Miracle-dz/UniFAR-FTUs huggingface dataset:
Pyrolysate formation from four different phenyl propanols and classification of the initial reaction pathways

Four phenyl propanols with different ring pendant groups; cinnamyl alcohol, p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol were subjected to analytical pyrolysis (pyrolysis-GC/MS) to evaluate the effects of hydroxyl and methoxyl groups on the pyrolysis mechanism and pyrolysate distribution. In the case of cinnamyl alcohol, which does not have phenolic hydroxyl group, over 40% of the starting material was recovered as an unreacted compound, indicating that the phenolic hydroxyl group accelerates pyrolysis reaction to produce gas, char, and pyrolysates. However, side chain reactions, such as formation of cinnamaldehyde and phenyl ethenyl, were produced in high yield as major products. The pyrolysis of p-coumaryl alcohol yielded many products that suffered from loss of the side-chain C1 or C2 carbons. Sinapyl alcohol and coniferyl alcohol exhibited similar pyrolysis behavior, with similar compounds detected as the major products. The basic reaction pathways are initiated by the homolytic cleavage of C–O and H–O bonds followed by C-C bond cleavage. Therefore, many of the pyrolysates were produced via reactions involving hydrogen and hydroxyl radicals. These initial reactions were classified into four principal categories: 1) donation of hydrogen radical, 2) abstraction of hydrogen radical, 3) donation of hydroxyl radical and 4) abstraction of hydroxyl radical. Isomerisation and methyl radical donation also occurred during the pyrolysis of phenyl propanols. Copyright © IJRETR, all rights reserved.
Antimutagenic properties of 3,5-disubstituted 2-thiohydantoins

3,5-Disubstituted 2-thiohydantoins, which were prepared by the reaction of allyl isothiocyanate (AITC) or 4-(methylthio)-3-butenyl isothiocyanate (MTBI) with various amino acids, were assayed for their inhibitory effects on the mutagenicity of 2-amino-3-methylimidazo[4,5-f]quinoline (IQ) on Salmonella typhimurium TA 98. When the 3,5-disubstituted 2-thiohydantoins (125−500 μg/mL) were simultaneously preincubated with the bacterial strain, IQ (0.5 μg/mL), and rat microsomal fraction (S9 mix) for 20 min, a dose-dependent inhibition of IQ mutagenicity was observed in the tested compounds except one prepared from MTBI and l-methionine. This inhibition ranged from 23% to 86%. The inhibitory effect either disappeared or was largely reduced to 5−19%, when the 3,5-disubstituted 2-thiohydantoins were added after metabolic activation of IQ with S9 mix. These results suggest that the 3,5-disubstituted 2-thiohydantoins probably acted as an inhibitor of the S9 mix-mediated metabolic activation of the mutagen. Keywords:.
Automatic Determination of Skeletal Maturity using Statistical Models of Appearance

—This work addresses the problem of automatic determination of skeletal maturity in children and young adults. Skeletal age assessment is important for diagnosing and monitoring growth and endocrine disorders. We have constructed a system which uses Statistical Models of Shape and Appearance to locate bones in a radiograph and to predict skeletal maturity. By analysing the performance on a dataset of about 600 digitised radiographs of normal children we show that different variants of Part+Geometry (P+G) models are sufﬁcient to initialise an automatic registration algorithm. We built global models of whole hand and local models of individual bones. We used the same P+G models to locate salient bones of the hand to initialise an Active Appearance Model (AAM) to match all the bones of the hand in a radiograph. We improved our age estimation results by using multiple local age group models and multiple local age estimators. We obtained an accuracy of 0 . 75 mm and 0 . 70 mm on sparse points placement for initialization of automatic registrations and Active Appearance models ﬁtting respectively. We achieved a sub-millimeter accuracy for automatic model annotation and for locating the bones in a new radiograph. Our skeletal maturity methodology achieved an accuracy in estimating Skeletal Age of mean absolute error of 0 . 41 ± 0 . 02 years and 0 . 47 ± 0 . 03 years for female and male respectively.
Role of calcium influx during the latent period in sea urchin fertilization

After ∼7–40 s following gamete fusion, a steadily increasing fraction of a sea urchin's zygotes initiate an activating calcium wave. The fertilization membrane then rises, the cell cycle resumes and development begins. This study focuses on the so‐called latent period that occurs between the time that gamete fusion occurs and the initiation of the activating calcium wave. We inhibited calcium influx during this period by adding lanthanum or by reducing external calcium with a buffer at various time points after insemination. Both of these treatments blocked the activation of eggs that had not yet started a wave at the time of treatment. This indicates that an influx of calcium is needed during the latent period to induce egg activation. These results support the sperm conduit model of egg activation in the sea urchin, where calcium flows from the sea through the fused sperms' acrosomal process into a cortical region of the eggs' endoplasmic reticulum.
Modeling Attributes Influencing Intelligent Information Software System using Exploratory Factor Analysis

: In today’s era Software development is hybrid with Software Reliability Model, using model parameters for validating software faulty dataset as it has many pitfalls choosing reliability model being dataset biasing, parameters on which choosing parameter estimation relevant to model based on statistical test, readiness index, functionality preserving, effort consumed on faulty datasets prediction. Model Estimation Modeling based on Least Square Error (LSE) for which factor analyzed using Exploratory Factor Analysis and Confirmatory Factor Analysis and model is validated using p-test with confidence interval of 95% on cloud service. Based on our eleven attribute of our dataset, Continuous Integration is most relevant among other factors. Defect Prediction is one the most crucial and critical task in successful operation of software working as can leads to faults which further causes failures and these failures are very ominous, thus reducing and preventing software defects is major challenging work
NUMERICAL INVESTIGATION OF AIR INJECTION DURING BIOMASS GASIFICATION IN A TWO-STAGE DOWNDRAFT CONFIGURATION

The partial combustion in a two-stage downdraft gasifier is critical for the process optimisation. The hot zone is indeed determining for tar cracking, the major issue in gasification. The conditions in this zone are strongly influenced by the way of injecting air inside the gasifier. In this paper, numerical simulations of the reactive flows within the oxidation zone are performed to characterise the influence of the injection surface area and the nozzle geometry on the process. Results show a transition in the flow pattern at an injection velocity around 34 m/s. Once this limit is reached, the nozzles suck up all the pyrolysis gas. Thus, it passes through a very hot zone, for an efficient tar cracking. Further increase in injection velocity accelerates the gas velocity in the hot zone. As a consequence, the residence time is shortened and the tars are less cracked. The main conclusions of this study can be summed up in three observations: • Air injection velocity determines the velocity in the combustion zone and thus the gas residence time in the hot zone; • A minimum injection velocity of about 34 m/s is necessary to guarantee a satisfactory flow pattern and a homogeneous temperature in the hot zone; above this level temperature profile remains almost unchanged whatever velocity is used; • Injection at a 34 m/s velocity and at an inclination of 5-10° from the vertical was found to be the optimum configuration studied. MCS 4 Lisbon, October 6-1
Entry and price competition in the over‐the‐counter drug market after deregulation: Evidence from Portugal

Abstract In the last two decades, many European countries allowed the sale of over‐the‐counter (OTC) drugs outside pharmacies. This was expected to lower retail prices through increased competition. Evidence of such price reductions is scarce. We assess the impact of supermarket and outlet entry in the OTC drug market on OTC prices charged by incumbent pharmacies using a difference‐in‐differences strategy. We use price data on five popular OTC drugs for all retailers located in Lisbon for three distinct points in time (2006, 2010, and 2015). Our results suggest that competitive pressure in the market is mainly exerted by supermarkets, which charge, on average, 20% lower prices than pharmacies. The entry of a supermarket among the main competitors of an incumbent pharmacy is associated with an average 4% to 6% decrease in prices relative to the control group. These price reductions are long‐lasting but fairly localized. We find no evidence of price reductions following OTC outlet entry. Additional results from a reduced‐form entry model and a propensity score matching difference‐in‐differences approach support the view that these effects are causal.
Sentiment Analysis Using Naïve Bayes Classifier

: Twitteris a web service and social communication platform which allow users to address their tweets in different domains. Public can easily and efficiently explicit their perspectives and ideas on a wide variety of cluster on topics via social networking websites. As online data is abundant through different platforms like social networks, twitter, Facebook, etc. Analysing the data is of paramount importance in drawing inference from the data. Hence, in our research, we try to perform sentiment analysis on twitter data by using a Naive Bayesian algorithm. By using our model, we can measure the customers opinions and perceptions and can be enhanced to any desired level depending on the data gathered from on line resources .
Hydrogen Rich Syngas Oxidative Catalytic Activity of Nickel and Inconel

Because of concerns regarding the effect of human activities on climate change, there is considerable interest in finding alternative fuel sources that produce lower fossil carbon emissions. One path is the use of hydrogen rich syngas as proposed for Integrated Coal and/or Biomass Gasification/Combine Cycle (ICGCC) systems. In addition, operating the combustor under lean, premixed conditions with lower temperatures is ideal to reduce the formation of NOx [1–3]. Syngas is composed primarily of H2, CO, and CO2 and while this seems a simple mixture, the presence of large amounts of gaseous hydrogen complicate matters because of its tendency to be extremely sensitive to preignition under oxidizing conditions. Indeed, at this time there is an unexplained discrepancy between measured and predicted ignition times and flame speeds for hydrogen enriched syngas fuels at the high pressures (10-20 bar) and low temperatures (600-800 K) typical of stationary gas turbine combustor inlets [3]. This discrepancy was highlighted in a paper published by Petersen et al. [2]. The discrepancy in predicted ignition time can be quite large; at 20 bar and 700 K, typical gas turbine combustor inlet conditions, it is about three orders of magnitude. This has a negative impact requiring an overly conservative design to avoid unwanted preignition events.
GerDraCor-Coref: A Coreference Corpus for Dramatic Texts in German

Dramatic texts are a highly structured literary text type. Their quantitative analysis so far has relied on analysing structural properties (e.g., in the form of networks). Resolving coreferences is crucial for an analysis of the content of the character speech, but developing automatic coreference resolution (CR) systems depends on the existence of annotated corpora. In this paper, we present an annotated corpus of German dramatic texts, a preliminary analysis of the corpus as well as some baseline experiments on automatic CR. The analysis shows that with respect to the reference structure, dramatic texts are very different from news texts, but more similar to other dialogical text types such as interviews. Baseline experiments show a performance of 28.8 CoNLL score achieved by the rule-based CR system CorZu. In the future, we plan to integrate the (partial) information given in the dramatis personae into the CR model.
Diversity and activity of cellulolytic bacteria, isolated from the gut contents of grass carp (Ctenopharyngodon idellus) (Valenciennes) fed on Sudan grass (Sorghum sudanense) or artificial feedstuffs

Herbivorous grass carp (Ctenopharyngodon idellus) has a powerful capability to digest cellulose from aquatic plants, depending on the cellulase complex produced by the cellulolytic bacterial community in the gastrointestinal (GI) tract. However, it remains uncertain which bacteria taxa may actively participate in the digestion of food fibre. In this study, a total of 499 cellulolytic bacteria from the gut content of grass carp fed on Sudan grass (242 strains) and artificial feedstuffs (257 strains) were randomly isolated and characterized using carboxymethyl-cellulose, microcrystalline cellulose and cellobiose agar media. The results showed that more than half of the isolates were capable of degrading carboxymethyl-cellulose and cellobiose, while the remaining isolates were restricted to microcrystalline cellulose decomposition, exclusively. The cellulolytic bacterial community was dominated by Aeromonas, followed by Enterobacter, Enterococcus, Citrobacter, Bacillus, Raoultella, Klebsiella, Hydrotalea, Pseudomonas, Brevibacillus and some unclassified bacteria, as revealed by 16S rDNA sequence analysis. Notably, grass carp fed on grass with high-fibre content harboured a higher diversity of cellulolytic bacteria than the ones fed on low-fibre feedstuffs. Our results provided evidence for a positive correlation between the content of food fibre and the diversity of cellulolytic bacteria in grass carp intestines. Thus, improving growth conditions and cellulase activities for GI cellulolytic microorganisms in grass carp intestines are critical for effective utilization of feedstuffs containing high fibre levels.
Fast Distributed Estimation of Probability Mass Functions over Anonymous Networks

—The aggregation and estimation of values over 1 networks is fundamental for distributed applications, such as 2 wireless sensor networks. Estimating the average, minimal and 3 maximal values has already been extensively studied in the 4 literature. In this paper, we focus on estimating the entire 5 distribution of values in a network with anonymous agents. 6 In particular, we compare two different estimation strategies in 7 terms of their convergence speed, accuracy and communication 8 cost. The ﬁrst strategy is deterministic and based on the average 9 consensus protocol, while the second strategy is probabilistic 10 and based on the max consensus protocol. We characterize 11 both strategies’ statistical performance, and present trade-offs 12 and guidelines for choosing the right estimation scheme. 13
SEMANTIC WEB MINING BASED ON BIG DATA USING RDF FOR WEB SEARCHE ’ S

- The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance Constructing transfer inference forest and effective assertion triples, the storage is largely reduced and the reasoning process is simplified and accelerated. Resource description framework is a basic representation of ontologies used to describe the knowledge in the Semantic Web. A large volume of Semantic Web data, the fast growth of ontology bases has brought significant challenges in performing efficient and scalable reasoning. Centralized reasoning methods are not sufficient to process large ontologies. Distributed reasoning methods are thus required to improve the scalability and performance of inferences. We have implemented Web PIE (Web-scale Inference Engine) and we demonstrate its performance on a cluster of up to 64 nodes. We have evaluated our system using very large real-world datasets (Bio2RDF, LLD, LDSR) and the LUBM synthetic benchmark, scaling up to 100 billion triples. Results show that our implementation scales linearly and vastly outperforms current systems in terms of maximum data size and inference speed
Predicting ALICE Grid throughput using recurrent neural networks

The Worldwide LHC Computing Grid (WLCG) is the infrastructure enabling the storage and processing of the large amount of data generated by the LHC experiments, and in particular the ALICE experiment. With the foreseen increase in the computing requirements of the future High Luminosity LHC experiments, a data placement strategy which increases the efficiency of the WLCG computing infrastructure becomes extremely relevant for the scientific success of the LHC scientific programme. Currently, the data placement at the ALICE Grid computing sites is determined by heuristic algorithms. Optimisation of the data storage could yield substantial benefits in terms of efficiency and time-to-result. This has however proven to be arduous due to the complexity of the problem. In this work we propose a modelisation of the behaviour of the system via principal component analysis, time series analysis and deep learning, starting from the detailed data collected by the MonALISA monitoring system. We show that it is possible to analyse and model the throughput of the ALICE Grid to a level that has not been possible before, comparing the performance of different deep learning architectures based on recurrent neural networks. Analyzing about six weeks of activity, the Grid I/O throughput trend is successfully predicted with a mean relative error of 4%, while the prediction of the throughput itself performs at 5%.
Invasive marine red alga introduced to New Zealand waters: First record of Grateloupia turuturu (Halymeniaceae, Rhodophyta)

Abstract The red alga Grateloupia turuturu is recorded from New Zealand waters for the first time. This species, native to Japan, is considered to be invasive in western Europe, North America, and Tasmania. The occurrence of G. turuturu is confirmed by molecular analysis of chloroplast‐encoded rbcL and mitochondrial cox2–3 spacer sequences as well as the vegetative and reproductive anatomy of both tetrasporophyte and gametophyte specimens.
Reconstruction and Change Management for Cultural Heritage

1. ABSTRACT Restorations and building projects in the building are playing an increasingly important role in the field of building management. Building inventory documentation therefore forms the basis for all types of planning. In the meantime, data acquisition using 3D laser scanners and subsequent evaluation has established itself in the field of internal service. This provides an effective planning tool for the architect, restorer and craftsman. We use unmanned aerial vehicles equipped with highresolution cameras which can produce hundreds of photos of the building from many angles within a short time. From these "photo clouds" a three-dimensional model of the building can be calculated by means of photogrammetric methods on highly parallel computer clusters.
Production of an epitope-specific antibody using recombinant repetitive oligonucleotides

This study was performed to produce a strong antibody against repeated peptides for a specific region of an antigen. Nine amino acids at C-terminal from human brain β-tubulin III were selected as a target sequence since it is well known for a high conservancy of sequence and resistance to antibody production. The synthetic oligonucleotides had six bases (5′-CGAC—GT-3′, 5′-TC—CAGC-3′) appended at each end and were repeatedly linked and directly joined by ligase in the presence of restriction enzymes (AccI, AvaI) to allow for the construction of head-to-tail repeats. Ligated oligonucleotides containing more than 10-unit repeats were eluted from agarose gels and cloned into the AccI site of a modified pGEX expression vector. The cloned repeated target sequences were subsequently expressed in a bacterial system. A large amount of glutathione-S-transferase-fusion protein containing the unit repeats was purified by affinity chromatography on glutathione-agarose beads and injected into Balb/c mice to produce polyclonal and monoclonal antibodies (Abs). Total protein from several mammalian brains was extracted and utilised to test the quality of the generated Abs in immunoblot assays. The generated Abs specifically recognised the 55-kDa human brain β-tubulin protein but not any other mammalian brain β-tubulins. The potential uses of this system could be applied to an effective epitope generation for an antibody and to the development of a new type of subunit vaccine.
Ischemic Heart Disease and Cognitive Prognosis in the First Year after Stroke

Importance: Cognitive impairment is the greatest single source of unmet need identified by stroke survivors. Knowledge of the factors that influence cognitive prognosis will lead to better preventive and rehabilitation strategies. Objective: To identify the factors that influence general cognitive function, memory and executive function in the first year after ischemic stroke. Design: Single centre longitudinal observational study. Setting: Hospital stroke service. Participants: A cohort of 179 patients identified within 7 days of first symptomatic ischaemic stroke were enrolled into a longitudinal cognitive study, STRATEGIC. General cognitive function, episodic memory and executive function were assessed in the first three months and again at one year after stroke. Lesion topography was defined by imaging (n=152) performed in the acute period. Cognitive evaluation was repeated at one year in 141 participants. Main Outcome Measures: Montreal Cognitive Assessment (MoCA) score at 1 year. Verbal free recall (Free and Cued Selective Reminding Test) and Digit Symbol Substitution Score provided secondary outcome measures of episodic memory and executive function respectively. Results: At 50+-19 days after stroke, diabetes mellitus and smoking were associated with MoCA score independent of other risk and demographic factors. Lesion vascular territory was independently associated with memory while white matter lesion burden was associated with executive function. In contrast to other risk factors, ischaemic heart disease was associated with change in cognitive scores and MoCA score at one year but not MoCA score at 3 months. IHD was the only factor significantly associated with change over time. This association was significant independent of other factors. Conclusions and Relevance: Associations between post-stroke cognition, and age, diabetes, smoking and white matter lesions, are likely to reflect the general effects of these factors on brain structure and function. These risk factors are not associated with change in cognitive function between 3 months and one year. In contrast, pre-existing ischemic heart disease was associated specifically with change in cognition over time. On average, patients with IHD showed decline in MoCA scores between 3 and 12 months while those free of IHD showed improvement. Intervention for IHD, alongside best-care stroke rehabilitation, merits investigation as a strategy to improve cognitive prognosis after stroke.
Identifying Explosive Cases with Unsupervised Machine Learning

An analysis of a combined dataset of Wave 1 and 2 cases, aligned at approximately Local Time Zero + 2 months with unsupervised machine learning methods such as PCA and deep autoencoder dimensionality reduction allows to clearly separate milder background cases from those with more rapid and aggressive onset of the epidemics. The analysis and findings of the study can be used in evaluation of possible epidemiological scenarios and as an effective modeling tool to design corrective and preventative measures to avoid developments with potentially heavy impact.