scholarly journals Lidar-Based Morphometry of Conical Hills in Temperate Karst Areas in Slovenia

2021 ◽  
Vol 13 (14) ◽  
pp. 2668
Author(s):  
Tamás Telbisz

Conical hills, or residual hills, are frequently mentioned landforms in the context of humid tropical karsts as they are dominant surface elements there. Residual hills are also present in temperate karsts, but generally in a less remarkable way. These landforms have not been thoroughly addressed in the literature to date, therefore the present article is the first attempt to morphometrically characterize temperate zone residual karst hills. We use the methods already developed for doline morphometry, and we apply them to the “inverse” topography using LiDAR-based digital terrain models (DTMs) of three Slovenian sample areas. The characteristics of hills and depressions are analysed in parallel, taking into account the rank of the forms. A common feature of hills and dolines is that, for both types, the empirical distribution of planform areas has a strongly positive skew. After logarithmic transformation, these distributions can be approximated by Inverse Gaussian, Normal, and Weibull distributions. Along with the rank, the planform area and vertical extent of the hills and dolines increase similarly. High circularity is characteristic only of the first-rank forms for both dolines and hills. For the sample areas, the the hill area ratios and the doline area ratios have similar values, but the total extent of the hills is slightly larger in each case. A difference between dolines and hills is that the shapes of hills are more similar to one another than those of dolines. The reason for this is that the larger, closed depressions are created by lateral coalescence, while the hills are residual forms carved from large blocks. Another significant difference is that the density of dolines is much higher than that of hills. This article is intended as a methodological starting point for a new topic, aiming at the comprehensive study of residual karst hills across different climatic areas.

Author(s):  
Vanajakshamma Velam ◽  
Vyshnavi Kancherla ◽  
Latheef Kasala ◽  
Anusha Kancherla ◽  
Mounica Reddy Pillaram

Abstract Background This study was an attempt to assess and compare the gender-wise lifestyle patterns and well-being status among the employees of a tertiary care teaching hospital. Material and Methods This is a cross-sectional, questionnaire-based study conducted at a tertiary care hospital between May and August 2019. A total of 777 employees belonging to both genders (male and female) and working at different professional levels were assessed. All the enrolled employees were subjected to a comprehensive study tool consisting of various dimensions of their health, which included physical, mental, social, spiritual and intellectual health dimensions. Results Among the participants, 327 (42.1%) were male and 450 (57.9%) were female. There was no significant difference in the mean age of male (37.91 ± 7.52) and female (36.85 ± 8.16) employees (p = 0.07). A significantly higher proportion of diabetes and hypertension were seen in male employees (9.8% and 14.4%, respectively) than in female (5.6% and 6.2% respectively). The overall well-being was better in male employees than in females and was statistically significant (p < 0.0001). We found that male employees had statistically significant better well-being in terms of physical, mental and social health whereas female employees had intellectual health. Conclusion The overall well-being in healthcare staff was good at our tertiary care hospital, and the outstanding/good well-being rate was higher in male employees than in female employees. Female employees experienced risks with regard to their physical health.


2021 ◽  
Vol 13 (12) ◽  
pp. 2417
Author(s):  
Savvas Karatsiolis ◽  
Andreas Kamilaris ◽  
Ian Cole

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.


Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3799
Author(s):  
Tim J. Fyfe ◽  
Peter J. Scammells ◽  
J. Robert Lane ◽  
Ben Capuano

(1) Background: Two first-in-class racemic dopamine D1 receptor (D1R) positive allosteric modulator (PAM) chemotypes (1 and 2) were identified from a high-throughput screen. In particular, due to its selectivity for the D1R and reported lack of intrinsic activity, compound 2 shows promise as a starting point toward the development of small molecule allosteric modulators to ameliorate the cognitive deficits associated with some neuropsychiatric disease states; (2) Methods: Herein, we describe the enantioenrichment of optical isomers of 2 using chiral auxiliaries derived from (R)- and (S)-3-hydroxy-4,4-dimethyldihydrofuran-2(3H)-one (d- and l-pantolactone, respectively); (3) Results: We confirm both the racemate and enantiomers of 2 are active and selective for the D1R, but that the respective stereoisomers show a significant difference in their affinity and magnitude of positive allosteric cooperativity with dopamine; (4) Conclusions: These data warrant further investigation of asymmetric syntheses of optically pure analogues of 2 for the development of D1R PAMs with superior allosteric properties.


Water ◽  
2014 ◽  
Vol 6 (2) ◽  
pp. 271-300 ◽  
Author(s):  
Jenni-Mari Vesakoski ◽  
Petteri Alho ◽  
Juha Hyyppä ◽  
Markus Holopainen ◽  
Claude Flener ◽  
...  

1982 ◽  
Vol 19 (A) ◽  
pp. 359-365 ◽  
Author(s):  
David Pollard

The theory of weak convergence has developed into an extensive and useful, but technical, subject. One of its most important applications is in the study of empirical distribution functions: the explication of the asymptotic behavior of the Kolmogorov goodness-of-fit statistic is one of its greatest successes. In this article a simple method for understanding this aspect of the subject is sketched. The starting point is Doob's heuristic approach to the Kolmogorov-Smirnov theorems, and the rigorous justification of that approach offered by Donsker. The ideas can be carried over to other applications of weak convergence theory.


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