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Preterm birth survivors are at a higher risk of growth and developmental disabilities compared to their term counterparts. Development of strategies to lower the complications of preterm birth forms the rising need of the hour. Appropriate nutrition is essential for the growth and development of preterm infants. Early administration of optimal nutrition to preterm birth survivors lowers the risk of adverse health outcomes and improves cognition in adulthood. A group of neonatologists, pediatricians, and nutrition experts convened to discuss and frame evidence-based recommendations for optimizing nutrition in preterm low birth weight (LBW) infants. The following were the primary recommendations of the panel: (1) enteral feeding is safe and may be preferred to parenteral nutrition due to the complications associated with the latter; however, parenteral nutrition may be a useful adjunct to enteral feeding in some critical cases; (2) early, fast, or continuous enteral feeding yields better outcomes compared to late, slow, or intermittent feeding, respectively; (3) routine use of nasogastric tubes is not advisable; (4) preterm infants can be fed while on ventilator or continuous positive airway pressure; (5) routine evaluation of gastric residuals and abdominal girth should be avoided; (6) expressed breast milk (EBM) is the first choice for feeding preterm infants due to its beneficial effects on cardiovascular, neurological, bone health, and growth outcomes; the second choice is donor pasteurized human milk; (7) EBM or donor milk may be fortified with human milk fortifiers, without increasing the osmolality of the milk, to meet the high protein requirements of preterm infants; (8) standard fortification is effective and safe but does not fulfill the high protein needs; (9) use of targeted and adjustable fortification, where possible, helps provide optimal nutrition; (10) optimizing weight gain in preterm infants prevents long-term cardiovascular complications; (11) checking for optimal weight and sucking/swallowing ability is essential prior to discharge of preterm infants; and (12) appropriate counseling and regular follow-up and monitoring after discharge will help achieve better long-term health outcomes. This consensus summary serves as a useful guide to clinicians in addressing the challenges and providing optimal nutrition to preterm LBW infants.

Numerical solutions for the optimal feedback stabilization of discrete time dynamical systems is the focus of this technical note. Set-theoretic notion of almost everywhere stability introduced by the Lyapunov measure, weaker than conventional Lyapunov function-based stabilization methods, is used for optimal stabilization. The linear Perron-Frobenius transfer operator is used to pose the optimal stabilization problem as an infinite dimensional linear program. Set-oriented numerical methods are used to obtain the finite dimensional approximation of the linear program. We provide conditions for the existence of stabilizing feedback controls and show the optimal stabilizing feedback control can be obtained as a solution of a finite dimensional linear program. The approach is demonstrated on stabilization of period two orbit in a controlled standard map.

We prove a necessary and sufficient condition for the existence of Lyapunov density for a system of coupled autonomous ordinary differential equations. In particular, we characterize the kinds of couplings that preserve almost everywhere uniform stability of the origin provided the isolated systems have an almost everywhere uniformly stable equilibrium point at the origin.

This paper, for the first time, explores the use of data-driven method to model the traffic dynamics of an interstate highway system with high resolution probe data. The dynamic mode decomposition and spatio-temporal pattern network were used to analyze traffic patterns on a 290 mile interstate highway across Iowa. The results show the data-driven methods can effectively detect the changes in traffic system dynamics during different time of day. Traffic dynamics during morning peak hours, evening peak hours and off-peak were very different on the studied road. In contrast, the trends over multiple months were similar during the same time periods. The study also found that inclement weather had a significant impact on the system dynamics. In future, the proposed methodology can be used to gain insights in the system dynamics of a traffic network. These models will be instrumental in optimal traffic control, traffic sensor placement and other policy decisions affecting the capacity of the network.

In this paper, we show through examples, how the existing definitions of information transfer, namely directed information and transfer entropy fail to capture true causal interaction between states in control dynamical system. Furthermore, existing definitions are shown to be too weak to have any implication on two of the most fundamental concepts in system theory, namely controllability and observability. We propose a new definition of information transfer, based on the ideas from dynamical system theory, and show that this new definition can not only capture true causal interaction between states, but also have implication on system controllability and observability properties. In particular, we show that non-zero transfer of information from input-to-state and state-to-output implies structural controllability and observability properties of the control dynamical system respectively. Analytical expression for information transfer between state-to-state, input-to-state, state-to-output, and input-to-output are provided for linear system. There is a natural extension of our proposed definition to define information transfer over n time steps and average information transfer over infinite time step. We show that the average information transfer in feedback control system between plant output and input is equal to the entropy of the open loop dynamics thereby re-deriving the Bode fundamental limitation results using the proposed definition of transfer.

Accurate and rapid monitoring of indoor air quality is critical to ensure occupant safety in the built environment. This is especially important in events where hazardous substances are released, and prompt estimation of contaminant distribution will facilitate quick evacuation and control response. The built environment is usually equipped with a finite set of sensors that measure local concentration of contaminants. The goal is to use this streaming dataset to estimate the contaminant concentration distribution in the complete domain. We accomplish this by integrating two powerful concepts. We utilize an operator theoretic approach – specifically the Perron-Frobenius (P-F) operator – to model the contaminant transport. Previous work has shown that the PF approach is a fast, effective, and accurate paradigm for sensor placement and contaminant transport prediction. The PF approach is integrated with an Ensemble Kalman Filter to rapidly estimate contaminant distribution under unknown release scenarios, given minimal sensor data. The framework is illustrated for two scenarios: a 2D problem involving an office space, and a 3D problem involving a furnished hotel room. Both examples show that the contaminant distribution is accurately predicted within a few sensor measurement cycles. The general applicability of the framework is illustrated by testing the framework for multiple, unknown release locations. This approach provides a unified, extendable framework for rapid contaminant estimation.

Gastrocolic Fistula is, in the majority of cases the pathological communication between stomach and transverse colon. It occurs mostly in adults, but they can be present in infants, as well, as a result of congenital abnormalities or iatrogenic procedures (i.e. migration of naso gastric tube that placed before). We report a case of 10 days old full term, male neonate, was on naso gastric tube feeds, had clinical features of necrotizing enterocolitis (NEC), diagnosed to have gastrocolic fistula on barium enema study and confirmed on CT abdomen. Intraoperatively, baby had multiple perforations with gastrocolic fistula. Histopathological examination was suggestive of NEC. We searched literature, we could find seven case reports.

Heating ventilation and air conditioning (HVAC) systems in residential and commercial buildings make up 16% of the United States energy consumption. Utilizing natural ventilation strategies is a low energy solution to reduce the energy used by building environmental control systems. Design of effective natural ventilation strategies is challenging because of inherent stochasticity in interior (machine loads, number of people) and exterior conditions (wind load, outside temperature). However, by exploiting the natural dynamics of building systems, efficient design and control seems possible. We explore this idea by introducing a stochastic approach to analyze the natural dynamics of building systems (under natural ventilation) by explicitly incorporating the effects of stochastic wind speeds and stochastic internal loads. We show that complex dynamics in the form of bi-stable behavior emerges when considering a single zone building with stochastic inputs. We show that neglecting these complex stochastic dynamics leads to inaccurate predictions in the thermal response, especially for natural ventilation. We compute the sensitivity of the system with respect to various system parameters which provide insight into developing robust design guidelines. The techniques presented aid in the design process, and are a step toward adaptive, efficient, robust control of natural ventilation systems.

SUMMARY In this paper, we study the problem of control of discrete-time linear time varying systems over uncertain channels. The uncertainty in the channels is modeled as a stochastic random variable. We use exponential mean square stability of the closed-loop system as a stability criterion. We show that fundamental limitations arise for the mean square exponential stabilization for the closed-loop system expressed in terms of statistics of channel uncertainty and the positive Lyapunov exponent of the open-loop uncontrolled system. Our results generalize the existing results known in the case of linear time invariant systems, where Lyapunov exponents are shown to emerge as the generalization of eigenvalues from linear time invariant systems to linear time varying systems. Simulation results are presented to verify the main results of this paper. Copyright © 2012 John Wiley & Sons, Ltd.

We study the problem of actuator and sensor placement in a linear advection partial differential equation (PDE). The problem is motivated by its application to actuator and sensor placement in building systems for the control and detection of a scalar quantity such as temperature and contaminants. We propose a gramian based approach to the problem of actuator and sensor placement. The special structure of the advection PDE is exploited to provide an explicit formula for the controllability and observability gramian in the form of a multiplication operator. The explicit formula for the gramian, as a function of actuator and sensor location, is used to provide test criteria for the suitability of a given sensor and actuator location. Furthermore, the solution obtained using gramian based criteria is interpreted in terms of the flow of the advective vector field. In particular, the almost everywhere stability property of the advective vector field is shown to play a crucial role in deciding the location of actuators and sensors. Simulation results are performed to support the main results of this paper.

There is increased research trend towards the use of Phasor Measurement Units (PMUs) for real-time stability monitoring and active feedback control of power system. In this paper, we address the problem of control of inter-area oscillations when the measurements from Phasor Measurement Units (PMUs) are corrupted with noise. Unlike existing results, we assume that the noise enters multiplicatively in system measurements. We provide systematic procedure based on the solution of Linear Matrix Inequalities (LMI) for the mean square stochastic stability verification of power system with measurement uncertainties. Furthermore, an optimization-based procedure is proposed for the synthesis of measurement-based feedback controller robust to wide area measurement noise. Finally, simulation results are provided to demonstrate the application of the developed framework on WSCC 9 bus system.

We study synchronization in scalar nonlinear systems connected over a linear network with stochastic uncertainty in their interactions. We provide a sufficient condition for the synchronization of such network systems expressed in terms of the parameters of the nonlinear scalar dynamics, the second and largest eigenvalues of the mean interconnection Laplacian, and the variance of the stochastic uncertainty. The sufficient condition is independent of network size thereby making it attractive for verification of synchronization in a large size network. The main contribution of this paper is to provide analytical characterization for the interplay of roles played by the internal dynamics of the nonlinear system, network topology, and uncertainty statistics in network synchronization. We show there exist important tradeoffs between these various network parameters necessary to achieve synchronization. We show for nearest neighbor networks with stochastic uncertainty in interactions there exists an optimal number of neighbors with maximum margin for synchronization. This proves in the presence of interaction uncertainty, too many connections among network components is just as harmful for synchronization as the lack of connection. We provide an analytical formula for the optimal gain required to achieve maximum synchronization margin thereby allowing us to compare various complex network topology for their synchronization property.

In this technical note, we study the mean square stability based analysis of stochastic continuous-time linear networked systems. The stochastic uncertainty is assumed to enter multiplicatively in system dynamics through input and output channels of the plant. Necessary and sufficient conditions for mean square exponential stability are expressed in terms of the input–output property of deterministic or nominal system dynamics captured by the mean square system norm and variance of channel uncertainty. The stability results can also be interpreted as a small-gain theorem for continuous-time stochastic systems. Linear matrix inequalities based optimization formulation is provided for the computation of mean square system norm for the stability analysis. For a special case of single-input channel uncertainty, we also prove a fundamental limitation result that arises in the mean square exponential stabilization of the continuous-time linear system. Overall, the contributions in this technical note generalize the existing results on stability analysis from discrete-time linear systems to continuous-time linear systems with multiplicative uncertainty. Simulation results are presented for Western System Coordinating Council 9-bus power system to demonstrate the application of the developed framework.

In this paper, we study the synchronization of identical nonlinear systems over large-scale network with uncertainty in the interconnections. We consider a special class of nonlinear systems which have a dissipative nonlinearity and the stability of such systems can be analyzed using absolute stability theory tools like the Positive Real Lemma, Bounded Real Lemma and dissipativity theory. We extend this analysis to the stochastic setting over a network where the interconnection weights are drive by Wiener process with given mean and variance. To capture the stability of the synchronized state, we study the notion of mean square stability from stochastic stability theory and formulate a network size-independent sufficient condition based on the theory of stochastic dissipative systems. We also compute a heuristic margin of synchronization for the networked systems to indicate the tolerance to stochastic uncertainty in interconnection links.

In our recent work [1], we introduced Lyapunov measure as a new tool to verify weaker set-theoretic notion of almost everywhere stability of stochastic nonlinear systems. A Linear transfer Perron-Frobenius operator for stochastic systems was used to provide an explicit formula for the Lyapunov measure, verifying almost everywhere almost sure stability of stochastic systems. The focus of this paper is on the computational aspect of the Lyapunov measure for stochastic systems. We used set-oriented numerical methods for the finite dimensional approximation of the linear operator and the Lyapunov measure. Stability results in the finite dimensional approximation space are also presented. In particular, we show the finite dimensional approximation leads to a further weaker notion of stability referred to as coarse stability.

In this paper, we derive results for the stochastic stability analysis and controller synthesis for continuous-time stochastic system. The important feature considered here is the multiplicative nature of the stochastic uncertainty in system dynamics. We generalize the existing small-gain type results for stability of discrete-time system with stochastic uncertainty in feedback loop to continuous-time dynamics. Further, LMI-based computable necessary and sufficient conditions are provided for the mean square stability of feedback system. The proposed stability analysis results are used for the synthesis of dynamic robust stabilizing feedback controller with stochastic uncertainty in the feedback loop between the plant and the controller. Fundamental limitation result for the mean square stabilization of system over stochastic channels is presented. Finally, we demonstrate the proposed framework on pendulum on a cart system.

Online monitoring of rotor angle stability in wide area power systems is an important task to avoid out-of-step instability conditions. In recent years, the installation of phasor measurement units (PMUs) on the power grids has increased significantly and, therefore, a large amount of real-time data is available for online monitoring of system dynamics. This paper proposes a PMU-based application for online monitoring of rotor angle stability. A technique based on Lyapunov exponents is used to determine if a power swing leads to system instability. The relationship between rotor angle stability and maximal Lyapunov exponent (MLE) is established. A computational algorithm is developed for the calculation of MLE in an operational environment. The effectiveness of the monitoring scheme is illustrated with a three-machine system and a 200-bus system model.

Extrauterine growth retardation is a major clinical problem in preterm infants. Aggressive nutritional interventions may play an important role to fight this prevalent problem. Parenteral nutrition is almost indispensable part of aggressive nutritional approach in neonates, because of certain limitations of enteral route during first few days of life. Formidable research work done in developed world has resolved certain important issues like dosage regimens. However, still we need to resolve quite a few unanswered queries. This is especially true for Indian context where we deal with a different population of neonates than the developed world. In this article we will discuss both the resolved and unresolved issues regarding neonatal parenteral nutrition and the research priorities for us.

Bubble continuous positive airway pressure (BCPAP) is a low-cost nasal CPAP delivery system with potential benefits for developing nations. The objective of the study was to compare the efficacy and safety of BCPAP with ventilator CPAP (VCPAP) in preterm neonates with moderate respiratory distress.In a pilot randomized controlled trial, 30 preterm neonates (gestation37 weeks) with Silverman-Anderson score between 5 to 7 and oxygen requirement30% within first 6 h of life were randomly allocated to BCPAP or VCPAP after informed parental consent. Proportion of neonates with success or failure, while using the allocated mode of CPAP delivery (primary outcome) was compared.The success rate was comparable [VCPAP: 80% (12/15) vs. BCPAP: 87% (13/15)] between the two groups. Dislodgement was commonest problem with equal frequency [10/15, (67%)] in each group.BCPAP appears to be a promising method of CPAP delivery in preterm neonates with moderate respiratory distress.

Motivated by the problem of microfluidic mixing, optimal control of advective mixing in Stokes fluid flows is considered. The velocity field is assumed to be induced by a finite set of spatially distributed force fields that can be modulated arbitrarily with time, and a passive material is advected by the flow. To quantify the degree of mixedness of a density field, we use a Sobolev space norm of negative index. We frame a finite-time optimal control problem for which we aim to find the modulation that achieves the best mixing for a fixed value of the action (the time integral of the kinetic energy of the fluid body) per unit mass. We derive the first-order necessary conditions for optimality that can be expressed as a two-point boundary value problem (TPBVP) and discuss some elementary properties that the optimal controls must satisfy. A conjugate gradient descent method is used to solve the optimal control problem and we present numerical results for two problems involving arrays of vortices. A comparison of the mixing performance shows that optimal aperiodic inputs give better results than sinusoidal inputs with the same energy.

Air quality has been an important issue in public health for many years. Sensing the level and distributions of impurities help in the control of building systems and mitigate long term health risks. Rapid detection of infectious diseases in large public areas like airports and train stations may help limit exposure and aid in reducing the spread of the disease. Complete coverage by sensors to account for any release scenario of chemical or biological warfare agents may provide the opportunity to develop isolation and evacuation plans that mitigate the impact of the attack. All these scenarios involve strategic placement of sensors to promptly detect and rapidly respond. This paper presents a data driven sensor placement algorithm based on a dynamical systems approach. The approach utilizes the finite dimensional Perron-Frobenius (PF) concept. The PF operator (or the Markov matrix) is used to construct an observability gramian that naturally incorporates sensor accuracy, location constraints, and sensing constraints. The algorithm determines the response times, sensor coverage maps, and the number of sensors needed. The utility of the procedure is illustrated using four examples: a literature example of the flow field inside an aircraft cabin and three air flow fields in different geometries. The effect of the constraints on the response times for different sensor placement scenarios is investigated. Knowledge of the response time and coverage of the multiple sensors aides in the design of mechanical systems and response mechanisms. The methodology provides a simple process for place sensors in a building, analyze the sensor coverage maps and response time necessary during extreme events, as well as evaluate indoor air quality. The theory established in this paper also allows for future work in topics related to construction of classical estimator problems for the sensors, real-time contaminant transport, and development of agent dispersion, contaminant isolation/removal, and evacuation strategies., Highlights • A robust sensor placement algorithm is developed that is capable of covering all possible release scenarios. • Placement algorithm includes constraints on sensing accuracy, placement constraints, and sensing constraints. • The algorithm provides sensor coverage maps, response times, and number of sensors for different scenarios. • Show applications involving indoor air quality, transmission of infectious diseases, and chemical and biological warfare.

Proportionate sharing of load changes in a microgrid without communication among the generators is a difficult problem. In this paper an exact power sharing algorithm is developed that uses an optimization-based technique to minimize the exchange of state information among the generators. To demonstrate the application of the developed framework we present simulation results for a system constructed using the data obtained from IEEE 14 bus system. Simulation results reveal that power sharing can be achieved using relatively few communication links among the generators.

In this paper, we present results for the vulnerability analysis of a power network to stochastic link failure attacks. We assume a network links are subjected to attacks where the link-based attack is modeled as stochastic perturbation to link weight. The objective is to determine which links in the network can tolerate the least amount of stochastic perturbation to maintain stochastic stability of a power network. We develop a system theoretical-based, analytical, and computation framework that allows us to rank links in the order of their critical importance to maintain stochastic stability of the network. The computational approach relies on solving a Linear Matrix Inequality (LMI). The developed framework is applied to a structure preserving model of the power network. The structure preserving model allows for the representation of original network topology, thereby identifying critical links connecting generators and load buses. Simulations are performed on IEEE 14 bus system to demonstrate the application of the developed framework.

We consider the problem of controllability degradation in dynamical networks subjected to malicious attacks. Attacks on the networks are assumed to be in the form of the removal of interconnection links. We formulate an optimization problem that seeks sets of links whose removal causes maximal degradation in the rank of the controllability gramian. We apply the alternating direction method of multipliers and sequential convex programming to find local solutions to this combinatorial optimization problem. We provide illustrative examples to demonstrate the utility of our results.

In this paper, we investigate the behaviour of networked systems under two different nonlinear protocols: 1) Pitchfork bifurcation nonlinearity in absolute flow 2) Hopf bifurcation nonlinearity in disagreement flow. The former one is an extension of the results proposed in [13] for the case when individual agents are free to choose a parameter that defines their own dynamics. Based on the eigenvalues of the linearized system, we characterize the set of equilibria that can be achieved under the above protocols. Furthermore, we provide sufficient conditions to achieve consensus for both protocols. Finally, we show that varying the values of the parameter leads to bifurcation for the entire network. For each protocol, we analyze system with two and three agents, and present a characterization of the steady-state behaviour of the entire system.

We study the vulnerability of large-scale linear dynamical networks to coordinated attacks. We consider scenarios in which an attacker can tamper with the links connecting the network components and can also manipulate input injections at the nodes. When these two types of attacks take place simultaneously, the attack is referred to as a coordinated attack. We assume that network links are attacked with a certain probability and that malicious data is injected at the input ports. We employ Markov jump linear systems to model link-based attacks and the system input matrix to model data injection attacks. System theoretic vulnerability metrics developed in earlier work are used to analyze network vulnerability to coordinated attacks. These measures of vulnerability allow us to characterize the impact of coordinated attacks and the difficulty associated with detecting them. Finally, we analyze the vulnerability of coordinated attacks on the New England 39 bus power network.

In this paper, we study controllability of two-dimensional integrable twist maps with bounded area-preserving time-dependent (control) perturbations. In contrast to the time-independent perturbation case of the Kolmogorov–Arnold–Moser theorem, there are no invariant sets other than the whole phase space if the perturbation is made a function of time. We give necessary and sufficient conditions for global controllability of these maps.

Active controllers that allow the compressor to operate safely at its peak pressure rise point by preventing surge and controlling entry into rotating stall need to be globally stabilizing. Bifurcation methods have emerged as a useful tool for the analysis of global stability and control of nonlinear dynamical systems such as the axial compressor. In this paper we show how bifurcation analysis can be used effectively to obtain answers to questions of global stability of the compressor dynamics. Since the bifurcation method does not directly provide information on transient behaviour, we demonstrate how bifurcation results need to be carefully interpreted to be of use when dealing with practical systems. We then present a novel nonlinear bifurcation-based stall/surge controller that is globally stabilizing. With such a controller, it becomes possible to avoid surge entirely and to prevent abrupt entry into rotating stall. The controller also eliminates the hysteresis between entry into and recovery from a rotating stall, and maintains system stability under all perturbations, small and large.

We develop a model-free approach for the short-term voltage stability monitoring of a power system. Finite time Lya-punov exponents are used as the certificate of stability. The time-series voltage data from phasor measurement units (PMU) are used to compute the Lyapunov exponent to predict voltage stability in real time. Issues related to practical implementation of the proposed method, such as phasor measurement noise, communication delay, and the finite window size for prediction, are also discussed. Furthermore, the stability certificate in the form of Lyapunov exponents is also used to determine the stability/instability contributions of the individual buses to the overall system stability and for computation of critical clearing time. Simulation results are provided for the IEEE 162-bus system to demonstrate the application of the developed method.

Aeroelastic flutter is a dynamic instability of fluid-structural system in which the structure exhibits a sustained, often diverging oscillation. Flutter behavior is self-feeding and destructive. Nonlinearities such as freeplay in rigid-body rotational stiffness of the structural system can have an effect on the onset of flutter and its amplitude. In particular there is experimental evidence that as the amount of freeplay increases, the freestream velocity at which the flutter instability occurs decreases. In this paper, we develop a modeling framework that allows us to predict this dependence of flutter velocity on the freeplay parameter. We model the airfoil system with freeplay nonlinearity as a feedback interconnection of linear system and sector bounded nonlinearity. Freeplay in stiffness is practically approximated as a hyperbola nonlinearity. Eigenvalue analysis at equilibrium points is used to predict onset of flutter and characterize a Hopf bifurcation of the system from stable to limit cycle behavior. Spectral analysis us used to characterize the limit cycle behavior. This analysis indicates the flutter onset velocity to be a function of freeplay region length. Follow-on research correlating recently obtained wind tunnel results to a three-dimensional extension of the model is outlined.

Existing sensing solutions facilitating continuous condition assessment of wind turbine blades are limited by a lack of scalability and clear link signal-to-prognosis. With recent advances in conducting polymers, it is now possible to deploy networks of thin film sensors over large areas, enabling low cost sensing of large-scale systems. Here, we propose to use a novel sensing skin consisting of a network of soft elastomeric capacitors (SECs). Each SEC acts as a surface strain gage transducing local strain into measurable changes in capacitance. Using surface strain data facilitates the extraction of physics-based features from the signals that can be used to conduct condition assessment. We investigate the performance of an SEC network at detecting damages. Diffusion maps are constructed from the time series data, and changes in point-wise diffusion distances evaluated to determine the presence of damage. Results are benchmarked against time-series data produced from off-the-shelf resistive strain gauges. This paper presents data from a preliminary study. Results show that the SECs are promising, but the capability to perform damage detection is currently reduced by the presence of parasitic noise in the signal.

We propose a system theoretic approach to the identification and mitigation of vulnerabilities to cyber attacks, in networks of dynamical systems. Using the controllability and observability gramians, we define a network's vulnerability in terms of the impact of an attack input and the degree of difficulty with which this impact can be detected. In this framework, a network is deemed as vulnerable if it is easy for an attacker to steer it to a certain state and yet such a state is hard to observe through the network's sensing mechanisms. We propose strategies for finding the optimal location of a small number of sensors that minimize the network's vulnerability. Such strategies are obtained as the solution of convex optimization problems, formulated so as to strike a balance between maximal reduction of the system's vulnerability and employing a minimal number of sensors. The utility of the developed framework is demonstrated on a standard IEEE nine bus power system network model.

UNLABELLED Bubble continuous positive airway pressure (BCPAP) is a low cost nasal CPAP delivery system with potential benefits to developing nations. OBJECTIVE To compare the efficacy and safety of BCPAP with ventilator-derived CPAP (VCPAP) in preterm neonates with respiratory distress. METHODS In a randomized controlled trial, preterm neonates with Silverman-Anderson score ≥ 4 and oxygen requirement >30% within first 6 h of life were randomly allocated to BCPAP or VCPAP. Proportion of neonates with success or failure was compared. RESULTS In all, 47 of 57 (82.5%) neonates from BCPAP group and 36 of 57 (63.2%) neonates from the VCPAP group completed CPAP successfully (p = 0.03). Neonates who failed CPAP had higher Silverman-Anderson score (p < 0.01), lower arterial to alveolar oxygenation ratio (p < 0.05) and needed surfactant more frequently (p < 0.01). CONCLUSION BCPAP has higher success rate than VCPAP for managing preterm neonates with early onset respiratory distress, with comparable safety.

Cyber threats are serious concerns for power systems. This paper proposes a risk assessment framework to enhance the resilience of power systems against cyber attacks. The Duality Element Relative Fuzzy Evaluation Method (DERFEM) is used to evaluate quantitatively security vulnerabilities within cyber systems of power systems; the Attack Graph is employed to identify intrusion scenarios that exploit multiple vulnerabilities; an System Stability Monitoring and Response System (SSMARS) is developed to monitor the impact of intrusion scenarios on power system dynamics in real time. SSMARS is a novel PMU application for the Smart Grid. It is designed be implemented in a RTO/ISO control center to assess and enhance power system security on line. SSMARS calculates the Conditional Lyapunov Exponents (CLEs) in real time based on the Phasor Measurement Unit (PMU) data. Power system stability is predicted through the values of CLEs. Control actions based on CLEs are suggested if power system instability is likely to happen. The effectiveness of SSMARS is illustrated with the IEEE 39 bus system model.

We state and prove a robustness result for the concept of almost everywhere uniform stability of an invariant attractor for a nonlinear autonomous differential equation. We also show that the concept of almost everywhere uniform stability rectifies the drawback of vanishing density on stable manifolds of hyperbolic equilibrium points which exists for a weaker concept of almost everywhere stability.

In this paper, we propose a novel approach based on tools from system theory and ergodic theory of dynamical systems for the identification of critical interactions responsible for the emergence of complex dynamics in network systems. We consider a network system with multiple uncertain random parameters and operating in non-equilibrium. The objective is to determine which of these multiple parameters are critical for maintaining the stability of non-equilibrium dynamics. We provide necessary condition, expressed in terms of variance of uncertainty and nominal system dynamics, to maintain the stability of network system. The condition is used for rank ordering uncertain parameters and to provide margin of stability for network system. The proposed method is applied for the identification of parameters responsible for limit cycle oscillations in biochemical network involved in yeast cell glycolysis and for robust synchronization in network of Kuramoto oscillators with uncertainty in coupling parameters.

In this work, we develop a data-driven method for the diagnosis of damage in mesoscale mechanical structures using an array of distributed sensor networks. The proposed approach relies on comparing intrinsic geometries of data sets corresponding to the undamaged and damaged states of the system. We use a spectral diffusion map approach to identify the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion maps. The low dimensional embedding of the data set corresponding to different damage levels is obtained using a singular value decomposition of the diffusion map. We construct appropriate metrics in the diffusion space to compare the different data sets corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. To achieve this goal, we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. Typical damage, such as crack or delamination, will lead to a loss of stiffness, is modeled by altering the stiffness of the laminate layer. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with a relatively small signal-to-noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm is advantageous by enabling the comparison of data from numerous sensors of similar or different types of data through data fusion, hereby making it attractive to exploit the distributed nature of sensor arrays. This distributed nature is further exploited for the purpose of damage localization. We perform extensive numerical simulations to demonstrate that the proposed method can successfully determine the extent of damage on the wind turbine blade and also localize the damage. We also present preliminary results for the application of the developed algorithm on the experimental data. These preliminary results obtained using experimental data are promising and is a topic of our ongoing investigation.

Rotor angle instability, if not corrected in time, may cause cascaded load and generator tripping events which will lead to a major blackout. In recent years, the Phasor Measurement Units (PMUs) have been undergoing fast deployment toward a hierarchical Wide-Area Measurement System (WAMS). Wide-area early warning systems for rotor angle instability are being developed. This paper proposes an online monitoring scheme for rotor angle stability based on PMU data. A loss of synchronism is predicted by Lyapunov Exponents. The relationship between rotor angle stability and the Maximal Lyapunov Exponent (MLE) is established. A computationally efficient algorithm for MLE calculation is developed for on-line applications. Simulation results on a 200-bus model are used to validate the effectiveness of the proposed method.

In this paper, we propose a model-free approach for short term voltage stability monitoring of power system. Our data-driven approach makes use of Phasor Measurement Units (PMU) data to generate the stability certificate for verifying the voltage stability. We employ Lyapunov exponent, a stability tool adapted from ergodic theory of dynamical system, to generate the stability certificate. The time-series voltage data from PMU is used for the online computation of Lyapunov exponent. The proposed method can not only be used to determine the voltage stability of the entire system but can also be used to determine stability/instability contribution of individual buses to the overall system stability. Simulation results are presented on WSCC nine bus system using different load models.

In this paper, we study the problem of stabilization of nonlinear system in Lure form with uncertainty at the input and output channels. The channel uncertainty is modeled using Bernoulli random variable. Generalization of Positive Real Lemma for stochastic systems are derived to prove the main result of this paper providing sufficient condition for the mean square exponential stability of the closed loop system with erasure channels at the input and output. We generalize this result to provide sufficient condition for stabilization over general uncertain channel at the input and perfect measurement channel at the output. The results in this paper provide synthesis method for the design of controller and observer that are robust to channel uncertainty. Due to nonlinear plant dynamics, the controller and observer design problem are coupled, however we provide explicit relation between the erasure probability of the input and output channels to maintain stability of the feedback control system.

Survival of preterm neonates in developing world has improved. Developing countries lag behind in nutritional management in NICU especially parenteral nutrition (PN). This randomized controlled trial was done to evaluate the effect of aggressive parenteral nutrition on nitrogen retention of sick VLBW and extremely low birth weight (ELBW) babies. From September 2009 to February 2010, total 34 babies were randomized to receive aggressive parenteral nutrition (APN)(n=17) or standard parenteral nutrition (SPN) (n=17). The average daily total and PN calory intake of babies in APN group was significantly higher during first week. APN was well-tolerated; however, nitrogen retention was not significantly higher in APN group. Aggressive parenteral nutrition in sick VLBW babies is feasible in developing world, though it did not improve nitrogen retention in first week of life.

To assess outcome of InSuRE (Intubation, Surfactant and Rapid Extubation) in managing preterm neonates with respiratory distress syndrome (RDS).Preterm neonates fulfilling inclusion criteria were enrolled after obtaining informed parental consent. Criteria for success of InSuRE was predefined. Proportion of neonates with success or failure of InSuRE was the primary outcome.From August 2008 through July 2009, 28 babies underwent InSuRE. Sixteen babies (57 %) succeeded InSuRE. Median birth weight in successful group was 1362.5 (850-2,150) g and in failure group was 1,805 (990-2,560) g (p = 0.015). Nasal continuous positive airway pressure (nCPAP) was started at 0.5 (0-5.0) h of life in successful group and at 3.0 (0.5-6.0) h in failure group (p = 0.005). Babies in successful group received surfactant at median age of 2.0 (1.0-6.0) h, and in failure group at 4.0 (2.0-8.0) h (p = 0.002). Two patients in successful group died of neonatal sepsis, while none died in failure group (p = 0.492).InSuRE is feasible in developing countries. However, we need large multicentric randomised controlled trials to prove the safety and efficacy in our settings.