Varietal identification plays a pivotal role in viticulture for several purposes. Nowadays, such identification is accomplished using ampelography and molecular markers, techniques requiring specific expertise and equipment. Deep learning, on the other hand, appears to be a viable and cost-effective alternative, as several recent studies claim that computer vision models can identify different vine varieties with high accuracy. Such works, however, limit their scope to a handful of selected varieties and do not provide accurate figures for external data validation. In the current study, five well-known computer vision models were applied to leaf images to verify whether the results presented in the literature can be replicated over a larger data set consisting of 27 varieties with 26 382 images. It was built over 2 years of dedicated field sampling at three geographically distinct sites, and a validation data set was collected from the Internet. Cross-validation results on the purpose-built data set confirm literature results. However, the same models, when validated against the independent data set, appear unable to generalize over the training data and retain the performances measured during cross validation. These results indicate that further enhancement have been done in filling such a gap and developing a more reliable model to discriminate among grape varieties, underlining that, to achieve this purpose, the image resolution appears to be a crucial factor in the development of such models.

We recently reported on the radio-frequency attenuation length of cold polar ice at Summit Station, Greenland, based on bi-static radar measurements of radio-frequency bedrock echo strengths taken during the summer of 2021. Those data also allow studies of (a) the relative contributions of coherent (such as discrete internal conducting layers with sub-centimeter transverse scale) vs incoherent (e.g. bulk volumetric) scattering, (b) the magnitude of internal layer reflection coefficients, (c) limits on signal propagation velocity asymmetries (‘birefringence’) and (d) limits on signal dispersion in-ice over a bandwidth of ~100 MHz. We find that (1) attenuation lengths approach 1 km in our band, (2) after averaging 10 000 echo triggers, reflected signals observable over the thermal floor (to depths of ~1500 m) are consistent with being entirely coherent, (3) internal layer reflectivities are ≈–60$\to$–70 dB, (4) birefringent effects for vertically propagating signals are smaller by an order of magnitude relative to South Pole and (5) within our experimental limits, glacial ice is non-dispersive over the frequency band relevant for neutrino detection experiments.

Over the last 25 years, radiowave detection of neutrino-generated signals, using cold polar ice as the neutrino target, has emerged as perhaps the most promising technique for detection of extragalactic ultra-high energy neutrinos (corresponding to neutrino energies in excess of 0.01 Joules, or 1017 electron volts). During the summer of 2021 and in tandem with the initial deployment of the Radio Neutrino Observatory in Greenland (RNO-G), we conducted radioglaciological measurements at Summit Station, Greenland to refine our understanding of the ice target. We report the result of one such measurement, the radio-frequency electric field attenuation length $L_\alpha$. We find an approximately linear dependence of $L_\alpha$ on frequency with the best fit of the average field attenuation for the upper 1500 m of ice: $\langle L_\alpha \rangle = ( ( 1154 \pm 121) - ( 0.81 \pm 0.14) \, ( \nu /{\rm MHz}) ) \,{\rm m}$ for frequencies ν ∈ [145 − 350] MHz.

The prismatic variety of fluoro-edenite, a new amphibole found in lavas from Mt Etna in Biancavilla (Catania Province, Sicily, Italy), has been characterized by μ-Raman and μ-FTIR spectroscopy. The wavenumbers at which the bands are detected in the μ-Raman and μ-FTIR spectra are compared with tremolite, asbestos the chemical and crystallographic characteristics of which are very similar to those of fluoro-edenite.

We present two pilot studies for the search and characterization of accretion events in star-forming dwarf galaxies. Our strategy consists of two complementary approaches: i) the direct search for stellar substructures around dwarf galaxies through deep wide-field imaging, and ii) the characterization of the chemical properties in these systems up to large galacto-centric distances. We show our results for two star-forming dwarf galaxies, the starburst irregular NGC 4449, and the extremely metal-poor dwarf DDO 68.

The oasis of Samarkand in the Middle Zeravshan Valley (modern Uzbekistan) was a major political and economic center in ancient western Central Asia. The chronology of its irrigation system was, until now, only constrained by the quality and quantity of archaeological findings and several different hypotheses have been proposed for it. We use a new approach combining archaeological surveying, radiocarbon dating, sedimentary analysis, and the numerical modeling of a flood event to offer new evidence for, and quantitative dating of, the development of irrigation system on the southern flank of the Middle Zeravshan Valley. We analyzed 13 bones and charcoals from 3 archaeological sites and obtained new 14C ages from Afrasiab (ancient Samarkand), a dwelling damaged by flooding in the 2nd century AD (site code: SAM-174) and the fortress of Kafir Kala. We established the origin of sedimentary deposits at the sites to infer the presence of the 2 most important canals of the southern flank: the Dargom and the Yanghiaryk. Finally, we show with a numerical model of overland flow that a natural flood was unlikely to have produced the damage observed at SAM-174. The combined results of the study indicate that the canals south of Samarkand existed, and were mainly developed, in the 2nd century AD and were not connected to the main feeding canal of Afrasiab at that time.

Star clusters are valuable tools for theoretical and observational astronomy across a wide range of disciplines from cosmology to stellar spectroscopy. For example, properties of globular clusters are used to constrain stellar evolutionary models, nucleosynthesis and chemical evolution, as well as the star formation and assembly histories of galaxies and the distribution of dark matter in present-day galaxies. Open clusters are widely used as stellar laboratories for the study of specific stellar phenomena (e.g., various emission-line stars, pulsating pre-MS stars, magnetic massive stars, binarity, stellar rotation, etc.). They also provide observational constraints on models of massive star evolution and of Galactic disk formation and chemical evolution.

We introduce the SMC in space and time, a large coordinated space and ground-based program to study star formation processes and history, as well as variable stars, structure, kinematics and chemical evolution of the whole SMC. Here, we present the Colour-Magnitude Diagrams (CMDs) resulting from HST/ACS photometry, aimed at deriving the star formation history (SFH) in six fields of the SMC. The fields are located in the central regions, in the stellar halo, and in the wing toward the LMC. The CMDs are very deep, well beyond the oldest Main Sequence Turn-Off, and will allow us to derive the SFH over the entire Hubble time.