Exploring the Effects of Emplacement Conditions on Explosion P/S Ratios across Local to Regional Distances

High-frequency (∼> 2 Hz) seismic P/S amplitude ratios are well-established as a discriminant to distinguish between natural earthquakes and underground explosions at regional distances (∼200–1500 km). As research shifts toward identifying lower-yield events, work has begun to investigate the potential of this discriminant for use at local distances (<200 km), in which initial results raise questions about its effectiveness. Here, we utilize data from several chemical explosion experiment series at the Nevada National Security Site in southern Nevada in the United States to study explosion Pg/Lg ratios across the range of local to regional distances. The experiments are conducted over differing emplacement conditions, with contrasting geologies and a variety of yields and depths of burial, including surface explosions. We first establish the similarities of Pg/Lg ratios from chemical explosions to those from historic nuclear tests and conclude that, as previous data have suggested, chemical explosion ratios are good proxies for nuclear tests. We then examine Pg/Lg ratios from the new experiment series as functions of distance, yield, depth of burial, and scaled depth of burial (SDOB). At far-local and regional distances, we observe consistently higher ratios from hard-rock explosions compared to ones in a weaker dry alluvium medium, consistent with prior regional distance results. No other trends with yield, depth of burial, or SDOB are strongly evident. Scatter in the observed ratios is very high, particularly at the shortest event-to-station distances, suggesting that small-scale path effects play a significant role. On average, the local distance explosion Pg/Lg ratios show remarkable consistency across all the variations in emplacement. Explosion source models will need to reproduce these results.

Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show that our method gives the significant and better result by comparing it to other methods.

New constraint of the Hubble constant by proper motions of radio components observed in AGN twin-jets

As the advent of precision cosmology, the Hubble constant (H 0) inferred from the Lambda Cold Dark Matter fit to the Cosmic Microwave Background data is increasingly in tension with the measurements from the local distance ladder. To approach its real value, we need more independent methods to measure, or to make constraint of, the Hubble constant. In this paper, we apply a plain method, which is merely based on the Friedman-Lemaître-Robertson-Walker cosmology together with geometrical relations, to constrain the Hubble constant by proper motions of radio components observed in AGN twin-jets. Under the assumption that the ultimate ejection strengths in both sides of the twin-jet concerned are intrinsically the same, we obtain a lower limit of H 0,min = 51.5 ± 2.3 km s−1 Mpc−1 from the measured maximum proper motions of the radio components observed in the twin-jet of NGC 1052.

Scoring Protein Sequence Alignments Using Deep Learning

Background: A high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a protein sequence, there are no methods to predict which SA may lead to more accurate models without actually building the models. In this work, we describe a method to predict the quality of a protein's SA. Methods: We created our own dataset by generating a variety of SAs for a set of 1,351 representative proteins and investigated various deep learning architectures to predict the local distance difference test (lDDT) scores of distance maps predicted with SAs as the input. These lDDT scores serve as indicators of the quality of the SAs. Results: Using two independent test datasets consisting of CASP13 and CASP14 targets, we show that our method is effective for scoring and ranking SAs when a pool of SAs is available for a protein sequence. With an example, we further discuss that SA selection using our method can lead to improved structure prediction.

Segmentation of Change in Surface Geometry Analysis for Cultural Heritage Applications

This work proposes a change-based segmentation method for applications to cultural heritage (CH) imaging to perform monitoring and assess changes at each surface point. It can be used as a support or component of the 3D sensors to analyze surface geometry changes. In this research, we proposed a new method to identify surface changes employing segmentation based on 3D geometrical data acquired at different time intervals. The geometrical comparison was performed by calculating point-to-point Euclidean distances for each pair of surface points between the target and source geometry models. Four other methods for local distance measurement were proposed and tested. In the segmentation method, we analyze the local histograms of the distances between the measuring points of the source and target models. Then the parameters of these histograms are determined, and predefined classes are assigned to target surface points. The proposed methodology was evaluated by considering two different case studies of restoration issues on CH surfaces and monitoring them over time. The results were presented with a colormap visualization for each category of the detected change in the analysis. The proposed segmentation method will help in the field of conservation and restoration for the documentation and quantification of geometrical surface change information. This analysis can help in decision-making for the assessment of damage and potential prevention of further damage, and the interpretation of measurement results.

The graviton Compton mass as Dark Energy

One of the greatest challenges of science is to understand the current accelerated expansion of the Universe. In this work we show that by considering the quantum nature of the gravitational field, its wavelength can be associated to an effective Compton mass. We propose that this mass can be interpreted as dark energy, with a Compton wavelength given by the size of the observable Universe, implying that the dark energy varies depending on this size. If we do so, we find that: 1.- Even without any free constant for dark energy, the evolution of the Hubble parameter is exactly the same as for the LCDM model, so this model has the same predictions as LCDM. 2.- The density rate of the dark energy is ΩΛ = 0.69 which is a very similar value as the one found by the Planck satellite ΩΛ = 0.684. 3.- The dark energy has this value because it corresponds to the actual size of the radius of the Universe, thus the coincidence problem has a very natural explanation. 4.- It is possible to find also a natural explanation to why observations inferred from the local distance ladder find the value H0 = 73 km/s/Mpc for the Hubble constant, we show that if we take the variability of the dark energy into account they should measure H0 = 67.3 km/s/Mpc as well. 5.- In this model the inflationary period contains a natural successful graceful exit.