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We present neutral hydrogen (H  i) and ionized hydrogen (H α) observations of 10 galaxies out to a redshift of 0.1. The H  i observations are from the first epoch (178 h) of the COSMOS H  i Large Extragalactic Survey (CHILES). Our sample is H  i biased and consists of 10 late-type galaxies with H  i masses that range from 1.8 × 107 M⊙ to 1.1 × 1010 M⊙. We find that although the majority of galaxies show irregularities in the morphology and kinematics, they generally follow the scaling relations found in larger samples. We find that the H  i and H α velocities reach the flat part of the rotation curve. We identify the large-scale structure in the nearby CHILES volume using DisPerSE with the spectroscopic catalogue from SDSS. We explore the gaseous properties of the galaxies as a function of location in the cosmic web. We also compare the angular momentum vector (spin) of the galaxies to the orientation of the nearest cosmic web filament. Our results show that galaxy spins tend to be aligned with cosmic web filaments and show a hint of a transition mass associated with the spin angle alignment. [ABSTRACT FROM AUTHOR]

Late-type disc galaxies are observed to have extended gas discs that are significantly larger than their optical discs. The Λ cold dark matter (ΛCDM) cosmological model predicts that luminous galaxies, such as the Milky-Way (MW), are hosts to a large number of low-mass dark matter sub-haloes (DMSHs), which are capable of interacting with the discs of galaxies. We aim to understand the kinematic impact of a DMSH collision with the extended gas disc of a galaxy. In order to study this, we use hydrodynamical simulations with a variety of parameters such as galaxy inclination, DMSH mass, and galaxy mass. To analyse the simulation results, we present two-dimensional (2D) kinematic and gas density maps of galaxies to investigate their unique features and structures. Along with this, we also implement a deep learning (DL) model to classify kinematic images of the simulated galaxies as either impacted (by the DMSH) or isolated. Through the use of these techniques, we have found that the impact of the DMSH causes features in both the 2D kinematic and gas density plots, most notably the development of a |${\sim }1\, \rm {kpc}$| size H  i hole in the gas-rich DMSH impact. Through the study of these maps, we identify certain features and the specific conditions required to induce them. The study of these features is important, as they can be observed with large upcoming H  i surveys such as WALLABY and SKA. [ABSTRACT FROM AUTHOR]

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles' datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey's dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys' sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model., Competing Interests: The authors declare that they have no competing interests. Md Khairul Islam and Cornelia Locher are affiliated with the CRC for Honey Bee Products, which is an umbrella organisation which brings together industry and government organisations with industry. Md Khairul Islam received a scholarship from the CRC for his PhD research and Cornelia Locher is a full time employee of the University of Western Australia and has therefore no financial dependence on the CRC., (© 2021 Islam et al.)