Miracle-dz/UniFAR-FTUs huggingface dataset:
PRBM_A_373033 2773..2782

Purpose: Previous studies have shown that academic procrastination not only affects middle school students’ academic performance but also causes them physical and psychological stress, as well as negative emotions. Therefore, it is necessary to explore the influencing factors of academic procrastination and its internal mechanisms. This study aims to explore the relationship between perceived parental autonomy support and academic procrastination, as well as the role of autonomous motivation and time management disposition. Participants and Methods: Cross-sectional data from 662 middle school students were collected using the Parental Autonomy Support Scale, Academic Procrastination Inventory for Middle School Students, Academic Self-Regulation Questionnaire and Adolescence Time Management Disposition Scale. SPSS and its PROCESS macro were used for data analysis. Results: After gender and age were controlled, the results showed that perceived parental autonomy support could not only directly predict middle school students’ academic procrastination but also predict three paths of procrastination: (1) the mediating role of autonomous motivation, (2) the mediating role of time management disposition, and (3) the chain mediating role of autonomous motivation and time management disposition. Conclusion: Autonomous motivation and time management disposition played a chain mediating role in the relationship between perceived parental autonomy support and academic procrastination in middle school students.
Utilizing Silver Nanocubes to Promote Förster Resonance Energy Transfer (FRET) Between Cadmium Selenide and Perovskite Quantum Dots Embedded in a Polymer Layer Upon Optical Excitation

Silver Nanocubes Förster Transfer Abstract In this work, we investigate the physical consequences of integrating a metal optical cavity into a mixed quantum dot donor-acceptor embedded in a polymer layer. The metal optical cavity is composed of silver nanocubes and the positively charged polymer PAH, and the quantum dots used are perovskite quantum dots emitting at 530 nm and CdSe/ZnS quantum dots emitting at 635 nm which are embedded in a PMMA layer. The system is optically excited with a 475 nm beam of varying power, and we seek to investigate whether the metal optical cavity is successful in promoting Forster Resonance Energy Transfer (FRET) between the quantum dots and if so with what efficiency. Based on the results, we will further improve the engineering of the system's electromagnetic environment. The implications of this research extend to nanophotonic devices and next generation opto-electronics through the conversion of incident infrared or UV photons into visible light.
Monte carlo em for data-association and its applications in computer vision

Estimating geometry from images is at the core of many computer vision applications, whether it concerns the imaging geometry, the geometry of the scene, or both. Examples include image mosaicking, pose estimation, multi baseline stereo, and structure from motion. All these problems can be modeled probabilistically and translate into well-understood statistical estimation problems, provided the correspondence between measurements in the different images is known. I will show that, if the correspondence is not known, the statistically optimal estimate for the geometry can be obtained using the expectation-maximization (EM) algorithm. In contrast to existing techniques, the EM algorithm avoids the estimation bias associated with computing a single “best” set of correspondences, but rather considers the distribution over all possible correspondences consistent with the data. While the latter computation is intractable in general, I show that it can be approximated well in practice using Markov chain Monte Carlo sampling. As part of this, I have designed an efficient sampler specifically tuned to the correspondence problem. The resulting Monte Carlo EM approach represents the first truly multiview algorithm for geometric estimation with unknown correspondence. This is especially relevant in the structure from motion domain, where the state of the art relies on robust estimation of two or three-view geometric constraints. In addition, I will show that the probabilistic approach I propose allows for a seamless and principled way of integrating prior knowledge, appearance models, and statistical models for occlusion and clutter.
Development of a Micro-Scale Motion Capture System for Analysis of Formicidae Biomechanics

ii Dedication.iii Acknowledgements.iv List of Figures.vii List of Tables.viii Chapter 1: Introduction.1 1.1 Background.1 1.2 Significance of Motion Capture in Biomechanical Analysis.2 1.3 Previous Research.3 1.3. 1 Ant Neck Research Exclusive to The Ohio State University.3 1.3. 2 Capture and Synthesis of Insect Motion.8 1.4 Focus of Thesis.10 1.5 Significance of Research.11 1.6 Overview of Thesis.11 Chapter 2: Materials and Methods.12 2.1 Ants.12 2.1.1 Obtainment of Colony.12 2.1.2 Care of Colony.13 2.1.3 Marking Procedure.14 2.2 Experimental Set-Up and Equipment.17 2.2.1 Camera Selection.17 2.2.2 Macro Lens Selection.18 2.3 Testing Environment Design Development.20 2.3. 1 Initial Prototype (1.1). 20 2.3. 2 Prototype 1.2.21 2.3. 3 Prototype 1.3. 22 2.4 Experimental Set-Up and Calibration.23
Cascading gravity is ghost free

We perform a full perturbative stability analysis of the 6D cascading gravity model in the presence of 3-brane tension. We demonstrate that for sufficiently large tension on the (flat) 3-brane, there are no ghosts at the perturbative level, consistent with results that had previously only been obtained in a specific 5D decoupling limit. These results establish the cascading gravity framework as a consistent infrared modification of gravity.
Magnetic properties of FePt and FePt–Al2O3 granular films by post annealing

FePt and FePt–Al2O3 alloy thin films were prepared by rf magnetron sputtering system, then post annealed in vacuum. The as-deposited films were in disordered state and the ordered L10 structure was obtained by post annealing. The temperature for L10 structure of FePt and FePt–Al2O3 films was about 400 and 550 °C, respectively. The effect of film thickness on magnetic properties was studied. The coercivity decreased rapidly from 8 to 1.5 kOe at the thickness below 10 nm for the FePt L10 films. The decrease of coercivity is due the diffusion and compositional deviation. The surface roughness is as low as 0.3 nm for FePt–Al2O3 films with coercivity of about 3.3 kOe.
ALCOHOLISM AND ITS EFFECT ON FAMILY; A SECONDARY STUDY

There isn’t many research on the relationship between alcoholism and dysfunctional families, and those that do tend to mention it only in passing as one issue among many. In order to comprehend the problem of alcoholism from both a theoretical and sociological standpoint, an attempt has been made to identify the many sociological views now in use and how they relate to the current issue.
THE MOST MASSIVE OBJECTS IN THE UNIVERSE

We calculate the expected mass of the most massive object in the universe, finding it to be a cluster of galaxies with total mass M200 = 3.8 × 1015 M☉ at z = 0.22, with the 1σ marginalized regions being 3.3 × 1015 M☉ < M200 < 4.4 × 1015 M☉ and 0.12 < z < 0.36. We restrict ourselves to self-gravitating bound objects and base our results on halo mass functions derived from N-body simulations. The mass and redshift distribution of the largest objects in the universe are potentially interesting tests of ΛCDM, probing the initial conditions, non-Gaussianity, and the behavior of gravity on large scales. We discuss A2163 and A370 as candidates for the most massive cluster in the universe, although uncertainties in their masses preclude definitive comparisons with theory. We find that the three most massive clusters in the South Pole Telescope (SPT) 178 and 2500 deg2 catalogs match predictions. Since the mass function evolves steeply with redshift, we also investigate the most unlikely clusters in the universe. We find that SPT-CL J2106–5844 is 2σ and XMMU J2235.3–2557 is 3σ inconsistent with ΛCDM, considering their respective redshifts and survey sizes. Our findings motivate further observations of the highest mass end of the mass function, particularly at z > 1, where a number of anomalously massive clusters have been discovered. Future surveys will explore larger volumes, and both the most massive object and the most unlikely object in the universe may be identified within the next decade.
Topological nodal-line semimetals in alkaline-earth stannides, germanides, and silicides

Based on first-principles calculations and an effective Hamiltonian analysis, we systematically investigate the electronic and topological properties of alkaline-earth compounds $A{X}_{2}$ $(A=\text{Ca}$, Sr, Ba; $X=\text{Si}$, Ge, Sn). Taking ${\mathrm{BaSn}}_{2}$ as an example, we find that when spin-orbit coupling is ignored, these materials are three-dimensional topological nodal-line semimetals characterized by a snakelike nodal loop in three-dimensional momentum space. Drumheadlike surface states emerge either inside or outside the loop circle on the (001) surface depending on surface termination, while complicated double-drumhead-like surface states appear on the (010) surface. When spin-orbit coupling is included, the nodal line is gapped and the system becomes a topological insulator with ${\mathbb{Z}}_{2}$ topological invariants (1;001). Since spin-orbit coupling effects are weak in light elements, the nodal-line semimetal phase is expected to be achievable in some alkaline-earth germanides and silicides.
The evolution of a national research funding system Radical transformation through layering and displacement

: This article outlines the evolution of a national research funding system over a timespan of more than 40 years and analyses the development from a rather stable Humboldt inspired floor funding model to a complex multi-tiered system in constant flux. Based on recent contributions to Historical Institutionalism it is shown how the system has changed gradually along a number of dimensions through layering- and displacement processes and how the sum of mainly minor adjustments over time has led to a radical transformation of the funding system as a whole. It is also shown that the traditional academically oriented research council system has been remarkable resilient against far-reaching restructuring attempts. This change resistance has, however, on several occasions led the political system to implement change through other funding channels which for prolonged periods of time has made the councils somewhat marginalized.
Optimal controller location in wireless networked control systems

The control performance of wireless networked control systems (WNCS) has been shown to heavily depend on the packet delivery quality of both the sensor‐to‐controller and controller‐to‐actuator communications. Such quality relies on the relative distance between the wireless transmitter and receiver, which naturally raises the challenging problem of controller placement in WNCS for optimal control performance. In this paper, we investigate the optimal controller location (OCL) problem in WNCS based on linear‐quadratic‐Gaussian control strategy. For the one‐hop network case where the controller can only be placed at either the sensor side or the actuator side, we derive a simple yet effective criterion to determine the OCL. For the more general multi‐hop case where the controller can be located at either one of the sensors, relays, or actuators, we obtain the necessary and sufficient condition under which the closed‐loop system is guaranteed to be stable. On the basis of these results, we further transform the OCL problem into an optimization problem that can be solved efficiently. Numerical results are provided to verify our analysis. Copyright © 2013 John Wiley & Sons, Ltd.
Extremal Random Walks on Trees

We study random walks on trees, where we iteratively move from one vertex to a randomly chosen adjacent vertex. We study two quantities arising in random walks: the hitting time and the mixing time. The hitting time is the expected number of steps to walk between a chosen pair of vertices. The mixing time is the expected number of steps before the distribution of the current state is proportional to its degree. For a fixed tree size, we prove that the star is the unique minimizing structure and the path is the unique maximizing structure for both quantities.
An Integrated Metabolomic Study of Osteoporosis: Discovery and Quantification of Hyocholic Acids as Candidate Markers

Osteoporosis is becoming a highly prevalent disease in a large proportion of the global aged population. Serum metabolite markers may be important for the treatment and early prevention of osteoporosis. Serum samples from 32 osteoporosis and 32 controls were analyzed by untargeted metabolomics and lipidomic approaches performed on an ultra-high performance liquid chromatography and high-resolution mass spectrometry (UHPLC-HRMS) system. To find systemic disturbance of osteoporosis, weighted gene correlation network analysis (WGCNA) and statistical methods were employed for data-mining. Then, an in-depth targeted method was utilized to determine potential markers from the family of key metabolites. As a result, 1,241 metabolites were identified from untargeted methods and WGCNA indicated that lipids metabolism is deregulated and glycerol phospholipids, sphingolipids, fatty acids, and bile acids (BA) are majorly affected. As key metabolites of lipids metabolism, 66 bile acids were scanned and 49 compounds were quantified by a targeted method. Interestingly, hyocholic acids (HCA) were found to play essential roles during the occurrence of osteoporosis and may be potential markers. These metabolites may be new therapeutic or diagnosis targets for the screening or treatment of osteoporosis. Quantified measurement of potential markers also enables the establishment of diagnostic models for the following translational research in the clinic.
Using Simulated Annealing to Factor Numbers

AbstractAlmost all public secure communication relies on the inability tofactor large numbers. There is no known analytic or classical numericmethod to rapidly factor large numbers. Shor[1] has shown that aquantum computer can factor numbers in polynomial time but thereis no practical quantum computer that can yet do such computations.We show that a simulated annealing[2] approach can be adapted toﬁnd factors of large numbers. 1 Approach Simulated annealing is an approach inspired by statistical mechanics whichby analogy views the values of a multivariable numeric problem as phys-ical states of particles. Simulated annealing is very useful practically forproblems such as the traveling salesperson problem and other so called NP-complete problems which have no known polynomial time numeric solutions.Let N be the given number to be factored, and use N in base 2 withndigits. We seek numbers Aand B (written in binary with respectively aand bdigits) such that A∗ B = N with Athe larger number (so a>= b). Now, a+b= nor n+1. For a given N there are at most n−2 possibilitiesfor {a,b}. (Remember that a>b, and the leftmost digit of both A and Bmust be a 1.) Factor A, with abinary digits, can have 1,2,3,. ,a1s in its1
Impact of cross-coronavirus immunity in post-acute sequelae of COVID-19

Beyond the unpredictable acute illness caused by SARS-CoV-2, one-fifth of infections unpredictably result in long-term persistence of symptoms despite the apparent clearance of infection. Insights into the mechanisms that underlie post-acute sequelae of COVID-19 (PASC) will be critical for the prevention and clinical management of long-term complications of COVID-19. Several hypotheses have been proposed that may account for the development of PASC, including persistence of virus or the dysregulation of immunity. Among the immunological changes noted in PASC, alterations in humoral immunity have been observed in some patient subsets. To begin to determine whether SARS-CoV-2 or other pathogen specific humoral immune responses evolve uniquely in PASC, we performed comprehensive antibody profiling against SARS-CoV-2 and a panel of endemic pathogens or routine vaccine antigens using Systems Serology in a cohort of patients with pre-existing rheumatic disease who either developed or did not develop PASC. A distinct humoral immune response was observed in individuals with PASC. Specifically, individuals with PASC harbored less inflamed and weaker Fc-receptor binding anti-SARS-CoV-2 antibodies and a significantly expanded and more inflamed antibody response against endemic Coronavirus OC43. Individuals with PASC, further, generated more avid IgM responses and developed an expanded inflammatory OC43 S2-specific Fc-receptor binding response, linked to cross reactivity across SARS-CoV-2 and common coronaviruses. These findings implicate previous common Coronavirus imprinting as a marker for the development of PASC.
Paradigms for Fast Parallel Approximability

1. Introduction 2. Basic concepts 3. Extremal graph properties 4. Rounding, interval partitioning and separation 5. Primal-dual method 6. Graph decomposition 7. Further parallel approximations 8. Non-approximability 9. Syntactical defined phrases Appendix: Definition of problems Bibliography Index.
Estimation of population mean of a stigmatized quantitative variable using double sampling

This paper considers the problem of estimation of population mean of a sensitive variable in double sampling. Randomized Response Technique (RRT) proposed by Chaudhuri and Adhikary (1990) is used to elicit the information of the sensitive character. An empirical study is included to show the relative efficiency of the proposed estimator over the usual estimator under the super-population model.
Elevator Optimization: Application of Spatial Process and Gibbs Random Field Approaches for Dumbwaiter Modeling and Multi-Dumbwaiter Systems

This research investigates analytical and quantitative methods for simulating elevator optimizations. To maximize overall elevator usage, we concentrate on creating a ”multiple-user positive-sum system” that is inspired by agent-based game theory. We deﬁne and create basic ”Dumbwaiter” models by attempting both the Spatial Process Approach and the Gibbs Random Field Approach. These two mathematical techniques approach the problem from diﬀerent points of view: the spatial process can give an analytical solution in continuous space and the Gibbs Random Field provides a discrete framework to ﬂexibly model the problem on a computer. Starting from the simplest case, we target the assumptions to provide concrete solutions to the models and develop a ”Multi-Dumbwaiter System. ” This paper examines, evaluates, and proves the ultimate success of such implemented strategies to design the basic elevator’s optimal policy; consequently, not only do we believe in the results’ practicality for industry, but also their potential for application.
Large-Scale LiDAR Consistent Mapping Using Hierarchical LiDAR Bundle Adjustment

Reconstructing an accurate and consistent large-scale LiDAR point cloud map is crucial for robotics applications. The existing solution, pose graph optimization, though it is time-efficient, does not directly optimize the mapping consistency. LiDAR bundle adjustment (BA) has been recently proposed to resolve this issue; however, it is too time-consuming on large-scale maps. To mitigate this problem, this paper presents a globally consistent and efficient mapping method suitable for large-scale maps. Our proposed work consists of a bottom-up hierarchical BA and a top-down pose graph optimization, which combines the advantages of both methods. With the hierarchical design, we solve multiple BA problems with a much smaller Hessian matrix size than the original BA; with the pose graph optimization, we smoothly and efficiently update the LiDAR poses. The effectiveness and robustness of our proposed approach have been validated on multiple spatially and timely large-scale public spinning LiDAR datasets, i.e., KITTI, MulRan and Newer College, and self-collected solid-state LiDAR datasets under structured and unstructured scenes. With proper setups, we demonstrate our work could generate a globally consistent map with around 12$\%$ of the sequence time.
Prayatul Matrix: A Direct Comparison Approach to Evaluate Performance of Supervised Machine Learning Models

—Performance comparison of supervised machine learning (ML) models are widely done in terms of different confusion matrix based scores obtained on test datasets. However, a dataset comprises several instances having different difﬁculty levels. Therefore, it is more logical to compare effectiveness of ML models on individual instances instead of comparing scores obtained for the entire dataset. In this paper, an alternative approach is proposed for direct comparison of supervised ML models in terms of individual instances within the dataset. A direct comparison matrix called Prayatul Matrix is introduced, which accounts for comparative outcome of two ML algorithms on different instances of a dataset. Five different performance measures are designed based on prayatul matrix. Efﬁcacy of the proposed approach as well as designed measures is analyzed with four classiﬁcation techniques on three datasets. Also analyzed on four large-scale complex image datasets with four deep learning models namely ResNet50V2, MobileNetV2, EfﬁcientNet, and XceptionNet. Results are evident that the newly designed measure are capable of giving more insight about the comparing ML algorithms, which were impossible with existing confusion matrix based scores like accuracy, precision and recall.
End-to-end Multilingual Coreference Resolution with Mention Head Prediction

This paper describes our approach to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our model is based on a state-of-the-art end-to-end coreference resolution system. Apart from joined multilingual training, we improved our results with mention head prediction. We also tried to integrate dependency information into our model. Our system ended up in third place. Moreover, we reached the best performance on two datasets out of 13.
Quintessence like behavior of symmetric teleparallel dark energy: Linear and nonlinear model

In Einstein's General Relativity (GR), the gravitational interactions are described by the spacetime curvature. Recently, other alternative geometric formulations and representations of GR have emerged in which the gravitational interactions are described by the so-called torsion or non-metricity. Here, we consider the recently proposed modified symmetric teleparallel theory of gravity or $f\left( Q\right) $ gravity, where $Q$ represents the non-metricity scalar. In this paper, motivated by several papers in the literature, we assume the power-law form of the function $% f\left( Q\right) $ as $f\left( Q\right) =\alpha Q^{n+1}+\beta $ (where $% \alpha $, $\beta $, and $n$ are free model parameters) that contains two models: Linear ($n=0$) and nonlinear ($n\neq 0$). Further, to add constraints to the field equations we assume the deceleration parameter form as a divergence-free parametrization. Then, we discuss the behavior of various cosmographic and cosmological parameters such as the jerk, snap, lerk, $Om$ diagnostic, cosmic energy density, isotropic pressure, and equation of state (EoS) parameter with a check of the violation of the strong energy condition (SEC) to obtain the acceleration phase of the Universe. Hence, we conclude that our cosmological $f(Q)$ models behave like quintessence dark energy (DE).