Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of conservation activities on people who are incarcerated: a case study based on qualitative and quantitative analyses

In the past two decades, conservationists and the corrections sector have implemented collaborative ecological restoration projects, creating more inclusive arenas for conservation. These venues provide people who are incarcerated with opportunities to have a positive impact on their environment, and for ecologists to communicate science and the importance of nature with people in nature-deprived environments. We provide examples of conservation programs and their associated media pieces nationwide, whose descriptions, to date, have been almost entirely anecdotal and without formal evaluation. In this study, a collaboration of ecologists and social scientists analyzed impacts on the “incarcerated citizen scientists” who participated in two conservation projects coordinated by these ecologists at the Salt Lake County Jail, Utah, using quantitative and qualitative approaches, including voluntary pre- and post-surveys. The quantitative results informed potential outcomes, but were inconclusive. However, the qualitative results revealed that a majority of the participants reported gaining knowledge about science and conservation, and that about a quarter of them reported psychological benefits from participating, such as feeling that they were able to give back to their community through the project. These results document the potential positive impacts that participation in ecological restoration projects can help promote well-being and community involvement, and to increase science knowledge from all participants. The results also reinforce the importance of collaborations between scientists who use quantitative and qualitative approaches and analytical tools, which, when combined, provide the capacity to measure, analyze, and interpret data from human participants. These considerations should be further explored with collaborations of natural scientists, social scientists, corrections staff, and people who are incarcerated as ecological restoration projects in correctional institutions become more prevalent.
Energy Harvesting in a System with a Two-Stage Flexible Cantilever Beam

The subject of the research contained in this paper is a new design solution for an energy harvesting system resulting from the combination of a quasi-zero-stiffness energy harvester and a two-stage flexible cantilever beam. Numerical tests were divided into two main parts-analysis of the dynamics of the system due to periodic, quasiperiodic, and chaotic solutions and the efficiency of energy generation. The results of numerical simulations were limited to zero initial conditions as they are the natural position of the static equilibrium. The article compares the energy efficiency for the selected range of the dimensionless excitation frequency. For this purpose, three cases of piezoelectric mounting were analyzed-only on the first stage of the beam, on the second and both stages. The analysis has been carried out with the use of diagrams showing difference of the effective values of the voltage induced on the piezoelectric electrodes. The results indicate that for effective energy harvesting, it is advisable to attach piezoelectric energy transducers to each step of the beam despite possible asynchronous vibrations.
Optimal Joint Power and Rate Adaptation for Awareness and Congestion Control in Vehicular Networks

To support inter-vehicular applications, vehicles broadcast beacons with information about their state. Congestion may occur when the load on the channel due to beaconing can prevent the transmission of other types of messages. In this paper, we propose a distributed algorithm for optimal joint adaptation of transmit power and beaconing rate for congestion and awareness control. Our approach is based on a network utility formulation of the congestion control problem, which allows us to induce a desired fairness notion and set different priorities for vehicles. We formulate the general problem but, since it is not convex, we assume Rayleigh fading and derive an algorithm, called PRAIOS, that solves the optimization problem in a decentralized way with convergence guarantees. Our results, validated with realistic simulations in both static and dynamic scenarios and compared with other proposals, show that PRAIOS quickly converges to close to optimal allocations, while keeping the maximum beaconing load at the desired level. They also show that it can control the load when the fading is not Rayleigh. Applications can dynamically set their requirements as constraints, that are enforced by the algorithm while complying with the maximum load, which allows seamless integration of operational requirements into the control framework.
Grassmann tensor renormalization group for the one-flavor lattice Gross–Neveu model with finite chemical potential

We apply the Grassmann tensor renormalization group (GTRG) to the one-flavor lattice Gross-Neveu model in the presence of chemical potential. We compute the fermion number density and its susceptibility and confirm the validity of GTRG for the finite density system. We introduce a method analogous to the reweighting method for Monte Carlo method and test it for some parameters.
Jets in strongly-coupled N = 4 super Yang-Mills theory

We study jets of massless particles in N=4 super Yang-Mills theory using the AdS/conformal field theory correspondence both at zero and finite temperature. We set up an initial state corresponding to a highly energetic quark/antiquark pair and follow its time evolution into two jets. At finite temperature the jets stop after traveling a finite distance, whereas at zero temperature they travel and spread forever. We map out the corresponding baryon number charge density and identify the generic late-time behavior of the jets as well as features that depend crucially on the initial conditions.
Nonlocal permittivity from a quasistatic model for a class of wire media

A simple quasi-static model applicable to a wide class of wire media is developed that explains strong non-locality in the dielectric response of wire media in clear physical terms of effective inductance and capacitance per unit length of a wire. The model is checked against known solutions and found to be in excellent agreement with the results obtained by much more sophisticated analytical and numerical methods. Special attention is given to suppression of the spatial dispersion effects in wire media.
DETAILED NUMERICAL SIMULATIONS ON THE FORMATION OF PILLARS AROUND H ii REGIONS

We study the structural evolution of turbulent molecular clouds under the influence of ionizing radiation emitted from a nearby massive star by performing a high-resolution parameter study with the iVINE code. The temperature is taken to be 10 K or 100 K, the mean number density is either 100 cm−3 or 300 cm−3. Furthermore, the turbulence is varied between Mach 1.5 and Mach 12.5, the main driving scale of the turbulence is varied between 1 pc and 8 pc. We vary the ionizing flux by an order of magnitude, corresponding to allowing between 0.5% and 5% of the mass in the domain to be ionized immediately. In our simulations, the ionizing radiation enhances the initial turbulent density distribution and thus leads to the formation of pillar-like structures observed adjacent to H ii regions in a natural way. Gravitational collapse occurs regularly at the tips of the structures. We find a clear correlation between the initial state of the turbulent cold cloud and the final morphology and physical properties of the structures formed. The most favorable regime for the formation of pillars is Mach 4–10. Structures and therefore stars only form if the initial density contrast between the high-density unionized gas and the gas that is going to be ionized is lower than the temperature contrast between the hot and the cold gas. The density of the resulting pillars is determined by a pressure equilibrium between the hot and the cold gas. A thorough analysis of the simulations shows that the complex kinematical and geometrical structure of the formed elongated filaments reflects that of observed pillars to an impressive level of detail. In addition, we find that the observed line-of-sight velocities allow for a distinct determination of different formation mechanisms. Comparing the current simulations to previous results and recent observations, we conclude that, e.g., the pillars of creation in M16 formed by the mechanism proposed here and not by the radiation driven implosion of pre-existing clumps.
Identification of Bacillus velezensis SBB and Its Antifungal Effects against Verticillium dahliae

Traditional control methods have drawbacks in controlling Verticillium wilt diseases caused by Verticillium dahliae Kleb. ; therefore, an efficient and environmentally friendly strategy for disease control must be identified and the mechanisms determined. In this study, a soil-isolated strain SBB was identified as Bacillus velezensis based on 16S rRNA, gyrA, and gyrB gene sequences. In vitro, strain SBB had excellent inhibitory effects on V. dahliae, with the highest inhibition rate of 70.94%. Moreover, strain SBB inhibited production of the conidia of V. dahliae and suppressed the production of microsclerotia and melanin. Through gas chromatograph–mass spectrometer analysis, nine compounds were detected from the volatile organic compounds produced by SBB, among which 2-nonanol, 2-heptanone, 6-methyl-2-heptanone, and 2-nonanone could completely inhibit V. dahliae growth. Strain SBB produced cellulase, amylase, protease, and siderophore. During inhibitory action on V. dahliae, strain SBB showed upregulated expression of genes encoding non-volatile inhibitory metabolites, including difficidin, bacilysin, and bacillaene, at 1.923-, 1.848-, and 1.448-fold higher, respectively. Thus, our study proved that strain SBB had an efficient antagonistic effect on V. dahliae, suggesting strain SBB can be used as a potential biological control agent against Verticillium wilt.
Residential CO2 Emissions in Europe and Carbon Taxation: A Country-Level Assessment

This paper examines the determinants of residential CO2 emissions, which are not covered by the European Union Emissions Trading System (EU ETS), in 19 European countries between 2000-2017. Using both static and dynamic panel models, we found strong relationships between CO2 emissions per capita, GDP per capita, energy prices and heating needs. We then assessed the impact of European carbon taxation and show that a €20/tonne CO2 tax lowers emissions by 1% on average. We found that this tax affects countries differently in terms of tax revenue-to-GDP ratio. Poland and the Czech Republic would have to pay the highest contribution, and Portugal and Denmark the lowest. Finally, we propose a scenario that equalizes countries’ tax burdens. We show that, were Europe to redistribute all tax revenues, the main beneficiaries would be Poland and Belgium, while Denmark and Luxembourg would have to pay a surtax.
Clinical effectiveness and prospects of methylene blue: A systematic review

Methylene blue (MB) is a well-known pharmaceutical ingredient that is thought to have a multi-targeted therapeutic effect as an anti-malarial and neuroprotective agent and has recently been identified as a treatment for coronavirus disease 2019 (COVID-19). In this review, we present an overview of relevant clinical trials, including ongoing trials, on the therapeutic uses of MB. A search for clinical trials on clinicaltrials.gov was performed using the terms "methylene blue" and "methylthionine chloride. " This review focuses on clinical trials of MB-based therapies applied to brain diseases, cancer imaging and diagnosis, infectious diseases such as malaria or COVID-19, and cardiovascular diseases. Nanoparticle-based delivery techniques have also been briefly discussed in addition to common delivery methods.
Language learners’ emotion regulation and enjoyment in an online collaborative writing program

Collaborative learning in online contexts is emotionally challenging for language learners. To achieve successful learning outcomes, language learners need to regulate their emotions and sustain positive emotions during the collaborative learning process. This study investigated language learners’ emotion regulation and enjoyment, the most extensively researched positive emotion in foreign language learning, in an online collaborative English learning environment. In the study, we collected data by surveying 336 Chinese students majoring in English who collaboratively completed a series of English language writing tasks in 108 online groups facilitated by a social media app (WeChat). Principal component analysis revealed two primary types of emotion regulation: peer regulation and group regulation. The analysis also revealed one factor underpinning enjoyment: enjoyment of online collaboration. Correlation analysis showed medium and positive relationships between peer regulation, group regulation, and enjoyment of online collaboration. Structural equation modeling analysis further found that group regulation exerted a medium-sized direct effect on enjoyment of online collaboration. Peer regulation affected enjoyment of online collaboration moderately and indirectly via group regulation. The theoretical and pedagogical implications of the findings can help to optimize face-to-face and online collaborative language learning activities.
Directed Coulomb explosion effect on proton acceleration by an intense laser pulse from a double-layer target

We examine ion acceleration by irradiating a hundred TW laser pulse on a double-layer target. It is shown analytically and by three-dimensional particle-in-cell simulations that higher energy protons are obtained by using material with a high charge-to-mass ratio in the first layer of a double-layer target, because a strong Coulomb explosion occurs in such a material. As a result, the protons keep accelerating for a longer time. Using the optimal conditions for the target, it is shown that high energy and high quality protons can be generated.
Studies of ICRF mode conversion with phase contrast imaging and full-wave simulations in Alcator C-Mod

Radio frequency (rf) waves in the ion cyclotron range of frequencies (ICRF) are widely used for heating fusion plasmas. In a multi-ion-species plasma, the launched fast waves convert to ion cyclotron waves and ion Bernstein waves in the vicinity of the two-ion hybrid resonance between the cyclotron resonances. The mode converted waves are of interest as actuators to optimize plasma performance through current drive and flow drive. Numerical simulations are essential to describe these processes accurately in a realistic tokamak geometry, and it is important that these simulation codes be validated against experiment. The phase contrast imaging (PCI) technique has been used on Alcator C-Mod to measure directly the rf waves. The measurements were compared to predictions of full-wave simulations using a synthetic diagnostic method. The measured mode converted wave intensity was found to be a factor of ∼50 weaker than what was expected from the linear wave theory in a strong mode conversion regime. The agreement improved when the wave intensity was weaker, which is a possible indication of nonlinear wave physics being involved. Thesis Supervisor: Miklos Porkolab Title: Professor of Physics Director, Plasma Science and Fusion Center
Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis

Abstract Despite major advances in research and treatment, identifying important genotype risk factors for high blood pressure remains challenging. Traditional genome-wide association studies (GWAS) focus on one single nucleotide polymorphism (SNP) at a time. We aim to select among over half a million SNPs along with time-varying phenotype variables via simultaneous modeling and variable selection, focusing on the most dangerous blood pressure levels at high quantiles. Taking advantage of rich data from a large-scale public health study, we develop and apply a novel quantile penalized generalized estimating equations (GEE) approach, incorporating several key aspects including ultra-high dimensional genetic SNPs, the longitudinal nature of blood pressure measurements, time-varying covariates, and conditional high quantiles of blood pressure. Importantly, we identify interesting new SNPs for high blood pressure. Besides, we find blood pressure levels are likely heterogeneous, where the important risk factors identified differ among quantiles. This comprehensive picture of conditional quantiles of blood pressure can allow more insights and targeted treatments. We provide an efficient computational algorithm and prove consistency, asymptotic normality, and the oracle property for the quantile penalized GEE estimators with ultra-high dimensional predictors. Moreover, we establish model-selection consistency for high-dimensional BIC. Simulation studies show the promise of the proposed approach. Supplementary materials for this article are available online.
On direct inverse of Stokes, Helmholtz and Laplacian operators in view of time-stepper-based Newton and Arnoldi solvers in incompressible CFD

Factorization of the incompressible Stokes operator linking pressure and velocity is revisited. The main purpose is to use the inverse of the Stokes operator with a large time step as a preconditioner for Newton and Arnoldi iterations applied to computation of steady three-dimensional flows and to study of their stability. It is shown that the Stokes operator can be inversed within an acceptable computational effort. This inverse includes fast direct inverses of several Helmholtz operators and iterative inverse of the pressure matrix. It is shown, additionally, that fast direct solvers can be attractive for the inverse of the Helmholtz and Laplace operators on fine grids and at large Reynolds numbers, as well as for other problems where convergence of iterative methods slows down. Implementation of the Stokes operator inverse to time-stepping-based formulation of the Newton and Arnoldi iterations is discussed.
A Novel Rapid Assessment of Mental Stress by Using PPG Signals Based on Deep Learning

People’s daily life is easily affected by mental stress, which can lead to mental illness in the long term. The current mental stress detection process is cumbersome, and the development of rapid assessment methods will make a great contribution to medical care. In view of this, this study used a pulse oximeter to obtain noninvasive photoplethysmography (PPG) signals, the measurement information was analyzed using heart rate variability (HRV), and the Poincaré plot of the heartbeat cycle was the output. Poincaré maps are used as input to deep learning (DL) to perform conditional prediction of mental stress. Finally, the results of conventional HRV and DL are compared. From the experimental results, the classification of the feature of the PPG signal (Poincaré plot) by the model is a meaningful and good result.
Examining a solar-climate link in diurnal temperature ranges

A recent study has suggested a link between the surface level diurnal temperaturerange (DTR) and variations in the cosmic ray (CR) flux. As the DTR is an effective proxyfor cloud cover, this result supports the notion that widespread cloud changes may beinduced by the CR flux. If confirmed, this would have significant implications for ourunderstanding of natural climate forcings. Here, we perform a detailed investigation of therelationships between DTR and solar activity (total solar irradiance and the CR flux) frommore than 60 years of NCEP/NCAR reanalysis data and observations from meteorologicalstation data. We find no statistically significant evidence to suggest that the DTR isconnected to either long-term solar periodicities (11 or 1.68-year) or short-term (dailytimescale) fluctuations in solar activity, and we attribute previous reports on the contrary toan incorrect estimation of the statistical significance of the data. If a CR–DTR relationshipexists, based on the estimated noise in DTR composites during Forbush decrease (FD)events, the DTR response would need to be larger than 0.03 C per 1% increase in the CRflux to be reliably detected. Compared with a much smaller rough estimate of 0.005 Cper 1% increase in the CR flux expected if previous claims that FD events cause reductionsin the cloud cover are valid, we conclude it is not possible to detect a solar relatedresponses in station-based or reanalysis-based DTR data sets related to a hypothesized CR–cloud link, as potential signals would be drowned in noise.
MSL for mixed logit model estimation - on shape of distributions

The use of Mixed Logit models (or Hybrid Logit) has grown rapidly during the past five years as research has demonstrated the applicability of the model and especially the software/purpose specific code has become available. In general, the method of Maximum Simulated Likelihood (MSL) is applied, although this only optimises the parameters of the a priori assumed distribution of the distributed terms. Few of the analyses so far have dealt with the interesting question of which distributions to apply. The distribution to be assumed for the coefficients is generally assumed prior to the estimation. This paper describes a study of extensive model estimations on synthetic data with known distribution. Several different distributions and combinations of distributions have been tested. These included fixed coefficients, normal distributed random coefficients, lognormal distributed random coefficients, bi-modal distributed random coefficients and combinations thereof (e.g. one normal and one lognormal distributed random coefficients), and finally, a normal disturbance added in addition to the Gumbel residual. The correct distribution did come out as significant in a MSL testing. However, in several cases other distributions did turn out to be significant. The paper concludes with guidelines on how to include error components in demand estimation and how to compare models that only differ in assumed distribution of the random coefficients. For the covering abstract see ITRD E126595.
General Master Theorems of Integrals with Applications

Many formulas of improper integrals are shown every day and need to be solved in different areas of science and engineering. Some of them can be solved, and others require approximate solutions or computer software. The main purpose of this research is to present new fundamental theorems of improper integrals that generate new formulas and tables of integrals. We present six main theorems with associated remarks that can be viewed as generalizations of Cauchy’s results and I.S. Gradshteyn integral tables. Applications to difficult problems are presented that cannot be solved with the usual techniques of residue or contour theorems. The solutions of these applications can be obtained directly, depending on the proposed theorems with an appropriate choice of functions and parameters.
An Extensive Study for Detection of Chronic Renal Disease by Utilizing Deep Learning Algorithm on Medical Images

Chronic Renal Disease considered as one of the serious, persistent illness which is caused by the impairment of the kidney. By the help of Machine Learning Techniques, as well as Deep Learning Techniques the illness can easily be detected at a previously stable and in a good time for the illness to be treated. Symptoms of Renal Disease cannot easily be found out. Therefore, the risk of it spreading at a higher rate is not accurately found out at early stages. It was earlier seen that the major factor resulting in renal disease was age, but from the past five years it is observed that even young individuals are being infected with this disease. So, with the help of the common symptoms, and with the images, a Deep Learning technique is proposed using image processing to identify renal disease. By the help of Transformers and Autoencoders, a model with better accuracies is proposed for Chronic Renal Diseases.