Controlling Tensegrity Robots through Evolution using Friction based Actuation

Traditional robotic structures have limitations in planetary exploration as their rigid structural joints are prone to damage in new and rough terrains. In contrast, robots based on tensegrity structures, composed of rods and tensile cables, offer a highly robust, lightweight, and energy efficient solution over traditional robots. In addition tensegrity robots can be highly configurable by rearranging their topology of rods, cables and motors. However, these highly configurable tensegrity robots pose a significant challenge for locomotion due to their complexity. This study investigates a control pattern for successful locomotion in tensegrity robots through an evolutionary algorithm. A twelve-rod hardware model is rapidly prototyped to utilize a new actuation method based on friction. A web-based physics simulation is created to model the twelve-rod tensegrity ball structure. Square-waves are used as control policies for the actuators of the tensegrity structure. Monte Carlo trials are run to find the most successful number of amplitudes for the square-wave control policy. From the results, an evolutionary algorithm is implemented to find the most optimized solution for locomotion of the twelve-rod tensegrity structure. The software pattern coupled with the new friction based actuation method can serve as the basis for highly efficient tensegrity robots in space exploration

Synergistic Effects of 3D ECM and Chemogradients on Neurite Outgrowth and Guidance: A Simple Modeling and Microfluidic Framework

During nervous system development, numerous cues within the extracellular matrix microenvironment (ECM) guide the growing neurites along specific pathways to reach their intended targets. Neurite motility is controlled by extracellular signal sensing through the growth cone at the neurite tip, including chemoattractive and repulsive cues. However, it is difficult to regenerate and restore neurite tracts, lost or degraded due to an injury or disease, in the adult central nervous system. Thus, it is important to evaluate the dynamic interplay between ECM and the concentration gradients of these cues, which would elicit robust neuritogenesis. Such information is critical in understanding the processes involved in developmental biology, and in developing high-fidelity neurite regenerative strategies post-injury, and in drug discovery and targeted therapeutics for neurodegenerative conditions. Here, we quantitatively investigated this relationship using a combination of mathematical modeling and in vitro experiments, and determined the synergistic role of guidance cues and ECM on neurite outgrowth and turning. Using a biomimetic microfluidic system, we have shown that cortical neurite outgrowth and turning under chemogradients (IGF-1 or BDNF) within 3D scaffolds is highly regulated by the source concentration of the guidance cue and the physical characteristics of the scaffold. A mechanistic-driven partial differential equation model of neurite outgrowth has been proposed, which could also be used prospectively as a predictive tool. The parameters for the chemotaxis term in the model are determined from the experimental data using our microfluidic assay. Resulting model simulations demonstrate how neurite outgrowth was critically influenced by the experimental variables, which was further supported by experimental data on cell-surface-receptor expressions. The model results are in excellent agreement with the experimental findings. This integrated approach represents a framework for further elucidation of biological mechanisms underlying neuronal responses of specialized cell types, during various stages of development, and under healthy or diseased conditions

Unusual signatures of the ferromagnetic transition in the heavy Fermion compound UMn2_2Al20_{20}

Magnetic susceptibility results for single crystals of the new cubic compounds UT$_2$Al$_{20}$ (T=Mn, V, and Mo) are reported. Magnetization, specific heat, resistivity, and neutron diffraction results for a single crystal and neutron diffraction and inelastic spectra for a powder sample are reported for UMn$_2$Al$_{20}$. For T = V and Mo, temperature independent Pauli paramagnetism is observed. For UMn$_2$Al$_{20}$, a ferromagnetic transition is observed in the magnetic susceptibility at $T_c$ = 20 K. The specific heat anomaly at $T_c$ is very weak while no anomaly in the resistivity is seen at $T_c$. We discuss two possible origins for this behavior of UMn$_2$Al$_{20}$: moderately small moment itinerant ferromagnetism, or induced local moment ferromagnetism.Comment: 5 pages, 5 figures, to be published in Phys. rev.

Distributed Community Detection in Dynamic Graphs

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied \emph{Planted Bisection Model} \sdG(n,p,q) where the node set $[n]$ of the network is partitioned into two unknown communities and, at every time step, each possible edge $(u,v)$ is active with probability $p$ if both nodes belong to the same community, while it is active with probability $q$ (with $q<<p$) otherwise. We also consider a time-Markovian generalization of this model. We propose a distributed protocol based on the popular \emph{Label Propagation Algorithm} and prove that, when the ratio $p/q$ is larger than $n^{b}$ (for an arbitrarily small constant $b>0$), the protocol finds the right "planted" partition in $O(\log n)$ time even when the snapshots of the dynamic graph are sparse and disconnected (i.e. in the case $p=\Theta(1/n)$).Comment: Version I

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance

Balancing Energy Dissipation in Data Gathering Wireless Sensor Networks Using Ant Colony Optimization

Abstract. Formulation of energy efficient protocols is of utmost importance for wireless sensor networks because of energy constraints of sensor nodes. When a number of nodes is deployed in a field located away from the base station, the nodes undergo unequal energy dissipation while transmitting information to the base station primarily due to two reasons: i) the difference in the distances of nodes from the base station and ii) the variation in inter-nodal distances. The schemes presented here better network lifetime by taking into account these two issues and try to equalize the energy dissipation by the nodes. While constructing the chain we also use Ant Colony Optimization algorithm instead of greedy approach used in PEGASIS. Application of ACO ensures that the chain formed is of shortest possible length and thus further helps enhance network performances by reducing the inter-nodal transmission distances as much as possible. Extensive simulations performed corroborates that the proposed schemes outperform PEGASIS by a significant margin

Cytokine secretion in breast cancer cells – MILLIPLEX assay data

© 2019 The Author(s) Metastatic breast cancer is the most advanced stage of breast cancer and the leading cause of breast cancer mortality. Although understanding of the cancer progression and metastasis process has improved, the bi-directional communication between the tumor cell and the tumor microenvironment is still not well understood. Breast cancer cells are highly secretory, and their secretory activity is modulated by a variety of inflammatory stimuli present in the tumor microenvironment. Here, we characterized the cytokine expression in human breast cancer cells (MDA-MB-231, MCF-7, T-47D, and BT-474) in vitro using 41 cytokine MILLIPLEX assay. Further, we compared cytokine expression in breast cancer cells to those in non-tumorigenic human breast epithelial MCF-10A cells

Antimicrobial efficacy of n-[3- chloro-(substituted aryl)-4-oxoazetidin- 1-yl] pyridine-4- carboxamides against resistant bacterial strains obtained from clinical isolates

Abstract Background: The treatment of infectious diseases is still an important and challenging problem due to emerging infectious diseases and increasing number of multi-drug resistant microbial pathogens which cause a variety of illnesses ranging from hospital-acquired pneumonia, bloodstream infections, urinary tract infections from catheters, abdominal infections and even meningitis. Methods: The main objective of the present study was to evaluate the antimicrobial efficacy and β-lactamase inhibitory activity of the synthesized 2-azetidinones against resistant bacterial strains obtained from clinical isolates. Results: The tested 2-azetidinones exhibited antimicrobial efficacy comparable to the standard drugs Ampicillin and Griseofelvin. Among the tested compounds, N-[3-chloro-2-(2,5-dimethoxyphenyl)-4-oxoazetidin-1-yl]pyridine-4-carboxamide(5o) exhibited the highest activity with MIC of 6.25 µg/mL (Gram +ve and Gram –ve bacteria),1.56 µg/mL (A. niger) and 3.12 µg/mL(A. terrus and P. chrysogenum) respectively. Also all the screened compounds (5d, 5f, 5h,5j,5o) exhibited more pronounced activity (MIC: 125 µg/mL) against resistant K. pneumonia obtained from clinical isolates compared to standard antibiotic Amoxycillin. The compounds when tested as admixtures with the standard antibiotic amoxicillin (1:2) exhibited similar antibacterial spectra in comparison to the most widely employed clinical combination Augmentin. The 2-azetidinonescan prove to be a cheaper alternative with similar potential β-lactamase inhibitory activity thereby proving their utility and benefit towards the development of anti-infectives for the treatment of infections caused by drug resistant microorganisms