Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Computation and Isomorphism Testing

In this thesis, we study quantum computation and algorithms for isomorphism problems. Some of the problems that we cover are fundamentally quantum and therefore require quantum techniques. For other problems, classical approaches are more appropriate; however, we often give quantum variants of our classical algorithms as well. The field of quantum computation aims to accelerate algorithms by exploiting the laws of quantum mechanics. Several quantum algorithms are known that are exponentially faster than the best classical algorithms known for the same problems, including several which are relevant to cryptography. Quantum computing is therefore of great importance. On the theory side, it has already transformed our notions of which problems are tractable and which are not. On the practical side, the construction of a quantum computer would render many popular techniques for encryption obsolete. We derive several results in quantum computation. Quantum algorithms are normally formulated in an abstract way that ignores practical details such as where different parts of the computation are located physically. Using naive methods for translating these algorithms into practical quantum computing technologies increases the time complexity by a linear factor, while previous work reduced this factor to a square root. We further reduce this factor to a constant at the cost of requiring additional space. This retroactively justifies an important assumption made in many quantum algorithms. Another interesting problem is to determine the query complexity of extracting different types of information from physical processes. In the case of deterministic processes, it is well known that quantum algorithms cannot outperform deterministic algorithms by more than an exponential factor. We show – somewhat surprisingly – that when the process is allowed to be randomized, there are problems that have a constant quantum query complexity but cannot be solved classically no matter how many queries are made. In fact, we show how to construct such an infinity-vs-one separation from any weaker separation between the classical and quantum query complexities. In an isomorphism problem, we seek to determine if two algebraic or combinatorial objects have the same structure. One of the most well-known of these is the graph isomorphism problem, which is interesting since it is suspected to be of complexity intermediate between P and the NP-complete problems. We transition from quantum computation to isomorphism testing by considering the tree isomorphism problem. While linear-time classical algorithms are known for this problem, we show that a promising framework for developing efficient quantum algorithms for graph isomorphism, known as the state preparation approach, can also solve tree isomorphism. While this result might seem modest, it is important to know that the state preparation approach works for trees, since otherwise there would be little hope of using it to obtain efficient algorithms for more interesting classes of…
Síndrome de Miller-Fisher en edad pediátrica: un debut inusual

Miller-Fisher syndrome is a peripheral neuropathy characterized by ophthalmoplegia, areflexia and ataxia. The incidence of this syndrome is estimated at 1 in a million, and just 14% of these occur on a pediatric age, making it an infrequent diagnosis in pediatrics. In this article we report a case of Miller-Fisher syndrome with an unusual onset on a pediatric patient along with infrequent radiological findings.
Single-cell transcriptomic landscape of immunometabolism reveals intervention candidates of ascorbate and aldarate metabolism, fatty-acid degradation and PUFA metabolism of T-cell subsets in healthy controls, psoriasis and psoriatic arthritis

Introduction The modulation of immunometabolic pathways is emerging as a promising therapeutic target for immune-mediated diseases. However, the immunometabolic features of psoriatic disease and the potential targets for immunometabolic intervention in the different T-cell subsets involved in its pathogenesis remain unclear. Methods In this study, we analyzed circulating blood single-cell data from healthy controls (HC), psoriasis (PSO), and psoriatic arthritis (PSA) patients, and revealed their metabolic features of T-cell subsets: CD4+ central memory T cells (TCMs), CD8+ effective memory T cells (TEMs), regulatory T cells (Tregs), mucosal-associated invariant T cells (MAITs ), and γδ T cells. Pearson test was performed to determine the linkages between differential metabolic and inflammatory pathways. Based on these results, we also analyzed the potential impacts of biological antibodies on differential metabolic pathways by comparing the immunometabolism differences between PSA patients without and with biological treatment. Results Our results suggest that upregulation of ascorbate and aldarate metabolism, as well as fatty acid degradation, may enhance the immune suppression of Tregs. Enhanced metabolism of alpha-linolenic acid, linoleic acid, and arachidonic acid may inhibit the pro-inflammatory functions of CD4+ TCMs and CD8+ TEMs in PSO and PSA, and protect the immune suppression of Tregs in PSA. We propose that supporting ascorbic acid and fatty acid metabolic pathways may be an adjunctive reprogramming strategy with adalimumab and etanercept therapy. Discussion These findings not only provide insights into immunometabolism characteristics of psoriatic disease, but also offer preliminary options for the auxiliary treatment of psoriasis.
MODELAGEM DA CURVA DE RETENÇÃO DE SOLOS ARENO ARGILOSOS UTILIZANDO DADOS SECUNDÁRIOS DA LITERATURA

A curva de retenção do solo é uma função característica necessária para a resolução de problemas geotécnicos relacionados com fluxo em solos não saturados. Estudo de processos no solo como: infiltração, drenagem, movimento de soluto e disponibilidade de água para as plantas são exemplos de estudo que requisitam o conhecimento da curva de retenção por se tratar de uma ferramenta conceitual que permite interpretar o comportamento de solos não saturados a partir de mudanças na sucção.
An operational matrix method for solving linear Fredholm‒Volterra integro-differential equations

The aim of this paper is to propose an efficient method to compute approximate solutions of linear Fredholm‒Volterra integro-differential equations (FVIDEs) using Taylor polynomials. More precisely, we present a method based on operational matrices of Taylor polynomials in order to solve linear FVIDEs. By using the operational matrices of integration and product for the Taylor polynomials, the problem for linear FVIDEs is transformed into a system of linear algebraic equations. The solution of the problem is obtained from this linear system after the incorporation of initial conditions. Numerical examples are presented to show the applicability and the efficiency of the method. Wherever possible, the results of our method are compared with those yielded by some other methods.
Pain Assessment and Need for Analgesics after Scaling and Root Planing in Patients with Stage II and Stage III Periodontitis

Background and Objectives: The most common treatment procedure for periodontitis and gingivitis is scaling and root planing, which is perceived as a painful dental treatment. The current study aimed to assess pain perception and analgesics consumption after scaling and root planing (SRP) in patients with stage II and stage III periodontitis. Materials and Methods: Before starting the SRP, all the periodontal parameters, such as probing depth (PD), bleeding on probing (BOP), and clinical attachment level (CAL), were measured. The anxiety level of the patients was also assessed using the modified dental anxiety scale (MDAS) questionnaire. Pain perception of the patients was recorded with the visual analog scale (VAS) after performing the SRP. Patients were asked to mark their pain level on the VAS sheet after two hours, four hours, eight hours, 24 h, and 48 h after the periodontal treatment. The following cut-off points were used for the pain intensity in the VAS: 0 = no pain, 1–4 = mild pain, 4–6 = moderate pain, and 7–10 = severe pain. Patients were advised to take analgesics if the pain was intolerable. Multivariate logistic regression was performed to conduct the association of all dependent variables and the pain perception of patients. A nonparametric Friedman test was conducted to assess pain perception at different times. Results: A total of 52 patients including 32 males and 20 females participated in the current study, with a mean age of 43.10 ± 12.33 years. Multivariate analyses showed that MDAS and analgesic consumption is significantly associated with pain perception. Other clinical variables are not associated with pain perception. The Friedman test exhibited that pain perception is significantly associated (p < 0.05) with time. Conclusions: Analgesic consumption and anxiety level are significantly associated with pain perception after SRP treatment.
Spatial-spectral locality preserving projection for hyperspectral image classification with limited training samples

ABSTRACT Locality preserving projection (LPP) is a popular unsupervised feature extraction (FE) method. In this paper, the spatial-spectral LPP (SSLPP) method is proposed, which uses both the spectral and spatial information of hyperspectral image (HSI) for FE. The proposed method consists of two parts. In the first part, unlabelled samples are selected in a spatially homogeneous neighbourhood from filtered HSI. In the second part, the transformation matrix is calculated by an LPP-based method and by using the spectral and spatial information of the selected unlabelled samples. Experimental results on Indian Pines (IP), Kennedy Space Center (KSC), and Pavia University (PU) datasets show that the performance of SSLPP is superior to spectral unsupervised, supervised, and semi-supervised FE methods in small and large sample size situations. Moreover, the proposed method outperforms other spatial-spectral semi-supervised FE methods for PU dataset, which has high spatial resolution. For IP and KSC datasets, spectral regularized local discriminant embedding (SSRLDE) has the best performance by using spectral and spatial information of labelled and unlabelled samples, and SSLPP is ranked just behind it. Experiments show that SSLPP is an efficient unsupervised FE method, which does not use training samples as preparation of them is so difficult, costly, and sometimes impractical. SSLPP results are much better than LPP. Also, it decreases the storage and calculation costs using less number of unlabelled samples.
Computational Investigations of Boundary Condition Effects on Simulations of Thermoacoustic Instabilities

This dissertation presents a formulation of the Continuous Sensitivity Equation Method (CSEM) applied to the Computational Fluid Dynamics (CFD) simulation of thermoacoustic instability problems. The proposed sensitivity analysis approach only requires a single run of the CFD simulation. Moreover, the sensitivities of field variables, pressure, velocity and temperature to boundary-condition parameters are directly obtained from the solution to sensitivity equations. Thermoacoustic instability is predicted by the Rayleigh criterion. The sensitivity of the Rayleigh index is computed utilizing the sensitivities of field variables. The application of the CSEM to thermoacoustic instability problems is demonstrated by two classic examples. The first example explores the effects of the heated wall temperature on the one-dimensional thermoacoustic convection. The sensitivity of the Rayleigh index, which is the indicator of thermoacoustic instabilities, is computed by the sensitivity of field variables. As the heat wall temperature increases, the sensitivity of the Rayleigh index decreases. The evolution from positive to negative sensitivity values suggests the transition from a destabilizing trend to stabilizing trend of the thermoacoustic system. Thermoacoustic instabilities in a self-excited Rijke tube are investigated following the relatively simple thermoacoustic convection problem. The complexity of simulating the Rijke tube increases in both dimensions and mechanisms which incorporate the species transport process and chemical reactions. As a representative model of the large lean premixed combustor, Rijke tube has been extensively studied. Quantitative sensitivity analysis sets the present work apart from previous research on the prediction and control of thermoacoustic instabilities. The effects of two boundary-condition parameters, i.e. the inlet mass flow rate and the equivalence
Oracle Properties, Bias Correction, and Bootstrap Inference for Adaptive Lasso for Time Series M‐Estimators

We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for possibly nonlinear time series models. In particular, we investigate the question of how to conduct inference on the parameters given an adaptive lasso model. Central to this study is the test of the hypothesis that a given adaptive lasso parameter equals zero, which therefore tests for a false positive. To this end, we introduce a recentered bootstrap procedure and show, theoretically and empirically through extensive Monte Carlo simulations, that the adaptive lasso can combine efficient parameter estimation, variable selection, and inference in one step. Moreover, we analytically derive a bias correction factor that is able to significantly improve the empirical coverage of the test on the active variables. Finally, we apply the adaptive lasso and the recentered bootstrap procedure to investigate the relation between the short rate dynamics and the economy, thereby providing a statistical foundation (from a model choice perspective) for the classic Taylor rule monetary policy model.
Globally asymptotic stability analysis for memristor‐based competitive systems of reaction–diffusion delayed neural networks

The main aim of this investigation is to present a mathematical criterion that guarantees the global asymptotic stability of special class of nonlinear neural systems. Indeed, a memristor‐based competitive nonlinear reaction–diffusion Cohen–Grossberg neural network system is chosen to be investigated. Chasing the accuracy and comprehensiveness, our model has to be improved with the following elements: neutrality of the nonlinear part and being effected under time delays in the both of straight and neutral parts of the nonlinearity. Since the proposed neural network is novel, so we need to make use of a novel Lyapunov functional correspondingly to globally asymptotic stabilization of the neural network under investigation. Prior to this process, it is necessary to present a uniqueness criterion for solutions of the proposed neural network. To this aim, the well‐known M‐matrix technique of the linear algebra is applied that guarantees existence of a unique equilibrium point of the considered Cohen–Grossberg neural network. Numerical perspective of this investigation leads us to some numerical prototypes that justify our stability criterion is applicable. This investigation will be finalized with interesting discussion on the nature of time delays that are characterized as fundamental elements in modeling of the neural networks.
A novel stochastically stable variational Bayesian Kalman filter for spacecraft attitude estimation

In the context of nonlinear systems, variational Bayesian filters play an important and alternative role to sampling‐based Monte Carlo methods. Although they have been used in conjunction with derivatives of Kalman filters, their behavior, both in terms of convergence, stability, and a solution without a stepwise approximation procedure, has not been well explored. In this paper, we propose a novel, stochastically stable, stepwise approximation‐free variational Bayesian Kalman filter (hence, termed as VBKF) for nonlinear systems. The proposed filter, unlike other posterior distribution estimation methods, is very flexible with the number of parameters that should be known a priori, offers an excellent convergence rate, stochastic stability, and more specifically in its ability to handle non‐Gaussian, unknown, inaccurate and drifting noise models. Furthermore, one of the key advantages of the proposed solution is that it is stepwise approximation‐free, and as such, less computationally demanding. We also prove that the proposed filter is stochastically stable using the Posterior Cramér–Rao Lower Bound. In addition to the stability, by using Lyapunov function, we show that the estimation errors are exponentially bounded by the mean square error under certain conditions. In addition to this, we also demonstrate the practical applicability of the proposed approach by developing a novel filter for estimating the attitude of spacecrafts using star cameras and gyro‐sensors. We then evaluate the efficacy of the novel filter for attitude estimation, and thus that of the VBKF, using a set of detailed simulations mimicking different initialization errors that are common in attitude estimation. Our results show that the proposed approach offers an excellent convergence rate within conventional 3σ$$ 3\sigma $$ bounds, better root mean square error when compared against several of the common, and state‐of‐the‐art methods.
Nano-g Optical Accelerometer With a Large Dynamic Range and Low Noise Floor

Optical microelectromechanical system (MEMS) devices are often used to measure vibrations with exceedingly small amplitudes, which are extensively used in acceleration and seismic sensors. However, due to the limitations of the sensing principle and the variation in ambient temperature, the measurement accuracy and dynamic range of these optical devices are relatively general. As a solution, this article presents the design of an electromagnetic feedback optical MEMS accelerometer with temperature control. Two single accelerometer chips form the acceleration sensing structure, assembled via manual microassembly. The electromagnetic feedback control system enables the sensor to operate in the linear range, and the temperature control system maintains a stable operating temperature for the equipment. The novel design of the sensing structure with two sets of control systems enables the accelerometer to have an extremely low noise floor, along with a wide dynamic range. Experimental results confirm that the device can achieve a voltage sensitivity of 2643 V/g and dynamic range values up to 3.9 mg. The self-noise of the sensor is less than 3 ng/ $\surd $ Hz in the frequency range from 0.8 to 7 Hz. The bias stability is 11.9 ng at ${t}$ = 1.8 s. Considering the excellent performance, the proposed device has significant application potential in seismic and gravity measurements.
On the regional boundary observability of semilinear time-fractional systems with Caputo derivative

This paper considers the regional boundary observability problem for semilinear time-fractional systems. The main objective is to reconstruct the initial state on a subregion of the boundary of the evolution domain of the considered fractional system using the output equation. We proceed by providing a link between the regional boundary observability of the considered semilinear system on the desired boundary subregion, and the regional observability of its linear part, in a well chosen subregion of the evolution domain. By adding some assumptions on the nonlinear term appearing in the considered system, we give the main theorem that allows us to reconstruct the initial state in the well-chosen subregion using the Hilbert uniqueness method (HUM). From it, we recover the initial state on the boundary subregion. Finally, we provide a numerical example that backs up the theoretical results presented in this paper with a satisfying reconstruction error.
Proteoglycans in Articular Cartilage and Their Contribution to Chondral Injury and Repair Mechanisms

Proteoglycans are vital components of the extracellular matrix in articular cartilage, providing biomechanical properties crucial for its proper functioning. They are key players in chondral diseases, specifically in the degradation of the extracellular matrix. Evaluating proteoglycan molecules can serve as a biomarker for joint degradation in osteoarthritis patients, as well as assessing the quality of repaired tissue following different treatment strategies for chondral injuries. Despite ongoing research, understanding osteoarthritis and cartilage repair remains unclear, making the identification of key molecules essential for early diagnosis and effective treatment. This review offers an overview of proteoglycans as primary molecules in articular cartilage. It describes the various types of proteoglycans present in both healthy and damaged cartilage, highlighting their roles. Additionally, the review emphasizes the importance of assessing proteoglycans to evaluate the quality of repaired articular tissue. It concludes by providing a visual and narrative description of aggrecan distribution and presence in healthy cartilage. Proteoglycans, such as aggrecan, biglycan, decorin, perlecan, and versican, significantly contribute to maintaining the health of articular cartilage and the cartilage repair process. Therefore, studying these proteoglycans is vital for early diagnosis, evaluating the quality of repaired cartilage, and assessing treatment effectiveness.
Social Network Analysis in R: A Software Review

In education research, social network analysis is being widely used to study different interactions and their overall implications. Recently, there has also been a surge in the development of software tools to implement social network analysis. In this article, we review two popular R packages, igraph and statnet suite, in the context of network summarization and modeling. We discuss different aspects of these packages and demonstrate some of their functionalities by analyzing a friendship network of lawyers. Finally, we end with recommendations for using these packages along with pointers to additional resources for network analysis in R.
Phased Multibeam Antennas Employing Generalized Joined Coupler Matrix

In this paper, we present a new type of feed networks to feed multibeam antennas, known as the generalized joined coupler (GJC) matrix. Using the GJC matrix, the same phase shifters for each beam can be tuned simultaneously to steer each beam independently. The configuration of the GJC matrices, the theory for synthesizing them and experimental results are reported.