The dynamics of Rn-222 cyclic flow within the shallow geological subsurface media as a daily temporal variated source for exhalation into the air.

Extensive research on the dynamics of radon gas (Rn-222) originating from the radioactive decay of radium (Ra-226) in geological subsurface media, sheds light on its periodic release into the atmosphere. Radon is a product of the uranium-238 decay chain found within rock and soil grains. While only a fraction of the generated radon escapes (emanates) into porous spaces due to nuclear recoil, it serves as the source for subsurface gas flows and for cyclic exhalation into the soil-atmosphere interface. Ongoing study of radon movement in shallow and deep subsoil, and its emergence at the surface, reveals complete semi-diurnal, diurnal, and seasonal gas flow cycles in the subsoil. Complementary emissions occur nocturnally as radon is released into the atmosphere. Moreover, two natural driving forces govern complex semi-diurnal and diurnal flows below and above the surface. Subsurface gas movement in porous media exhibits nonlinear behavior influenced by surface temperature gradients, resulting in downward flow to depths of up to 100 m. This flow exhibits daily periodicity with depth-dependent time delays, correlating with the diurnal surface temperature cycle. Additionally, pore gas transport into and out of open boreholes responds linearly to semi-diurnal barometric pressure changes, known as barometric pumping. Beyond subsurface phenomena, Europe and Australia increasingly employ nocturnal radon measurements to study atmospheric stability and air quality, assuming that variations in local stationary near-surface radon concentrations reflect atmospheric mixing processes. Recognizing mechanisms governing radon's temporal changes within geological subsurface media highlights the need for continuous underground radon monitoring to validate variations in daily radon exhalation to the surface. On the other hand, monitoring radon at considerable depths minimizes climatic contributions and enhances the ability to discern non-periodic pre-seismic radon signals, independent of atmospheric compulsion. This research offers potential insights into seismic precursors and the complex interplay between subsurface geodynamics and atmospheric conditions.

Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method.

Grapevine (Vitis vinifera L.) currently includes thousands of cultivars. Discrimination between these varieties, historically done by ampelography, is done in recent decades mostly by genetic analysis. However, when aiming to identify archaeobotanical remains, which are mostly charred with extremely low genomic preservation, the application of the genomic approach is rarely successful. As a result, variety-level identification of most grape remains is currently prevented. Because grape pips are highly polymorphic, several attempts were made to utilize their morphological diversity as a classification tool, mostly using 2D image analysis technics. Here, we present a highly accurate varietal classification tool using an innovative and accessible 3D seed scanning approach. The suggested classification methodology is machine-learning-based, applied with the Iterative Closest Point (ICP) registration algorithm and the Linear Discriminant Analysis (LDA) technique. This methodology achieved classification results of 91% to 93% accuracy in average when trained by fresh or charred seeds to test fresh or charred seeds, respectively. We show that when classifying 8 groups, enhanced accuracy levels can be achieved using a "tournament" approach. Future development of this new methodology can lead to an effective seed classification tool, significantly improving the fields of archaeobotany, as well as general taxonomy.

Augmenting GPS IWV estimations using spatio-temporal cloud distribution extracted from satellite data.

Water vapor (WV) is the most variable greenhouse gas in the troposphere, therefore investigation of its spatio-temporal distribution and motion is of great importance in meteorology and climatology studies. Here, we suggest a new strategy for augmenting integrated water vapor (IWV) estimations using both remote sensing satellites and global positioning system (GPS) tropospheric path delays. The strategy is based first on the ability to estimate METEOSAT-10 7.3 µm WV pixel values by extracting the mathematical dependency between the IWV amount calculated from GPS zenith wet delays (ZWD) and the METEOSAT-10 data. We then use the surface temperature differences between ground station measurements and METEOSAT-10 10.8 µm infra-red (IR) channel to identify spatio-temporal cloud distribution structures. As a last stage, the identified cloud features are mapped into the GPS-IWV distribution map when preforming the interpolation between adjusted GPS station inside the network. The suggested approach improves the accuracy of estimated regional IWV maps, in comparison with radiosonde data, thus enables to obtain the total water amount at the atmosphere, both in the form of clouds and vapor. Mean and root mean square (RMS) difference between the GPS-IWV estimations, using the spatio-temporal clouds distribution, and radiosonde data are reduced from 1.77 and 2.81 kg/m2 to 0.74 and 2.04 kg/m2, respectively. Furthermore, by improving the accuracy of the estimated regional IWV maps distribution it is possible to increase the accuracy of regional Numerical Weather Prediction (NWP) platforms.