivers, as well as significant negative correlations with electric conductivity, nitrogen and chlorophyll a for Tietê River, indicate the negative effects of the water quality deterioration on the fish condition factor. The work contributes to the expansion of knowledge about P. lineatus, the most important commercial fish of the middle Tietê River basin, which is severely impacted by unsustainable human actions.
Neural Dynamics of Theta Wave in Children with Attention Deficit Hyper activity Disorder

Attention deficit hyperactivity disorder having variable cluster of hyperactivity, impulsivity and inattention symptoms affecting the normal cognitive and behavioral function of an individual ADHD is characterized by decreased attention span and impulsivity. EEG power were investigated in patients with attention deficit hyperactivity disorder (ADHD) diagnosed as per DSMV criteria. EEG were conducted for 30 ADHD children and 30 healthy controls of comparable age (8-9 yr) and sex. The EEG power tests include absolute power of theta, ADHD patients showed significantly higher theta absolute power, (p<0.005). This study supplants the tenet of maturational arrest of higher cognitive function of alertness and attention in ADHD.
Adaptive Fuzzy Tracking Control of Flexible-Joint Robots With Full-State Constraints

This paper reports our study on adaptive fuzzy tracking control for flexible-joint robots with full state constraints. In the control design, fuzzy systems are adopted to identify the totally unknown nonlinear functions and can properly avoid burdensome computations. The tan-type barrier Lyapunov functions are used to deal with state constraints so that even without state constraints, the controller is still valid. By combining the method of backstepping design with adaptive fuzzy control approaches, a novel simpler controller is successfully constructed to ensure that the output tracking errors converge to a sufficiently small neighborhood of the origin, while the constraints on the system states will not be violated during operation. Finally, comparison simulations are presented to demonstrate the effectiveness of the proposed control schemes.
Keep Calm and Carry on: The Short- vs. Long-Run Effects of Mindfulness Meditation on (Academic) Performance

IZA DP No. 15723 NOVEMBER 2022 Keep Calm and Carry On: The Shortvs. Long-Run Effects of Mindfulness Meditation on (Academic) Performance* Mindfulness-based meditation practices are becoming increasingly popular in Western societies, including in the business world and in education. While the scientific literature has largely documented the benefits of mindfulness meditation for mental health, little is still known about potential spillovers of these practices on other important life outcomes, such as performance. We address this question through a field experiment in an educational setting. We study the causal impact of mindfulness meditation on academic performance through a randomized evaluation of a well-known 8-week mindfulness meditation training delivered to university students on campus. As expected, the intervention improves students’ mental health and non-cognitive skills. However, it takes time before students’ performance can benefit from mindfulness meditation: we find that, if anything, the intervention marginally decreases average grades in the short run, i.e., during the exam period right after the end of the intervention, whereas it significantly increases academic performance, by about 0.4 standard deviations, in the long run (ca. 6 months after the end of intervention). We investigate the underlying mechanisms and discuss the implications of our results. JEL Classification: I21, C93, I12, I31