Physiological and subjective thermal response from Indians

A controlled laboratory experiment was carried out on forty Indian male college students for evaluating the effect of indoor thermal environment on occupants' response and thermal comfort. During experiment, indoor temperature varied from 21 degrees C to 33 degrees C, and the variables like relative humidity, airflow, air temperature and radiant temperature were recorded along with skin (T-sk) and oral temperature (T-core) from the subjects. From T-sk and T-c, body temperature (T-b) was evaluated. Subjective Thermal Sensation Vote (TSV) was recorded using ASHRAE 7-point scale. In PMV model, Fanger's T-sk equation was used to accommodate adaptive response. Stepwise regression analysis result showed T-b was better predictor of TSV than T-sk and T-core. Regional skin temperature response, lower sweat threshold temperature with no dipping sweat and higher cutaneous sweating threshold temperature were observed as thermal adaptive responses. Using PMV model, thermal comfort zone was evaluated as (22.46-25.41) degrees C with neutral temperature of 23.91 degrees C, whereas using TSV response, wider comfort zone was estimated as (23.25-2632) degrees C with neutral temperature at 24.83 degrees C. It was observed that PMV-model overestimated the actual thermal response. Interestingly, these subjects were found to be less sensitive to hot but more sensitive to cold. A new TSV-PPD relation (PPDnew) was obtained with an asymmetric distribution of hot-cold thermal sensation response in Indians. (C) 2013 Elsevier Ltd. All rights reserved.

PMV model is insufficient to capture subjective thermal response from Indians

A controlled laboratory experiment was carried out on forty Indian male college students for evaluating the effect of indoor thermal environment on occupants' response and thermal comfort. During experiment, indoor temperature varied from 21 degrees C to 33 degrees C, and the variables like relative humidity, airflow, air temperature and radiant temperature were recorded along with subject's physiological parameters (skin (T-sk) and oral temperature (T-c)) and subjective thermal sensation responses (TSV). From T-sk and T-c, body temperature (T-b) was evaluated. Subjective Thermal Sensation Vote (TSV) was recorded using ASHRAE 7-point scale. In PMV model, Fanger's T-sk equation was used to accommodate adaptive response. Step-wise regression analysis result showed T-b was better predictor of TSV than T-sk and T-c. Regional skin temperature response, suppressed sweating without dipping, lower sweating threshold temperature and higher cutaneous threshold for sweating were observed as thermal adaptive responses. These adaptive responses cannot be considered in PMV model. To incorporate subjective adaptive response, mean skin temperature (T-sk) is considered in dry heat loss calculation. Along with these, PMV-model and other two methodologies are adopted to calculate PMV values and results are compared. However, recent literature is limited to measure the sweat rate in Indians and consideration of constant Ersw in PMV model needs to be corrected. Using measured T-sk in PMV model (Method(1)), thermal comfort zone corresponding to 0.5 <= PMV <= 0.5 was evaluated as (22.46-25.41) degrees C with neutral temperature of 23.91 degrees C, similarly while using TSV response, wider comfort zone was estimated as (23.25-26.32) degrees C with neutral temperature at 24.83 degrees C, which was further increased to with TSV-PPDnew, relation. It was observed that PMV-model overestimated the actual thermal response. Interestingly, these subjects were found to be less sensitive to hot but more sensitive to cold. A new TSV-PPD relation (PPDnew) was obtained from the population distribution of TSV response with an asymmetric distribution of hot-cold thermal sensation response from Indians. The calculations of human thermal stress according to steady state energy balance models used on PMV model seem to be inadequate to evaluate human thermal sensation of Indians. Relevance to industry: The purpose of this paper is to estimate thermal comfort zone and optimum temperature for Indians. It also highlights that PMV model seems to be inadequate to evaluate subjective thermal perception in Indians. These results can be used in feedback control of HVAC systems in residential and industrial buildings. (C) 2014 Elsevier B.V. All rights reserved.

Patterned growth and differentiation of neural cells on polymer derived carbon substrates with micro/nano structures in vitro

The growth of neuroblastoma (N2a) and Schwann cells has been explored on polymer derived carbon substrates of varying micro and nanoscale geometries: resorcinol-formaldehyde (RE) gel derived carbon films and electrospun nanofibrous (similar to 200 nm diameter) mat and SU-8 (a negative photoresist) derived carbon micro-patterns. MTT assay and complementary lactate dehydrogenase (LDH) assay established cytocompatibility of RE derived carbon films and fibers over a period of 6 days in culture. The role of length scale of surface patterns in eliciting lineage-specific adaptive response along, across and on the interspacing between adjacent micropatterns (i.e., ``on'', ``across'' and ``off'') has been assayed. Textural features were found to affect 3',5'-cyclic AMP sodium salt-induced neurite outgrowth, over a wide range of length scales: from similar to 200 nm (carbon fibers) to similar to 60 mu m (carbon patterns). Despite their innate randomness, carbon nanofibers promoted preferential differentiation of N2a cells into neuronal lineage, similar to ordered micro-patterns. Our results, for the first time, conclusively demonstrate the potential of RE-gel and SU-8 derived carbon substrates as nerve tissue engineering platforms for guided proliferation and differentiation of neural cells in vitro. (C) 2013 Elsevier Ltd. All rights reserved.

Imaging cellular signalling: many `moving tales' in MAP kinase odyssey

Cellular signalling events are at the core of every adaptive response. Signalling events link environmental changes to physiological responses, consequently allowing cellular and organismal sustenance and survival. Classical approaches to study cellular signalling have relied on a variety of cell disruptive techniques which yield limited kinetic information, while the underlying events are much more complex. In this article, we discuss how modern live cell imaging microscopy has found increasing utilization in revealing spatio temporal dynamics of various signalling pathways. Utilizing the well studied mitogen-activated protein kinase (MAPK) signalling cascade as a template, the design, construction and utilization of `mobile' (translocation proficient) biosensors, suitable for studying MAPK signalling in living cells are described in detail. Experimental setup and results obtained from these biosensors, based on different proteins involved in the MAPK signalling cascade, have been described along with the setup of a microscope optimal for live cell imaging applications. Utilizing the ability to activate or deactivate signalling pathways using defined activators and specific pharmacological inhibitors, we also show how these sensors can yield unique spatial and temporal kinetic information of signalling in living cells.

Imaging cellular signalling: many `moving tales' in MAP kinase odyssey

Cellular signalling events are at the core of every adaptive response. Signalling events link environmental changes to physiological responses, consequently allowing cellular and organismal sustenance and survival. Classical approaches to study cellular signalling have relied on a variety of cell disruptive techniques which yield limited kinetic information, while the underlying events are much more complex. In this article, we discuss how modern live cell imaging microscopy has found increasing utilization in revealing spatio temporal dynamics of various signalling pathways. Utilizing the well studied mitogen-activated protein kinase (MAPK) signalling cascade as a template, the design, construction and utilization of `mobile' (translocation proficient) biosensors, suitable for studying MAPK signalling in living cells are described in detail. Experimental setup and results obtained from these biosensors, based on different proteins involved in the MAPK signalling cascade, have been described along with the setup of a microscope optimal for live cell imaging applications. Utilizing the ability to activate or deactivate signalling pathways using defined activators and specific pharmacological inhibitors, we also show how these sensors can yield unique spatial and temporal kinetic information of signalling in living cells.

An adaptive system of vigilance in spotted deer (Axis axis) herds in response to predation

Spotted deer or chital (Axis axis), a major prey species in southern India, lives in large groups. To understand the benefits of group living, we carried out observations on chital herds under natural conditions. Individual and group vigilance showed a negative correlation with herd size, whereas the latter had a positive correlation with proportion of vigilant individuals. Furthermore, individual vigilance was negatively correlated with proportion of individuals vigilant and positively correlated with group vigilance. These results are explained in the context of a three-phase vigilance system, probably operative in the chital herd, under specified ecological conditions. We surmise that this system allows for adaptation to predation risk and has possibly co-evolved with the optimal hunting strategy of the predator.

Effective treatment of infectious diseases: A nonlinear adaptive control theoretic approach

A nonlinear adaptive system theoretic approach is presented in this paper for effective treatment of infectious diseases that affect various organs of the human body. The generic model used does not represent any specific disease. However, it mimics the generic immunological dynamics of the human body under pathological attack, including the response to external drugs. From a system theoretic point of view, drugs can be interpreted as control inputs. Assuming a set of nominal parameters in the mathematical model, first a nonlinear controller is designed based on the principle of dynamic inversion. This treatment strategy was found to be effective in completely curing "nominal patients". However, in some cases it is ineffective in curing "realistic patients". This leads to serious (sometimes fatal) damage to the affected organ. To make the drug dosage design more effective, a model-following neuro-adaptive control design is carried out using neural networks, which are trained (adapted) online. From simulation studies, this adaptive controller is found to be effective in killing the invading microbes and healing the damaged organ even in the presence of parameter uncertainties and continuing pathogen attack.

Adaptive optimal load balancing in a nonhomogeneous multiserver system with a central job scheduler

A model comprising several servers, each equipped with its own queue and with possibly different service speeds, is considered. Each server receives a dedicated arrival stream of jobs; there is also a stream of generic jobs that arrive to a job scheduler and can be individually allocated to any of the servers. It is shown that if the arrival streams are all Poisson and all jobs have the same exponentially distributed service requirements, the probabilistic splitting of the generic stream that minimizes the average job response time is such that it balances the server idle times in a weighted least-squares sense, where the weighting coefficients are related to the service speeds of the servers. The corresponding result holds for nonexponentially distributed service times if the service speeds are all equal. This result is used to develop adaptive quasi-static algorithms for allocating jobs in the generic arrival stream when the load parameters are unknown. The algorithms utilize server idle-time measurements which are sent periodically to the central job scheduler. A model is developed for these measurements, and the result mentioned is used to cast the problem into one of finding a projection of the root of an affine function, when only noisy values of the function can be observed

Optimal blood glucose regulation of diabetic patients using single network adaptive critics

Diabetes is a long-term disease during which the body's production and use of insulin are impaired, causing glucose concentration level to increase in the bloodstream. Regulating blood glucose levels as close to normal as possible leads to a substantial decrease in long-term complications of diabetes. In this paper, an intelligent online feedback-treatment strategy is presented for the control of blood glucose levels in diabetic patients using single network adaptive critic (SNAC) neural networks (which is based on nonlinear optimal control theory). A recently developed mathematical model of the nonlinear dynamics of glucose and insulin interaction in the blood system has been revised and considered for synthesizing the neural network for feedback control. The idea is to replicate the function of pancreatic insulin, i.e. to have a fairly continuous measurement of blood glucose and a situation-dependent insulin injection to the body using an external device. Detailed studies are carried out to analyze the effectiveness of this adaptive critic-based feedback medication strategy. A comparison study with linear quadratic regulator (LQR) theory shows that the proposed nonlinear approach offers some important advantages such as quicker response, avoidance of hypoglycemia problems, etc. Robustness of the proposed approach is also demonstrated from a large number of simulations considering random initial conditions and parametric uncertainties. Copyright (C) 2009 John Wiley & Sons, Ltd.

Model-following Neuro-adaptive approach for robust control of high performance aircrafts

Based on dynamic inversion, a relatively straightforward approach is presented in this paper for nonlinear flight control design of high performance aircrafts, which does not require the normal and lateral acceleration commands to be first transferred to body rates before computing the required control inputs. This leads to substantial improvement of the tracking response. Promising results are obtained from six degree-offreedom simulation studies of F-16 aircraft, which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response (including elimination of non-minimum phase behavior) and reduced control magnitude. Next, a model-following neuron-adaptive design is augmented the nominal design in order to assure robust performance in the presence of parameter inaccuracies in the model. Note that in the approach the model update takes place adaptively online and hence it is philosophically similar to indirect adaptive control. However, unlike a typical indirect adaptive control approach, there is no need to update the individual parameters explicitly. Instead the inaccuracy in the system output dynamics is captured directly and then used in modifying the control. This leads to faster adaptation, which helps in stabilizing the unstable plant quicker. The robustness study from a large number of simulations shows that the adaptive design has good amount of robustness with respect to the expected parameter inaccuracies in the model.

Stochastic analysis of epidemics on adaptive time varying networks

Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an ``adaptive threshold,'' i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.

Cybernetic modeling of adaptive prediction of environmental changes by microorganisms

Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy. (C) 2013 Published by Elsevier Inc.

Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

A neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of C-M alpha. Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.

A Quasi-Newton Adaptive Algorithm for Generalized Symmetric Eigenvalue Problem

In this paper, we first recast the generalized symmetric eigenvalue problem, where the underlying matrix pencil consists of symmetric positive definite matrices, into an unconstrained minimization problem by constructing an appropriate cost function, We then extend it to the case of multiple eigenvectors using an inflation technique, Based on this asymptotic formulation, we derive a quasi-Newton-based adaptive algorithm for estimating the required generalized eigenvectors in the data case. The resulting algorithm is modular and parallel, and it is globally convergent with probability one, We also analyze the effect of inexact inflation on the convergence of this algorithm and that of inexact knowledge of one of the matrices (in the pencil) on the resulting eigenstructure. Simulation results demonstrate that the performance of this algorithm is almost identical to that of the rank-one updating algorithm of Karasalo. Further, the performance of the proposed algorithm has been found to remain stable even over 1 million updates without suffering from any error accumulation problems.

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).