Morphological Control of Cilia-Inspired Asymmetric Movements Using Nonlinear Soft Inflatable Actuators

Soft robotic systems typically follow conventional control schemes, where actuators are supplied with dedicated inputs that are regulated through software. However, in recent years an alternative trend is being explored, where the control architecture can be simplified by harnessing the passive mechanical characteristics of the soft robotic system. This approach is named “morphological control”, and it can be used to decrease the number of components (tubing, valves and regulators) required by the controller. In this paper, we demonstrate morphological control of bio-inspired asymmetric motions for systems of soft bending actuators that are interconnected with passive flow restrictors. We introduce bending actuators consisting out of a cylindrical latex balloon in a flexible PVC shell. By tuning the radii of the tube and the shell, we obtain a nonlinear relation between internal pressure and volume in the actuator with a peak and valley in pressure. Because of the nonlinear characteristics of the actuators, they can be assembled in a system with a single pressure input where they bend in a discrete, preprogrammed sequence. We design and analyze two such systems inspired by the asymmetric movements of biological cilia. The first replicates the swept area of individual cilia, having a different forward and backward stroke, and the second generates a travelling wave across an array of cilia.

Impact of dust-cloud-radiation interactions on surface albedo: a case study of "Tiramisu" snow in Urumqi, China

Dust-cloud-surface radiation interactions (DCRI) is a complex nonlinear relation referring to the influences of both atmospheric dust and dust-on-snow on surface albedo. A “Tiramisu” snow event occurred on December 1st, 2018, in Urumqi, China, providing an excellent testbed for exploring the comprehensive effect induced by atmospheric dust and those deposited atop fresh snowpack on surface radiation. A detailed analysis indicates that the decrease of snow albedo by 0.17–0.26 (22–34%) is contributed by the effects both the dust-cloud interactions and dust-on-snow at synoptic scale in this case. In particular, dust well mixed with ice clouds at altitudes of 2.5–5.5 km disrupted the “seeder-feeder” structure of clouds and heterogeneous ice nucleation. Dust-induced changes in the low layer of ice cloud (3.3–5.5 km) under a low temperature of –20 °C resulted in a 31.8% increase in the ice particle radius and 84.6% in the ice water path, which acted to indirectly buffer the incident solar radiation reaching the surface. Dust particles deposited on the snow surface further caused snow darkening since the snow albedo was found to decrease by 11.8–23.3%. These findings underscore the importance of considering the comprehensive effect of dust-cloud-radiation interactions in the future.

Blind Inversion of Multidimensional Seismic Data Using Sequential Tikhonov and Total Variation Regularizations (STTVR)

The acoustic impedance (AI) model is key data for seismic interpretation, usually obtained from its nonlinear relation with seismic reflectivity. Common approaches use initial geological and seismic information to constraint the AI model estimation. When no accurate prior information is available, these approaches may dictate false results at some parts of the model. The regularization of ill-posed underdetermined problems requires some constraints to restrict the possible results. Available seismic inversion methods mostly use Tikhonov or total variation (TV) regularizations with some adjustments. Tikhonov regularization assumes smooth variation in the AI model, and it is incurious about the rapid changes in the model. TV allows rapid changes, and it is more stable in presence of noisy data. In a detailed realistic earth model that AI changes gradually, TV creates a stair-casing effect, which could lead to misinterpretation. This could be avoided by using TV and Tikhonov regularization sequentially in the alternating direction method of multipliers (ADMM) and creating the AI model. The result of implementing the proposed algorithm (STTVR) on 2D synthetic and real seismic sections shows that the smaller details in the lithological variations are accounted for as well as the general trend. STTVR can calculate major AI variations without any additional low-frequency constraints. The temporal and spatial transition of the calculated AI in real seismic data is gradual and close to a real geological setting.

Envy effects on conflict dynamics in supervised work groups

At the end of the last century, chaos theory principles have helped organizational theorists to analyze several aspects of organizations and to account for their dynamic evolution. However, most of contributions remained relegated as qualitative discussions of organizational phenomena. In this paper, starting from effort allocations of subordinates in supervised work groups which were observed in a human participants interaction, we found evidence of nonlinear relation between the colleagues’ effort. In order to explain the large variability of behavior we observed, we consider those activated by unfavorable social comparison and propose a dynamical model. A theoretical dynamic model based on the empirical results appears to be powerful for modeling repeated interactions in a work group. Research and intervention design should focus on individual intolerance and beliefs about the reciprocal capacities between subordinates, which, according to our study, appear to play a key role in the inefficiency of equilibria observed in supervised work groups.

On a nonlinear relation for computing the overpartition function

In 1939, H. S. Zuckerman provided a Hardy-Ramanujan-Rademacher-type convergent series that can be used to compute an isolated value of the overpartition function p (n). Computing p (n) by this method requires arithmetic with very high-precision approximate real numbers and it is complicated. In this paper, we provide a formula to compute the values of p (n) that requires only the values of p (k) with k ≤ n/2. This formula is combined with a known linear homogeneous recurrence relation for the overpartition function p (n) to obtain a simple and fast computation of the value of p (n). This new method uses only (large) integer arithmetic and it is simpler to program.

Theoretical study on amplifying strong exciton–photon coupling based on surface plasmon in a hybridized perovskite nanowire-metal film-perovskite nanowire structure

We theoretically report a strong light–matter interaction in a sandwich structure composed of hybridized inorganic–organic perovskite nanowires, silica (SiO2) films and a silver (Ag) film. Surface plasmon effectively enhances the strong exciton–photon coupling strength of perovskite nanowires, which depends on reduction of effective mode volume and local field enhancement. By calculation, we find that the thicknesses of SiO2 and Ag films can affect the coupling strength. With the suitable thickness of SiO2 (5 nm) and Ag (30 nm) films, Rabi splitting can reach 319 meV, while without an Ag film Rabi splitting is only 270 meV. Furthermore, Rabi splitting shows a negative correlation with SiO2 film thickness within a limited range. Still, it has a nonlinear relation with Ag film thickness because the imaginary part of the effective index for the hybridized mode shows a nonlinear relation with Ag film thickness. Our structure provides suitable parameters for the experiment and has potential application in nano-lasers.

Parental Acceptance of Children’s Storytelling Robots: A Projection of the Uncanny Valley of AI

Parent–child story time is an important ritual of contemporary parenting. Recently, robots with artificial intelligence (AI) have become common. Parental acceptance of children’s storytelling robots, however, has received scant attention. To address this, we conducted a qualitative study with 18 parents using the research technique design fiction. Overall, parents held mixed, though generally positive, attitudes toward children’s storytelling robots. In their estimation, these robots would outperform screen-based technologies for children’s story time. However, the robots’ potential to adapt and to express emotion caused some parents to feel ambivalent about the robots, which might hinder their adoption. We found three predictors of parental acceptance of these robots: context of use, perceived agency, and perceived intelligence. Parents’ speculation revealed an uncanny valley of AI: a nonlinear relation between the human likeness of the artificial agent’s mind and affinity for the agent. Finally, we consider the implications of children’s storytelling robots, including how they could enhance equity in children’s access to education, and propose directions for research on their design to benefit family well-being.

Structure and drivers of ocean mixing north of Svalbard in summer and fall 2018

The Arctic Ocean is a major sink for heat and salt for the global ocean. Ocean mixing contributes to this sink by mixing the Atlantic- and Pacific-origin waters with surrounding waters. We investigate the drivers of ocean mixing north of Svalbard, in the Atlantic sector of the Arctic, based on observations collected during two research cruises in summer and fall 2018. Estimates of vertical turbulent heat flux from the Atlantic Water layer up to the mixed layer reach 30 W m−2 in the core of the boundary current, and average to 8 W m−2, accounting for ∼1 % of the total heat loss of the Atlantic layer in the region. In the mixed layer, there is a nonlinear relation between the layer-integrated dissipation and wind energy input; convection was active at a few stations and was responsible for enhanced turbulence compared to what was expected from the wind stress alone. Summer melting of sea ice reduces the temperature, salinity and depth of the mixed layer and increases salt and buoyancy fluxes at the base of the mixed layer. Deeper in the water column and near the seabed, tidal forcing is a major source of turbulence: diapycnal diffusivity in the bottom 250 m of the water column is enhanced during strong tidal currents, reaching on average 10−3 m2 s−1. The average profile of diffusivity decays with distance from the seabed with an e-folding scale of 22 m compared to 18 m in conditions with weaker tidal currents. A nonlinear relation is inferred between the depth-integrated dissipation in the bottom 250 m of the water column and the tidally driven bottom drag and is used to estimate the bottom dissipation along the continental slope of the Eurasian Basin. Computation of an inverse Froude number suggests that nonlinear internal waves forced by the diurnal tidal currents (K1 constituent) can develop north of Svalbard and in the Laptev and Kara seas, with the potential to mix the entire water column vertically. Understanding the drivers of turbulence and the nonlinear pathways for the energy to turbulence in the Arctic Ocean will help improve the description and representation of the rapidly changing Arctic climate system.