Miracle-dz/UniFAR-FTUs huggingface dataset:
Grain boundary in phosphorene and its unique roles on C and O doping

First-principles calculations are performed to determine the structures of grain boundary (GB) in 2D phosphorene and two typical GBs have been predicted: A-GB and Z-GB defects. The effects of a single substitutional C (O) dopant atom on the energetics and electronic properties were further investigated. Our results indicate that the grain boundary region is reactive and C or O impurity atoms prefer to be incorporated into the GB region atoms instead of the phosphorene bulk region. Particularly, it was found that the formation of along the GBs is an exothermic process. Furthermore, both C and O doping inside the grain boundary defects give rise to magnetism in phosphorene. The band structures are also dramatically tuned by the C (O) doping. The study suggests that GBs in 2D phosphorene provide an accessible structure such that the electronic and magnetic properties can be effectively tailored by C or O doping.
Dienogest May Reduce Estradiol- and Inflammatory Cytokine-Induced Cell Viability and Proliferation and Inhibit the Pathogenesis of Endometriosis: A Cell Culture- and Mouse Model-Based Study

Dienogest (DNG) is a therapeutic medication used in endometriosis treatment. Limited data are available regarding its mechanism of action on endometrial cells. Using in vivo and in vitro models, we investigated whether DNG treatment causes significant biological changes in human endometrial stromal cells (ESCs). The markers related to the pathogenesis of endometriosis in ESCs were evaluated using estradiol, tumor necrosis factor alpha (TNF-α), interleukin 1β (IL-1β), and IL-32, administered alone or in combination with DNG. Implanted endometrial tissues were compared between C57BL/6 mice that did or did not receive DNG treatment by using size measurements and immunohistochemistry. A significant decrease in cell viability, protein kinase B (AKT) phosphorylation, and the expression of p21-activated kinase 4 and vascular endothelial growth factor were observed in ESCs treated with estradiol plus DNG. Cell viability, AKT phosphorylation, and proliferating cell nuclear antigen (PCNA) expression also decreased significantly after TNF-α plus DNG treatment. Treatment with IL-1β or IL-32 plus DNG significantly decreased cell viability or PCNA expression, respectively. The size of the implanted endometrial tissue significantly decreased in mice treated with DNG, accompanied by decreased PCNA expression. Thus, DNG may reduce cell viability and proliferation induced by estradiol, TNF-α, IL-1β, and IL-32, and inhibit the endometriosis pathogenesis by decreasing PCNA expression.
Existence of Static Dyonic Black Holes in $4d$ $N = 1$ Supergravity With Finite Energy

We prove the existence and the uniqueness of the static dyonic black holes in four dimensional $N=1$ supergravity theory coupled vector and scalar multiplets. We set the near-horizon geometry to be a product of two Einstein surfaces, whereas the asymptotic geometry has to be a space of constant scalar curvature. Using these data, we show that there exist a unique solution for scalar fields which interpolates these regions.
A review on large k minimal spectral k-partitions and Pleijel's Theorem

In this survey, we review the properties of minimal spectral $k$-partitions in the two-dimensional case and revisit their connections with Pleijel's Theorem. We focus on the large $k$ problem (and the hexagonal conjecture) in connection with two recent preprints by J. Bourgain and S. Steinerberger on the Pleijel Theorem. This leads us also to discuss some conjecture by I. Polterovich, in relation with square tilings. We also establish a Pleijel Theorem for Aharonov-Bohm Hamiltonians and deduce from it, via the magnetic characterization of the minimal partitions, some lower bound for the number of critical points of a minimal partition.
Arthur packets for 𝑝-adic groups by way of microlocal vanishing cycles of perverse sheaves, with examples

In this article we propose a geometric description of Arthur packets for p p -adic groups using vanishing cycles of perverse sheaves. Our approach is inspired by the 1992 book by Adams, Barbasch and Vogan on the Langlands classification of admissible representations of real groups and follows the direction indicated by Vogan in his 1993 paper on the Langlands correspondence. Using vanishing cycles, we introduce and study a functor from the category of equivariant perverse sheaves on the moduli space of certain Langlands parameters to local systems on the regular part of the conormal bundle for this variety. In this article we establish the main properties of this functor and show that it plays the role of microlocalization in the work of Adams, Barbasch and Vogan. We use this to define ABV-packets for pure rational forms of p p -adic groups and propose a geometric description of the transfer coefficients that appear in Arthur’s main local result in the endoscopic classification of representations. This article includes conjectures modelled on Vogan’s work, including the prediction that Arthur packets are ABV-packets for p p -adic groups. We gather evidence for these conjectures by verifying them in numerous examples.
BOUNDED REDUCTIVE SUBALGEBRAS OF sln

Let g be a reductive Lie algebra and k g be a reductive in g subalge- bra. A (g;k)-module M is a g-module for which any element m 2 M is contained in a nite-dimensional k-submodule of M. We say that a (g;k)-module M is bounded if there exists a constant CM such that the Jordan{Holder multiplicities of any simple nite- dimensional k-module in every nite-dimensional k-submodule of M are bounded by CM. In the present paper we describe explicitly all reductive in sln subalgebras k which admit a bounded simple innite-dimension al (sln;k)-module. Our technique is based on sym- plectic geometry and the notion of spherical variety. We also characterize the irreducible components of the associated varieties of simple bounded (g;k)-modules.
Calmodulin Binding Domains in Critical Risk Proteins Involved in Neurodegeneration

Neurodegeneration leads to multiple early changes in cognitive, emotional, and social behaviours and ultimately progresses to dementia. The dysregulation of calcium is one of the earliest potentially initiating events in the development of neurodegenerative diseases. A primary neuronal target of calcium is the small sensor and effector protein calmodulin that, in response to calcium levels, binds to and regulates hundreds of calmodulin binding proteins. The intimate and entangled relationship between calmodulin binding proteins and all phases of Alzheimer’s disease has been established, but the relationship to other neurodegenerative diseases is just beginning to be evaluated. Risk factors and hallmark proteins from Parkinson’s disease (PD; SNCA, Parkin, PINK1, LRRK2, PARK7), Huntington’s disease (HD; Htt, TGM1, TGM2), Lewy Body disease (LBD; TMEM175, GBA), and amyotrophic lateral sclerosis/frontotemporal disease (ALS/FTD; VCP, FUS, TDP-43, TBK1, C90rf72, SQSTM1, CHCHD10, SOD1) were scanned for the presence of calmodulin binding domains and, within them, appropriate binding motifs. Binding domains and motifs were identified in multiple risk proteins, some of which are involved in multiple neurodegenerative diseases. The potential calmodulin binding profiles for risk proteins involved in HD, PD, LBD, and ALS/FTD coupled with other studies on proven binding proteins supports the central and potentially critical role for calmodulin in neurodegenerative events.
Bayesian Gaussian processes for sequential prediction, optimisation and quadrature

We develop a family of Bayesian algorithms built around Gaussian processes for various problems posed by sensor networks. We firstly introduce an iterative Gaussian process for multi-sensor inference problems, and show how our algorithm is able to cope with data that may be noisy, missing, delayed and/or correlated. Our algorithm can also effectively manage data that features changepoints, such as sensor faults. Extensions to our algorithm allow us to tackle some of the decision problems faced in sensor networks, including observation scheduling. Along these lines, we also propose a general method of global optimisation, Gaussian process global optimisation (GPGO), and demonstrate how it may be used for sensor placement. Our algorithms operate within a complete Bayesian probabilistic framework. As such, we show how the hyperparameters of our system can be marginalised by use of Bayesian quadrature, a principled method of approximate integration. Similar techniques also allow us to produce full posterior distributions for any hyperparameters of interest, such as the location of changepoints. We frame the selection of the positions of the hyperparameter samples required by Bayesian quadrature as a decision problem, with the aim of minimising the uncertainty we possess about the values of the integrals we are approximating. Taking this approach, we have developed sampling for Bayesian quadrature (SBQ), a principled competitor to Monte Carlo methods. We conclude by testing our proposals on real weather sensor networks. We further benchmark GPGO on a wide range of canonical test problems, over which it achieves a significant improvement on its competitors. Finally, the efficacy of SBQ is demonstrated in the context of both prediction and optimisation.
Engineering nonreciprocal wave dispersion in a nonlocal micropolar metabeam

Active metamaterials with electronic control schemes can exhibit nonreciprocal and/or complex elastic coefficients that result in non-Hermitian wave phenomena. Here, we investigate theoretically and experimentally a non-Hermitian micropolar metabeam with piezoelectric elements and electronic nonlocal feed-forward control. Since the nonlocal feed-forward control breaks spatial reciprocity, the proposed metabeam supports nonreciprocal flexural wave propagation, featuring unidirectional amplification/attenuation and non-Hermitian skin effect. Theoretical homogenization modeling is developed to consider the nonlocal effect into an effective complex bending stiffness. The unidirectional wave amplification/attenuation is attributed to the energy conversion between electrical power and mechanical work. The non-Hermitian skin effect, characterized by a winding number, is the manifestation of the flexural nonreciprocity and admits an extensive number of localized bulk eigenmodes on open boundaries. The nonlocal metabeam is also employed to engineer the anomalous wave dispersion such as tunable roton-like dispersion and band tilting. The nonlocal micropolar metabeam could pave the ways for designing non-Hermitian topological mechanical metamaterials featuring programmable nonreciprocal wave transmission and engineering roton-like wave dispersion relations under ambient environments.
On a problem by Hans Feichtinger

In this paper, we solve a spectral problem about positive semi-definite trace-class pseudodifferential operators on modulation spaces which was posed by H. Feichtinger. Later, C. Heil and D. Larson rephrased the problem in the broader setting of positive semi-definite trace-class operators on a separable Hilbert space. Our solution consists in constructing a counterexample that solves Hans Feichtinger's problem by first solving this second problem.
Automated Impact Damage Detection Technique for Composites Based on Thermographic Image Processing and Machine Learning Classification

Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.
Optimal control with reset-renewable resources

We consider both discrete and continuous control problems constrained by a fixed budget of some resource, which may be renewed upon entering a preferred subset of the state space. In the discrete case, we consider both deterministic and stochastic shortest path problems with full budget resets in all preferred nodes. In the continuous case, we derive augmented PDEs of optimal control, which are then solved numerically on the extended state space with a full/instantaneous budget reset on the preferred subset. We introduce an iterative algorithm for solving these problems efficiently. The method's performance is demonstrated on a range of computational examples, including the optimal path planning with constraints on prolonged visibility by a static enemy observer. In addition, we also develop an algorithm that works on the original state space to solve a related but simpler problem: finding the subsets of the domain "reachable-within-the-budget". This manuscript is an extended version of the paper accepted for publication by SIAM J. on Control and Optimization. In the journal version, Section 3 and the Appendix were omitted due to space limitations.
Parameter Selection and Performance Study in Particle Swarm Optimization

The present paper describes the Particle Swarm Optimization (PSO) technique and how different parameters in the algorithm may be selected in order to achieve faster convergence to the solution for a given optimization problem. PSO has become a common heuristic technique in the optimization community with many researchers exploring the concepts, issues and applications of the algorithm. PSO has undergone many changes since its introduction in 1995. As researchers have learnt about the technique, they have derived new versions, new applications and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of the particle swarming, including variations in the algorithm, current and ongoing research, and applications. In this paper we first analyze the impact that the inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provide guidelines for selecting these two parameters.
Using Catalan words and a $q$-shuffle algebra to describe a PBW basis for the positive part of $U_q({\widehat {\mathfrak{sl}}}_2)$

The positive part $U^+_q$ of $U_q({\widehat {\mathfrak{sl}}}_2)$ has a presentation with two generators $A,B$ that satisfy the cubic $q$-Serre relations. In 1993 I. Damiani obtained a PBW basis for $U^+_q$, consisting of some elements $\lbrace E_{n\delta+\alpha_0}\rbrace_{n=0}^\infty$, $\lbrace E_{n\delta+\alpha_1}\rbrace_{n=0}^\infty$, $\lbrace E_{n\delta}\rbrace_{n=1}^\infty$ that are defined recursively. Our goal is to describe these elements in closed form. We achieve this goal using Catalan words and a $q$-shuffle algebra.
Negative curves on algebraic surfaces

We study curves of negative self-intersection on algebraic surfaces. Our main result shows there exist smooth complex projective surfaces X, related to Hilbert modular surfaces, such that X contains reduced, irreducible curves C of arbitrarily negative self-intersection C 2 . Previously the only known examples of surfaces for which C 2 was not bounded below were in positive characteristic, and the general expectation was that no examples could arise over the complex numbers. Indeed, we show that the idea underlying the examples in positive characteristic cannot produce examples over the complex number field, and thus our complex examples require a different approach.
On positive scalar curvature cobordisms and the conformal Laplacian on end-periodic manifolds

We show that the periodic $\eta$-invariants introduced by Mrowka--Ruberman--Saveliev~\cite{MRS3} provide obstructions to the existence of cobordisms with positive scalar curvature metrics between manifolds of dimensions $4$ and $6$. The proof combines a relative version of the Schoen--Yau minimal surface technique with an end-periodic index theorem for the Dirac operator. As a result, we show that the bordism groups $\Omega^{\spin,+}_{n+1}(S^1 \times BG)$ are infinite for any non-trivial group $G$ which is the fundamental group of a spin spherical space form of dimension $n=3$ or $5$.
Systemic inflammation and risk of multiple sclerosis – A presymptomatic case-control study

Background C-reactive protein (CRP) is a marker of systemic inflammation. Increased levels of CRP in young persons have been suggested to decrease the risk of multiple sclerosis (MS). Objectives To assess CRP as a risk factor for MS. Methods Levels of CRP were measured with a high-sensitive immunoassay in biobank samples from 837 individuals who later developed MS and 984 matched controls. The risk of developing MS was analysed by conditional logistic regression on z-scored CRP values. Results Levels of CRP were not associated with MS risk. Conclusions We found no association between CRP levels and risk of MS development.
Association Between ABO Blood Group and Gestational Diabetes Mellitus in Pregnant Women at King Abdulaziz University Hospital: A Retrospective Study

Objective Gestational diabetes mellitus (GDM) can occur during pregnancy. One of the leading causes of it is a hormone produced by the placenta that interferes with glucose absorption and causes glucose buildup in the bloodstream. Genetic variations between ethnicities are believed to be associated with GDM, and there has been some research on the association of ABO blood group with GDM in different populations. However, the results so far are inconsistent, and there is no conclusive evidence on how ABO blood group affects the occurrence of GDM. This study aims to examine the link between ABO blood group and GDM in pregnant women at King Abdulaziz University Hospital. Methodology A retrospective cohort study was conducted on a group of GDM patients between 2019 and 2022 using data collected from the patients’ medical records at King Abdulaziz University Hospital. Results The overall prevalence of GDM was high at 74.7%, and the percentage of patients with A, O, B, and AB blood group who had GDM was 42.9%, 41.41%, 12.1%, and 3.59%, respectively. However, there was no significant difference in Rh status or any other clinical characteristic between the participants who had GDM and those who did not have GDM. Conclusion The present findings indicate that blood group is not associated with the development of GDM in this cohort from Saudi Arabia. However, more studies are required in the future to corroborate these findings.
Pilot Forum: Ferramenta de Fórum Multidisciplinar com Recursos de Gamificação para o Ambiente Acadêmico

A baixa adesão ao uso da monitoria presencial e a falta de um ambiente virtual adequado para solução de dúvidas comuns dos estudantes ao longo do curso de graduação motivaram o presente trabalho, que busca compreender a aplicabilidade de um fórum de discussão perene e multidisciplinar no ambiente acadêmico. O fórum foi desenvolvido usando a ferramenta Discourse e aplicada para 44 estudantes de Engenharia de Software da Universidade do Estado de Santa Catarina (UDESC). Para avaliar a percepção e validar a usabilidade e intenção de uso da ferramenta, foram pesquisas quantitativas, seguindo a técnica GQM (Goal Question Metric). Foi possível alcançar um resultado positivo quanto à utilização da ferramenta, sendo possível concluir que os estudantes universitários têm interesse em participar de fóruns colaborativos, onde seja possível consultar dúvidas, fazer perguntas e responder colegas, mesmo de fases distintas.
Tensor Robust Principal Component Analysis: Better recovery with atomic norm regularization

This paper studies tensor-based Robust Principal Component Analysis (RPCA) using atomic-norm regularization. Given the superposition of a sparse and a low-rank tensor, we present conditions under which it is possible to exactly recover the sparse and low-rank components. Our results improve on existing performance guarantees for tensor-RPCA, including those for matrix RPCA. Our guarantees also show that atomic-norm regularization provides better recovery for tensor-structured data sets than other approaches based on matricization. In addition to these performance guarantees, we study a nonconvex formulation of the tensor atomic-norm and identify a class of local minima of this nonconvex program that are globally optimal. We demonstrate the strong performance of our approach in numerical experiments, where we show that our nonconvex model reliably recovers tensors with ranks larger than all of their side lengths, significantly outperforming other algorithms that require matricization.
Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images

Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image. A trained depth belief network (DBN) processes and divides fusion images, while a Softmax classifier determines the land type. As tested, the proposed approach can categorise all types of land. Traditional methods of detecting distant sensing photographs have limitations that can be overcome by using convolutional neural networks (CNN). Traditional techniques are incapable of combining deep learning elements while doing badly in classification. After PCA decreases data dimensionality, deep learning is applied to generate effective features that employ deep learning after PCA has reduced the dimensionality of the data. Principal component analysis is commonly used because of its effectiveness in attaining linear dimension reduction. It may be used on its own or as a starting point for further study into various different dimensionality reduction approaches. Data can be altered by remapping onto a new set of orthogonal axes using a process known as projection-based principal component analysis. Following remote sensing of land resources, the pictures were classified using a support vector machine. Euroset satellite images are used to assess the suggested approach. Accuracy and kappa have both increased. It was accurate and within 95.83 % of the planned figures. The classification findings' kappa value and reasoning time were 95.87 % and 128 milliseconds, respectively. Both the model's performance and the classification effect are excellent.