Miracle-dz/UniFAR-FTUs huggingface dataset:
Teachers in Distance Education During the COVID-19 Pandemic. Context of Mainstream, Inclusive and Special Education

The aim of the article is to present the results of research on distance education conducted during the COVID-19 pandemic in the Silesian Voivodeship (Poland). The research focuses on the work of teachers in mainstream schools and those who work with students with special educational needs. The author presents: the difficulties indicated by teachers, the support they experience and the solutions they apply, especially to students with special educational needs. The study was based on a quantitative paradigm, using a diagnostic survey method and online survey questionnaires, sent to respondents in the LimeServey system. 958 teachers representing primary and secondary schools participated in the study.The teachers indicated the difficulties of distance learning, such as the inability to monitor the progress of students, problems with the organization of group activities and the use of activating methods in teaching. They found the support in interpersonal contacts and self-study work; they rarely used the help of specialists. Half of the respondents worked with students with special educational needs. The largest group of such students were those with specific learning difficulties. Students with disabilities accounted for about 36% of those students identified by teachers. The teachers working with students with special educational needs experienced technical problems on the part of the student and psychophysical problems resulting from the specific condition of the student with special educational needs.The results of the research and the recommendations were presented to the school authorities in order to improve the quality of distance education and raise the standards of teachers.
Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection

Communication using modern internet technologies has revolutionized the ways humans exchange information. Despite the numerous advantages offered by such technology, its applicability is still limited due to problems stemming from personal attacks and pseudoattacks. On social media platforms, these toxic contents may take the form of texts (e.g., online chats, emails), speech, and even images and movie clips. Because the cyberbullying of an individual via the use of such toxic digital content may have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect, using machine learning approaches, cyberbullying on social media. It is important to use word embedding techniques to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words. The extracted embeddings are used to decide if a digital input contains cyberbullying contents. Supplying strong word representations to classification methods is a key facet of such detection approaches. In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, using three basic machine learning models and four deep learning models. The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.
Stability of the shadow projection and the left-curtain coupling

The (left-)curtain coupling, introduced by Beiglb\"ock and the author is an extreme element of the set of "martingale" couplings between two real probability measures in convex order. It enjoys remarkable properties with respect to order relations and a minimisation problem inspired by the theory of optimal transport. An explicit representation and a number of further noteworthy attributes have recently been established by Henry-Labord\`ere and Touzi. In the present paper we prove that the curtain coupling depends continuously on the prescribed marginals and quantify this with Lipschitz estimates. Moreover, we investigate the Markov composition of curtain couplings as a way of associating Markovian martingales with peacocks.
USO DO ALGORITMO GENÉTICO NO PLANEJAMENTO FLORESTAL CONSIDERANDO SEUS OPERADORES DE SELEÇÃO

This study tested and analyzed four selection operators (Elitist, Tournament, Roulette wheels and Bi-classist) and defined the best one. The forest planning problem test was based on the Johnson & Schermann (1977) type I model encompassing 52 eucalyptus stands, where 254 forest management prescriptions were created. The genetic algorithm (GA) was built in Visual Basic ® Microsoft® and its sets of parameters were: initial population (300), crossover (10%), mutation (10%) and replacement (60%). The measuring variables were: minimum, median and maximum values; coefficient of variation for the fitness and the processing time. It was also applied the nonparametric Kruskal-Wallis test with 5% of the probability to check the differences among selection operators of 30 samples. The results showed that the selection operators presented different efficiency and effectiveness according to Kruskal- Wallis test for 5% of probability. The decreasing sequence of efficiency was: Roulette wheels, Tournament, Elitist and Bi-classist. The lower percentage deviations matched from the exact solution were: 2.75% (Elitist), 2.15% (Tournament), 0.90% (Roulette wheels) and 2.40% (Bi-classist). The best selection operator tested was the one that follows the Roulette wheels.
Multipoint fine-scale linkage disequilibrium mapping: importance of modeling background LD

In linkage disequilibrium (LD) mapping, use of information on multiple markers simultaneously is expected to lead to greater power to detect association and smaller confidence intervals (CIs) for the location of the variant of interest than would be obtained from single-point analysis. Among the important challenges facing case-control LD mapping methods are (i) even when an appropriate control sample is available, there may be background LD in the control sample which must be taken into account in the analysis, especially when fine-scale data are collected, and (ii) in practice, genotype rather than haplotype data are often available, limiting the applicability of some methods. Furthermore, in cases when genotype data can, in principle, be incorporated, it can be computationally challenging. We focus on simultaneous solution of these problems in the context of the Decay of Haplotype Sharing (DHS) method. We develop a computationally efficient method that allows for genotype or haplotype data on many loci and incorporates background LD based on a Markov model of order η. The case of a Markov model of order 2 is implemented in free software. In addition, we demonstrate that failure to adequately model background LD can potentially have a major effect on the analysis, and we develop and apply methods for assessing the adequacy of the model for background LD.
Healing of Unilateral Maxillary Sinusitis by Endodontic and Periodontal Treatment of Maxillary Teeth

Inflammatory conditions of dental origin may spread to the bone tissue, causing its destruction, and to anatomical structures located in the vicinity of the tooth affected with inflammation. Maxillary premolars and molars may develop inflammatory lesions of the Schneiderian membrane and lead to tooth-borne lesions in the maxillary sinuses. Unilateral inflammation of the maxillary sinuses should be diagnosed and treated. The aim of this study was to determine whether and after what time from the applied endodontic or nonsurgical periodontal treatment the inflammation in the maxillary sinus was diminished (assessed by the decrease in the Schneiderian membrane hypertrophy). A retrospective study was performed to analyze the records of endodontically, periodontally, or endodontically-periodontally treated patients with unilateral inflammation of the maxillary sinuses along with diagnostic Cone Beam Computed Tomography. The method for determination of the inflammation was measurements registered in millimeters in Carestream software. The analysis included the situation before treatment and 3 months, 6 months, and 12 months after completion of the treatment. Regardless of the origin of the maxillary sinus lesion, healing of inflammation of the sinus has been reported after the implementation of causative treatment of the maxillary tooth. Dental treatment reduces the need to implement conservative or surgical ENT treatment.
Genetic structure of dinoflagellate symbionts in coral recruits differs from that of parental or local adults

Abstract The symbiotic relationship between dinoflagellate algae in the family Symbiodiniaceae and scleractinian corals forms the base of the tropical reef ecosystem. In scleractinian corals, recruits acquire symbionts either “vertically” from the maternal colony or initially lack symbionts and acquire them “horizontally” from the environment. Regardless of the mode of acquisition, coral species and individual colonies harbor only a subset of the highly diverse complex of species/taxa within the Symbiodiniaceae. This suggests a genetic basis for specificity, but local environmental conditions and/or symbiont availability may also play a role in determining which symbionts within the Symbiodiniaceae are initially taken up by the host. To address the relative importance of genetic and environmental drivers of symbiont uptake/establishment, we examined the acquisition of these dinoflagellate symbionts in one to three‐month‐old recruits of Orbicella faveolata to compare symbiont types present in recruits to those of parental populations versus co‐occurring adults in their destination reef. Variation in chloroplast 23S ribosomal DNA and in three polymorphic microsatellite loci was examined. We found that, in general, symbiont communities within adult colonies differed between reefs, suggesting that endemism is common among symbiont populations of O. faveolata on a local scale. Among recruits, initial symbiont acquisition was selective. O. faveolata recruits only acquired a subset of locally available symbionts, and these generally did not reflect symbiont populations in adults at either the parental or the outplant reef. Instead, symbiont communities within new recruits at a given outplant site and region tended to be similar to each other, regardless of parental source population. These results suggest temporal variation in the local symbiont source pool, although other possible drivers behind the distinct difference between symbionts within O. faveolata adults and new generations of recruits may include different ontogenetic requirements and/or reduced host selectivity in early ontogeny.
Leveraging the Influence of Power Grid Links in Renewable Energy Power Generation

Smart grid construction provides the basic conditions for grid connection of renewable energy power generation. However, the grid connection of large-scale intermittent renewable energy sources increases the complexity of operational control of power systems. With the increase in intermittent renewable energy grid connections, distribution system operators must optimize and integrate these new participants to ensure the flexibility and stability of smart grids. Dividing the smart grid into logical clusters helps to overcome the problems caused by intermittent renewable energy grid connections. In this study, we propose a 2-step modeling approach that includes both link and leverage analyses to detect the network partitioning and assess the stability of the smart grids. Our experimental results of the link analysis show that, despite the identical scores in modularity and Silhouette Coefficients (SC), the total computational time of the linear programming model for linkage (CD1) is 29.8% shorter than that of the quadratic programming model for linkage (CD2) on 7 networks with fewer than 200 nodes, whereas CD2 is 29.5% faster than CD1 on 19 larger networks with more than 200 nodes. The leverage results of benchmark networks indicate that the computational time of each instance with the proposed linear programming model for leverage (ID1) and quadratic programming model for leverage (ID2) was substantially reduced, and the Critical Node Problem (CNP) results of medium- and large-scale networks were better than those reported in the literature, which play a significant role in smart grid optimization.
Tree species classification from complex laser scanning data in Mediterranean forests using deep learning

Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three‐dimensional vegetation structure at high resolution, but the scalability of using these data for large‐scale forest monitoring is limited by reliance on intensive manual data processing, including the use of stem maps generated in the field to determine tree species. New methods from data science have the capacity to automate this identification process, reducing the hurdles towards automated inventories with TLS. In particular, contemporary developments in point cloud processing methods, alongside large increases in the computing power of consumer‐level graphics processing units, provide new opportunities. Here, we apply a deep learning‐based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre‐processing such as leaf‐wood separation or quantitative reconstructions. Using modern network architectures and data augmentation techniques, and without extensive pre‐processing, we are able to achieve high overall and per‐species accuracy that is comparable or higher than in existing work while using data from a water‐limited ecosystem complicated by structural convergence and multi‐stem trees. Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle—individual species identification—and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring.
Alcohol Recognition and Desire to Drink of Extended Alcohol Brand Logos

Alcohol companies in Thailand have adopted surrogate marketing that uses similar logos on non-alcoholic products. We aimed to assess variations of the alcohol recognition using reaction time and desire to drink among consumers exposed to original logos and modified logos (i.e., black logos, partial logos, logos on non-alcoholic beverages and other merchandise). Participants aged ≥19 years took part in this cross-sectional study. The primary independent variables were types of logos: original logos, modified logos (i.e., black logos, partial logos, logos on non-alcoholic beverages, and logos on other merchandise). An in-house-developed online survey randomly presented the logos. Alcohol recognition and the desire to drink alcohol were assessed. The study included 1185 participants. More time (estimated coefficient of reaction time <0.5 s) was required to recognize the modified logos than the original logos. Younger participants (19–24 years) reacted significantly faster than the older participants (>25 years) after seeing all types of logos. The desire to drink alcohol (<0.5 point) upon seeing the modified logos was lower than the original logos. No significant difference in the desire was observed between the younger and older participants upon seeing the original and partial logos. The modified logos reminded consumers of the alcohol products of that brand with a tiny difference in reaction time and the desire to drink without practical significance.
Extraction of a Triterpene Solution and Evaluation of the Hypolipidemic Efficacy of the Pleurotus tuber-regium (Fr.) Sing Sclerotium

The total triterpenes in edible mushrooms have high medicinal value, and the sclerotium has various biological activities, such as the regulation of blood pressure and blood glucose. In this study, the total triterpenes of the Pleurotus tuber-regium (Fr.) Sing Sclerotium (PTRSS) were extracted, and their hypolipidemic effects were also investigated. The infrared spectra showed that the total triterpenes were consistent with the characteristic structures of the total triterpenes before and after purification. The binding abilities of total triterpenes to sodium glycocholate, sodium taurocholate, and sodium cholate were investigated, and all of them had a good binding ability to cholate. In vivo experiments showed that zebrafish tolerated the total triterpenes from the mushroom nuclei at a maximum concentration of 500 µg/mL. A correlation analysis showed that the total triterpenes from the mushroom nuclei reduced the lipid accumulation in zebrafish induced by a high-fat diet, and the lipid-lowering effect showed a correlation with dose.
A Search for Tidal Stellar Debris from the Magellanic Clouds: Survey Results from the First Two Years

ASearchforTidalStellarDebrisfromtheMagellanicClouds:SurveyResultsfromtheFirstTwoYearsS.R. Ma jewski1;2,J.C. OstheimerW.E. Kunkel3K.V. Johnston4R.J.Patterson1andC.almaAbstract. Aspartofalargersurveysubstructureinthehaloviap osition-velo citydistributionsofgiantstars,wehavb eenlo okingforevidenceoftidalstellardebrisinaring eldsaroundtheMagellanicClouds.Werep orthereontheresultsof rsttwoyearsork,whichgivesstrongevidenceforthepresenceoftidaldebrisinour elds,andwhichareroughlyconsistentwithmo delsofMagellanicdebris.1. Intro ductionAnimp ortantdiscriminanb etweenleadingmo delsfortheoriginofMagellanicStreamisthepresenceofastellarcounterparttoHIgasstream:rampressuestrippingofgasbyaputativehotGalactichalowouldactonlyonMagellanicgaswhilegravitationaltidalstrippingwouldactonb othandstars.Severalpreviousattemptsto ndtidalstellardebrishavefailedcarb onstars,Astars,orothermainsequencestarsintheMagellanicStream(Mathewsonetal.1979,Recillas-Cruz1982,Br uckHPackardFoundationellow;CottrellScholar2LasCampanasObservatory,Carnegieatories,Casilla601,LaSerena,Chile3InstituteforAdvancedStudy,Princeton,NJ085401
Sentiment Analysis on Covid-19 Vaccine

The coronavirus disease (COVID-19) pandemic continues to have an influence on the global population's health and well-being. In this pandemic situation, vaccination plays an major role in everyone life. The pandemic has transformed our perspective of the world to a new level. COVID-19 is spreading rapidly, and the only way to stop it is to vaccinate the whole people. Using Machine Learning (ML), this paper assesses people's opinions regarding COVID - 19 vaccine among college students. The COVID-19 vaccination was examined using social network analysis. For the greatest findings and accuracy, sentiment analysis can be done using the Naive Bayes Algorithm, Support Vector Machine, and Logistic Regression. The datasets can be collected as actual statistics.
Rhizobacteria Mitigate the Negative Effect of Aluminum on Pea Growth by Immobilizing the Toxicant and Modulating Root Exudation

High soil acidity is one of the main unfavorable soil factors that inhibit the growth and mineral nutrition of plants. This is largely due to the toxicity of aluminum (Al), the mobility of which increases significantly in acidic soils. Symbiotic microorganisms have a wide range of beneficial properties for plants, protecting them against abiotic stress factors. This report describes the mechanisms of positive effects of plant growth-promoting rhizobacteria Pseudomonas fluorescens SPB2137 on four pea (Pisum sativum L.) genotypes grown in hydroponics and treated with 80 µM AlCl3. In batch culture, the bacteria produced auxins, possessed 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity, alkalized the medium and immobilized Al, forming biofilm-like structures and insoluble phosphates. Inoculation with Ps. fluorescens SPB2137 increased root and/or shoot biomass of Al-treated plants. The bacteria alkalized the nutrient solution and transferred Al from the solution to the residue, which contained phosphorus that was exuded by roots. As a result, the Al concentration in roots decreased, while the amount of precipitated Al correlated negatively with its concentration in the solution, positively with the solution pH and negatively with Al concentration in roots and shoots. Treatment with Al induced root exudation of organic acids, amino acids and sugars. The bacteria modulated root exudation via utilization and/or stimulation processes. The effects of Al and bacteria on plants varied depending on pea genotype, but all the effects had a positive direction and the variability was mostly quantitative. Thus, Ps. fluorescens SPB2137 improved the Al tolerance of pea due to immobilization and exclusion of toxicants from the root zone.
Photon-counting single-molecule spectroscopy for studying conformational dynamics and macromolecular interactions

Single-molecule methods have the potential to provide information about conformational dynamics and molecular interactions that cannot be obtained by other methods. Removal of ensemble averaging provides several benefits, including the ability to detect heterogeneous populations and the ability to observe asynchronous reactions. Single-molecule diffusion methodologies using fluorescence resonance energy transfer (FRET) are developed to monitor conformational dynamics while minimizing perturbations introduced by interactions between molecules and surfaces. These methods are used to perform studies of the folding of Chymotrypsin Inhibitor 2, a small, single-domain protein, and of single-stranded DNA (ssDNA) homopolymers. Confocal microscopy is used in combination with sensitive detectors to detect bursts of photons from fluorescently labeled biomolecules as they diffuse through the focal volume. These bursts are analyzed to extract fluorescence resonance energy transfer (FRET) efficiency. Advances in data acquisition and analysis techniques that are providing a more complete picture of the accessible molecular information are discussed. Photon Arrival-time Interval Distribution (PAID) analysis is a new method for monitoring macromolecular interactions by fluorescence detection with simultaneous determination of coincidence, brightness, diffusion time, and occupancy (proportional to concentration) of fluorescently-labeled molecules undergoing diffusion in a confocal detection volume. This method is based on recording the time of arrival of all detected photons, and then plotting the two-dimensional histogram of photon pairs, where one axis is the time interval between each pair of photons 1 and 2, and the second axis is the number of other photons detected in the time interval between photons 1 and 2. PAID is related to Fluorescence Correlation Spectroscopy (FCS) by a collapse of this histogram onto the time interval axis. PAID extends auto- and cross-correlation FCS by measuring the brightness of fluorescent species. A data-fitting model is developed, which is used to simultaneously determine coincidence, brightness, diffusion time, and occupancy from experiments performed on fluorophore-labeled dsDNA test samples. Using simulations, the performance of PAID is compared with existing methods. The statistical accuracy of the parameters extracted using PAID exceeds or matches the accuracy of the other methods, while providing additional information.