scholarly journals TYPES OF RIVERS FEEDING SEDIMENTARY BASINS ON THE SOUTHEASTERN EDGE OF THE SIBERIAN PLATFORM DURING THE RIPHEAN: SKETCH OF RECONSTRUCTION

2021 ◽  
Vol 40 (4) ◽  
pp. 99-117
Author(s):  
A.V. Maslov ◽  
◽  
V.N. Podkovyrov ◽  
Keyword(s):  
Bottom Sediments ◽  
Individual Data ◽  
Average Data ◽  
The Real ◽  
Geological Situation ◽  
Data Points ◽  
Clayey Rocks ◽  
Data Point ◽  
Clay Rocks ◽  
Metamorphic Terranes

Some geochemical features of fine-grained clastic/clay rocks of a number of Riphean formations of the Yudoma-Maya region of southeastern Yakutia are considered. A comparison of the distribution character of the data points of individual and average samples of clay rocks in the (La/Yb)N–Eu/Eu* and (La/Yb)N–Th diagrams, which show the composition of the bottom sediments of the estuarine parts of modern rivers of various categories, allowed us to make a number of conclusions. Thus, individual data points on the (La/Yb)N–Eu/Eu* diagram are concentrated mainly in the fields of bottom sediments of rivers feeding on erosion products of mainly sedimentary (categories 1 and 2) and volcanic rocks (category 4), as well as their overlapping zones. The average data point of the clay rocks of the Trekhgorka Formation in this diagram is located in the field of bottom sediments of rivers draining magmatic/metamorphic terranes (category 3). The average data points of shales of the Dim, Talyn, and Svetly formations are located in the zone of overlapping fields of bottom sediments of rivers of categories 1, 2, and 3, i.e., they contain erosion products of both sedimentary formations and magmatic and metamorphic complexes. This is quite well consistent with the real geological situation. At the same time, in the composition of clayey rocks of higher stratigraphic levels of Riphean, erosion products of magmatic/metamorphic terranes are either absent or their proportion is very small. On the (La/Yb)N–Th graph, most of the individual data points of Riphean clayey rocks tend to be in the fields 1, 2, and 4. Their average data points are mainly confined to the overlapping zone of the fields of bottom sediments of rivers of categories 1 and 2. The average data point of clay rocks of Ust-Kirba Formation is located in the field of bottom sediments of rivers that feed on products of erosion from rocks of volcanic provinces. It also does not contradict the real geological situation.

scholarly journals Categories of Vendian catchments – sources of fine-grained aluminosiliciclastic materials for the Serebryanka and Sylvitsa group deposits (Middle Urals)

LITOSFERA ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 751-770
Author(s):  
A. V. Maslov
Keyword(s):  
Bottom Sediments ◽  
Analytical Data ◽  
Western Slope ◽  
Middle Urals ◽  
The Middle Urals ◽  
Fine Grained ◽  
Sedimentary Sequences ◽  
Catchment Areas ◽  
Clay Rocks ◽  
Metamorphic Terranes

Research subject. The article discusses a number of geochemical features (distribution of lanthanides and Th, values of (La/Yb)N and Eu/Eu*) of fine-grained clastic/clay rocks of various Vendian regional stages of the western slope of the Middle Urals. The results were interpreted in the context of data on the composition of the catchment areas of a number of modern rivers and information on the distribution of the same characteristics in suspended matter and modern bottom sediments of the “downstream North Dvina River–White Sea”.Methods and materials. The content of La, Sm, Eu, Gd, Yb, and Th in as many as 200 samples of shales and mudstones of the Serebryanka and Sylvitsa groups served as a research material. The research method was an analysis of the localization of individual and average data points of clayey rocks of the Laplandian, Redkinian, Belomorian and Kotlinian regional stages on paired diagrams (La/Yb)N–Eu/Eu* and (La/Yb)N–Th developed on the basis of analytical data on the composition of bottom sediments of estuarine parts of different categories/classes of modern rivers. Results. It was shown that the sedimentary sequences of the western slope of the Middle Urals are mainly composed of fine-grained clastic material belonging to categories 1 (sediments of world major rivers) and 2 (sediments of rivers draining sedimentary/mixed sedimentary formations). This material is likely to have entered the sedimentation area from the east, from the growing Kadomian Varanger-Kanin-Timan folded-thrust belt/Timan orogen, or/and from the west (Riphean sedimentary sequences of Kama-Belsk aulacogen). In addition, the igneous and metamorphic rocks of the East European Platform basement composing the structures, which could be classified as “igneous/metamorphic terranes” under modern classification, were likely to have made a certain contribution. Conclusions. The obtained results agree well with the conclusions obtained earlier as a result of mineralogical and petrographic studies of the conglomerates and sandstones of the Serebryanka and Sylvitsa groups, as well as with the data from a previous geochemical analysis of clay rocks.

Averaging Trials Versus Averaging Trial Peaks: Impact on Study Outcomes

2017 ◽  
Vol 33 (3) ◽  
pp. 233-236 ◽  
Author(s):  
Kevin D. Dames ◽  
Jeremy D. Smith ◽  
Gary D. Heise
Keyword(s):  
Phase Shift ◽  
Average Data ◽  
Paired Samples ◽  
Individual Trial ◽  
Dependent Variables ◽  
Data Points ◽  
Multiple Trials ◽  
Average Profile ◽  
Better Than ◽  
Peak Value

Gait data are commonly presented as an average of many trials or as an average across participants. Discrete data points (eg, maxima or minima) are identified and used as dependent variables in subsequent statistical analyses. However, the approach used for obtaining average data from multiple trials is inconsistent and unclear in the biomechanics literature. This study compared the statistical outcomes of averaging peaks from multiple trials versus identifying a single peak from an average profile. A series of paired-samples t tests were used to determine whether there were differences in average dependent variables from these 2 methods. Identifying a peak value from the average profile resulted in significantly smaller magnitudes of dependent variables than when peaks from multiple trials were averaged. Disagreement between the 2 methods was due to temporal differences in trial peak locations. Sine curves generated in MATLAB confirmed this misrepresentation of trial peaks in the average profile when a phase shift was introduced. Based on these results, averaging individual trial peaks represents the actual data better than choosing a peak from an average trial profile.

scholarly journals QOS ANALYSIS OF WIRELESS NETWORKS BASED ON LOW MOBILITY PROTOCOL

2019 ◽  
Vol 01 (02) ◽  
pp. 126-138
Author(s):  
Bindhu V
Keyword(s):  
Quality Of Service ◽  
Performance Measure ◽  
Point Problem ◽  
Delivery Ratio ◽  
Mobile Wireless ◽  
Data Points ◽  
Data Point ◽  
Hidden Data ◽  
Existing Structure

The decision on the possible trails to be taken by an information that is to be transmitted from a data point to its destined point enact to have more significance on the performance measure of the transmission. The mobile wireless network is also one such network whose quality of service are determined from the ways the trails are entrenched. This wireless adhoc network that is independent of the previously existing structure forms a topology that undergoes spontaneous changes with the closely existing data points or devices. This type of networks are well suited for many adverse situation and environment, but certain attributes of the wireless adhoc such as insubstantial bandwidth, more over head in paths , hidden data point problem, energy restraints, motility interfered routing make them visage difficulties in having a proper routing, empowering MANET to achieve lesser quality in the service provided. So this paper quality of service analysis for the MANET using high power low mobility protocol ensures a routing path entrenchment between highly energetic with diminished motility data points to improvise the quality of service. The QoS analysis is done based on the delay, throughput and the packet delivery ratio to measure its performance.

scholarly journals Synrift sandstones and clayey rocks: bulk chemical composition and location on a number of discriminant paleogeodynamic diagrams

2019 ◽  
pp. 439-465
Author(s):  
A. V. Maslov ◽  
V. N. Podkovyrov ◽  
E. Z. Gareev ◽  
A. D. Nozhkin
Keyword(s):  
Chemical Composition ◽  
Continental Margins ◽  
Bulk Chemical Composition ◽  
East European ◽  
Fine Grained ◽  
Sedimentary Deposits ◽  
Data Points ◽  
Bulk Chemical ◽  
Clayey Rocks ◽  
Active Continental Margins

The bulk chemical composition of synrift sandstones and associated clayey rocks has been analized, and the distribution of the fields they form has been studied on discriminant paleogeodynamic SiO2K2O/Na2O [Roser, Korsch, 1986] and DF1DF2 [Verma, Armstrong-Altrin, 2013] diagrams. The studied sandstones in terms of bulk chemical composition mainly correspond to greywacke, lititic, arkose and subarkose psammites; Sublitites and quartz arenites are also found. A significant part in the analyzed data massif consists of psammites, in which log(Na2O/K2O)-1.0; missing on the Pettijohn classification chart. This confirms our conclusion, based on the results of mineralogical and petrographic studies, that the sedimentary infill of rift structures unites immature sandstones, the detrital framework of which was formed due to erosion of local sources, represented by various magmatic and sedimentary formations. Synrift clayey rocks, compared with sandstones, are composed of more mature fine-grained siliciclastics. As follows from the distribution of figurative data points of clayey rocks on the F1F2 diagram [Roser, Korsch, 1988], its sources were mainly sedimentary deposits. The content of most of the main rock-forming oxides in the synrift sandstones is almost the same as in silt-sandstone rocks present in the Upper Precambrian-Phanerozoic sedimentary mega-complex of the East European Plate, but at the same time differs significantly from the Proterozoic and Phanerozoic cratonic sediments, as well as from the average composition upper continental crust. It is shown that the distribution of the fields of syntift sandstones and clayey rocks on the SiO2K2O/Na2O diagram does not have any distinct features, and their figurative data points are localized in the areas of terrigenous rocks of passive and active continental margins. On the DF1DF2 diagram, the fields of the studied psammites and clayey rocks are located in areas of riftogenous and collisional environments. We have proposed a different position of the border between these areas in the diagram, which will require further verification.

Sphericity Evaluation Based on Minimum Circumscribed Sphere Method

2012 ◽  
Vol 433-440 ◽  
pp. 3146-3151 ◽  
Author(s):  
Fan Wu Meng ◽  
Chun Guang Xu ◽  
Juan Hao ◽  
Ding Guo Xiao
Keyword(s):  
Convex Hull ◽  
Early Stage ◽  
Measured Data ◽  
Efficient Approach ◽  
Critical Data ◽  
Material Condition ◽  
Data Points ◽  
Point Set ◽  
Data Point

The search of sphericity evaluation is a time-consuming work. The minimum circumscribed sphere (MCS) is suitable for the sphere with the maximum material condition. An algorithm of sphericity evaluation based on the MCS is introduced. The MCS of a measured data point set is determined by a small number of critical data points according to geometric criteria. The vertices of the convex hull are the candidates of these critical data points. Two theorems are developed to solve the sphericity evaluation problems. The validated results show that the proposed strategy offers an effective way to identify the critical data points at the early stage of computation and gives an efficient approach to solve the sphericity problems.

The Research of Data Points Quality Assessment Method in Reverse Engineering

2009 ◽  
Vol 419-420 ◽  
pp. 445-448 ◽  
Author(s):  
Jun Ting Cheng ◽  
Wei Ling Zhao ◽  
Can Zhao ◽  
Xue Dong Xie
Keyword(s):  
Reverse Engineering ◽  
Data Quality ◽  
Quality Assessment ◽  
Assessment Method ◽  
Measured Data ◽  
Quality Data ◽  
Data Quality Assessment ◽  
Quality Assessment Method ◽  
Data Points ◽  
Data Point

In the field of reverse engineering, data quality assessment is a very important work in the detection, the result of data quality assessment will directly or indirectly affect the detection and the following manufacturing process quality. Data quality assessment can be used in the camera calibration, the model and model reconstruction comparison, and so on. In this paper, on the basis of the existing method of calculating each point error, and multipurpose use of average and standard error and some other concepts of mathematical statistics, and then improve a novel and simple calculating error method. This method is applicable to many groups of one-to-one ideal data and the measured data comparison, and it can be more intuitive to reflect the error of overall data, as well as the error distribution, and it can be more efficient to determine the measured data is reasonable or not. In this paper, the data point quality which is collected in the reverse engineering is assessed, and it can see that the method which is proposed in this article has some advantages in the data point quality assessment field.

scholarly journals Parameterization Method on B-Spline Curve

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
H. Haron ◽  
A. Rehman ◽  
D. I. S. Adi ◽  
S. P. Lim ◽  
T. Saba
Keyword(s):  
Computer Model ◽  
Basis Function ◽  
Real Object ◽  
Reconstruction Process ◽  
B Spline ◽  
Parameterization Method ◽  
Spline Basis ◽  
Data Points ◽  
Data Point ◽  
2D And 3D

The use of computer graphics in many areas allows a real object to be transformed into a three-dimensional computer model (3D) by developing tools to improve the visualization of two-dimensional (2D) and 3D data from series of data point. The tools involved the representation of 2D and 3D primitive entities and parameterization method using B-spline interpolation. However, there is no parameterization method which can handle all types of data points such as collinear data points and large distance of two consecutive data points. Therefore, this paper presents a new parameterization method that is able to solve those drawbacks by visualizing the 2D primitive entity of scanned data point of a real object and construct 3D computer model. The new method has improved a hybrid method by introducing exponential parameterization method in the beginning of the reconstruction process, followed by computing B-spline basis function to find maximum value of the function. The improvement includes solving a linear system of the B-spline basis function using numerical method. Improper selection of the parameterization method may lead to the singularity matrix of the system linear equations. The experimental result on different datasets show that the proposed method performs better in constructing the collinear and two consecutive data points compared to few parameterization methods.

scholarly journals Similarity to a single set

2016 ◽  
Author(s):  
Lee Naish
Keyword(s):  
Lung Cancer ◽  
Similarity Measure ◽  
Diagnostic Tests ◽  
Similarity Measures ◽  
The Other ◽  
Data Set ◽  
Data Points ◽  
Data Point ◽  
Binary Classifiers ◽  
Single Set

Identifying patterns and associations in data is fundamental to discovery in science. This work investigates a very simple instance of the problem, where each data point consists of a vector of binary attributes, and attributes are treated equally. For example, each data point may correspond to a person and the attributes may be their sex, whether they smoke cigarettes, whether they have been diagnosed with lung cancer, etc. Measuring similarity of attributes in the data is equivalent to measuring similarity of sets - an attribute can be mapped to the set of data points which have the attribute. Furthermore, there is one identified base set (or attribute) and only similarity to that set is considered - the other sets are just ranked according to how similar they are to the base set. For example, if the base set is lung cancer sufferers, the set of smokers may well be high in the ranking. Identifying set similarity or correlation has many uses and is often the first step in determining causality. Set similarity is also the basis for comparing binary classifiers such as diagnostic tests for any data set. More than a hundred set similarity measures have been proposed in the literature is but there is very little understanding of how best to choose a similarity measure for a given domain. This work discusses numerous properties that similarity measures can have, weakening some previously proposed definitions so they are no longer incompatible, and identifying important forms of symmetry which have not previously been considered. It defines ordering relations over similarity measures and shows how some properties of a domain can be used to help choose a similarity measure which will perform well for that domain.

scholarly journals Similarity to a single set

2016 ◽  
Author(s):  
Lee Naish
Keyword(s):  
Lung Cancer ◽  
Similarity Measure ◽  
Diagnostic Tests ◽  
Similarity Measures ◽  
The Other ◽  
Data Set ◽  
Data Points ◽  
Data Point ◽  
Binary Classifiers ◽  
Single Set

Identifying patterns and associations in data is fundamental to discovery in science. This work investigates a very simple instance of the problem, where each data point consists of a vector of binary attributes, and attributes are treated equally. For example, each data point may correspond to a person and the attributes may be their sex, whether they smoke cigarettes, whether they have been diagnosed with lung cancer, etc. Measuring similarity of attributes in the data is equivalent to measuring similarity of sets - an attribute can be mapped to the set of data points which have the attribute. Furthermore, there is one identified base set (or attribute) and only similarity to that set is considered - the other sets are just ranked according to how similar they are to the base set. For example, if the base set is lung cancer sufferers, the set of smokers may well be high in the ranking. Identifying set similarity or correlation has many uses and is often the first step in determining causality. Set similarity is also the basis for comparing binary classifiers such as diagnostic tests for any data set. More than a hundred set similarity measures have been proposed in the literature is but there is very little understanding of how best to choose a similarity measure for a given domain. This work discusses numerous properties that similarity measures can have, weakening some previously proposed definitions so they are no longer incompatible, and identifying important forms of symmetry which have not previously been considered. It defines ordering relations over similarity measures and shows how some properties of a domain can be used to help choose a similarity measure which will perform well for that domain.

Context-Sensitive Heuristics in Statistical Reasoning

1993 ◽  
Vol 46 (4) ◽  
pp. 779-811 ◽  
Author(s):  
Fergus Bolger ◽  
Nigel Harvey
Keyword(s):  
Weighted Average ◽  
Statistical Reasoning ◽  
Separate Component ◽  
Serial Dependence ◽  
Context Sensitive ◽  
Different Types ◽  
Future Data ◽  
Data Points ◽  
Data Point

Previous work has shown that people use anchor-and-adjust heuristics to forecast future data points from previous ones in the same series. We report three experiments that show that they use different versions of this heuristic for different types of series. To forecast an untrended series, our subjects always took a weighted average of the long-term mean of the series and the last data point. In contrast, the way that they forecast a trended series depended on the serial dependences in it. When these were low, people forecast by adding a proportion of the last difference in the series to the last data point. When stronger serial dependences made this difference less similar to the next one, they used a version of the averaging heuristic that they employed for untrended series. This could take serial dependences into account and included a separate component for trend. These results suggest that people use a form of the heuristic that is well adapted to the nature of the series that they are forecasting. However, we also found that the size of their adjustments tended to be suboptimal. They overestimated the degree of serial dependence in the data but underestimated trends. This biased their forecasts.

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