Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Detection Algorithm Based on Graph Convolutional Network with Dynamic Cluster Centers Selection

Efficiently describing and discovering communities in a network is as one of the most challenging problems in community detection. The traditional extended community discovery methods are highly dependent on the initial structural centers of the network, and the expansion process only consider the network topology information, resulting in poor community detection effect. To alleviate the above problems, this paper proposes a GCN-based unsupervised learning method with dynamic cluster centers selection for community detection (GCN- DC). Firstly, some important labeled nodes are obtained by the central nodes selection method and these nodes have the characteristics of high local influence but long distance. Secondly, a center node update algorithm is developed to update the initial cluster center to make up for the error in the initial cluster center selecting process and to construct a balanced training set for the purpose of achieving the best results. Finally, an unsupervised community detection algorithm with the trained GCN model is performed on the entire network nodes to uncover the underlying community structure. Extensive experiments conducted on 6 datasets have demonstrated the superiority of our proposal compared to 7 classical algorithms.
Nitric Oxide Regulates Seed Germination by Integrating Multiple Signalling Pathways

Seed germination is of great significance for plant development and crop yield. Recently, nitric oxide (NO) has been shown to not only serve as an important nitrogen source during seed development but also to participate in a variety of stress responses in plants to high salt, drought, and high temperature. In addition, NO can affect the process of seed germination by integrating multiple signaling pathways. However, due to the instability of NO gas activity, the network mechanism for its fine regulation of seed germination remains unclear. Therefore, this review aims to summarize the complex anabolic processes of NO in plants, to analyze the interaction mechanisms between NO-triggered signaling pathways and different plant hormones such as abscisic acid (ABA) and gibberellic acid (GA), ethylene (ET) and reactive oxygen species (ROS) signaling molecules, and to discuss the physiological responses and molecular mechanisms of seeds during the involvement of NO in abiotic stress, so as to provide a reference for solving the problems of seed dormancy release and improving plant stress tolerance.
The classification of patient blood samples with machine learning techniques

After implementing the Jaminan Kesehatan Nasional (JKN) in Indonesia, health system inequity, payment non-compliance and additional expenditure still exists. To better deal with the problems in their healthcare system, this study uses a variety of machine learning algorithms to classify patient blood samples for improving the efficiency of healthcare system. The study shows that most of the algorithms are up to 70% accuracy and the accuracy will rise with only important variables.
AI Based approaches for identification of COVID and non-COVID Pneumonia

The COVID–19 pandemic affected millions of lives worldwide, disrupting every aspect of economic and social life, presenting unprecedented challenges to the public health sector. Early and accurate detection of the disease is important to contain the pandemic and minimize further loss of lives and livelihood. Artificial Intelligence and Machine Learning (AI/ML), like in many other domains, have also been instrumental in tackling the outbreak of Coronavirus. From detection to treatment, AI/ML has been used by various industries for faster and automated response. RT-PCR being the standard detection technique for COVID-19, takes few hours to provide results. X-ray and CT scan images of the lungs provide a clear representation of the presence of virus, indicating the amount of damage incurred by the lungs. In this paper, an attempt has been made to differentiate COVID-19 CT scan images from that of non COVID-19 using AI/ML. A detailed study has been done on the artificial neural networks used worldwide by various researchers for this classification. Convolution Neural Network (CNN) being the most effective classifier for images, has been used in this study to build a neural network (Covi-net) that can classify COVID-19 and nonCOVID-19 images with 97.6% accuracy.
Pharmacological Cardioprotection against Ischemia Reperfusion Injury—The Search for a Clinical Effective Therapy

Pharmacological conditioning aims to protect the heart from myocardial ischemia-reperfusion injury (IRI). Despite extensive research in this area, today, a significant gap remains between experimental findings and clinical practice. This review provides an update on recent developments in pharmacological conditioning in the experimental setting and summarizes the clinical evidence of these cardioprotective strategies in the perioperative setting. We start describing the crucial cellular processes during ischemia and reperfusion that drive acute IRI through changes in critical compounds (∆GATP, Na+, Ca2+, pH, glycogen, succinate, glucose-6-phosphate, mitoHKII, acylcarnitines, BH4, and NAD+). These compounds all precipitate common end-effector mechanisms of IRI, such as reactive oxygen species (ROS) generation, Ca2+ overload, and mitochondrial permeability transition pore opening (mPTP). We further discuss novel promising interventions targeting these processes, with emphasis on cardiomyocytes and the endothelium. The limited translatability from basic research to clinical practice is likely due to the lack of comorbidities, comedications, and peri-operative treatments in preclinical animal models, employing only monotherapy/monointervention, and the use of no-flow (always in preclinical models) versus low-flow ischemia (often in humans). Future research should focus on improved matching between preclinical models and clinical reality, and on aligning multitarget therapy with optimized dosing and timing towards the human condition.
Association between Cerebrospinal Fluid Soluble TREM2, Alzheimer’s Disease and Other Neurodegenerative Diseases

Background: Cerebrospinal fluid (CSF) soluble triggering receptor expressed on myeloid cells 2 (sTREM2) is a potential biomarker and therapy target for neurodegenerative diseases (NDDs). The purpose of this meta-analysis was to investigate the association between CSF sTREM2 level and NDDs, and to reveal the dynamic changes in CSF sTREM2 level in Alzheimer’s disease (AD) continuum. Methods: We systematically searched PubMed, Embase, Web of Science, and Cochrane Library databases for observational studies, which compared the levels of CSF sTREM2 between NDDs and controls. Sources of heterogeneity were analyzed using sensitivity analysis, subgroup analysis and meta-regression. We assessed pooled data using a random-effects model. Results: Twenty-two observational studies which included 5716 participates were identified. Compared with the controls, the whole AD continuum group showed a significant increase in CSF sTREM2 level (standardized mean difference [SMD]: 0.41, 95% confidence intervals [CI]: 0.24, 0.58, p < 0.001). The mild cognitive impairment (MCI) group displayed the largest effect size (SMD, 0.49 [95% CI: 0.10, 0.88], p = 0.014), followed by the AD cohort (SMD, 0.40 [95% CI: 0.18, 0.63], p < 0.001). The increase in sTREM2 in the preclinical stage of AD (pre-AD) group was the lowest (SMD, 0.29 [95% CI: 0.03, 0.55], p = 0.031). Other NDDs also showed an increase in the CSF sTREM2 levels compared with control groups (SMD, 0.77 [95% CI: 0.37, 1.16], p < 0.001). Conclusions: The pooled data confirmed that NDDs are associated with increased CSF sTREM2 level, thereby suggesting the CSF sTREM2 as a potential dynamic biomarker and therapy target for NDDs.
Preparation and Characteristics of Polyethylene Oxide/Curdlan Nanofiber Films by Electrospinning for Biomedical Applications

In this study, polyethylene oxide (PEO) and curdlan solutions were used to prepare PEO/curdlan nanofiber films by electrospinning using deionized water as the solvent. In the electrospinning process, PEO was used as the base material, and its concentration was fixed at 6.0 wt. %. Moreover, the concentration of curdlan gum varied from 1.0 to 5.0 wt. %. For the electrospinning conditions, various operating voltages (12–24 kV), working distances (12–20 cm) and feeding rates of polymer solution (5–50 μL/min) were also modified. Based on the experimental results, the optimum concentration for the curdlan gum was 2.0 wt. %. Additionally, the most suitable operating voltage, working distance and feeding rate for the electrospinning process were 19 kV, 20 cm and 9 μL/min, respectively, which can help to prepare relatively thinner PEO/curdlan nanofibers with higher mesh porosity and without the formation of beaded nanofibers. Finally, the PEO/curdlan nanofiber instant films containing 5.0 wt. % quercetin inclusion complex were used to perform wetting and disintegration processes. It was found that the instant film can be dissolved significantly on the low-moisture wet wipe. On the other hand, when the instant film touched water, it can be disintegrated very quickly within 5 s, and the quercetin inclusion complex was dissolved in water efficiently. Furthermore, when the instant film encountered the water vapor at 50 °C, it almost completely disintegrated after immersion for 30 min. The results indicate that the electrospun PEO/curdlan nanofiber film is highly feasible for biomedical applications consisting of instant masks and quick-release wound dressings, even in the water vapor environment.
Tutte polynomial in functional magnetic resonance imaging

Methods of graph theory are applied to the processing of functional magnetic resonance images. Specifically the Tutte polynomial is used to analyze such kind of images. Functional Magnetic Resonance Imaging provide us connectivity networks in the brain which are represented by graphs and the Tutte polynomial will be applied. The problem of computing the Tutte polynomial for a given graph is #P-hard even for planar graphs. For a practical application the maple packages “GraphTheory” and “SpecialGraphs” will be used. We will consider certain diagram which is depicting functional connectivity, specifically between frontal and posterior areas, in autism during an inferential text comprehension task. The Tutte polynomial for the resulting neural networks will be computed and some numerical invariants for such network will be obtained. Our results show that the Tutte polynomial is a powerful tool to analyze and characterize the networks obtained from functional magnetic resonance imaging.
On the Satisfaction of Modulus and Ambiguity Function Constraints in Radar Waveform Optimization for Detection

Patton, Lee K. , Ph.D., Ph.D. in Engineering Program, Department of Electrical Engineering, Wright State University, 2009. On the Satisfaction of Modulus and Ambiguity Function Constraints in Radar Waveform Optimization for Detection. We consider the design of radar systems that are capable of using knowledge of their interference environment to dynamically design transmit waveforms that afford optimum signal-to-interference-plus-noise ratio while satisfying modulus and ambiguity function constraints. We begin by establishing the inextricable nature of modulus constraints in the waveform optimization problem. We then extend the state of the art in waveform optimization to accommodate these constraints. This is done by solving a secondary optimization problem using the method of alternating projections. We demonstrate that this approach can be a computationally efficient alternative to dynamic programming methods. We then consider the multiple-target detection problem, which is the basis for introducing ambiguity function constraints into the waveform design process. We formulate the waveform optimization problem for several receiver architectures, and solve these problems using sequential quadratic programming and interior point methods. Finally, we address the need for a more computationally tractable approach by considering a number of suboptimal formulations. This includes a novel formulation based on a parametrization of nonlinear frequency modulation.
AVALIAÇÃO DA INFLUÊNCIA DO TEMPO DE ESTUFA NA RESISTÊNCIA MECÂNICA DE JUNTAS ADESIVAS PARA APLICAÇÃO EM PARA-LAMAS AGRÍCOLAS

The use of structural adhesives has increased on automotives and mobility sectors, manly on the union of thin metal sheets. When compared to some others joints options, adhesive joints results on light, cheaper and high aesthetics quality structures. When it comes to structural qualities, adhesive joints have a more uniform stress distribution in relation to spot welding, resulting in more reliable joint. On the other hand, a considerable part of structural adhesives has a polymeric nature, what provides a low performance when exposed to higher temperatures. Aiming to validate the use adhesive joints on vehicles, it is necessary to understand how the temperature and time exposure influence adhesive resistance. The present work evaluates the temperature and exposition time which an agricultural rear fender is exposed during an electrocoating painting process oven cure, as well the effect it causes on single lap joints specimens mechanical resistance. For this purpose, two different classes of adhesives were used on specimens: i) acrylic adhesive and ii) toughened structural acrylic adhesive. The single lap joints specimens resistance were evaluated using lap-shear test, with a 2,65 mm thickness SAE J403 1008 substrate. Cohesive failure was the manly failure mode found on the specimens, leading to conclude they had a proper substrate adhesion. As result, the toughened structural acrylic adhesive had a strength increase regardless of the oven holding time, when compared to not heated specimens. However, for the joints created using the acrylic adhesive, the results were found around the average of the unheated specimens, with a wide data dispersion which is probably due to adhesive thickness control method used. Furthermore, the aspects related to the joints adhesive thickness control method used, which could be the answer to the data dispersion, are discussed.
Image matching tracking algorithm based on local cross-entropy

The cross-entropy value illuminates the information difference between the template image and the real-time image on the average.In this paper,aiming at solving the problem of the radiation distortion and geometry distortion in the system of the photoelectric pod,the image matching tracking algorithm based on cross -entropy is proposed.Since the cross -entropy minimum rule is propitious to image matching with abundant information,the algorithm mentioned is provided with the very good anti- noise properties,and the capability of resisting radiation distortion and geometry distortion.The results of simulation experiment show that this algorithm has steady matching tracking ability and well adaptability when radiation distortion happens.So it is a practical algorithm.
On foliations of R n÷~ by minimal hypersurfaces

This paper sets forth several basic theorems regarding foliations of En÷, by minimal hypersurfaces; here n + 1---8, for reasons discussed below. First we outline our primary results. In §1, we prove a local result (Theorem 1.1), concerning smoothness. It is shown that any a priori merely continuous foliation of an open subset of ~n+l by minimal hypersurfaces is actually Lipschitz, and oriented by a Lipschitz unit normal. This result is sharp, as evidenced by several accompanying examples, and perhaps surprisingly so, since minimal hypersurfaces are themselves always real analytic. We proceed in §2 to study the global structure of such foliations, when they are defined on all of Euclidean space. In this regard, Theorem 2.3 establishes that, in any dimension, the leaf space of the foliation is E, the real line. Geometers, it seems, are often unaware that this is far from being true for general hypersurface foliations of En+~, even when leaves are assumed proper and real-analytic, even if n + 1 = 2 (cf. §2 below). In Theorem 2.4, we specialize to foliations which have an asymptotically regular leaf. This natural hypothesis always obtains in the critical case n + 1 = 8, and the theorem states that such a foliation is diffeomorphic to the cartesian product of a contractible areaminimizing hypersurface with E, and that, near infinity, each leaf looks like the central cone over a homology ( n 1)-sphere embedded "symmetrically" in the unit sphere S ~ (cf. remarks following Theorem 2.4). Our interest in foliations of N n+' by minimal hypersurfaces derives from a very natural problem which has yet to be solved. We precede its statement with a few words of background. Recall that a solution of the minimal surface equation in N" is a function whose graph in R n÷l is a minimal hypersurface. The classical Bernstein problem, which asks whether an entire solution is necessarily a linear polynomial (i.e. whether its graph must be an affine hyperplane) is well-known, and has a long, fascinating history. Bombieri, DeGiorgi, and Giusti solved it completely in 1968 ([BDG]), building upon a number of other important works (e.g. [B], [FW], [DI, [A], and [SJ]) in doing so. Their result (the "Bernstein Theorem") states that
Speech Refinement Using Custom Filter for Developing Robust S2S Dataset

Neural Network-based speech-to-speech (S2S) translators require a robust and well-refined dataset of audio signals, from which they make automatic translations. These signals, during recording, are usually accompanied by some noises, which normally alter the information conveyed by the original noiseless signal. This type of problem affects the accuracy of the translation system. Although many de-noising techniques have been proposed by researchers for removing noises from raw audio signals, however, this paper presents a novel approach for noise removal from the audio signal using custom filter based on Short-Time Fourier Transform (STFT). By using the LJ Speech Dataset publicly available on Kaggle website, experimental results show that the research technique boosts signal strength (SNR) by 1.001db on average, thus making it an effective method of eliminating noise from speech and other useful acoustic signals.
Effective resistance of toroidal graphs; some sharper results and applications

The average effective resistance of a graph can be computed as a function of its eigenvalues. Using this formula and focusing on toroidal d-dimensional graphs, we study the role of the network topology and specifically of the graph dimension in determining the resistance. Considering sequences of graphs of increasing size, we study the asymptotical behavior of the effective resistance, proving that it is (asymptotically) inversely proportional to the dimension. These findings are relevant in many applications, including distributed estimation and control of sensor networks and clock networks.
Influence of carbon fibre layers on the strength of thermally modified laminated veneer lumber

Obstract Thermally modification of wood is an environment-friendly alternative method for improving several properties of wood without the use of chemicals. The compressive strength (CS) parallel to the grain of reinforced laminated veneer lumber (LVL) manufactured from heat treated beech ( Fagus orientalis ) veneers and carbon fibre was determined. Thermally modification was performed at 140°C, 160°C, 180°C, and 200 °C according to thermal treatment process. Carbon fibre were added as a reinforcement layer between wood veneers bonded with phenol-formaldehyde (PF), polyvinyl acetate (PVAc) polyurethane adhesives (PU) to improve properties of LVL. Results showed that reinforcing LVL panels with carbon fibre increased both density and CS. The PF adhesive showed better results for reinforced LVL panels with carbon fibre. The anatomical structure and density of the wood material significantly affect its mechanical properties, including compressive strength parallel to the grains. Wood density had a strong significant linear relationship with CS.
Disturbance Observer-Based Fixed-Time Tracking Control for a Tilt Trirotor Unmanned Aerial Vehicle

This article proposed a new fixed-time trajectory tracking control strategy based on the disturbance observer (DOB) for a tilt trirotor unmanned aerial vehicle (TTUAV), which is subject to unknown external disturbances. To facilitate the TTUAV's trajectory tracking controller design, the dynamic model of the TTUAV is divided into two subsystems, the position subsystem (outer-loop) and the attitude subsystem (inner-loop). For the dynamics of the TTUAV's outer loop, the unknown external disturbances consist of the unknown wind disturbances, the unknown aerodynamic frictions forces, the lateral force generated by the rear servo, and the states coupling between the outer loop and the inner loop. Then, a nonlinear DOB is designed to estimate the unknown external disturbances, and a nonsingular terminal sliding-mode controller (NTSMC) is further developed to drive the TTUAV's position tracking errors to zero in a fixed time. For the dynamics of the TTUAV's inner loop, the unknown external disturbances are mainly introduced by the aerodynamic frictions torques. Then, a modified sliding mode controller is designed to reject the unknown external disturbances, and to ensure that the attitude tracking errors converge to zero exponentially. In addition, a composite stability analysis based on Lyapunov stability theory is provided to guarantee the fixed-time convergence of the closed-loop dynamics of the TTUAV. Finally, the results of the real-time experiments are presented to verify the effectiveness of the proposed control strategy.
Modified Kolmogorov-Smirnov Test Procedures with Application to Arbitrarily Right-Censored Data

The classical Kolmogorov one-sample goodness-of-fit and Smirnov two-sample test procedures are modified in an attempt to obtain increased power when applied to uncensored data. Both are then generalized for use with arbitrarily right-censored data. Motivation for the formulation of the generalized procedures is provided heuristically by appealing to the basic concept upon which the procedures for uncensored data are based; a theoretical motivation is provided by asymptotic weak convergence results. The size and power of the generalized Smirnov two-sample procedure are evaluated for small and moderate sample sizes using Monte Carlo simulations. Results of the simulations are also used to make comparisons with the Gehan-Wilcoxon and logrank two-sample test procedures. The generalized Smirnov procedure is found to maintain the size of the test in nearly all the cases studied, although it is conservative in small samples of heavily censored data. It is found to be more powerful than both the Gehan-Wilcoxon and the logrank procedures for the non-Lehmann alternatives considered.