Miracle-dz/UniFAR-FTUs huggingface dataset:
Pervaporation-esterification coupling. II: Modelling of the influence of different operating parameters

A previous paper showed that the coupling of esterification (reversible) reactions to pervaporation (membrane process) can lead a quasi-complete conversion of reactants. The experimental conditions used were not the best to reach high conversion during a length of time. The influences of different parameters, such as the catalyst concentration, the ratios of membrane area to volume of solution, the ratios of initial number of mole of acid to alcohol, and the reaction and pervaporation temperature on the rate of conversion were studied experimentally and theoretically on the basis of the previously described model
End-to-end Alternating Optimization for Blind Super Resolution

Previous methods decompose the blind super-resolution (SR) problem into two sequential steps: \textit{i}) estimating the blur kernel from given low-resolution (LR) image and \textit{ii}) restoring the SR image based on the estimated kernel. This two-step solution involves two independently trained models, which may not be well compatible with each other. A small estimation error of the first step could cause a severe performance drop of the second one. While on the other hand, the first step can only utilize limited information from the LR image, which makes it difficult to predict a highly accurate blur kernel. Towards these issues, instead of considering these two steps separately, we adopt an alternating optimization algorithm, which can estimate the blur kernel and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely \textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR image based on the predicted kernel, and \textit{Estimator} estimates the blur kernel with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, \textit{Estimator} utilizes information from both LR and SR images, which makes the estimation of the blur kernel easier. More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}. Extensive experiments on synthetic datasets and real-world images show that our model can largely outperform state-of-the-art methods and produce more visually favorable results at a much higher speed. The source code is available at \url{https://github.com/greatlog/DAN.git}.
Goosefoot – a plant that likes drought. The goosefoot family pollen season in 2019 in Poland, Hungary and Slovakia

Almost all the species of the Chenopodiaceae family present in our flora flower from July–August to the autumn. Unfortunately, allergies do not take a vacation. Warm, dry July and August weather should limit pollen emissions. However, similarly to most plants in dry habitats, goosefoot are well adapted to such conditions and does not provide even a short reprieve to pollen allergic patients. However, goosefoot pollen does not have a very large allergenic significance; despite the long pollen season lasting about 3 months, pollen concentrations in the air are low and very rarely exceed the concentration of 30 grains/m3. This study compares Chenopodiaceae pollen seasons in Poland, Hungary and Slovakia in 2019. The investigations were carried out using the volumetric method (Hirst type pollen sampler). Seasonal pollen index was estimated as the sum of daily average pollen concentrations in the given season. The pollen season ranges from June to September, depending on the geographical latitude. In Hungary and Slovakia there are much longer pollen seasons than in Poland. Pollen of goosefoot family contains the panallergen profilins, which are responsible for cross-reactivity among pollen-sensitized patients. In 2019 the pollen season of goosefoot started first in Hungary, in Kaposvar on June 7th and in Slovakia, in Žilina, on June 8th; in Poland pollen season started much later, on June 14th in Szczecin and Opole. At the latest, a pollen season ended in Nitria (Slovakia) on October 16th; in Kecskemet (Hungary) on October 3rd. In Poland the season ended much earlier than in Hungary and Slovakia already on August 25th. The differences of pollen season durations are considerable, the number of days ranged from 72 to 128. The dynamics of the pollen seasons of goosefoot family show similarities within a given country and considerable differences between these countries. However, the differences of the highest airborne concentration between the countries are not considerable (25 pollen grains/m3 in Poland, 49 pollen grains/m3 in Hungary, and 30 pollen grains/m3 in Slovakia. The maximum values of seasonal pollen count in Polish cities occurred between July 26th and August 29th, in Hungarian cities between August 27th and 30th, and in Slovakian cities between August 7th and 28th. Pollen season was characterized by extremely different total annual pollen SPI, in Poland from 116 to 360; in Hungary and Slovakia within the limits 290 to 980. Droughts that occur more frequently during the summer facilitate the spread of species of the goosefoot family due to the possibility of these plants gaining new habitats.
QAConv: Question Answering on Informative Conversations

This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source. We focus on informative conversations, including business emails, panel discussions, and work channels. Unlike open-domain and task-oriented dialogues, these conversations are usually long, complex, asynchronous, and involve strong domain knowledge. In total, we collect 34,608 QA pairs from 10,259 selected conversations with both human-written and machine-generated questions. We use a question generator and a dialogue summarizer as auxiliary tools to collect and recommend questions. The dataset has two testing scenarios: chunk mode and full mode, depending on whether the grounded partial conversation is provided or retrieved. Experimental results show that state-of-the-art pretrained QA systems have limited zero-shot performance and tend to predict our questions as unanswerable. Our dataset provides a new training and evaluation testbed to facilitate QA on conversations research.
CHANGES OF CONVENTIONAL NUTRIENT METABOLISM IN BURNED RATS

Twenty healthy male Wistar rats with body weight of about 200 g were divided randomly into burn group and control group and fed individually in rat metabolic cages to study the burn effect of rat on conventional nutrient metabolism. The results showed that the burn could decrease to certain degree ( P 0.01 or 0.05) of food intake,amount of feces and urine,content of protein,fat,calcium,phosphorus in feces,and on the other side,it could increase ( P 0.01 or 0.05) the digestibility rate of protein,fat,calcium,phosphorus from the feed,and also decreased the nitrogen content in urine.
Effect of In–CNHC Bonds on the Synthesis, Structure, and Reactivity of Dialkylindium Alkoxides: How Indium Compares to Gallium

The investigation of the reactivity of dialkylindium alkoxides toward N-heterocyclic carbenes (NHCs) has shown that both the character of the In–CNHC bond and alkyl and alkoxide substituents have a significant effect on the formation of R2InOR(NHC) complexes and the distribution of products. The reactions of simple dimethylindium alkoxides with the N-heterocyclic carbenes 1,3-bis(2,4,6-trimethylphenyl)imidazolin-2-ylidene (SIMes) and 1,3-bis(2,4,6-trimethylphenyl)imidazol-2-ylidene (IMes) lead to the monomeric complexes Me2InOR(NHC), as shown by the isolation of Me2InOMe(NHC) (NHC = IMes (3), SIMes (4)). Compounds Me2InOR(NHC) are unstable in solution and instantly disproportionate, which can be associated with a weaker In–CNHC bond in comparison with stable gallium analogues. As a result, Me3In(NHC) (NHC = IMes (1), SIMes (2)) adducts, as well as Mitsubishi-type methylindium alkoxides, are formed. The exchange of a simple alkoxy group with chelating (S)-methyl lactate (S-melac) has resulted in the more s.
Tether-cutting and Overlying Magnetic Reconnections in an MHD Simulation of Prominence-cavity System

We investigate the magnetic reconnection in an MHD simulation of a coronal magnetic flux rope (MFR) confined by a helmet streamer, where a prominence-cavity system forms. This system includes a hot cavity surrounding a prominence with prominence horns and a central hot core above the prominence. The evolution of the system from quasi-equilibrium to eruption can be divided into four phases: quasi-static, slow-rise, fast-rise, and propagation phases. The emerged MFR initially stays quasi-static and magnetic reconnection occurs at the overlying high-Q (squashing factor) apex region, which gradually evolves into a hyperbolic flux tube (HFT). The decrease of the integrated magnetic tension force (above the location of the overlying reconnection) is due to the removal of overlying confinement by the enhanced overlying reconnection between the MFR and the overlying fields at the apex HFT, thus engines the slow rise of the MFR with a nearly constant velocity. Once the MFR reaches the regime of torus instability, another HFT immediately forms at the dip region under the MFR, followed by the explosive flare reconnection. The integrated resultant force (above the location of the flare reconnection) exponentially increases, which drives the exponential fast rise of the MFR. The system enters the propagation phase, once its apex reaches the height of about one solar radius above the photosphere. The simulation reproduces the main processes of one group of prominence eruptions especially those occurring on the quiet Sun.
Data for: Does Bitcoin add value to global industry portfolios? _CRC2019

Bitcoin has been increasingly viewed as a new form of investment, yet its role as an asset in a diversified industry portfolio is not well understood. In this paper, we explore the dynamic interdependence between Bitcoin and the ten global industry sectors classified by the Global Industry Classification Standard. We find, in accordance with previous literature, that Bitcoin is relatively isolated from traditional industries. While the near-zero correlation with traditional financial assets offers some diversification benefits to investors, these benefits are counterbalanced by the volatility of the asset. Bitcoin’s optimal presence in a minimum variance portfolio is only about 1 percent – a weight that is robust to various methods for estimating the return covariance matrix. Bitcoin’s optimal weight in portfolios maximizing Sharpe and Sortino ratios are on the magnitude of 10 to 20 percent. Hence, the value of Bitcoin as an asset in a diversified portfolio critically depends on investors’ views about the future of Blockchain technology. JEL classification: C32, G11, G15
SpikeMS: Deep Spiking Neural Network for Motion Segmentation

Spiking Neural Networks (SNN) are the so-called third generation of neural networks which attempt to more closely match the functioning of the biological brain. They inherently encode temporal data, allowing for training with less energy usage and can be extremely energy efficient when coded on neuromorphic hardware. In addition, they are well suited for tasks involving event-based sensors, which match the event-based nature of the SNN. However, SNNs have not been as effectively applied to real-world, large-scale tasks as standard Artificial Neural Networks (ANNs) due to the algorithmic and training complexity. To exacerbate the situation further, the input representation is unconventional and requires careful analysis and deep understanding. In this paper, we propose SpikeMS, the first deep encoder-decoder SNN architecture for the real-world large-scale problem of motion segmentation using the event-based DVS camera as input. To accomplish this, we introduce a novel spatio-temporal loss formulation that includes both spike counts and classification labels in conjunction with the use of new techniques for SNN backpropagation. In addition, we show that SpikeMS is capable of incremental predictions, or predictions from smaller amounts of test data than it is trained on. This is invaluable for providing outputs even with partial input data for low-latency applications and those requiring fast predictions. We evaluated SpikeMS on challenging synthetic and real-world sequences from EV-IMO, EED and MOD datasets and achieving results on a par with a comparable ANN method, but using potentially 50 times less power.
The mathematical theory of splitting-merging patterns in phase transition dynamics

Abstract For nonlinear hyperbolic systems of conservation laws in one space variable, we establish the existence of nonclassical entropy solutions exhibiting nonlinear interactions between shock waves with strong strength. The proposed theory is relevant in the theory of phase transition dynamics, and the solutions under consideration enjoy a splitting–merging pattern, consisting of (compressive) classical and (undercompressive) nonclassical waves, interacting together as well as with classical waves of weaker strength. Our analysis is based on three novel ideas. First, a generalization of Hayes–LeFloch’s nonclassical Riemann solver is introduced for systems and is based on prescribing, on one hand, a kinetic relation for the propagation of nonclassical undercompressive shocks and, on the other hand, a nucleation criterion that selects between classical and nonclassical behavior. Second, we extend LeFloch-Shearer’s theorem to systems and we prove that the presence of a nucleation condition implies that only a finite number of splitting and merging cycles can occur. Third, our arguments of nonlinear stability build upon recent work by the last two authors who identified a natural total variation functional for scalar conservation laws and, specifically, for systems of conservation laws we introduce here novel functionals which measure the total variation and wave interaction of nonclassical and classical waves.
CN-LBP: Complex Networks-Based Local Binary Patterns for Texture Classification

To overcome the limitations of original local binary patterns (LBPs), this article proposes a new texture descriptor aided by complex networks (CNs) and LBPs, named CN-LBP. Specifically, we first abstract a texture image (TI) as directed graphs over different bands with the help of pixel distance, intensity, and gradient (magnitude and angle). Second, several CN feature measurements, including clustering coefficient, in-degree centrality, out-degree centrality, and eigenvector centrality), are selected to decipher the texture features, which generates four feature images retaining the image information as much as possible. Third, given the original TIs, gradient images (GIs), and generated feature images, we can obtain the discriminative representation of TIs based on a uniform LBP (ULBP). Finally, the feature vector is obtained by jointly calculating and concatenating spatial histograms. In contrast to the original LBP, the proposed texture descriptor contains more detailed image information and shows resistance to imaging and noise. Experiment results on four datasets show that the proposed texture descriptor can significantly im prove the classification accuracies compared to the state-of-the-art LBP-based variants and deep learning-based methods.
Effect of Alpha Lipoic Acid on Polycystic Ovary Syndrome with Insulin Resistance

Background: Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in reproductive age women where insulin resistance plays an important role. Insulin resistance makes the first line treatment, clomiphene citrate (CC) treatment, become ineffective. Alpha Lipoic Acid (ALA) is believed to be an alternative treatment for CC-resistant PCOS. Objectives: The aim of the study is to understand the effect of ALA in Insuline receptor substrate 1 (IRS-1) expression, Glucosa transporter 4 (GLUT-4) expression, and folliculogenesis in insulin resistant PCOS rat model. Methods: This was an experimental study with randomized posttest only control group design. 30 females rat injected with testosterone propionate (TP) 1mg/100gram bodyweight for 28 days then divided into 3 groups. Negative control group receive no other treatment, positive control group receive a placebo for 14 days, and treatment group receive ALA for 14 days. IRS-1 and GLUT-4 expression is evaluated with immunohistochemistry, while folliculogenesis is evaluated by counting the number of follicle in each stage. Results: Mean IRS-1 expression in muscle in treatment group is significantly higher than other groups (4.28±1.05; 3.02±1.03; 1.86±0.83, p <0.01 respectively). Mean GLUT-4 expression in treatment group is significantly higher than other groups (4.28±0.91; 3.20±1.14; 1.40±0.55, p <0.01 respectively). Mean number of follicle in each stage in treatment group are significantly reduced than other groups (all p <0.05). Conclusion: ALA increase the expression of IRS-1 and GLUT-4, and also reduce the number of follicle in each stage of folliculogenesis.
Evaluation of Cross-Section and Wing Length in Free Vibration Analysis of Aircraft Wings

This study presents the numerical free vibration analysis of aircraft wings created using different airfoil cross sections such as NACA 0009, NACA 2424, and NACA 4415. Aircraft wings were made of different lengths. Numerical frequency analyses were conducted Taguchi L9 orthogonal array with two control factors including three levels and so nine numerical modal analyses were performed. Airfoil cross sections and lengths of aircraft wings were used as the first and the second control factors. To detect the control factors with optimal levels, analysis of signal-to-noise (S/N) ratio was employed. In addition, analysis of variance (ANOVA) at the 95 % confidence level was implemented to carry out percent contributions of airfoil cross sections and lengths of aircraft wings on free vibration. As can be summarized from this study, the maximum free vibration behavior was obtained by using NACA 2424 wing profiles with a length of 5 meters. Also, the most dominant control factors were found to be airfoil type with 85.21 % effect and wing length with 12.87 % effect, according to ANOVA.
International Students' Perceptions of Virtual Service Learning Program Amidst COVID-19 Pandemic

This article presents two Thai international students' accounts of their lived experiences during a virtual service learning (community service) program due to global COVID-19 pandemic Semi-structured interview and participants' reflection data were thematically analyzed through the lens of positive and negative emotions so as to explore participants' perceptions of virtual service learning Findings indicate that the pandemic situation was not a barrier to contributing positively to society One of the positive effects of the community service activities is that such activities could build students' creativity It was also found that despite bad internet connectivity in their respective areas, the final-year international students were able to adapt to both academic and non-academic environments This indicates that all the international students should maintain their positive feelings when completing their service learning program
Pengaruh Kekayaan Daerah Dan Belanja Modal Terhadap Kinerja Penyelenggaraan Pemerintah Daerah: Pengujian Kompleksitas Pemerintah Sebagai Variabel Moderasi

This study aims to examine the effect of regional wealth and capital expenditure on the performance of local government administration, and to see whether government complexity will affect the relationship betwen regional wealth and capital expenditure on the perfomance of local government administration. Population in this study were districts/cities in West Sumatera Province in 2013-2017. The analytical tool used in this study is moderate regression analysis. The results of this study indicate that regional wealth doesn’t affect the performance of local government administration, and capital expenditure negatively affects the performance of local government administration. Government complexity doesn’t moderate the relationship between regional wealth and capital expenditure on the performance of local government administration. The results of this study also show that complexity of the government weakens the relationship between regional wealth and capital expenditure on the performance of local government administration
Density, Cities and Air Pollution: A Global View

In this paper, we take a global view at air pollution looking at countries and cities worldwide. In doing so, we revisit the relationship between population density and air pollution, using i) a large panel of countries with data from 1960 to 2010, and ii) a unique and large sample of more than 1200 (big) cities around the world, combining pollution data with satellite data on built-up areas, population and light intensity at night at the grid-cell level for the last two decades. At the country level, we find that higher density in urban areas is associated with lower CO2 and PM2.5 emissions per capita. This result is supported at the city level; denser cities show lower emissions per capita. Our finding is robust to several controls and different estimation techniques and identification strategies. In our city level analysis, we also investigate the role of various characteristics of cities, in particular their average income, size and spatial structure (indicating within-city differences in density). We find evidence of an Environmental Kuznets Curve between economic development and pollution and that a polycentric city structure leads to lower pollution in the largest urban areas, while monocentricity seems beneficial for smaller cities.
Predicting speech intelligibility from EEG using a dilated convolutional network

—Objective: Currently, only behavioral speech understanding tests are available, which require active participation of the person. As this is infeasible for certain populations, an objective measure of speech intelligibility is required. Recently, brain imaging data has been used to establish a relationship between stimulus and brain response. Linear models have been successfully linked to speech intelligibility but require per-subject training. We present a deep-learning-based model incorporating dilated convolutions that can be used to predict speech intelligibility without subject-speciﬁc (re)training. Methods: We evaluated the performance of the model as a function of input segment length, EEG frequency band and receptive ﬁeld size while comparing it to a baseline model. Next, we evaluated performance on held-out data and ﬁnetun-ing. Finally, we established a link between the accuracy of our model and the state-of-the-art behavioral MATRIX test. Results: The model signiﬁcantly outperformed the baseline for every input segment length (p ≤ 10 − 9 ≤ 10 − 9 ≤ 10 − 9 ), for all EEG frequency bands except the theta band (p ≤ 0 . 001 ≤ 0 . 001 ≤ 0 . 001 ) and for receptive ﬁeld sizes larger than 125 ms (p ≤ 0 . 05 ≤ 0 . 05 ≤ 0 . 05 ). Ad-ditionally, ﬁnetuning signiﬁcantly increased the accuracy (p ≤ 0 . 05 ≤ 0 . 05 ≤ 0 . 05 ) on a held-out dataset. Finally, a signiﬁcant correlation (r=0.59, p=0.0154) was found between the speech reception threshold estimated using the behavioral MATRIX test and our objective method. Conclusion: Our proposed dilated convolutional model can be used as a proxy for speech intelligibility. Signiﬁcance: Our method is the ﬁrst to predict the speech reception threshold from EEG for unseen subjects, contributing to objective measures