Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant, Antibiofilm & Biological Activities of Summer Savory

The Hepatic disease all over the world had a more attention towards the prevention methods; planned diet can effective and protective. Therefore, this study was aimed to examine the antioxidant and hepatoprotective role of the dried savory leaves (1 and 2%) and its dried hydroethanolic extract (25 and 50 ppm) against carbon tetrachloride (CCl 4 ) induced liver damage in rats. Also, the effect of savory leaves extract on biofilm formation of some microbial strains was studied. Liver injury group revealed significant increase in serum liver parameters such as AST, ALT and ALP activities and malondialdehyde (MDA) levels. Whereas, reduction in superoxide dismutase (SOD) activity was done. Furthermore, the liver injury rats recorded hepatic histological several and severe alteration due to hepatocellular damage by CCl 4 . Treatments by the dried savory leaves and its extract at different levels resulted significantly improved the liver function and oxidative stress parameters; also, suppresses the histological alteration in liver. Both Gram-positive and Gram-negative bacteria have the ability to form biofilm with varying degrees. The savory ethanolic extract has an antibiofilm effect against the studied bacteria strains. Also, savory leaves extract exhibited scavenging activity due to their containing from phenolic
A Real Time Hearing Loss Simulator

Summary Several hearing loss simulators ( HLS ) have been developed to demonstrate the e ﬀ ects of hearing loss on auditory perception to normal hearing (NH) listeners, and to facilitate prediction of the perception of sound products by hearing impaired customers. This paper describes a real-time HLS based on an inverse, compressive GammaChirp (GC) ﬁlterbank, and how it was used to temporarily handicap NH listeners participating in a traditional notched-noise (NN) masking experiment (e.g. [1]) with a 2-kHz signal frequency. Sets of NN thresholds were obtain with a wide range of symmetric and asymmetric notches at two noise spectrum levels while participants listened to the sounds presented both with and without the HLS . The NN data were used to derive auditory ﬁlter shapes and input/output (IO) functions, which demonstrate that the HLS can simulate the elevation of pure tone threshold and the ﬂattening of the input/output function commonly observed in sensory-neural hearing loss. the
Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification

Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation.
Monotone finite-difference schemes for stochastic Hamilton-Jacobi equations

The aim of this paper is to provide a general framework for the construction of monotone finite difference schemes to approximate viscosity solutions of pathwise (stochastic) Hamilton-Jacobi equations. The arguments are adapted from the classical viscosity solution setting, with a more careful analysis needed to treat the irregular time dependence. In particular, for paths with non-vanishing quadratic variation, it is necessary for the schemes to involve certain mild approximations of the driving paths. The first result is a qualitative argument that gives essentially optimal conditions for the approximating paths in order for the scheme to converge. This same result is then made qualitative, and then used to compute rates of convergence that extend those obtained in the classical setting. Specific examples are given in the case of equations driven by Brownian motion. A scheme is also presented that uses scaled random works to establish convergence in law.
Solutions of Some Linear Fractional Partial Differential Equations in Mathematical Physics

In this article, we use double Laplace transform method to find solution of general linear fractional partial differential equation in terms of Mittag-Leffler function subject to the initial and boundary conditions. The efficiency of the method is illustrated by considering fractional wave and diffusion equations, Klein-Gordon equation, Burger’s equation, Fokker-Planck equation, KdV equation, and KdV-Burger’s equation of mathematical physics.
Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject’s chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors’ location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland–Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R2 > 0.99), a Pearson’s correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.
Similarity Search Of Freesound Environmental Sound Based On Their Enhanced Multiscale Fractal Dimension

In this paper, we propose a new acoustic feature signature based on the multiscale fractal dimension extracted from sound signals for the content-based retrieval of environmental sounds such as ﬁeld-recording sounds shared through Freesound. The multiscale fractal dimension de-rived from the fractal theory is known as a descriptor representing several features of the sound waveform. We report the basic characteristics of the enhanced multiscale fractal dimen-sion (EMFD) extracted from each sound signal. Furthermore, we developed a similarity search system for environmental sounds using EMFD and Mel frequency cepstral coefﬁcients 39 (MFCC39). We have compared the descrip-tiveness of EMFD signature and MFCC39 for the search purpose and found some competitive aspects of EMFD signature against MFCC39. These results show that EMFD signature is useful for describing the features of environmental sound and applicable to the search of large-scale sound databases.
Diagnosing Multicollinearity in Exponential Random Graph Models

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past 10 years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, yielding inconsistent model estimates and problematizing inference from parameters. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor (VIF) from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the VIFs for each model. The distribution of VIFs is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between VIFs and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity, and guidelines for interpretation are discussed.
Experiments in optics and photonics engineering education at Penn State

During the fall semester of 2022 an experimental first-year seminar on applied optics was taught for the first time at The Pennsylvania State University. The ongoing goals of the course are to recruit students to electrical engineering and to retain them while steering students towards a specialization in optics and photonics. This was attempted through hands-on experiments demonstrating basic concepts in optics, and tours of laboratories on campus that demonstrate the wide range of applications of electrical and optical engineering. Experiments included aligning fiber optics, building telescopes, building pinhole cameras, experiments with fluorescence spectroscopy, transmission spectroscopy, and demonstrations of polarization. In addition to the first year seminar, the following semester a senior level experiment-based course on applied spectroscopy was offered as well. In addition to experiments, the senior level course includes peer instruction. While the audience for the two courses differ, the goals are much the same to recruit and retain students in optics and photonics. The effectiveness of course presentation in each case is evaluated based on student response and feedback. The two courses’ materials will be compared and improvements will be discussed.
Rapid communication Sensitivity to disparity corrugations in peripheral vision

Abstract Disparity discrimination thresholds are known to increase with both retinal eccentricity and distance from the horopter.However, little is known about how the detectability of cyclopean gratings varies with retinal position. Thresholds for disparitycorrugations were measured as a function of corrugation frequency for different visual eccentricities. Subjects viewed annulardisplays of random dot stereograms, and judged in which of two intervals a circumferential disparity modulation was present. Forany given eccentricity, visual sensitivity to disparity corrugations was bandpass. As eccentricity increased from 3.5 to 21.0°,peak-to-trough thresholds were found to increase, the optimal corrugation frequency for detection decreased, and the upper cutoffcorrugation frequency also decreased. The M-Scaling functions of Rovamo and Virsu were used to replot the data in terms ofcycles per unit cortical distance. Peak detection frequency was constant at 0.8 cycles per mm of cortex after this rescaling,demonstrating that acuity for disparity modulations is approximately M-scaled beyond the fovea. © 1998 Published by ElsevierScience Ltd. All rights reserved.
Chebyshev collocation method for solving second order ODEs using integration matrices

The spectral collocation method for solving two-point boundary value problems for second order differential equations is implemented, based on representing the solution as an expansion in Chebyshev polynomials. The approach allows a stable calculation of both the spectral representation of the solution and its pointwise representation on any required grid in the definition domain of the equation and additional conditions of the multipoint problem. For the effective construction of SLAE, the solution of which gives the desired coefficients, the Chebyshev matrices of spectral integration are actively used. The proposed algorithms have a high accuracy for moderate-dimension systems of linear algebraic equations. The matrix of the system remains well-conditioned and, with an increase in the number of collocation points, allows finding solutions with ever-increasing accuracy.
The effects of periodic and non-periodic inputs on the dynamics of a medial entorhinal cortex layer II stellate cell model

THE EFFECTS OF PERIODIC AND NON-PERIODIC INPUTS ON THE DYNAMICS OF A MEDIAL ENTORHINAL CORTEX LAYER II STELLATE CELL MODEL by Dongwook Kim Various neuron types exhibit sub-threshold and firing frequency resonance in which the sub-threshold membrane potential or firing frequency responses to periodic inputs peak at a preferred frequency (or frequencies). Previous experimental work has shown that medial entorhinal cortex layer II stellate cells (SCs) exhibit sub-threshold and firing frequency resonance in the theta frequency band (4 10 Hz). In this thesis we seek to understand the biophysical and dynamic mechanism underlying these phenomena and how they are related. We studied the effects of sinusoidal current and synaptic conductance inputs at various frequencies, with and without noise, on the supra-threshold dynamics of a SC model. For current inputs, our results show that while the SC model exhibits a single frequency preference peak (in the theta frequency band) for low sinusoidal input levels, it exhibits three preferred frequency peaks for larger input levels. These additional peaks occur at frequencies that are roughly a multiple of the “theta” one. For synaptic conductance inputs, we observe an additional peak in the signal gain which occurs at a much higher frequency (in the high gamma frequency band). These findings depart from the linear prediction. The corresponding linearized model does not exhibit three preferred frequency peaks for current inputs and a much higher frequency for conductance inputs under the same conditions (such as parameters, noise, amplitude of inputs and maximal synaptic conductance) in the nonlinear model. Previous experimental work has shown high-frequency Poisson-distributed trains of combined excitatory and inhibitory conductanceand current-based synaptic inputs reduce amplitude of subthreshold oscillations of SCs. The second goal of this thesis is to investigate the mechanism underlying these phenomena in the context of the model. More specially, we studied the effects of both conductanceand current-based synaptic inputs at various maximal conductance values on a SC model. Our numerical simulations show that conductance-based synaptic inputs reduce the amplitude of SC’s subthreshold oscillations for low enough value of the maximal synaptic conductance value but amplify these oscillations at a higher range. These results are in contrast to the experimental results. THE EFFECTS OF PERIODIC AND NON-PERIODIC INPUTS ON THE DYNAMICS OF A MEDIAL ENTORHINAL CORTEX LAYER II STELLATE CELL MODEL
SGFNet: Semantic-Guided Fusion Network for RGB-Thermal Semantic Segmentation

Recently, semantic segmentation based on RGB and thermal infrared (TIR) images has become a research hotspot because of its stability in the weak light environment. However, most of the current methods ignore the differences between the two modalities of data and do not use semantic information in multi-modal fusion. In this paper, we propose a novel Semantic-Guided Fusion Network (SGFNet) for RGB-Thermal semantic segmentation, which makes full use of semantic information in the multi-modal fusion. Our SGFNet consists of an asymmetric encoder with TIR branch and RGB branch and a decoder. We concentrate on enhancing the multi-modal feature representation in the encoder with a pattern of fusion and enhancement. Specifically, considering that TIR images are stable under weak light conditions, we first propose a Semantic Guidance Head to extract semantic information in the TIR branch. In the RGB branch, we propose a Multi-modal Coordination and Distillation Unit to fuse multi-modal features first. Then, we propose a Cross-level and Semantic-guided Enhancement Unit to enhance the fused features with cross-level information and semantic information. We arrange these two units at all stages of the RGB branch to generate features with strong representation abilities at different levels. For the decoder, to obtain large receptive fields and fine edges, we improve the Lawin ASPP decoder by introducing edge information extracted from the low-level features, proposing the edge-aware Lawin ASPP decoder. With our encoder and decoder working together, our SGFNet can identify objects accurately and segment objects finely. Extensive experiments on the MFNet dataset demonstrate the superior performance of the proposed SGFNet compared with state-of-the-art methods. The code and results of our method are available at https://github.com/kw717/SGFNet.
Partial differential equations and dynamical systems applied to problems coming from physics and biology

This thesis falls within the broad framework of partial differential equations and dynamical systems, and focuses more specifically on two independent topics. The first one is the study of the discrete coagulation-fragmentation equations with diffusion. Using duality lemma we establish new $L^p$ estimates for polynomial moments of the solutions, under an assumption of convergence of the diffusion coefficients. These moment estimates are then used to obtain new results of smoothness and to prove that strong enough fragmentation can prevent gelation even in the diffusive case. The second topic is the one of computer-assisted proofs for dynamical systems. We improve and apply a method enabling to a posteriori validate numerical solutions, which is based on Banach's fixed point theorem. More precisely, we extend the range of applicability of the method to include operators with a dominant linear tridiagonal part, we improve an existing technique allowing to compute and validate invariant manifolds, and we introduce an new technique that significantly improves the usage of polynomial interpolation for a posteriori validation methods. Then, we apply those techniques to prove the existence of traveling waves for the suspended bridge equation, and to study inhomogeneous steady states of a cross-diffusion system.
Multiscale modelling and splitting approaches for fluids composed of Coulomb-interacting particles

ABSTRACT We consider fluids composed of Coulomb-interacting particles, which aremodelled by the Fokker--Planck equation with a collision operator.Based on modelling the transport and collision of the particles,we propose new, computationally efficient, algorithms based on splitting the equations of motion into a global Newtonian transport equation, where the effects of an external electric field are considered, and a local Coulomb interaction stochastic differential equation, which determinesthe new velocities of the particle. Two different numerical schemes, one deterministic and the other stochastic, as well as an Hamiltonian splitting approach, are proposed for coupling the interactionand transport equations. Results are presented for two- and multi-particle systems with different approximations for the Coulomb interaction.Methodologically, the transport part is modelled by thekinetic equations and the collision part is modelled bythe Langevin equations with Coulomb collisions.Such splitting approaches allow concentrating on different solver methodsfor each different part. Further, we solve multiscale problems involving an external electrostatic field.We apply a multiscale approach so that we can decompose the different time-scales of the transport and the collision parts.We discuss the benefits of the different splitting approaches and theirnumerical analysis.
Study of the Matrix Effect on the Plasma Characterization of Heavy Elements in Soil Sediments using LIBS with a Portable Echelle Spectrometer

Laser-induced breakdown spectroscopy (LIBS) has been applied to perform a study of the matrix effect on the plasma characterization of soil sediment targets. The plasma is generated by focusing a pulsed Nd: YAG laser on the target in air at atmospheric pressure. The plasma emission spectrum was detected using a portable Echelle spectrometer (Mechelle 7500 — Multichannel Instruments, Stockholm, Sweden) with intensified CCD camera. Spectroscopic analysis of plasma evolution of laser produced plasmas has been characterized in terms of their spectra, and electron temperature. Four heavy elements V, Pb, Mn and Co were determined in the obtained spectra. The LTE and optically thin plasma conditions were verified for the produced plasma. The electron temperature and density were determined using the emission intensity and stark broadening, respectively, of the spectral lines of the heavy elements in the soil sediments. The electron temperature does not change with concentration. For environmental applications, the obtained results showed the capability of the proposed LIBS setup with the portable Mechelle 7500 spectrometer to be applied in-situ for real-time measurements of the variation of the matrix elemental composition of soil sediments by following up only a single element as a marker for the composition of the soil sediment without need of analysis of the other elements.
Evidence-Based Dietary Practices to Improve Osteoarthritis Symptoms: An Umbrella Review

While there is some research investigating whole foods or diets that are easily understood and accessible to patients with osteoarthritis, specific nutrients or nutraceuticals are more commonly identified. Unfortunately, guidelines and evidence surrounding individual nutrients, extracts, and nutraceuticals are conflicting and are more difficult to interpret and implement for patients with osteoarthritis. The purpose of this umbrella review is to provide a comprehensive understanding of the existing evidence of whole foods and dietary patterns effects on osteoarthritis-related outcomes to inform evidence-based recommendations for healthcare professionals and identify areas where more research is warranted. A literature search identified relevant systematic reviews/meta-analyses using five databases from inception to May 2022. Five systematic reviews/meta-analyses were included in the current umbrella review. Most evidence supported the Mediterranean diet improving osteoarthritis-related outcomes (e.g., pain, stiffness, inflammation, biomarkers of cartilage degeneration). There was little to no evidence supporting the effects of fruits and herbs on osteoarthritis-related outcomes; however, there was some suggestion that specific foods could potentiate symptom improvement through antioxidative mechanisms. The overall lack of homogeneity between the studies limits the conclusions that can be made and highlights the need for quality research that can identify consumer-accessible foods to improve osteoarthritis-related symptoms.