Miracle-dz/UniFAR-FTUs huggingface dataset:
Superresolution Video Reconstruction with a Hybrid POCS Algorithm

In the paper the Hybrid Projection onto to Convex Sets (HPOCS) scheme for video superresolution reconstruction is proposed, which aims to integrate slightly different, but unique, information in temporally adjacent video frames to generate much higher resolution reconstruction frames. In the proposed algorithm, image matching is utilized to estimate translational motion among various frames firstly, and then APRX algorithm is adopted to access the Point Spread Function (PSF) and the ideal high resolution (HR) video frame, finally, image reconstruction is carried out within the projection onto convex sets framework. Experimental results computed from simulation video sequence and actual video sequences show dramatic resolution improvement over original frame, bilinear interpolation frame and POCS reconstruction result. The edges in the HPOCS reconstruction result are much clearer and the details are more prominent
Risk-sensitive investment in a finite-factor model

A new jump diffusion regime-switching model is introduced, which allows for linking jumps in asset prices with regime changes. We prove the existence and uniqueness of the solution to the risk-sensitive asset management criterion maximization problem in this setting. We provide an ODE for the optimal value function, which may be efficiently solved numerically. Relevant probability measure changes are discussed in the appendix. The recently introduced approach of Klebaner and Liptser (2013) is used to prove the martingale property of the relevant density processes.
Devya Distribution and Its Applications

A new one parameter lifetime distribution named, ‘Devya Distribution’ for modeling lifetime data from engineering and biomedical science, has been proposed. Its statistical and mathematical properties including shape, moments, coefficient of variation, skewness, kurtosis, hazard rate function, mean residual life function, stochastic ordering, mean deviations, Bonferroni and Lorenz curves have been discussed. The condition under which the proposed distribution is over-dispersed, equi-dispersed, and under-dispersed has been given along with other one parameter lifetime distributions. The method of maximum likelihood and the method of moments have been discussed for estimating its parameter. The goodness of fit of the proposed distribution over one parameter exponential, Lindley, Shanker, Akash, Aradhana, Sujatha, and Amarendra distributions have been given with two real lifetime data sets.
Partial spatial coherence illumination in digital holographic microscopy: quantitative analysis of the resulting noise reduction

Imaging applications needing illumination with sufficiently high power density often request coherent light such as provided by laser or super-luminescent diodes. However the very high spatial coherence of those sources can generate coherent speckle noise and multiple reflection effects that may degrade the resulting image quality. In order to overcome such issues, we have shown that using partial spatial coherence illumination in interferometric digital holographic microscopy (DHM) greatly improves the image quality. Two models are here proposed to quantitatively assess the noise reduction as a function of both the spatial coherence, and the distance between the noise source and the recorded plane. We emphasize that these approaches may be useful in numerous imaging situations not restricted to DHM systems. The first developed model uses the discretization of the field of view in the plane of the noise source. This model is more intuitive but encounters some limitations. The second model, based on a continuous approach, corroborates the discrete model and extends it when necessary. Experimental validation of both models has been performed with a DHM, whose illumination has an adjustable spatial coherence. The noise was generated using a microscope slide with de-agglomerated particles. The relative standard deviation of fluctuations due to noise is shown to be inversely proportional to the product D|d| when this quantity is high, where D is the diameter of a pupil leading the spatial coherence and d is the defocus distance of the noise source. The continuous model is applicable in any case.
Schumann resonances variation observed from Electromagnetic monitoring stations

Schumann resonances(SR) are a set of frequencies of electromagnetic(EM) waves in the natural cavity formed by the earth′s surface and its ionosphere,in the extremely low frequency(ELF) range,caused by natural electrical activity of the earth and its atmospheric environment.Because of the resonance interaction,EM energy of SR is stronger than the other frequencies′. In recent years,studies have found that earthquakes would disturb the stable parameters and spectra of SR,so using SR anomaly to implement earthquake monitoring is effective.However,finding out the EM field background variation is the basis of distinguishing the seismic EM anomaly.In this paper,utilizing ELF electromagnetic monitoring stations′ natural electromagnetic field data,we processed and analyzed electromagnetic field components′ power spectrum curve of SR frequencies,and obtained Schumann resonance background change with season and latitude.Comparing SR′ electromagnetic field curves of one station,we can see electromagnetic field intensity changes with seasons obviously.This is weaker in winter and stronger in summer,and the amplitude of sinusoidal variation differs by two orders of magnitude.Comparing the same frequency′s graphs of different stations,it is observed that electromagnetic field intensity is enhancing with the decreasing of the latitude.The background variation study will provide physical basis for distinguishing the earthquake electromagnetic animaly.
Recognition of Yoga Asana from Real-Time Videos using Blaze-pose

: Yoga is a broad concept that connotes union. Considering yoga’s spiritual and health benefits, it is now practiced by millions of people worldwide. This paper proposes a lightweight and robust architecture that could recognize yoga asana from video input. Most of the existing techniques use either expensive hardware configuration such as Kinect or specialized feature extraction techniques from raw inputs for each asana. Even though these produce decent accuracy in a controlled environment, they are complex to design and often fail in most real-time cases with complex backgrounds. The problem with the existing asana recognition methods from the literature is that they either demand high-end configurations or do not produce key points while recognition, which is crucial in pose correction employed at a later stage. The proposed model is so computationally e ffi cient that it can be deployed even in entry-level smartphones, browsers, and smart TVs. Pose estimation is done initially using state-of-the-art Blaze Pose architecture. Transformations are applied after that to achieve scale and position independence. convolutional neural networks (CNN) and long-short-term memory (LSTM) networks are being used to train the model from the extracted key points. The CNN network from the novel architecture can be leveraged to extract spatial features, whereas LSTM networks understand the features through time stages. After precise tuning of hyperparameters, our system achieves a training accuracy of 95.29% and a test accuracy of 98.65% at 30 frames per second (FPS). To the best of our knowledge, this is the first computationally e ffi cient work, which processes video input at 30 FPS and achieves decent accuracy compared to existing research works from the literature.
Modeling of PZT-induced Lamb wave propagation in structures by using a novel two-layer spectral finite element

This paper presents a novel two-layer spectral finite element model, consisting of PZT wafer and host structure, to simulate PZT-induced Lamb wave propagation in beam-like and plate-like structures. Based on the idea of equal displacement on the interface between PZT wafer and host structure, the one-dimensional spectral beam element of PZT-host beam and two-dimensional spectral plate element of PZT-host plate are considered as one hybrid element, respectively. A novel approach is proposed by taking the coupling effect of piezoelectric transducers in the thickness direction into account. The dynamic equation of the two-layer spectral element is derived from Hamilton’s principle. Validity of the developed spectral finite element is verified through numerical simulation. The result indicates that, compared with the conventional finite element method (FEM) based on elasticity, the proposed spectral finite element is proved to have a high accuracy in modeling Lamb wave propagation, meanwhile, significantly improve the calculation efficiency.
Comprehensive Experimental Studies on Vortex Dynamics over Military Wing Configurations in IAR

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The Porous Medium Equation

dynamics. We have arrived at an interesting concept, seeing solutions as continuous curves moving around in an infinite-dimensional metric space X (here, the function space L1(Ω)). Viewing solutions as continuous curves in a general space is the starting point of the abstract theory of differential equations, a way that we will travel quite often. In the so-called Abstract Dynamics it is typical to forget the variable x in the notation and look at the map t 7→ u(t) ∈ X, where u(t) is the abbreviated form for u(·, t). Remarks. (1) Note that the theorem allows to define the value u(t) of a limit solution (in particular, of a weak solution) u at any time t > 0 as a well-defined element of L1(Ω). Actually, in many cases, as when Φ is superlinear and f is bounded, it is an element of L∞(Ω). (2) If u0 and f are bounded the initial regularity is better. In that case the initial data are taken in the Lp sense: ũ(t) → ũ(0) in Lp(Ω), for every p 0; if u0 is continuous, then the convergence takes place uniformly in x as t → 0, see Section 7.5. 1. (3) Unfortunately, there are no equivalent L1 estimates for the Dirichlet Problem with nonhomogeneous data g 6= 0. We end this subsection with a simple but very useful consequence. Corollary 6.3 Let u be a limit solution with data u0 ∈ L1(Ω) and f ∈ L1(Q). If t1 > 0, then ũ(x, t) = u(x, t + t1) is the limit solution with data ũ0(x) = u(x, t1) and forcing term f(x, t) = f(x, t + t1). This important result is immediate for the approximations. We leave the details to the reader. Remark. Let us note that any concept of limit solution depends on the type of admissible approximations and on the functional setting in which limits are taken. The definition we propose applies in the L1 setting. If needed, these solutions will be called L1-limit solutions. For an extension see Section 6.6. 6.2 Theory of very weak solutions The continuous dependence with respect to the L1-norm is a powerful property. It has allowed us to extend the existence result for weak solutions of the preceding section and
The value of diffusion tensor imaging (DTI) and tractography in patients with intraspinal tumours: a preliminary study

Poster: "ECR 2013 / C-0877 / The value of diffusion tensor imaging (DTI) and tractography in patients with intraspinal tumours: a preliminary study" by: "F. Cartes-Zumelzu, S. Ingorokva, H. Kostron, G. Feuchtner, C. Kremser, C. Thome; Innsbruck/AT"
Generation Z’s Brand Love for Skincare Products: Do Religious Beliefs Matter?

To date, there has been no prior research examining the relationship between brand love as a determinant of brand loyalty and e-WOM specifically for halal skincare products. This study investigates how Muslim Generation Z might love a brand because of their religious beliefs and brand identity, resulting in brand loyalty and e-WOM that benefits the company. The questionnaire for this study was completed online by 120 participants from three cities in Central Java, Indonesia, namely Semarang, Salatiga, and Surakarta. The model was tested using PLS-SEM. The results of the research revealed that religious beliefs and brand identification affected brand love, whereas brand love affected brand loyalty and e-WOM. From this research, it can be seen that branding cannot be separated from faith. Muslims like brands that reflect their religious values and allow them to identify as Muslims. Therefore, marketers must have a comprehensive understanding of the halal concept in the manufacturing and production processes of halal skincare.
Segmentation non-supervisée de champs de données multi-composantes

In this paper, we revisit some methods of unsupervised segmentation on multi-component datasets. The first method lies on the Llyod’s algorithm, also known as Kmeans algorithm; we suggest an original approach to initialize this algorithm, by using data obtained during the building of the minimum spanning tree (MST) [7]. The same kind of initialization has been applied for a second method inspired by works of Shi and Malik [11] based upon the Laplacian of a fully connected graph. The very important problem of the choice of the metric for characterizing similarities between two points of multi-dimensionnal datasets is also discussed. We present results obtained with both segmentation methods on multispectral images and on asteroid reflectance spectra.
Optimizing b‐values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model

Abstract Purpose To define an optimal set of b‐values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model. Methods Simulations of diffusion signals were performed to define an optimal set of b‐values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal b‐values set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one. Results The total relative error on simulations decreased of about 40% when adopting the optimal set of 13 b‐values (0 10 20 40 50 60 200 300 400 1200 1300 1400 1500 s/mm2), showing significant differences and increased precision on D and f estimates with respect to simulations with a non‐optimized b‐values set. Similarly, in vivo acquisitions demonstrated a dependency of IVIM parameters on the b‐values array, with differences between the optimal set of b‐values and a clinical non‐optimized acquisition. IVIM parameters were compatible to literature values, with D (0.679/0.701 [0.022/0.008] ·10−3mm2 /s), f (5.49/5.80 [0.70/1.14] %), and D* (8.25/7.67 [0.92/0.83] ·10−3mm2 /s) median [interquartile range] estimates for white matter/gray matter in volunteers and D (0.709/0.715/1.06 [0.035/0.023/0.271] ·10−3mm2 /s), f (7.08/7.84/21.54 [1.20/1.06/6.05] %), and D* (10.85/11.84/2.32 [1.38/2.32/4.94] ·10−3mm2 /s) for white matter/gray matter/Gross Tumor Volume in patients with skull‐base chordoma tumor. Conclusions The definition of an optimal b‐values set can improve the estimation of quantitative IVIM parameters. This allows setting up an optimized approach that can be adopted for IVIM studies in the brain.
Does corporate social responsibility (CSR) have spillover effect?——Based on the binary relationship of supply chains in China

ABSTRACT With data sampled from listed suppliers and customers in China from 2010 to 2017, the presenting work investigates how corporate social responsibility (CSR) is realised from the perspective of supply chains. Results found that socially responsible customers improved CSR performance of suppliers, revealing a significant CSR spillover effect among supply chains and further suggesting customers’ bargaining power as an important realisation path. In addition, both the CSR sensitivity of customers and the CSR fulfilment ability of suppliers strengthened CSR spillover effect in supply chains. More importantly, suppliers’ CSR performance is positive feedback to customers’ need rather than a publicity stunt to cater to customers. This paper expands current understanding on corporate behavioural coordination from the perspective of supply chains and provides insights for promoting CSR fulfilment in the Chinese scenario.
Harnessing Distrust: News, Credibility Heuristics, and War in an Authoritarian Regime

ABSTRACT To evaluate the credibility of political information, citizens rely on simple logical rules-of-thumb or heuristics based on various resources, such as personal experience and popular wisdom. It is often assumed that contrary to dependence on the media, personal experience and popular wisdom help citizens to build alternative understandings of political events. However, little is known about how citizens use heuristics in authoritarian settings. Relying on focus groups, this study uses Russian citizens’ reception of the regime propaganda regarding Ukraine in 2016–17 as a case study to investigate the credibility heuristics of citizens living in an autocratic state during war. Deploying both qualitative and quantitative analysis of citizens’ discourse, I identify the main heuristics used to evaluate the credibility of propaganda. I show that citizens perceive regime propaganda with distrust and often rely on popular wisdom and personal experience to identify bias. However, this does not necessarily guarantee a critical attitude toward regime propaganda. Citizens use these resources to evaluate propaganda’s credibility selectively depending on their political alignment. Indeed, their reliance on personal experience and popular wisdom undermines the authority of state media in general. However, propaganda resonates with the distrust toward media and politics that permeates citizens’ experiences. As a result, the reliance on these resources for interpreting political information can amplify, rather than erode, the credibility of specific news stories. These results contribute to the understanding of both how propaganda is received and credibility heuristics are used in an authoritarian environment.
Nutritional Quality and Antioxidant Activity of Selected Edible Wild Mushrooms

Five species of edible wild mushrooms were selected to determine the proximate composition, vitamins (retinol, α-tocopherol, γ-tocopherol, thiamin, riboflavin, and ascorbic acid) and mineral contents (Fe, Zn, Co, Ca, Mg, K, and Na), antioxidant activity, and total phenolics. Hygrophorus sp. showed significant higher ( p<0.05) fat content as compared to other wild mushrooms with the mean value of 6.57%. However, Polyporus tenuiculus was found to be a good source of fiber while Hygrocybe sp. was good in α-tocopherol among the mushrooms tested. The iron and calcium contents were the highest in Hyrocybe (175.64 μg/g) and Hygrophorus sp. (81.70mg/100g) respectively. The in vitro antioxidant activities of petroleum ether (PE) and methanolic extracts of the edible wild mushrooms were comparable to the cultivated oyster mushroom. The radical scavenging activity was highest for PE extract of Pleurotus sp. (85%) while methanolic extract of Hygrocybe sp. exhibited the highest (94%) chelating effect, in correlation with its significantly higher ( p<0.05) phenolics content (42.21 mg GAE/g extract). In conclusion, edible wild mushrooms can be an excellent source of micronutrients and antioxidant components.
A Model of Voter Dynamics with Information Flow: Differential Equations vs Individual- Based Analysis

A MODEL OF VOTER DYNAMICS WITH INFORMATION FLOW: DIFFERENTIAL EQUATIONS VS INDIVIDUAL-BASED ANALYSIS Cassandra Cvetkovic University of Guelph, 2013 Advisor: Dr. Monica Cojocaru The models will show how ones weekly contacts, media outlets and willingness to accept information, formulate the population’s vote on an issue. The voting population is separated into four groups: aged 20-39 and 40+, with and without a university degree. Scenarios illustrate the influence of outside information, varying initial opinions and effectiveness of accepting information when each group make up an equal portion of the population. The equations are expanded to an agent-based model with each person maintaining individual characteristics. Scenarios are observed with distributions amongst the groups, depicting how the make-up of a population influences if the issue passes with a majority vote. The models can be applied to test scenarios for the vote on a given issue; we show Finland’s, Japan’s and Australias stance on carbon tax implementation.
Resistance to antibiotics in clinical isolates of Escherichia coli producing and non-producing Extended Spectrum Betalactamases (ESBL) obtained from urine cultures in patients with urinary tract infection.

Objective: Identify resistance patterns against various antibiotics in Escherichia coli producers and non-producers of extended spectrum beta-lactamases in urinary infections in a population of the Toluca Valley, Mexico Introduction: Escherichia coli is a bacterium that is part of the normal biota of the human being, but under certain conditions it can produce diseases such as gastrointestinal and urinary tract infections for which is the main responsible. Enterobacteriaceae such as Escherichia are extended spectrum betalactamases (ESBL) producers, which makes their treatment difficult due to a high rate of resistance to antibiotics. Methods: 155 samples were collected from patients with suspected urinary tract infection without exclusion criteria such as age or gender. Automated equipment was used for the identification of the etiological agent and sensitivity tests. For the determination of ESBL, the double disc technique was used. Results: 35 strains of Escherichia coli were obtained, of which 45.72 % have ESBL, these strains show 100 % resistance to betalactams, as well as high resistance to quinolones and tetracycline ranging from 70 to 100 %. On the other hand, the resistance shown by non-ESBL producing strains is variable both for betalactams and for other antibiotics. Conclusion: Treatment for urinary tract infections has become increasily difficult due to the high rate of resistance to antibiotics, mainly ESBL-producing strains such as the E coli strains in the present study. Where high rates of resistance to different antibiotics used in clinical practice are shown.
Image Segmentation Using Hybrid Genetic Algorithm

In the imaging segmentation, the Minimum Error is a better method because of its simplicity and having good result for small targets with more yawp. But it is not fit for a real-time system because its calculation is burdensome and will spend lots of time to segment images. In solving this problem an effective hybrid genetic algorithm (GASA) is proposed which combines the capacity of GA to reach the global optimum with the capability of SA to gain the local one. This text makes use of the fleetness and stability of GASA to reduce the calculation. GASA can not only enhance the rate and efficiency of algorithmic constringency but also effectively avoid appearing precocity and plunging into local optimum. This method with steady performance can completely satisfy the accuracy and speed′s requirements of a real-time system and provide a better effect on image segmentation.