Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro development and acclimatization of Cyrtopodium aliciae L. Linden & Rolfe, an endemic species of the Chapada Diamantina

ABSTRACT: Orchids are valued as ornamental plants, bioindicators, and medicinal plants, which implies that some species may be over-collected. Some inhabit very fragile environments and are under threat by the misuse of habitats and anthropogenic impacts. The search for beautiful plants and flowers has increased the number of facilities for micropropagation either by seeding or by cloning plants using in vitro techniques. However, not all species have appropriate media for growth and development that would help in conservation efforts. Cyrtopodium aliciae is an endemic species of rupestrian grassland in Brazil t. It has appeal as an ornamental plant or for use in hybridisation programs dueo its small size and white brownish-purple dotted flowers. This study compared three different media, namely ½ concentration Murashige and Skoog (MS), Vacin and Wendt, and Knudson C, during plant growth and their effect on the acclimatization of Cyrtopodium aliciae. The number and length of shoots and roots, increase in mass, and survival in vitro and ex vitro were analyzed. The experiment was conducted as completely random with a factorial arrangement of treatments (3 × 3) with 10 repetitions per treatment containing 10 plants for the in vitro experiment and 3 repetitions of 10 plants for the ex vitro experiment. Cyrtopodium aliciae performed better in the ½ concentration MS medium with a higher increase in mass, plant development, and survival under both in vitro and ex vitro conditions.
An R and S-Plus® Companion to Multivariate Analysis

Casella, G. , and Berger, R. L. (2002), Statistical Inference, Belmont, CA: Duxbury Press. Davison, A. C. , and Hinkley, D. V. (1999), Bootstrap Methods and Their Applications, Cambridge, U.K.: Cambridge University Press. Efron, B. , and Tibshirani, R. J. (1993), An Introduction to the Bootstrap, New York: Chapman & Hall/CRC. Eubank, R. L. (1999), Nonparametric Regression and Spline Smoothing (2nd ed.), New York: Marcel Dekker. Evans, M. J., and Rosenthal, J. S. (2004), Probability and Statistics, New York: W. H. Freeman and Company. Fishman, G. S. (2000), Monte Carlo: Concepts, Algorithms, and Applications, New York: Springer-Verlag. Fletcher, R. (1987), Practical Methods of Optimization, New York: Wiley. Gentle, J. E. (2002), Elements of Computational Statistics, New York: SpringerVerlag. (2004), “Courses in Statistical Computing and Computational Statistics,” The American Statistician, 58, 2–5. Givens, G. H. , and Hoeting, J. A. (2005), website for Computational Statistics, available at http://www.stat.colostate.edu/computationalstatistics/. Lange, K. (1999), Numerical Analysis for Statisticians, New York: SpringerVerlag. Monahan, J. F. (2001), Numerical Methods of Statistics, Cambridge, U.K.: Cambridge University Press. Robert, C. P., and Casella, G. (2004), Monte Carlo Statistical Methods, New York: Springer-Verlag.
Electrospinning of Poly (Acrylamide), Poly (Acrylic Acid) and Poly (Vinyl Alcohol) Nanofibers: Characterization and Optimization Study on the Effect of Different Parameters on Mean Diameter Using Taguchi Design of Experiment Method

In this study, nanofibers of poly (acrylic acid) (PAAc), polyacrylamide (PAAm) and poly (vinyl alcohol) (PVOH) were prepared using the electrospinning technique. Based on the Taguchi DOE (design of experiment) method, the effects of electrospinning parameters, i.e., needle tip to collector distance, polymer solution concentration, applied voltage, polymer solution feed rate and polymer type, on the diameter and morphology of polymer nanofibers were evaluated. Analyses of the experiments for the diameters of the polymer nanofibers showed that the type of polymer was the most significant factor. The optimal combination to obtain the smallest diameters with minimum deviations for electrospun polymer nanofibers was also determined. For this purpose, the appropriate factor levels were determined as follows: polymer PAAm, applied voltage 10 kV, delivery rate 0.1 mL/h, needle tip to collector distance 10 cm, and polymer solution concentration 8%, to obtain the thinnest nanofibers. This combination was further validated by conducting a confirmation experiment, and the average diameter of the polymer nanofibers was found to be close to the optimal conditions estimated by the Taguchi DOE method.
Towards Cost-Effective Crystalline Silicon Based Flexible Solar Cells: Integration Strategy by Rational Design of Materials, Process, and Devices

Towards Cost-Effective Crystalline Silicon Based Flexible Solar Cells: Integration Strategy by Rational Design of Materials, Process, and Devices Rabab R. Bahabry The solar cells market has an annual growth of more than 30 percent over the past 15 years. At the same time, the cost of the solar modules diminished to meet both of the rapid global demand and the technological improvements. In particular for the crystalline silicon solar cells, the workhorse of this technology. The objective of this doctoral thesis is enhancing the efficiency of c-Si solar cells while exploring the cost reduction via innovative techniques. Contact metallization and ultra-flexible wafer based c-Si solar cells are the main areas under investigation. First, Silicon-based solar cells typically utilize screen printed Silver (Ag) metal contacts which affect the optimal electrical performance. To date, metal silicide-based ohmic contacts are occasionally used for the front contact grid lines. In this work, investigation of the microstructure and the electrical characteristics of nickel monosilicide (NiSi) ohmic contacts on the rear side of c-Si solar cells has been carried out. Significant enhancement in the fill factor leading to increasing the total power conversion efficiency is observed. Second, advanced classes of modern application require a new generation of 5 versatile solar cells showcasing extreme mechanical resilience. However, silicon is a brittle material with a fracture strains <1%. Highly flexible Si-based solar cells are available in the form thin films which seem to be disadvantageous over thick Si solar cells due to the reduction of the optical absorption with less active Si material. Here, a complementary metal oxide semiconductor (CMOS) technology based integration strategy is designed where corrugation architecture to enable an ultra-flexible solar cell module from bulk mono-crystalline silicon solar wafer with 17% efficiency. This periodic corrugated array benefits from an interchangeable solar cell segmentation scheme which preserves the active silicon thickness and achieves flexibility via interdigitated back contacts. These cells can reversibly withstand high mechanical stress as the screen-printed metals have fracture strain >15%. Furthermore, the integration of the cells is demonstrated on curved surfaces for a fully functional system. Finally, the developed flexing approach is used to fabricate three-dimensional dome-shaped cells to reduce the optical coupling losses without the use of the expensive solar tracking/tilting systems.
Hierarchical Fringe Tracking, sky coverage and AGNs at the VLTI

Hierarchical Fringe Tracking (HFT) is a fringe tracking concept optimizing the sensitivity in optical long baseline by reducing to an absolute minimum the number of measurements used to correct the OPD fluctuations. By nature, the performances of an HFT do not decreases with the number of apertures of the interferometer and are set only by the flux delivered by the individual telescopes. This a critical feature for future interferometers with large number of apertures both for homodyne and heterodyne operation. Here we report the design and first optical bench tests of integrated optics HFT chips for a 4 telescopes interferometer such as the VLTI. These tests validate the HFT concept and confirm previous estimates that we could track accurately fringes on the VLTI up to nearly K~15.9 with the UTs and K~12.2 with the ATs with a J+H+K fringe tracker with one HFT chip per band. This is typically 2.5 magnitudes fainter than the best potential performance of the current ABCD fringe tracker in the K band. An active longitudinal and transverse chromatic dispersion correction allows the optimization of broad band fiber injections and instrumental contrast. We also present a preliminary evaluation of the potential of such a gain of sensitivity for the observations of AGNs with the VLTI.
Quality of Life of the Population of Kazakhstan: Assessment of the Main Parameters and Identification of Problem Areas

The purpose of the article is to study the main parameters characterizing the quality of life of the population of Kazakhstan in modern economic conditions and develop proposals for its improvement. The methodology of the study is based on a holistic approach, including the use of objective indicators of the quality of life that determine the material conditions and means of human livelihood, and subjective assessments of the population, social groups of their material and social well-being. Measuring and evaluating the level and quality of life of the population of Kazakhstan based on an integrated approach includes the use of the following methods: component analysis, normative and stratification, integral indicators, and subjective assessments of the population. The leading blocks of the life support system are considered, including the assessment of income and consumption in the conditions of maintaining socio-economic income differentiation; the social qualitative parameters of the life support system of the population, the most important indicators of the implementation of priority areas of development of labor potential in the innovative economy, modernization of housing policy are given.
Approximations for modelling CO chemistry in GMCs: a comparison of approaches

We examine several different simplified approaches for modelling the chemistry of CO in three-dimensional numerical simulations of turbulent molecular clouds. We compare the different models both by looking at the behaviour of integrated quantities such as the mean CO fraction or the cloud-averaged CO-to-H2 conversion factor, and also by studying the detailed distribution of CO as a function of gas density and visual extinction. In addition, we examine the extent to which the density and temperature distributions depend on our choice of chemical model. We find that all of the models predict the same density PDF and also agree very well on the form of the temperature PDF for temperatures T > 30 K, although at lower temperatures, some differences become apparent. All of the models also predict the same CO-to-H2 conversion factor, to within a factor of a few. However, when we look more closely at the details of the CO distribution, we find larger differences. The more complex models tend to produce less CO and more atomic carbon than the simpler models, suggesting that the C/CO ratio may be a useful observational tool for determining which model best fits the observational data. Nevertheless, the fact that these chemical differences do not appear to have a strong effect on the density or temperature distributions of the gas suggests that the dynamical behaviour of the molecular clouds on large scales is not particularly sensitive to how accurately the small-scale chemistry is modelled.
Synchrony and chaos in coupled oscillators and neural networks

by Sridhar Raghavachari This disssertation studies the dynamics of ensembles of coupled, dynamical elements with discrete and continuous time dynamics. Speci c problems include the appearance of synchronous behavior in an ensemble of dynamical elements. We show that the dynamics of coupled map lattices with connectivity that scales with inter-site distance exhibit a transition from spatial disorder to spatially uniform temporal chaos as the scaling is varied. We investigate the eigenvalue spectrum of the stochastic matrix characterizing uctuations from the uniform state numerically and show that the spectrum is bounded,real and the largest eigenvalue (corresponding to the uniform solution) has a gap separating it from the remaining N-1 eigenvalues which correspond to nonuniform solutions. The width of this gap depends on the scaling exponent. We relate the stability of the uniform state with this gap and show that the state is globally stable even in a strongly chaotic region of the uncoupled map. Bursting is a prototypical pattern of voltage oscillations of membrane potentials of biological cells, where the membrane potential alternates between fast oscillations and a slow drift. These complex oscillations arise as a result of interactions between the kinetics of fast and slow ion channels. While bursting in isolated cells is well understood, the study of populations of interacting bursters is less developed. We Sridhar Raghavachari study a one-dimensional continuum model of bursting and show that synchronous bursting is extinguished by a spatial wave of bursting separating active and quiescent cells when the coupling is weak. This result places bounds on the measured values of coupling strength between secretory cells in the pancreas. Rhythmic behavior in animals, such as locomotion, digestion and respiration is controlled by the interactions of cellular and synaptic mechanisms acting on several time scales. We explore a simple rhythmic circuit model with two cells reciprocally inhibiting each other with fast and slow time scale inhibition. The interaction between the fast oscillations of the cells and the slow inhibition results in a complex pattern of bursting. This form of bursting originates from a network interaction, as isolated cells can only oscillate but not burst. This result is useful in understanding the design of central pattern generators in invertebrates where multiple time scale inhibition is common. To my parents and Arpita.
Cross-Domain Industrial Intrusion Detection Deep Model Trained With Imbalanced Data

Constrained by the high acquisition and labeling cost, traffic data in industrial control systems (ICSs) are usually extremely imbalanced. Deep-learning-based (DL) industrial control intrusion detection systems (IDSs) are not applicable to dynamic networks and show a limited detection performance. In this article, we enhanced the information transmission link in adversarial domain adaptation (DA) and proposed an information-enhanced adversarial DA (IADA) method. Our method could train a cross-domain industrial intrusion detection deep model with imbalanced data and maintained high detection accuracy. The experimental results based on SCADA network layer data showed that the detection accuracy of the gated recurrent unit model trained in IADA reached 93.7% and 91.3% in the two transfer tasks with a significant cross-domain discrepancy.
Decomposition-Based Multiobjective Optimization Algorithms With Adaptively Adjusting Weight Vectors and Neighborhoods

The decomposition-based multiobjective optimization algorithm (MOEA/D) is an effective method of solving a multiobjective optimization problem (MOP). The main idea of MOEA/D is that the objectives are weighted through different vectors to form different subproblems, and an optimal solution set is obtained by co-evolution in a certain neighborhood. However, with the increase of objectives, the number of nondominated solutions increases exponentially, resulting in the deteriorated capability of searching for optimal solutions. In addition, for an optimization problem with the complex Pareto front (PF), the selection pressure of nondominated solutions is insufficient. To make evolution more efficient, an MOEA/D with adaptively adjusting weight vectors and neighborhoods (MOEA/D-AAWNs) is developed in this article. First, the evolutionary direction of each subproblem is analyzed and the Sparsity function (Spa) is proposed to measure the population density on the PF. By using Spa, a method of generating uniform vectors is presented to improve the diversity of solutions. Besides, a method of adaptively adjusting neighborhoods is given. It adjusts neighborhoods according to the number of iterations and the Spa value of its corresponding subproblem. In this way, the computational resource can be effectively allocated, leading to the improvement in evolutionary efficiency. The proposed algorithm is applied to solve a series of benchmark optimization instances, and the experimental results show that the proposed algorithm outperforms comparison algorithms in runtime, convergence, and diversity.
Artificial Neural Network-Based Modeling for Estimating the Effects of Various Random Fluctuations on DC/Analog/RF Characteristics of GAA Si Nanosheet FETs

Advanced field-effect transistors (FETs), such as gate-all-around (GAA) nanowire (NW) and nanosheet (NS) devices, have been highly scaled; therefore, they are critically affected even by a microscopic fluctuation. As the GAA NS device is considered a promising candidate beyond 5-nm technology, it is essential to analyze the effects of these fluctuations on dc and analog/radio frequency (RF) characteristics for future applications. In this article, we for the first time demonstrate that the machine learning (ML)-aided numerical device simulation approach can be used to model the effects of various fluctuations on the characteristics of GAA NS FETs (NSFETs). Among various fluctuations, we mainly focus on work function fluctuation (WKF), random dopant fluctuation (RDF), and interface trap fluctuation (ITF). The independent and combined effects of these fluctuations on the characteristics of NSFETs are studied. Except for transfer and output characteristics, analog and RF parameters, such as gate capacitance, transconductance, cutoff frequency, 3-dB frequency, and transconductance efficiency, are analyzed in detail. The main aim of this work is to show the capability and generality of ML in modeling various electrical characteristics of the explored NSFETs. The results show that the ML-based technique is fast and efficient, which accelerates the overall process and gives engineering acceptable accurate results.
Human Identification Model Considering Biometrics Features

In the medical field, brain classification is an effective technique for identifying a person through his brain print based on the hidden biometrics of high specificity included in the magnetic resonance images(MRI) of the brain, as this privacy strongly contributes to the issue of verification and identification of the person. In this paper, the brain print is extracted from the MRI obtained from 50 healthy people, which were passed through several pre-processing techniques in order to be used in the classification stage through convolutional neural network model, among those pre-classification stages, data collection after extracting the influential features for each image, which was based on linear discrimination analysis (LDA). The experimental results showed the importance of using LDA for feature extraction and adoption as input for K-NN and CNN classifiers. The classifiers proved successful in the classification if the features extracted with the help of LDA were adopted. Where CNN had the ability to classify with an accuracy of 99%, 82% for K-NN. The final stage in identifying a person through a brain fingerprint relied mainly on the model's success in classifying and predicting the remaining data in the testing stage.
A Preconditioning Paradox: Contrasting Effects of Initial Phyllosphere and Early Leaf Decomposer Microfungi on Subsequent Colonization by Leaf Decomposing Non-Unit-Restricted Basidiomycetes

Fungal interactions during leaf decomposition can facilitate or inhibit other fungi. This experiment focused on whether preconditioning of leaf litter by microfungi that were confined to one leaf (Unit-Restricted) made leaf litter less likely to be colonized and decomposed by basidiomycetes that bind litter into mats (Non-Unit-Restricted) than non-preconditioned litter. Leaves of Manilkara bidentata in litterbags were preconditioned by incubating them for 0, 1, 2 or 3 months in flat litter/seed rain baskets 10 cm above the forest floor to avoid colonization by basidiomycete fungi. Preconditioned and non-preconditioned leaves were transferred to 5 replicate basidiomycete fungal mats of Gymnopus johnstonii for 6 weeks. Both attachment by basidiomycete fungi and percent mass loss after 6 weeks decreased significantly with increasing preconditioning time. In non-preconditioned leaves, gamma irradiation did not affect mass loss or percent white-rot despite having significantly increased numbers of basidiomycete fungal connections as compared to non-irradiated leaves. In non-preconditioned leaves, more basidiomycetes attachmented to non-irradiated than irradiated leaves suggest facilitation by phyllosphere microfungi. While basidiomycete colonization was initially facilitated by phyllosphere fungi, we inferred that degradation of resource quality led to fewer fungal attachments and less mass loss after 1–3 months of preconditioning by microfungi. The date suggest there is a 1-month time window for basidiomycete fungi to incorporate fallen leaves into their litter mats.
A Probabilistic Approach to the Two-Dimensional Navier-Stokes Equations

We turn the Navier-Stokes equations for a 2-dimensional viscous incompressible fluid into a system of functional integrals in the trajectory space of a suitable diffusion process. Using probabilistic techniques as Girsanov's transformation and Bismut-Elworthy formula, we prove the existence of a unique global solution of this system in a constructive way.
Arousal-valence recognition using CNN with STFT feature-combined image

A novel ocular-features-combining method, called short-time Fourier transform (STFT) feature-combined image, and a simple convolutional neural networks (CNNs) model are proposed for arousal-valence recognition. The STFT feature-combined image aims to represent information on two ocular features (pupil size and eye movements) as a single image. The CNN model consists of two convolutional layers and uses STFT feature-combined image as an input. The experimental results demonstrate the effectiveness of the proposed method, and show that CNN model is not only effective for emotion-recognition methods based on other modalities, but also effective for ocular-feature-based emotion recognition.
The changing face of telesupervision and digital training in response to COVID‐19

Abstract The COVID‐19 pandemic has rapidly changed the ways in which marriage and family therapists (MFTs)/couple and family therapists (CFTs) engage in clinical supervision. Traditional face‐to‐face supervisory relationships have transitioned to telesupervision, which refers to supervisors using the internet as a training medium for their supervisees. Supervisors and supervisees alike are necessarily adapting to telesupervision relationships in an evolving world. As emergency protocols begin to give way to more routine procedures, it is important for supervisors to engage in meaningful conversations around the benefits, the challenges, and the future of telesupervision. This article discusses the various facets of telesupervision, including ethical implications, supervision modalities, the virtual supervisory alliance, the impact of telesupervision upon self‐of‐the‐therapist work, and the potential benefits of telesupervision.