Paulsinger4759

This dataset includes 1032 runs from a biomass downdraft gasifier integrated with power production unit that is fed by 86 different types of biomasses from different groups (e.g. wood and woody biomasses, herbaceous and agricultural biomasses, animal biomasses, mixed biomasses and contaminated biomasses) and under various operating conditions. EN450 The dataset covers elemental and proximate analysis of various biomasses, operation conditions and the net output power from the biomass gasification-power production (BG-PP) in each case/run. This article has been submitted via another Elsevier journal as a co-submission, titled "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant" [1]. In fact, this dataset has been used to train and test the developed Artificial neural network modeling of a downdraft BG-PP in our original research paper [1].The dataset contains the following three measures that are widely used to determine cognitive load in humans Detection Response Task - response time, pupil diameter, and eye gaze. These measures were recorded from 28 participants while they underwent tasks that are designed to permeate three different cognitive difficulty levels. The dataset will be useful to those researchers who seek to employ low cost, non-invasive sensors to detect cognitive load in humans and to develop algorithms for human-system automation. One such application is found in Advanced Driver Assistance Systems where eye-trackers are employed to monitor the alertness of the drivers. The dataset would also be helpful to researchers who are interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine system automation. The data is collected by the authors at the Department of Electrical & Computer Engineering in collaboration with the Faculty of Human Kinetics at the University of Windsor under the guidance of their Research Ethics Board.In 2019, 10 million new cases of tuberculosis have been reported worldwide. Our data reports genetic analyses of a Mycobacterium tuberculosis strain SBH321 isolated from a 31-year-old female with pulmonary tuberculosis. The genomic DNA of the strain was extracted from pure culture and subjected to sequencing using Illumina platform. M. tuberculosis strain SBH321 consists of 4,374,895 bp with G+C content of 65.59%. The comparative analysis by SNP-based phylogenetic analysis using maximum-likelihood method showed that our strain belonging to sublineage of the Ural family of Europe-America-Africa lineage (Lineage 4) and clustered with M. tuberculosis strain OFXR-4 from Taiwan. The whole genome sequence is deposited at DDBJ/ENA/GenBank under the accession WCJH00000000 (SRR10230353).The dataset contains tree height data collected in 200 mangrove and non-mangrove trees sampled in various sites in Malaysia. Different height measurement methods were performed, including visual measurements (stick, thumb rule) and precision field instruments (clinometer, laser rangefinder and altimeter), which were compared against benchmark values obtained using an unmanned aerial vehicle (UAV) and a Leica distometer. The core data have been analysed and interpreted in the paper by Saliu et al. An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs [1], in which the accuracy of each method for tree height measurement was discussed.Flue-Gas Desulphurization (FGD) is a fundamental process commonly adopted for the treatment of exhausts deriving from both stationary and mobile sources. The removal of SO2 from flue gasses can be made through different technologies and absorption offers the highest versatility for a large spectrum of applications. The data presented in this paper derive from FGD experiments carried out in a pilot wet scrubber equipped with a structured packing (Hastelloy C-22, Mellapak 250.X). The experiments aim to determine the SO2 removal efficiency from a simulated flue-gas in different operating conditions, similar to those observed in common wet FGD processes. Experimental data are reported in terms of gas velocity, concentration of SO2 in the flue-gas, liquid/gas feed ratio, fluids temperature and pressure. The dataset also includes the measurements of several working parameters, i.e. pressure drops in the column, wash water pH, relative humidity of the outlet gas and temperatures of gas and liquid flowing out of the FGD unit. The collection of these data could be useful in future studies and in the analysis of FGD units, also to design/improve large-scale absorption columns with structured packing, using various scrubbing liquids and in different operating conditions.Enterococcus gallinarum is a gram positive facultatively anaerobic bacteria that is typically found in mammalian intestinal tracts. It is generally not considered pathogenic to humans and is rarely reported. Here, we present the draft genome sequence data of Enterococcus gallinarum strain EGR748 isolated from a human clinical sample, and sequenced using the Illumina HiSeq 4000 system. The estimated whole genome size of the strain was 3,730,000 bp with a G + C content of 40.43%. The de novo assembly of the genome generated 55 contigs with an N50 of 208,509 bp. In addition, the Maximum Likelihood phylogenetic analysis based on the 16S rRNA sequence data accurately clustered EGR748 with other E. gallinarum strains. The data may be useful to demonstrate the capacity of this enterococcal species becoming the causal agents of nosocomial blood-stream infections. The genome dataset has been deposited at DDBJ/ENA/GenBank under the accession number JAABOR000000000.The present research datasets were processed for the article "The global homogenization of urban form. An assessment of 194 cities across time" [1]. They consist of land cover spatial layers, longitude and latitude point data and tabulated data with computed landscape metrics and the characterization of urban form of 194 cities for 1990 and 2015. Contiguous urban fabric at 30 m spatial resolution was derived from the Atlas of Urban Expansion database for 1990 and 2015 [2]. Landscape metrics were computed as quantitative measures of composition and spatial arrangement of each city and dimensions of the database were reduced employing correlation and principal components analysis. Hierarchical clustering was employed to group cities according to the similarity of their urban form and analysis of variance was applied to test for significant differences between them. The spatial layers contained in this article can be complemented with past and future land cover data to model urban form change at broader temporal scales.