Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and robust geometric correction for mosaicking UAV images with narrow overlaps

ABSTRACT Image mosaicking is essential for monitoring a wide target area using unmanned aircraft vehicle (UAV) images. An image mosaicking process requires accurate and robust geometric correction of individual images with respect to the reference plane. Tiepoint-based geometric correction methods developed so far usually assume wide overlaps between adjacent images. This article focuses on fast monitoring applications where UAVs fly very fast and image mosaics are to be generated immediately. In this case, wide overlaps might not be ensured. For this reason, we investigate a fast and robust geometric correction method for mosaicking UAV images with narrow overlaps. To ensure quickness in geometric correction, an image resampling approach using a resampling grid is presented. To ensure accuracy and robustness in geometric correction, existing transformation models are analysed in depth, and optimal models are proposed. Our proposed method shows the potential for fast monitoring applications. We also show that while existing transformation models work for images with a large overlap, perspective transformation models with full orientation parameters may suffer in images with a narrow overlap. We hope that our results can be useful when implementing an optimal solution that can simultaneously handle UAV images with different overlaps.
Aspects of Wilson loops in 3- and 4-dimensional gauge theories

In this thesis we study aspects of Wilson loops in some threeand four-dimensional gauge theories. In three dimensions, we construct the classically 1/2 BPS Wilson loops in N = 4 superconformal Chern-Simons matter theories. We consider a circular or linear quiver with Chern-Simons levels k, −k and 0. In the cases with no vanishing Chern-Simons levels, we find a pair of Wilson loops for each pair of adjacent nodes on the quiver connected by a hypermultiplet. This classical pairwise degeneracy has since been found to be lifted by quantum corrections. We also show that these Wilson loops are cohomologically equivalent to certain combinations of 1/4 BPS Wilson loops whose expectation value can be evaluated by the appropriate localized matrix model. We analyze the M-theory duals of those Wilson loops and comment on their degeneracy. In four dimensions, we study Wilson loops in planar N = 4 super Yang-Mills theory. We derive the Lax operator for a very large family of classical minimal surface solutions in H3 describing Wilson loops in N = 4 SYM theory. These solutions, constructed in [1], are associated with a hyperelliptic Riemann surface of odd genus. The procedure which we use to construct the Lax operator was introduced in [2]. Here we apply the procedure to the very large family of minimal surfaces found in [1]. We verify that the algebraic curve derived from the Lax operator is none other than this hyperelliptic surface. We also study operator insertions into the 1/2 BPS straight Wilson line in N = 4 SYM. Deformations of this straight line operator may be mapped to certain operator insertions into the straight line. The operator insertions are classified according to the OSp(2, 2|4) symmetry group preserved by the straight line. We study the multiplet structure of these insertions and present a method to compute the anomalous dimensions and three-point function structure constants of these insertions.
Observability-Based Guidance and Sensor Placement

Observability-Based Guidance and Sensor Placement Brian T. Hinson Chair of the Supervisory Committee: Associate Professor Kristi Morgansen Hill Aeronautics and Astronautics Control system performance is highly dependent on the quality of sensor information available. In a growing number of applications, however, the control task must be accomplished with limited sensing capabilities. This thesis addresses these types of problems from a control-theoretic point-of-view, leveraging system nonlinearities to improve sensing performance. Using measures of observability as an information quality metric, guidance trajectories and sensor distributions are designed to improve the quality of sensor information. An observability-based sensor placement algorithm is developed to compute optimal sensor configurations for a general nonlinear system. The algorithm utilizes a simulation of the nonlinear system as the source of input data, and convex optimization provides a scalable solution method. The sensor placement algorithm is applied to a study of gyroscopic sensing in insect wings. The sensor placement algorithm reveals information-rich areas on flexible insect wings, and a comparison to biological data suggests that insect wings are capable of acting as gyroscopic sensors. An observability-based guidance framework is developed for robotic navigation with limited inertial sensing. Guidance trajectories and algorithms are developed for range-only and bearing-only navigation that improve navigation accuracy. Simulations and experiments with an underwater vehicle demonstrate that the observability measure allows tuning of the navigation uncertainty.
DEVELOPMENT OF SPIRITUALITY ASSESSMENT OF NURSING INSTRUMENTS IN NURSES AT LABUANG BAJI HOSPITAL MAKASSAR

Spiritual care is carried out by nurses for patients, to prevent more severe health effects and warn of their illness, besides fulfilling spiritual needs that are holistic care to improve patient rehabilitation and quality of life when patients are treated. The aim of the study was to find out the development of an instrument for assessing the spiritual aspects of patients. This research is a qualitative research, the researcher synthesizes and constructs an instrument for assessing the patient's spiritual aspects, while the informants are five expert nurses, nursing practitioners and experts in the field of nursing science specifying spiritual aspects. The stages of the instrument development process are the construction stage, the preparation of the grid, the development of variable dimensions and indicators, theoretical and empirical validation, expert panels and review revisions based on input from the expert panel. The results show that after carrying out the instrument development process, this research has found five dimensions of spiritual aspects assessment, namely: the dimensions of spiritual experience, positive emotions, the meaning of life, rituals (for non-Muslims) and rituals (for Muslims). While discussing spiritual experience is a manifestation of spirituality within a person in the form of specific and unique experiences related to his relationship with God at various levels, one's spirituality is related to the purpose and meaning of life as a whole as a manifestation of his relationship with God, spirituality has four dimensions, namely the meaning of life, positive emotions, spiritual experiences, and rituals
Corrigendum: Modelling of bulk superconductor magnetisation (2015 Supercond. Sci. Technol. 28 053002)

This paper presents a topical review of the current state of the art in modelling the magnetization of bulk superconductors, including both (RE)BCO (where RE = rare earth or Y ) and MgB 2 materials. Such modelling is a powerful tool to understand the physical mechanisms of their magnetization, to assist in interpretation of experimental results, and to predict the performance of practical bulk superconductor-based devices, which is particularly important as many superconducting applications head towards the commercialization stage of their development in the coming years. In addition to the analytical and numerical techniques currently used by researchers for modelling such materials, the commonly used practical techniques to magnetize bulk superconductors are summarized with a particular focus on pulsed ﬁ eld magnetization (PFM), which is promising as a compact, mobile and relatively inexpensive magnetizing technique. A number of numerical models developed to analyse the issues related to PFM and optimise the technique are described in detail, including understanding the dynamics of the magnetic ﬂ ux penetration and the in ﬂ uence of material inhomogeneities, thermal properties, pulse duration, magnitude and shape, and the shape of the magnetization coil(s). The effect of externally applied magnetic ﬁ elds in different con ﬁ gurations on the attenuation of the trapped ﬁ eld is also discussed. A number of novel and hybrid bulk superconductor structures are described, including improved thermal conductivity structures and ferromagnet – superconductor structures, which have been designed to overcome some of the issues related to bulk superconductors and their magnetization and enhance the intrinsic properties of bulk superconductors acting as trapped ﬁ eld magnets. Finally, the use of hollow bulk cylinders/tubes for shielding is analysed.
Mean geometry for 2D random fields: Level perimeter and level total curvature integrals

We introduce the level perimeter integral and the total curvature integral associated with a real valued function f defined on the plane R^2 as integrals allowing to compute the perimeter of the excursion set of f above level t and the total (signed) curvature of its boundary for almost every level t. Thanks to the Gauss-Bonnet theorem, the total curvature is directly related to the Euler Characteristic of the excursion set. We show that the level perimeter and the total curvature integrals can be explicitly computed in two different frameworks: piecewise constant functions (also called here elementary functions) and smooth (at least C^2) functions. Considering 2D random fields (in particular considering shot noise random fields), we compute their mean perimeter and total curvature integrals, and this provides new explicit computations of the mean perimeter and Euler Characteristic densities of excursion sets, beyond the Gaussian framework.
CELL-CENTERED FINITE VOLUME SOLUTION OF THE TWO-DIMENSIONAL NAVIER-STOKES EQUATIONS

Cell-centered finite volume method with multistage time-stepping is successfully applied to two-dimensional mass-weighted, time-averaged Navier-Stokes equations for the computation of viscous flows. In the cellcentered scheme, flow quantities are associated with the center of a cell. Convective fluxes at the cell faces are evaluated by means of upwind Roe Flux Differencing Scheme (Roe FDS) with Monotone Upwind Schemes for Scalar Conservation Laws (MUSCL) approach. Green? s theorem is employed for evaluation of gradients in computation of viscous fluxes. Five stage hybrid time-stepping scheme is implemented for integration to steady state. Convergence is accelerated by utilizing local time stepping and residual smoothing. The accuracy of the present Navier-Stokes solver is verified by comparing flat-plate laminar boundary-layer solutions with theoretical solutions of Blasius and by comparing laminar airfoil solutions with those available in literature. Convergence down to machine zero attained in the computations indicates a good sign for the efficiency of the present solver. Turbulence closure for Reynolds stresses is obtained using two-layer algebraic eddy viscosity model of Baldwin and Lomax. Computed results for turbulent flows are validated with available experimental results.
Synchrotron radiation-based l 1-norm regularization on micro-CT imaging in shale structure analysis

Abstract Synchrotron radiation (SR) X-ray tomography is a main technique in realizing imaging of an object section by section. We apply this technique in recovering shale microstructures which becomes a hot topic recently in non-conventional oil and gas exploration. We first set the experimental setup using SR sources at BL13W1 beamline at Shanghai Synchrotron Radiation Facility to obtain the measured data of the shale sample, and then we establish the l 1 {l_{1}} -norm constrained regularization model to reconstruct the microstructure of the shale. A fast convergent gradient descent method is developed to realize this purpose. It indicates from our test results that the new method possesses higher resolution of imaging than the conventional results given by the commercial software.
Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation

Simple Summary Leukemia is a type of cancer that affects white blood cells and can lead to serious health problems and death. Diagnosing leukemia is currently performed through a combination of morphological and molecular criteria, which can be time-consuming and, in some cases, unreliable. Computer-aided diagnosis (CAD) systems based on deep-learning methods have shown promise in improving diagnosis efficiency and accuracy. However, these systems suffer from the “black box problem,” which can lead to incorrect classifications. This research proposes a novel deep-learning approach with visual explainability for ALL diagnoses based on robust white blood cell nuclei segmentation to provide a highly reliable and interpretable classification. The aim is to develop a CAD system that can assist physicians in diagnosing leukemia more efficiently, potentially improving patient outcomes. The findings of this research may impact the research community by providing a more reliable and explainable deep-learning-based approach to blood disorder diagnosis. Abstract Leukemia is a significant health challenge, with high incidence and mortality rates. Computer-aided diagnosis (CAD) has emerged as a promising approach. However, deep-learning methods suffer from the “black box problem”, leading to unreliable diagnoses. This research proposes an Explainable AI (XAI) Leukemia classification method that addresses this issue by incorporating a robust White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism. The segmentation of WBC is achieved by combining image processing and U-Net techniques, resulting in improved overall performance. The segmented images are fed into modified ResNet-50 models, where the MLP classifier, activation functions, and training scheme have been tested for leukemia subtype classification. Additionally, we add visual explainability and feature space analysis techniques to offer an interpretable classification. Our segmentation algorithm achieves an Intersection over Union (IoU) of 0.91, in six databases. Furthermore, the deep-learning classifier achieves an accuracy of 99.9% on testing. The Grad CAM methods and clustering space analysis confirm improved network focus when classifying segmented images compared to non-segmented images. Overall, the proposed visual explainable CAD system has the potential to assist physicians in diagnosing leukemia and improving patient outcomes.
Outlier Rejection by Means of Median Matrices for Polarimetric SAR Covariance Symmetry Classification

This letter exploits the intrinsic selectivity properties of the median to enhance the covariance symmetry classification in polarimetric synthetic aperture radar (PolSAR) images. More in detail, the median matrices are utilized to properly detect and remove outliers in the data belonging to a reference window, in turn used to estimate the covariance structure of the pixel under test. Hence, the scene is classified in terms of the structures assumed by the covariance under specific symmetric scattering mechanisms. To do this, for each pixel under test, the data in a reference window are filtered through the application of a generalized inner product (GIP)-based procedure involving the median matrix in its computation. The filtered data are then used as input to a model order selection (MOS)-based procedure for the final scene classification. Tests conducted on L-band real-recorded SAR data show the effectiveness of the devised framework.
Progress Towards Achieving the Challenge of Indian Summer Monsoon Climate Simulation in a Coupled Ocean‐Atmosphere Model

Simulation of the spatial and temporal structure of the monsoon intraseasonal oscillations (MISOs), which have effects on the seasonal mean and annual cycle of Indian summer monsoon (ISM) rainfall, remains a grand challenge for the state‐of‐the‐art global coupled models. Biases in simulation of the amplitude and northward propagation of MISOs and related dry rainfall bias over ISM region in climate models are limiting the current skill of monsoon prediction. Recent observations indicate that the convective microphysics of clouds may be critical in simulating the observed MISOs. The hypothesis is strongly supported by high fidelity in simulation of the amplitude and space‐time spectra of MISO by a coupled climate model, when our physically based modified cloud microphysics scheme is implemented in conjunction with a modified new Simple Arakawa Schubert (nSAS) convective parameterization scheme. Improved simulation of MISOs appears to have been aided by much improved simulation of the observed high cloud fraction and convective to stratiform rain fractions and resulted into a much improved simulation of the ISM rainfall, monsoon onset, and the annual cycle.
Short-term prediction of ICU admission for COVID-19 inpatients

Since the COVID-19 outbreak, many hospitals suffered from a surge of some high-risk inpatients needing to be admitted to the ICU. In this study, we propose a method predicting the likelihood of COVID-19 inpatients’ admission to the ICU within a time frame of 12 hours. Four steps, the Bayesian Ridge Regression-based missing value imputation, the synthesis of training samples by the combination of two rows (the first and another row) of each patient, customized oversampling, and XGBoost classifier, are used for the proposed method. In the experiment, the AUC-ROC and F-score of our method is compared with those of other methods using various imputation techniques and classifiers. Our method achieves the best performance among the methods.
Growth Kinetics of Listeria monocytogenes and Salmonella enterica on Dehydrated Vegetables during Rehydration and Subsequent Storage

Dehydrated vegetables have low water activities and do not support the proliferation of pathogenic bacteria. Once rehydrated, vegetables can be incorporated into other foods or held for later use. The aim of this study was to examine the survival and proliferation of Listeria monocytogenes and Salmonella enterica on dehydrated vegetables during rehydration and subsequent storage. Carrots, corn, onion, bell peppers, and potatoes were heat dehydrated, inoculated at 4 log CFU/g, and rehydrated at either 5 or 25 °C for 24 h. Following rehydration, vegetables were stored at 5, 10, or 25 °C for 7 d. Both L. monocytogenes and S. enterica survived on all vegetables under all conditions examined. After 24 h of rehydration at 5 °C, pathogen populations on the vegetables were generally <1.70 log CFU/g, whereas rehydration at 25 °C resulted in populations of 2.28 to 6.25 log CFU/g. The highest growth rates during storage were observed by L. monocytogenes on potatoes and S. enterica on carrots (2.37 ± 0.61 and 1.63 ± 0.18 log CFU/g/d, respectively) at 25 °C when rehydration occurred at 5 °C. Results indicate that pathogen proliferation on the vegetables is both rehydration temperature and matrix dependent and highlight the importance of holding rehydrated vegetables at refrigeration temperatures to hinder pathogen proliferation. Results from this study inform time and temperature controls for the safety of these food products.
Deep Analysis of Clinical Parameters and Temporal Evolution of Glycemic Parameters Based on CGM Data for the Characterization of Severe Hypoglycemia in a Cohort of Children and Adolescents with Type 1 Diabetes

This study aims to evaluate the determinants and clinical markers of patients at risk for severe hypoglycemia (SH) in children and adolescents with type 1 diabetes. In the EPI-GLUREDIA study, clinical parameters and continuous glucose monitoring metrics from children and adolescents with type 1 diabetes were retrospectively analyzed between July 2017 and June 2022. Their clinical parameters were collected during traditional and quarterly medical consultations according to whether they experienced severe hypoglycemia or not. Then, continuous glucose monitoring metrics were analyzed on days surrounding SH during specific periods. According to the glycemic parameters, glycemic hemoglobin and glycemic mean were significantly lower in the three months preceding a SH compared with during three normal months (p < 0.05). Moreover, the time spent in hypoglycemia(time below the range, TBR<3.3) and its strong correlation (R = 0.9, p < 0.001) with the frequency of SH represent a sensitive and specific clinical parameter to predict SH (cut-off: 9%, sensitivity: 71%, specificity: 63%). The second finding of the GLUREDIA study is that SH is not an isolated event in the glycemic follow-up of our T1DM patients. Indeed, most of the glycemic parameters (i.e., glycemic mean, glycemic variability, frequency of hypoglycemia, and glycemic targets) vary considerably in the month preceding an SH (all p < 0.05), whereas most of these studied glycemic parameters remain stable in the absence of a severe acute complication (all p > 0.05). Furthermore, the use of ROC curves allowed us to determine for each glycemic parameter a sensitive or specific threshold capable of more accurately predicting SH. For example, a 10% increase in the frequency of hypoglycemia predicts a risk of near SH with good combination of sensitivity and specificity (sensitivity: 80%, specificity: 60%). The GLUREDIA study aimed to target clinical and glycemic parameters to predict patients at risk for SH. First, we identified TBR<3.3 < 9% as a sensitive and specific tool to reduce the frequency of SH. In addition, SH was not an isolated event but rather it was accompanied by glycemic disturbances in the 30 days before SH.
Spectral Observations of Superthin Galaxies

We conduct spectral observations of 138 superthin galaxies (STGs) with high radial-to-vertical stellar disk scale ratios with the Dual Imaging Spectrograph on the 3.5 m telescope at the Apache Point Observatory (APO) to obtain the ionized gas rotation curves with R ∼5000 resolution. We also performed near-infrared (NIR) H and Ks photometry for 18 galaxies with the NICFPS camera on the 3.5 m telescope. The spectra, the NIR photometry, and published optical and NIR photometry are used for modeling that utilizes the thickness of the stellar disk and rotation curves simultaneously. The projection and dust extinction effects are taken into account. We evaluate eight models that differ in their free parameters and constraints. As a result, we estimated the masses and scale lengths of the galactic dark halos. We find systematic differences between the properties of our red and blue STGs. The blue STGs have a large fraction of dynamically underevolved galaxies whose vertical velocity dispersion is low in both gas and stellar disks. The dark halo-to-disk scale ratio is shorter in the red STGs than in the blue ones, but in a majority of all STGs, this ratio is under 2. The optical color (r − i) of the STGs correlates with their rotation curve maximum, vertical velocity dispersion in stellar disks, and mass of the dark halo. We conclude that there is a threshold central surface density of 50 M ⊙ pc−2 below which we do not observe very thin, rotationally supported galactic disks.