Flow patterns generated by oblate medusan jellyfish: field measurements and laboratory analyses

Flow patterns generated by medusan swimmers such as jellyfish are known to differ according the morphology of the various animal species. Oblate medusae have been previously observed to generate vortex ring structures during the propulsive cycle. Owing to the inherent physical coupling between locomotor and feeding structures in these animals, the dynamics of vortex ring formation must be robustly tuned to facilitate effective functioning of both systems. To understand how this is achieved, we employed dye visualization techniques on scyphomedusae (Aurelia aurita) observed swimming in their natural marine habitat. The flow created during each propulsive cycle consists of a toroidal starting vortex formed during the power swimming stroke, followed by a stopping vortex of opposite rotational sense generated during the recovery stroke. These two vortices merge in a laterally oriented vortex superstructure that induces flow both toward the subumbrellar feeding surfaces and downstream. The lateral vortex motif discovered here appears to be critical to the dual function of the medusa bell as a flow source for feeding and propulsion. Furthermore, vortices in the animal wake have a greater volume and closer spacing than predicted by prevailing models of medusan swimming. These effects are shown to be advantageous for feeding and swimming performance, and are an important consequence of vortex interactions that have been previously neglected

Highly skewed current-phase relation in superconductor-topological insulator-superconductor Josephson junctions

Three-dimensional topological insulators (TI's) in proximity with superconductors are expected to exhibit exotic phenomena such as topological superconductivity (TSC) and Majorana bound states (MBS), which may have applications in topological quantum computation. In superconductor-TI-superconductor Josephson junctions, the supercurrent versus the phase difference between the superconductors, referred to as the current-phase relation (CPR), reveals important information including the nature of the superconducting transport. Here, we study the induced superconductivity in gate-tunable Josephson junctions (JJs) made from topological insulator BiSbTeSe2 with superconducting Nb electrodes. We observe highly skewed (non-sinusoidal) CPR in these junctions. The critical current, or the magnitude of the CPR, increases with decreasing temperature down to the lowest accessible temperature (T ~ 20 mK), revealing the existence of low-energy modes in our junctions. The gate dependence shows that close to the Dirac point the CPR becomes less skewed, indicating the transport is more diffusive, most likely due to the presence of electron/hole puddles and charge inhomogeneity. Our experiments provide strong evidence that superconductivity is induced in the highly ballistic topological surface states (TSS) in our gate-tunable TI- based JJs. Furthermore, the measured CPR is in good agreement with the prediction of a model which calculates the phase dependent eigenstate energies in our system, considering the finite width of the electrodes as well as the TSS wave functions extending over the entire circumference of the TI

An integrated neural network algorithm for optimum performance assessment of auto industry with multiple outputs and corrupted data and noise

In the real world encountering with noisy and corrupted data is unavoidable. Auto industry sector (AIS) as a one of the significant industry encounters with noisy and corrupted data regarding to its rapid development. Therefore, developing the performance assessment in this situation is so helpful for this industry. As Data envelopment Analysis (DEA) could not deal with noisy and corrupted data, the alternative method(s) is very important. As one of excellent and promising feature of artificial neural networks (ANNs) are theirs flexibility and robustness in noisy situation, they are a good alternative. This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques for efficiency assessment in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores of auto industry in various countries, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of AIS on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Another feature of proposed algorithm is its ability to calculate efficiency for multiple outputs. An example using real data is presented for illustrative purposes. In the application to the auto industries, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. To test the robustness of the efficiency results of the proposed method, the ability of proposed ANN algorithm in dealing with noisy and corrupted data is compared with Data Envelopment Analysis (DEA). Results of the robustness check show that the proposed algorithm is much more robust to the noise and corruption in input data than DEA

Casimir force in the presence of a magnetodielectric medium

In this article we investigate the Casimir effect in the presence of a medium by quantizing the Electromagnetic (EM) field in the presence of a magnetodielectric medium by using the path integral formalism. For a given medium with definite electric and magnetic susceptibilities, explicit expressions for the Casimir force are obtained which are in agree with the original Casimir force between two conducting parallel plates immersed in the quantum electromagnetic vacuum.Comment: 8 pages, 1 figur

Effect of hook and bait size on catch efficiency in the Persian Gulf recreational fisheries

The effect of hook and bait sizes on the catch efficiency and size composition of Spangled Emperor Lethrinus nebulosus, Orange‐spotted Grouper Epinephelus coioides, and Narrowbarred Mackerel Scomberomorus commerson was investigated in the recreational and semi‐subsistence handline fishery in the Persian Gulf. Based on expectations that increasing hook and bait sizes would decrease the catch efficiency of the smaller individuals while maintaining the catch efficiency of larger fish, we investigated the effect of increasing hook and bait sizes. For all three species, the results indicated slightly lower catch efficiency for the smaller fish when larger hooks were used. Furthermore, the results demonstrated a significant increase in catch efficiency for the larger sizes of Spangled Emperor and Orange‐spotted Grouper when fished with larger hooks, an effect that increased with fish size for both species. Additionally, the overall catch efficiency did not vary significantly when increasing hook and bait sizes for the three species investigated. This study shows that fishing with larger hooks and larger bait would change the exploitation pattern of these species toward higher proportions of larger fish in the catches. Moreover, based on the size distribution of the species on the fishing grounds during the study period, the use of larger hooks and bait would lead to significant increases in the total number of Spangled Emperor caught (41% increase; 95% confidence interval [CI] = 17–69%) and the total number of Orange‐spotted Grouper caught (151% increase; 95% CI = 132–336%), respectively. The results indicated a similar effect for Narrowbarred Mackerel; however, the effect was far less profound than for the two other species and was not significant for any size‐classes

Inherent-opening-controlled pattern formation in carbon nanotube arrays

We have introduced inherent openings into densely packed carbon nanotube arrays to study self-organized pattern formation when the arrays undergo a wetting–dewetting treatment from nanotube tips. These inherent openings, made of circular or elongated hollows in nanotube mats, serve as dewetting centres, from where liquid recedes from. As the dewetting centres initiate dry zones and the dry zones expand, surrounding nanotubes are pulled away from the dewetting centres by liquid surface tension. Among short nanotubes, the self-organized patterns are consistent with the shape of the inherent openings, i.e. slender openings lead to elongated trench-like structures, and circular holes result in relatively round nest-like arrangements. Nanotubes in a relatively high mat are more connected, like in an elastic body, than those in a short mat. Small cracks often initialize themselves in a relatively high mat, along two or more adjacent round openings; each of the cracks evolves into a trench as liquid dries up. Self-organized pattern control with inherent openings needs to initiate the dewetting process above the nanotube tips. If there is no liquid on top, inherent openings barely enlarge themselves after the wetting–dewetting treatment

A Novel Protocol for Stevia Rebaudiana (Bert.) Regeneration

Stevia rebaudiana Bertoni has sweet substances (stevioside) in its leaves that are free of calories and their consumption is beneficial for diabetic patients and is also helpful in high blood pressure also. Because of low capability in seed germination, tissue culture is an appropriate method for propagation of this plant. In the current study, optimization of stevia in vitro cultivation via direct organogenesis with different explants, light intensities and plant hormones has been examined. These treatments included BAP (at 0.5, 1, 1.5 and 2mg/l) in combination with  2,4-D, IBA and NAA (each with concentrations of 0.1, 0.2 and 0.5mg/l) and different light intensities (Dark, 2000, 4000 and 6000 lux). MS was utilized as a basal medium. Results indicated the highest rate of organogenesis (85%) occurred on the axillary buds explants with a medium containing 1.5mg/l BAP + 0.1mg/l NAA under 6000 lux light intensity. Also, the highest range of primary organ per explant (42) with 0.3cm length was achieved at this condition. The most efficient medium for rhizogenesis i.e. 100% root production along with the highest root number (11 with approximately 7.13cm length) was obtained in presence of activated charcoal and 1mg/l of IBA. At the end of rhizogenesis experiments, the plantlet length and node multiplicity were 12.8cm and 7 respectively. Greenhouse cultivation of these plantlets was successful

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification