Getting a kick out of it: multimodal signalling during male–male encounters in the foot-flagging frog Micrixalus aff. saxicola from the Western Ghats of India

Several anuran species use multimodal signals to communicate in diverse social contexts. Our study describes acoustic and visual behaviours of the Small Torrent Frog (Micrixalus aff. saxicola), a diurnal frog endemic to the Western Ghats of India. During agonistic interactions males display advertisement calls, foot-flagging and tapping (foot lifting) behaviours to signal the readiness to defend perching sites in perennial streams. Results from a quantitative video analysis of male–male interactions indicate that footflagging displays were used as directional signals toward the opponent male, but were less abundant than calls. The acoustic and visual signals were not functionally linked. The call of Micrixalus aff. saxicola thereby did not act as an alert signal. Analysis of behavioural transitions revealed that kicking behaviours (physical attacks) significantly elicited kicks from interacting males. We suggest that foot-flagging displays ritualized from this frequently observed fighting technique to reduce physical attacks.

Getting a kick out of it: multimodal signalling during male-male encounters in the foot-flagging frog Micrixalus aff. saxicola from the Western Ghats of India

Several anuran species use multimodal signals to communicate in diverse social contexts. Our study describes acoustic and visual behaviours of the Small Torrent Frog (Micrixalus aff. saxicola), a diurnal frog endemic to the Western Ghats of India. During agonistic interactions males display advertisement calls, foot-flagging and tapping (foot lifting) behaviours to signal the readiness to defend perching sites in perennial streams. Results from a quantitative video analysis of male-male interactions indicate that foot-flagging displays were used as directional signals toward the opponent male, but were less abundant than calls. The acoustic and visual signals were not functionally linked. The call of Micrixalus aff. saxicola thereby did not act as an alert signal. Analysis of behavioural transitions revealed that kicking behaviours (physical attacks) significantly elicited kicks from interacting males. We suggest that foot-flagging displays ritualized from this frequently observed fighting technique to reduce physical attacks.

Multimodal shape oscillations of droplets excited by an air stream

The shape dynamics of droplets exposed to an air jet at intermediate droplet Reynolds numbers is investigated. High speed imaging and hot-wire anemometry are employed to examine the mechanism of droplet oscillation. The theory that the vortex shedding behind the droplet induces oscillation is examined. In these experiments, no particular dominant frequency is found in the wake region of the droplet. Hence the inherent free-stream disturbances prove to be driving the droplet oscillations. The modes of droplet oscillation show a band of dominant frequencies near the corresponding natural frequency, further proving that there is no particular forcing frequency involved. In the frequency spectrum of the lowest mode of oscillation for glycerol at the highest Reynolds number, no response is observed below the threshold frequency corresponding to the viscous dissipation time scale. This selective suppression of lower frequencies in the case of glycerol is corroborated by scaling arguments. The influence of surface tension on the droplet oscillation is studied using ethanol as a test fluid. Since a lower surface tension reduces the natural frequency, ethanol shows lower excited frequencies. The oscillation levels of different fluids are quantified using the droplet aspect ratio and correlated in terms of Weber number and Ohnesorge number. (C) 2014 Elsevier Ltd. All rights reserved.

Multimodal therapy for complete regression of malignant melanoma using constrained nonlinear optimal dynamic inversion

Using a realistic nonlinear mathematical model for melanoma dynamics and the technique of optimal dynamic inversion (exact feedback linearization with static optimization), a multimodal automatic drug dosage strategy is proposed in this paper for complete regression of melanoma cancer in humans. The proposed strategy computes different drug dosages and gives a nonlinear state feedback solution for driving the number of cancer cells to zero. However, it is observed that when tumor is regressed to certain value, then there is no need of external drug dosages as immune system and other therapeutic states are able to regress tumor at a sufficiently fast rate which is more than exponential rate. As model has three different drug dosages, after applying dynamic inversion philosophy, drug dosages can be selected in optimized manner without crossing their toxicity limits. The combination of drug dosages is decided by appropriately selecting the control design parameter values based on physical constraints. The process is automated for all possible combinations of the chemotherapy and immunotherapy drug dosages with preferential emphasis of having maximum possible variety of drug inputs at any given point of time. Simulation study with a standard patient model shows that tumor cells are regressed from 2 x 107 to order of 105 cells because of external drug dosages in 36.93 days. After this no external drug dosages are required as immune system and other therapeutic states are able to regress tumor at greater than exponential rate and hence, tumor goes to zero (less than 0.01) in 48.77 days and healthy immune system of the patient is restored. Study with different chemotherapy drug resistance value is also carried out. (C) 2014 Elsevier Ltd. All rights reserved.

A novel acoustic-vibratory multimodal duet

The communication strategy of most crickets and bushcrickets typically consists of males broadcasting loud acoustic calling songs, while females perform phonotaxis, moving towards the source of the call. Males of the pseudophylline bushcricket species Onomarchus uninotatus produce an unusually low-pitched call, and we found that the immediate and most robust response of females to the male acoustic call was a bodily vibration, or tremulation, following each syllable of the call. We hypothesized that these bodily oscillations might send out a vibrational signal along the substrate on which the female stands, which males could use to localize her position. We quantified these vibrational signals using a laser vibrometer and found a clear phase relationship of alternation between the chirps of the male acoustic call and the female vibrational response. This system therefore constitutes a novel multimodal duet with a reliable temporal structure. We also found that males could localize the source of vibration but only if both the acoustic and vibratory components of the duet were played back. This unique multimodal duetting system may have evolved in response to higher levels of bat predation on searching bushcricket females than calling males, shifting part of the risk associated with partner localization onto the male. This is the first known example of bushcricket female tremulation in response to a long-range male acoustic signal and the first known example of a multimodal duet among animals.

Location of concentrators in a computer communication network: a stochastic automation search method

The following problem is considered. Given the locations of the Central Processing Unit (ar;the terminals which have to communicate with it, to determine the number and locations of the concentrators and to assign the terminals to the concentrators in such a way that the total cost is minimized. There is alao a fixed cost associated with each concentrator. There is ail upper limit to the number of terminals which can be connected to a concentrator. The terminals can be connected directly to the CPU also In this paper it is assumed that the concentrators can bo located anywhere in the area A containing the CPU and the terminals. Then this becomes a multimodal optimization problem. In the proposed algorithm a stochastic automaton is used as a search device to locate the minimum of the multimodal cost function . The proposed algorithm involves the following. The area A containing the CPU and the terminals is divided into an arbitrary number of regions (say K). An approximate value for the number of concentrators is assumed (say m). The optimum number is determined by iteration later The m concentrators can be assigned to the K regions in (mk) ways (m > K) or (km) ways (K>m).(All possible assignments are feasible, i.e. a region can contain 0,1,…, to concentrators). Each possible assignment is assumed to represent a state of the stochastic variable structure automaton. To start with, all the states are assigned equal probabilities. At each stage of the search the automaton visits a state according to the current probability distribution. At each visit the automaton selects a 'point' inside that state with uniform probability. The cost associated with that point is calculated and the average cost of that state is updated. Then the probabilities of all the states are updated. The probabilities are taken to bo inversely proportional to the average cost of the states After a certain number of searches the search probabilities become stationary and the automaton visits a particular state again and again. Then the automaton is said to have converged to that state Then by conducting a local gradient search within that state the exact locations of the concentrators are determined This algorithm was applied to a set of test problems and the results were compared with those given by Cooper's (1964, 1967) EAC algorithm and on the average it was found that the proposed algorithm performs better.

Agents Based Algorithms for Design Parameter Estimation in Contaminant Transport Inverse Problems

A considerable amount of work has been dedicated on the development of analytical solutions for flow of chemical contaminants through soils. Most of the analytical solutions for complex transport problems are closed-form series solutions. The convergence of these solutions depends on the eigen values obtained from a corresponding transcendental equation. Thus, the difficulty in obtaining exact solutions from analytical models encourages the use of numerical solutions for the parameter estimation even though, the later models are computationally expensive. In this paper a combination of two swarm intelligence based algorithms are used for accurate estimation of design transport parameters from the closed-form analytical solutions. Estimation of eigen values from a transcendental equation is treated as a multimodal discontinuous function optimization problem. The eigen values are estimated using an algorithm derived based on glowworm swarm strategy. Parameter estimation of the inverse problem is handled using standard PSO algorithm. Integration of these two algorithms enables an accurate estimation of design parameters using closed-form analytical solutions. The present solver is applied to a real world inverse problem in environmental engineering. The inverse model based on swarm intelligence techniques is validated and the accuracy in parameter estimation is shown. The proposed solver quickly estimates the design parameters with a great precision.

Detection of multiple source locations using a glowworm metaphor with applications to collective robotics

This paper presents a glowworm swarm based algorithm that finds solutions to optimization of multiple optima continuous functions. The algorithm is a variant of a well known ant-colony optimization (ACO) technique, but with several significant modifications. Similar to how each moving region in the ACO technique is associated with a pheromone value, the agents in our algorithm carry a luminescence quantity along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luminescence and have a circular sensor range. The glowworms depend on a local-decision domain to compute their movements. Simulations demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a multimodal function. The above optimization scenario solves problems where a collection of autonomous robots is used to form a mobile sensor network. In particular, we address the problem of detecting multiple sources of a general nutrient profile that is distributed spatially on a two dimensional workspace using multiple robots.

Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains

We present the theoretical foundations for the multiple rendezvous problem involving design of local control strategies that enable groups of visibility-limited mobile agents to split into subgroups, exhibit simultaneous taxis behavior towards, and eventually rendezvous at, multiple unknown locations of interest. The theoretical results are proved under certain restricted set of assumptions. The algorithm used to solve the above problem is based on a glowworm swarm optimization (GSO) technique, developed earlier, that finds multiple optima of multimodal objective functions. The significant difference between our work and most earlier approaches to agreement problems is the use of a virtual local-decision domain by the agents in order to compute their movements. The range of the virtual domain is adaptive in nature and is bounded above by the maximum sensor/visibility range of the agent. We introduce a new decision domain update rule that enhances the rate of convergence by a factor of approximately two. We use some illustrative simulations to support the algorithmic correctness and theoretical findings of the paper.

Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources

This paper presents a glowworm metaphor based distributed algorithm that enables a collection of minimalist mobile robots to split into subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple radiation sources such as nuclear/hazardous chemical spills and fire-origins in a fire calamity. The algorithm is based on a glowworm swarm optimization (GSO) technique that finds multiple optima of multimodal functions. The algorithm is in the same spirit as the ant-colony optimization (ACO) algorithms, but with several significant differences. The agents in the glowworm algorithm carry a luminescence quantity called luciferin along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luciferin. The key feature that is responsible for the working of the algorithm is the use of an adaptive local-decision domain, which we use effectively to detect the multiple source locations of interest. The glowworms have a finite sensor range which defines a hard limit on the local-decision domain used to compute their movements. Extensive simulations validate the feasibility of applying the glowworm algorithm to the problem of multiple source localization. We build four wheeled robots called glowworms to conduct our experiments. We use a preliminary experiment to demonstrate the basic behavioral primitives that enable each glowworm to exhibit taxis behavior towards source locations and later demonstrate a sound localization task using a set of four glowworms.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

An ensemble of B-DNA dinucleotide geometries lead to characteristic nucleosomal DNA structure and provide plasticity required for gene expression

Background: A nucleosome is the fundamental repeating unit of the eukaryotic chromosome. It has been shown that the positioning of a majority of nucleosomes is primarily controlled by factors other than the intrinsic preference of the DNA sequence. One of the key questions in this context is the role, if any, that can be played by the variability of nucleosomal DNA structure. Results: In this study, we have addressed this question by analysing the variability at the dinucleotide and trinucleotide as well as longer length scales in a dataset of nucleosome X-ray crystal structures. We observe that the nucleosome structure displays remarkable local level structural versatility within the B-DNA family. The nucleosomal DNA also incorporates a large number of kinks. Conclusions: Based on our results, we propose that the local and global level versatility of B-DNA structure may be a significant factor modulating the formation of nucleosomes in the vicinity of high-plasticity genes, and in varying the probability of binding by regulatory proteins. Hence, these factors should be incorporated in the prediction algorithms and there may not be a unique `template' for predicting putative nucleosome sequences. In addition, the multimodal distribution of dinucleotide parameters for some steps and the presence of a large number of kinks in the nucleosomal DNA structure indicate that the linear elastic model, used by several algorithms to predict the energetic cost of nucleosome formation, may lead to incorrect results.

``Clickable'', Trifunctional Magnetite Nanoparticles and Their Chemoselective Biofunctionalization

A multifunctional iron oxide based nanoformulation for combined cancer-targeted therapy and multimodal imaging has been meticulously designed and synthesized using a chemoselective ligation approach. Novel superparamagnetic magnetite nanoparticles simultaneously functionalized with amine, carboxyl, and azide groups were fabricated through a sequence of stoichiometrically controllable partial succinylation and Cu (II) catalyzed diazo transfer on the reactive amine termini of 2-aminoethylphosphonate grafted magnetite nanoparticles (MNPs). Functional moieties associated with MNP surface were chemoselectively conjugated with rhodamine B isothiocyanate (RITC), propargyl folate (FA), and paclitaxel (PTX) via tandem nucleophic addition of amine to isothithiocyanates, Cu (I) catalyzed azide-alkyne click chemistry and carbodiimide-promoted esterification. An extensive in vitro study established that the bioactives chemoselectively appended to the magnetite core bequeathed multifunctionality to the nanoparticles without any loss of activity of the functional molecules. Multifunctional nanoparticles, developed in the course of the study, could selectively target and induce apoptosis to folate-receptor (FR) overexpressing cancer cells with enhanced efficacy as compared to the free drug. In addition, the dual optical and magnetic properties of the synthesized nanoparticles aided in the real-time tracking of their intracellular pathways also as apoptotic events through dual fluorescence and MR-based imaging.

Development of microstructure and texture in Copper during warm accumulative roll bonding

The evolution of microstructure and texture gradient in warm Accumulative Roll Bonded Cu-Cu multilayer has been studied. Grain size distribution is multimodal and exhibits variation from middle to surface layer. Evolution of texture is largely influenced by shear, in addition to rolling deformation. This leads to the formation of a texture comprising of high fraction of Brass and rolling direction-rotated cube components. Partial recrystallization was observed. Deformed and recrystallized grains were separated using a partition scheme based on grain orientation spread and textures were analyzed for both the partition. Retention of deformation texture components in recrystallized grains suggests the mechanism of recrystallization as continuous recrystallization. Shear deformation plays an important role in grain refinement through continuous recrystallization. (C) 2012 Elsevier Inc. All rights reserved.