Miracle-dz/UniFAR-FTUs huggingface dataset:
The treatment of missing ‘not at random’ geological data for ore grade modelling

Geostatistics aims to spatially model multiple correlated attributes with some under-sampled variables. Missing samples can distort the data set statistics, and for the model to be consistent, it is necessary to understand the mechanisms that cause the missing data (MD). There are three mechanisms that can govern MD: Missing Completely at Random (MCAR), Missing at Random (MAR) and Missing Not At Random (MNAR). More specifically, when there is a systematic difference between measured and the missing samples, the data is subject to the MNAR mechanism, which leads to biases in the final model if not dealt with adequately. A case is presented where an under-sampled attribute, U (ppm), is modelled to infer its values over the study area through co-simulation approach and an univariate simulation performed on an artificially complete set, considering the MNAR mechanism, which shows a reduction in the relative estimation errors compared to the co-simulated model.
An Aerological Study on the Pre-summer Rainy Season in Japan

The amount of precipitation is examined in relation to the upper wind field and temperature field in the pre-summer rainy season in Japan,, June and July, for the years 1950 and 1951. It may be well explained as a result of vertical motions of baroclinic unstable waves in the westerlies. In the atmospheric structure during this season, there can be found a similarity to the structure of baroclinic unstable wave. It is found from the data of 1946, 1950 and 1951 that the horizontal distribution of the mean zonal velocity also has some relations with the mechanism of precipitation, which suggests that a more complete,three-dimensionaltheory of baroclinic instability is needed. Some brief remarkes are given on the relations between the basic flow and the disturbances.
Shock wave structure in rarefied polyatomic gases with large relaxation time for the dynamic pressure

The shock wave structure in rarefied polyatomic gases is analyzed based on extended thermodynamics (ET). In particular, the case with large relaxation time for the dynamic pressure, which corresponds to large bulk viscosity, is considered by adopting the simplest version of extended thermodynamics with only 6 independent fields (ET6); the mass density, the velocity, the temperature and the dynamic pressure. Recently, the validity of the theoretical predictions by ET was confirmed by the numerical analysis based on the kinetic theory in [S Kosuge and K Aoki: Phys. Rev. Fluids, Vol. 3, 023401 (2018)]. It was shown that numerical results using the polyatomic version of ellipsoidal statistical model agree with the theoretical predictions by ET for small or moderately large Mach numbers. In the present paper, first, we compare the theoretical predictions by ET6 with the ones by kinetic theory for large Mach number under the same assumptions, that is, the gas is polytropic and the bulk viscosity is proportional to the temperature. Second, the shock wave structure for large Mach number in a non-polytropic gas is analyzed with the particular interest in the effect of the temperature dependence of specific heat and the bulk viscosity on the shock wave structure. Through the analysis of the case of a rarefied carbon dioxide (CO2) gas, it is shown that these temperature dependences play important roles in the precise analysis of the structure for strong shock waves.
Efficient seamline determination for UAV image mosaicking using edge detection

ABSTRACT Image mosaicking of data from individual high-resolution unmanned aerial vehicle (UAV) images is required to obtain sufficient coverage over extensive roads. During the mosaicking process, seamlines may be generated due to differences in illumination or projection between individual images, or the presence of moving objects. This study presents an efficient seamline determination technique based on edge detection for UAV road surface images. The algorithm can be divided into three main steps. First, we detect the edges in the overlapping intensity image within the road boundary. Next, we obtain an automatic seamline passing through regions of non-attraction in areas of overlap. Finally, we adjust the values of the overlapping region using the values of the corresponding individual images by following the coordinates of the seamline detected in the second step, ultimately creating an image mosaic. The experiment using UAV images of a road surface demonstrates that the proposed method produces a satisfactory result. The proposed method can be applied for quick mosaicking of UAV images intended for maintaining road safety.
Littlewood–Paley decomposition in quantum calculus

ABSTRACT We introduce Littlewood–Paley decomposition related to q-Rubin's operator, this allows us to provide a dyadic characterization of Sobolev, Hölder and Lebesgue spaces associated with the q-Rubin's operators and to establish some embedding results for these spaces. We construct the paraproduct operators associated with the q-Rubin's operators and we establish its action on the Sobolev and Hölder spaces.
Fully Distributed Resilience for Adaptive Exponential Synchronization of Heterogeneous Multiagent Systems Against Actuator Faults

Cooperative control of multiagent systems (MAS) on communication networks has received a great deal of attention, mostly for the case of homogeneous agents, which all have the same dynamics. An advantage of cooperative synchronization mechanisms is their local distributed nature, which makes them scalable to large networks. However, most existing design mechanisms require some global information, such as the leader's dynamics or global graph information, so that the control protocols are technically not fully distributed. Moreover, the distributed nature of the control protocols makes them susceptible to faults or uncertainties. In this paper, we study heterogeneous MAS, where all agents may have different dynamics. We provide adaptive resilience mechanisms for rejecting actuator faults, and guarantee exponential convergence of synchronization errors, whereas most existing results on actuator faults guarantee only boundedness of errors. Finally, we provide algorithms that are fully distributed, requiring no knowledge of either the leader's dynamics or of graph properties.
Stability analysis and control design for a kind of nonlinear descriptor system via T-S fuzzy model with actuator saturation

This paper analyzes the stability for a class of nonlinear descriptor system via T-S fuzzy model with actuator saturation and proposes a controller design method for the system. A set invariance condition is first presented such that the closed-loop system is regular and impulse free, and under which an ellipsoid is contractively invariant. Then the methods of estimating the attraction domain for typical shape reference sets are obtained based on the set invariance condition. The conditions for estimating the attraction domains are described by optimization problems for the given feedback gain matrices. Finally, the controller design methods are obtained by solving the optimization linear matrix inequalities (LMIs) problems which derives the largest contractively invariant ellipsoid. A positive matrix is the key to this study because it can overcome the difficulty of an infeasible LMI due to the singular system characteristics and allows us to define the contractive invariant set in a manner that resembles normal state systems. The study may lay a foundation for further studies in the synthesis of a class of nonlinear descriptor systems via T-S fuzzy models with actuator saturation.
EXACT TRAVELING WAVE SOLUTIONS OF THE OSMOSIS $k(2,3)$ EQUATION

In this paper, we study the bifurcation of $k(2, 3)$ equation $u_{t}+(u^{2})_{x}-(u^{3})_{xxx}=0$ with osmosis dispersion. Using the qualitative analysis methods of dynamical system and numerical simulation by Maple programs, illustrative phase portraits corresponding to traveling wave system are presented, and expressions of a periodic cusp wave, kink-like and antikink-like wave solutions, and implicit expression of soliton are explicitly given after integrals.
Helical bottleneck effect in 3D homogeneous isotropic turbulence

We present the results of modelling the development of homogeneous and isotropic turbulence with a large-scale source of energy and a source of helicity distributed over scales. We use the shell model for numerical simulation of the turbulence at high Reynolds number. The results show that the helicity injection leads to a significant change in the behavior of the energy and helicity spectra in scales larger and smaller than the energy injection scale. We suggest the phenomenology for direct turbulent cascades with the helicity effect, which reduces the efficiency of the spectral energy transfer. Therefore the energy is accumulated and redistributed so that non-linear interactions will be sufficient to provide a constant energy flux. It can be interpreted as the ‘helical bottleneck effect’ which, depending on the parameters of the injection helicity, reminds one of the well-known bottleneck effect at the end of inertial range. Simulations which included the infrared part of the spectrum show that the inverse cascade hardly develops under distributed helicity forcing.
Identification and characterization of the out-of-plane resonance in a dielectric elastomer to drive an agile robotic cube

A dielectric elastomer (DE) vibrates and resonates mechanically under an alternating voltage and the vibrations undergo a transition from the in-plane to the out-of-plane state. In this study, a phase diagram was established experimentally to illustrate this transition and an electromechanical model was proposed to predict the transition using the boundary conditions. The resonance frequencies were calculated and were shown to be consistent with the experimentally obtained values. A novel vibration-driven robotic cube was then designed and powered by the dielectric elastomer at the 1st order out-of-plane resonance. The agile robotic cube is ultralight and demonstrated a speedy response of 125 mm/s (1.8 body length/s), which is superior to the locomotive dynamics of the existing DE-based robotic devices.
Thermoelastic damping of graphene nanobeams by considering the size effects of nanostructure and heat conduction

Abstract Thermoelastic damping of nanobeams by considering the size effects of nanostructure and heat conduction is studied herein. The size effect of nanostructure is investigated based on Euler–Bernoulli beam assumptions in the framework of nonlocal strain gradient elasticity, and the size dependence of heat conduction is taken into account by incorporating phase-lagging and nonlocal effects. Closed-form solutions of thermoelastic damping and quality factor characterized by thermoelastic coupling are derived. Graphene nanoribbon is chosen as a nanobeam. The effects of relaxation time, aspect ratio, elastic modulus, thermal expansion, and thermal conductivity on quality factor of graphene nanobeams are discussed in detail.
Magnetically assisted explosions of weakly magnetized stars

Abstract We carried out high resolution simulations of weakly-magnetized core-collapse supernovae in two-dimensional axisymmetry in order to see the influence of the magnetic field and rotation on the explosion. We found that the magnetic field amplified by magnetorotational instability (MRI) has a great positive impact on the explosion by enhancing the neutrino heating, provided that the progenitor has large angular momentum close to the highest value found in stellar evolution calculations. We also found that even for progenitors neither involving strong magnetic flux nor large angular momentum, the magnetic field is greatly amplified by the convection aand rotation, and this leads to the boost of the explosion again by enhancing the neutrino heating.
Essays on Random Forest Ensembles

ESSAYS ON RANDOM FOREST ENSEMBLES Matthew Olson Abraham J. Wyner A random forest is a popular machine learning ensemble method that has proven successful in solving a wide range of classification problems. While other successful classifiers, such as boosting algorithms or neural networks, admit natural interpretations as maximum likelihood, a suitable statistical interpretation is much more elusive for a random forest. In the first part of this thesis, we demonstrate that a random forest is a fruitful framework in which to study AdaBoost and deep neural networks. We explore the concept and utility of interpolation, the ability of a classifier to perfectly fit its training data. In the second part of this thesis, we place a random forest on more sound statistical footing by framing it as kernel regression with the proximity kernel. We then analyze the parameters that control the bandwidth of this kernel and discuss useful generalizations.
An intelligent fault diagnosis framework for raw vibration signals: adaptive overlapping convolutional neural network

Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing to their efficiency in extracting representative features. However, there is always an undesirable shift variant property embedded in raw vibration signals, which hinders the direct use of raw signals in fault diagnosis networks. A convolutional neural network (CNN) is a widely used and efficient method to extract features in various fields for its excellent sparse connectivity, equivalent representation and weight sharing properties. However, raw CNN is time-consuming and has a marginal problem. Heuristically, we construct a fault diagnosis framework called adaptive overlapping CNN (AOCNN) to deal with one dimension (1D) raw vibration signals directly. The shift variant problem is dealt with by the adaptive convolutional layer and the root-mean-square (RMS) pooling layer, and the marginal problem embedded in the CNN is relieved by employing the overlapping layer. Meanwhile, the AOCNN is also characterized by adopting different convolutional strides and diverse activation functions in feature extraction network training and usage. Furthermore, sparse filtering is embedded into the AOCNN, and experiments on a bearing dataset and a gearbox dataset are conducted to verify the validity of the proposed method separately. When compared with other state-of-the-art methods this method reveals its superiority.
The case of extended supersymmetry and a study in superspace

In this dissertation we study quantum field theories with extended supersymmetry in four and three dimensions. In $d = 4$ we study $N = 2$ supersymmetric theories using projective superspace formalism extensively. We discover the full non-Abelian action for super Yang-Mills (SYM) theory in projective superspace by studying its relation to harmonic superspace under a suitable Wick rotation of the latter's internal two-dimensional sphere. We also show that a Chern-Simons action for SYM in 'full' $N = 2$ superspace can be written down that reproduces both the harmonic and projective SYM actions. The projective formalism allows simplication in computing Feynman supergraphs because the $N = 2$ rules imply simpler D-algebra than the $N = 1$ case. Also, integrals over its one-dimensional internal space are simpler to handle than the two-dimensional counterparts in the harmonic case. Furthermore, these calculations are simplified drastically in background field formalism and to construct it in projective formalism, we have to choose different representations for quantum and background fields. This also means that the standard power counting arguments are applicable and finiteness beyond 1-loop for $N = 2$ SYM becomes manifest. We then study the hyperkahler moduli space of $N = 2$ SYM compactified on a circle. Recently, it was shown that the Darboux coordinates on the moduli space are an efficient description of the hyperkahler metric and we give simple construction of the integral equation describing these coordinates using projective superspace. We apply this result to study the moduli space of $d = 5 N = 1$ SYM compactified on a torus and we obtain results in agreement with the literature. Lastly, in $d = 3$ we study the free energy of $N = 3$ Chern-Simons theories associated with affine ADE quivers and conjecture a general expression for free energy of $D_{n}$ quivers. Through the AdS/CFT correspondence, this leads to a prediction for the volume of certain class of tri-Sasaki Einstein manifolds. As a consistency check of our expression, we add massive fundamental flavour fields and verify that the free energy decreases in accordance with F-theorem once they are integrated out.
An image processing method for automated evaluation of the area of hysteresis loops

Measuring the magnetic hysteresis loop and energy loss characteristics of magnetic materials helps students to understand the ferromagnetic properties of the materials. In this study, a conventional experiment is combined with automated image processing to learn about the fundamentals of the hysteresis loop by measuring its area. We process a loop image acquired using a digital camera to calculate the area. The results of this easy and instructive experiment are substantially compatible with theory and are more precise than those obtained when using the tedious conventional method of manually evaluating the loop area. The proposed method is discussed as a clear-cut and a fast tool to reproduce the results with pragmatic accuracy.
Asymptotic behavior of the Random Logistic Model and of parallel Bayesian logspline density estimators

ASYMPTOTIC BEHAVIOR OF THE RANDOM LOGISTIC MODEL AND OF PARALLEL BAYESIAN LOGSPLINE DENSITY ESTIMATORS MAY 2018 KONSTANDINOS KOTSIOPOULOS B.S., UNIVERSITY OF CRETE, GREECE M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professors Richard S. Ellis, Erin Conlon and Alexey Miroshnikov This dissertation is comprised of two separate projects. The first concerns a Markov chain called the Random Logistic Model. For r ∈ (0, 4] and x ∈ [0, 1] the logistic map fr(x) = rx(1 − x) defines, for t ∈ N, the dynamical system xr(t + 1) = f(xr(t)) on [0, 1], where xr(1) = x. The interplay between this dynamical system and the Markov chain xr,N (t) defined by perturbing the logistic map by truncated Gaussian noise scaled by 1/ √ N , where N → ∞, is studied. A natural question is whether one can quantify this interplay via probabilistic limit theorems for xr,N (t). There are two possible limits: the vanishing-noise limit N → ∞ for fixed t ∈ N, then taking t → ∞, and the ergodic limit t → ∞ followed by the vanishing-noise limit N → ∞. Both lead to a set of probabilistic limit theorems where the underlying deterministic dynamics take over. A particular case of interest is for r = 4. In the second project we perform an asymptotic analysis of Bayesian parallel density estimators which are based on logspline density estimation presented in [25]. The parallel estimator we introduce is in the spirit of the kernel density estimator presented by Neiswanger, Wang and Xing [17]. We provide a numerical procedure that produces the density estimator itself in place of the sampling algorithm. We derive an error bound for the mean integrated squared error for the full data posterior estimator and investigate the parameters that arise from the logspline density estimation and the numerical approximation procedure. Our investigation leads to the choice of parameters that result in the error bound scaling appropriately in relation to them.
Silicon nanospheres for directional scattering in thin-film solar cells

Abstract. Reducing active layer thickness of solar cell stresses on efficient light trapping mechanisms to keep the cell efficiency intact. Directional light scattering and promising refractive index of silicon nanoparticles make them encouraging scattering centers for thin-film silicon solar cells. Finite-difference time-domain simulations are used to study the optical properties of silicon nanospheres embedded in the top and bottom buffer layer of solar cells. Diameter of a silicon nanoparticle plays a crucial role in the forward and backward scattering of incident light into the cell. Silicon nanospheres outperform commonly used metallic and dielectric nanospheres and trapped the incident light over a broad spectrum. Silicon nanospheres require special attention when placed in both the buffer layers of the solar cell simultaneously, and lateral displacement of the silicon nanospheres at the top buffer layer with respect to nanospheres at the bottom buffer layer is beneficial. Lateral displacement of nanospheres provides a total quantum efficiency of 51.49% in comparison to 21.9% of the pristine cell. These exceptional scattering competencies of silicon nanospheres make them a promising candidate for photovoltaic applications. Silicon scatterers may be used with well-established fabrication techniques.
The research on NURBS adaptive interpolation technology

In order to solve the problems of Research on NURBS Adaptive Interpolation Technology, such as interpolation time bigger, calculation more complicated, and NURBS curve step error are not easy changed and so on. This paper proposed a study on the algorithm for NURBS adaptive interpolation method of NURBS curve and simulation. We can use NURBS adaptive interpolation that calculates (xi, yi, zi). Simulation results show that the proposed NURBS curve interpolator meets the high-speed and high-accuracy interpolation requirements of CNC systems. The interpolation of NURBS curve should be finished. The simulation results show that the algorithm is correct; it is consistent with a NURBS curve interpolation requirements.
Investigating Environmental Determinants of Soil-Transmitted Helminths Transmission using GPS Tracking and Metagenomics Technologies

Background The Global Health community aims to eliminate soil-transmitted helminth (STH) infections by 2030. Current preventive methods such as Mass Drug Administration, WASH practices, and health education needs to be complimented to halt transmission. We tracked the movement of hookworm-infected and non-infected persons and investigated soil factors in the places they frequented within an endemic community to further understand the role of human movement and sources of infections. Methods 59 positive and negative participants wore GPS tracking devices for 10 consecutive days and their movement data captured in real time. The data was overlaid on the community map to determine where each group differentially spent most of their time. Soil samples were collected from these identified sites and other communal places. Physical and chemical properties were determined for each sample using standard methods and helminth eggs cultured into larvae using the Baermann technique. Bivariate and multivariate analyses were used to determine associations between larvae counts and soil factors. Helminth species were identified with metagenomic sequencing and their distributions mapped to sampling sites in the community. Results The study found that there was no significant difference in the average larvae counts in soil between sites assessed by infected and non-infected participants (P=0.59). However, soil factors, such as pH, carbon and sandy-loamy texture were associated with high larvae counts (P<0.001) while nitrogen and clay content were associated with low counts(P<0.001). The dominant helminth species identified were Panagrolaimus superbus (an anhydrobiotic helminth), Parastrongyloides trichosuri (a parasite of small mammals), Trichuris trichuria (whipworm), and Ancylostoma caninum (dog hookworm). Notably, no Necator americanus was identified in any soil sample. Conclusion This study provides important insights into the association between soil factors and soil-transmitted helminths. These findings contribute to our understanding of STH epidemiology and support evidence-based decision-making for elimination strategies.