DIN EN 6019:1990-11, Luft- und Raumfahrt; Prüfverfahren für metallische Werkstoffe; Empfohlenes Verfahren für die Bestimmung der R-Kurve und K

1990 ◽  
Keyword(s):  
K Index

An Empirical Study of the Relationship between Seasonal Precipitation and Thermodynamic Environment in Puerto Rico

2019 ◽  
Vol 34 (2) ◽  
pp. 277-288 ◽  
Author(s):  
Paul W. Miller ◽  
Thomas L. Mote ◽  
Craig A. Ramseyer
Keyword(s):  
Puerto Rico ◽  
Annual Rainfall ◽  
Seasonal Precipitation ◽  
Surface Precipitation ◽  
Precipitation Forecasting ◽  
Caribbean Islands ◽  
K Index ◽  
Groundwater Reserves ◽  
The Tropics ◽  
The Relationship

Abstract With limited groundwater reserves and few reservoirs, Caribbean islands such as Puerto Rico are largely dependent on regular rainfall to meet societal and ecological water needs. Thus, the ability to anticipate seasonal rainfall shortages, such as the 2015 drought, is particularly important, yet few reliable tools exist for this purpose. Consequently, interpolated surface precipitation observations from the Daymet archive are summarized on daily, annual, and seasonal time scales and compared to the host thermodynamic environment as characterized by the Gálvez–Davison index (GDI), a convective potential parameter designed specifically for the tropics. Complementing the Daymet precipitation totals, ≥1.1 million WSR-88D volume scans between 2002 and 2016 were analyzed for echo tops ≥ 10 000 ft (~3 km) to establish a radar-inferred precipitation activity database for Puerto Rico. The 15-yr record reveals that the GDI outperforms several midlatitude-centric thermodynamic indices, explaining roughly 25% of daily 3-km echo top (ET) activity during each of Puerto Rico’s primary seasons. In contrast, neither mean-layer CAPE, the K index, nor total totals explain more than 11% during any season. When aggregated to the seasonal level, the GDI strongly relates to 3-km ET (R2 = 0.65) and Daymet precipitation totals (R2 = 0.82) during the early rainfall season (ERS; April–July), with correlations weaker outside of this period. The 4-month ERS explains 51% (41%) of the variability to Puerto Rico’s annual rainfall during exceptionally wet (dry) years. These findings are valuable for climate downscaling studies predicting Puerto Rico’s hydroclimate in future atmospheric states, and they could potentially be adapted for operational seasonal precipitation forecasting.

scholarly journals K Index in cerebrospinal fluid: a valid tool in multiple sclerosis diagnosis

2014 ◽  
Vol 10 (3) ◽  
pp. 167-171 ◽  
Author(s):  
Massimo Pieri ◽  
Fabio Duranti ◽  
Diego Centonze ◽  
Fabio Buttari ◽  
Sergio Bernardini ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Cerebrospinal Fluid ◽  
K Index ◽  
Multiple Sclerosis Diagnosis

scholarly journals Back-Calculation of Fish Size in Diet Analysis of Piscivorous Predators: A New Index for the Alien Silurus glanis

2021 ◽  
Vol 13 (8) ◽  
pp. 4322
Author(s):  
Renato Bacchetta ◽  
Andrea Marotta ◽  
Alessandro Nessi ◽  
Paolo Tremolada
Keyword(s):  
Diet Analysis ◽  
Native Range ◽  
Top Predator ◽  
Silurus Glanis ◽  
K Index ◽  
Caudal Vertebrae ◽  
Back Calculation ◽  
Control Capability ◽  
The Mean ◽  
Bone Remains

The wels catfish Silurus glanis has been constantly spreading in many European basins, outside its native range. Being a voracious predator, it is considered to have a severe impact on local fish communities. In the Ticino River (Northern Italy), bones of S. glanis were found in feces from the top predator Lutra lutra. To estimate the control capability of L. lutra for this species and to back-calculate S. glanis’ size from its bone remains, whole skeletons from 27 differently sized S. glanis specimens were analyzed. A double pharyngeal element and all caudal vertebrae emerged as significant items for species identification. The mean length of the pharyngeal element was directly related to fish mass, while for vertebrae, a K-index was proposed to identify the position of each vertebra along the spine and, from this, to calculate the original fish mass. This methodology allowed us to establish that the length of the preyed S. glanis was 85–435 mm, and the ages were between 0+ and 2+ years. The proposed methodology opens new perspectives for more detailed studies on the efficiency of predation by piscivorous species on allochthonous ones.

F Minus K Index

2018 ◽  
pp. 1383-1383
Author(s):  
Richard Temple
Keyword(s):  
K Index

scholarly journals Interrelation among different instability indices of the troposphere over Dhaka associated with thunderstorms/nor'westers over Bangladesh during the pre-monsoon season

MAUSAM ◽  
2021 ◽  
Vol 58 (3) ◽  
pp. 361-368
Author(s):  
SAMARENDRA KARMAKAR ◽  
MD. MAHBUB ALAM
Keyword(s):  
Monsoon Season ◽  
Stability Index ◽  
Dew Point ◽  
Standard Errors ◽  
Regression Equations ◽  
K Index ◽  
Total Index ◽  
Level Of Significance ◽  
Almost All ◽  
Instability Indices

Attempts have been made to correlate different instability indices among themselves statistically. The study reveals that the Showalter Stability Index (SI) has moderate to good correlations with different instability indices except Dew-point Index (DPI), Vertical Total Index (VT), Modified Vertical Total Index (MVT) and Modified K-Index (MK). Most of the correlations co-efficient are found to be significant up to 99% level of significance except Dry Instability Index (DII), which has correlation with SI up to 95% level of significance. Lifted Index (LI) has moderate to good correlation with different instability indices except DII, K-Index (KI) and MVT. Most of the correlations co-efficient are significant up to 99% level of significance except VT, SWEAT Index (SWI) and MKI, which have correlation with LI up to 95% level of significance. Unmodified instability indices have moderate to strong correlation with the corresponding modified instability indices, having 99% level of significance. The correlation co-efficient of VT and MVT, SWI and Modified SWEAT Index (MSWI), and KI and MKI are comparatively large. Standard errors of estimate are small in almost all the cases except a few. The regression equations obtained are likely to be helpful in the computation of different instability indices.

scholarly journals Variability of Weddell Sea ionospheric anomaly as deduced from observations at the Akademik Vernadsky station

2021 ◽  
pp. 47-55
Author(s):  
A. Zalizovski ◽  
◽  
I. Stanislawska ◽  
V. Lisachenko ◽  
O. Charkina ◽  
...  
Keyword(s):  
Diurnal Variation ◽  
Electron Density ◽  
Geomagnetic Activity ◽  
Dynamic Equilibrium ◽  
Doppler Frequency ◽  
Weddell Sea ◽  
K Index ◽  
Weddell Sea Anomaly ◽  
Index Level ◽  
Solar Ultraviolet

Ionospheric Weddell Sea anomaly is an inversion of diurnal variation of the electron density in the ionosphere over Antarctic Peninsula, Weddell Sea, and neighbor territories observed during Antarctic summer. This paper aims at analyzing the reaction of the ionosphere during the Weddell Sea anomaly to changes in solar and geomagnetic activity as deduced from the data of vertical sounding of the ionosphere conducted at the Akademik Vernadsky station. The aim is achieved by comparing the monthly median values of the critical frequencies of the ionosphere (foF2) during Weddell Sea anomaly for the years of high and low solar activity; as well as by comparison of median December height-time diagrams (HT-diagrams) of foF2 calculated separately for the time intervals characterized by low or high levels of F10.7 and K indices for the period from 2007 till 2016. It was experimentally demonstrated that the Weddell Sea anomaly depends on the levels of solar ultraviolet flux and local K indices. The biggest nighttime maximum of ionization corresponds to low K indices and high values of F10.7. The most accurate inversion of diurnal variation of electron density in the F region is observed under the low values of K index and low F10.7 flux. The growth of geomagnetic activity decreases the nighttime ionization under both low and high levels of F10.7 fluxes and leads to a blur of the night maximum. Visible virtual heights of maximums increase together with F10.7 independently of the K index level. Blurring of the night maximum can be explained by destruction of the field of thermospheric winds supporting the nighttime anomaly, and/or by increasing role of plasma drifts in comparison with wind impact. The growth of visible virtual height of the nighttime maximum with increasing solar F10.7 flux could be explained by the gain of equatorward thermospheric wind with increasing solar ultraviolet flux that leads to growth of plasma upwelling effect. The Doppler frequency shift of the signals reflected from the ionosphere during nighttime in presence of the Weddell Sea anomaly is close to zero which could be explained by a stable F2 layer formed as a result of dynamic equilibrium between photochemical processes and upward plasma transport.

I – J – K : INDEX / INTELLIGENCE / JE / KITSCH

2019 ◽  
Vol N° 200 (2) ◽  
pp. 159-175
Author(s):  
Étienne Candel ◽  
Caroline Courbières ◽  
Gustavo Gomez-Mejia ◽  
Sarah Labelle
Keyword(s):  
K Index

Exploratory Data Analysis

2018 ◽  
pp. 112-136
Keyword(s):  
Data Analysis ◽  
Probability Space ◽  
Exploratory Data Analysis ◽  
Spatial Location ◽  
K Index ◽  
3D Data ◽  
Gaussian Density ◽  
Exploratory Data ◽  
Nearest Neighborhood ◽  
Quadrat Analysis

Exploratory data analysis (EDA) tries to summarize datasets main characteristics such as nearest neighborhood indexes, standard deviation, scatterplots or quadrat analysis. This EDA chapter is divided into several sections to cover myGeoffice© options not forgetting the graphical mode when facing outputs: file data input (after all, any analysis demands data); Descriptive study of the variable (mean, kurtosis, distribution plot, etc.); 2D-3D data posting (spatial location of the data samples); Cutoff layout map (a spatial colorful plot according to the data samples values that are higher and lower against any particular threshold); G and Kipley's K Index (to disclose clustered, uniform and random space sampling); Kernel Gaussian density (a non-parametric way to estimate the probability space density function of a variable); T-Student and F-tests (a parametric approach to check statistical differences between two sub-regions), including a brief section regarding the two-way ANOVA technique; Quadrat analysis (comparison of the statistically expected and actual counts of objects within spatial sampling areas to test randomness and clustering); XX profile scatterplot (silhouette view of the data along XX axis); and YY profile scatterplot (silhouette view of the data along YY axis).

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