TO STUDY THE POSSIBILITIES OF USING THE SEISMOLOGICAL NETWORK OF THE ALTAI-SAYAN REGION FOR REGIME VIBRO-SEISMIC OBSERVATIONS

The results of deep seismic studies based on P - and S-wave data on the East-Stanov fragment of the reference 700-kilometer geophysical profile 8-DV are presented. Deep seismic sections of the upper crust (up to a depth of 20 km) with the distribution of the velocities of longitudinal and transverse waves are constructed. The P - wave velocities in the upper part of the section vary from 4-5 km / s within the Upper Zeya and Amur-Zeya depressions to 5.5-6.0 km/s within mountain ranges and plateaus; at depths of 10-20 km, lenses of high-velocity rocks up to 6.7-7.0 km/s are distinguished in the profile alignment. According to the S - waves in the upper part of the section, the velocity values are generally 3.0-3.2 km/s; reduced velocity values of 2.5-2.6 km / s are observed in the Upper Zey depression. At depths of 5-20 km within the section, according to the transverse wave data, a number of sections with reduced and increased velocity values are distinguished, respectively, up to 3.4-3.5 km/s and 3.75-3.8 km/s. The correlation of the selected anomalies according to the data of P-and S-waves is carried out.

Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating snowpack model output for avalanche forecasting

Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant for their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models, which consist of multidimensional sequences describing the snow characteristics of grain type, hardness, and age. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. By emulating aspects of the human avalanche hazard assessment process, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build an understanding of how to interpret and trust operational snowpack simulations.

Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating of snowpack model output for avalanche forecasting

Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant to their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. Through emulating a human avalanche hazard assessment approach, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build understanding in how to interpret and when to trust operational snowpack simulations.

Efficient merging of genome profile alignments

Motivation Whole-genome alignment (WGA) methods show insufficient scalability toward the generation of large-scale WGAs. Profile alignment-based approaches revolutionized the fields of multiple sequence alignment construction methods by significantly reducing computational complexity and runtime. However, WGAs need to consider genomic rearrangements between genomes, which make the profile-based extension of several whole-genomes challenging. Currently, none of the available methods offer the possibility to align or extend WGA profiles. Results Here, we present genome profile alignment, an approach that aligns the profiles of WGAs and that is capable of producing large-scale WGAs many times faster than conventional methods. Our concept relies on already available whole-genome aligners, which are used to compute several smaller sets of aligned genomes that are combined to a full WGA with a divide and conquer approach. To align or extend WGA profiles, we make use of the SuperGenome data structure, which features a bidirectional mapping between individual sequence and alignment coordinates. This data structure is used to efficiently transfer different coordinate systems into a common one based on the principles of profiles alignments. The approach allows the computation of a WGA where alignments are subsequently merged along a guide tree. The current implementation uses progressiveMauve and offers the possibility for parallel computation of independent genome alignments. Our results based on various bacterial datasets up to several hundred genomes show that we can reduce the runtime from months to hours with a quality that is negligibly worse than the WGA computed with the conventional progressiveMauve tool. Availability and implementation GPA is freely available at https://lambda.informatik.uni-tuebingen.de/gitlab/ahennig/GPA. GPA is implemented in Java, uses progressiveMauve and offers a parallel computation of WGAs. Supplementary information Supplementary data are available at Bioinformatics online.

ANALISIS PROFIL PENGARUH BAHAN ORGANIK TERHADAP KONSENTRASI BESI FERRO DAN SERAPANNYA DI LAHAN RAWA PASANG SURUT

<p>This study aimed to determine the similarity of the characteristics of each type of organic matter in suppressing the solubility of iron in soil and absorption in plants. This research was conducted in two stages. The first stage was conducted in the greenhouse to study the effect of organic matter to iron solubility in acidic sulphate soil. The research used a factorial design with 1 control and 3 replications. The first factor was type of organic matter used, B1 = rice straw; B2 = weeds; B3 = Combination of 50% rice straw and 50% weeds. The second factor was the incubation period of organic matter I1 = 2 weeks, I2 = 4 weeks, I3 = 8 weeks, and I4 = 12 weeks. The second stage was analyzing the profiles of the type of organic matter in order to evaluate the similarity of the characteristics of each type of organic matter. Based on the profile alignment, it was found that the three types of organic matter were not aligned. The types of organic matter had different roles in suppressing the solubility of iron in soil and its absorption in plants. There is a need to do a comparative analysis with Tukey method to the three types of organic matter.</p>