Design and Modeling of an Active Five-Axis Compliant Micromanipulator

This paper presents the design and modeling of an active five-axis compliant micromanipulator whose tip orientation can be independently controlled by large angles about two axes and the tip-position can be controlled in three dimensions. These features enable precise control of the contact point of the tip and the tip-sample interaction forces with three-dimensional nanoscale objects, including those features that are conventionally inaccessible. Control of the tip-motion is realized by means of electromagnetic actuation combined with a novel kinematic and structural design of the micromanipulator, which, in addition, also ensures compatibility with existing high-resolution motion-measurement systems. The design and analysis of the manipulator structure and those of the actuation system are first presented. Quasi-static and dynamic lumped-parameter (LP) models are then derived for the five-axis compliant micromanipulator. Finite element (FE) analysis is employed to validate these models, which are subsequently used to study the effects of tip orientation on the mechanical characteristics of the five-axis micromanipulator. Finally, a prototype of the designed five-axis manipulator is fabricated by means of focused ion-beam milling (FIB).

Stress states in individual Si particles of a cast Al-Si alloy: Micro-Raman analysis and microstructure based modeling

The stress states in Si particles of cast Al-Si based alloys depend on its morphology and the heat treatment given to the alloy. The Si particles fracture less on modification and fracture more in the heat treated condition. An attempt has been made in this work to study the effect of heat treatment and Si modification on the stress states of the particles. Such understanding will be valuable for predicting the ductility of the alloy. The stress states of Si particles are estimated by Raman technique and compared with the microstructure-based FEM simulations. Combination of Electron Back-Scattered Diffraction (EBSD) and frequency shift, polarized micro-Raman technique is applied to determine the stress states in Si particles with (111) orientations. Stress states are measured in the as-received state and under uniaxial compression. The residual stress, the stress in the elastic-plastic regime and the stress which causes fracture of the particles is estimated by Raman technique. FEM study demonstrates that the stress distribution is uniform in modified Si, whereas the unmodified Si shows higher and more complex stress states. The onset of plastic flow is observed at sharp corners of the particles and is followed by localization of strain between particles. Clustering of particles generates more inhomogeneous plastic strain in the matrix. Particle stress estimated by Raman technique is in agreement with FEM calculations. (C) 2014 Elsevier B.V. All rights reserved.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Role of electrical resistance of electrodes in modeling of discharging and charging of flooded lead-acid batteries

Electrical resistance of both the electrodes of a lead-acid battery increases during discharge due to formation of lead sulfate, an insulator. Work of Metzendorf 1] shows that resistance increases sharply at about 65% conversion of active materials, and battery stops discharging once this critical conversion is reached. However, these aspects are not incorporated into existing mathematical models. Present work uses the results of Metzendorf 1], and develops a model that includes the effect of variable resistance. Further, it uses a reasonable expression to account for the decrease in active area during discharge instead of the empirical equations of previous work. The model's predictions are compared with observations of Cugnet et al. 2]. The model is as successful as the non-mechanistic models existing in literature. Inclusion of variation in resistance of electrodes in the model is important if one of the electrodes is a limiting reactant. If active materials are stoichiometrically balanced, resistance of electrodes can be very large at the end of discharge but has only a minor effect on charging of batteries. The model points to the significance of electrical conductivity of electrodes in the charging of deep discharged batteries. (C) 2014 Elsevier B.V. All rights reserved.

LANDSCAPE DYNAMICS MODELING THROUGH INTEGRATED MARKOV, FUZZY-AHP AND CELLULAR AUTOMATA

Multi temporal land use information were derived using two decades remote sensing data and simulated for 2012 and 2020 with Cellular Automata (CA) considering scenarios, change probabilities (through Markov chain) and Multi Criteria Evaluation (MCE). Agents and constraints were considered for modeling the urbanization process. Agents were nornmlized through fiizzyfication and priority weights were assigned through Analytical Hierarchical Process (AHP) pairwise comparison for each factor (in MCE) to derive behavior-oriented rules of transition for each land use class. Simulation shows a good agreement with the classified data. Fuzzy and AHP helped in analyzing the effects of agents of growth clearly and CA-Markov proved as a powerful tool in modelling and helped in capturing and visualizing the spatiotemporal patterns of urbanization. This provided rapid land evaluation framework with the essential insights of the urban trajectory for effective sustainable city planning.

Structural studies on Mycobacterium tuberculosis RecA: Molecular plasticity and interspecies variability

Structures of crystals of Mycobacterium tuberculosis RecA, grown and analysed under different conditions, provide insights into hitherto underappreciated details of molecular structure and plasticity. In particular, they yield information on the invariant and variable features of the geometry of the P-loop, whose binding to ATP is central for all the biochemical activities of RecA. The strengths of interaction of the ligands with the P-loop reveal significant differences. This in turn affects the magnitude of the motion of the `switch' residue, Gln195 in M. tuberculosis RecA, which triggers the transmission of ATP-mediated allosteric information to the DNA binding region. M. tuberculosis RecA is substantially rigid compared with its counterparts from M smegmatis and E. coli, which exhibit concerted internal molecular mobility. The interspecies variability in the plasticity of the two mycobacterial proteins is particularly surprising as they have similar sequence and 3D structure. Details of the interactions of ligands with the protein, characterized in the structures reported here, could be useful for design of inhibitors against M. tuberculosis RecA.

Using long time series of agricultural-derived nitrates for estimating catchment transit times

The estimation of water and solute transit times in catchments is crucial for predicting the response of hydrosystems to external forcings (climatic or anthropogenic). The hydrogeochemical signatures of tracers (either natural or anthropogenic) in streams have been widely used to estimate transit times in catchments as they integrate the various processes at stake. However, most of these tracers are well suited for catchments with mean transit times lower than about 4-5 years. Since the second half of the 20th century, the intensification of agriculture led to a general increase of the nitrogen load in rivers. As nitrate is mainly transported by groundwater in agricultural catchments, this signal can be used to estimate transit times greater than several years, even if nitrate is not a conservative tracer. Conceptual hydrological models can be used to estimate catchment transit times provided their consistency is demonstrated, based on their ability to simulate the stream chemical signatures at various time scales and catchment internal processes such as N storage in groundwater. The objective of this study was to assess if a conceptual lumped model was able to simulate the observed patterns of nitrogen concentration, at various time scales, from seasonal to pluriannual and thus if it was relevant to estimate the nitrogen transit times in headwater catchments. A conceptual lumped model, representing shallow groundwater flow as two parallel linear stores with double porosity, and riparian processes by a constant nitrogen removal function, was applied on two paired agricultural catchments which belong to the Research Observatory ORE AgrHys. The Global Likelihood Uncertainty Estimation (GLUE) approach was used to estimate parameter values and uncertainties. The model performance was assessed on (i) its ability to simulate the contrasted patterns of stream flow and stream nitrate concentrations at seasonal and inter-annual time scales, (ii) its ability to simulate the patterns observed in groundwater at the same temporal scales, and (iii) the consistency of long-term simulations using the calibrated model and the general pattern of the nitrate concentration increase in the region since the beginning of the intensification of agriculture in the 1960s. The simulated nitrate transit times were found more sensitive to climate variability than to parameter uncertainty, and average values were found to be consistent with results from others studies in the same region involving modeling and groundwater dating. This study shows that a simple model can be used to simulate the main dynamics of nitrogen in an intensively polluted catchment and then be used to estimate the transit times of these pollutants in the system which is crucial to guide mitigation plans design and assessment. (C) 2015 Elsevier B.V. All rights reserved.

Modeling of solar radiation management: a comparison of simulations using reduced solar constant and stratospheric sulphate aerosols

The climatic effects of Solar Radiation Management (SRM) geoengineering have been often modeled by simply reducing the solar constant. This is most likely valid only for space sunshades and not for atmosphere and surface based SRM methods. In this study, a global climate model is used to evaluate the differences in the climate response to SRM by uniform solar constant reduction and stratospheric aerosols. Our analysis shows that when global mean warming from a doubling of CO2 is nearly cancelled by both these methods, they are similar when important surface and tropospheric climate variables are considered. However, a difference of 1 K in the global mean stratospheric (61-9.8 hPa) temperature is simulated between the two SRM methods. Further, while the global mean surface diffuse radiation increases by similar to 23 % and direct radiation decreases by about 9 % in the case of sulphate aerosol SRM method, both direct and diffuse radiation decrease by similar fractional amounts (similar to 1.0 %) when solar constant is reduced. When CO2 fertilization effects from elevated CO2 concentration levels are removed, the contribution from shaded leaves to gross primary productivity (GPP) increases by 1.8 % in aerosol SRM because of increased diffuse light. However, this increase is almost offset by a 15.2 % decline in sunlit contribution due to reduced direct light. Overall both the SRM simulations show similar decrease in GPP (similar to 8 %) and net primary productivity (similar to 3 %). Based on our results we conclude that the climate states produced by a reduction in solar constant and addition of aerosols into the stratosphere can be considered almost similar except for two important aspects: stratospheric temperature change and the consequent implications for the dynamics and the chemistry of the stratosphere and the partitioning of direct versus diffuse radiation reaching the surface. Further, the likely dependence of global hydrological cycle response on aerosol particle size and the latitudinal and height distribution of aerosols is discussed.

Analogous Synaptic Plasticity Profiles Emerge from Disparate Channel Combinations

An open question within the Bienenstock-Cooper-Munro theory for synaptic modification concerns the specific mechanism that is responsible for regulating the sliding modification threshold (SMT). In this conductance-based modeling study on hippocampal pyramidal neurons, we quantitatively assessed the impact of seven ion channels (R- and T-type calcium, fast sodium, delayed rectifier, A-type, and small-conductance calcium-activated (SK) potassium and HCN) and two receptors (AMPAR and NMDAR) on a calcium-dependent Bienenstock-Cooper-Munro-like plasticity rule. Our analysis with R- and T-type calcium channels revealed that differences in their activation-inactivation profiles resulted in differential impacts on how they altered the SMT. Further, we found that the impact of SK channels on the SMT critically depended on the voltage dependence and kinetics of the calcium sources with which they interacted. Next, we considered interactions among all the seven channels and the two receptors through global sensitivity analysis on 11 model parameters. We constructed 20,000 models through uniform randomization of these parameters and found 360 valid models based on experimental constraints on their plasticity profiles. Analyzing these 360 models, we found that similar plasticity profiles could emerge with several nonunique parametric combinations and that parameters exhibited weak pairwise correlations. Finally, we used seven sets of virtual knock-outs on these 360 models and found that the impact of different channels on the SMT was variable and differential. These results suggest that there are several nonunique routes to regulate the SMT, and call for a systematic analysis of the variability and state dependence of the mechanisms underlying metaplasticity during behavior and pathology.

On the variability in fracture toughness of `ductile' bulk metallic glasses

The mode I fracture toughness, K-Ic, of ductile bulk metallic glasses (BMGs) exhibits a high degree of specimen-to-specimen variability. By conducting fracture experiments in modes I and II, we demonstrate that the observed high variability in mode I, vis-a-vis mode II, is a result of highly variable propensity for the conversion of shear bands into cracks in mode I whereas in mode II, crack growth direction is fixed. Thus, the measured variability in K-Ic is intrinsic to the nature of BMGs. (C) 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

COMPUTATIONAL MODELING OF A MULTI-LAYERED PIEZO-COMPOSITE BEAM MADE UP OF MFC

The Variational Asymptotic Method (VAM) is used for modeling a coupled non-linear electromechanical problem finding applications in aircrafts and Micro Aerial Vehicle (MAV) development. VAM coupled with geometrically exact kinematics forms a powerful tool for analyzing a complex nonlinear phenomena as shown previously by many in the literature 3 - 7] for various challenging problems like modeling of an initially twisted helicopter rotor blades, matrix crack propagation in a composite, modeling of hyper elastic plates and various multi-physics problems. The problem consists of design and analysis of a piezocomposite laminate applied with electrical voltage(s) which can induce direct and planar distributed shear stresses and strains in the structure. The deformations are large and conventional beam theories are inappropriate for the analysis. The behavior of an elastic body is completely understood by its energy. This energy must be integrated over the cross-sectional area to obtain the 1-D behavior as is typical in a beam analysis. VAM can be used efficiently to approximate 3-D strain energy as closely as possible. To perform this simplification, VAM makes use of thickness to width, width to length, width multiplied by initial twist and strain as small parameters embedded in the problem definition and provides a way to approach the exact solution asymptotically. In this work, above mentioned electromechanical problem is modeled using VAM which breaks down the 3-D elasticity problem into two parts, namely a 2-D non-linear cross-sectional analysis and a 1-D non-linear analysis, along the reference curve. The recovery relations obtained as a by-product in the cross-sectional analysis earlier are used to obtain 3-D stresses, displacements and velocity contours. The piezo-composite laminate which is chosen for an initial phase of computational modeling is made up of commercially available Macro Fiber Composites (MFCs) stacked together in an arbitrary lay-up and applied with electrical voltages for actuation. The expressions of sectional forces and moments as obtained from cross-sectional analysis in closed-form show the electro-mechanical coupling and relative contribution of electric field in individual layers of the piezo-composite laminate. The spatial and temporal constitutive law as obtained from the cross-sectional analysis are substituted into 1-D fully intrinsic, geometrically exact equilibrium equations of motion and 1-D intrinsic kinematical equations to solve for all 1-D generalized variables as function of time and an along the reference curve co-ordinate, x(1).

Girsanov Transformation-Based Reliability Modeling and Testing of Actively Controlled Structures

The problem of estimation of the time-variant reliability of actively controlled structural dynamical systems under stochastic excitations is considered. Monte Carlo simulations, reinforced with Girsanov transformation-based sampling variance reduction, are used to tackle the problem. In this approach, the external excitations are biased by an additional artificial control force. The conflicting objectives of the two control forces-one designed to reduce structural responses and the other to promote limit-state violations (but to reduce sampling variance)-are noted. The control for variance reduction is fashioned after design-point oscillations based on a first-order reliability method. It is shown that for structures that are amenable to laboratory testing, the reliability can be estimated experimentally with reduced testing times by devising a procedure based on the ideas of the Girsanov transformation. Illustrative examples include studies on a building frame with a magnetorheologic damper-based isolation system subject to nonstationary random earthquake excitations. (C) 2014 American Society of Civil Engineers.

Decreasing intensity of monsoon low-frequency intraseasonal variability over India

Understanding the changing nature of the intraseasonal oscillatory (ISO) modes of Indian summer monsoon manifested by active and break phase, and their association with extreme rainfall events are necessary for probabilistic estimation of flood-related risks in a warming climate. Here, using ground-based observed rainfall, we define an index to measure the strength of monsoon ISOs and show that the relative strength of the northward-propagating low-frequency ISO (20-60 days) modes have had a significant decreasing trend during the past six decades, possibly attributed to the weakening of large-scale circulation in the region during monsoon season. This reduction is compensated by a gain in synoptic-scale (3-9 days) variability. The decrease in low-frequency ISO variability is associated with a significant decreasing trend in the percentage of extreme events during the active phase of the monsoon. However, this decrease is balanced by significant increasing trends in the percentage of extreme events in the break and transition phases. We also find a significant rise in the occurrence of extremes during early and late monsoon months, mainly over eastern coastal regions. Our study highlights the redistribution of rainfall intensity among periodic (low-frequency) and non-periodic (extreme) modes in a changing climate scenario.

A common stochastic accumulator with effector-dependent noise can explain eye-hand coordination

The computational architecture that enables the flexible coupling between otherwise independent eye and hand effector systems is not understood. By using a drift diffusion framework, in which variability of the reaction time (RT) distribution scales with mean RT, we tested the ability of a common stochastic accumulator to explain eye-hand coordination. Using a combination of behavior, computational modeling and electromyography, we show how a single stochastic accumulator to threshold, followed by noisy effector-dependent delays, explains eye-hand RT distributions and their correlation, while an alternate independent, interactive eye and hand accumulator model does not. Interestingly, the common accumulator model did not explain the RT distributions of the same subjects when they made eye and hand movements in isolation. Taken together, these data suggest that a dedicated circuit underlies coordinated eye-hand planning.