Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental and Numerical Assessment of the Hot Sheet Formability of Martensitic Stainless Steels

Hot formed sheet components made of Martensitic Stainless Steels (MSS) can achieve ultra-high strengths in combination with very high corrosion resistance. This enables to manufacture complex lightweight sheet components with longer lifespan. Nevertheless, the hot formability of MSS sheets has not been accurately evaluated considering high temperatures and complex stress and strain states. In this work, the hot sheet formability of three MSS alloys under thermomechanical process conditions was investigated. Initially, mechanical properties of this sheet material were determined by uniaxial tensile test. Finite Element Method (FEM) simulation of a hot deep drawing process was performed under consideration of thermo physical calculated material models using the software JMatPro® and Simufact Forming® 15.0. The resulting strains and cooling rates developed locally in the work piece during the forming process were estimated. The numerical results were validated experimentally. Round cups were manufactured by hot deep drawing process. The resulting maximum drawing depth and hardness were measured. In general, all three alloys developed very good formability at forming temperatures between 700 and 900 °C and increased hardness values. However, they are highly susceptible to chemical composition, austenitization temperature, dwell time, and flange gap. A statistic approach is given to explain the correlation between hardness and its influencing factors.
The Role of Assessment as Learning to Support Pre-service Mathematics Teachers in Designing Mathematics Test Instruments Based on ESD

This study aims to describe the role of Assessment as Learning for pre-service mathematics teachers in designing mathematical test instruments. Assessment as Learning is useful as a quality control tool for the designed mathematics test instrument. This is qualitative research with a phenomenological approach, involving four pre-service mathematics teachers. Data were obtained through the design of mathematical test instruments and in-depth interviews with the participants. The test instruments designed was then adjusted to the results of the interview. The research results reveal that Assessment as Learning is important in supporting students to monitor the results of their work; environmental aspects are the most difficult aspects to be integrated into mathematical problems; students have more difficulty in completing Peer-Assessment than Self-Assessment; the problems raised in the economic aspects are only around prices, and Self-Assessment and Peer-Assessment are very helpful in detecting errors and deficiencies in the questions designed. It is concluded that Assessment as Learning helps and supports pre-service mathematics teachers in designing mathematics test instruments to create good quality mathematics test instruments. This research contributes to pre-service mathematics teachers conducting the Assessment as Learning to minimize errors in the mathematical test instruments.
Bayesian Analysis of Quasi-periodic Pulsations in Stellar Flares

Quasi-periodic pulsations (QPPs) are routinely observed in a range of wavelengths during flares, but in most cases the mechanism responsible is unknown. We present a method to detect and characterize QPPs in time series such as light curves for solar or stellar flares based on forward modeling and Bayesian analysis. We include models for QPPs as oscillations with finite lifetimes and nonmonotonic amplitude modulation, such as wave trains formed by dispersive evolution in structured plasmas. By quantitatively comparing different models using Bayes factors, we characterize the QPPs according to five properties: sinusoidal or nonsinusoidal, finite or indefinite duration, symmetric or asymmetric perturbations, monotonic or nonmonotonic amplitude modulation, and constant or varying period of oscillation. We demonstrate our method and show examples of these five characteristics by analyzing QPPs in white-light stellar flares observed by the Kepler space telescope. Different combinations of properties may be able to identify particular physical mechanisms and so improve our understanding of QPPs and allow their use as seismological diagnostics. We propose that three observational classes of QPPs can be distinguished: decaying harmonic oscillations, finite wave trains, and nonsinusoidal pulsations.
Water/moisture vapor permeabilities and thermal wear comfort of the Coolmax®/bamboo/tencel included PET and PP composite yarns and their woven fabrics

Abstract This study examined the water and moisture vapor permeabilities and the heat retention rate related to the wear comfort of the woven fabrics made from different types of composite yarn, the sheath/core composite yarn structure and non-circular cross-section of the constituent filaments (Coolmax®) in the core region played a very important role with high fabric porosity by including a capillary absorption in the wicking property. The fabric structures with high porosity composed of Coolmax® sheath/core yarns, and spun yarns with non-circular cross-sectional fibers (Coolmax®) affected strongly the fast drying rate due to moisture vapor diffusion of composite yarn fabrics. The moisture vapor permeability of the composite yarn fabrics was highly dependent on the porosity in the sheath/core yarn structures, In addition, the heat retention rate of the composite yarn fabric was strongly influenced by the high porosity due to the sheath/core and bulky staple yarn structures as well as the low thermal conductivity of the constituent fibers.
Phenylalanine Alleviates Postharvest Chilling Injury of Plum Fruit by Modulating Antioxidant System and Enhancing the Accumulation of Phenolic Compounds

SUMMARY Research background Low temperature storage causes chilling injury in plum (Prunus domestica L.) fruits. Consequently, any treatments with beneficial effects against these symptoms would achieve attention. For this purpose, phenylalanine treatments were applied on ‘Stanley’ plum fruits. The main purpose of the present study is to investigate the influence of the exogenous application of phenylalanine on fruit quality, chilling tolerance, and antioxidant capacity of ‘Stanley’ plums during cold storage. Experimental approach Phenylalanine at different concentrations was applied on ‘Stanley’ plums. Following phenylalanine application, plums were cold stored. Chilling injury, antioxidant capacity, electrolyte leakage, malondialdehyde, proline and internal contents of anthocyanin, flavonoids, phenols, ascorbic acid and some antioxidant enzymes were assessed. Results and conclusions Phenylalanine treatment signiﬁcantly alleviated chilling injury in plum fruits by enhancing antioxidant capacity and increasing the activity of phenylalanine ammonia lyase enzyme (PAL). Phenylalanine-treated fruits had higher mass fractions of ascorbic acid, anthocyanins, flavonoids and phenols, as well as a higher total antioxidant activity than the control fruits during low temperature storage. Phenylalanine at 7.5 mM was the most effective treatment in enhancing the activity of PAL, the accumulation of phenolic compounds and in reducing the severity of chilling injury. Treatments delayed mass loss and maintained fruit firmness. In addition, the application of 7.5 mM phenylalanine improved the activities of antioxidant enzymes (superoxide dismutase, catalase and ascorbate peroxidase), decreased the accumulation of hydrogen peroxide, and increased the endogenous content of proline. Moreover, phenylalanine maintained membrane integrity, manifested by a reduced electrolyte leakage and malondialdehyde accumulation. Novelty and scientific contribution In the current study, chilling injury had a positive correlation with the activities of PAL and antioxidant enzymes. However, negative correlations were observed between the chilling injury and ascorbic acid mass fraction, and antioxidant capacity. Considering the results, phenylalanine treatment could be an encouraging approach to alleviate the severity of chilling injury and thus preserve nutritional quality of plums during low temperature storage.
New Vanadium(V) Complexes with Salicylaldehyde Semicarbazone Derivatives: Synthesis, Characterization, and in vitro Insulin‐Mimetic Activity − Crystal Structure of [VvO2(salicylaldehyde semicarbazone)]

The new dioxo(semicarbazone)vanadium(V) complexes cis-VO2L, where L = salicylaldehyde semicarbazone (L1), salicylaldehyde 4-n-butylsemicarbazone (L2), or salicylaldehyde 4-(2-naphthyl)semicarbazone (L3), have been synthesized, characterized by 1H and 13C NMR and FTIR spectroscopy and tested for bioactivity as potential insulin-mimetic agents. All dioxovanadium(V) complexes exhibited essentially no in vitro insulin-mimetic activity, but the VO2L2 complex developed activity in the presence of ascorbic acid, similar to that of vanadyl sulfate. The molecular structure of the novel complex VO2L1 has been solved by X-ray diffraction methods. It crystallizes in the tetragonal space group P42/n with a = 12.7674(7), c = 11.5308(5) A, and Z = 8. The vanadium atom is in a distorted square-pyramidal coordination, with L1 acting as a tridentate ligand through its azomethyne nitrogen atom, carbonyl oxygen atom and deprotonated phenol oxygen atom. The coordination sphere is completed by two oxo ligands at cis positions. (© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2004)
Simulation and Optimization of Biodiesel Production by Soybean Oil Transesterification in Non-Ideal Continuous Stirred-Tank Reactor

This research examines the transesterification reaction of soybean oil with methanol in order to model and simulate a non-ideal continuous stirred-tank reactor. A mathematical model has been developed for the reactor. The biodiesel production process was optimized by application of factorial design 25 and response surface methodology. Factorial design and response surface analysis were combined with modeling and simulation to determine the operating conditions that maximize biodiesel production and minimize reactor volume. The optimum results obtained were conversion (0.95), molar ratio alcohol/oil (6:1), temperature (60.5 oC), ? (0.75), bypass (0.10) and reactor volume (1,500 L).
Moderating Uncivil User Comments by Humans or Machines? The Effects of Moderation Agent on Perceptions of Bias and Credibility in News Content

Abstract Studies have shown that uncivil comments under an online news article may result in biased perceptions of the news content, and explicit comment moderation has the potential to mitigate this adverse effect. Using an online experiment, the present study extends this line of research with the examination of how interface cues signalling different agents (human vs. machine) in moderating uncivil comments affect a reader’s judgment of the news and how prior belief in machine heuristic moderates such effects. The results indicated that perceptions of news bias were attenuated when uncivil comments were moderated by a machine (as opposed to a human) agent, which subsequently engendered greater perceived credibility of the news story. Additionally, such indirect effects were more prominent among readers who strongly believed that machine operations are generally accurate and reliable than those with a weaker prior belief in this rule of thumb.
Biotrickling filtration of air contaminated with ethanol

A biotrickling filter (BTF) for treating high ethanol loads was operated for one year and the effect of operating conditions was studied. The BTF was operated in a range of ethanol inlet concentrations of 0.2–15.0 g m−3 and at three different residence times (30, 65 and 130 s). The experiments show that removal efficiency decreased with increasing ethanol inlet concentration and decreasing air residence time. Removal efficiency varied in the range of 60–100%. A maximum elimination capacity of 970 g m−3 h−1 was obtained for an inlet load of 1610 g m−3 h−1. At a constant residence time, the carbon dioxide (CO2) production rate varied with ethanol inlet concentration. BTF presented the maximum CO2 production rate in the range of inlet concentration of 3.0–7.0 g m−3. Two strategies for controlling biomass accumulation were applied: one consisted in periodical washing; the other combined periodical washing with nutrient starvation by consuming less water and energy. Both strategies led to maintaining the BTF stable, with high adaptability and reproducibility. Copyright © 2007 Society of Chemical Industry
Artificial Neural Network-Based Control of Switched Reluctance Motor for Torque Ripple Reduction

Switched reluctance motor is acquiring major attention because of its simple design, economic development, and reduced dependability. These attributes make switched reluctance motors superior to other variable speed machines. The major challenge associated with the development of a switched reluctance motor is its high torque ripple. Torque ripple produces noise and vibration, resulting in degradation of its performance. Various techniques are developed to cope with torque ripples. Practically, there exists not a single mature technique for the minimization of torque ripples in switched reluctance motors. In this research, a switched reluctance motor is modelled and analysed. Its speed and current control are implemented through artificial neural networks. Artificial neural network is found to be a promising technique as compared with other techniques because of its accuracy, reduced complexity, stability, and generalization. The Levenberg–Marquardt algorithm is utilized in artificial neural networks due to its fast and stable convergence for training and testing. It is found from research that artificial neural network-based improved control shows better performance of the switched reluctance motor. Realization of this technique is further validated from its mean square error analysis. Operating parameters of the switched reluctance motor are improved significantly. Simulation environment is created in Matlab/Simulink.
Mobility Speed Effect and Neural Network Optimization for Deep MIMO Beamforming in mmWave Networks

Beamforming for millimetre-wave (mmWave) frequencies has been studied for many years. It is considered as an important enabling technology for communications in these high-frequency ranges and it received a lot of attention in the research community. The special characteristics of the mmWave band made the beamforming problem a challenging one because it depends on many environmental and operational factors. These challenges made any model-based architecture fit only special applications, working scenarios, and specific environment geometry. All these reasons increased the need for more general machine learning based beamforming systems that can work in different environments and conditions. This increased the need for an extended adjustable dataset that can serve as a tool for any machine learning technique to build an efficient beamforming architecture. Deep MIMO dataset has been used in many architectures and designs and has proved its benefits and flexibility to fit in many cases. In this paper, we study the extension of collaborative beamforming that includes many cooperating base stations by studying the impact of User Equipment (UE) speed ranges on the beamforming performance, optimizing the parameters of the neural network architecture of the beamforming design, and suggesting the optimal design that gives the best performance for as a small dataset as possible. Suggested architecture can achieve the same performance achieved before with up to 33% reduction in the dataset size used to train the system which provides a huge reduction in the data collection and processing time.
A study of the composition and structure of the cell-wall mucopeptide of micrococcus lysodeikticus.

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Clinical outcomes and predictors of response for adalimumab in patients with moderately to severely active ulcerative colitis: a KASID prospective multicenter cohort study

Background/Aims This study assessed the efficacy and safety of adalimumab (ADA) and explored predictors of response in Korean patients with ulcerative colitis (UC). Methods A prospective, observational, multicenter study was conducted over 56 weeks in adult patients with moderately to severely active UC who received ADA. Clinical response, remission, and mucosal healing were assessed using the Mayo score. Results A total of 146 patients were enrolled from 17 academic hospitals. Clinical response rates were 52.1% and 37.7% and clinical remission rates were 24.0% and 22.0% at weeks 8 and 56, respectively. Mucosal healing rates were 39.0% and 30.1% at weeks 8 and 56, respectively. Prior use of anti-tumor necrosis factor-α (anti-TNF-α) did not affect clinical and endoscopic responses. The ADA drug level was significantly higher in patients with better outcomes at week 8 (P<0.05). In patients with lower endoscopic activity, higher body mass index, and higher serum albumin levels at baseline, the clinical response rate was higher at week 8. In patients with lower Mayo scores and C-reactive protein levels, clinical responses, and mucosal healing at week 8, the clinical response rate was higher at week 56. Serious adverse drug reactions were identified in 2.8% of patients. Conclusions ADA is effective and safe for induction and maintenance in Korean patients with UC, regardless of prior anti-TNF-α therapy. The ADA drug level is associated with the efficacy of induction therapy. Patients with better short-term outcomes were predictive of those with an improved long-term response.
What can we learn about urban air quality with regard to the first outbreak of the COVID-19 pandemic? A case study from central Europe

Abstract. Motor vehicle road traffic in central Budapest was reduced by approximately 50 % of its ordinary level for several weeks as a consequence of various limitation measures introduced to mitigate the first outbreak of the COVID-19 pandemic in 2020. The situation was utilised to assess the real potentials of urban traffic on air quality. Concentrations of NO, NO2, CO, O3, SO2 and particulate matter (PM) mass, which are ordinarily monitored in cities for air quality considerations, aerosol particle number size distributions, which are not rarely measured continuously on longer runs for research purposes, and meteorological properties usually available were collected and jointly evaluated in different pandemic phases. The largest changes occurred over the severest limitations (partial lockdown in the Restriction phase from 28 March to 17 May 2020). Concentrations of NO, NO2, CO, total particle number (N6–1000) and particles with a diameter < 100 nm declined by 68 %, 46 %, 27 %, 24 % and 28 %, respectively, in 2020 with respect to the average reference year comprising 2017–2019. Their quantification was based on both relative difference and standardised anomaly. The change rates expressed as relative concentration difference due to relative reduction in traffic intensity for NO, NO2, N6–1000 and CO were 0.63, 0.57, 0.40 and 0.22 (%/%), respectively. Of the pollutants which reacted in a sensitive manner to the change in vehicle circulation, it is the NO2 that shows the most frequent exceedance of the health limits. Intentional tranquillising of the vehicle flow has considerable potential for improving the air quality. At the same time, the concentration levels of PM10 mass, which is the most critical pollutant in many European cities including Budapest, did not seem to be largely affected by vehicles. Concentrations of O3 concurrently showed an increasing tendency with lower traffic, which was explained by its complex reaction mechanism. Modelling calculations indicated that spatial gradients of NO and NO2 within the city became further enhanced by reduced vehicle flow.