An inclined plane remote lab

Author(s):  
J. P. C. Lima ◽  
J. P. S. Simao ◽  
I. N. Silva ◽  
P. C. Nicolete ◽  
J. B. Silva ◽  
...  
Keyword(s):  
2021 ◽  
Vol 33 (6) ◽  
pp. 061707
Author(s):  
Alexander E. Dubinov ◽  
Djamilya N. Iskhakova ◽  
Valeria A. Lyubimtseva

1997 ◽  
Vol 36 (2) ◽  
pp. 135-143 ◽  
Author(s):  
Bhabani Shankar Dandapat ◽  
Anadi Shankar Gupta

1935 ◽  
Vol 19 (2) ◽  
pp. 207-214 ◽  
Author(s):  
C. W. Brown ◽  
E. E. Ghiselli ◽  
F. M. Henry
Keyword(s):  

2015 ◽  
Vol 112 (42) ◽  
pp. 12938-12943 ◽  
Author(s):  
Tzer Han Tan ◽  
Jesse L. Silverberg ◽  
Daniela S. Floss ◽  
Maria J. Harrison ◽  
Christopher L. Henley ◽  
...  

Experimental studies show that plant root morphologies can vary widely from straight gravity-aligned primary roots to fractal-like root architectures. However, the opaqueness of soil makes it difficult to observe how environmental factors modulate these patterns. Here, we combine a transparent hydrogel growth medium with a custom built 3D laser scanner to directly image the morphology of Medicago truncatula primary roots. In our experiments, root growth is obstructed by an inclined plane in the growth medium. As the tilt of this rigid barrier is varied, we find Medicago transitions between randomly directed root coiling, sinusoidal root waving, and normal gravity-aligned morphologies. Although these root phenotypes appear morphologically distinct, our analysis demonstrates the divisions are less well defined, and instead, can be viewed as a 2D biased random walk that seeks the path of steepest decent along the inclined plane. Features of this growth response are remarkably similar to the widely known run-and-tumble chemotactic behavior of Escherichia coli bacteria, where biased random walks are used as optimal strategies for nutrient uptake.


2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.


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