Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vitro Evaluation of the Antifungal Effect of AgNPs on Fusarium oxysporum f. sp. lycopersici

The application of nanomaterials in the agri-food industry can lead us to the formulation of new sustainable and effective pesticides for the control of fungi such as Fusarium oxysporum f. sp. lycopersici (Fol). This is a fungal plant pathogen for the tomato plant. In this work, silver nanoparticles (AgNPs) were synthesized by a green methodology from Geranium leaf extract as a reducing agent. The poisoned food technique was used to determine the percentage of inhibition of Fol mycelial growth by the action of AgNPs. They were characterized by transmission electron microscopy (TEM, JEOL JEM-2100, Tokyo, Japan) and ultraviolet-visible spectroscopy (UV-VIS, DU 730 Beckman Coulter, Brea, CA, USA). Five different concentrations of AgNPs (10, 20, 40, 75, and 150 mg/L) were evaluated in vitro in order to determine the minimum inhibitory concentration (MIC) as well as the behavior of their antifungal activity in tomato fruit. Nanoparticles with spherical morphology and average diameters of 38.5 ± 18.5 nm were obtained. The maximum percentage of inhibition on the mycelial growth of Fol was 94.6 ± 0.1%, which was obtained using the AgNPs concentration of 150 mg/L and it was determined that the MIC corresponds to 75 mg/L. On the other hand, in a qualitative way, it was possible to observe an external inhibitory effect in the tomato fruit from the concentration of 10 mg/L. Finally, we can conclude that AgNPs are a viable alternative for alternative formulations applied in the agri-food industry as pesticide solutions.
Systematic Review of Deep Learning Techniques for Lung Cancer Detection

—Cancer is the leading cause of deaths across the globe and 10 million people died of cancer and particularly 2.21 million new cases registered besides 1.80 million deaths, according to WHO, in 2020. Malignant cancer is caused by multiplication and growth of lung cells. In this context, exploiting technological innovations for automatic detection of lung cancer early is to be given paramount importance. Towards this end significant progress has been made and deep learning model such as Convolutional Neural Network (CNN) is found superior in processing lung CT or MRI images for disease diagnosis. Lung cancer detection in the early stages of the disease helps in better treatment and cure of the disease. In this paper, we made a systematic review of deep learning methods for detection of lung cancer. It reviews peer reviewed journal papers and conferences from 2012 to 2021. Literature review throws light on synthesis of different existing methods covering machine learning (ML), deep learning and artificial intelligence (AI). It provides insights of different deep learning methods in terms of their pros and cons and arrives at possible research gaps. This paper gives knowledge to the reader on different aspects of lung cancer detection which can trigger further research possibilities to realize models that can be used in Clinical Decision Support Systems (CDSSs) required by healthcare units.
Multiwavelength ultrashort pulse generator using a diverging time-lens

Abstract. A self-starting optoelectronic oscillator that employs a diverging time lens and a Mach–Zehnder modulator (MZM) in a fiber-extended cavity to generate a multiwavelength ultrashort pulse train is demonstrated. The switching window formed by the MZM is narrowed by the use of a diverging time lens, which is a phase modulator in our study. A wavelength assignment scheme is deployed to simultaneously suppress the pedestals of pulses on different wavelengths. A detailed analysis is given, and the results are presented by experiment. We have generated 25 GHz optical pulses simultaneously on four wavelengths as a proof-of-concept demonstration. The pulse width of the optical pulse, the phase noise, and the timing jitter performance of the generated microwave signal are experimentally measured.
The Relationship between Anger Expression and Performance Score in Parents and Coaches: The Mediating Role of Self-Efficacy and Assertiveness

Background: Youth sport provides regular physical activity for millions of children. It is a global system, which includes coaches, referees, athletes, and parent spectators; consequently, the behavior of each member can influence the experiences of others. This study aimed to investigate the mediating role of self-efficacy and assertiveness in the relationship between the degree of anger expression and the performance children’s score in a group of parents and coaches. Methods: 200 parents (96 fathers, 104 mothers) and 200 coaches (119 males, 81 females) responded to the General Self-efficacy Scale, The Scale for Interpersonal Behavior, and the State–Trait Anger Expression Inventory-2; an indicator of performance was asked of each coach. The age range of parents was 28–59 years (M = 41.39, SD = 7.07), while that of coaches was 27–43 years (M = 35.91, SD = 3.35). Results: Results revealed that self-efficacy and assertiveness were the direct and indirect predictors of performance score. Conclusions: The study provided an understanding of the nature and magnitude of this profoundly interpersonal experience. Future studies may advance relevant education programs and effective interventions aimed at reducing angry expressions and lack of communication.
Study of Speckle Pattern Quality Assessment used in Digital Image Correlation

In the use of digital image correlation(DIC) technique for full-field deformation measurement,the tested object surface must be covered with a random gray speckle pattern,which deforms together with the object surface as a carrier of deformation information.However,in practical applications,the speckle patterns on tested surface may show distinctly different intensity distribution characteristics and have an important influence on DIC measurements.How to assess the overall quality of different speckle patterns with a simple yet effective parameter is an interesting but confusing problem,and is also helpful to optimize the technique.In this paper,a novel,simple,easy-to-calculate yet effective global parameter,called mean gray intensity gradient,is proposed for quality assessment of the speckle patterns used in DIC.To verify this new concept,five different speckle patterns were numerically translated,and the displacements measured with DIC were compared with the exact ones.The errors are evaluated in terms of mean bias error and standard deviation error.It shows that both mean bias error and standard deviation of the measured displacement are closely related to the mean gray intensity gradient of the speckle pattern used,and a good speckle pattern should be of large mean intensity gradient.
Phosphor-free white light-emitting diodes*

The multiple color-matching schemes that could improve the color rendering index for phosphor-free white LEDs are discussed. Then we review a few of the recent research directions for phosphor-free white LEDs, which include the development of monolithic GaN-based white LEDs and hybrid integrated GaN-based and AlGaInP-based white LEDs. These development paths will pave the way toward commercial application of phosphor-free white LEDs in the coming years.
A fast correlation method for image matching using pixels abstract

The common method for image matching is correlation matching.The calculating number was very large when correlation function was used between two matching images.Two steps approach was proposed in this paper for image matching that was coarse and exact way.Firstly every pixel's gray level was summed up along every column to calculate the mean of column pixels.Then one dimension signal was coarsely matched using one order correlation function.Secondly the points with larger correlation matching values were chosen as the better matching points.These less points were used in the original image as the coordinate origins to calculate the two orders correlation functions.The point with maximum correlation value would be the optimal matching position.Experiences with three kinds of images prove that this approach is faster than conventional one and the matching result is accurate.
Is the Nomenclature of Massive Rotator Cuff Tear Used Correctly? Systematic Review

Background: The term massive refers to a quantitative measure of the rotator cuff tears. Irreparable rotator cuff tear is indicated by ratings that qualitatively evaluate the repairability of the tendon. These two terms should be considered separately. Methods: Between 2016 and 2022, publications in the last about 5 years were searched from Pubmed and MEDLINE online databases. The search terms (irreparable OR massive) AND (rotator cuff tear), (repairable OR massive) AND (rotator cuff tear), (massive OR retracted) AND (rotator cuff tear), (irreparable OR retracted) AND (rotator cuff tear) ”were used. For each search term, it was checked whether the title of the search term was mentioned in the title of the article, and whether the title and the content were compatible in the studies in which the searched term was mentioned in the title. Results: Of the 934 scanned articles, 69 of them were evaluated in terms of study title and material-method compatibility. 22 articles with the term “massive rotator cuff tear” in the title were identified. It was seen that in 11 of these 22 articles (50%), the title and the material method were compatible and in 11 of them (50%) the title and the content were incompatible, and the term “massive” was used instead of the term “irreparable”. Conclusions: We see that most of the studies on massive tears were actually done to emphasize irreparable tears. Since most of the massive tears are repairable, the quality feature of massive tears, which are in the group that poses a problem, as irreparable rotator cuff tear or massive irreparable rotator cuff tear should be emphasized. Keywords: Rotator cuff tears, Shoulder, Arthroscopy, Ireparable, Massive
Various Vertical Coordinate Systems Used for Numerical Weather Prediction

Abstract For numerical weather prediction with primitive equations (the Eulerian hydrodynamic equations modified by the assumption of hydrostatic equilibrium), various coordinate systems are used to represent the vertical structure of the atmosphere. In this paper, we review the essential features of prediction equations, satisfying the conservation of mass and total energy, in various vertical coordinate systems. We formulate the equations of horizontal motion, hydrostatic balance, mass continuity, and thermodynamics using a generalized vertical coordinate in which any variable that gives a single-valued monotonic relationship with a geometric height can be used as a vertical coordinate. Conditions to conserve total energy in a generalized vertical coordinate are investigated. Various prediction schemes using pressure, height, and potential temperature as a vertical coordinate are derived from the set of basic equations in the generalized coordinate system. These three coordinate systems are unique in th.
Confusion Detection from Facial Expression using Deep Neural Network

Confusion is the most frequently observed emotion in daily life and can greatly affect the effectiveness and efficiency of communication. Detecting the confusion from learners and resolving timely is critical for achieving successful teaching in education. Most Facial Expression Recognition (FER) research works focus only on detecting six basic emotions: happiness, sadness, anger, fear, disgust, and surprise. Even though the confusion detection problem gains more attention from researchers recently, analysis of both spatial and temporal information with sufficient data is still short. In this study, we present a spatial-temporal network for confusion detection on video level which was trained on BAUM-1 database, as far as we know, this is the largest public video dataset which confusion is labeled. The model includes ResNet-18 Convolutional Neural Network (CNN), and Long-Short Term Memory (LSTM) recurrent neural network (RNN). By cascading these two deep learning structures, our method yields 73% accuracy which outperforms the baseline LSTM network that yields 67% on the same BAUM-1s dataset. We also test our proposed method with our confusion video dataset which was collected by recording 15 participants under uncontrolled environment. The model was able to predict 1 instance of 30 consecutive facial images within 0.04 seconds and got 66% of accuracy.
Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions, and public behavior.
Effects of Reynolds number and density on a Jet-in-Hot-Coflow

The Delft Jet-in-Hot-Coflow (DJHC) burner mimics flameless combustion for research purposes. Although the burner is intended to be used with natural gas in the jet, in this research it is operated with an inert air jet, to gain insight in the characteristics of the flow field without the further complication of combustion. This thesis reports on the effects of Reynolds number and density ratios ( ρjet ρcoflow ) on radial and axial velocities, Reynolds stresses, location of the virtual origin, and decay rate, all at the centreline, using LDA and CARS. Four different cases are studied, one of which should have the same parameters as studied by Oldenhof et al.[1], though without the flame, but has a different density ratio. The other three are ceteris paribus cases with one different density ratio and one different Reynolds number of the jet, two cases have the same Reynolds number and the same density ratio, but other parameters are different (e.g. temperature and velocity). The results obtained in this thesis are quite different from results obtained by Lenze [2], Ebrahimi [3] and Hussein et al. [4], who reported on studies of turbulent free jets. These deviations can be caused by the relativelty low turbulence in the cases studied in this thesis, compared to other investigations and the presence of a coflow in this thesis.
Stabilization of unstable and chaotic models of population dynamics by target oriented and stochastic control

Structured discrete population models are able to fit and predict chaotic exp erimental data. However, most of the chaos control techniques in the literature have been designed and analyzed in a one-dimensional setting. By introducing ta rget oriented control for discrete dynamical systems, we prove the possibility to s abilize a chosen state for a wide range of structured population models. The re sults are illustrated with introducing a control in the celebrated LPA model [1] describing a flour beetle dynamics. Moreover, we show that the new control allows to s tabilize periodic solutions for higher order difference equations, such as th e delayed Ricker model, for which previous target oriented methods were not design ed [2]. The second part of our talk outlines the possibility to stabilize otherwise unstab le or even chaotic equation by noise only [3]. However, for chaotic maps sta bilization comes at a price: the interval of allowed initial conditions becomes incredib ly small. A combination of a stochastic control with eventual noise control allows to relax conditions on both the initial value and the parameters of the equation.
Moringa oleifera Lam Leaf Extract Stimulates NRF2 and Attenuates ARV-Induced Toxicity in Human Liver Cells (HepG2)

The World Health Organization (WHO) reported that there are 37 million individuals living with the human immunodeficiency virus (HIV) worldwide, with the majority in South Africa. This chronic disease is managed by the effective use of antiretroviral (ARV) drugs. However, with prolonged use, ARV drug-induced toxicity remains a clinically complex problem. This study investigated the toxicity of ARV drugs on mitochondria and the NRF2 antioxidant pathway and its possible amelioration using Moringa oleifera Lam (MO) leaf extracts. This medicinal plant has a range of functional bioactive compounds. Liver (HepG2) cells were treated with individual ARV drugs: Tenofovir disoproxil fumarate (TDF), Emtricitabine (FTC), and Lamivudine (3TC) for 96 h, followed by MO leaf extracts for 24 h. Intracellular ROS, cytotoxicity, lipid peroxidation, total and reduced glutathione (GSH), ATP, and mitochondrial polarisation were determined. Finally, protein (pNRF2, NRF2, SOD2, CAT, and Sirt3) and mRNA (NRF2, CAT, NQO1 SOD2, Sirt3, and PGC1α) expression were measured using Western blot and qPCR, respectively. TDF, FTC, and 3TC significantly increased intracellular ROS and extracellular levels of both MDA and LDH. ARVs also reduced the GSH and ATP levels and altered the mitochondrial polarization. Further, ARVs reduced the expression of NRF2 SOD2, Sirt3, CAT, NQO1, UCP2 and PGC1α mRNA and consequently pNRF2, NRF2, SOD2, Sirt3 and CAT protein. In contrast, there was a significant reduction in the extracellular MDA and LDH levels post-MO treatment. MO significantly reduced intracellular ROS while significantly increasing GSH, ATP, and mitochondrial membrane polarization. The addition of MO to ARV-treated cells significantly upregulated the expression of NRF2, SOD2, Sirt3, CAT, UCP2, PGC1α, and NQO1 mRNA and pNRF2, NRF2, SOD2, Sirt3 proteins. Thus, MO ameliorates ARV-induced hepatotoxicity by scavenging oxidants by inducing the NRF2 antioxidant pathway. MO shows great therapeutic potential and may be considered a potential supplement to ameliorate ARV drug toxicity.
Association between neighbourhood composition, kindergarten educator-reported distance learning barriers, and return to school concerns during the first wave of the COVID-19 pandemic in Ontario, Canada

Abstract Introduction Research to date has established that the COVID-19 pandemic has not impacted everyone equitably. Whether this unequitable impact was seen educationally with regards to educator reported barriers to distance learning, concerns and mental health is less clear. Objective The objective of this study was to explore the association between the neighbourhood composition of the school and kindergarten educator-reported barriers and concerns regarding children’s learning during the first wave of COVID-19 related school closures in Ontario, Canada. Methods In the spring of 2020, we collected data from Ontario kindergarten educators (n = 2569; 74.2% kindergarten teachers, 25.8% early childhood educators; 97.6% female) using an online survey asking them about their experiences and challenges with online learning during the first round of school closures. We linked the educator responses to 2016 Canadian Census variables based on schools’ postal codes. Bivariate correlations and Poisson regression analyses were used to determine if there was an association between neighbourhood composition and educator mental health, and the number of barriers and concerns reported by kindergarten educators. Results There were no significant findings with educator mental health and school neighbourhood characteristics. Educators who taught at schools in neighbourhoods with lower median income reported a greater number of barriers to online learning (e.g., parents/guardians not submitting assignments/providing updates on their child’s learning) and concerns regarding the return to school in the fall of 2020 (e.g., students’ readjustment to routines). There were no significant associations with educator reported barriers or concerns and any of the other Census neighbourhood variables (proportion of lone parent families, average household size, proportion of population that do no speak official language, proportion of population that are recent immigrants, or proportion of population ages 0-4). Conclusions Overall, our study suggests that the neighbourhood composition of the children’s school location did not exacerbate the potential negative learning experiences of kindergarten students and educators during the COVID-19 pandemic, although we did find that educators teaching in schools in lower-SES neighbourhoods reported more barriers to online learning during this time. Taken together, our study suggests that remediation efforts should be focused on individual kindergarten children and their families as opposed to school location.
Analysis of Frequency Selective Surface with U-Shaped Geometry

In this paper the frequency selective surface, FSS, with U-shaped geometry is analyzed. Employing the Wave Concept Iterative Procedure, WCIP, and the Method of Moments, MoM, numerical results for the FSS frequency response are presented and discussed. The first frequency of resonance is calculated using an approximate expression. Numerical results obtained by the use of WCIP and the MoM, as well as the results of the first resonance that were obtained with the approximate expression, present very good concordance. Based on the electric current distribution, the role of the U armsâ€™ length in the FSS frequency of resonance is examined. Â Four FSS were fabricated and experimentally characterized. Compared to numerical ones, the measured results presented a very good agreement. The achieved frequency characteristics, such as reduction in the frequency of resonance up to approximately 50% in comparison to dipole, and the geometry simplicity, instigate the pursuit of new features of the FSS U-shaped geometry.
Vector addition theorem and its diagonalization

The conventional vector addition theorem is written in a compact notation. Then a new and succinct derivation of the vector addition theorem is presented that is as close to the derivation of the scalar addition theorem. Newly derived expressions in this new derivation are used to diagonalize the vector addition theorem. The diagonal form of the vector addition theorem is important in the design of fast algorithms for computational wave physics such as computational electromagnetics and computational acoustics. AMS subject classifications: 43A45, 43A65, 43A90, 47L90, 78M99 PACS: 03.50. De, 85.40. Bh, 89.20. Ff
The Missing Tail Risk in Option Prices

This paper contributes to the literature on deviations from rational expectations in financial markets and to the literature on evaluating density forecasts. We first develop a novel statistic to evaluate the overall accuracy of distributional forecasts, and find two methods that yield accurate distributional forecasts. We then propose another statistic to examine the relative accuracy over the entire distribution range. Our results indicate more oil price realizations in the left tail than predicted. We argue that this finding points to a persistent behavioral forecasting bias and a departure from the rational expectations hypothesis. Investors hence underestimate left tail risk and under-insure against very low oil prices