Miracle-dz/UniFAR-FTUs huggingface dataset:
SAR Change Detection to Evaluate Flood Event Characteristics, Pre- and Post-Restoration of a Floodplain

A method using Sentinel-1 synthetic aperture radar (SAR) data to evidence flood changes after floodplain restoration is proposed. A Natural Flood Management (NFM) project on the Ouse river in southern England, undertaken by the Sussex Flow Initiative, was analysed to ascertain any reduction of previous flood risk. GIS operations were conducted on the results of the change detection analysis to identify how flood area, form and compactness were affected after the NFM installation, and how these changes relate to the project aims. Flood records based on internet-published drone footage verified the change detection methodology. A scorecard was developed to evaluate the benefits and disadvantages of spatial changes seen in post-restoration floods in comparison to inundation before the measures were installed. Evaluation results were used in the annual report of the SFI project to demonstrate the attenuation of floodwaters in accordance with the aims of the project.
Hepatic antioxidant and gut ecological modulation properties of long-term intake of tea (Camellia sinensis L.) flower extract in vivo

Tea (Camellia sinensis L.) flower extract (TFE) is a new type of tea beverage. The aim of this study was to explore the possible function after intake of TFE for a fixed period. In the study, 200 mg/kg body weight (BW)/day (d) of TFE was given to mice for 14 weeks. The results showed that the levels of hepatic superoxide dismutase and reduced glutathione were increased but the formation of malondialdehyde was reduced, compared to the normal control (NC) group. Meanwhile, administration of TFE contributed to the prior number of colonic goblet cells (1,505 ± 124 vs. 1,162 ± 112, per mm2) and enhancement of colonic messenger RNA expression of mucin 2 and Claudin5. Additionally, TFE intervention modulated the composition and metabolic pathways of gut microbiota with an important role in dietary metabolism. Representatively, the relative abundance of genera Bacteroides, Prevotella, and Lachnospiracea_incertae_sedis, and the levels of short-chain fatty acids (SCFAs) and immunoglobulin A were increased. Taken together, long-term intake of TFE could promote hepatic antioxidant and modulate gut ecological status. These results could provide a reference for the development of TFE as a functional beverage.
Strong Lensing Cosmography in the Frontier Fields

Abstract The wealth of strong lensing features observed in the Frontier Fields clusters offers insights on the nature of dark energy. The large number of multiple-images systems with redshifts allows to simultaneously estimate the lens model parameters and the cosmological parameters involved in the distances calculations. In particular for the ΛCDM model, it is possible to estimate the matter density Ω m and the dark energy equations parameters w X . In this talk, I will present recent analyses of systematic errors based on Frontier Fields observed and simulated data.
A Qualitative Study : Critical Success Factors of Public Private Partnerships in Indonesia

This research aims to analyze the Critical Success Factors (CSF) of Public Private Partnership (PPP) Indonesia that are very significant due at the ontological level and sociological level. The problem is very interesting to be analyzed by conducting qualitative research method. Data were collected through indepth interview, documentation and observation. Data were analyzed by using data reduction, data display and data verification and supported by triangulation. The results are categorized into several themes. Based on these results as per ontological level and sociological level are useful for improving Public Private Partnership policy and practice and providing information to stakeholders related as inputs for making better regulation on Public Private Partnership as well as for public officials and practitioners.
A discrete two-level model for charge pricing of electric vehicles based on evolution game theory

According to the present vehicle-to-grid situation and existing pricing strategy in China, this paper proposed a two-level evolution game model for pricing analysis problem. The model contains two level game models: the up level game operated between monopoly electricity sale enterprise and small electricity sale enterprise; the second level is game between electricity sale enterprise and EV user; a pay-off matrix in ternary nonlinear equation form is built to descries the evolution process under different strategies. After the Linearization of nonlinear equations, the evolution stable strategy (ESS) is deduced for evolution game model. Analysis work is carried out depending on the ESS. Finally, the numerical results are given by computer simulation. Several suggestions of pricing strategies in China’s electricity market are obtained.
Impacts of the COVID-19 Pandemic on Student Performance and Perceptions of Learning

The COVID-19 pandemic has greatly affected higher education, but when learning shifted to an online environment it provided an opportunity to gain insight into how classroom format influences student performance and preferences. Thus, our objective was to assess student performance across classroom types, along with student perceptions of learning, to better understand the learning environment at the height of COVID-19. We evaluated student performance by final grade in undergraduate animal genetics (ANEQ 328) and large animal physiology (ANEQ 305) courses across four semesters, from before the pandemic through the return to in-person learning. In addition, students received surveys with questions regarding their learning experience. Survey analysis showed a consistent influence of COVID-19 within the classroom, mostly due to secondary effects. Student performance increased (P < 0.05) or was maintained in the pandemic centered semesters compared to pre-pandemic scores. When in-person learning resumed, scores in the ANEQ 305 course were worse (P < 0.05) than any previous semester but scores in the ANEQ 328 course returned to pre-pandemic levels. Overall, this study indicated that student performance was not negatively affected by the online transition during COVID-19 in these two courses, but it did decrease student satisfaction with their learning.
Schizophrenia MEG Network Analysis Based on Kernel Granger Causality

Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (p=0.001). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (p=0.0018). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (p=0.012); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.
An iterative method for 2D inverse scattering problems by alternating reconstruction of medium properties and wavefields: theory and application to the inversion of elastic waveforms

We study a reconstruction algorithm for the general inverse scattering problem based on the estimate of not only medium properties, as in more conventional approaches, but also wavefields propagating inside the computational domain. This extended set of unknowns is justified as a way to prevent local minimum stagnation, which is a common issue for standard methods. At each iteration of the algorithm, (i) the model parameters are obtained by solution of a convex problem, formulated from a special bilinear relationship of the data with respect to properties and wavefields (where the wavefield is kept fixed), and (ii) a better estimate of the wavefield is calculated, based on the previously reconstructed properties. The resulting scheme is computationally convenient since step (i) can greatly benefit from parallelization and the wavefield update (ii) requires modeling only in the known background model, which can be sped up considerably by factorization-based direct methods. The inversion method is successfully tested on synthetic elastic datasets.
The Applications of Artificial Intelligence in Managing Project Processes and Targets: A Systematic Analysis

Artificial intelligence (AI) has emerged as the defining technology of the 21 st century and has far-reaching impacts on project management (PM). This study assesses the applications of AI in managing project processes and targets through a systematic analysis of publications from 2017 to 2021. The analysis has revealed interesting insights, trends, gaps, and issues. This study informs the researchers and practitioners of the status of AI applications in the management of project processes and targets. It helps stimulate research efforts that can lead to more advances in applying AI to augment PM practices.
Simultaneously modelling clustered marginal counts and multinomial proportions with zero inflation with application to analysis of osteoporotic fractures data

Osteoporotic fractures are known to be highly recurring. We investigate bone‐dependent and bone‐independent risk factors of osteoporotic fracture frequency and relative proportions at various body locations by using the data from the osteoporotic fracture study that was conducted by the National Health and Nutrition Examination Survey, 2007–2008. We propose a new zero‐inflated baseline category multinomial mixed model to characterize the clustered count responses and multinomial proportions by subject simultaneously while taking account of zero inflation and randomness of cluster sizes. Our approach gives additional insights into the risk factors of osteoporotic fracture frequencies at various body locations. This joint modelling of fracture frequency also allows us to characterize relative proportion patterns at various body locations by subject between men and women across age. These findings have clear policy relevance to appropriate osteoporotic fracture prevention and resource allocation.
Cosmic acceleration with tachyon field

In this study, we examine two models of the scalar field, that is, a normal scalar field and a tachyon scalar field in [Formula: see text] gravity to describe cosmic acceleration of the universe, where [Formula: see text], [Formula: see text] and [Formula: see text] are Ricci curvature scalar, trace of energy–momentum tensor and kinetic energy of scalar field [Formula: see text], respectively. Using the minimal-coupling Lagrangian [Formula: see text], for both the scalar models we obtain a viable cosmological system, where [Formula: see text] and [Formula: see text] are real constants. While a normal scalar field gives a system describing expansion from the deceleration to the late-time acceleration, tachyon field together with [Formula: see text] in the system produces a quintessential expansion which is very close to de Sitter point, where we find a new condition [Formula: see text] for inflation.
Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction

Learning exercises that activate students’ additional cognitive understanding of course concepts facilitate contextualizing the content knowledge and developing higher-order thinking and problem-solving skills. Student-generated instructional materials such as course summaries and problem sets are amongst the instructional strategies that reflect active learning and constructivist philosophy. The contributions of this work are twofold: 1) We introduce a practical implementation of inside-outside learning strategy in an undergraduate deep learning course and will share our experiences in incorporating student-generated instructional materials learning strategy in course design, and 2) We develop a context-aware deep learning framework to draw insights from the student-generated materials for (i) Detecting anomalies in group activities and (ii) Predicting the median quiz performance of students in each group. This work opens up an avenue for effectively implementing a constructivism learning strategy in large-scale and online courses to build a sense of community between learners while providing an automated tool for instructors to identify at-risk groups.
Logarithmic Spiral Arm Pitch Angle of Spiral Galaxies: Measurement and Relationship to Galactic Structure and Nuclear Supermassive Black Hole Mass

In this dissertation, I explore the geometric structure of spiral galaxies and how the visible structure can provide information about the central mass of a galaxy, the density of its galactic disk, and the hidden mass of the supermassive black hole in its nucleus. In order to quantitatively measure the logarithmic spiral pitch angle (a measurement of tightness of the winding) of galactic spiral arms, I led an effort in our research group (the Arkansas Galaxy Evolution Survey) to modify existing two-dimensional fast Fourier transform software to increase its efficacy and accuracy. Using this software, I was able to lead an effort to calculate a black hole mass function (BHMF) for spiral galaxies in our local Universe. This work effectively provides us with a census of local black holes and establishes an endpoint on the evolutionary history of the BHMF for spiral galaxies. Furthermore, my work has indicated a novel fundamental relationship between the pitch angle of a galaxy’s spiral arms, the maximum density of neutral atomic hydrogen in its disk, and the stellar mass of its bulge. This result provides strong support for the density wave theory of spiral structure in disk galaxies and poses a critical question of the validity of rival theories for the genesis of spiral structure in disk galaxies. DEDICATION I dedicate this dissertation to my grandmother, Jacqueline Davis, and my aunt, Janet Green, who passed away during my time as a graduate student.
Characterization of Glutamate-Gated Chloride Channel in Tribolium castaneum

Simple Summary The glutamate-gated chloride channel (GluCl) is an important neurotransmitter receptor in the nervous system of invertebrates, and it is also a promising target for insecticide discovery. In the present study, three GluCl variants generated by alternative splicing of exon 3 were cloned from the red flour beetle Tribolium castaneum, and their sequence features, genomic structures, and expression profiles as well as the homology modeling of TcGluCl were studied. These results provide valuable information for studying the specific function of the insect GluCl variant. Abstract The glutamate-gated chloride channels (GluCls) play essential roles in signal transduction by regulating fast inhibitory synaptic transmission in the nervous system of invertebrates. While there is only one GluCl subunit in the insect, the diversity of insect GluCls is broadened by alternative splicing. In the present study, three TcGluCl variant genes were cloned from the red flour beetle Tribolium castaneum. Analysis of the characteristics of TcGluCls including sequence features, genomic structures, and alternative splicing revealed that TcGluCls had the typical structural features of GluCls and showed high homologies with the GluCls from other insect orders. The TcGluCl-encoding gene consists of nine exons and three variants (TcGluCl-3a, TcGluCl-3b, and TcGluCl-3c) were generated by the alternative splicing of exon 3, which was a highly conserved alternative splicing site in insect GluCls. Homology modeling of TcGluCl-3a showed that the exon 3 coding protein located at the N-terminal extracellular domain, and there were no steric clashes encountered between the exon 3 coding region and ivermectin/glutamate binding pocket, which indicated that the alternative splicing of exon 3 might have no impact on the binding of GluCls to glutamate and insecticide. In addition to the head tissue, TcGluCl-3a and TcGluCl-3c also had high expressions in the ovary and testis of T. castaneum, whereas TcGluCl-3b showed high expression in the midgut, suggesting the diverse physiological functions of TcGluCl variants in T. castaneum. The total TcGluCl and three variants showed the highest expression levels in the early stage larvae. The expressions of TcGluCl, TcGluCl-3b, and TcGluCl-3c were significantly increased from the late-stage larvae to the early stage pupae and indicated that the TcGluCl might be involved in the growth and development of T. castaneum. These results are helpful to further understand the molecular characteristics of insect GluCls and provide foundations for studying the specific function of the GluCl variant.
Students’ common difficulties and approaches while solving conceptual problems with non-identical light bulbs in series and parallel

We discuss a study of student reasoning difficulties and approaches while solving problems about the brightness of non-identical light bulbs connected in series and parallel. The questions about the light bulbs can be solved quantitatively even though they were posed as conceptual problems. We compare the performance of introductory physics students with that of a set of physics PhD students and find that these problems related to non-identical light bulbs are difficult even for PhD students. We also conducted individual interviews with six introductory students to obtain an in-depth understanding of their approaches and rationale for solving the problems in a particular way. We discuss the conceptual difficulties displayed in the interviews and in the written responses in which introductory physics students were asked to explain their reasoning. In addition to confirming some misconceptions which have previously been observed in the context of equal wattage light bulbs, use of unequal wattage light bulbs in this research reveals misconceptions not documented previously.
Cotangent bundles over all the Hermitian symmetric spaces

We construct the N = 2 supersymmetric nonlinear sigma models on the cotangent bundles over all the compact and non-compact Hermitian symmetric spaces. In order to construct them we use the projective superspace formalism which is an N = 2 off-shell superfield formulation in four-dimensional space-time. This formalism allows us to obtain the explicit expression of N = 2 supersymmetric nonlinear sigma models on the cotangent bundles over any Hermitian symmetric spaces in terms of the N =1 superfields, once the Kähler potentials of the base manifolds are obtained. Starting with N = 1 supersymmetric Kähler nonlinear sigma models on the Hermitian symmetric spaces, we extend them into the N = 2 supersymmetric models by using the projective superspace formalism and derive the general formula for the cotangent bundles over all the compact and non-compact Hermitian symmetric spaces. We apply to the formula for the non-compact Hermitian symmetric space E7/E6 × U(1)1.
Minimum Bias Measurements at the LHC

Inclusive charged particle measurements at hadron colliders probe the low-energy nonperturbative region of QCD. Pseudorapidity distributions of charged-particles produced in pp collisions at 13 TeV have been measured by the CMS experiment. The ATLAS collaboration has measured the inclusive charged particle multiplicity and its dependence on transverse momentum and pseudorapidity in special data sets with low LHC beam current, recorded at a center-of-mass energy of 13 TeV. The measurements present the first detailed studies in inclusive phase spaces with a minimum transverse momentum of 100 MeV and 500 MeV. The distribution of electromagnetic and hadronic energy in the very forward phase-space has been measured with the CASTOR calorimeters located at a pseudorapidity of -5.2 to -6.6 in the very forward region of CMS. The energy distributions are very powerful benchmarks to study the performance of MPI in hadronic interactions models at 13 TeV collision energy. All measurements are compared with predictions of various Monte Carlo generator predictions and are found to provide strong constraints on these.
Chaotic Iterations for SN Transport

Abstract An iterative solution method is introduced for SN transport calculations called “chaotic” iterations. For SN sweeps on parallel-decomposed meshes, a full-parallel sweep can be employed, in which processors must wait to start a sweep until incoming boundary data are received from one or more neighboring processes. This causes delays in the computation that affects efficiency. The parallel block Jacobi (PBJ) method, by contrast, is a splitting method in which all processor-local sweeps are computed using incoming data from the previous iteration with no waiting. This eliminates the delay associated with full-parallel sweeps but adversely impacts the iterative convergence rate. The chaotic iteration is a hybrid of the two possibilities, using current incoming data from neighboring processors when available and previous iteration data otherwise. Whether the boundary data are available or not depends on the communication between processes. It can be viewed as a splitting that changes from one iteration to the next, making the iteration chaotic. In this article, we prove that several iteration schemes associated with the chaotic splitting converge. The analysis presumes some splitting has been imposed at any given iteration, and so the results also apply to fixed, as well as chaotic, splittings. We present numerical results showing the convergence rate of the chaotic iterations method is between the full sweep method and the PBJ method. The numerical results also compare timings between the methods. Notably, for most of the test problems in this article, the chaotic iterations method is at least as fast as the PBJ method.
Regularization Based Anderson Rubin Tests for Many Instruments

This paper studies the asymptotic validity of the regularized Anderson Rubin (AR) tests in linear models with large number of instruments. The regularized AR tests use informationreduction methods to provide robust inference in instrumental variable (IV) estimation for data rich environments. We derive the asymptotic properties of the tests. Their asymptotic distribution depend on unknown nuisance parameters. A bootstrap method is used to obtain more reliable inference. The regularized tests are robust to many moment conditions in the sense that they are valid for both few and many instruments, and even for more instruments than the sample size. Our simulations show that the proposed AR tests work well and have better performance than competing AR tests when the number of instruments is very large. The usefulness of the regularized tests is shown by proposing confidence intervals for the Elasticity of Intertemporal Substitution (EIS).