Notes

Teen Security Style since Associate of Problem Behavior.
In the present article, we present a data compilation reflecting recrystallized microstructures and the corresponding mechanical properties of an equiatomic, single-phase face-centered cubic (FCC) CrFeNi medium-entropy alloy (MEA). For the analysis, interpretation, and discussion of the data, the reader can refer to the original research article entitled "Effects of temperature on mechanical properties and deformation mechanisms of the equiatomic CrFeNi medium-entropy alloy", see Ref. (Schneider and Laplanche, Acta Mater. 204, 2020). The data related to recrystallized microstructures comprise raw backscatter electron (BSE) micrographs (tif-files) obtained using a scanning electron microscope (SEM) for six grain sizes in the range [10-160 µm], optical micrographs of the alloy with the largest grain size (d = 327 µm), pdf-reports and tables presenting the corresponding grain-size distributions (d, accounting for grain boundaries only) and crystallite-size distributions (c, which accounts for both grain and anneraw stress-strain curves can be found. Compression tests for alloys with different grain sizes were performed at room temperature. Additional compression tests and tensile tests for the grain size d = 160 µm were performed at temperatures between 77 K and 873 K. Characteristic mechanical properties, such as yield stresses at 0.2% plastic strain (σ0.2% ) and Hall-Petch parameters (σ0 and ky ) are given for all temperatures in the tables below. Moreover, the Hall-Petch parameters as well as the mechanical data reported in the present study could be used for data mining and implemented in programs used for alloy design.The dataset reported data of survival rate, growth performance, branching architecture derived from 107 different poplar clones, including 104 introduced poplar clones (Populus deltoides) and 3 local extended poplar clones, measured within a long-term field experiment in lowland plantations in China. After 24 growing seasons from 1992 to 2015, the suvival rate for all the 107 clones, and tree height, diameter at breast-height (1.3 m), and tree volume of each tree were measured and calculated for all the 107 clones, in total of 301 alive trees (one to 9 trees for each clone), in three replicated plots. Subsequently, a total of 17 potential clones were selected by approximately 15% selection intensity, and 17 sample trees (one mean tree for each clone) were harvested for the investigation of the branching number, branching angle and base diameter of first-order branches. For the interpretation of the results of this experiment the readers are referred to Ref. [1]. The data presented in this article will aid selection of superior poplar clones for study and future applications in the similar lowland sites. The data on the suvival rate and growth performance of 107 poplar clones can help farmers and breeders to designing optimal schemes to increase timber yield and log assortment in poplar plantations. Raw data on tree structure parameters and branching traits can be used to evaluate the different performance of the clones, testing their different spacing and rotation requirement, and also designing innovative plantation schemes.A dataset of four draft genome sequences of Bifidobacterium strains is presented. PYR-41 All four genome assemblies are high-quality drafts characterized by high completeness and low contamination levels. GC content of the genomes varied in the range between 59.27% and 62.77%. Genome sequences were annotated for further functional and taxonomical analyses of the respective Bifidobacterium strains. Genetic determinants of probiotic capabilities, including the genes, related to utilization of human milk oligosaccharides and mucin, as well as the genes, encoding bile salt hydrolase were identified. The genome of B. bifidum VKPM=Ac-1784 has been shown to possess two bacteriocin gene clusters. The dataset expands knowledge on genomic diversity of probiotic strains of Bifidobacterium genus. The dataset is available under PRJNA656137 accession number in NCBI database and under zyv26t6x5r accession number in Mendeley Data repository.This is data on the microbial diversity in the floating cyanobacterial community and sediment samples from the lake Solenoe (Novosibirsk region, Russia) obtained by metagenomic methods. Such a detailed data of the microbial diversity of the Novosibirsk oblast lake ecosystem was carried out for the first time. The purpose of our work was to reveal microbial taxonomic diversity and abundance, metabolic pathways and new enzyme findings the studied lake ecosystem using the next-generation sequencing (NGS) technology and metagenomic analysis. The data was obtained using metagenomics DNA whole genome sequencing (WGS) on Illumina NextSeq and NovaSeq. The raw sequence data used for analysis is available in NCBI under the Sequence Read Archive (SRA) with the BioProjects and SRA accession numbers PRJNA493912 (SRR7943696), PRJNA493952 (SRR7943839) and PRJNA661775 (SRR12601635, SRR12601634, SRR12601633) corresponding to floating cyanobacterial community and sediment layers samples, respectively.The present article provides chemical, paleontological and mineralogical data obtained during an archaeometric characterization of 40 samples (33 pottery sherds, 5 clay samples, 1 sand sample and 1 red earth pigment) collected in the Via dei Sepolcri ceramic workshop in Pompeii, Italy. The workshop was still active during the 79 CE eruption of Mt. Vesuvius and the archaeometric data obtained in our investigation reveal distinct differences between pottery and geological raw materials belonging to an early 'Phase 1' production (from the beginning of the 1st century CE to the 62 CE earthquake) and a subsequent 'Phase 2' production (from the 62 CE earthquake to the 79 CE eruption). These data inform the discussions and interpretations presented in the article entitled "A pottery workshop in Pompeii unveils new insights on the Roman ceramics crafting tradition and raw materials trade", edited by Grifa et al. [1].Since the launch of the InvestSmart™ initiative in 2014, the government agencies in Malaysia have been actively engaging community and university students via their outreach programs to promote investment literacy. Given this background, the state of the investment literacy of Malaysian undergraduates and their readiness to invest is intriguing. Therefore, this article offers a dataset of Malaysian undergraduates' readiness to invest and the role that investment literacy and social influence play in their readiness to invest. Using a non-probability sampling technique, 500 undergraduate students in Malaysia were engaged to participate voluntarily in this survey. Descriptive statistics are presented in this paper. The dataset provides insights into the current state of investment literacy among Malaysian undergraduates, the sources of information on stock investment, and the readiness of these undergraduates to participate in the stock market.The dataset describes regional brain c-Fos expression and a component of maternal nest building behavior ("straw carrying") in 5 late term pregnant rabbits that had been allowed to interact with straw (a nest building material) for a discrete period (30 min), during which repetitive straw carrying behavior was initiated. Animals were sacrificed for brain c-Fos immunoreactivity 1 h after straw was placed into their cage. Regional brain c-Fos expression Neuronal c-Fos expression is known to associate with a sustained increase in neuronal excitation above resting levels, primarily due to its induction in response to increased glutamatergic input and corresponding activation of the NMDA receptor. In practice, c-Fos expression is taken to be an indication of an increase in "neuronal activity". Importantly, there is a lag of approximately 20 to 30 min between the onset of the stimulus that caused increased excitation, and the initiation of neuronal c-Fos expression, and c-Fos has a cellular half-life of approximately 1 h. Thus, the pattern of brain c-Fos expression within a brain histological section represents a composite snapshot of "superimposed" regional activations that occurred within approximately 30 min to 2 h prior to sacrifice. Behavioral variables Behavioral variables included in the present dataset are those that reflect the repetitive nature of straw carrying (straw carrying cycle frequency), as well as individual subcomponents of this behavior (collecting straw, interacting with the nest site), and indicators of the "rigidity" of expression of these subcomponents across all cycle repetitions (standard deviations of time spent collecting straw, time spent interacting with nest site). Exploratory Factor Analysis (EFA) with cluster rotation was applied in an exploratory manner in order to clarify correlational relationships between regional c-Fos expression and specific behavioral variables.A current and fully-referenced dataset of resources and technologies for rice provision system is presented in this paper. These data served as model input data for the first multi-objective spatio-temporal optimisation of Philippine rice value chains. Data on available farmland area and their characteristics, such as paddy rice yield, rice farming costs and GHG emissions, are reported. As scenarios were developed for optimal rice value chains of integrated food and non-food production, estimates on the spatio-temporal demands on food, energy, fuels and chemical are presented. Data on sale prices and GHG emission factors of the raw materials and products are also compiled. Processing and transporting technologies involved in the modelling have their economic and operating parameters presented in this paper. This dataset has been collated through academic journals, technical papers and government agencies; all of which have been properly referenced. These data are valuable to various stakeholders of the rice industry across the globe aiming to understand rice value chains optimisation studies and to conduct further scenario development under different conditions and assumptions.Recently, the use of the citizen-sensors (people generating and sharing real data by social media) for detecting and disseminating emergency events in real-time have shown a considerable increase because people at the place of the event, as well as elsewhere, can quickly post relevant information on this type of alerts. Here, we present an emergency events dataset called UrbangEnCy. The dataset contains over 25500 texts in Spanish posted on Twitter from January 19th to August 19th, 2020, with emergencies and non-emergencies related content in Ecuador. We obtained, cleaned and, filtered these tweets and, then we selected the location and temporal data as well as tweet content. Besides, the data set includes annotations regarding the type of tweet (emergency / non-emergency) as well as additional nomenclature used to describe emergencies in the Center for immediate response service to emergencies (ECU 911) of Ecuador and international emergency services agencies (ESAs). UrbangEnCy dataset facilitates evaluating data science performance, machine learning, and natural language processing algorithms used with supervised and unsupervised problems re- related to text mining and pattern recognition. The dataset is freely and publicly available at https//doi.org/10.17632/4x37zz82k8.
My Website: https://www.selleckchem.com/products/pyr-41.html