Insect outbreak shifts the direction of selection from fast to slow growth rates in the long-lived conifer Pinus ponderosa

Treesearch

Raul de la Mata; Sharon Hood; Anna Sala

2017-01-01

Long generation times limit species' rapid evolution to changing environments. Trees provide critical global ecosystem services, but are under increasing risk of mortality because of climate change-mediated disturbances, such as insect outbreaks. The extent to which disturbance changes the dynamics and strength of selection is unknown, but has important...

Distributed Decision Making in a Dynamic Network Environment

DTIC Science & Technology

1990-01-01

protocols, particularly when traffic arrival statistics are varying or unknown, and loads are high. Both nonpreemptive and preemptive repeat disciplines are...The simulation model allows general value functions, continuous time operation, and preemptive or nonpreemptive service. For reasons of tractability... nonpreemptive LIFO, (4) nonpreemptive LIFO with discarding, (5) nonpreemptive HOL, (6) nonpreemp- tive HOL with discarding, (7) preemptive repeat HOL, (8

Recent CESAR (Center for Engineering Systems Advanced Research) research activities in sensor based reasoning for autonomous machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pin, F.G.; de Saussure, G.; Spelt, P.F.

1988-01-01

This paper describes recent research activities at the Center for Engineering Systems Advanced Research (CESAR) in the area of sensor based reasoning, with emphasis being given to their application and implementation on our HERMIES-IIB autonomous mobile vehicle. These activities, including navigation and exploration in a-priori unknown and dynamic environments, goal recognition, vision-guided manipulation and sensor-driven machine learning, are discussed within the framework of a scenario in which an autonomous robot is asked to navigate through an unknown dynamic environment, explore, find and dock at the panel, read and understand the status of the panel's meters and dials, learn the functioningmore » of a process control panel, and successfully manipulate the control devices of the panel to solve a maintenance emergency problems. A demonstration of the successful implementation of the algorithms on our HERMIES-IIB autonomous robot for resolution of this scenario is presented. Conclusions are drawn concerning the applicability of the methodologies to more general classes of problems and implications for future work on sensor-driven reasoning for autonomous robots are discussed. 8 refs., 3 figs.« less

Visual identification and similarity measures used for on-line motion planning of autonomous robots in unknown environments

NASA Astrophysics Data System (ADS)

Martínez, Fredy; Martínez, Fernando; Jacinto, Edwar

2017-02-01

In this paper we propose an on-line motion planning strategy for autonomous robots in dynamic and locally observable environments. In this approach, we first visually identify geometric shapes in the environment by filtering images. Then, an ART-2 network is used to establish the similarity between patterns. The proposed algorithm allows that a robot establish its relative location in the environment, and define its navigation path based on images of the environment and its similarity to reference images. This is an efficient and minimalist method that uses the similarity of landmark view patterns to navigate to the desired destination. Laboratory tests on real prototypes demonstrate the performance of the algorithm.

A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Liu, Jiechao; Jayakumar, Paramsothy; Stein, Jeffrey L.; Ersal, Tulga

2016-11-01

This paper investigates the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. The context of this work is large autonomous ground vehicles that manoeuvre at high speed within unknown, unstructured, flat environments and have significant vehicle dynamics-related constraints. Five different representations of vehicle dynamics models are considered: four variations of the two degrees-of-freedom (DoF) representation as lower fidelity models and a fourteen DoF representation with combined-slip Magic Formula tyre model as a higher fidelity model. It is concluded that the two DoF representation that accounts for tyre nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm in order to operate the vehicle at its limits within an environment that includes large obstacles. For less challenging environments, however, the two DoF representation with linear tyre model and constant axle loads is sufficient.

Robot navigation research using the HERMIES mobile robot

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnett, D.L.

1989-01-01

In recent years robot navigation has attracted much attention from researchers around the world. Not only are theoretical studies being simulated on sophisticated computers, but many mobile robots are now used as test vehicles for these theoretical studies. Various algorithms have been perfected for navigation in a known static environment; but navigation in an unknown and dynamic environment poses a much more challenging problem for researchers. Many different methodologies have been developed for autonomous robot navigation, but each methodology is usually restricted to a particular type of environment. One important research focus of the Center for Engineering Systems Advanced researchmore » (CESAR) at Oak Ridge National Laboratory, is autonomous navigation in unknown and dynamic environments using the series of HERMIES mobile robots. The research uses an expert system for high-level planning interfaced with C-coded routines for implementing the plans, and for quick processing of data requested by the expert system. In using this approach, the navigation is not restricted to one methodology since the expert system can activate a rule module for the methodology best suited for the current situation. Rule modules can be added the rule base as they are developed and tested. Modules are being developed or enhanced for navigating from a map, searching for a target, exploring, artificial potential-field navigation, navigation using edge-detection, etc. This paper will report on the various rule modules and methods of navigation in use, or under development at CESAR, using the HERMIES-IIB robot as a testbed. 13 refs., 5 figs., 1 tab.« less

Signal detection via residence-time asymmetry in noisy bistable devices.

PubMed

Bulsara, A R; Seberino, C; Gammaitoni, L; Karlsson, M F; Lundqvist, B; Robinson, J W C

2003-01-01

We introduce a dynamical readout description for a wide class of nonlinear dynamic sensors operating in a noisy environment. The presence of weak unknown signals is assessed via the monitoring of the residence time in the metastable attractors of the system, in the presence of a known, usually time-periodic, bias signal. This operational scenario can mitigate the effects of sensor noise, providing a greatly simplified readout scheme, as well as significantly reduced processing procedures. Such devices can also show a wide variety of interesting dynamical features. This scheme for quantifying the response of a nonlinear dynamic device has been implemented in experiments involving a simple laboratory version of a fluxgate magnetometer. We present the results of the experiments and demonstrate that they match the theoretical predictions reasonably well.

Cell fate determination dynamics in bacteria

NASA Astrophysics Data System (ADS)

Kuchina, Anna; Espinar, Lorena; Cagatay, Tolga; Garcia-Ojalvo, Jordi; Suel, Gurol

2010-03-01

The fitness of an organism depends on many processes that serve the purpose to adapt to changing environment in a robust and coordinated fashion. One example of such process is cellular fate determination. In the presence of a variety of alternative responses each cell adopting a particular fate represents a ``choice'' that must be tightly regulated to ensure the best survival strategy for the population taking into account the broad range of possible environmental challenges. We investigated this problem in the model organism B.Subtilis which under stress conditions differentiates terminally into highly resistant spores or initiates an alternative transient state of competence. The dynamics underlying cell fate choice remains largely unknown. We utilize quantitative fluorescent microscopy to track the activities of genes involved in these responses on a single-cell level. We explored the importance of temporal interactions between competing cell fates by re- engineering the differentiation programs. I will discuss how the precise dynamics of cellular ``decision-making'' governed by the corresponding biological circuits may enable cells to adjust to diverse environments and determine survival.

Trust-Based Access Control Model from Sociological Approach in Dynamic Online Social Network Environment

PubMed Central

Kim, Seungjoo

2014-01-01

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information. PMID:25374943

Trust-based access control model from sociological approach in dynamic online social network environment.

PubMed

Baek, Seungsoo; Kim, Seungjoo

2014-01-01

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information.

Safe Exploration Algorithms for Reinforcement Learning Controllers.

PubMed

Mannucci, Tommaso; van Kampen, Erik-Jan; de Visser, Cornelis; Chu, Qiping

2018-04-01

Self-learning approaches, such as reinforcement learning, offer new possibilities for autonomous control of uncertain or time-varying systems. However, exploring an unknown environment under limited prediction capabilities is a challenge for a learning agent. If the environment is dangerous, free exploration can result in physical damage or in an otherwise unacceptable behavior. With respect to existing methods, the main contribution of this paper is the definition of a new approach that does not require global safety functions, nor specific formulations of the dynamics or of the environment, but relies on interval estimation of the dynamics of the agent during the exploration phase, assuming a limited capability of the agent to perceive the presence of incoming fatal states. Two algorithms are presented with this approach. The first is the Safety Handling Exploration with Risk Perception Algorithm (SHERPA), which provides safety by individuating temporary safety functions, called backups. SHERPA is shown in a simulated, simplified quadrotor task, for which dangerous states are avoided. The second algorithm, denominated OptiSHERPA, can safely handle more dynamically complex systems for which SHERPA is not sufficient through the use of safety metrics. An application of OptiSHERPA is simulated on an aircraft altitude control task.

Regulating Cortical Neurodynamics for Past, Present and Future

NASA Astrophysics Data System (ADS)

Liljenström, Hans

2002-09-01

Behaving systems, biological as well as artificial, need to respond quickly and accurately to changes in the environment. The response is dependent on stored memories, and novel situations should be learnt for the guidance of future behavior. A highly nonlinear system dynamics is required in order to cope with a complex and changing environment, and this dynamics should be regulated to match the demands of the current situation, and to predict future behavior. In many cases the dynamics should be regulated to minimize processing time. We use computer simulations of cortical structures in order to investigate how the neurodynamics of these systems can be regulated for optimal performance in an unknown and changing environment. In particular, we study how cortical oscillations can serve to amplify weak signals and sustain an input pattern for more accurate information processing, and how chaotic-like behavior could increase the sensitivity in initial, exploratory states. We mimic regulating mechanisms based on neuromodulators, intrinsic noise levels, and various synchronizing effects. We find optimal noise levels where system performance is maximized, and neuromodulatory strategies for an efficient pattern recognition, where the anticipatory state of the system plays an important role.

Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

NASA Astrophysics Data System (ADS)

Sutrisno; Widowati; Solikhin

2016-06-01

In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

Kinematics and Dynamics of Motion Control Based on Acceleration Control

NASA Astrophysics Data System (ADS)

Ohishi, Kiyoshi; Ohba, Yuzuru; Katsura, Seiichiro

The first IEEE International Workshop on Advanced Motion Control was held in 1990 pointed out the importance of physical interpretation of motion control. The software servoing technology is now common in machine tools, robotics, and mechatronics. It has been intensively developed for the numerical control (NC) machines. Recently, motion control in unknown environment will be more and more important. Conventional motion control is not always suitable due to the lack of adaptive capability to the environment. A more sophisticated ability in motion control is necessary for compliant contact with environment. Acceleration control is the key technology of motion control in unknown environment. The acceleration control can make a motion system to be a zero control stiffness system without losing the robustness. Furthermore, a realization of multi-degree-of-freedom motion is necessary for future human assistance. A human assistant motion will require various control stiffness corresponding to the task. The review paper focuses on the modal coordinate system to integrate the various control stiffness in the virtual axes. A bilateral teleoperation is a good candidate to consider the future human assistant motion and integration of decentralized systems. Thus the paper reviews and discusses the bilateral teleoperation from the control stiffness and the modal control design points of view.

Bayesian Tracking within a Feedback Sensing Environment: Estimating Interacting, Spatially Constrained Complex Dynamical Systems from Multiple Sources of Controllable Devices

DTIC Science & Technology

2014-07-25

composition of simple temporal structures to a speaker diarization task with the goal of segmenting conference audio in the presence of an unknown number of...application domains including neuroimaging, diverse document selection, speaker diarization , stock modeling, and target tracking. We detail each of...recall performance than competing methods in a task of discovering articles preferred by the user • a gold-standard speaker diarization method, as

Locally optimal control under unknown dynamics with learnt cost function: application to industrial robot positioning

NASA Astrophysics Data System (ADS)

Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane; Nyiri, Eric

2017-01-01

Recent methods of Reinforcement Learning have enabled to solve difficult, high dimensional, robotic tasks under unknown dynamics using iterative Linear Quadratic Gaussian control theory. These algorithms are based on building a local time-varying linear model of the dynamics from data gathered through interaction with the environment. In such tasks, the cost function is often expressed directly in terms of the state and control variables so that it can be locally quadratized to run the algorithm. If the cost is expressed in terms of other variables, a model is required to compute the cost function from the variables manipulated. We propose a method to learn the cost function directly from the data, in the same way as for the dynamics. This way, the cost function can be defined in terms of any measurable quantity and thus can be chosen more appropriately for the task to be carried out. With our method, any sensor information can be used to design the cost function. We demonstrate the efficiency of this method through simulating, with the V-REP software, the learning of a Cartesian positioning task on several industrial robots with different characteristics. The robots are controlled in joint space and no model is provided a priori. Our results are compared with another model free technique, consisting in writing the cost function as a state variable.

Autonomous Navigation, Dynamic Path and Work Flow Planning in Multi-Agent Robotic Swarms Project

NASA Technical Reports Server (NTRS)

Falker, John; Zeitlin, Nancy; Leucht, Kurt; Stolleis, Karl

2015-01-01

Kennedy Space Center has teamed up with the Biological Computation Lab at the University of New Mexico to create a swarm of small, low-cost, autonomous robots, called Swarmies, to be used as a ground-based research platform for in-situ resource utilization missions. The behavior of the robot swarm mimics the central-place foraging strategy of ants to find and collect resources in an unknown environment and return those resources to a central site.

Conformational changes in intact dengue virus reveal serotype-specific expansion

PubMed Central

Lim, Xin-Xiang; Chandramohan, Arun; Lim, Xin Ying Elisa; Bag, Nirmalya; Sharma, Kamal Kant; Wirawan, Melissa; Wohland, Thorsten; Lok, Shee-Mei; Anand, Ganesh S.

2017-01-01

Dengue virus serotype 2 (DENV2) alone undergoes structural expansion at 37 °C (associated with host entry), despite high sequence and structural homology among the four known serotypes. The basis for this differential expansion across strains and serotypes is unknown and necessitates mapping of the dynamics of dengue whole viral particles to describe their coordinated motions and conformational changes when exposed to host-like environments. Here we capture the dynamics of intact viral particles of two serotypes, DENV1 and DENV2, by amide hydrogen/deuterium exchange mass spectrometry (HDXMS) and time resolved Förster Resonance Energy Transfer. Our results show temperature-dependent dynamics hotspots on DENV2 and DENV1 particles with DENV1 showing expansion at 40 °C but not at 37 °C. HDXMS measurement of virion dynamics in solution offers a powerful approach to identify potential epitopes, map virus-antibody complex structure and dynamics, and test effects of multiple host-specific perturbations on viruses and virus-antibody complexes. PMID:28186093

A Bayesian nonparametric approach to dynamical noise reduction

NASA Astrophysics Data System (ADS)

Kaloudis, Konstantinos; Hatjispyros, Spyridon J.

2018-06-01

We propose a Bayesian nonparametric approach for the noise reduction of a given chaotic time series contaminated by dynamical noise, based on Markov Chain Monte Carlo methods. The underlying unknown noise process (possibly) exhibits heavy tailed behavior. We introduce the Dynamic Noise Reduction Replicator model with which we reconstruct the unknown dynamic equations and in parallel we replicate the dynamics under reduced noise level dynamical perturbations. The dynamic noise reduction procedure is demonstrated specifically in the case of polynomial maps. Simulations based on synthetic time series are presented.

A single footshock causes long-lasting hypoactivity in unknown environments that is dependent on the development of contextual fear conditioning.

PubMed

Daviu, Núria; Fuentes, Silvia; Nadal, Roser; Armario, Antonio

2010-09-01

Exposure to a single session of footshocks induces long-lasting inhibition of activity in unknown environments that markedly differ from the shock context. Interestingly, these effects are not necessarily associated to an enhanced anxiety and interpretation of this hypoactivity remains unclear. In the present experiment we further studied this phenomenon in male Sprague-Dawley rats. In a first experiment, a session of three shocks resulted in hypoactivity during exposure, 6-12days later, to three different unknown environments. This altered behaviour was not accompanied by a greater hypothalamic-pituitary-adrenal (HPA) activation, although greater HPA activation paralleling higher levels of freezing was observed in the shock context. In a second experiment we used a single shock and two procedures, one with pre-exposure to the context before the shock and another with immediate shock that did not induce contextual fear conditioning. Hypoactivity and a certain level of generalization of fear (freezing) to the unknown environments only appeared in the group that developed fear conditioning, but no evidence for enhanced anxiety in the elevated plus-maze was found in any group. The results suggest that if animals are able to associate an aversive experience with a distinct unknown environment, they would display more cautious behaviour in any unknown environment and such strategy persists despite repeated experience with different environments. This long-lasting cautious behaviour was not associated to greater HPA response to the unknown environment that was however observed in the shock context. The present findings raised some concerns about interpretation of long-lasting behavioural changes caused by brief stressors. Copyright 2010 Elsevier Inc. All rights reserved.

A Novel Real-Time Reference Key Frame Scan Matching Method.

PubMed

Mohamed, Haytham; Moussa, Adel; Elhabiby, Mohamed; El-Sheimy, Naser; Sesay, Abu

2017-05-07

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions' environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.

Adaptive walking of a quadrupedal robot based on layered biological reflexes

NASA Astrophysics Data System (ADS)

Zhang, Xiuli; Mingcheng, E.; Zeng, Xiangyu; Zheng, Haojun

2012-07-01

A multiple-legged robot is traditionally controlled by using its dynamic model. But the dynamic-model-based approach fails to acquire satisfactory performances when the robot faces rough terrains and unknown environments. Referring animals' neural control mechanisms, a control model is built for a quadruped robot walking adaptively. The basic rhythmic motion of the robot is controlled by a well-designed rhythmic motion controller(RMC) comprising a central pattern generator(CPG) for hip joints and a rhythmic coupler (RC) for knee joints. CPG and RC have relationships of motion-mapping and rhythmic couple. Multiple sensory-motor models, abstracted from the neural reflexes of a cat, are employed. These reflex models are organized and thus interact with the CPG in three layers, to meet different requirements of complexity and response time to the tasks. On the basis of the RMC and layered biological reflexes, a quadruped robot is constructed, which can clear obstacles and walk uphill and downhill autonomously, and make a turn voluntarily in uncertain environments, interacting with the environment in a way similar to that of an animal. The paper provides a biologically inspired architecture, with which a robot can walk adaptively in uncertain environments in a simple and effective way, and achieve better performances.

Camera pose estimation for augmented reality in a small indoor dynamic scene

NASA Astrophysics Data System (ADS)

Frikha, Rawia; Ejbali, Ridha; Zaied, Mourad

2017-09-01

Camera pose estimation remains a challenging task for augmented reality (AR) applications. Simultaneous localization and mapping (SLAM)-based methods are able to estimate the six degrees of freedom camera motion while constructing a map of an unknown environment. However, these methods do not provide any reference for where to insert virtual objects since they do not have any information about scene structure and may fail in cases of occlusion of three-dimensional (3-D) map points or dynamic objects. This paper presents a real-time monocular piece wise planar SLAM method using the planar scene assumption. Using planar structures in the mapping process allows rendering virtual objects in a meaningful way on the one hand and improving the precision of the camera pose and the quality of 3-D reconstruction of the environment by adding constraints on 3-D points and poses in the optimization process on the other hand. We proposed to benefit from the 3-D planes rigidity motion in the tracking process to enhance the system robustness in the case of dynamic scenes. Experimental results show that using a constrained planar scene improves our system accuracy and robustness compared with the classical SLAM systems.

Determination of power system component parameters using nonlinear dead beat estimation method

NASA Astrophysics Data System (ADS)

Kolluru, Lakshmi

Power systems are considered the most complex man-made wonders in existence today. In order to effectively supply the ever increasing demands of the consumers, power systems are required to remain stable at all times. Stability and monitoring of these complex systems are achieved by strategically placed computerized control centers. State and parameter estimation is an integral part of these facilities, as they deal with identifying the unknown states and/or parameters of the systems. Advancements in measurement technologies and the introduction of phasor measurement units (PMU) provide detailed and dynamic information of all measurements. Accurate availability of dynamic measurements provides engineers the opportunity to expand and explore various possibilities in power system dynamic analysis/control. This thesis discusses the development of a parameter determination algorithm for nonlinear power systems, using dynamic data obtained from local measurements. The proposed algorithm was developed by observing the dead beat estimator used in state space estimation of linear systems. The dead beat estimator is considered to be very effective as it is capable of obtaining the required results in a fixed number of steps. The number of steps required is related to the order of the system and the number of parameters to be estimated. The proposed algorithm uses the idea of dead beat estimator and nonlinear finite difference methods to create an algorithm which is user friendly and can determine the parameters fairly accurately and effectively. The proposed algorithm is based on a deterministic approach, which uses dynamic data and mathematical models of power system components to determine the unknown parameters. The effectiveness of the algorithm is tested by implementing it to identify the unknown parameters of a synchronous machine. MATLAB environment is used to create three test cases for dynamic analysis of the system with assumed known parameters. Faults are introduced in the virtual test systems and the dynamic data obtained in each case is analyzed and recorded. Ideally, actual measurements are to be provided to the algorithm. As the measurements are not readily available the data obtained from simulations is fed into the determination algorithm as inputs. The obtained results are then compared to the original (or assumed) values of the parameters. The results obtained suggest that the algorithm is able to determine the parameters of a synchronous machine when crisp data is available.

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

PubMed

Kauppi, Jukka-Pekka; Martikainen, Kalle; Ruotsalainen, Ulla

2010-12-01

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns. Copyright © 2010 Elsevier Ltd. All rights reserved.

Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments

PubMed Central

Yao, Yao; Marchal, Kathleen; Van de Peer, Yves

2014-01-01

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485

Ratbot automatic navigation by electrical reward stimulation based on distance measurement in unknown environments.

PubMed

Gao, Liqiang; Sun, Chao; Zhang, Chen; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

2013-01-01

Traditional automatic navigation methods for bio-robots are constrained to configured environments and thus can't be applied to tasks in unknown environments. With no consideration of bio-robot's own innate living ability and treating bio-robots in the same way as mechanical robots, those methods neglect the intelligence behavior of animals. This paper proposes a novel ratbot automatic navigation method in unknown environments using only reward stimulation and distance measurement. By utilizing rat's habit of thigmotaxis and its reward-seeking behavior, this method is able to incorporate rat's intrinsic intelligence of obstacle avoidance and path searching into navigation. Experiment results show that this method works robustly and can successfully navigate the ratbot to a target in the unknown environment. This work might put a solid base for application of ratbots and also has significant implication of automatic navigation for other bio-robots as well.

Stochastic Online Learning in Dynamic Networks under Unknown Models

DTIC Science & Technology

2016-08-02

Repeated Game with Incomplete Information, IEEE International Conference on Acoustics, Speech, and Signal Processing. 20-MAR-16, Shanghai, China...in a game theoretic framework for the application of multi-seller dynamic pricing with unknown demand models. We formulated the problem as an...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning

Driving a car with custom-designed fuzzy inferencing VLSI chips and boards

NASA Technical Reports Server (NTRS)

Pin, Francois G.; Watanabe, Yutaka

1993-01-01

Vehicle control in a-priori unknown, unpredictable, and dynamic environments requires many calculational and reasoning schemes to operate on the basis of very imprecise, incomplete, or unreliable data. For such systems, in which all the uncertainties can not be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. Two types of computer boards including custom-designed VLSI chips were developed to add a fuzzy inferencing capability to real-time control systems. All inferencing rules on a chip are processed in parallel, allowing execution of the entire rule base in about 30 microseconds, and therefore, making control of 'reflex-type' of motions envisionable. The use of these boards and the approach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation in a-priori unknown environments are first discussed. Then how the human-like navigation scheme implemented on one of the qualitative inferencing boards was installed on a test-bed platform to investigate two control modes for driving a car in a-priori unknown environments on the basis of sparse and imprecise sensor data is described. In the first mode, the car navigates fully autonomously, while in the second mode, the system acts as a driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right and speed up or slow down depending on the obstacles perceived by the sensors. Experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulation results as well as indoors and outdoors experiments are presented and discussed to illustrate the feasibility and robustness of autonomous navigation and/or safety enhancing driver's aid using the new fuzzy inferencing hardware system and some human-like reasoning schemes which may include as little as six elemental behaviors embodied in fourteen qualitative rules.

Natural Environment Illumination: Coherent Interactive Augmented Reality for Mobile and Non-Mobile Devices.

PubMed

Rohmer, Kai; Jendersie, Johannes; Grosch, Thorsten

2017-11-01

Augmented Reality offers many applications today, especially on mobile devices. Due to the lack of mobile hardware for illumination measurements, photorealistic rendering with consistent appearance of virtual objects is still an area of active research. In this paper, we present a full two-stage pipeline for environment acquisition and augmentation of live camera images using a mobile device with a depth sensor. We show how to directly work on a recorded 3D point cloud of the real environment containing high dynamic range color values. For unknown and automatically changing camera settings, a color compensation method is introduced. Based on this, we show photorealistic augmentations using variants of differential light simulation techniques. The presented methods are tailored for mobile devices and run at interactive frame rates. However, our methods are scalable to trade performance for quality and can produce quality renderings on desktop hardware.

A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots.

PubMed

Sherwin, Tyrone; Easte, Mikala; Chen, Andrew Tzer-Yeu; Wang, Kevin I-Kai; Dai, Wenbin

2018-02-14

Location-aware services are one of the key elements of modern intelligent applications. Numerous real-world applications such as factory automation, indoor delivery, and even search and rescue scenarios require autonomous robots to have the ability to navigate in an unknown environment and reach mobile targets with minimal or no prior infrastructure deployment. This research investigates and proposes a novel approach of dynamic target localisation using a single RF emitter, which will be used as the basis of allowing autonomous robots to navigate towards and reach a target. Through the use of multiple directional antennae, Received Signal Strength (RSS) is compared to determine the most probable direction of the targeted emitter, which is combined with the distance estimates to improve the localisation performance. The accuracy of the position estimate is further improved using a particle filter to mitigate the fluctuating nature of real-time RSS data. Based on the direction information, a motion control algorithm is proposed, using Simultaneous Localisation and Mapping (SLAM) and A* path planning to enable navigation through unknown complex environments. A number of navigation scenarios were developed in the context of factory automation applications to demonstrate and evaluate the functionality and performance of the proposed system.

A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots

PubMed Central

Sherwin, Tyrone; Easte, Mikala; Wang, Kevin I-Kai; Dai, Wenbin

2018-01-01

Location-aware services are one of the key elements of modern intelligent applications. Numerous real-world applications such as factory automation, indoor delivery, and even search and rescue scenarios require autonomous robots to have the ability to navigate in an unknown environment and reach mobile targets with minimal or no prior infrastructure deployment. This research investigates and proposes a novel approach of dynamic target localisation using a single RF emitter, which will be used as the basis of allowing autonomous robots to navigate towards and reach a target. Through the use of multiple directional antennae, Received Signal Strength (RSS) is compared to determine the most probable direction of the targeted emitter, which is combined with the distance estimates to improve the localisation performance. The accuracy of the position estimate is further improved using a particle filter to mitigate the fluctuating nature of real-time RSS data. Based on the direction information, a motion control algorithm is proposed, using Simultaneous Localisation and Mapping (SLAM) and A* path planning to enable navigation through unknown complex environments. A number of navigation scenarios were developed in the context of factory automation applications to demonstrate and evaluate the functionality and performance of the proposed system. PMID:29443906

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

PubMed Central

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855

A hybrid search algorithm for swarm robots searching in an unknown environment.

PubMed

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.

Sequential reconstruction of driving-forces from nonlinear nonstationary dynamics

NASA Astrophysics Data System (ADS)

Güntürkün, Ulaş

2010-07-01

This paper describes a functional analysis-based method for the estimation of driving-forces from nonlinear dynamic systems. The driving-forces account for the perturbation inputs induced by the external environment or the secular variations in the internal variables of the system. The proposed algorithm is applicable to the problems for which there is too little or no prior knowledge to build a rigorous mathematical model of the unknown dynamics. We derive the estimator conditioned on the differentiability of the unknown system’s mapping, and smoothness of the driving-force. The proposed algorithm is an adaptive sequential realization of the blind prediction error method, where the basic idea is to predict the observables, and retrieve the driving-force from the prediction error. Our realization of this idea is embodied by predicting the observables one-step into the future using a bank of echo state networks (ESN) in an online fashion, and then extracting the raw estimates from the prediction error and smoothing these estimates in two adaptive filtering stages. The adaptive nature of the algorithm enables to retrieve both slowly and rapidly varying driving-forces accurately, which are illustrated by simulations. Logistic and Moran-Ricker maps are studied in controlled experiments, exemplifying chaotic state and stochastic measurement models. The algorithm is also applied to the estimation of a driving-force from another nonlinear dynamic system that is stochastic in both state and measurement equations. The results are judged by the posterior Cramer-Rao lower bounds. The method is finally put into test on a real-world application; extracting sun’s magnetic flux from the sunspot time series.

Intelligent on-line fault tolerant control for unanticipated catastrophic failures.

PubMed

Yen, Gary G; Ho, Liang-Wei

2004-10-01

As dynamic systems become increasingly complex, experience rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challenge for control engineers. Traditional control design techniques are not adequate to cope with these systems, which may suffer from unanticipated dynamic failures. In this research work, we investigate the on-line fault tolerant control problem and propose an intelligent on-line control strategy to handle the desired trajectories tracking problem for systems suffering from various unanticipated catastrophic faults. Through theoretical analysis, the sufficient condition of system stability has been derived and two different on-line control laws have been developed. The approach of the proposed intelligent control strategy is to continuously monitor the system performance and identify what the system's current state is by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal to compensate for the unknown system failure dynamics by using an artificial neural network as an on-line estimator to approximate the unexpected and unknown failure dynamics. The first control law is derived directly from the Lyapunov stability theory, while the second control law is derived based upon the discrete-time sliding mode control technique. Both control laws have been implemented in a variety of failure scenarios to validate the proposed intelligent control scheme. The simulation results, including a three-tank benchmark problem, comply with theoretical analysis and demonstrate a significant improvement in trajectory following performance based upon the proposed intelligent control strategy.

Crystallization by Particle Attachment in Synthetic, Biogenic, and Geologic Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Yoreo, James J.; Gilbert, Pupa U.; Sommerdijk, Nico

Field and laboratory observations show that crystals commonly form by the addition and attachment of particles that range from multi-ion complexes to fully formed nanoparticles. These non-classical pathways to crystallization are diverse, in contrast to classical models that consider the addition of monomeric chemical species. We review progress toward understanding crystal growth by particle attachment processes and show that multiple pathways result from the interplay of free energy landscapes and reaction dynamics. Much remains unknown about the fundamental aspects; particularly the relationships between solution structure, interfacial forces, and particle motion. Developing a predictive description that connects molecular details to ensemblemore » behavior will require revisiting long-standing interpretations of crystal formation in synthetic systems and patterns of mineralization in natural environments.« less

Dynamic Modeling from Flight Data with Unknown Time Skews

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2016-01-01

A method for estimating dynamic model parameters from flight data with unknown time skews is described and demonstrated. The method combines data reconstruction, nonlinear optimization, and equation-error parameter estimation in the frequency domain to accurately estimate both dynamic model parameters and the relative time skews in the data. Data from a nonlinear F-16 aircraft simulation with realistic noise, instrumentation errors, and arbitrary time skews were used to demonstrate the approach. The approach was further evaluated using flight data from a subscale jet transport aircraft, where the measured data were known to have relative time skews. Comparison of modeling results obtained from time-skewed and time-synchronized data showed that the method accurately estimates both dynamic model parameters and relative time skew parameters from flight data with unknown time skews.

Resource Management in Constrained Dynamic Situations

NASA Astrophysics Data System (ADS)

Seok, Jinwoo

Resource management is considered in this dissertation for systems with limited resources, possibly combined with other system constraints, in unpredictably dynamic environments. Resources may represent fuel, power, capabilities, energy, and so on. Resource management is important for many practical systems; usually, resources are limited, and their use must be optimized. Furthermore, systems are often constrained, and constraints must be satisfied for safe operation. Simplistic resource management can result in poor use of resources and failure of the system. Furthermore, many real-world situations involve dynamic environments. Many traditional problems are formulated based on the assumptions of given probabilities or perfect knowledge of future events. However, in many cases, the future is completely unknown, and information on or probabilities about future events are not available. In other words, we operate in unpredictably dynamic situations. Thus, a method is needed to handle dynamic situations without knowledge of the future, but few formal methods have been developed to address them. Thus, the goal is to design resource management methods for constrained systems, with limited resources, in unpredictably dynamic environments. To this end, resource management is organized hierarchically into two levels: 1) planning, and 2) control. In the planning level, the set of tasks to be performed is scheduled based on limited resources to maximize resource usage in unpredictably dynamic environments. In the control level, the system controller is designed to follow the schedule by considering all the system constraints for safe and efficient operation. Consequently, this dissertation is mainly divided into two parts: 1) planning level design, based on finite state machines, and 2) control level methods, based on model predictive control. We define a recomposable restricted finite state machine to handle limited resource situations and unpredictably dynamic environments for the planning level. To obtain a policy, dynamic programing is applied, and to obtain a solution, limited breadth-first search is applied to the recomposable restricted finite state machine. A multi-function phased array radar resource management problem and an unmanned aerial vehicle patrolling problem are treated using recomposable restricted finite state machines. Then, we use model predictive control for the control level, because it allows constraint handling and setpoint tracking for the schedule. An aircraft power system management problem is treated that aims to develop an integrated control system for an aircraft gas turbine engine and electrical power system using rate-based model predictive control. Our results indicate that at the planning level, limited breadth-first search for recomposable restricted finite state machines generates good scheduling solutions in limited resource situations and unpredictably dynamic environments. The importance of cooperation in the planning level is also verified. At the control level, a rate-based model predictive controller allows good schedule tracking and safe operations. The importance of considering the system constraints and interactions between the subsystems is indicated. For the best resource management in constrained dynamic situations, the planning level and the control level need to be considered together.

A Novel Real-Time Reference Key Frame Scan Matching Method

PubMed Central

Mohamed, Haytham; Moussa, Adel; Elhabiby, Mohamed; El-Sheimy, Naser; Sesay, Abu

2017-01-01

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems. PMID:28481285

An efficient incremental learning mechanism for tracking concept drift in spam filtering

PubMed Central

Sheu, Jyh-Jian; Chu, Ko-Tsung; Li, Nien-Feng; Lee, Cheng-Chi

2017-01-01

This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email’s header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1) Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email’s content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment. PMID:28182691

Mass properties measurement system dynamics

NASA Technical Reports Server (NTRS)

Doty, Keith L.

1993-01-01

The MPMS mechanism possess two revolute degrees-of-freedom and allows the user to measure the mass, center of gravity, and the inertia tensor of an unknown mass. The dynamics of the Mass Properties Measurement System (MPMS) from the Lagrangian approach to illustrate the dependency of the motion on the unknown parameters.

From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence.

PubMed

Vidaki, Athina; Kayser, Manfred

2017-12-21

Human genetic variation is a major resource in forensics, but does not allow all forensically relevant questions to be answered. Some questions may instead be addressable via epigenomics, as the epigenome acts as an interphase between the fixed genome and the dynamic environment. We envision future forensic applications of DNA methylation analysis that will broaden DNA-based forensic intelligence. Together with genetic prediction of appearance and biogeographic ancestry, epigenomic lifestyle prediction is expected to increase the ability of police to find unknown perpetrators of crime who are not identifiable using current forensic DNA profiling.

Landing Hazard Avoidance Display

NASA Technical Reports Server (NTRS)

Abernathy, Michael Franklin (Inventor); Hirsh, Robert L. (Inventor)

2016-01-01

Landing hazard avoidance displays can provide rapidly understood visual indications of where it is safe to land a vehicle and where it is unsafe to land a vehicle. Color coded maps can indicate zones in two dimensions relative to the vehicles position where it is safe to land. The map can be simply green (safe) and red (unsafe) areas with an indication of scale or can be a color coding of another map such as a surface map. The color coding can be determined in real time based on topological measurements and safety criteria to thereby adapt to dynamic, unknown, or partially known environments.

Stochasticity and determinism in models of hematopoiesis.

PubMed

Kimmel, Marek

2014-01-01

This chapter represents a novel view of modeling in hematopoiesis, synthesizing both deterministic and stochastic approaches. Whereas the stochastic models work in situations where chance dominates, for example when the number of cells is small, or under random mutations, the deterministic models are more important for large-scale, normal hematopoiesis. New types of models are on the horizon. These models attempt to account for distributed environments such as hematopoietic niches and their impact on dynamics. Mixed effects of such structures and chance events are largely unknown and constitute both a challenge and promise for modeling. Our discussion is presented under the separate headings of deterministic and stochastic modeling; however, the connections between both are frequently mentioned. Four case studies are included to elucidate important examples. We also include a primer of deterministic and stochastic dynamics for the reader's use.

Normalized value coding explains dynamic adaptation in the human valuation process.

PubMed

Khaw, Mel W; Glimcher, Paul W; Louie, Kenway

2017-11-28

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

Incoherent manipulation of the photoactive yellow protein photocycle with dispersed pump-dump-probe spectroscopy.

PubMed

Larsen, Delmar S; van Stokkum, Ivo H M; Vengris, Mikas; van Der Horst, Michael A; de Weerd, Frank L; Hellingwerf, Klaas J; van Grondelle, Rienk

2004-09-01

Photoactive yellow protein is the protein responsible for initiating the "blue-light vision" of Halorhodospira halophila. The dynamical processes responsible for triggering the photoactive yellow protein photocycle have been disentangled with the use of a novel application of dispersed ultrafast pump-dump-probe spectroscopy, where the photocycle can be started and interrupted with appropriately tuned and timed laser pulses. This "incoherent" manipulation of the photocycle allows for the detailed spectroscopic investigation of the underlying photocycle dynamics and the construction of a fully self-consistent dynamical model. This model requires three kinetically distinct excited-state intermediates, two (ground-state) photocycle intermediates, I(0) and pR, and a ground-state intermediate through which the protein, after unsuccessful attempts at initiating the photocycle, returns to the equilibrium ground state. Also observed is a previously unknown two-photon ionization channel that generates a radical and an ejected electron into the protein environment. This second excitation pathway evolves simultaneously with the pathway containing the one-photon photocycle intermediates.

Incoherent Manipulation of the Photoactive Yellow Protein Photocycle with Dispersed Pump-Dump-Probe Spectroscopy

PubMed Central

Larsen, Delmar S.; van Stokkum, Ivo H. M.; Vengris, Mikas; van der Horst, Michael A.; de Weerd, Frank L.; Hellingwerf, Klaas J.; van Grondelle, Rienk

2004-01-01

Photoactive yellow protein is the protein responsible for initiating the “blue-light vision” of Halorhodospira halophila. The dynamical processes responsible for triggering the photoactive yellow protein photocycle have been disentangled with the use of a novel application of dispersed ultrafast pump-dump-probe spectroscopy, where the photocycle can be started and interrupted with appropriately tuned and timed laser pulses. This “incoherent” manipulation of the photocycle allows for the detailed spectroscopic investigation of the underlying photocycle dynamics and the construction of a fully self-consistent dynamical model. This model requires three kinetically distinct excited-state intermediates, two (ground-state) photocycle intermediates, I0 and pR, and a ground-state intermediate through which the protein, after unsuccessful attempts at initiating the photocycle, returns to the equilibrium ground state. Also observed is a previously unknown two-photon ionization channel that generates a radical and an ejected electron into the protein environment. This second excitation pathway evolves simultaneously with the pathway containing the one-photon photocycle intermediates. PMID:15345564

CRYSTAL GROWTH. Crystallization by particle attachment in synthetic, biogenic, and geologic environments.

PubMed

De Yoreo, James J; Gilbert, Pupa U P A; Sommerdijk, Nico A J M; Penn, R Lee; Whitelam, Stephen; Joester, Derk; Zhang, Hengzhong; Rimer, Jeffrey D; Navrotsky, Alexandra; Banfield, Jillian F; Wallace, Adam F; Michel, F Marc; Meldrum, Fiona C; Cölfen, Helmut; Dove, Patricia M

2015-07-31

Field and laboratory observations show that crystals commonly form by the addition and attachment of particles that range from multi-ion complexes to fully formed nanoparticles. The particles involved in these nonclassical pathways to crystallization are diverse, in contrast to classical models that consider only the addition of monomeric chemical species. We review progress toward understanding crystal growth by particle-attachment processes and show that multiple pathways result from the interplay of free-energy landscapes and reaction dynamics. Much remains unknown about the fundamental aspects, particularly the relationships between solution structure, interfacial forces, and particle motion. Developing a predictive description that connects molecular details to ensemble behavior will require revisiting long-standing interpretations of crystal formation in synthetic systems, biominerals, and patterns of mineralization in natural environments. Copyright © 2015, American Association for the Advancement of Science.

Pattern Recognition Algorithm for High-Sensitivity Odorant Detection in Unknown Environments

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2012-01-01

In a realistic odorant detection application environment, the collected sensory data is a mix of unknown chemicals with unknown concentrations and noise. The identification of the odorants among these mixtures is a challenge in data recognition. In addition, deriving their individual concentrations in the mix is also a challenge. A deterministic analytical model was developed to accurately identify odorants and calculate their concentrations in a mixture with noisy data.

Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics.

PubMed

Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam

2017-07-01

In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Self-evaluation on Motion Adaptation for Service Robots

NASA Astrophysics Data System (ADS)

Funabora, Yuki; Yano, Yoshikazu; Doki, Shinji; Okuma, Shigeru

We suggest self motion evaluation method to adapt to environmental changes for service robots. Several motions such as walking, dancing, demonstration and so on are described with time series patterns. These motions are optimized with the architecture of the robot and under certain surrounding environment. Under unknown operating environment, robots cannot accomplish their tasks. We propose autonomous motion generation techniques based on heuristic search with histories of internal sensor values. New motion patterns are explored under unknown operating environment based on self-evaluation. Robot has some prepared motions which realize the tasks under the designed environment. Internal sensor values observed under the designed environment with prepared motions show the interaction results with the environment. Self-evaluation is composed of difference of internal sensor values between designed environment and unknown operating environment. Proposed method modifies the motions to synchronize the interaction results on both environment. New motion patterns are generated to maximize self-evaluation function without external information, such as run length, global position of robot, human observation and so on. Experimental results show that the possibility to adapt autonomously patterned motions to environmental changes.

Environmental statistics and optimal regulation

NASA Astrophysics Data System (ADS)

Sivak, David; Thomson, Matt

2015-03-01

The precision with which an organism can detect its environment, and the timescale for and statistics of environmental change, will affect the suitability of different strategies for regulating protein levels in response to environmental inputs. We propose a general framework--here applied to the enzymatic regulation of metabolism in response to changing nutrient concentrations--to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, and the costs associated with enzyme production. We find: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.

A Combination of Ontogeny and CNS Environment Establishes Microglial Identity.

PubMed

Bennett, F Chris; Bennett, Mariko L; Yaqoob, Fazeela; Mulinyawe, Sara B; Grant, Gerald A; Hayden Gephart, Melanie; Plowey, Edward D; Barres, Ben A

2018-05-22

Microglia, the brain's resident macrophages, are dynamic CNS custodians with surprising origins in the extra-embryonic yolk sac. The consequences of their distinct ontogeny are unknown but critical to understanding and treating brain diseases. We created a brain macrophage transplantation system to disentangle how environment and ontogeny specify microglial identity. We find that donor cells extensively engraft in the CNS of microglia-deficient mice, and even after exposure to a cell culture environment, microglia fully regain their identity when returned to the CNS. Though transplanted macrophages from multiple tissues can express microglial genes in the brain, only those of yolk-sac origin fully attain microglial identity. Transplanted macrophages of inappropriate origin, including primary human cells in a humanized host, express disease-associated genes and specific ontogeny markers. Through brain macrophage transplantation, we discover new principles of microglial identity that have broad applications to the study of disease and development of myeloid cell therapies. Copyright © 2018 Elsevier Inc. All rights reserved.

Seeking the foundations of cognition in bacteria: From Schrödinger's negative entropy to latent information

NASA Astrophysics Data System (ADS)

Ben Jacob, Eshel; Shapira, Yoash; Tauber, Alfred I.

2006-01-01

We reexamine Schrödinger's reflections on the fundamental requirements for life in view of new observations about bacterial self-organization and the emerging understanding of gene-network regulation mechanisms and dynamics. Focusing on the energy, matter and thermodynamic imbalances provided by the environment, Schrödinger proposed his consumption of negative entropy requirement for life. We take the criteria further and propose that, besides “negative entropy”, organisms extract latent information embedded in the complexity of their environment. By latent information we refer to the non-arbitrary spatio-temporal patterns of regularities and variations that characterize the environmental dynamics. Hence it can be used to generate an internal condensed description (model or usable information) of the environment which guides the organisms functioning. Accordingly, we propose that Schrödinger's criterion of “consumption of negative entropy” is not sufficient and “consumption of latent information” is an additional fundamental requirement of Life. In other words, all organisms, including bacteria, the most primitive (fundamental) ones, must be able to sense the environment and perform internal information processing for thriving on latent information embedded in the complexity of their environment. We then propose that by acting together, bacteria can perform this most elementary cognitive function more efficiently as can be illustrated by their cooperative behavior (colonial or inter-cellular self-organization). As a member of a complex superorganism-the colony-each unit (bacteria) must possess the ability to sense and communicate with the other units comprising the collective and perform its task within a distribution of tasks. Bacterial communication thus entails collective sensing and cooperativity. The fundamental (primitive) elements of cognition in such systems include interpretation of (chemical) messages, distinction between internal and external information, and some self vs., non-self distinction (peers and cheaters). We outline how intra-cellular self-organization together with genome plasticity and membrane dynamics might, in principle, provide the intra-cellular mechanisms needed for these fundamental cognitive functions. In regard to intra-cellular processes, Schrödinger postulated that new physics is needed to explain the convertion of the genetically stored information into a functioning cell. At present, his ontogenetic dilemma is generally perceived to be solved and is attributed to a lack of knowledge when it was proposed. So it is widely accepted that there is no need for some unknown laws of physics to explain cellular ontogenetic development. We take a different view and in Schrödinger's foot steps suggest that yet unknown physics principles of self-organization in open systems are missing for understanding how to assemble the cell's component into an information-based functioning “machine”.

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring

PubMed Central

Niewiadomska-Szynkiewicz, Ewa; Sikora, Andrzej; Marks, Michał

2016-01-01

Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper. PMID:27649186

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring.

PubMed

Niewiadomska-Szynkiewicz, Ewa; Sikora, Andrzej; Marks, Michał

2016-09-14

Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper.

1. photocopy of postcard (from Glenwood Springs Lodge & Pool, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. photocopy of postcard (from Glenwood Springs Lodge & Pool, Inc., Date unknown) Photographer unknown, Date unknown GENERAL VIEW OF LODGE, HOT SPRINGS POOL AND ENVIRONS - Hot Springs Lodge, Garfield County, CO

Extremum seeking with bounded update rates

DOE PAGES

Scheinker, Alexander; Krstić, Miroslav

2013-11-16

In this work, we present a form of extremum seeking (ES) in which the unknown function being minimized enters the system’s dynamics as the argument of a cosine or sine term, thereby guaranteeing known bounds on update rates and control efforts. We present general n-dimensional optimization and stabilization results as well as 2D vehicle control, with bounded velocity and control efforts. For application to autonomous vehicles, tracking a source in a GPS denied environment with unknown orientation, this ES approach allows for smooth heading angle actuation, with constant velocity, and in application to a unicycle-type vehicle results in control abilitymore » as if the vehicle is fully actuated. Our stability analysis is made possible by the classic results of Kurzweil, Jarnik, Sussmann, and Liu, regarding systems with highly oscillatory terms. In our stability analysis, we combine the averaging results with a semi-global practical stability result under small parametric perturbations developed by Moreau and Aeyels.« less

Dynamic modelling and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances

NASA Astrophysics Data System (ADS)

Yang, Xinxin; Ge, Shuzhi Sam; He, Wei

2018-04-01

In this paper, both the closed-form dynamics and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances are developed. The dynamic model of the system is described with assumed modes approach and Lagrangian method. The flexible manipulators are represented as Euler-Bernoulli beams. Based on singular perturbation technique, the displacements/joint angles and flexible modes are modelled as slow and fast variables, respectively. A sliding mode control is designed for trajectories tracking of the slow subsystem under unknown but bounded disturbances, and an adaptive sliding mode control is derived for slow subsystem under unknown slowly time-varying disturbances. An optimal linear quadratic regulator method is proposed for the fast subsystem to damp out the vibrations of the flexible manipulators. Theoretical analysis validates the stability of the proposed composite controller. Numerical simulation results demonstrate the performance of the closed-loop flexible space robot system.

Epistemic and aleatory uncertainty in the study of dynamic human-water systems

NASA Astrophysics Data System (ADS)

Di Baldassarre, Giuliano; Brandimarte, Luigia; Beven, Keith

2016-04-01

Here we discuss epistemic and aleatory uncertainty in the study of dynamic human-water systems (e.g. socio-hydrology), which is one of the main topics of Panta Rhei, the current scientific decade of the International Association of Hydrological Sciences (IAHS). In particular, we identify three types of lack of understanding: (i) known unknowns, which are things we know we don't know; (ii) unknown unknowns, which are things we don't know we don't know; and (iii) wrong assumptions, things we think we know, but we actually don't know. We posit that a better understanding of human-water interactions and feedbacks can help coping with wrong assumptions and known unknowns. Moreover, being aware of the existence of unknown unknowns, and their potential capability to generate surprises or black swans, suggest the need to rely more on bottom-up approaches, based on social vulnerabilities and possibilities of failures, and less on top-down approaches, based on optimization and quantitative predictions.

Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data.

PubMed

Zhu, Yuanheng; Zhao, Dongbin; Li, Xiangjun

2017-03-01

H ∞ control is a powerful method to solve the disturbance attenuation problems that occur in some control systems. The design of such controllers relies on solving the zero-sum game (ZSG). But in practical applications, the exact dynamics is mostly unknown. Identification of dynamics also produces errors that are detrimental to the control performance. To overcome this problem, an iterative adaptive dynamic programming algorithm is proposed in this paper to solve the continuous-time, unknown nonlinear ZSG with only online data. A model-free approach to the Hamilton-Jacobi-Isaacs equation is developed based on the policy iteration method. Control and disturbance policies and value are approximated by neural networks (NNs) under the critic-actor-disturber structure. The NN weights are solved by the least-squares method. According to the theoretical analysis, our algorithm is equivalent to a Gauss-Newton method solving an optimization problem, and it converges uniformly to the optimal solution. The online data can also be used repeatedly, which is highly efficient. Simulation results demonstrate its feasibility to solve the unknown nonlinear ZSG. When compared with other algorithms, it saves a significant amount of online measurement time.

Assessing Temporal Behavior in LIDAR Point Clouds of Urban Environments

NASA Astrophysics Data System (ADS)

Schachtschneider, J.; Schlichting, A.; Brenner, C.

2017-05-01

Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

Landing Site and Traverse Plan Development for Resource Prospector

NASA Technical Reports Server (NTRS)

Elphic, R. C.; Colaprete, A.; Shirley, M.; McGovern, A.; Beyer, R.; Siegler, M. A.

2017-01-01

Resource Prospector (RP) will be the first lunar surface robotic expedition to explore the character and feasibility of in situ resource utilization at the lunar poles. It is aimed at determining where, and how much, hydrogen-bearing and other volatiles are sequestered in polar cold traps. To meet its goals, the mission should land where the likelihood of finding polar volatiles is high [1,2,3]. The operational environment is challenging: very low sun elevations, long shadows cast by even moderate relief, cryogenic subsurface temperatures, unknown regolith properties, and very dynamic sun and Earth communications geometries force a unique approach to landing, traverse design and mission operations.

Resource Prospector Landing Site and Traverse Plan Development

NASA Technical Reports Server (NTRS)

Elphic, R. C.; Colaprete, A.; Shirley, M.; McGovern, A.; Beyer, R.

2016-01-01

Resource Prospector (RP) will be the first lunar surface robotic expedition to explore the character and feasibility of in situ resource utilization at the lunar poles. It is aimed at determining where, and how much, hydrogen-bearing and other volatiles are sequestered in polar cold traps. To meet its goals, the mission should land where the likelihood of finding polar volatiles is high. The operational environment is challenging: very low sun elevations, long shadows cast by even moderate relief, cryogenic subsurface temperatures, unknown regolith properties, and very dynamic sun and Earth communications geometries force a unique approach to landing, traverse design and mission operations.

Landing Site and Traverse Plan Development for Resource Prospector

NASA Technical Reports Server (NTRS)

Elphic, R. C.; Colaprete, A.; Shirley, M.; A.McGovern; Beyer, R.; Siegler, M. A.

2017-01-01

Resource Prospector (RP) will be the first lunar surface robotic expedition to explore the character and feasibility of in situ resource utilization at the lunar poles. It is aimed at determining where, and how much, hydrogen-bearing and other volatiles are sequestered in polar cold traps. To meet its goals, the mission should land where the likelihood of finding polar volatiles is high. The operational environment is challenging: very low sun elevations, long shadows cast by even moderate relief, cryogenic subsurface temperatures, unknown regolith properties, and very dynamic sun and Earth communications geometries force a unique approach to landing, traverse design and mission operations.

Quantum coherence and entanglement control for atom-cavity systems

NASA Astrophysics Data System (ADS)

Shu, Wenchong

Coherence and entanglement play a significant role in the quantum theory. Ideal quantum systems, "closed" to the outside world, remain quantum forever and thus manage to retain coherence and entanglement. Real quantum systems, however, are open to the environment and are therefore susceptible to the phenomenon of decoherence and disentanglement which are major hindrances to the effectiveness of quantum information processing tasks. In this thesis we have theoretically studied the evolution of coherence and entanglement in quantum systems coupled to various environments. We have also studied ways and means of controlling the decay of coherence and entanglement. We have studied the exact qubit entanglement dynamics of some interesting initial states coupled to a high-Q cavity containing zero photon, one photon, two photons and many photons respectively. We have found that an initially correlated environmental state can serve as an enhancer for entanglement decay or generation processes. More precisely, we have demonstrated that the degree of entanglement, including its collapse as well as its revival times, can be significantly modified by the correlated structure of the environmental modes. We have also studied dynamical decoupling (DD) technique --- a prominent strategy of controlling decoherence and preserving entanglement in open quantum systems. We have analyzed several DD control methods applied to qubit systems that can eliminate the system-environment coupling and prolong the quantum coherence time. Particularly, we have proposed a new DD sequence consisting a set of designed control operators that can universally protected an unknown qutrit state against colored phase and amplitude environment noises. In addition, in a non-Markovian regime, we have reformulated the quantum state diffusion (QSD) equation to incorporate the effect of the external control fields. Without any assumptions on the system-environment coupling and the size of environment, we have consistently solved the control dynamics of open quantum systems using this stochastic QSD approach. By implementing the QSD equation, our numerical results have revealed that how the control efficacy depends on the designed time points and shapes of the applied control pulses, and the environment memory time scale.

Parameter identifiability of linear dynamical systems

NASA Technical Reports Server (NTRS)

Glover, K.; Willems, J. C.

1974-01-01

It is assumed that the system matrices of a stationary linear dynamical system were parametrized by a set of unknown parameters. The question considered here is, when can such a set of unknown parameters be identified from the observed data? Conditions for the local identifiability of a parametrization are derived in three situations: (1) when input/output observations are made, (2) when there exists an unknown feedback matrix in the system and (3) when the system is assumed to be driven by white noise and only output observations are made. Also a sufficient condition for global identifiability is derived.

Flying at no mechanical energy cost: disclosing the secret of wandering albatrosses.

PubMed

Sachs, Gottfried; Traugott, Johannes; Nesterova, Anna P; Dell'Omo, Giacomo; Kümmeth, Franz; Heidrich, Wolfgang; Vyssotski, Alexei L; Bonadonna, Francesco

2012-01-01

Albatrosses do something that no other birds are able to do: fly thousands of kilometres at no mechanical cost. This is possible because they use dynamic soaring, a flight mode that enables them to gain the energy required for flying from wind. Until now, the physical mechanisms of the energy gain in terms of the energy transfer from the wind to the bird were mostly unknown. Here we show that the energy gain is achieved by a dynamic flight manoeuvre consisting of a continually repeated up-down curve with optimal adjustment to the wind. We determined the energy obtained from the wind by analysing the measured trajectories of free flying birds using a new GPS-signal tracking method yielding a high precision. Our results reveal an evolutionary adaptation to an extreme environment, and may support recent biologically inspired research on robotic aircraft that might utilize albatrosses' flight technique for engineless propulsion.

Flying at No Mechanical Energy Cost: Disclosing the Secret of Wandering Albatrosses

PubMed Central

Sachs, Gottfried; Traugott, Johannes; Nesterova, Anna P.; Dell'Omo, Giacomo; Kümmeth, Franz; Heidrich, Wolfgang

2012-01-01

Albatrosses do something that no other birds are able to do: fly thousands of kilometres at no mechanical cost. This is possible because they use dynamic soaring, a flight mode that enables them to gain the energy required for flying from wind. Until now, the physical mechanisms of the energy gain in terms of the energy transfer from the wind to the bird were mostly unknown. Here we show that the energy gain is achieved by a dynamic flight manoeuvre consisting of a continually repeated up-down curve with optimal adjustment to the wind. We determined the energy obtained from the wind by analysing the measured trajectories of free flying birds using a new GPS-signal tracking method yielding a high precision. Our results reveal an evolutionary adaptation to an extreme environment, and may support recent biologically inspired research on robotic aircraft that might utilize albatrosses' flight technique for engineless propulsion. PMID:22957014

The structural impact of DNA mismatches

PubMed Central

Rossetti, Giulia; Dans, Pablo D.; Gomez-Pinto, Irene; Ivani, Ivan; Gonzalez, Carlos; Orozco, Modesto

2015-01-01

The structure and dynamics of all the transversion and transition mismatches in three different DNA environments have been characterized by molecular dynamics simulations and NMR spectroscopy. We found that the presence of mismatches produced significant local structural alterations, especially in the case of purine transversions. Mismatched pairs often show promiscuous hydrogen bonding patterns, which interchange among each other in the nanosecond time scale. This therefore defines flexible base pairs, where breathing is frequent, and where distortions in helical parameters are strong, resulting in significant alterations in groove dimension. Even if the DNA structure is plastic enough to absorb the structural impact of the mismatch, local structural changes can be propagated far from the mismatch site, following the expected through-backbone and a previously unknown through-space mechanism. The structural changes related to the presence of mismatches help to understand the different susceptibility of mismatches to the action of repairing proteins. PMID:25820425

Wideband Interferometric Sensing and Imaging Polarimetry

NASA Technical Reports Server (NTRS)

Verdi, James Salvatore; Kessler, Otto; Boerner, Wolfgang-Martin

1996-01-01

Wideband Interferometric Sensing and Imaging Polarimetry (WISIP) has become an important, indispensible tool in wide area military surveillance and global environmental monitoring of the terrestrial and planetary covers. It enables dynamic, real time optimal feature extraction of significant characteristics of desirable targets and/or target sections with simultaneous suppression of undesirable background clutter and propagation path speckle at hitherto unknown clarity and never before achieved quality. WISIP may be adopted to the detection, recognition, and identification (DRI) of any stationary, moving or vibrating targets or distributed scatterer segments versus arbitrary stationary, dynamical changing and/or moving geo-physical/ecological environments, provided the instantaneous 2x2 phasor and 4x4 power density matrices for forward propagation/backward scattering, respectively, can be measured with sufficient accuracy. For example, the DRI of stealthy, dynamically moving inhomogeneous volumetric scatter environments such as precipitation scatter, the ocean/sea/lake surface boundary layers, the littoral coastal surf zones, pack ice and snow or vegetative canopies, dry sands and soils, etc. can now be successfully realized. A comprehensive overview is presented on how these modern high resolution/precision, complete polarimetric co-registered signature sensing and imaging techniques, complemented by full integration of novel navigational electronic tools, such as DGPS, will advance electromagnetic vector wave sensing and imaging towards the limits of physical realization. Various examples utilizing the most recent image data take sets of airborne, space shuttle, and satellite imaging systems demonstrate the utility of WISIP.

Laser-Based Slam with Efficient Occupancy Likelihood Map Learning for Dynamic Indoor Scenes

NASA Astrophysics Data System (ADS)

Li, Li; Yao, Jian; Xie, Renping; Tu, Jinge; Feng, Chen

2016-06-01

Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.

Map generation in unknown environments by AUKF-SLAM using line segment-type and point-type landmarks

NASA Astrophysics Data System (ADS)

Nishihta, Sho; Maeyama, Shoichi; Watanebe, Keigo

2018-02-01

Recently, autonomous mobile robots that collect information at disaster sites are being developed. Since it is difficult to obtain maps in advance in disaster sites, the robots being capable of autonomous movement under unknown environments are required. For this objective, the robots have to build maps, as well as the estimation of self-location. This is called a SLAM problem. In particular, AUKF-SLAM which uses corners in the environment as point-type landmarks has been developed as a solution method so far. However, when the robots move in an environment like a corridor consisting of few point-type features, the accuracy of self-location estimated by the landmark is decreased and it causes some distortions in the map. In this research, we propose AUKF-SLAM which uses walls in the environment as a line segment-type landmark. We demonstrate that the robot can generate maps in unknown environment by AUKF-SLAM, using line segment-type and point-type landmarks.

Numerical and experimental studies of particle flow in a high-pressure boundary-layer wind tunnel

NASA Technical Reports Server (NTRS)

White, B. R.

1984-01-01

The approach was to simulate the surface environment of Venus as closely as practicable and to conduct experiments to determine threshold wind speeds, particle flux, particle velocities, and the characteristics of various aeolian bedforms. The Venus Wind Tunnel (VWT) is described and the experimental procedures that were developed to make the high-pressure wind tunnel measurements are presented. In terrestrial simulations of aeolian activity, it is possible to conduct experiments under pressures and temperatures found in natural environments. Because of the high pressures and temperatures, Venusian simulations are difficult to achieve in this regard. Consequently, extrapolation of results to Venue potentially involves unknown factors. The experimental rationale was developed in the following way: The VWT enables the density of the Venusian atmosphere to be reproduced. Density is the principal atmospheric property for governing saltation threshold, particle flux, and the ballistics of airborne particles (equivalent density maintains dynamic similarity of gas flow). When operated at or near Earth's ambient temperature, VWT achieves Venusian atmospheric density at pressures of about 30 bar, or about one third less than those on Venus, although still maintaining dynamic similarity to Venus.

Parallel Simulation of Three-Dimensional Free Surface Fluid Flow Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

BAER,THOMAS A.; SACKINGER,PHILIP A.; SUBIA,SAMUEL R.

1999-10-14

Simulation of viscous three-dimensional fluid flow typically involves a large number of unknowns. When free surfaces are included, the number of unknowns increases dramatically. Consequently, this class of problem is an obvious application of parallel high performance computing. We describe parallel computation of viscous, incompressible, free surface, Newtonian fluid flow problems that include dynamic contact fines. The Galerkin finite element method was used to discretize the fully-coupled governing conservation equations and a ''pseudo-solid'' mesh mapping approach was used to determine the shape of the free surface. In this approach, the finite element mesh is allowed to deform to satisfy quasi-staticmore » solid mechanics equations subject to geometric or kinematic constraints on the boundaries. As a result, nodal displacements must be included in the set of unknowns. Other issues discussed are the proper constraints appearing along the dynamic contact line in three dimensions. Issues affecting efficient parallel simulations include problem decomposition to equally distribute computational work among a SPMD computer and determination of robust, scalable preconditioners for the distributed matrix systems that must be solved. Solution continuation strategies important for serial simulations have an enhanced relevance in a parallel coquting environment due to the difficulty of solving large scale systems. Parallel computations will be demonstrated on an example taken from the coating flow industry: flow in the vicinity of a slot coater edge. This is a three dimensional free surface problem possessing a contact line that advances at the web speed in one region but transitions to static behavior in another region. As such, a significant fraction of the computational time is devoted to processing boundary data. Discussion focuses on parallel speed ups for fixed problem size, a class of problems of immediate practical importance.« less

Attitude determination using an adaptive multiple model filtering Scheme

NASA Technical Reports Server (NTRS)

Lam, Quang; Ray, Surendra N.

1995-01-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Astrophysics Data System (ADS)

Lam, Quang; Ray, Surendra N.

1995-05-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Unified sensor management in unknown dynamic clutter

NASA Astrophysics Data System (ADS)

Mahler, Ronald; El-Fallah, Adel

2010-04-01

In recent years the first author has developed a unified, computationally tractable approach to multisensor-multitarget sensor management. This approach consists of closed-loop recursion of a PHD or CPHD filter with maximization of a "natural" sensor management objective function called PENT (posterior expected number of targets). In this paper we extend this approach so that it can be used in unknown, dynamic clutter backgrounds.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

PubMed Central

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently. PMID:28255297

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

PubMed

Ni, Jianjun; Wu, Liuying; Shi, Pengfei; Yang, Simon X

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Simulation-based Extraction of Key Material Parameters from Atomic Force Microscopy

NASA Astrophysics Data System (ADS)

Alsafi, Huseen; Peninngton, Gray

Models for the atomic force microscopy (AFM) tip and sample interaction contain numerous material parameters that are often poorly known. This is especially true when dealing with novel material systems or when imaging samples that are exposed to complicated interactions with the local environment. In this work we use Monte Carlo methods to extract sample material parameters from the experimental AFM analysis of a test sample. The parameterized theoretical model that we use is based on the Virtual Environment for Dynamic AFM (VEDA) [1]. The extracted material parameters are then compared with the accepted values for our test sample. Using this procedure, we suggest a method that can be used to successfully determine unknown material properties in novel and complicated material systems. We acknowledge Fisher Endowment Grant support from the Jess and Mildred Fisher College of Science and Mathematics,Towson University.

Firing rate dynamics in the hippocampus induced by trajectory learning.

PubMed

Ji, Daoyun; Wilson, Matthew A

2008-04-30

The hippocampus is essential for spatial navigation, which may involve sequential learning. However, how the hippocampus encodes new sequences in familiar environments is unknown. To study the impact of novel spatial sequences on the activity of hippocampal neurons, we monitored hippocampal ensembles while rats learned to switch from two familiar trajectories to a new one in a familiar environment. Here, we show that this novel spatial experience induces two types of changes in firing rates, but not locations of hippocampal place cells. First, place-cell firing rates on the two familiar trajectories start to change before the actual behavioral switch to the new trajectory. Second, repeated exposure on the new trajectory is associated with an increased dependence of place-cell firing rates on immediate past locations. The result suggests that sequence encoding in the hippocampus may involve integration of information about the recent past into current state.

Emergence of polysaccharide membrane walls through macro-space partitioning via interfacial instability.

PubMed

Okeyoshi, Kosuke; Okajima, Maiko K; Kaneko, Tatsuo

2017-07-21

Living organisms in drying environments build anisotropic structures and exhibit directionality through self-organization of biopolymers. However, the process of macro-scale assembly is still unknown. Here, we introduce a dissipative structure through a non-equilibrium process between hydration and deposition in the drying of a polysaccharide liquid crystalline solution. By controlling the geometries of the evaporation front in a limited space, multiple nuclei emerge to grow vertical membrane walls with macroscopic orientation. Notably, the membranes are formed through rational orientation of rod-like microassemblies along the dynamic three-phase contact line. Additionally, in the non-equilibrium state, a dissipative structure is ultimately immobilized as a macroscopically partitioned space by multiple vertical membranes. We foresee that such oriented membranes will be applicable to soft biomaterials with direction controllability, and the macroscopic space partitionings will aid in the understanding of the space recognition ability of natural products under drying environments.

Firing Rate Dynamics in the Hippocampus Induced by Trajectory Learning

PubMed Central

Wilson, Matthew A.

2008-01-01

The hippocampus is essential for spatial navigation, which may involve sequential learning. However, how the hippocampus encodes new sequences in familiar environments is unknown. To study the impact of novel spatial sequences on the activity of hippocampal neurons, we monitored hippocampal ensembles while rats learned to switch from two familiar trajectories to a new one in a familiar environment. Here, we show that this novel spatial experience induces two types of changes in firing rates, but not locations of hippocampal place cells. First, place-cell firing rates on the two familiar trajectories start to change before the actual behavioral switch to the new trajectory. Second, repeated exposure on the new trajectory is associated with an increased dependence of place-cell firing rates on immediate past locations. The result suggests that sequence encoding in the hippocampus may involve integration of information about the recent past into current state. PMID:18448645

A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims.

PubMed

Doroodgar, Barzin; Liu, Yugang; Nejat, Goldie

2014-12-01

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing a human operator to cooperate and share such tasks with a rescue robot as navigation, exploration, and victim identification. In this paper, we present a unique hierarchical reinforcement learning-based semi-autonomous control architecture for rescue robots operating in cluttered and unknown urban search and rescue (USAR) environments. The aim of the controller is to enable a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A direction-based exploration technique is integrated in the controller to expand the search area of the robot via the classification of regions and the rubble piles within these regions. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed HRL-based semi-autonomous controller to unknown cluttered scenes with different sizes and varying types of configurations.

Coupled dynamics of body mass and population growth in response to environmental change.

PubMed

Ozgul, Arpat; Childs, Dylan Z; Oli, Madan K; Armitage, Kenneth B; Blumstein, Daniel T; Olson, Lucretia E; Tuljapurkar, Shripad; Coulson, Tim

2010-07-22

Environmental change has altered the phenology, morphological traits and population dynamics of many species. However, the links underlying these joint responses remain largely unknown owing to a paucity of long-term data and the lack of an appropriate analytical framework. Here we investigate the link between phenotypic and demographic responses to environmental change using a new methodology and a long-term (1976-2008) data set from a hibernating mammal (the yellow-bellied marmot) inhabiting a dynamic subalpine habitat. We demonstrate how earlier emergence from hibernation and earlier weaning of young has led to a longer growing season and larger body masses before hibernation. The resulting shift in both the phenotype and the relationship between phenotype and fitness components led to a decline in adult mortality, which in turn triggered an abrupt increase in population size in recent years. Direct and trait-mediated effects of environmental change made comparable contributions to the observed marked increase in population growth. Our results help explain how a shift in phenology can cause simultaneous phenotypic and demographic changes, and highlight the need for a theory integrating ecological and evolutionary dynamics in stochastic environments.

Coupled dynamics of body mass and population growth in response to environmental change

PubMed Central

Ozgul, Arpat; Childs, Dylan Z.; Oli, Madan K.; Armitage, Kenneth B.; Blumstein, Daniel T.; Olson, Lucretia E.; Tuljapurkar, Shripad; Coulson, Tim

2017-01-01

Environmental change has altered the phenology, morphological traits and population dynamics of many species1,2. However, the links underlying these joint responses remain largely unknown due to a paucity of long-term data and the lack of an appropriate analytical framework3. Here, we investigate the link between phenotypic and demographic responses to environmental change using a novel methodology and an exceptional long-term (1976–2008) dataset from a hibernating mammal (the yellow-bellied marmot) inhabiting a dynamic subalpine habitat. We demonstrate how earlier emergence from hibernation and earlier weaning of young has led to a longer growing season and larger body masses prior to hibernation. The resulting shift in both the phenotype and the relationship between phenotype and fitness components led to a decline in adult mortality, which in turn triggered an abrupt increase in population size in recent years. Direct and trait-mediated effects of environmental change had comparable contributions to the observed dramatic increase in population growth. Our results help explain how a shift in phenology can cause simultaneous phenotypic and demographic changes, and highlight the need for a theory integrating ecological and evolutionary dynamics in stochastic environments4,5. PMID:20651690

Robot navigation research at CESAR (Center for Engineering Systems Advanced Research)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnett, D.L.; de Saussure, G.; Pin, F.G.

1989-01-01

A considerable amount of work has been reported on the problem of robot navigation in known static terrains. Algorithms have been proposed and implemented to search for an optimum path to the goal, taking into account the finite size and shape of the robot. Not as much work has been reported on robot navigation in unknown, unstructured, or dynamic environments. A robot navigating in an unknown environment must explore with its sensors, construct an abstract representation of its global environment to plan a path to the goal, and update or revise its plan based on accumulated data obtained and processedmore » in real-time. The core of the navigation program for the CESAR robots is a production system developed on the expert-system-shell CLIPS which runs on an NCUBE hypercube on board the robot. The production system can call on C-compiled navigation procedures. The production rules can read the sensor data and address the robot's effectors. This architecture was found efficient and flexible for the development and testing of the navigation algorithms; however, in order to process intelligently unexpected emergencies, it was found necessary to be able to control the production system through externally generated asynchronous data. This led to the design of a new asynchronous production system, APS, which is now being developed on the robot. This paper will review some of the navigation algorithms developed and tested at CESAR and will discuss the need for the new APS and how it is being integrated into the robot architecture. 18 refs., 3 figs., 1 tab.« less

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Control of Complex Dynamic Systems by Neural Networks

NASA Technical Reports Server (NTRS)

Spall, James C.; Cristion, John A.

1993-01-01

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Utilizing Molecular Dynamics ' Multipotent Methodologies to Measure Microscopic Motions of DNA Molecules: A Magniloquent Manuscript On DNA's Means and Mannerisms

NASA Astrophysics Data System (ADS)

Kingsland, Addie

DNA is an amazing molecule which is the basic template for all genetics. It is the primary molecule for storing biological information, and has many applications in nanotechnology. Double-stranded DNA may contain mismatched base pairs beyond the Watson-Crick pairs guanine-cytosine and adenine-thymine. To date, no one has found a physical property of base pair mismatches which describes the behavior of naturally occurring mismatch repair enzymes. Many materials properties of DNA are also unknown, for instance, when pulling DNA in different configurations, different energy differences are observed with no obvious reason why. DNA mismatches also affect their local environment, for instance changing the quantum yield of nearby azobenzene moieties. We utilize molecular dynamics computer simulations to study the structure and dynamics for both matched and mismatched base pairs, within both biological and materials contexts, and in both equilibrium and biased dynamics. We show that mismatched pairs shift further in the plane normal to the DNA strand and are more likely to exhibit non-canonical structures, including the e-motif. Base pair mismatches alter their local environment, affecting the trans- to cis- photoisomerization quantum yield of azobenzene, as well as increasing the likelihood of observing the e-motif. We also show that by using simulated data, we can give new insights on theoretical models to calculate the energetics of pulling DNA strands apart. These results, all relatively inexpensive on modern computer hardware, can help guide the design of DNA-based nanotechnologies, as well as give new insights into the functioning of mismatch repair systems in cancer prevention.

Calcium transient prevalence across the dendritic arbour predicts place field properties.

PubMed

Sheffield, Mark E J; Dombeck, Daniel A

2015-01-08

Establishing the hippocampal cellular ensemble that represents an animal's environment involves the emergence and disappearance of place fields in specific CA1 pyramidal neurons, and the acquisition of different spatial firing properties across the active population. While such firing flexibility and diversity have been linked to spatial memory, attention and task performance, the cellular and network origin of these place cell features is unknown. Basic integrate-and-fire models of place firing propose that such features result solely from varying inputs to place cells, but recent studies suggest instead that place cells themselves may play an active role through regenerative dendritic events. However, owing to the difficulty of performing functional recordings from place cell dendrites, no direct evidence of regenerative dendritic events exists, leaving any possible connection to place coding unknown. Using multi-plane two-photon calcium imaging of CA1 place cell somata, axons and dendrites in mice navigating a virtual environment, here we show that regenerative dendritic events do exist in place cells of behaving mice, and, surprisingly, their prevalence throughout the arbour is highly spatiotemporally variable. Furthermore, we show that the prevalence of such events predicts the spatial precision and persistence or disappearance of place fields. This suggests that the dynamics of spiking throughout the dendritic arbour may play a key role in forming the hippocampal representation of space.

Calcium transient prevalence across the dendritic arbor predicts place field properties

PubMed Central

Sheffield, Mark E. J.; Dombeck, Daniel A.

2014-01-01

Establishing the hippocampal cellular ensemble that represents an animal’s environment involves the emergence and disappearance of place fields in specific CA1 pyramidal neurons1–4, and the acquisition of different spatial firing properties across the active population5. While such firing flexibility and diversity have been linked to spatial memory, attention and task performance6,7, the cellular and network origin of these place cell features is unknown. Basic integrate-and-fire models of place firing propose that such features result solely from varying inputs to place cells8,9, but recent studies3,10 instead suggest that place cells themselves may play an active role through regenerative dendritic events. However, due to the difficulty of performing functional recordings from place cell dendrites, no direct evidence of regenerative dendritic events exists, leaving any possible connection to place coding unknown. Using multi-plane two-photon calcium imaging of CA1 place cell somata, axons, and dendrites in mice navigating a virtual environment, we show that regenerative dendritic events do exist in place cells of behaving mice and, surprisingly, their prevalence throughout the arbor is highly spatiotemporally variable. Further, we show that the prevalence of such events predicts the spatial precision and persistence or disappearance of place fields. This suggests that the dynamics of spiking throughout the dendritic arbor may play a key role in forming the hippocampal representation of space. PMID:25363782

Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai

2017-03-01

This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.

X-Ray Spectroscopy of Photoionized Plasmas

NASA Technical Reports Server (NTRS)

Kallman, Tim

2008-01-01

Spectroscopy allows study of sources on small spatial scales, and can provide detailed diagnostic information about elemental abundances, temperature, density and gas dynamics. For compact sources such as accreting black holes in active galactic nuclei (AGN) and X-ray binaries X-ray spectra provide truly unique insight. Observations using Chandra and XMM have revealed components of gas in these systems which were previously unknown or poorly studied. Interpretation of these data presents modeling and analysis challenges, and requires an understanding of atomic physics, ionization and spectrum formation in a radiation-dominated environment. In this talk I will discuss examples, and how they have contributed to our understanding of accreting sources and the nearby gas.

Panarchy use in environmental science for risk and resilience planning

USGS Publications Warehouse

Angeler, David G.; Allen, Craig R.; Garmestani, Ahjond S.; Gunderson, Lance H.; Linkov, Igor

2016-01-01

Environmental sciences have an important role in informing sustainable management of built environments by providing insights about the drivers and potentially negative impacts of global environmental change. Here, we discuss panarchy theory, a multi-scale hierarchical concept that accounts for the dynamism of complex socio-ecological systems, especially for those systems with strong cross-scale feedbacks. The idea of panarchy underlies much of system resilience, focusing on how systems respond to known and unknown threats. Panarchy theory can provide a framework for qualitative and quantitative research and application in the environmental sciences, which can in turn inform the ongoing efforts in socio-technical resilience thinking and adaptive and transformative approaches to management.

Adaptive Actor-Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2016-01-01

This paper is concerned with the problem of integral sliding-mode control for a class of nonlinear systems with input disturbances and unknown nonlinear terms through the adaptive actor-critic (AC) control method. The main objective is to design a sliding-mode control methodology based on the adaptive dynamic programming (ADP) method, so that the closed-loop system with time-varying disturbances is stable and the nearly optimal performance of the sliding-mode dynamics can be guaranteed. In the first step, a neural network (NN)-based observer and a disturbance observer are designed to approximate the unknown nonlinear terms and estimate the input disturbances, respectively. Based on the NN approximations and disturbance estimations, the discontinuous part of the sliding-mode control is constructed to eliminate the effect of the disturbances and attain the expected equivalent sliding-mode dynamics. Then, the ADP method with AC structure is presented to learn the optimal control for the sliding-mode dynamics online. Reconstructed tuning laws are developed to guarantee the stability of the sliding-mode dynamics and the convergence of the weights of critic and actor NNs. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

PubMed Central

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments. PMID:22666056

Adapting an ant colony metaphor for multi-robot chemical plume tracing.

PubMed

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.

Dynamic Distribution and Layouting of Model-Based User Interfaces in Smart Environments

NASA Astrophysics Data System (ADS)

Roscher, Dirk; Lehmann, Grzegorz; Schwartze, Veit; Blumendorf, Marco; Albayrak, Sahin

The developments in computer technology in the last decade change the ways of computer utilization. The emerging smart environments make it possible to build ubiquitous applications that assist users during their everyday life, at any time, in any context. But the variety of contexts-of-use (user, platform and environment) makes the development of such ubiquitous applications for smart environments and especially its user interfaces a challenging and time-consuming task. We propose a model-based approach, which allows adapting the user interface at runtime to numerous (also unknown) contexts-of-use. Based on a user interface modelling language, defining the fundamentals and constraints of the user interface, a runtime architecture exploits the description to adapt the user interface to the current context-of-use. The architecture provides automatic distribution and layout algorithms for adapting the applications also to contexts unforeseen at design time. Designers do not specify predefined adaptations for each specific situation, but adaptation constraints and guidelines. Furthermore, users are provided with a meta user interface to influence the adaptations according to their needs. A smart home energy management system serves as running example to illustrate the approach.

Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.

PubMed

Sokoloski, Sacha

2017-09-01

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

Nonholonomic Ofject Tracking with Optical Sensors and Ofject Recognition Feedback

NASA Technical Reports Server (NTRS)

Goddard, R. E.; Hadaegh, F.

1994-01-01

Robotic controllers frequently operate under constraints. Often, the constraints are imperfectly or completely unknown. In this paper, the Lagrangian dynamics of a planar robot arm are expressed as a function of a globally unknown consraint.

High-predation habitats affect the social dynamics of collective exploration in a shoaling fish.

PubMed

Ioannou, Christos C; Ramnarine, Indar W; Torney, Colin J

2017-05-01

Collective decisions play a major role in the benefits that animals gain from living in groups. Although the mechanisms of how groups collectively make decisions have been extensively researched, the response of within-group dynamics to ecological conditions is virtually unknown, despite adaptation to the environment being a cornerstone in biology. We investigate how within-group interactions during exploration of a novel environment are shaped by predation, a major influence on the behavior of prey species. We tested guppies ( Poecilia reticulata ) from rivers varying in predation risk under controlled laboratory conditions and find the first evidence of differences in group interactions between animals adapted to different levels of predation. Fish from high-predation habitats showed the strongest negative relationship between initiating movements and following others, which resulted in less variability in the total number of movements made between individuals. This relationship between initiating movements and following others was associated with differentiation into initiators and followers, which was only observed in fish from high-predation rivers. The differentiation occurred rapidly, as trials lasted 5 min, and was related to shoal cohesion, where more diverse groups from high-predation habitats were more cohesive. Our results show that even within a single species over a small geographical range, decision-making in a social context can vary with local ecological factors.

Method for detection of selected chemicals in an open environment

NASA Technical Reports Server (NTRS)

Duong, Tuan (Inventor); Ryan, Margaret (Inventor)

2009-01-01

The present invention relates to a space-invariant independent component analysis and electronic nose for detection of selective chemicals in an unknown environment, and more specifically, an approach to analysis of sensor responses to mixtures of unknown chemicals by an electronic nose in an open and changing environment. It is intended to fill the gap between an alarm, which has little or no ability to distinguish among chemical compounds causing a response, and an analytical instrument, which can distinguish all compounds present but with no real-time or continuous event monitoring ability.

Decentralized control algorithms of a group of vehicles in 2D space

NASA Astrophysics Data System (ADS)

Pshikhopov, V. K.; Medvedev, M. Y.; Fedorenko, R. V.; Gurenko, B. V.

2017-02-01

The problem of decentralized control of group of robots, described by kinematic and dynamic equations of motion in the plane, is considered. Group performs predetermined rectangular area passing at a fixed speed, keeping the line and a uniform distribution. The environment may contain a priori unknown moving or stationary obstacles. Decentralized control algorithms, based on the formation of repellers in the state space of robots, are proposed. These repellers form repulsive forces generated by dynamic subsystems that extend the state space of robots. These repulsive forces are dynamic functions of distances and velocities of robots in the area of operation of the group. The process of formation of repellers allows to take into account the dynamic properties of robots, such as the maximum speed and acceleration. The robots local control law formulas are derived based on positionally-trajectory control method, which allows to operate with non-linear models. Lyapunov function in the form of a quadratic function of the state variables is constructed to obtain a nonlinear closed-loop control system. Due to the fact that a closed system is decomposed into two independent subsystems Lyapunov function is also constructed as two independent functions. Numerical simulation of the motion of a group of five robots is presented. In this simulation obstacles are presented by the boundaries of working area and a movable object of a given radius, moving rectilinear and uniform. Obstacle speed is comparable to the speeds of the robots in a group. The advantage of the proposed method is ensuring the stability of the trajectories and consideration of the limitations on the speed and acceleration at the trajectory planning stage. Proposed approach can be used for more general robots' models, including robots in the three-dimensional environment.

Interplay between population firing stability and single neuron dynamics in hippocampal networks

PubMed Central

Slomowitz, Edden; Styr, Boaz; Vertkin, Irena; Milshtein-Parush, Hila; Nelken, Israel; Slutsky, Michael; Slutsky, Inna

2015-01-01

Neuronal circuits' ability to maintain the delicate balance between stability and flexibility in changing environments is critical for normal neuronal functioning. However, to what extent individual neurons and neuronal populations maintain internal firing properties remains largely unknown. In this study, we show that distributions of spontaneous population firing rates and synchrony are subject to accurate homeostatic control following increase of synaptic inhibition in cultured hippocampal networks. Reduction in firing rate triggered synaptic and intrinsic adaptive responses operating as global homeostatic mechanisms to maintain firing macro-stability, without achieving local homeostasis at the single-neuron level. Adaptive mechanisms, while stabilizing population firing properties, reduced short-term facilitation essential for synaptic discrimination of input patterns. Thus, invariant ongoing population dynamics emerge from intrinsically unstable activity patterns of individual neurons and synapses. The observed differences in the precision of homeostatic control at different spatial scales challenge cell-autonomous theory of network homeostasis and suggest the existence of network-wide regulation rules. DOI: http://dx.doi.org/10.7554/eLife.04378.001 PMID:25556699

Enzymatic dynamics into the Eisenia fetida (Savigny, 1826) gut during vermicomposting of coffee husk and market waste in a tropical environment.

PubMed

Ordoñez-Arévalo, Berenice; Guillén-Navarro, Karina; Huerta, Esperanza; Cuevas, Raúl; Calixto-Romo, M Angeles

2018-01-01

Epigeic worms modify microbial communities through their digestive processes, thereby influencing the decomposition of organic matter in vermicomposting systems. Nevertheless, the enzyme dynamics within the gut of tropically adapted earthworms is unknown, and the enzymes involved have not been simultaneously studied. The activities of 19 hydrolytic enzymes within three different sections of the intestine of Eisenia fetida were determined over a fasting period and at 24 h and 30, 60, and 90 days of vermicomposting, and data were evaluated by multivariate analyses. There were found positive correlations between the maximal activity of glycosyl hydrolases and one esterase with the anterior intestine (coincident with the reduction of hemicellulose in the substrate) and the activity of the protease α-chymotrypsin with posterior intestine. The results suggest that activities of enzymes change in a coordinated manner within each gut section, probably influenced by selective microbial enzyme enrichment and by the availability of nutrients throughout vermicomposting.

Psychophysical evidence for auditory motion parallax.

PubMed

Genzel, Daria; Schutte, Michael; Brimijoin, W Owen; MacNeilage, Paul R; Wiegrebe, Lutz

2018-04-17

Distance is important: From an ecological perspective, knowledge about the distance to either prey or predator is vital. However, the distance of an unknown sound source is particularly difficult to assess, especially in anechoic environments. In vision, changes in perspective resulting from observer motion produce a reliable, consistent, and unambiguous impression of depth known as motion parallax. Here we demonstrate with formal psychophysics that humans can exploit auditory motion parallax, i.e., the change in the dynamic binaural cues elicited by self-motion, to assess the relative depths of two sound sources. Our data show that sensitivity to relative depth is best when subjects move actively; performance deteriorates when subjects are moved by a motion platform or when the sound sources themselves move. This is true even though the dynamic binaural cues elicited by these three types of motion are identical. Our data demonstrate a perceptual strategy to segregate intermittent sound sources in depth and highlight the tight interaction between self-motion and binaural processing that allows assessment of the spatial layout of complex acoustic scenes.

Non-Contact Temperature Requirements (NCTM) for drop and bubble physics

NASA Technical Reports Server (NTRS)

Hmelo, Anthony B.; Wang, Taylor G.

1989-01-01

Many of the materials research experiments to be conducted in the Space Processing program require a non-contaminating method of manipulating and controlling weightless molten materials. In these experiments, the melt is positioned and formed within a container without physically contacting the container's wall. An acoustic method, which was developed by Professor Taylor G. Wang before coming to Vanderbilt University from the Jet Propulsion Laboratory, has demonstrated the capability of positioning and manipulating room temperature samples. This was accomplished in an earth-based laboratory with a zero-gravity environment of short duration. However, many important facets of high temperature containerless processing technology have not been established yet, nor can they be established from the room temperature studies, because the details of the interaction between an acoustic field an a molten sample are largely unknown. Drop dynamics, bubble dynamics, coalescence behavior of drops and bubbles, electromagnetic and acoustic levitation methods applied to molten metals, and thermal streaming are among the topics discussed.

Variable and complex food web structures revealed by exploring missing trophic links between birds and biofilm.

PubMed

Kuwae, Tomohiro; Miyoshi, Eiichi; Hosokawa, Shinya; Ichimi, Kazuhiko; Hosoya, Jun; Amano, Tatsuya; Moriya, Toshifumi; Kondoh, Michio; Ydenberg, Ronald C; Elner, Robert W

2012-04-01

Food webs are comprised of a network of trophic interactions and are essential to elucidating ecosystem processes and functions. However, the presence of unknown, but critical networks hampers understanding of complex and dynamic food webs in nature. Here, we empirically demonstrate a missing link, both critical and variable, by revealing that direct predator-prey relationships between shorebirds and biofilm are widespread and mediated by multiple ecological and evolutionary determinants. Food source mixing models and energy budget estimates indicate that the strength of the missing linkage is dependent on predator traits (body mass and foraging action rate) and the environment that determines food density. Morphological analyses, showing that smaller bodied species possess more developed feeding apparatus to consume biofilm, suggest that the linkage is also phylogenetically dependent and affords a compelling re-interpretation of niche differentiation. We contend that exploring missing links is a necessity for revealing true network structure and dynamics. © 2012 Blackwell Publishing Ltd/CNRS.

Altered Cytoskeleton as a Mitochondrial Decay Signature in the Retinal Pigment Epithelium

PubMed Central

Sripathi, Srinivasa R.; He, Weilue; Sylvester, O’Donnell; Neksumi, Musa; Um, Ji-Yeon; Dluya, Thagriki; Bernstein, Paul S.; Jahng, Wan Jin

2016-01-01

Mitochondria mediate energy metabolism, apoptosis, and aging, while mitochondrial disruption leads to age-related diseases that include age-related macular degeneration (AMD). Descriptions of mitochondrial morphology have been non-systematic and qualitative, due to lack of knowledge on the molecular mechanism of mitochondrial dynamics. The current study analyzed mitochondrial size, shape, and position quantitatively in retinal pigment epithelial cells (RPE) using a systematic computational model to suggest mitochondrial trafficking under oxidative environment. Our previous proteomic study suggested that prohibitin is a mitochondrial decay biomarker in the RPE. The current study examined the prohibitin interactome map using immunoprecipitation data to determine the indirect signaling on cytoskeletal changes and transcriptional regulation by prohibitin. Immunocytochemistry and immunoprecipitation demonstrated that there is a positive correlation between mitochondrial changes and altered filaments as well as prohibitin interactions with kinesin and unknown proteins in the RPE. Specific cytoskeletal and nuclear protein-binding mechanisms may exist to regulate prohibitin-mediated reactions as key elements, including vimentin and p53, to control apoptosis in mitochondria and the nucleus. Prohibitin may regulate mitochondrial trafficking through unknown proteins that include 110 kDa protein with myosin head domain and 88 kDa protein with cadherin repeat domain. Altered cytoskeleton may represent a mitochondrial decay signature in the RPE. The current study suggests that mitochondrial dynamics and cytoskeletal changes are critical for controlling mitochondrial distribution and function. Further, imbalance of retrograde vs. anterograde mitochondrial trafficking may initiate the pathogenic reaction in adult-onset neurodegenerative diseases. PMID:27029380

Altered Cytoskeleton as a Mitochondrial Decay Signature in the Retinal Pigment Epithelium.

PubMed

Sripathi, Srinivas R; He, Weilue; Sylvester, O'Donnell; Neksumi, Musa; Um, Ji-Yeon; Dluya, Thagriki; Bernstein, Paul S; Jahng, Wan Jin

2016-06-01

Mitochondria mediate energy metabolism, apoptosis, and aging, while mitochondrial disruption leads to age-related diseases that include age-related macular degeneration. Descriptions of mitochondrial morphology have been non-systematic and qualitative, due to lack of knowledge on the molecular mechanism of mitochondrial dynamics. The current study analyzed mitochondrial size, shape, and position quantitatively in retinal pigment epithelial cells (RPE) using a systematic computational model to suggest mitochondrial trafficking under oxidative environment. Our previous proteomic study suggested that prohibitin is a mitochondrial decay biomarker in the RPE. The current study examined the prohibitin interactome map using immunoprecipitation data to determine the indirect signaling on cytoskeletal changes and transcriptional regulation by prohibitin. Immunocytochemistry and immunoprecipitation demonstrated that there is a positive correlation between mitochondrial changes and altered filaments as well as prohibitin interactions with kinesin and unknown proteins in the RPE. Specific cytoskeletal and nuclear protein-binding mechanisms may exist to regulate prohibitin-mediated reactions as key elements, including vimentin and p53, to control apoptosis in mitochondria and the nucleus. Prohibitin may regulate mitochondrial trafficking through unknown proteins that include 110 kDa protein with myosin head domain and 88 kDa protein with cadherin repeat domain. Altered cytoskeleton may represent a mitochondrial decay signature in the RPE. The current study suggests that mitochondrial dynamics and cytoskeletal changes are critical for controlling mitochondrial distribution and function. Further, imbalance of retrograde versus anterograde mitochondrial trafficking may initiate the pathogenic reaction in adult-onset neurodegenerative diseases.

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaosmore » of PMSM and show the effectiveness and robustness of the proposed method.« less

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.

PubMed

Luo, Shaohua

2014-09-01

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

Automated adaptive inference of phenomenological dynamical models.

PubMed

Daniels, Bryan C; Nemenman, Ilya

2015-08-21

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.

Automated adaptive inference of phenomenological dynamical models

PubMed Central

Daniels, Bryan C.; Nemenman, Ilya

2015-01-01

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved. PMID:26293508

Towards fenceless boundaries for solar powered insect biobots.

PubMed

Latif, Tahmid; Whitmire, Eric; Novak, Tristan; Bozkurt, Alper

2014-01-01

Demonstration of remote navigation with instrumented insects, such as the Madagascar Hissing Cockroach, Gromphadorhina portentosa, has enabled the concept of biobotic agents for search and rescue missions and environmental monitoring applications. The biobots can form the nodes of a mobile sensor network to be established, for example, in unknown and dynamic environments after natural disasters to pinpoint surviving victims. We demonstrate here, for the first time, the concept of an invisible fence for insect biobots with an ultimate goal of keeping insect biobots within a certain distance of each other or a base station to ensure a reliable wireless network. For extended mission durations, this fenceless boundary would also be used to guide insects towards light sources for autonomous solar charging of their on-board batteries.

Development of force adaptation during childhood.

PubMed

Konczak, Jürgen; Jansen-Osmann, Petra; Kalveram, Karl-Theodor

2003-03-01

Humans learn to make reaching movements in novel dynamic environments by acquiring an internal motor model of their limb dynamics. Here, the authors investigated how 4- to 11-year-old children (N = 39) and adults (N = 7) adapted to changes in arm dynamics, and they examined whether those data support the view that the human brain acquires inverse dynamics models (IDM) during development. While external damping forces were applied, the children learned to perform goal-directed forearm flexion movements. After changes in damping, all children showed kinematic aftereffects indicative of a neural controller that still attempted to compensate the no longer existing damping force. With increasing age, the number of trials toward complete adaptation decreased. When damping was present, forearm paths were most perturbed and most variable in the youngest children but were improved in the older children. The findings indicate that the neural representations of limb dynamics are less precise in children and less stable in time than those of adults. Such controller instability might be a primary cause of the high kinematic variability observed in many motor tasks during childhood. Finally, the young children were not able to update those models at the same rate as the older children, who, in turn, adapted more slowly than adults. In conclusion, the ability to adapt to unknown forces is a developmental achievement. The present results are consistent with the view that the acquisition and modification of internal models of the limb dynamics form the basis of that adaptive process.

Visualizing the response of a gut bacterial population to antibiotic perturbations

NASA Astrophysics Data System (ADS)

Schlomann, Brandon; Wiles, Travis; Guillemin, Karen; Parthasarathy, Raghuveer

Each of our intestines is home to a vast ecosystem composed of trillions of bacteria in a dynamic environment. Bacterial communities face fluctuations in nutrient influx, invasions by new microbes, physical disturbances from peristalsis, and, perhaps, the arrival of antibiotic drugs. Metagenomic profiling has shown that antibiotic treatments can cause major changes in the composition of species present in the gut, at timescales shorter than a day. How this happens is unknown, as these dynamics have never been observed directly. I'll present recent work that addresses this by using well-defined microbial communities in a model organism, the zebrafish. Light Sheet Fluorescence Microscopy is used to image a commensal species of Vibrioresponding to antibiotic perturbations in the guts of live, larval fish. We find that sub-lethal concentrations of different classes of antibiotics induce similar physical responses in Vibrio, namely filamentation and reduction of motility. The arrested bacteria then aggregate and can be ejected via peristalsis, resulting in large population collapses. These observations suggest that antibiotics can cause large disruptions to gut ecosystems even in low concentrations, and that physical processes may be important drivers of response dynamics.

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai; Zhang, Huaguang

2016-05-01

An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

Development of autonomous grasping and navigating robot

NASA Astrophysics Data System (ADS)

Kudoh, Hiroyuki; Fujimoto, Keisuke; Nakayama, Yasuichi

2015-01-01

The ability to find and grasp target items in an unknown environment is important for working robots. We developed an autonomous navigating and grasping robot. The operations are locating a requested item, moving to where the item is placed, finding the item on a shelf or table, and picking the item up from the shelf or the table. To achieve these operations, we designed the robot with three functions: an autonomous navigating function that generates a map and a route in an unknown environment, an item position recognizing function, and a grasping function. We tested this robot in an unknown environment. It achieved a series of operations: moving to a destination, recognizing the positions of items on a shelf, picking up an item, placing it on a cart with its hand, and returning to the starting location. The results of this experiment show the applicability of reducing the workforce with robots.

Streptococcus massiliensis in the human mouth: a phylogenetic approach for the inference of bacterial habitats.

PubMed

Póntigo, F; Silva, C; Moraga, M; Flores, S V

2015-12-29

Streptococcus is a diverse bacterial lineage. Species of this genus occupy a myriad of environments inside humans and other animals. Despite the elucidation of several of these habitats, many remain to be identified. Here, we explore a methodological approach to reveal unknown bacterial environments. Specifically, we inferred the phylogeny of the Mitis group by analyzing the sequences of eight genes. In addition, information regarding habitat use of species belonging to this group was obtained from the scientific literature. The oral cavity emerged as a potential, previously unknown, environment of Streptococcus massiliensis. This phylogeny-based prediction was confirmed by species-specific polymerase chain reaction (PCR) amplification. We propose employing a similar approach, i.e., use of bibliographic data and molecular phylogenetics as predictive methods, and species-specific PCR as confirmation, in order to reveal other unknown habitats in further bacterial taxa.

Life-table studies revealed significant effects of deforestation on the development and survivorship of Anopheles minimus larvae.

PubMed

Wang, Xiaoming; Zhou, Guofa; Zhong, Daibin; Wang, Xiaoling; Wang, Ying; Yang, Zhaoqing; Cui, Liwang; Yan, Guiyun

2016-06-06

Many developing countries are experiencing rapid ecological changes such as deforestation and shifting agricultural practices. These environmental changes may have an important consequence on malaria due to their impact on vector survival and reproduction. Despite intensive deforestation and malaria transmission in the China-Myanmar border area, the impact of deforestation on malaria vectors in the border area is unknown. We conducted life table studies on Anopheles minimus larvae to determine the pupation rate and development time in microcosms under deforested, banana plantation, and forested environments. The pupation rate of An. minimus was 3.8 % in the forested environment. It was significantly increased to 12.5 % in banana plantations and to 52.5 % in the deforested area. Deforestation reduced larval-to-pupal development time by 1.9-3.3 days. Food supplementation to aquatic habitats in forested environments and banana plantations significantly increased larval survival rate to a similar level as in the deforested environment. Deforestation enhanced the survival and development of An. minimus larvae, a major malaria vector in the China-Myanmar border area. Experimental determination of the life table parameters on mosquito larvae under a variety of environmental conditions is valuable to model malaria transmission dynamics and impact by climate and environmental changes.

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

PubMed

Fei, Juntao; Lu, Cheng

2018-04-01

In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

Dual Rate Adaptive Control for an Industrial Heat Supply Process Using Signal Compensation Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chai, Tianyou; Jia, Yao; Wang, Hong

The industrial heat supply process (HSP) is a highly nonlinear cascaded process which uses a steam valve opening as its control input, the steam flow-rate as its inner loop output and the supply water temperature as its outer loop output. The relationship between the heat exchange rate and the model parameters, such as steam density, entropy, and fouling correction factor and heat exchange efficiency are unknown and nonlinear. Moreover, these model parameters vary in line with steam pressure, ambient temperature and the residuals caused by the quality variations of the circulation water. When the steam pressure and the ambient temperaturemore » are of high values and are subjected to frequent external random disturbances, the supply water temperature and the steam flow-rate would interact with each other and fluctuate a lot. This is also true when the process exhibits unknown characteristic variations of the process dynamics caused by the unexpected changes of the heat exchange residuals. As a result, it is difficult to control the supply water temperature and the rates of changes of steam flow-rate well inside their targeted ranges. In this paper, a novel compensation signal based dual rate adaptive controller is developed by representing the unknown variations of dynamics as unmodeled dynamics. In the proposed controller design, such a compensation signal is constructed and added onto the control signal obtained from the linear deterministic model based feedback control design. Such a compensation signal aims at eliminating the unmodeled dynamics and the rate of changes of the currently sample unmodeled dynamics. A successful industrial application is carried out, where it has been shown that both the supply water temperature and the rate of the changes of the steam flow-rate can be controlled well inside their targeted ranges when the process is subjected to unknown variations of its dynamics.« less

Dynamic parameter identification of robot arms with servo-controlled electrical motors

NASA Astrophysics Data System (ADS)

Jiang, Zhao-Hui; Senda, Hiroshi

2005-12-01

This paper addresses the issue of dynamic parameter identification of the robot manipulator with servo-controlled electrical motors. An assumption is made that all kinematical parameters, such as link lengths, are known, and only dynamic parameters containing mass, moment of inertia, and their functions need to be identified. First, we derive dynamics of the robot arm with a linear form of the unknown dynamic parameters by taking dynamic characteristics of the motor and servo unit into consideration. Then, we implement the parameter identification approach to identify the unknown parameters with respect to individual link separately. A pseudo-inverse matrix is used for formulation of the parameter identification. The optimal solution is guaranteed in a sense of least-squares of the mean errors. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example of the parameter identification. Simulations and experiments for both open loop and close loop controls are carried out. Comparison of the results confirms the correctness and usefulness of the parameter identification and the derived dynamic model.

Real-time on-line space research laboratory environment monitoring with off-line trend and prediction analysis

NASA Astrophysics Data System (ADS)

Jules, Kenol; Lin, Paul P.

2007-06-01

With the International Space Station currently operational, a significant amount of acceleration data is being down-linked, processed and analyzed daily on the ground on a continuous basis for the space station reduced gravity environment characterization, the vehicle design requirements verification and science data collection. To help understand the impact of the unique spacecraft environment on the science data, an artificial intelligence monitoring system was developed, which detects in near real time any change in the reduced gravity environment susceptible to affect the on-going experiments. Using a dynamic graphical display, the monitoring system allows science teams, at any time and any location, to see the active vibration disturbances, such as pumps, fans, compressor, crew exercise, re-boost and extra-vehicular activities that might impact the reduced gravity environment the experiments are exposed to. The monitoring system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many increments (an increment usually lasts 6 months) collected onboard the station for selected disturbances. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential systems failures. The monitoring system has two operating modes: online and offline. Both near real-time on-line vibratory disturbance detection and off-line detection and trend analysis are discussed in this paper.

Colonization of over a thousand Cibicidoides wuellerstorfi (foraminifera: Schwager, 1866) on artificial substrates in seep and adjacent off-seep locations in dysoxic, deep-sea environments

NASA Astrophysics Data System (ADS)

Burkett, Ashley M.; Rathburn, Anthony E.; Elena Pérez, M.; Levin, Lisa A.; Martin, Jonathan B.

2016-11-01

After ~1 yr on the seafloor at water depths of ~700 m on Hydrate Ridge in the Pacific, eight colonization experiments composed primarily of a plastic mesh cube (from here on refered to as SEA3, for Seafloor Epibenthic Attachment Cubes) were colonized by 1076 Cibicidoides wuellerstorfi on ~1841 cm2 of experimental substrate. This species is typically considered an indicator of well-oxygenated conditions, and recruitment of such large numbers in bottom waters with low dissolved oxygen availability (0.24-0.37 mL/L) indicate that this taxon may not be as limited by oxygen as previously thought. Clues about substrate preferences were evident from the distribution, or lack thereof, of individuals among plastic mesh, coated steel frame, wooden dowels and reflective tape. Abundance, individual size distributions within cage populations and isotopic biogeochemistry of living foraminifera colonizing experimental substrates were compared between active seep and adjacent off-seep experiment locations, revealing potential differences between these environments. Few studies have examined foraminiferal colonization of hard substrates in the deep-sea and to our knowledge no previous study has compared foraminiferal colonization of active seep and off-seep substrates from the same region. This study provides initial results of recruitment, colonization, geochemical and morphological aspects of the paleoceanographically significant species, C. wuellerstorfi, from dynamic deep-sea environments. Further experimental deployments of SEA3s will provide a means to assess relatively unknown ecologic dynamics of important foraminiferal deep-sea species.

Bayesian deterministic decision making: a normative account of the operant matching law and heavy-tailed reward history dependency of choices.

PubMed

Saito, Hiroshi; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato

2014-01-01

The decision making behaviors of humans and animals adapt and then satisfy an "operant matching law" in certain type of tasks. This was first pointed out by Herrnstein in his foraging experiments on pigeons. The matching law has been one landmark for elucidating the underlying processes of decision making and its learning in the brain. An interesting question is whether decisions are made deterministically or probabilistically. Conventional learning models of the matching law are based on the latter idea; they assume that subjects learn choice probabilities of respective alternatives and decide stochastically with the probabilities. However, it is unknown whether the matching law can be accounted for by a deterministic strategy or not. To answer this question, we propose several deterministic Bayesian decision making models that have certain incorrect beliefs about an environment. We claim that a simple model produces behavior satisfying the matching law in static settings of a foraging task but not in dynamic settings. We found that the model that has a belief that the environment is volatile works well in the dynamic foraging task and exhibits undermatching, which is a slight deviation from the matching law observed in many experiments. This model also demonstrates the double-exponential reward history dependency of a choice and a heavier-tailed run-length distribution, as has recently been reported in experiments on monkeys.

Adaptive Approximation-Based Regulation Control for a Class of Uncertain Nonlinear Systems Without Feedback Linearizability.

PubMed

Wang, Ning; Sun, Jing-Chao; Han, Min; Zheng, Zhongjiu; Er, Meng Joo

2017-09-06

In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error compensation pertaining to single-hidden-layer feedforward network (SLFN) from the Lyapunov synthesis, a series of SLFN-based approximators are explicitly constructed to exactly dominate completely unknown dynamics. By the virtue of significant advancements on the API technique, an adaptive API methodology is eventually established in combination with SLFN-based adaptive approximators, and it contributes to a recursive mechanism for the AARC scheme. As a consequence, the output regulation error can asymptotically converge to the origin, and all other signals of the closed-loop system are uniformly ultimately bounded. Simulation studies and comprehensive comparisons with backstepping- and API-based approaches demonstrate that the proposed AARC scheme achieves remarkable performance and superiority in dealing with unknown dynamics.

Online Solution of Two-Player Zero-Sum Games for Continuous-Time Nonlinear Systems With Completely Unknown Dynamics.

PubMed

Fu, Yue; Chai, Tianyou

2016-12-01

Regarding two-player zero-sum games of continuous-time nonlinear systems with completely unknown dynamics, this paper presents an online adaptive algorithm for learning the Nash equilibrium solution, i.e., the optimal policy pair. First, for known systems, the simultaneous policy updating algorithm (SPUA) is reviewed. A new analytical method to prove the convergence is presented. Then, based on the SPUA, without using a priori knowledge of any system dynamics, an online algorithm is proposed to simultaneously learn in real time either the minimal nonnegative solution of the Hamilton-Jacobi-Isaacs (HJI) equation or the generalized algebraic Riccati equation for linear systems as a special case, along with the optimal policy pair. The approximate solution to the HJI equation and the admissible policy pair is reexpressed by the approximation theorem. The unknown constants or weights of each are identified simultaneously by resorting to the recursive least square method. The convergence of the online algorithm to the optimal solutions is provided. A practical online algorithm is also developed. Simulation results illustrate the effectiveness of the proposed method.

A family of dynamic models for large-eddy simulation

NASA Technical Reports Server (NTRS)

Carati, D.; Jansen, K.; Lund, T.

1995-01-01

Since its first application, the dynamic procedure has been recognized as an effective means to compute rather than prescribe the unknown coefficients that appear in a subgrid-scale model for Large-Eddy Simulation (LES). The dynamic procedure is usually used to determine the nondimensional coefficient in the Smagorinsky (1963) model. In reality the procedure is quite general and it is not limited to the Smagorinsky model by any theoretical or practical constraints. The purpose of this note is to consider a generalized family of dynamic eddy viscosity models that do not necessarily rely on the local equilibrium assumption built into the Smagorinsky model. By invoking an inertial range assumption, it will be shown that the coefficients in the new models need not be nondimensional. This additional degree of freedom allows the use of models that are scaled on traditionally unknown quantities such as the dissipation rate. In certain cases, the dynamic models with dimensional coefficients are simpler to implement, and allow for a 30% reduction in the number of required filtering operations.

The Roles of Feedback and Feedforward as Humans Learn to Control Unknown Dynamic Systems.

PubMed

Zhang, Xingye; Wang, Shaoqian; Hoagg, Jesse B; Seigler, T Michael

2018-02-01

We present results from an experiment in which human subjects interact with an unknown dynamic system 40 times during a two-week period. During each interaction, subjects are asked to perform a command-following (i.e., pursuit tracking) task. Each subject's performance at that task improves from the first trial to the last trial. For each trial, we use subsystem identification to estimate each subject's feedforward (or anticipatory) control, feedback (or reactive) control, and feedback time delay. Over the 40 trials, the magnitudes of the identified feedback controllers and the identified feedback time delays do not change significantly. In contrast, the identified feedforward controllers do change significantly. By the last trial, the average identified feedforward controller approximates the inverse of the dynamic system. This observation provides evidence that a fundamental component of human learning is updating the anticipatory control until it models the inverse dynamics.

Intracellular Electric Field and pH Optimize Protein Localization and Movement

PubMed Central

Cunningham, Jessica; Estrella, Veronica; Lloyd, Mark; Gillies, Robert; Frieden, B. Roy; Gatenby, Robert

2012-01-01

Mammalian cell function requires timely and accurate transmission of information from the cell membrane (CM) to the nucleus (N). These pathways have been intensively investigated and many critical components and interactions have been identified. However, the physical forces that control movement of these proteins have received scant attention. Thus, transduction pathways are typically presented schematically with little regard to spatial constraints that might affect the underlying dynamics necessary for protein-protein interactions and molecular movement from the CM to the N. We propose messenger protein localization and movements are highly regulated and governed by Coulomb interactions between: 1. A recently discovered, radially directed E-field from the NM into the CM and 2. Net protein charge determined by its isoelectric point, phosphorylation state, and the cytosolic pH. These interactions, which are widely applied in elecrophoresis, provide a previously unknown mechanism for localization of messenger proteins within the cytoplasm as well as rapid shuttling between the CM and N. Here we show these dynamics optimize the speed, accuracy and efficiency of transduction pathways even allowing measurement of the location and timing of ligand binding at the CM –previously unknown components of intracellular information flow that are, nevertheless, likely necessary for detecting spatial gradients and temporal fluctuations in ligand concentrations within the environment. The model has been applied to the RAF-MEK-ERK pathway and scaffolding protein KSR1 using computer simulations and in-vitro experiments. The computer simulations predicted distinct distributions of phosphorylated and unphosphorylated components of this transduction pathway which were experimentally confirmed in normal breast epithelial cells (HMEC). PMID:22623963

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

Command of active matter by topological defects and patterns

NASA Astrophysics Data System (ADS)

Peng, Chenhui; Turiv, Taras; Guo, Yubing; Wei, Qi-Huo; Lavrentovich, Oleg D.

2016-11-01

Self-propelled bacteria are marvels of nature with a potential to power dynamic materials and microsystems of the future. The challenge lies in commanding their chaotic behavior. By dispersing swimming Bacillus subtilis in a liquid crystalline environment with spatially varying orientation of the anisotropy axis, we demonstrate control over the distribution of bacterial concentration, as well as the geometry and polarity of their trajectories. Bacteria recognize subtle differences in liquid crystal deformations, engaging in bipolar swimming in regions of pure splay and bend but switching to unipolar swimming in mixed splay-bend regions. They differentiate topological defects, heading toward defects of positive topological charge and avoiding negative charges. Sensitivity of bacteria to preimposed orientational patterns represents a previously unknown facet of the interplay between hydrodynamics and topology of active matter.

Impact of meteorological inflow uncertainty on tracer transport and source estimation in urban atmospheres

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lucas, Donald D.; Gowardhan, Akshay; Cameron-Smith, Philip

2015-08-08

Here, a computational Bayesian inverse technique is used to quantify the effects of meteorological inflow uncertainty on tracer transport and source estimation in a complex urban environment. We estimate a probability distribution of meteorological inflow by comparing wind observations to Monte Carlo simulations from the Aeolus model. Aeolus is a computational fluid dynamics model that simulates atmospheric and tracer flow around buildings and structures at meter-scale resolution. Uncertainty in the inflow is propagated through forward and backward Lagrangian dispersion calculations to determine the impact on tracer transport and the ability to estimate the release location of an unknown source. Ourmore » uncertainty methods are compared against measurements from an intensive observation period during the Joint Urban 2003 tracer release experiment conducted in Oklahoma City.« less

Quality Measures for Digital Business Ecosystems Formation

NASA Astrophysics Data System (ADS)

Raza, Muhammad; Hussain, Farookh Khadeer; Chang, Elizabeth

To execute a complex business task, business entities may need to collaborate with each other as individually they may not have the capability or willingness to perform the task on its own. Such collaboration can be seen implemented in digital business ecosystems in the form of simple coalitions using multi-agent systems or by employing Electronic Institutions. A major challenge is choosing optimal partners who will deliver the agreed commitments, and act in the coalition’s interest. Business entities are scaled according to their quality level. Determining the quality of previously unknown business entities and predicting the quality of such an entity in a dynamic environment are crucial issues in Business Ecosystems. A comprehensive quality management system grounded in the concepts of Trust and Reputation can help address these issues.

Bio-nano interactions detected by nanochannel electrophoresis.

PubMed

Luan, Binquan

2016-08-01

Engineered nanoparticles have been widely used in industry and are present in many consumer products. However, their bio-safeties especially in a long term are largely unknown. Here, a nanochannel-electrophoresis-based method is proposed for detecting the potential bio-nano interactions that may further lead to damages to human health and/or biological environment. Through proof-of-concept molecular dynamics simulations, it was demonstrated that the transport of a protein-nanoparticle complex is very different from that of a protein along. By monitoring the change of ionic currents induced by a transported analyte as well as the transport velocities of the analyte, the complex (with bio-nano interaction) can be clearly distinguished from the protein alone (with no interaction with tested nanoparticles). © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In situ structure and dynamics of DNA origami determined through molecular dynamics simulations

PubMed Central

Yoo, Jejoong; Aksimentiev, Aleksei

2013-01-01

The DNA origami method permits folding of long single-stranded DNA into complex 3D structures with subnanometer precision. Transmission electron microscopy, atomic force microscopy, and recently cryo-EM tomography have been used to characterize the properties of such DNA origami objects, however their microscopic structures and dynamics have remained unknown. Here, we report the results of all-atom molecular dynamics simulations that characterized the structural and mechanical properties of DNA origami objects in unprecedented microscopic detail. When simulated in an aqueous environment, the structures of DNA origami objects depart from their idealized targets as a result of steric, electrostatic, and solvent-mediated forces. Whereas the global structural features of such relaxed conformations conform to the target designs, local deformations are abundant and vary in magnitude along the structures. In contrast to their free-solution conformation, the Holliday junctions in the DNA origami structures adopt a left-handed antiparallel conformation. We find the DNA origami structures undergo considerable temporal fluctuations on both local and global scales. Analysis of such structural fluctuations reveals the local mechanical properties of the DNA origami objects. The lattice type of the structures considerably affects global mechanical properties such as bending rigidity. Our study demonstrates the potential of all-atom molecular dynamics simulations to play a considerable role in future development of the DNA origami field by providing accurate, quantitative assessment of local and global structural and mechanical properties of DNA origami objects. PMID:24277840

In situ structure and dynamics of DNA origami determined through molecular dynamics simulations.

PubMed

Yoo, Jejoong; Aksimentiev, Aleksei

2013-12-10

The DNA origami method permits folding of long single-stranded DNA into complex 3D structures with subnanometer precision. Transmission electron microscopy, atomic force microscopy, and recently cryo-EM tomography have been used to characterize the properties of such DNA origami objects, however their microscopic structures and dynamics have remained unknown. Here, we report the results of all-atom molecular dynamics simulations that characterized the structural and mechanical properties of DNA origami objects in unprecedented microscopic detail. When simulated in an aqueous environment, the structures of DNA origami objects depart from their idealized targets as a result of steric, electrostatic, and solvent-mediated forces. Whereas the global structural features of such relaxed conformations conform to the target designs, local deformations are abundant and vary in magnitude along the structures. In contrast to their free-solution conformation, the Holliday junctions in the DNA origami structures adopt a left-handed antiparallel conformation. We find the DNA origami structures undergo considerable temporal fluctuations on both local and global scales. Analysis of such structural fluctuations reveals the local mechanical properties of the DNA origami objects. The lattice type of the structures considerably affects global mechanical properties such as bending rigidity. Our study demonstrates the potential of all-atom molecular dynamics simulations to play a considerable role in future development of the DNA origami field by providing accurate, quantitative assessment of local and global structural and mechanical properties of DNA origami objects.

Adaptive Control Based Harvesting Strategy for a Predator-Prey Dynamical System.

PubMed

Sen, Moitri; Simha, Ashutosh; Raha, Soumyendu

2018-04-23

This paper deals with designing a harvesting control strategy for a predator-prey dynamical system, with parametric uncertainties and exogenous disturbances. A feedback control law for the harvesting rate of the predator is formulated such that the population dynamics is asymptotically stabilized at a positive operating point, while maintaining a positive, steady state harvesting rate. The hierarchical block strict feedback structure of the dynamics is exploited in designing a backstepping control law, based on Lyapunov theory. In order to account for unknown parameters, an adaptive control strategy has been proposed in which the control law depends on an adaptive variable which tracks the unknown parameter. Further, a switching component has been incorporated to robustify the control performance against bounded disturbances. Proofs have been provided to show that the proposed adaptive control strategy ensures asymptotic stability of the dynamics at a desired operating point, as well as exact parameter learning in the disturbance-free case and learning with bounded error in the disturbance prone case. The dynamics, with uncertainty in the death rate of the predator, subjected to a bounded disturbance has been simulated with the proposed control strategy.

Single-cell resolution of intracellular T cell Ca2+ dynamics in response to frequency-based H2O2 stimulation.

PubMed

Kniss-James, Ariel S; Rivet, Catherine A; Chingozha, Loice; Lu, Hang; Kemp, Melissa L

2017-03-01

Adaptive immune cells, such as T cells, integrate information from their extracellular environment through complex signaling networks with exquisite sensitivity in order to direct decisions on proliferation, apoptosis, and cytokine production. These signaling networks are reliant on the interplay between finely tuned secondary messengers, such as Ca 2+ and H 2 O 2 . Frequency response analysis, originally developed in control engineering, is a tool used for discerning complex networks. This analytical technique has been shown to be useful for understanding biological systems and facilitates identification of the dominant behaviour of the system. We probed intracellular Ca 2+ dynamics in the frequency domain to investigate the complex relationship between two second messenger signaling molecules, H 2 O 2 and Ca 2+ , during T cell activation with single cell resolution. Single-cell analysis provides a unique platform for interrogating and monitoring cellular processes of interest. We utilized a previously developed microfluidic device to monitor individual T cells through time while applying a dynamic input to reveal a natural frequency of the system at approximately 2.78 mHz stimulation. Although our network was much larger with more unknown connections than previous applications, we are able to derive features from our data, observe forced oscillations associated with specific amplitudes and frequencies of stimuli, and arrive at conclusions about potential transfer function fits as well as the underlying population dynamics.

Amoeba-Inspired Heuristic Search Dynamics for Exploring Chemical Reaction Paths.

PubMed

Aono, Masashi; Wakabayashi, Masamitsu

2015-09-01

We propose a nature-inspired model for simulating chemical reactions in a computationally resource-saving manner. The model was developed by extending our previously proposed heuristic search algorithm, called "AmoebaSAT [Aono et al. 2013]," which was inspired by the spatiotemporal dynamics of a single-celled amoeboid organism that exhibits sophisticated computing capabilities in adapting to its environment efficiently [Zhu et al. 2013]. AmoebaSAT is used for solving an NP-complete combinatorial optimization problem [Garey and Johnson 1979], "the satisfiability problem," and finds a constraint-satisfying solution at a speed that is dramatically faster than one of the conventionally known fastest stochastic local search methods [Iwama and Tamaki 2004] for a class of randomly generated problem instances [ http://www.cs.ubc.ca/~hoos/5/benchm.html ]. In cases where the problem has more than one solution, AmoebaSAT exhibits dynamic transition behavior among a variety of the solutions. Inheriting these features of AmoebaSAT, we formulate "AmoebaChem," which explores a variety of metastable molecules in which several constraints determined by input atoms are satisfied and generates dynamic transition processes among the metastable molecules. AmoebaChem and its developed forms will be applied to the study of the origins of life, to discover reaction paths for which expected or unexpected organic compounds may be formed via unknown unstable intermediates and to estimate the likelihood of each of the discovered paths.

The walking behaviour of pedestrian social groups and its impact on crowd dynamics.

PubMed

Moussaïd, Mehdi; Perozo, Niriaska; Garnier, Simon; Helbing, Dirk; Theraulaz, Guy

2010-04-07

Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its "non-aerodynamic" shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.

Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach.

PubMed

Noel, Jean-Paul; Blanke, Olaf; Magosso, Elisa; Serino, Andrea

2018-06-01

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons' RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant's peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons' RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli. NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic properties are unexplained. We demonstrate with a psychophysical approach that PPS enlarges as incoming stimulus velocity increases, efficiently preventing contacts with faster approaching objects. We present a neurocomputational model of multisensory PPS implementing neural adaptation to persistent stimulation to propose a neurophysiological mechanism underlying this effect.

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

NASA Astrophysics Data System (ADS)

Habibi, Hamed; Rahimi Nohooji, Hamed; Howard, Ian

2017-09-01

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

Terminal Sliding Mode-Based Consensus Tracking Control for Networked Uncertain Mechanical Systems on Digraphs.

PubMed

Chen, Gang; Song, Yongduan; Guan, Yanfeng

2018-03-01

This brief investigates the finite-time consensus tracking control problem for networked uncertain mechanical systems on digraphs. A new terminal sliding-mode-based cooperative control scheme is developed to guarantee that the tracking errors converge to an arbitrarily small bound around zero in finite time. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network is used at each node to approximate the local unknown dynamics. The control schemes are implemented in a fully distributed manner. The proposed control method eliminates some limitations in the existing terminal sliding-mode-based consensus control methods and extends the existing analysis methods to the case of directed graphs. Simulation results on networked robot manipulators are provided to show the effectiveness of the proposed control algorithms.

Dynamics of Viral Abundance and Diversity in a Sphagnum-Dominated Peatland: Temporal Fluctuations Prevail Over Habitat.

PubMed

Ballaud, Flore; Dufresne, Alexis; Francez, André-Jean; Colombet, Jonathan; Sime-Ngando, Télesphore; Quaiser, Achim

2015-01-01

Viruses impact microbial activity and carbon cycling in various environments, but their diversity and ecological importance in Sphagnum-peatlands are unknown. Abundances of viral particles and prokaryotes were monitored bi-monthly at a fen and a bog at two different layers of the peat surface. Viral particle abundance ranged from 1.7 x 10(6) to 5.6 x 10(8) particles mL(-1), and did not differ between fen and bog but showed seasonal fluctuations. These fluctuations were positively correlated with prokaryote abundance and dissolved organic carbon, and negatively correlated with water-table height and dissolved oxygen. Using shotgun metagenomics we observed a shift in viral diversity between winter/spring and summer/autumn, indicating a seasonal succession of viral communities, mainly driven by weather-related environmental changes. Based on the seasonal asynchrony between viral and microbial diversity, we hypothesize a seasonal shift in the active microbial communities associated with a shift from lysogenic to lytic lifestyles. Our results suggest that temporal variations of environmental conditions rather than current habitat differences control the dynamics of virus-host interactions in Sphagnum-dominated peatlands.

Research on pressure tactile sensing technology based on fiber Bragg grating array

NASA Astrophysics Data System (ADS)

Song, Jinxue; Jiang, Qi; Huang, Yuanyang; Li, Yibin; Jia, Yuxi; Rong, Xuewen; Song, Rui; Liu, Hongbin

2015-09-01

A pressure tactile sensor based on the fiber Bragg grating (FBG) array is introduced in this paper, and the numerical simulation of its elastic body was implemented by finite element software (ANSYS). On the basis of simulation, fiber Bragg grating strings were implanted in flexible silicone to realize the sensor fabrication process, and a testing system was built. A series of calibration tests were done via the high precision universal press machine. The tactile sensor array perceived external pressure, which is demodulated by the fiber grating demodulation instrument, and three-dimension pictures were programmed to display visually the position and size. At the same time, a dynamic contact experiment of the sensor was conducted for simulating robot encountering other objects in the unknown environment. The experimental results show that the sensor has good linearity, repeatability, and has the good effect of dynamic response, and its pressure sensitivity was 0.03 nm/N. In addition, the sensor also has advantages of anti-electromagnetic interference, good flexibility, simple structure, low cost and so on, which is expected to be used in the wearable artificial skin in the future.

Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends.

PubMed

Thomas, Diana M; Weedermann, Marion; Fuemmeler, Bernard F; Martin, Corby K; Dhurandhar, Nikhil V; Bredlau, Carl; Heymsfield, Steven B; Ravussin, Eric; Bouchard, Claude

2014-02-01

Obesity prevalence in the United States appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and nonsocial influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. The dynamic model predicts that: obesity prevalence is a function of birthrate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of overweight, obesity, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9% respectively. The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. Copyright © 2013 The Obesity Society.

Swing-free transport of suspended loads. Summer research report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basher, A.M.H.

1996-02-01

Transportation of large objects using traditional bridge crane can induce pendulum motion (swing) of the object. In environments such as factory the energy contained in the swinging mass can be large and therefore attempts to move the mass onto target while still swinging can cause considerable damage. Oscillations must be damped or allowed to decay before the next process can take place. Stopping the swing can be accomplished by moving the bridge in a manner to counteract the swing which sometimes can be done by skilled operator, or by waiting for the swing to damp sufficiently that the object canmore » be moved to the target without risk of damage. One of the methods that can be utilized for oscillation suppression is input preshaping. The validity of this method depends on the exact knowledge of the system dynamics. This method can be modified to provide some degrees of robustness with respect to unknown dynamics but at the cost of the speed of transient response. This report describes investigations on the development of a controller to dampen the oscillations.« less

Decentralised output feedback control of Markovian jump interconnected systems with unknown interconnections

NASA Astrophysics Data System (ADS)

Li, Li-Wei; Yang, Guang-Hong

2017-07-01

The problem of decentralised output feedback control is addressed for Markovian jump interconnected systems with unknown interconnections and general transition rates (TRs) allowed to be unknown or known with uncertainties. A class of decentralised dynamic output feedback controllers are constructed, and a cyclic-small-gain condition is exploited to dispose the unknown interconnections so that the resultant closed-loop system is stochastically stable and satisfies an H∞ performance. With slack matrices to cope with the nonlinearities incurred by unknown and uncertain TRs in control synthesis, a novel controller design condition is developed in linear matrix inequality formalism. Compared with the existing works, the proposed approach leads to less conservatism. Finally, two examples are used to illustrate the effectiveness of the new results.

An Efficient Solution Method for Multibody Systems with Loops Using Multiple Processors

NASA Technical Reports Server (NTRS)

Ghosh, Tushar K.; Nguyen, Luong A.; Quiocho, Leslie J.

2015-01-01

This paper describes a multibody dynamics algorithm formulated for parallel implementation on multiprocessor computing platforms using the divide-and-conquer approach. The system of interest is a general topology of rigid and elastic articulated bodies with or without loops. The algorithm divides the multibody system into a number of smaller sets of bodies in chain or tree structures, called "branches" at convenient joints called "connection points", and uses an Order-N (O (N)) approach to formulate the dynamics of each branch in terms of the unknown spatial connection forces. The equations of motion for the branches, leaving the connection forces as unknowns, are implemented in separate processors in parallel for computational efficiency, and the equations for all the unknown connection forces are synthesized and solved in one or several processors. The performances of two implementations of this divide-and-conquer algorithm in multiple processors are compared with an existing method implemented on a single processor.

Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism.

PubMed

Liu, Zhi; Chen, Ci; Zhang, Yun; Chen, C L P

2015-03-01

To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Nonlinear microrheology and molecular imaging to map microscale deformations of entangled DNA networks

NASA Astrophysics Data System (ADS)

Wu, Tsai-Chin; Anderson, Rae

We use active microrheology coupled to single-molecule fluorescence imaging to elucidate the microscale dynamics of entangled DNA. DNA naturally exists in a wide range of lengths and topologies, and is often confined in cell nucleui, forming highly concentrated and entangled biopolymer networks. Thus, DNA is the model polymer for understanding entangled polymer dynamics as well as the crowded environment of cells. These networks display complex viscoelastic properties that are not well understood, especially at the molecular-level and in response to nonlinear perturbations. Specifically, how microscopic stresses and strains propagate through entangled networks, and what molecular deformations lead to the network stress responses are unknown. To answer these important questions, we optically drive a microsphere through entangled DNA, perturbing the system far from equilibrium, while measuring the resistive force the DNA exerts on the bead during and after bead motion. We simultaneously image single fluorescent-labeled DNA molecules throughout the network to directly link the microscale stress response to molecular deformations. We characterize the deformation of the network from the molecular-level to the mesoscale, and map the stress propagation throughout the network. We further study the impact of DNA length (11 - 115 kbp) and topology (linear vs ring DNA) on deformation and propagation dynamics, exploring key nonlinear features such as tube dilation and power-law relaxation.

Anticipatory Neurofuzzy Control

NASA Technical Reports Server (NTRS)

Mccullough, Claire L.

1994-01-01

Technique of feedback control, called "anticipatory neurofuzzy control," developed for use in controlling flexible structures and other dynamic systems for which mathematical models of dynamics poorly known or unknown. Superior ability to act during operation to compensate for, and adapt to, errors in mathematical model of dynamics, changes in dynamics, and noise. Also offers advantage of reduced computing time. Hybrid of two older fuzzy-logic control techniques: standard fuzzy control and predictive fuzzy control.

Combining environment-driven adaptation and task-driven optimisation in evolutionary robotics.

PubMed

Haasdijk, Evert; Bredeche, Nicolas; Eiben, A E

2014-01-01

Embodied evolutionary robotics is a sub-field of evolutionary robotics that employs evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-)adjusting themselves to previously unknown or dynamically changing conditions autonomously, without human oversight. This paper addresses one of the major challenges that such systems face, viz. that the robots must satisfy two sets of requirements. Firstly, they must continue to operate reliably in their environment (viability), and secondly they must competently perform user-specified tasks (usefulness). The solution we propose exploits the fact that evolutionary methods have two basic selection mechanisms-survivor selection and parent selection. This allows evolution to tackle the two sets of requirements separately: survivor selection is driven by the environment and parent selection is based on task-performance. This idea is elaborated in the Multi-Objective aNd open-Ended Evolution (monee) framework, which we experimentally validate. Experiments with robotic swarms of 100 simulated e-pucks show that monee does indeed promote task-driven behaviour without compromising environmental adaptation. We also investigate an extension of the parent selection process with a 'market mechanism' that can ensure equitable distribution of effort over multiple tasks, a particularly pressing issue if the environment promotes specialisation in single tasks.

Antibiotics in the aquatic environments: A review of the European scenario.

PubMed

Carvalho, Isabel T; Santos, Lúcia

2016-09-01

The discovery of antibiotics is considered one of the most significant scientific achievements of the 20th century, revolutionizing both human and veterinary medicine. However, antibiotics have been recently recognized as an emerging class of environmental contaminants since they have been massively administrated in humans and animals and persist in the environment through a complex vicious cycle of transformation and bioaccumulation. The diffusion of antibiotics in the environment, particularly in natural water systems, contributes to the development and global dissemination of antibiotic resistance. This phenomenon is one of the most important challenges to the health care sector in the 21st century. As a result, studies on the occurrence, fate, and effects of antibiotics in European aqueous environments have increased in the last years. Nevertheless, their potential aquatic ecotoxicity and human toxicity via environmental exposure routes remain unknown. Consequently, antibiotics are not regulated through the current European environmental water quality standards, which requires evidence concerning their widespread environmental contamination and intrinsic hazard. In this context, this literature review summarizes the state of knowledge on the occurrence of antibiotics in the different aqueous environmental systems across the Europe, as reported since 2000. Relating this subject to antibiotic consumption and their dynamic behavior in the environment, the acquired insights provide an improved understanding on aquatic pollution by antibiotics to outline the European scenario. Moreover, it addresses challenges, prospects for future research, and typical topics to stimulate discussion. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Weak Shall Inherit: Bacteriocin-Mediated Interactions in Bacterial Populations

PubMed Central

Majeed, Hadeel; Lampert, Adam; Ghazaryan, Lusine; Gillor, Osnat

2013-01-01

Background Evolutionary arms race plays a major role in shaping biological diversity. In microbial systems, competition often involves chemical warfare and the production of bacteriocins, narrow-spectrum toxins aimed at killing closely related strains by forming pores in their target’s membrane or by degrading the target’s RNA or DNA. Although many empirical and theoretical studies describe competitive exclusion of bacteriocin-sensitive strains by producers of bacteriocins, the dynamics among producers are largely unknown. Methodology/Principal findings We used a reporter-gene assay to show that the bacterial response to bacteriocins’ treatment mirrors the inflicted damage Potent bacteriocins are lethal to competing strains, but at sublethal doses can serve as strong inducing agents, enhancing their antagonists’ bacteriocin production. In contrast, weaker bacteriocins are less toxic to their competitors and trigger mild bacteriocin expression. We used empirical and numerical models to explore the role of cross-induction in the arms race between bacteriocin-producing strains. We found that in well-mixed, unstructured environments where interactions are global, producers of weak bacteriocins are selectively advantageous and outcompete producers of potent bacteriocins. However, in spatially structured environments, where interactions are local, each producer occupies its own territory, and competition takes place only in “no man’s lands” between territories, resulting in much slower dynamics. Conclusion/Significance The models we present imply that producers of potent bacteriocins that trigger a strong response in neighboring bacteriocinogenic strains are doomed, while producers of weak bacteriocins that trigger a mild response in bacteriocinogenic strains flourish. This counter-intuitive outcome might explain the preponderance of weak bacteriocin producers in nature. However, the described scenario is prolonged in spatially structured environments thus promoting coexistence, allowing migration and evolution, and maintaining bacterial diversity. PMID:23704942

Estimation of nonlinear pilot model parameters including time delay.

NASA Technical Reports Server (NTRS)

Schiess, J. R.; Roland, V. R.; Wells, W. R.

1972-01-01

Investigation of the feasibility of using a Kalman filter estimator for the identification of unknown parameters in nonlinear dynamic systems with a time delay. The problem considered is the application of estimation theory to determine the parameters of a family of pilot models containing delayed states. In particular, the pilot-plant dynamics are described by differential-difference equations of the retarded type. The pilot delay, included as one of the unknown parameters to be determined, is kept in pure form as opposed to the Pade approximations generally used for these systems. Problem areas associated with processing real pilot response data are included in the discussion.

Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.

PubMed

Fu, Yue; Fu, Jun; Chai, Tianyou

2015-12-01

In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

Graph Theory-Based Pinning Synchronization of Stochastic Complex Dynamical Networks.

PubMed

Li, Xiao-Jian; Yang, Guang-Hong

2017-02-01

This paper is concerned with the adaptive pinning synchronization problem of stochastic complex dynamical networks (CDNs). Based on algebraic graph theory and Lyapunov theory, pinning controller design conditions are derived, and the rigorous convergence analysis of synchronization errors in the probability sense is also conducted. Compared with the existing results, the topology structures of stochastic CDN are allowed to be unknown due to the use of graph theory. In particular, it is shown that the selection of nodes for pinning depends on the unknown lower bounds of coupling strengths. Finally, an example on a Chua's circuit network is given to validate the effectiveness of the theoretical results.

Bone loss and human adaptation to lunar gravity

NASA Technical Reports Server (NTRS)

Keller, T. S.; Strauss, A. M.

1992-01-01

Long-duration space missions and establishment of permanently manned bases on the Moon and Mars are currently being planned. The weightless environment of space and the low-gravity environments of the Moon and Mars pose an unknown challenge to human habitability and survivability. Of particular concern in the medical research community today is the effect of less than Earth gravity on the human skeleton, since the limits, if any, of human endurance in low-gravity environments are unknown. This paper provides theoretical predictions on bone loss and skeletal adaptation to lunar and other nonterrestrial-gravity environments based upon the experimentally derived relationship, density approximately (mass x gravity)(exp 1/8). The predictions are compared to skeletal changes reported during bed rest, immobilization, certrifugation, and spaceflight. Countermeasures to reduce bone losses in fractional gravity are also discussed.

Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurt Derr; Milos Manic

Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhancedmore » by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.« less

Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

PubMed

Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu

2016-03-01

The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.

FITTING NONLINEAR ORDINARY DIFFERENTIAL EQUATION MODELS WITH RANDOM EFFECTS AND UNKNOWN INITIAL CONDITIONS USING THE STOCHASTIC APPROXIMATION EXPECTATION–MAXIMIZATION (SAEM) ALGORITHM

PubMed Central

Chow, Sy- Miin; Lu, Zhaohua; Zhu, Hongtu; Sherwood, Andrew

2014-01-01

The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation–maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed. PMID:25416456

Chaotic dynamics in the (47171) Lempo triple system

NASA Astrophysics Data System (ADS)

Correia, Alexandre C. M.

2018-05-01

We investigate the dynamics of the (47171) Lempo triple system, also known by 1999 TC36. We derive a full 3D N-body model that takes into account the orbital and spin evolution of all bodies, which are assumed triaxial ellipsoids. We show that, for reasonable values of the shapes and rotational periods, the present best fitted orbital solution for the Lempo system is chaotic and unstable in short time-scales. The formation mechanism of this system is unknown, but the orbits can be stabilised when tidal dissipation is taken into account. The dynamics of the Lempo system is very rich, but depends on many parameters that are presently unknown. A better understanding of this systems thus requires more observations, which also need to be fitted with a complete model like the one presented here.

Remembering to learn: independent place and journey coding mechanisms contribute to memory transfer.

PubMed

Bahar, Amir S; Shapiro, Matthew L

2012-02-08

The neural mechanisms that integrate new episodes with established memories are unknown. When rats explore an environment, CA1 cells fire in place fields that indicate locations. In goal-directed spatial memory tasks, some place fields differentiate behavioral histories ("journey-dependent" place fields) while others do not ("journey-independent" place fields). To investigate how these signals inform learning and memory for new and familiar episodes, we recorded CA1 and CA3 activity in rats trained to perform a "standard" spatial memory task in a plus maze and in two new task variants. A "switch" task exchanged the start and goal locations in the same environment; an "altered environment" task contained unfamiliar local and distal cues. In the switch task, performance was mildly impaired, new firing maps were stable, but the proportion and stability of journey-dependent place fields declined. In the altered environment, overall performance was strongly impaired, new firing maps were unstable, and stable proportions of journey-dependent place fields were maintained. In both tasks, memory errors were accompanied by a decline in journey codes. The different dynamics of place and journey coding suggest that they reflect separate mechanisms and contribute to distinct memory computations. Stable place fields may represent familiar relationships among environmental features that are required for consistent memory performance. Journey-dependent activity may correspond with goal-directed behavioral sequences that reflect expectancies that generalize across environments. The complementary signals could help link current events with established memories, so that familiarity with either a behavioral strategy or an environment can inform goal-directed learning.

REMEMBERING TO LEARN: INDEPENDENT PLACE AND JOURNEY CODING MECHANISMS CONTRIBUTE TO MEMORY TRANSFER

PubMed Central

Bahar, Amir S.; Shapiro, Matthew L.

2012-01-01

The neural mechanisms that integrate new episodes with established memories are unknown. When rats explore an environment, CA1 cells fire in place fields that indicate locations. In goal-directed spatial memory tasks, some place fields differentiate behavioral histories (journey-dependent place fields) while others do not (journey-independent place fields). To investigate how these signals inform learning and memory for new and familiar episodes, we recorded CA1 and CA3 activity in rats trained to perform a standard spatial memory task in a plus maze and in two new task variants. A switch task exchanged the start and goal locations in the same environment; an altered environment task contained unfamiliar local and distal cues. In the switch task, performance was mildly impaired, new firing maps were stable, but the proportion and stability of journey-dependent place fields declined. In the altered environment, overall performance was strongly impaired, new firing maps were unstable, and stable proportions of journey-dependent place fields were maintained. In both tasks, memory errors were accompanied by a decline in journey codes. The different dynamics of place and journey coding suggest that they reflect separate mechanisms and contribute to distinct memory computations. Stable place fields may represent familiar relationships among environmental features that are required for consistent memory performance. Journey-dependent activity may correspond with goal directed behavioral sequences that reflect expectancies that generalize across environments. The complementary signals could help link current events with established memories, so that familiarity with either a behavioral strategy or an environment can inform goal-directed learning. PMID:22323731

Dual adaptive dynamic control of mobile robots using neural networks.

PubMed

Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

2009-02-01

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

Enabling Autonomous Navigation for Affordable Scooters.

PubMed

Liu, Kaikai; Mulky, Rajathswaroop

2018-06-05

Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping those in need navigate to their destinations in a hassle-free manner. In this paper, we propose to improve the safety and autonomy of navigation by designing a cutting-edge autonomous scooter, thus allowing people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings. We focus on indoor navigation scenarios for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. To achieve semi-LiDAR functionality, we leverage the gyros-based pose data to compensate the laser motion in real time and create synthetic mapping of simple environments with regular shapes and deep hallways. Laser range finders are suitable for long ranges with limited resolution. Stereo vision, on the other hand, provides 3D structural data of nearby complex objects. To achieve simultaneous fine-grained resolution and long range coverage in the mapping of cluttered and complex environments, we dynamically fuse the measurements from the stereo vision camera system, the synthetic laser scanner, and the LiDAR. We propose solutions to self-correct errors in data fusion and create a hybrid map to assist the scooter in achieving collision-free navigation in an indoor environment.

Intelligence-aided multitarget tracking for urban operations - a case study: counter terrorism

NASA Astrophysics Data System (ADS)

Sathyan, T.; Bharadwaj, K.; Sinha, A.; Kirubarajan, T.

2006-05-01

In this paper, we present a framework for tracking multiple mobile targets in an urban environment based on data from multiple sources of information, and for evaluating the threat these targets pose to assets of interest (AOI). The motivating scenario is one where we have to track many targets, each with different (unknown) destinations and/or intents. The tracking algorithm is aided by information about the urban environment (e.g., road maps, buildings, hideouts), and strategic and intelligence data. The tracking algorithm needs to be dynamic in that it has to handle a time-varying number of targets and the ever-changing urban environment depending on the locations of the moving objects and AOI. Our solution uses the variable structure interacting multiple model (VS-IMM) estimator, which has been shown to be effective in tracking targets based on road map information. Intelligence information is represented as target class information and incorporated through a combined likelihood calculation within the VS-IMM estimator. In addition, we develop a model to calculate the probability that a particular target can attack a given AOI. This model for the calculation of the probability of attack is based on the target kinematic and class information. Simulation results are presented to demonstrate the operation of the proposed framework on a representative scenario.

Command of active matter by topological defects and patterns.

PubMed

Peng, Chenhui; Turiv, Taras; Guo, Yubing; Wei, Qi-Huo; Lavrentovich, Oleg D

2016-11-18

Self-propelled bacteria are marvels of nature with a potential to power dynamic materials and microsystems of the future. The challenge lies in commanding their chaotic behavior. By dispersing swimming Bacillus subtilis in a liquid crystalline environment with spatially varying orientation of the anisotropy axis, we demonstrate control over the distribution of bacterial concentration, as well as the geometry and polarity of their trajectories. Bacteria recognize subtle differences in liquid crystal deformations, engaging in bipolar swimming in regions of pure splay and bend but switching to unipolar swimming in mixed splay-bend regions. They differentiate topological defects, heading toward defects of positive topological charge and avoiding negative charges. Sensitivity of bacteria to preimposed orientational patterns represents a previously unknown facet of the interplay between hydrodynamics and topology of active matter. Copyright © 2016, American Association for the Advancement of Science.

Robust interval-based regulation for anaerobic digestion processes.

PubMed

Alcaraz-González, V; Harmand, J; Rapaport, A; Steyer, J P; González-Alvarez, V; Pelayo-Ortiz, C

2005-01-01

A robust regulation law is applied to the stabilization of a class of biochemical reactors exhibiting partially known highly nonlinear dynamic behavior. An uncertain environment with the presence of unknown inputs is considered. Based on some structural and operational conditions, this regulation law is shown to exponentially stabilize the aforementioned bioreactors around a desired set-point. This approach is experimentally applied and validated on a pilot-scale (1 m3) anaerobic digestion process for the treatment of raw industrial wine distillery wastewater where the objective is the regulation of the chemical oxygen demand (COD) by using the dilution rate as the manipulated variable. Despite large disturbances on the input COD and state and parametric uncertainties, this regulation law gave excellent performances leading the output COD towards its set-point and keeping it inside a pre-specified interval.

Rapid hyperosmotic-induced Ca 2+ responses in Arabidopsis thaliana exhibit sensory potentiation and involvement of plastidial KEA transporters

DOE PAGES

Stephan, Aaron B.; Kunz, Hans-Henning; Yang, Eric; ...

2016-08-15

How plant roots initially sense osmotic stress in an environment of dynamic water availabilities remains largely unknown. Plants can perceive water limitation imposed by soil salinity or, potentially, by drought in the form of osmotic stress. Rapid osmotic stress-induced intracellular calcium transients provide the opportunity to dissect quantitatively the sensory mechanisms that transmit osmotic stress under environmental and genetic perturbations in plants. Here, we describe a phenomenon whereby prior exposure to osmotic stress increases the sensitivity of the rapid calcium responses to subsequent stress. Furthermore, mutations in plastidial K + exchange antiporter (KEA)1/2 and KEA3 transporters were unexpectedly found tomore » reduce the rapid osmotic stress-induced calcium elevation. These findings advance the understanding of the mechanisms underlying the rapid osmotic stress response in plants.« less

Rapid hyperosmotic-induced Ca 2+ responses in Arabidopsis thaliana exhibit sensory potentiation and involvement of plastidial KEA transporters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stephan, Aaron B.; Kunz, Hans-Henning; Yang, Eric

How plant roots initially sense osmotic stress in an environment of dynamic water availabilities remains largely unknown. Plants can perceive water limitation imposed by soil salinity or, potentially, by drought in the form of osmotic stress. Rapid osmotic stress-induced intracellular calcium transients provide the opportunity to dissect quantitatively the sensory mechanisms that transmit osmotic stress under environmental and genetic perturbations in plants. Here, we describe a phenomenon whereby prior exposure to osmotic stress increases the sensitivity of the rapid calcium responses to subsequent stress. Furthermore, mutations in plastidial K + exchange antiporter (KEA)1/2 and KEA3 transporters were unexpectedly found tomore » reduce the rapid osmotic stress-induced calcium elevation. These findings advance the understanding of the mechanisms underlying the rapid osmotic stress response in plants.« less

Spatial curvilinear path following control of underactuated AUV with multiple uncertainties.

PubMed

Miao, Jianming; Wang, Shaoping; Zhao, Zhiping; Li, Yuan; Tomovic, Mileta M

2017-03-01

This paper investigates the problem of spatial curvilinear path following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties. Firstly, in order to design the appropriate controller, path following error dynamics model is constructed in a moving Serret-Frenet frame, and the five degrees of freedom (DOFs) dynamic model with multiple uncertainties is established. Secondly, the proposed control law is separated into kinematic controller and dynamic controller via back-stepping technique. In the case of kinematic controller, to overcome the drawback of dependence on the accurate vehicle model that are present in a number of path following control strategies described in the literature, the unknown side-slip angular velocity and attack angular velocity are treated as uncertainties. Whereas in the case of dynamic controller, the model parameters perturbations, unknown external environmental disturbances and the nonlinear hydrodynamic damping terms are treated as lumped uncertainties. Both kinematic and dynamic uncertainties are estimated and compensated by designed reduced-order linear extended state observes (LESOs). Thirdly, feedback linearization (FL) based control law is implemented for the control model using the estimates generated by reduced-order LESOs. For handling the problem of computational complexity inherent in the conventional back-stepping method, nonlinear tracking differentiators (NTDs) are applied to construct derivatives of the virtual control commands. Finally, the closed loop stability for the overall system is established. Simulation and comparative analysis demonstrate that the proposed controller exhibits enhanced performance in the presence of internal parameter variations, external unknown disturbances, unmodeled nonlinear damping terms, and measurement noises. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A Real-Time Reaction Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles in Unknown Environments

PubMed Central

Yan, Zheping; Li, Jiyun; Zhang, Gengshi; Wu, Yi

2018-01-01

A novel real-time reaction obstacle avoidance algorithm (RRA) is proposed for autonomous underwater vehicles (AUVs) that must adapt to unknown complex terrains, based on forward looking sonar (FLS). To accomplish this algorithm, obstacle avoidance rules are planned, and the RRA processes are split into five steps Introduction only lists 4 so AUVs can rapidly respond to various environment obstacles. The largest polar angle algorithm (LPAA) is designed to change detected obstacle’s irregular outline into a convex polygon, which simplifies the obstacle avoidance process. A solution is designed to solve the trapping problem existing in U-shape obstacle avoidance by an outline memory algorithm. Finally, simulations in three unknown obstacle scenes are carried out to demonstrate the performance of this algorithm, where the obtained obstacle avoidance trajectories are safety, smooth and near-optimal. PMID:29393915

A Real-Time Reaction Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles in Unknown Environments.

PubMed

Yan, Zheping; Li, Jiyun; Zhang, Gengshi; Wu, Yi

2018-02-02

A novel real-time reaction obstacle avoidance algorithm (RRA) is proposed for autonomous underwater vehicles (AUVs) that must adapt to unknown complex terrains, based on forward looking sonar (FLS). To accomplish this algorithm, obstacle avoidance rules are planned, and the RRA processes are split into five steps Introduction only lists 4 so AUVs can rapidly respond to various environment obstacles. The largest polar angle algorithm (LPAA) is designed to change detected obstacle's irregular outline into a convex polygon, which simplifies the obstacle avoidance process. A solution is designed to solve the trapping problem existing in U-shape obstacle avoidance by an outline memory algorithm. Finally, simulations in three unknown obstacle scenes are carried out to demonstrate the performance of this algorithm, where the obtained obstacle avoidance trajectories are safety, smooth and near-optimal.

A general CPL-AdS methodology for fixing dynamic parameters in dual environments.

PubMed

Huang, De-Shuang; Jiang, Wen

2012-10-01

The algorithm of Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy exhibits its efficiency in solving stochastic point location (SPL) problems. However, there is one bottleneck for this CPL-AdS strategy which is that, when the dimension of the feature, or the number of divided subintervals for each iteration, d is large, the decision table for elimination process is almost unavailable. On the other hand, the larger dimension of the features d can generally make this CPL-AdS strategy avoid oscillation and converge faster. This paper presents a generalized universal decision formula to solve this bottleneck problem. As a matter of fact, this decision formula has a wider usage beyond handling out this SPL problems, such as dealing with deterministic point location problems and searching data in Single Instruction Stream-Multiple Data Stream based on Concurrent Read and Exclusive Write parallel computer model. Meanwhile, we generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter λ in both informative and deceptive environments. Furthermore, we employed different learning automata in the generalized CPL-AdS method to find out if faster learning algorithm will lead to better realization of the generalized CPL-AdS method. All of these aforementioned contributions are vitally important whether in theory or in practical applications. Finally, extensive experiments show that our proposed approaches are efficient and feasible.

Initial Considerations for Navigation and Flight Dynamics of a Crewed Near-Earth Object Mission

NASA Technical Reports Server (NTRS)

Holt, Greg N.; Getchius, Joel; Tracy, William H.

2011-01-01

A crewed mission to a Near-Earth Object (NEO) was recently identified as a NASA Space Policy goal and priority. In support of this goal, a study was conducted to identify the initial considerations for performing the navigation and flight dynamics tasks of this mission class. Although missions to a NEO are not new, the unique factors involved in human spaceflight present challenges that warrant special examination. During the cruise phase of the mission, one of the most challenging factors is the noisy acceleration environment associated with a crewed vehicle. Additionally, the presence of a human crew necessitates a timely return trip, which may need to be expedited in an emergency situation where the mission is aborted. Tracking, navigation, and targeting results are shown for sample human-class trajectories to NEOs. Additionally, the benefit of in-situ navigation beacons on robotic precursor missions is presented. This mission class will require a longer duration flight than Apollo and, unlike previous human missions, there will likely be limited communication and tracking availability. This will necessitate the use of more onboard navigation and targeting capabilities. Finally, the rendezvous and proximity operations near an asteroid will be unlike anything previously attempted in a crewed spaceflight. The unknown gravitational environment and physical surface properties of the NEO may cause the rendezvous to behave differently than expected. Symbiosis of the human pilot and onboard navigation/targeting are presented which give additional robustness to unforeseen perturbations.

Thaumarchaeal amoA and nirK Gene Abundance Patterns Reveal Spatiotemporal Dynamics of Ammonia-oxidizing Archaeal Populations in Monterey Bay, CA

NASA Astrophysics Data System (ADS)

Tolar, B. B.; Reji, L.; Smith, J. M.; Chavez, F.; Francis, C.

2016-12-01

Thaumarchaeaota are among the most abundant microorganisms on the planet, and are significant players in the global nitrogen cycle. All cultivated members of the phylum are capable of performing the first and rate-limiting step of nitrification - the aerobic oxidation of ammonia to nitrite. In marine environments, ammonia-oxidizing archaea (AOA) have been found to greatly outnumber their bacterial counterparts. However, much about their ecology remains largely unknown. Monterey Bay, a non-estuarine embayment on the central California coast, is an ideal site for studying the dynamics of natural thaumarchaeal assemblages, given the highly dynamic nature of the Bay waters with seasonal upwelling episodes and the associated steep gradients in environmental variables. In the present study, we examined thaumarchaeal population dynamics in the upper Monterey Bay water column (0-500 m) using multiple molecular markers. Following high-resolution spatiotemporal sampling (i.e., up to 10 depths sampled monthly over a period of 2 years) at two stations in the Bay, we quantified thaumarchaeal functional genes - the ammonia monooxygenase (amoA) gene and its `shallow' and `deep' marine ecotypes, and variants of the marine nitrite reductase (nirK) gene. The abundances of both genes were regressed against environmental variables to gain insights into factors shaping their spatiotemporal dynamics in the Bay. Gene abundances at both stations varied with depth and season, with winter months generally having several orders of magnitude greater abundances. Statistical analyses point to differential controls on the gene abundances, with depth and temperature potentially being the major environmental determinants of thaumarchaeal population size. Our results also highlight the importance of employing multiple marker genes to gain a more highly resolved picture of thaumarchaeal population dynamics in complex environmental systems such as the coastal ocean.

Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks

PubMed Central

Kucyi, Aaron; Salomons, Tim V.; Davis, Karen D.

2013-01-01

Human minds often wander away from their immediate sensory environment. It remains unknown whether such mind wandering is unsystematic or whether it lawfully relates to an individual’s tendency to attend to salient stimuli such as pain and their associated brain structure/function. Studies of pain–cognition interactions typically examine explicit manipulation of attention rather than spontaneous mind wandering. Here we sought to better represent natural fluctuations in pain in daily life, so we assessed behavioral and neural aspects of spontaneous disengagement of attention from pain. We found that an individual’s tendency to attend to pain related to the disruptive effect of pain on his or her cognitive task performance. Next, we linked behavioral findings to neural networks with strikingly convergent evidence from functional magnetic resonance imaging during pain coupled with thought probes of mind wandering, dynamic resting state activity fluctuations, and diffusion MRI. We found that (i) pain-induced default mode network (DMN) deactivations were attenuated during mind wandering away from pain; (ii) functional connectivity fluctuations between the DMN and periaqueductal gray (PAG) dynamically tracked spontaneous attention away from pain; and (iii) across individuals, stronger PAG–DMN structural connectivity and more dynamic resting state PAG–DMN functional connectivity were associated with the tendency to mind wander away from pain. These data demonstrate that individual tendencies to mind wander away from pain, in the absence of explicit manipulation, are subserved by functional and structural connectivity within and between default mode and antinociceptive descending modulation networks. PMID:24167282

Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks.

PubMed

Kucyi, Aaron; Salomons, Tim V; Davis, Karen D

2013-11-12

Human minds often wander away from their immediate sensory environment. It remains unknown whether such mind wandering is unsystematic or whether it lawfully relates to an individual's tendency to attend to salient stimuli such as pain and their associated brain structure/function. Studies of pain-cognition interactions typically examine explicit manipulation of attention rather than spontaneous mind wandering. Here we sought to better represent natural fluctuations in pain in daily life, so we assessed behavioral and neural aspects of spontaneous disengagement of attention from pain. We found that an individual's tendency to attend to pain related to the disruptive effect of pain on his or her cognitive task performance. Next, we linked behavioral findings to neural networks with strikingly convergent evidence from functional magnetic resonance imaging during pain coupled with thought probes of mind wandering, dynamic resting state activity fluctuations, and diffusion MRI. We found that (i) pain-induced default mode network (DMN) deactivations were attenuated during mind wandering away from pain; (ii) functional connectivity fluctuations between the DMN and periaqueductal gray (PAG) dynamically tracked spontaneous attention away from pain; and (iii) across individuals, stronger PAG-DMN structural connectivity and more dynamic resting state PAG-DMN functional connectivity were associated with the tendency to mind wander away from pain. These data demonstrate that individual tendencies to mind wander away from pain, in the absence of explicit manipulation, are subserved by functional and structural connectivity within and between default mode and antinociceptive descending modulation networks.

40 CFR - Unknown Title

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 6 2014-07-01 2014-07-01 false Section Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) AMBIENT AIR MONITORING REFERENCE AND EQUIVALENT METHODS Procedures for Determining Comparability Between Candidate Methods and Reference Method...

Fast grasping of unknown objects using principal component analysis

NASA Astrophysics Data System (ADS)

Lei, Qujiang; Chen, Guangming; Wisse, Martijn

2017-09-01

Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Crop rotations and poultry litter impact dynamic soil chemical properties and soil biota long-term

USDA-ARS?s Scientific Manuscript database

Dynamic soil physiochemical interactions with conservation agricultural practices and soil biota are largely unknown. Therefore, this study aims to quantify long-term (12-yr) impacts of cover crops, poultry litter, crop rotations, and conservation tillage and their interactions on soil physiochemica...

Mass properties measurement system: Dynamics and statics measurements

NASA Technical Reports Server (NTRS)

Doty, Keith L.

1993-01-01

This report presents and interprets experimental data obtained from the Mass Properties Measurement System (MPMS). Statics measurements yield the center-of-gravity of an unknown mass and dynamics measurements yield its inertia matrix. Observations of the MPMS performance has lead us to specific design criteria and an understanding of MPMS limitations.

The ANISA Model of Education: A Critique. Issues in Native Education.

ERIC Educational Resources Information Center

Four Worlds Development Project, Lethbridge (Alberta).

The ANISA model of education (D. Streets and D. Jordan) classifies curriculum content into four areas--the physical environment, the human environment, the unknown environment, and the self--and encourages horizontal integration between content areas. The ANISA model holds that the process of learning consists of differentiation, integration, and…

High Pressure Oxidizer Turbopump (HPOTP) inducer dynamic design environment

NASA Technical Reports Server (NTRS)

Herda, D. A.; Gross, R. S.

1995-01-01

The dynamic environment must be known to evaluate high pressure oxidizer turbopump inducer fatigue life. This report sets the dynamic design loads for the alternate turbopump inducer as determined by water-flow rig testing. Also, guidelines are given for estimating the dynamic environment for other inducer and impeller applications.

Towards Cluster-Assembled Materials of True Monodispersity in Size and Chemical Environment: Synthesis, Dynamics and Activity

DTIC Science & Technology

2016-10-27

AFRL-AFOSR-UK-TR-2016-0037 Towards cluster-assembled materials of true monodispersity in size and chemical environment: Synthesis, Dynamics and...Towards cluster-assembled materials of true monodispersity in size and chemical environment: synthesis, dynamics and activity 5a.  CONTRACT NUMBER 5b...report Towards cluster-assembled materials of true monodispersity in size and chemical environment: Synthesis, Dynamics and Activity Ulrich Heiz

Learning Unknown Event Models

DTIC Science & Technology

2014-07-01

Intelligence (www.aaai.org). All rights reserved. knowledge engineering, but it is often impractical due to high environment variance, or unknown events...distribution unlimited 13. SUPPLEMENTARY NOTES In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence , 27-31 July 2014...autonomy for responding to unexpected events in strategy simulations. Computational Intelligence , 29(2), 187-206. Leake, D. B. (1991), Goal-based

Dynamic activation of basilar membrane macrophages in response to chronic sensory cell degeneration in aging mouse cochleae

PubMed Central

Frye, Mitchell D.; Yang, Weiping; Zhang, Celia; Xiong, Binbin; Hu, Bo Hua

2016-01-01

In the sensory epithelium, macrophages have been identified on the scala tympani side of the basilar membrane. These basilar membrane macrophages are the spatially closest immune cells to sensory cells and are able to directly respond to and influence sensory cell pathogenesis. While basilar membrane macrophages have been studied in acute cochlear stresses, their behavior in response to chronic sensory cell degeneration is largely unknown. Here we report a systematic observation of the variance in phenotypes, the changes in morphology and distribution of basilar membrane tissue macrophages in different age groups of C57BL/6J mice, a mouse model of age-related sensory cell degeneration. This study reveals that mature, fully differentiated tissue macrophages, not recently infiltrated monocytes, are the major macrophage population for immune responses to chronic sensory cell death. These macrophages display dynamic changes in their numbers and morphologies as age increases, and the changes are related to the phases of sensory cell degeneration. Notably, macrophage activation precedes sensory cell pathogenesis, and strong macrophage activity is maintained until sensory cell degradation is complete. Collectively, these findings suggest that mature tissue macrophages on the basilar membrane are a dynamic group of cells that are capable of vigorous adaptation to changes in the local sensory epithelium environment influenced by sensory cell status. PMID:27837652

Dynamic activation of basilar membrane macrophages in response to chronic sensory cell degeneration in aging mouse cochleae.

PubMed

Frye, Mitchell D; Yang, Weiping; Zhang, Celia; Xiong, Binbin; Hu, Bo Hua

2017-02-01

In the sensory epithelium, macrophages have been identified on the scala tympani side of the basilar membrane. These basilar membrane macrophages are the spatially closest immune cells to sensory cells and are able to directly respond to and influence sensory cell pathogenesis. While basilar membrane macrophages have been studied in acute cochlear stresses, their behavior in response to chronic sensory cell degeneration is largely unknown. Here we report a systematic observation of the variance in phenotypes, the changes in morphology and distribution of basilar membrane tissue macrophages in different age groups of C57BL/6J mice, a mouse model of age-related sensory cell degeneration. This study reveals that mature, fully differentiated tissue macrophages, not recently infiltrated monocytes, are the major macrophage population for immune responses to chronic sensory cell death. These macrophages display dynamic changes in their numbers and morphologies as age increases, and the changes are related to the phases of sensory cell degeneration. Notably, macrophage activation precedes sensory cell pathogenesis, and strong macrophage activity is maintained until sensory cell degradation is complete. Collectively, these findings suggest that mature tissue macrophages on the basilar membrane are a dynamic group of cells that are capable of vigorous adaptation to changes in the local sensory epithelium environment influenced by sensory cell status. Copyright © 2016 Elsevier B.V. All rights reserved.

Output feedback control of a quadrotor UAV using neural networks.

PubMed

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Fractional-order active fault-tolerant force-position controller design for the legged robots using saturated actuator with unknown bias and gain degradation

NASA Astrophysics Data System (ADS)

Farid, Yousef; Majd, Vahid Johari; Ehsani-Seresht, Abbas

2018-05-01

In this paper, a novel fault accommodation strategy is proposed for the legged robots subject to the actuator faults including actuation bias and effective gain degradation as well as the actuator saturation. First, the combined dynamics of two coupled subsystems consisting of the dynamics of the legs subsystem and the body subsystem are developed. Then, the interaction of the robot with the environment is formulated as the contact force optimization problem with equality and inequality constraints. The desired force is obtained by a dynamic model. A robust super twisting fault estimator is proposed to precisely estimate the defective torque amplitude of the faulty actuator in finite time. Defining a novel fractional sliding surface, a fractional nonsingular terminal sliding mode control law is developed. Moreover, by introducing a suitable auxiliary system and using its state vector in the designed controller, the proposed fault-tolerant control (FTC) scheme guarantees the finite-time stability of the closed-loop control system. The robustness and finite-time convergence of the proposed control law is established using the Lyapunov stability theory. Finally, numerical simulations are performed on a quadruped robot to demonstrate the stable walking of the robot with and without actuator faults, and actuator saturation constraints, and the results are compared to results with an integer order fault-tolerant controller.

Cell reprogramming modelled as transitions in a hierarchy of cell cycles

NASA Astrophysics Data System (ADS)

Hannam, Ryan; Annibale, Alessia; Kühn, Reimer

2017-10-01

We construct a model of cell reprogramming (the conversion of fully differentiated cells to a state of pluripotency, known as induced pluripotent stem cells, or iPSCs) which builds on key elements of cell biology viz. cell cycles and cell lineages. Although reprogramming has been demonstrated experimentally, much of the underlying processes governing cell fate decisions remain unknown. This work aims to bridge this gap by modelling cell types as a set of hierarchically related dynamical attractors representing cell cycles. Stages of the cell cycle are characterised by the configuration of gene expression levels, and reprogramming corresponds to triggering transitions between such configurations. Two mechanisms were found for reprogramming in a two level hierarchy: cycle specific perturbations and a noise induced switching. The former corresponds to a directed perturbation that induces a transition into a cycle-state of a different cell type in the potency hierarchy (mainly a stem cell) whilst the latter is a priori undirected and could be induced, e.g. by a (stochastic) change in the cellular environment. These reprogramming protocols were found to be effective in large regimes of the parameter space and make specific predictions concerning reprogramming dynamics which are broadly in line with experimental findings.

Dynamic Model Predicting Overweight, Obesity, and Extreme Obesity Prevalence Trends

PubMed Central

Thomas, Diana M.; Weedermann, Marion; Fuemmeler, Bernard F.; Martin, Corby K.; Dhurandhar, Nikhil V.; Bredlau, Carl; Heymsfield, Steven B.; Ravussin, Eric; Bouchard, Claude

2013-01-01

Objective Obesity prevalence in the United States (US) appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. Design and Methods A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and non-social influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. Results The dynamic model predicts that: obesity prevalence is a function of birth rate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of obesity, overweight, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9%, respectively. Conclusions The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. PMID:23804487

The impact of vegan production on the kimchi microbiome.

PubMed

Zabat, Michelle A; Sano, William H; Cabral, Damien J; Wurster, Jenna I; Belenky, Peter

2018-09-01

Despite previous inquiry into the fermentative bacterial community of kimchi, there has been little insight into the impacts of starting ingredients on the establishment and dynamics of the microbial community. Recently some industrial producers have begun to utilize vegan production methods that omit fermented seafood ingredients. The community-level impacts of this change are unknown. In this study, we investigated the differences in the taxonomic composition of the microbial communities of non-vegan kimchi and vegan kimchi prepared through quick fermentation at room temperature. In addition to tracking the community dynamics over the fermentation process, we looked at the impact of the constituent ingredients and the production facility environment on the microbial community of fermenting kimchi. Our results indicate that the bacterial community of the prepared vegan product closely mirrors the progression and final structure of the non-vegan final product. We also found that room temperature-fermented kimchi differs minimally from more traditional cold-fermented kimchi. Finally, we found that the bacterial community of the starting ingredients show a low relative abundance of the lactic acid bacteria in fermented kimchi, whereas the production facility is dominated by these bacteria. Copyright © 2018 Elsevier Ltd. All rights reserved.

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

A strong magnetic field around the supermassive black hole at the centre of the Galaxy.

PubMed

Eatough, R P; Falcke, H; Karuppusamy, R; Lee, K J; Champion, D J; Keane, E F; Desvignes, G; Schnitzeler, D H F M; Spitler, L G; Kramer, M; Klein, B; Bassa, C; Bower, G C; Brunthaler, A; Cognard, I; Deller, A T; Demorest, P B; Freire, P C C; Kraus, A; Lyne, A G; Noutsos, A; Stappers, B; Wex, N

2013-09-19

Earth's nearest candidate supermassive black hole lies at the centre of the Milky Way. Its electromagnetic emission is thought to be powered by radiatively inefficient accretion of gas from its environment, which is a standard mode of energy supply for most galactic nuclei. X-ray measurements have already resolved a tenuous hot gas component from which the black hole can be fed. The magnetization of the gas, however, which is a crucial parameter determining the structure of the accretion flow, remains unknown. Strong magnetic fields can influence the dynamics of accretion, remove angular momentum from the infalling gas, expel matter through relativistic jets and lead to synchrotron emission such as that previously observed. Here we report multi-frequency radio measurements of a newly discovered pulsar close to the Galactic Centre and show that the pulsar's unusually large Faraday rotation (the rotation of the plane of polarization of the emission in the presence of an external magnetic field) indicates that there is a dynamically important magnetic field near the black hole. If this field is accreted down to the event horizon it provides enough magnetic flux to explain the observed emission--from radio to X-ray wavelengths--from the black hole.

Identification of vortexes obstructing the dynamo mechanism in laboratory experiments

NASA Astrophysics Data System (ADS)

Limone, A.; Hatch, D. R.; Forest, C. B.; Jenko, F.

2013-06-01

The magnetohydrodynamic dynamo effect explains the generation of self-sustained magnetic fields in electrically conducting flows, especially in geo- and astrophysical environments. Yet the details of this mechanism are still unknown, e.g., how and to which extent the geometry, the fluid topology, the forcing mechanism, and the turbulence can have a negative effect on this process. We report on numerical simulations carried out in spherical geometry, analyzing the predicted velocity flow with the so-called singular value decomposition, a powerful technique that allows us to precisely identify vortexes in the flow which would be difficult to characterize with conventional spectral methods. We then quantify the contribution of these vortexes to the growth rate of the magnetic energy in the system. We identify an axisymmetric vortex, whose rotational direction changes periodically in time, and whose dynamics are decoupled from those of the large scale background flow, that is detrimental for the dynamo effect. A comparison with experiments is carried out, showing that similar dynamics were observed in cylindrical geometry. These previously unexpected eddies, which impede the dynamo effect, offer an explanation for the experimental difficulties in attaining a dynamo in spherical geometry.

A strong magnetic field around the supermassive black hole at the centre of the Galaxy

NASA Astrophysics Data System (ADS)

Eatough, R. P.; Falcke, H.; Karuppusamy, R.; Lee, K. J.; Champion, D. J.; Keane, E. F.; Desvignes, G.; Schnitzeler, D. H. F. M.; Spitler, L. G.; Kramer, M.; Klein, B.; Bassa, C.; Bower, G. C.; Brunthaler, A.; Cognard, I.; Deller, A. T.; Demorest, P. B.; Freire, P. C. C.; Kraus, A.; Lyne, A. G.; Noutsos, A.; Stappers, B.; Wex, N.

2013-09-01

Earth's nearest candidate supermassive black hole lies at the centre of the Milky Way. Its electromagnetic emission is thought to be powered by radiatively inefficient accretion of gas from its environment, which is a standard mode of energy supply for most galactic nuclei. X-ray measurements have already resolved a tenuous hot gas component from which the black hole can be fed. The magnetization of the gas, however, which is a crucial parameter determining the structure of the accretion flow, remains unknown. Strong magnetic fields can influence the dynamics of accretion, remove angular momentum from the infalling gas, expel matter through relativistic jets and lead to synchrotron emission such as that previously observed. Here we report multi-frequency radio measurements of a newly discovered pulsar close to the Galactic Centre and show that the pulsar's unusually large Faraday rotation (the rotation of the plane of polarization of the emission in the presence of an external magnetic field) indicates that there is a dynamically important magnetic field near the black hole. If this field is accreted down to the event horizon it provides enough magnetic flux to explain the observed emission--from radio to X-ray wavelengths--from the black hole.

Spectral decomposition of nonlinear systems with memory

NASA Astrophysics Data System (ADS)

Svenkeson, Adam; Glaz, Bryan; Stanton, Samuel; West, Bruce J.

2016-02-01

We present an alternative approach to the analysis of nonlinear systems with long-term memory that is based on the Koopman operator and a Lévy transformation in time. Memory effects are considered to be the result of interactions between a system and its surrounding environment. The analysis leads to the decomposition of a nonlinear system with memory into modes whose temporal behavior is anomalous and lacks a characteristic scale. On average, the time evolution of a mode follows a Mittag-Leffler function, and the system can be described using the fractional calculus. The general theory is demonstrated on the fractional linear harmonic oscillator and the fractional nonlinear logistic equation. When analyzing data from an ill-defined (black-box) system, the spectral decomposition in terms of Mittag-Leffler functions that we propose may uncover inherent memory effects through identification of a small set of dynamically relevant structures that would otherwise be obscured by conventional spectral methods. Consequently, the theoretical concepts we present may be useful for developing more general methods for numerical modeling that are able to determine whether observables of a dynamical system are better represented by memoryless operators, or operators with long-term memory in time, when model details are unknown.

Environmental statistics and optimal regulation.

PubMed

Sivak, David A; Thomson, Matt

2014-09-01

Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies--such as constitutive expression or graded response--for regulating protein levels in response to environmental inputs. We propose a general framework-here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient-to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.

Unraveling Trichoderma species in the attine ant environment: description of three new taxa.

PubMed

Montoya, Quimi Vidaurre; Meirelles, Lucas Andrade; Chaverri, Priscila; Rodrigues, Andre

2016-05-01

Fungus-growing "attine" ants forage diverse substrates to grow fungi for food. In addition to the mutualistic fungal partner, the colonies of these insects harbor a rich microbiome composed of bacteria, filamentous fungi and yeasts. Previous work reported some Trichoderma species in the fungus gardens of leafcutter ants. However, no studies systematically addressed the putative association of Trichoderma with attine ants, especially in non-leafcutter ants. Here, a total of 62 strains of Trichoderma were analyzed using three molecular markers (ITS, tef1 and rpb2). In addition, 30 out of 62 strains were also morphologically examined. The strains studied correspond to the largest sampling carried out so far for Trichoderma in the attine ant environment. Our results revealed the richness of Trichoderma in this environment, since we found 20 Trichoderma species, including three new taxa described in the present work (Trichoderma attinorum, Trichoderma texanum and Trichoderma longifialidicum spp. nov.) as well as a new phylogenetic taxon (LESF 545). Moreover, we show that all 62 strains grouped within different clades across the Trichoderma phylogeny, which are identical or closely related to strains derived from several other environments. This evidence supports the transient nature of the genus Trichoderma in the attine ant colonies. The discovery of three new species suggests that the dynamic foraging behavior of these insects might be responsible for accumulation of transient fungi into their colonies, which might hold additional fungal taxa still unknown to science.

An extreme magneto-ionic environment associated with the fast radio burst source FRB 121102

NASA Astrophysics Data System (ADS)

Michilli, D.; Seymour, A.; Hessels, J. W. T.; Spitler, L. G.; Gajjar, V.; Archibald, A. M.; Bower, G. C.; Chatterjee, S.; Cordes, J. M.; Gourdji, K.; Heald, G. H.; Kaspi, V. M.; Law, C. J.; Sobey, C.; Adams, E. A. K.; Bassa, C. G.; Bogdanov, S.; Brinkman, C.; Demorest, P.; Fernandez, F.; Hellbourg, G.; Lazio, T. J. W.; Lynch, R. S.; Maddox, N.; Marcote, B.; McLaughlin, M. A.; Paragi, Z.; Ransom, S. M.; Scholz, P.; Siemion, A. P. V.; Tendulkar, S. P.; van Rooy, P.; Wharton, R. S.; Whitlow, D.

2018-01-01

Fast radio bursts are millisecond-duration, extragalactic radio flashes of unknown physical origin. The only known repeating fast radio burst source—FRB 121102—has been localized to a star-forming region in a dwarf galaxy at redshift 0.193 and is spatially coincident with a compact, persistent radio source. The origin of the bursts, the nature of the persistent source and the properties of the local environment are still unclear. Here we report observations of FRB 121102 that show almost 100 per cent linearly polarized emission at a very high and variable Faraday rotation measure in the source frame (varying from +1.46 × 105 radians per square metre to +1.33 × 105 radians per square metre at epochs separated by seven months) and narrow (below 30 microseconds) temporal structure. The large and variable rotation measure demonstrates that FRB 121102 is in an extreme and dynamic magneto-ionic environment, and the short durations of the bursts suggest a neutron star origin. Such large rotation measures have hitherto been observed only in the vicinities of massive black holes (larger than about 10,000 solar masses). Indeed, the properties of the persistent radio source are compatible with those of a low-luminosity, accreting massive black hole. The bursts may therefore come from a neutron star in such an environment or could be explained by other models, such as a highly magnetized wind nebula or supernova remnant surrounding a young neutron star.

An extreme magneto-ionic environment associated with the fast radio burst source FRB 121102.

PubMed

Michilli, D; Seymour, A; Hessels, J W T; Spitler, L G; Gajjar, V; Archibald, A M; Bower, G C; Chatterjee, S; Cordes, J M; Gourdji, K; Heald, G H; Kaspi, V M; Law, C J; Sobey, C; Adams, E A K; Bassa, C G; Bogdanov, S; Brinkman, C; Demorest, P; Fernandez, F; Hellbourg, G; Lazio, T J W; Lynch, R S; Maddox, N; Marcote, B; McLaughlin, M A; Paragi, Z; Ransom, S M; Scholz, P; Siemion, A P V; Tendulkar, S P; Van Rooy, P; Wharton, R S; Whitlow, D

2018-01-10

Fast radio bursts are millisecond-duration, extragalactic radio flashes of unknown physical origin. The only known repeating fast radio burst source-FRB 121102-has been localized to a star-forming region in a dwarf galaxy at redshift 0.193 and is spatially coincident with a compact, persistent radio source. The origin of the bursts, the nature of the persistent source and the properties of the local environment are still unclear. Here we report observations of FRB 121102 that show almost 100 per cent linearly polarized emission at a very high and variable Faraday rotation measure in the source frame (varying from +1.46 × 10 5 radians per square metre to +1.33 × 10 5 radians per square metre at epochs separated by seven months) and narrow (below 30 microseconds) temporal structure. The large and variable rotation measure demonstrates that FRB 121102 is in an extreme and dynamic magneto-ionic environment, and the short durations of the bursts suggest a neutron star origin. Such large rotation measures have hitherto been observed only in the vicinities of massive black holes (larger than about 10,000 solar masses). Indeed, the properties of the persistent radio source are compatible with those of a low-luminosity, accreting massive black hole. The bursts may therefore come from a neutron star in such an environment or could be explained by other models, such as a highly magnetized wind nebula or supernova remnant surrounding a young neutron star.

Rendezvous with connectivity preservation for multi-robot systems with an unknown leader

NASA Astrophysics Data System (ADS)

Dong, Yi

2018-02-01

This paper studies the leader-following rendezvous problem with connectivity preservation for multi-agent systems composed of uncertain multi-robot systems subject to external disturbances and an unknown leader, both of which are generated by a so-called exosystem with parametric uncertainty. By combining internal model design, potential function technique and adaptive control, two distributed control strategies are proposed to maintain the connectivity of the communication network, to achieve the asymptotic tracking of all the followers to the output of the unknown leader system, as well as to reject unknown external disturbances. It is also worth to mention that the uncertain parameters in the multi-robot systems and exosystem are further allowed to belong to unknown and unbounded sets when applying the second fully distributed control law containing a dynamic gain inspired by high-gain adaptive control or self-tuning regulator.

Adaptive robust control of a class of non-affine variable-speed variable-pitch wind turbines with unmodeled dynamics.

PubMed

Bagheri, Pedram; Sun, Qiao

2016-07-01

In this paper, a novel synthesis of Nussbaum-type functions, and an adaptive radial-basis function neural network is proposed to design controllers for variable-speed, variable-pitch wind turbines. Dynamic equations of the wind turbine are highly nonlinear, uncertain, and affected by unknown disturbance sources. Furthermore, the dynamic equations are non-affine with respect to the pitch angle, which is a control input. To address these problems, a Nussbaum-type function, along with a dynamic control law are adopted to resolve the non-affine nature of the equations. Moreover, an adaptive radial-basis function neural network is designed to approximate non-parametric uncertainties. Further, the closed-loop system is made robust to unknown disturbance sources, where no prior knowledge of disturbance bound is assumed in advance. Finally, the Lyapunov stability analysis is conducted to show the stability of the entire closed-loop system. In order to verify analytical results, a simulation is presented and the results are compared to both a PI and an existing adaptive controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

NASA Astrophysics Data System (ADS)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

Panarchy use in environmental science for risk and resilience ...

EPA Pesticide Factsheets

Environmental sciences have an important role in informing sustainable management of built environments by providing insights about the drivers and potentially negative impacts of global environmental change. Here, we discuss panarchy theory, a multi-scale hierarchical concept that accounts for the dynamism of complex socio-ecological systems, especially for those systems with strong cross-scale feedbacks. The idea of panarchy underlies much of system resilience, focusing on how systems respond to known and unknown threats. Panarchy theory can provide a framework for qualitative and quantitative research and application in the environmental sciences, which can in turn inform the ongoing efforts in socio-technical resilience thinking and adaptive and transformative approaches to management. The environmental sciences strive for understanding, mitigating and reversing the negative impacts of global environmental change, including chemical pollution, to maintain sustainability options for the future, and therefore play an important role for informing management.

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.

PubMed

Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai

2017-03-01

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

Distributed subterranean exploration and mapping with teams of UAVs

NASA Astrophysics Data System (ADS)

Rogers, John G.; Sherrill, Ryan E.; Schang, Arthur; Meadows, Shava L.; Cox, Eric P.; Byrne, Brendan; Baran, David G.; Curtis, J. Willard; Brink, Kevin M.

2017-05-01

Teams of small autonomous UAVs can be used to map and explore unknown environments which are inaccessible to teams of human operators in humanitarian assistance and disaster relief efforts (HA/DR). In addition to HA/DR applications, teams of small autonomous UAVs can enhance Warfighter capabilities and provide operational stand-off for military operations such as cordon and search, counter-WMD, and other intelligence, surveillance, and reconnaissance (ISR) operations. This paper will present a hardware platform and software architecture to enable distributed teams of heterogeneous UAVs to navigate, explore, and coordinate their activities to accomplish a search task in a previously unknown environment.

Dynamic load environment of bridge-mounted sign support structures : research implementation plan.

DOT National Transportation Integrated Search

2005-09-01

Welded aluminum highway sign support trusses must withstand in-service dynamic loads, which largely : constitute the fatigue environment. Sources of these dynamic loads include the natural wind and seismic : environment, the artificial wind environme...

Parametric system identification of catamaran for improving controller design

NASA Astrophysics Data System (ADS)

Timpitak, Surasak; Prempraneerach, Pradya; Pengwang, Eakkachai

2018-01-01

This paper presents an estimation of simplified dynamic model for only surge- and yaw- motions of catamaran by using system identification (SI) techniques to determine associated unknown parameters. These methods will enhance the performance of designing processes for the motion control system of Unmanned Surface Vehicle (USV). The simulation results demonstrate an effective way to solve for damping forces and to determine added masses by applying least-square and AutoRegressive Exogenous (ARX) methods. Both methods are then evaluated according to estimated parametric errors from the vehicle’s dynamic model. The ARX method, which yields better estimated accuracy, can then be applied to identify unknown parameters as well as to help improving a controller design of a real unmanned catamaran.

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

NASA Astrophysics Data System (ADS)

Jha, Devesh K.; Chattopadhyay, Pritthi; Sarkar, Soumik; Ray, Asok

2016-05-01

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.

Sex-based differences in the adaptive value of social behavior contrasted against morphology and environment.

PubMed

Vander Wal, E; Festa-Bianchet, M; Réale, D; Coltman, D W; Pelletier, F

2015-03-01

The adaptive nature of sociality has long been a central question in ecology and evolution. However, the relative importance of social behavior for fitness, compared to morphology and environment, remains largely unknown. We assessed the importance of sociality for fitness (lamb production and survival) in a population of mark6d bighorn sheep (Ovis canadensis) over 16 years (n = 1022 sheep-years). We constructed social networks from observations (n = 38,350) of group membership (n = 3150 groups). We then tested whether consistent individual differences in social behavior (centrality) exist and evaluated their relative importance compared to factors known to affect fitness: mass, age, parental effects, and population density. Sheep exhibited consistent individual differences in social centrality. Controlling for maternal carryover effects and age, the positive effect of centrality in a social network on adult female lamb production and survival was equal or greater than the effect of body mass or population density. Social centrality had less effect on male survival and no effect on adult male lamb production or lamb survival. Through its effect on lamb production and survival, sociality in fission-fusion animal societies may ultimately influence population dynamics equally or more than morphological or environmental effects.

Population extinction under bursty reproduction in a time-modulated environment

NASA Astrophysics Data System (ADS)

Vilk, Ohad; Assaf, Michael

2018-06-01

In recent years nondemographic variability has been shown to greatly affect dynamics of stochastic populations. For example, nondemographic noise in the form of a bursty reproduction process with an a priori unknown burst size, or environmental variability in the form of time-varying reaction rates, have been separately found to dramatically impact the extinction risk of isolated populations. In this work we investigate the extinction risk of an isolated population under the combined influence of these two types of nondemographic variation. Using the so-called momentum-space Wentzel-Kramers-Brillouin (WKB) approach and accounting for the explicit time dependence in the reaction rates, we arrive at a set of time-dependent Hamilton equations. To this end, we evaluate the population's extinction risk by finding the instanton of the time-perturbed Hamiltonian numerically, whereas analytical expressions are presented in particular limits using various perturbation techniques. We focus on two classes of time-varying environments: periodically varying rates corresponding to seasonal effects and a sudden decrease in the birth rate corresponding to a catastrophe. All our theoretical results are tested against numerical Monte Carlo simulations with time-dependent rates and also against a numerical solution of the corresponding time-dependent Hamilton equations.

CFD-RANS prediction of individual exposure from continuous release of hazardous airborne materials in complex urban environments

NASA Astrophysics Data System (ADS)

Efthimiou, G. C.; Andronopoulos, S.; Bartzis, J. G.; Berbekar, E.; Harms, F.; Leitl, B.

2017-02-01

One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict individual exposure (maximum dosages) of an airborne material which is released continuously from a point source. The present work addresses the question whether the computational fluid dynamics (CFD)-Reynolds-averaged Navier-Stokes (RANS) methodology can be used to predict individual exposure for various exposure times. This is feasible by providing the two RANS concentration moments (mean and variance) and a turbulent time scale to a deterministic model. The whole effort is focused on the prediction of individual exposure inside a complex real urban area. The capabilities of the proposed methodology are validated against wind-tunnel data (CUTE experiment). The present simulations were performed 'blindly', i.e. the modeller had limited information for the inlet boundary conditions and the results were kept unknown until the end of the COST Action ES1006. Thus, a high uncertainty of the results was expected. The general performance of the methodology due to this 'blind' strategy is good. The validation metrics fulfil the acceptance criteria. The effect of the grid and the turbulence model on the model performance is examined.

Autonomous Underwater Navigation and Optical Mapping in Unknown Natural Environments.

PubMed

Hernández, Juan David; Istenič, Klemen; Gracias, Nuno; Palomeras, Narcís; Campos, Ricard; Vidal, Eduard; García, Rafael; Carreras, Marc

2016-07-26

We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario.

Autonomous Underwater Navigation and Optical Mapping in Unknown Natural Environments

PubMed Central

Hernández, Juan David; Istenič, Klemen; Gracias, Nuno; Palomeras, Narcís; Campos, Ricard; Vidal, Eduard; García, Rafael; Carreras, Marc

2016-01-01

We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario. PMID:27472337

Autonomous exploration and mapping of unknown environments

NASA Astrophysics Data System (ADS)

Owens, Jason; Osteen, Phil; Fields, MaryAnne

2012-06-01

Autonomous exploration and mapping is a vital capability for future robotic systems expected to function in arbitrary complex environments. In this paper, we describe an end-to-end robotic solution for remotely mapping buildings. For a typical mapping system, an unmanned system is directed to enter an unknown building at a distance, sense the internal structure, and, barring additional tasks, while in situ, create a 2-D map of the building. This map provides a useful and intuitive representation of the environment for the remote operator. We have integrated a robust mapping and exploration system utilizing laser range scanners and RGB-D cameras, and we demonstrate an exploration and metacognition algorithm on a robotic platform. The algorithm allows the robot to safely navigate the building, explore the interior, report significant features to the operator, and generate a consistent map - all while maintaining localization.

Dynamically analyzing cell interactions in biological environments using multiagent social learning framework.

PubMed

Zhang, Chengwei; Li, Xiaohong; Li, Shuxin; Feng, Zhiyong

2017-09-20

Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.

Rocket Engine Nozzle Side Load Transient Analysis Methodology: A Practical Approach

NASA Technical Reports Server (NTRS)

Shi, John J.

2005-01-01

During the development stage, in order to design/to size the rocket engine components and to reduce the risks, the local dynamic environments as well as dynamic interface loads must be defined. There are two kinds of dynamic environment, i.e. shock transients and steady-state random and sinusoidal vibration environments. Usually, the steady-state random and sinusoidal vibration environments are scalable, but the shock environments are not scalable. In other words, based on similarities only random vibration environments can be defined for a new engine. The methodology covered in this paper provides a way to predict the shock environments and the dynamic loads for new engine systems and new engine components in the early stage of new engine development or engine nozzle modifications.

Study the Seasonal Variability of Plankton and Forage Fish in the Gulf of Khambhat Using Npzfd Model

NASA Astrophysics Data System (ADS)

Kumar, V.; Kumar, S.

2016-02-01

Numerical modelling of marine ecology exploits several assumptions and it is indeed quite challenging to include marine ecological phenomena into a mathematical framework with too many unknown parameters. The governing ordinary differential equations represent the interaction of the biological and chemical processes in a marine environment. The key concern in the development of a numerical models are parameterizations based on output, viz., mathematical modelling of ecological system mainly depends on parameters and its variations. Almost, all constituents of each trophic level of marine food web are depended on phytoplankton, which are mostly influenced by initial slope of P-I curve and nutrient stock in the study domain. Whereas, the earlier plankton dynamic models rarely include a compartment of small fish and as an agent in zooplankton mortality, which is most important for the modelling of higher trophic level of marine species. A compartment of forage fish in the modelling of plankton dynamics has been given more realistic mortality rates of plankton, viz., they feed upon phytoplankton and zooplankton. The inclusion of an additional compartment increases complexity of earlier plankton dynamics model as it introduces additional unknown parameters, which has been specified for performing the numerical simulations.As a case study we applied our analysis to explain the aquatic ecology of Gulf of Khambhat (19o 48' N - 22o20' N, 65o E - 72o40' E), west coast of India, which has rich bio-diversity and a high productive area in the form of plankton and forage fish. It has elevated turbidity and varying geography location, viz., one of the regions among world's ocean with high biological productivity.The model presented in this study is able to bring out the essential features of the observed data; that includes the bimodal oscillations in the observed data, monthly mean chlorophyll-a in the SeaWiFs/MODIS Aqua data and in-situ data of plankton. The additional compartment of forage fish and detritus in NPZFD model represents a major structural difference from the earlier NPZ model.

Modeling equine race surface vertical mechanical behaviors in a musculoskeletal modeling environment.

PubMed

Symons, Jennifer E; Fyhrie, David P; Hawkins, David A; Upadhyaya, Shrinivasa K; Stover, Susan M

2015-02-26

Race surfaces have been associated with the incidence of racehorse musculoskeletal injury, the leading cause of racehorse attrition. Optimal race surface mechanical behaviors that minimize injury risk are unknown. Computational models are an economical method to determine optimal mechanical behaviors. Previously developed equine musculoskeletal models utilized ground reaction floor models designed to simulate a stiff, smooth floor appropriate for a human gait laboratory. Our objective was to develop a computational race surface model (two force-displacement functions, one linear and one nonlinear) that reproduced experimental race surface mechanical behaviors for incorporation in equine musculoskeletal models. Soil impact tests were simulated in a musculoskeletal modeling environment and compared to experimental force and displacement data collected during initial and repeat impacts at two racetracks with differing race surfaces - (i) dirt and (ii) synthetic. Best-fit model coefficients (7 total) were compared between surface types and initial and repeat impacts using a mixed model ANCOVA. Model simulation results closely matched empirical force, displacement and velocity data (Mean R(2)=0.930-0.997). Many model coefficients were statistically different between surface types and impacts. Principal component analysis of model coefficients showed systematic differences based on surface type and impact. In the future, the race surface model may be used in conjunction with previously developed the equine musculoskeletal models to understand the effects of race surface mechanical behaviors on limb dynamics, and determine race surface mechanical behaviors that reduce the incidence of racehorse musculoskeletal injury through modulation of limb dynamics. Copyright © 2015 Elsevier Ltd. All rights reserved.

Differential flatness properties and adaptive control of the hypothalamic-pituitary-adrenal axis model

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

It is shown that the model of the hypothalamic-pituitary-adrenal gland axis is a differentially flat one and this permits to transform it to the so-called linear canonical form. For the new description of the system's dynamics the transformed control inputs contain unknown terms which depend on the system's parameters. To identify these terms an adaptive fuzzy approximator is used in the control loop. Thus an adaptive fuzzy control scheme is implemented in which the unknown or unmodeled system dynamics is approximated by neurofuzzy networks and next this information is used by a feedback controller that makes the state variables (CRH - corticotropin releasing hormone, adenocortocotropic hormone - ACTH, cortisol) of the hypothalamic-pituitary-adrenal gland axis model converge to the desirable levels (setpoints). This adaptive control scheme is exclusively implemented with the use of output feedback, while the state vector elements which are not directly measured are estimated with the use of a state observer that operates in the control loop. The learning rate of the adaptive fuzzy system is suitably computed from Lyapunov analysis, so as to assure that both the learning procedure for the unknown system's parameters, the dynamics of the observer and the dynamics of the control loop will remain stable. The performed Lyapunov stability analysis depends on two Riccati equations, one associated with the feedback controller and one associated with the state observer. Finally, it is proven that for the control scheme that comprises the feedback controller, the state observer and the neurofuzzy approximator, an H-infinity tracking performance can be succeeded.

An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot

NASA Astrophysics Data System (ADS)

Clark, Evan B.; Bramall, Nathan E.; Christner, Brent; Flesher, Chris; Harman, John; Hogan, Bart; Lavender, Heather; Lelievre, Scott; Moor, Joshua; Siegel, Vickie

2018-07-01

The development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42'09.3''N 147°37'23.2''W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa.

APPLICATION OF U.S. EPA METHODS TO THE ANALYSIS OF PHARMACEUTICALS AND PERSONAL CARE PRODUCTS IN THE ENVIRONMENT: DETERMINATION OF CLOFIBRIC ACID IN SEWAGE EFFLUENT BY GC-MS

EPA Science Inventory

An emerging area of research concerns pharmaceuticals and personal care products in the
environment and their possible impact on biota and ecosystems. The long term effects of
constant perfusion of PPCPs into the aquatic environment are presently unknown. Some
compoun...

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

Chain representations of Open Quantum Systems and Lieb-Robinson like bounds for the dynamics

NASA Astrophysics Data System (ADS)

Woods, Mischa

2013-03-01

This talk is concerned with the mapping of the Hamiltonian of open quantum systems onto chain representations, which forms the basis for a rigorous theory of the interaction of a system with its environment. This mapping progresses as an interaction which gives rise to a sequence of residual spectral densities of the system. The rigorous mathematical properties of this mapping have been unknown so far. Here we develop the theory of secondary measures to derive an analytic, expression for the sequence solely in terms of the initial measure and its associated orthogonal polynomials of the first and second kind. These mappings can be thought of as taking a highly nonlocal Hamiltonian to a local Hamiltonian. In the latter, a Lieb-Robinson like bound for the dynamics of the open quantum system makes sense. We develop analytical bounds on the error to observables of the system as a function of time when the semi-infinite chain in truncated at some finite length. The fact that this is possible shows that there is a finite ``Speed of sound'' in these chain representations. This has many implications of the simulatability of open quantum systems of this type and demonstrates that a truncated chain can faithfully reproduce the dynamics at shorter times. These results make a significant and mathematically rigorous contribution to the understanding of the theory of open quantum systems; and pave the way towards the efficient simulation of these systems, which within the standard methods, is often an intractable problem. EPSRC CDT in Controlled Quantum Dynamics, EU STREP project and Alexander von Humboldt Foundation

A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

NASA Astrophysics Data System (ADS)

Tsumori, Kenji; Ozawa, Seiichi

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

Group prioritisation with unknown expert weights in incomplete linguistic context

NASA Astrophysics Data System (ADS)

Cheng, Dong; Cheng, Faxin; Zhou, Zhili; Wang, Juan

2017-09-01

In this paper, we study a group prioritisation problem in situations when the expert weights are completely unknown and their judgement preferences are linguistic and incomplete. Starting from the theory of relative entropy (RE) and multiplicative consistency, an optimisation model is provided for deriving an individual priority vector without estimating the missing value(s) of an incomplete linguistic preference relation. In order to address the unknown expert weights in the group aggregating process, we define two new kinds of expert weight indicators based on RE: proximity entropy weight and similarity entropy weight. Furthermore, a dynamic-adjusting algorithm (DAA) is proposed to obtain an objective expert weight vector and capture the dynamic properties involved in it. Unlike the extant literature of group prioritisation, the proposed RE approach does not require pre-allocation of expert weights and can solve incomplete preference relations. An interesting finding is that once all the experts express their preference relations, the final expert weight vector derived from the DAA is fixed irrespective of the initial settings of expert weights. Finally, an application example is conducted to validate the effectiveness and robustness of the RE approach.

ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS

PubMed Central

MIAO, HONGYU; XIA, XIAOHUA; PERELSON, ALAN S.; WU, HULIN

2011-01-01

Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice. PMID:21785515

Robust ADP Design for Continuous-Time Nonlinear Systems With Output Constraints.

PubMed

Fan, Bo; Yang, Qinmin; Tang, Xiaoyu; Sun, Youxian

2018-06-01

In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

Nonperturbative Treatment of non-Markovian Dynamics of Open Quantum Systems

NASA Astrophysics Data System (ADS)

Tamascelli, D.; Smirne, A.; Huelga, S. F.; Plenio, M. B.

2018-01-01

We identify the conditions that guarantee equivalence of the reduced dynamics of an open quantum system (OQS) for two different types of environments—one a continuous bosonic environment leading to a unitary system-environment evolution and the other a discrete-mode bosonic environment resulting in a system-mode (nonunitary) Lindbladian evolution. Assuming initial Gaussian states for the environments, we prove that the two OQS dynamics are equivalent if both the expectation values and two-time correlation functions of the environmental interaction operators are the same at all times for the two configurations. Since the numerical and analytical description of a discrete-mode environment undergoing a Lindbladian evolution is significantly more efficient than that of a continuous bosonic environment in a unitary evolution, our result represents a powerful, nonperturbative tool to describe complex and possibly highly non-Markovian dynamics. As a special application, we recover and generalize the well-known pseudomodes approach to open-system dynamics.

A neural coding scheme reproducing foraging trajectories

NASA Astrophysics Data System (ADS)

Gutiérrez, Esther D.; Cabrera, Juan Luis

2015-12-01

The movement of many animals may follow Lévy patterns. The underlying generating neuronal dynamics of such a behavior is unknown. In this paper we show that a novel discovery of multifractality in winnerless competition (WLC) systems reveals a potential encoding mechanism that is translatable into two dimensional superdiffusive Lévy movements. The validity of our approach is tested on a conductance based neuronal model showing WLC and through the extraction of Lévy flights inducing fractals from recordings of rat hippocampus during open field foraging. Further insights are gained analyzing mice motor cortex neurons and non motor cell signals. The proposed mechanism provides a plausible explanation for the neuro-dynamical fundamentals of spatial searching patterns observed in animals (including humans) and illustrates an until now unknown way to encode information in neuronal temporal series.

Distributed Synchronization Control of Multiagent Systems With Unknown Nonlinearities.

PubMed

Su, Shize; Lin, Zongli; Garcia, Alfredo

2016-01-01

This paper revisits the distributed adaptive control problem for synchronization of multiagent systems where the dynamics of the agents are nonlinear, nonidentical, unknown, and subject to external disturbances. Two communication topologies, represented, respectively, by a fixed strongly-connected directed graph and by a switching connected undirected graph, are considered. Under both of these communication topologies, we use distributed neural networks to approximate the uncertain dynamics. Decentralized adaptive control protocols are then constructed to solve the cooperative tracker problem, the problem of synchronization of all follower agents to a leader agent. In particular, we show that, under the proposed decentralized control protocols, the synchronization errors are ultimately bounded, and their ultimate bounds can be reduced arbitrarily by choosing the control parameter appropriately. Simulation study verifies the effectiveness of our proposed protocols.

Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics.

PubMed

Li, Yongming; Sui, Shuai; Tong, Shaocheng

2017-02-01

This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

Student Errors in Dynamic Mathematical Environments

ERIC Educational Resources Information Center

Brown, Molly; Bossé, Michael J.; Chandler, Kayla

2016-01-01

This study investigates the nature of student errors in the context of problem solving and Dynamic Math Environments. This led to the development of the Problem Solving Action Identification Framework; this framework captures and defines all activities and errors associated with problem solving in a dynamic math environment. Found are three…

Design of a DNA chip for detection of unknown genetically modified organisms (GMOs).

PubMed

Nesvold, Håvard; Kristoffersen, Anja Bråthen; Holst-Jensen, Arne; Berdal, Knut G

2005-05-01

Unknown genetically modified organisms (GMOs) have not undergone a risk evaluation, and hence might pose a danger to health and environment. There are, today, no methods for detecting unknown GMOs. In this paper we propose a novel method intended as a first step in an approach for detecting unknown genetically modified (GM) material in a single plant. A model is designed where biological and combinatorial reduction rules are applied to a set of DNA chip probes containing all possible sequences of uniform length n, creating probes capable of detecting unknown GMOs. The model is theoretically tested for Arabidopsis thaliana Columbia, and the probabilities for detecting inserts and receiving false positives are assessed for various parameters for this organism. From a theoretical standpoint, the model looks very promising but should be tested further in the laboratory. The model and algorithms will be available upon request to the corresponding author.

Generation of metal nanoparticles from silver and copper objects: nanoparticle dynamics on surfaces and potential sources of nanoparticles in the environment.

PubMed

Glover, Richard D; Miller, John M; Hutchison, James E

2011-11-22

The use of silver nanoparticles (AgNPs) in antimicrobial applications, including a wide range of consumer goods and apparel, has attracted attention because of the unknown health and environmental risks associated with these emerging materials. Of particular concern is whether there are new risks that are a direct consequence of their nanoscale size. Identifying those risks associated with nanoscale structure has been difficult due to the fundamental challenge of detecting and monitoring nanoparticles in products or the environment. Here, we introduce a new strategy to directly monitor nanoparticles and their transformations under a variety of environmental conditions. These studies reveal unprecedented dynamic behavior of AgNPs on surfaces. Most notably, under ambient conditions at relative humidities greater than 50%, new silver nanoparticles form in the vicinity of the parent particles. This humidity-dependent formation of new particles was broadly observed for a variety of AgNPs and substrate surface coatings. We hypothesize that nanoparticle production occurs through a process involving three stages: (i) oxidation and dissolution of silver from the surface of the particle, (ii) diffusion of silver ion across the surface in an adsorbed water layer, and (iii) formation of new, smaller particles by chemical and/or photoreduction. Guided by these findings, we investigated non-nanoscale sources of silver such as wire, jewelry, and eating utensils that are placed in contact with surfaces and found that they also formed new nanoparticles. Copper objects display similar reactivity, suggesting that this phenomenon may be more general. These findings challenge conventional thinking about nanoparticle reactivity and imply that the production of new nanoparticles is an intrinsic property of the material that is not strongly size dependent. The discovery that AgNPs and CuNPs are generated spontaneously from manmade objects implies that humans have long been in direct contact with these nanomaterials and that macroscale objects represent a potential source of incidental nanoparticles in the environment. © 2011 American Chemical Society

Distributed Containment Control for Multiple Unknown Second-Order Nonlinear Systems With Application to Networked Lagrangian Systems.

PubMed

Mei, Jie; Ren, Wei; Li, Bing; Ma, Guangfu

2015-09-01

In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges

NASA Technical Reports Server (NTRS)

Tao, Gang; Joshi, Suresh M.

2008-01-01

In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pin, F.G.

Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-basedmore » approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.« less

Handling qualities of large flexible control-configured aircraft

NASA Technical Reports Server (NTRS)

Swaim, R. L.

1979-01-01

The approach to an analytical study of flexible airplane longitudinal handling qualities was to parametrically vary the natural frequencies of two symmetric elastic modes to induce mode interactions with the rigid body dynamics. Since the structure of the pilot model was unknown for such dynamic interactions, the optimal control pilot modeling method is being applied and used in conjunction with pilot rating method.

Distance, flow and PCR inhibition: eDNA dynamics in two headwater streams

Treesearch

Stephen F. Jane; Taylor M. Wilcox; Kevin S. McKelvey; Michael K. Young; Michael K. Schwartz; Winsor H. Lowe; Benjamin H. Letcher; Andrew R. Whiteley

2014-01-01

Environmental DNA (eDNA) detection has emerged as a powerful tool for monitoring aquatic organisms, but much remains unknown about the dynamics of aquatic eDNA over a range of environmental conditions. DNA concentrations in streams and rivers will depend not only on the equilibrium between DNA entering the water and DNA leaving the system through degradation, but also...

Disturbance rejection control for vibration suppression of piezoelectric laminated thin-walled structures

NASA Astrophysics Data System (ADS)

Zhang, S. Q.; Li, H. N.; Schmidt, R.; Müller, P. C.

2014-02-01

Thin-walled piezoelectric integrated smart structures are easily excited to vibrate by unknown disturbances. In order to design and simulate a control strategy, firstly, an electro-mechanically coupled dynamic finite element (FE) model of smart structures is developed based on first-order shear deformation (FOSD) hypothesis. Linear piezoelectric constitutive equations and the assumption of constant electric field through the thickness are considered. Based on the dynamic FE model, a disturbance rejection (DR) control with proportional-integral (PI) observer using step functions as the fictitious model of disturbances is developed for vibration suppression of smart structures. In order to achieve a better dynamic behavior of the fictitious model of disturbances, the PI observer is extended to generalized proportional-integral (GPI) observer, in which sine or polynomial functions can be used to represent disturbances resulting in better dynamics. Therefore the disturbances can be estimated either by PI or GPI observer, and then the estimated signals are fed back to the controller. The DR control is validated by various kinds of unknown disturbances, and compared with linear-quadratic regulator (LQR) control. The results illustrate that the vibrations are better suppressed by the proposed DR control.

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models.

PubMed

Daunizeau, J; Friston, K J; Kiebel, S J

2009-11-01

In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

PubMed

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

PubMed Central

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world. PMID:26528176

The monitoring system for vibratory disturbance detection in microgravity environment aboard the international space station

NASA Technical Reports Server (NTRS)

Laster, Rachel M.

2004-01-01

Scientists in the Office of Life and Microgravity Sciences and Applications within the Microgravity Research Division oversee studies in important physical, chemical, and biological processes in microgravity environment. Research is conducted in microgravity environment because of the beneficial results that come about for experiments. When research is done in normal gravity, scientists are limited to results that are affected by the gravity of Earth. Microgravity provides an environment where solid, liquid, and gas can be observed in a natural state of free fall and where many different variables are eliminated. One challenge that NASA faces is that space flight opportunities need to be used effectively and efficiently in order to ensure that some of the most scientifically promising research is conducted. Different vibratory sources are continually active aboard the International Space Station (ISS). Some of the vibratory sources include crew exercise, experiment setup, machinery startup (life support fans, pumps, freezer/compressor, centrifuge), thruster firings, and some unknown events. The Space Acceleration Measurement System (SAMs), which acts as the hardware and carefully positioned aboard the ISS, along with the Microgravity Environment Monitoring System MEMS), which acts as the software and is located here at NASA Glenn, are used to detect these vibratory sources aboard the ISS and recognize them as disturbances. The various vibratory disturbances can sometimes be harmful to the scientists different research projects. Some vibratory disturbances are recognized by the MEMS's database and some are not. Mainly, the unknown events that occur aboard the International Space Station are the ones of major concern. To better aid in the research experiments, the unknown events are identified and verified as unknown events. Features, such as frequency, acceleration level, time and date of recognition of the new patterns are stored in an Excel database. My task is to carefully synthesize frequency and acceleration patterns of unknown events within the Excel database into a new file to determine whether or not certain information that is received i s considered a real vibratory source. Once considered as a vibratory source, further analysis is carried out. The resulting information is used to retrain the MEMS to recognize them as known patterns. These different vibratory disturbances are being constantly monitored to observe if, in any way, the disturbances have an effect on the microgravity environment that research experiments are exposed to. If the disturbance has little or no effect on the experiments, then research is continued. However, if the disturbance is harmful to the experiment, scientists act accordingly by either minimizing the source or terminating the research and neither NASA's time nor money is wasted.

Influence of vectors' risk-spreading strategies and environmental stochasticity on the epidemiology and evolution of vector-borne diseases: the example of Chagas' disease.

PubMed

Pelosse, Perrine; Kribs-Zaleta, Christopher M; Ginoux, Marine; Rabinovich, Jorge E; Gourbière, Sébastien; Menu, Frédéric

2013-01-01

Insects are known to display strategies that spread the risk of encountering unfavorable conditions, thereby decreasing the extinction probability of genetic lineages in unpredictable environments. To what extent these strategies influence the epidemiology and evolution of vector-borne diseases in stochastic environments is largely unknown. In triatomines, the vectors of the parasite Trypanosoma cruzi, the etiological agent of Chagas' disease, juvenile development time varies between individuals and such variation most likely decreases the extinction risk of vector populations in stochastic environments. We developed a simplified multi-stage vector-borne SI epidemiological model to investigate how vector risk-spreading strategies and environmental stochasticity influence the prevalence and evolution of a parasite. This model is based on available knowledge on triatomine biodemography, but its conceptual outcomes apply, to a certain extent, to other vector-borne diseases. Model comparisons between deterministic and stochastic settings led to the conclusion that environmental stochasticity, vector risk-spreading strategies (in particular an increase in the length and variability of development time) and their interaction have drastic consequences on vector population dynamics, disease prevalence, and the relative short-term evolution of parasite virulence. Our work shows that stochastic environments and associated risk-spreading strategies can increase the prevalence of vector-borne diseases and favor the invasion of more virulent parasite strains on relatively short evolutionary timescales. This study raises new questions and challenges in a context of increasingly unpredictable environmental variations as a result of global climate change and human interventions such as habitat destruction or vector control.

Influence of Vectors’ Risk-Spreading Strategies and Environmental Stochasticity on the Epidemiology and Evolution of Vector-Borne Diseases: The Example of Chagas’ Disease

PubMed Central

Pelosse, Perrine; Kribs-Zaleta, Christopher M.; Ginoux, Marine; Rabinovich, Jorge E.; Gourbière, Sébastien; Menu, Frédéric

2013-01-01

Insects are known to display strategies that spread the risk of encountering unfavorable conditions, thereby decreasing the extinction probability of genetic lineages in unpredictable environments. To what extent these strategies influence the epidemiology and evolution of vector-borne diseases in stochastic environments is largely unknown. In triatomines, the vectors of the parasite Trypanosoma cruzi, the etiological agent of Chagas’ disease, juvenile development time varies between individuals and such variation most likely decreases the extinction risk of vector populations in stochastic environments. We developed a simplified multi-stage vector-borne SI epidemiological model to investigate how vector risk-spreading strategies and environmental stochasticity influence the prevalence and evolution of a parasite. This model is based on available knowledge on triatomine biodemography, but its conceptual outcomes apply, to a certain extent, to other vector-borne diseases. Model comparisons between deterministic and stochastic settings led to the conclusion that environmental stochasticity, vector risk-spreading strategies (in particular an increase in the length and variability of development time) and their interaction have drastic consequences on vector population dynamics, disease prevalence, and the relative short-term evolution of parasite virulence. Our work shows that stochastic environments and associated risk-spreading strategies can increase the prevalence of vector-borne diseases and favor the invasion of more virulent parasite strains on relatively short evolutionary timescales. This study raises new questions and challenges in a context of increasingly unpredictable environmental variations as a result of global climate change and human interventions such as habitat destruction or vector control. PMID:23951018

Adaptation of S. cerevisiae to Fermented Food Environments Reveals Remarkable Genome Plasticity and the Footprints of Domestication.

PubMed

Legras, Jean-Luc; Galeote, Virginie; Bigey, Frédéric; Camarasa, Carole; Marsit, Souhir; Nidelet, Thibault; Sanchez, Isabelle; Couloux, Arnaud; Guy, Julie; Franco-Duarte, Ricardo; Marcet-Houben, Marina; Gabaldon, Toni; Schuller, Dorit; Sampaio, José Paulo; Dequin, Sylvie

2018-07-01

The budding yeast Saccharomyces cerevisiae can be found in the wild and is also frequently associated with human activities. Despite recent insights into the phylogeny of this species, much is still unknown about how evolutionary processes related to anthropogenic niches have shaped the genomes and phenotypes of S. cerevisiae. To address this question, we performed population-level sequencing of 82 S. cerevisiae strains from wine, flor, rum, dairy products, bakeries, and the natural environment (oak trees). These genomic data enabled us to delineate specific genetic groups corresponding to the different ecological niches and revealed high genome content variation across the groups. Most of these strains, compared with the reference genome, possessed additional genetic elements acquired by introgression or horizontal transfer, several of which were population-specific. In addition, several genomic regions in each population showed evidence of nonneutral evolution, as shown by high differentiation, or of selective sweeps including genes with key functions in these environments (e.g., amino acid transport for wine yeast). Linking genetics to lifestyle differences and metabolite traits has enabled us to elucidate the genetic basis of several niche-specific population traits, such as growth on galactose for cheese strains. These data indicate that yeast has been subjected to various divergent selective pressures depending on its niche, requiring the development of customized genomes for better survival in these environments. These striking genome dynamics associated with local adaptation and domestication reveal the remarkable plasticity of the S. cerevisiae genome, revealing this species to be an amazing complex of specialized populations.

Environmental Statistics and Optimal Regulation

PubMed Central

2014-01-01

Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies–such as constitutive expression or graded response–for regulating protein levels in response to environmental inputs. We propose a general framework–here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient–to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones. PMID:25254493

Adaptive super-twisting observer for estimation of random road excitation profile in automotive suspension systems.

PubMed

Rath, J J; Veluvolu, K C; Defoort, M

2014-01-01

The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system.

Adaptive Super-Twisting Observer for Estimation of Random Road Excitation Profile in Automotive Suspension Systems

PubMed Central

Rath, J. J.; Veluvolu, K. C.; Defoort, M.

2014-01-01

The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321

Blob-Spring Model for the Dynamics of Ring Polymer in Obstacle Environment

NASA Astrophysics Data System (ADS)

Lele, Ashish K.; Iyer, Balaji V. S.; Juvekar, Vinay A.

2008-07-01

The dynamical behavior of cyclic macromolecules in a fixed obstacle (FO) environment is very different than the behavior of linear chains in the same topological environment; while the latter relax by a snake-like reptational motion from their chain ends the former can relax only by contour length fluctuations since they are endless. Duke, Obukhov and Rubinstein proposed a scaling model (the DOR model) to interpret the dynamical scaling exponents shown by Monte Carlo simulations of rings in a FO environment. We present a model (blob-spring model) to describe the dynamics of flexible and non-concatenated ring polymer in FO environment based on a theoretical formulation developed for the dynamics of an unentangled fractal polymer. We argue that the perpetual evolution of ring perimeter by the motion of contour segments results in an extra frictional load. Our model predicts self-similar dynamics with scaling exponents for the molecular weight dependence of diffusion coefficient and relaxation times that are in agreement with the scaling model proposed by Obukhov et al.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Estimating Topology of Discrete Dynamical Networks

NASA Astrophysics Data System (ADS)

Guo, Shu-Juan; Fu, Xin-Chu

2010-07-01

In this paper, by applying Lasalle's invariance principle and some results about the trace of a matrix, we propose a method for estimating the topological structure of a discrete dynamical network based on the dynamical evolution of the network. The network concerned can be directed or undirected, weighted or unweighted, and the local dynamics of each node can be nonidentical. The connections among the nodes can be all unknown or partially known. Finally, two examples, including a Hénon map and a central network, are illustrated to verify the theoretical results.

Dynamic modeling and Super-Twisting Sliding Mode Control for Tethered Space Robot

NASA Astrophysics Data System (ADS)

Zhao, Yakun; Huang, Panfeng; Zhang, Fan

2018-02-01

Recent years, tethered space capturing systems have been considered as one of the most promising solutions for active space debris removal due to the increasing threat of space debris to spacecraft and astronauts. In this paper, one of the tethered space capturing systems, Tethered Space Robot (TSR), is investigated. TSR includes a space platform, a space tether, and a gripper as the terminal device. Based on the assumptions that the platform and the gripper are point masses and the tether is rigid, inextensible and remaining straight, the dynamic model of TSR is presented, in which the disturbances from space environment is considered. According to the previous study, the in-plane and out-of-plane angles of the tether oscillate periodically although the tether is released to the desired length. A super-twisting adaptive sliding mode control scheme is designed for TSR to eliminate the vibration of the tether to assure a successful capture in station-keeping phase. Both uncontrolled and controlled situations are simulated. The simulation results show that the proposed controller is effective. Additionally, after comparing with normal sliding mode control algorithm, it is verified that the proposed control scheme can avoid the chattering of normal sliding mode control and is robust for unknown boundary perturbations.

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

PubMed Central

Teng, Santani

2017-01-01

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research. This article is part of the themed issue ‘Auditory and visual scene analysis’. PMID:28044019

A traffic priority language for collision-free navigation of autonomous mobile robots in dynamic environments.

PubMed

Bourbakis, N G

1997-01-01

This paper presents a generic traffic priority language, called KYKLOFORTA, used by autonomous robots for collision-free navigation in a dynamic unknown or known navigation space. In a previous work by X. Grossmman (1988), a set of traffic control rules was developed for the navigation of the robots on the lines of a two-dimensional (2-D) grid and a control center coordinated and synchronized their movements. In this work, the robots are considered autonomous: they are moving anywhere and in any direction inside the free space, and there is no need of a central control to coordinate and synchronize them. The requirements for each robot are i) visual perception, ii) range sensors, and iii) the ability of each robot to detect other moving objects in the same free navigation space, define the other objects perceived size, their velocity and their directions. Based on these assumptions, a traffic priority language is needed for each robot, making it able to decide during the navigation and avoid possible collision with other moving objects. The traffic priority language proposed here is based on a set of primitive traffic priority alphabet and rules which compose pattern of corridors for the application of the traffic priority rules.

Time of day determines Arabidopsis transcriptome and growth dynamics under mild drought.

PubMed

Dubois, Marieke; Claeys, Hannes; Van den Broeck, Lisa; Inzé, Dirk

2017-02-01

Drought stress is a major problem for agriculture worldwide, causing significant yield losses. Plants have developed highly flexible mechanisms to deal with drought, including organ- and developmental stage-specific responses. In young leaves, growth is repressed as an active mechanism to save water and energy, increasing the chances of survival but decreasing yield. Despite its importance, the molecular basis for this growth inhibition is largely unknown. Here, we present a novel approach to explore early molecular mechanisms controlling Arabidopsis leaf growth inhibition following mild drought. We found that growth and transcriptome responses to drought are highly dynamic. Growth was only repressed by drought during the day, and our evidence suggests that this may be due to gating by the circadian clock. Similarly, time of day strongly affected the extent, specificity, and in certain cases even direction of drought-induced changes in gene expression. These findings underscore the importance of taking into account diurnal patterns to understand stress responses, as only a small core of drought-responsive genes are affected by drought at all times of the day. Finally, we leveraged our high-resolution data to demonstrate that phenotypic and transcriptome responses can be matched to identify putative novel regulators of growth under mild drought. © 2016 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach.

PubMed

Cichy, Radoslaw Martin; Teng, Santani

2017-02-19

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research.This article is part of the themed issue 'Auditory and visual scene analysis'. © 2017 The Authors.

Dry intrusions: Lagrangian climatology and impact on the boundary layer

NASA Astrophysics Data System (ADS)

Raveh-Rubin, Shira; Wernli, Heini

2017-04-01

Dry air intrusions (DIs) are large-scale descending airstreams. A DI is typically referred to as a coherent airstream in the cold sector of an extratropical cyclone. Emerging evidence suggests that DIs are linked to severe surface wind gusts. However, there is yet no strict Lagrangian definition of DIs, and so their climatological frequency, dynamical characteristics as well as their seasonal and spatial distributions are unknown. Furthermore, the dynamical interaction between DIs and the planetary boundary layer is not fully understood. Here, we suggest a Lagrangian definition for DI air parcels, namely a minimum pressure increase along a trajectory of 400 hPa in 48 hours. Based on this criterion, the open questions are addressed by: (i) a novel global Lagrangian climatology for the ECMWF ERA-Interim reanalysis dataset for the years 1979-2014; (ii) a case study illustrating the interaction between DIs and the boundary layer. We find that DIs occur predominantly in winter. DIs coherently descend from the upper troposphere (their stratospheric origin is small), to the mid- and low levels, where they mix with their environment and diverge. Different physical characteristics typify DIs in the different regions and seasons. Finally, we demonstrate the different mechanisms by which DIs can destabilize the boundary layer and facilitate the formation of strong surface winds.

Stability and Aggregation of Silver and Titanium Dioxide Nanoparticles in Seawater: Role of Salinity and Dissolved Organic Matter

EPA Science Inventory

The behavior and fate of nanoparticles (NPs) in the marine environment is largely unknown and has the potential to have important environmental and human health implications. The aggregation state and fate of NPs in the marine environment is greatly influenced by their interactio...

Metagenomic assessment of the interplay between the environment and the genetic diversification of Acinetobacter

PubMed Central

Touchon, Marie; Brisse, Sylvain; Rocha, Eduardo P.C.

2017-01-01

Summary Most bacteria have poorly characterized environmental reservoirs and unknown closely related species. This hampers the study of bacterial evolutionary ecology because both the environment and the genetic background of ancestral lineages are unknown. We combined metagenomics, comparative genomics and phylogenomics to overcome this limitation, to identify novel taxa and to propose environments where they can be isolated. We applied this method to characterize the ecological distribution of known and novel lineages of Acinetobacter spp. We observed two major environmental transitions at deep phylogenetic levels, splitting the genus into three ecologically differentiated clades. One of these has rapidly shifted towards host‐association by acquiring genes involved in bacteria‐eukaryote interactions. We show that environmental perturbations affect species distribution in predictable ways: bovines have very diverse communities of Acinetobacter, unless they were administered antibiotics, in which case they show highly uniform communities of Acinetobacter spp. that resemble those of humans. Our results uncover the diversity of bacterial lineages, overpassing the limitations of classical cultivation methods and highlight the role of the environment in shaping their evolution. PMID:28967182

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

PubMed

Hua, Changchun; Zhang, Liuliu; Guan, Xinping

2017-01-01

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

A neural coding scheme reproducing foraging trajectories

PubMed Central

Gutiérrez, Esther D.; Cabrera, Juan Luis

2015-01-01

The movement of many animals may follow Lévy patterns. The underlying generating neuronal dynamics of such a behavior is unknown. In this paper we show that a novel discovery of multifractality in winnerless competition (WLC) systems reveals a potential encoding mechanism that is translatable into two dimensional superdiffusive Lévy movements. The validity of our approach is tested on a conductance based neuronal model showing WLC and through the extraction of Lévy flights inducing fractals from recordings of rat hippocampus during open field foraging. Further insights are gained analyzing mice motor cortex neurons and non motor cell signals. The proposed mechanism provides a plausible explanation for the neuro-dynamical fundamentals of spatial searching patterns observed in animals (including humans) and illustrates an until now unknown way to encode information in neuronal temporal series. PMID:26648311

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

NASA Astrophysics Data System (ADS)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

Dynamic Multitasking Countermeasures to Improve Sustained Attention

DTIC Science & Technology

2013-07-12

Apr-2012 31-Dec-2012 Approved for Public Release; Distribution Unlimited Final Report: Dynamic multitasking countermeasures to improve sustained...ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 vigilance, multitasking REPORT DOCUMENTATION PAGE 11. SPONSOR... multitasking countermeasures to improve sustained attention while driving Report Title The unknown at the outset of this work was if high cognitive load

Inexplicable or Simply Unexplained? The Management of Maize Seed in Mexico

PubMed Central

Dyer, George A.; López-Feldman, Alejandro

2013-01-01

Farmer management of plant germplasm pre-dates crop domestication, but humans’ role in crop evolution and diversity remains largely undocumented and often contested. Seemingly inexplicable practices observed throughout agricultural history, such as exchanging or replacing seed, continue to structure crop populations across the developing world. Seed management practices can be construed as events in the life history of crops and management data used to model crop demography, but this requires suitable quantitative data. As a prerequisite to addressing the causes and implications of maize seed management, we describe its patterns of variation across Mexico by drawing from the literature and new analysis. We find that rates of seed replacement, introduction and diffusion differ significantly across regions and altitudinal zones, but interactions among explanatory factors can obscure patterns of variation. The type, source, geographic origin and ownership of seed help explain observed rates. Yet, controlling for the characteristics of germplasm barely reduces interregional differences vastly exceeding variation across elevations. With few exceptions, monotonic altitudinal trends are absent. Causal relationships between management practices and the physical environment could determine farmers’ wellbeing and crop conservation in the face of climate change. Scarce and inconsistent data on management nevertheless could prevent an understanding of these relationships. Current conceptions on the management and dynamics of maize diversity are founded on a patchwork of observations in surprisingly few and dissimilar environments. Our estimates of management practices should shed light on differences in maize population dynamics across Mexico. Consistency with previous studies spanning over a decade suggests that common sets of forces are present within large areas, but causal associations remain unknown. The next step in explaining crop diversity should address variation in seed management across space and time simultaneously while identifying farmers’ values and motivations as underlying forces. PMID:23840847

Inexplicable or simply unexplained? The management of maize seed in Mexico.

PubMed

Dyer, George A; López-Feldman, Alejandro

2013-01-01

Farmer management of plant germplasm pre-dates crop domestication, but humans' role in crop evolution and diversity remains largely undocumented and often contested. Seemingly inexplicable practices observed throughout agricultural history, such as exchanging or replacing seed, continue to structure crop populations across the developing world. Seed management practices can be construed as events in the life history of crops and management data used to model crop demography, but this requires suitable quantitative data. As a prerequisite to addressing the causes and implications of maize seed management, we describe its patterns of variation across Mexico by drawing from the literature and new analysis. We find that rates of seed replacement, introduction and diffusion differ significantly across regions and altitudinal zones, but interactions among explanatory factors can obscure patterns of variation. The type, source, geographic origin and ownership of seed help explain observed rates. Yet, controlling for the characteristics of germplasm barely reduces interregional differences vastly exceeding variation across elevations. With few exceptions, monotonic altitudinal trends are absent. Causal relationships between management practices and the physical environment could determine farmers' wellbeing and crop conservation in the face of climate change. Scarce and inconsistent data on management nevertheless could prevent an understanding of these relationships. Current conceptions on the management and dynamics of maize diversity are founded on a patchwork of observations in surprisingly few and dissimilar environments. Our estimates of management practices should shed light on differences in maize population dynamics across Mexico. Consistency with previous studies spanning over a decade suggests that common sets of forces are present within large areas, but causal associations remain unknown. The next step in explaining crop diversity should address variation in seed management across space and time simultaneously while identifying farmers' values and motivations as underlying forces.

Simulating Open Quantum Systems with Hamiltonian Ensembles and the Nonclassicality of the Dynamics

NASA Astrophysics Data System (ADS)

Chen, Hong-Bin; Gneiting, Clemens; Lo, Ping-Yuan; Chen, Yueh-Nan; Nori, Franco

2018-01-01

The incoherent dynamical properties of open quantum systems are generically attributed to an ongoing correlation between the system and its environment. Here, we propose a novel way to assess the nature of these system-environment correlations by examining the system dynamics alone. Our approach is based on the possibility or impossibility to simulate open-system dynamics with Hamiltonian ensembles. As we show, such (im)possibility to simulate is closely linked to the system-environment correlations. We thus define the nonclassicality of open-system dynamics in terms of the nonexistence of a Hamiltonian-ensemble simulation. This classifies any nonunital open-system dynamics as nonclassical. We give examples for open-system dynamics that are unital and classical, as well as unital and nonclassical.

Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

PubMed

Si, Wenjie; Dong, Xunde; Yang, Feifei

2018-03-01

This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Systems Engineering Survey of Artificial Intelligence and Smart Sensor Networks in a Network-Centric Environment

DTIC Science & Technology

2009-09-01

problems, to better model the problem solving of computer systems. This research brought about the intertwining of AI and cognitive psychology . Much of...where symbol sequences are sequential intelligent states of the network, and must be classified as normal, abnormal , or unknown. These symbols...is associated with abnormal behavior; and abcbc is associated with unknown behavior, as it fits no known behavior. Predicted outcomes from

Gamma ray observatory dynamics simulator in Ada (GRODY)

NASA Technical Reports Server (NTRS)

1990-01-01

This experiment involved the parallel development of dynamics simulators for the Gamma Ray Observatory in both FORTRAN and Ada for the purpose of evaluating the applicability of Ada to the NASA/Goddard Space Flight Center's flight dynamics environment. The experiment successfully demonstrated that Ada is a viable, valuable technology for use in this environment. In addition to building a simulator, the Ada team evaluated training approaches, developed an Ada methodology appropriate to the flight dynamics environment, and established a baseline for evaluating future Ada projects.

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry Georgievich; Hafiychuk, Vasyl Nmn; Osipov, Viatcheslav V.; Patterson-Hine, F. Ann

2010-01-01

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Predicting Adaptive Behavior in the Environment from Central Nervous System Dynamics

PubMed Central

Proekt, Alex; Wong, Jane; Zhurov, Yuriy; Kozlova, Nataliya; Weiss, Klaudiusz R.; Brezina, Vladimir

2008-01-01

To generate adaptive behavior, the nervous system is coupled to the environment. The coupling constrains the dynamical properties that the nervous system and the environment must have relative to each other if adaptive behavior is to be produced. In previous computational studies, such constraints have been used to evolve controllers or artificial agents to perform a behavioral task in a given environment. Often, however, we already know the controller, the real nervous system, and its dynamics. Here we propose that the constraints can also be used to solve the inverse problem—to predict from the dynamics of the nervous system the environment to which they are adapted, and so reconstruct the production of the adaptive behavior by the entire coupled system. We illustrate how this can be done in the feeding system of the sea slug Aplysia. At the core of this system is a central pattern generator (CPG) that, with dynamics on both fast and slow time scales, integrates incoming sensory stimuli to produce ingestive and egestive motor programs. We run models embodying these CPG dynamics—in effect, autonomous Aplysia agents—in various feeding environments and analyze the performance of the entire system in a realistic feeding task. We find that the dynamics of the system are tuned for optimal performance in a narrow range of environments that correspond well to those that Aplysia encounter in the wild. In these environments, the slow CPG dynamics implement efficient ingestion of edible seaweed strips with minimal sensory information about them. The fast dynamics then implement a switch to a different behavioral mode in which the system ignores the sensory information completely and follows an internal “goal,” emergent from the dynamics, to egest again a strip that proves to be inedible. Key predictions of this reconstruction are confirmed in real feeding animals. PMID:18989362

Solar-powered ventilation of African termite mounds.

PubMed

Ocko, Samuel A; King, Hunter; Andreen, David; Bardunias, Paul; Turner, J Scott; Soar, Rupert; Mahadevan, L

2017-09-15

How termite mounds function to facilitate climate control is still only partially understood. Recent experimental evidence in the mounds of a single species, the south Asian termite Odontotermes obesus , suggests that the daily oscillations of radiant heating associated with diurnal insolation patterns drive convective flow within them. How general this mechanism is remains unknown. To probe this, we consider the mounds of the African termite Macrotermes michaelseni , which thrives in a very different environment. By directly measuring air velocities and temperatures within the mound, we see that the overall mechanisms and patterns involved are similar to that in the south Asian species. However, there are also some notable differences between the physiology of these mounds associated with the temporal variations in radiant heating patterns and CO 2 dynamics. Because of the difference between direct radiant heating driven by the position of the sun in African conditions, and the more shaded south Asian environments, we see changes in the convective flows in the two types of mounds. Furthermore, we also see that the south Asian mounds show a significant overturning of stratified gases, once a day, while the African mounds have a relatively uniform concentration of CO 2 Overall, our observations show that despite these differences, termite architectures can harness periodic solar heating to drive ventilation inside them in very different environments, functioning as an external lung, with clear implications for human engineering. © 2017. Published by The Company of Biologists Ltd.

IMPLICIT DUAL CONTROL BASED ON PARTICLE FILTERING AND FORWARD DYNAMIC PROGRAMMING.

PubMed

Bayard, David S; Schumitzky, Alan

2010-03-01

This paper develops a sampling-based approach to implicit dual control. Implicit dual control methods synthesize stochastic control policies by systematically approximating the stochastic dynamic programming equations of Bellman, in contrast to explicit dual control methods that artificially induce probing into the control law by modifying the cost function to include a term that rewards learning. The proposed implicit dual control approach is novel in that it combines a particle filter with a policy-iteration method for forward dynamic programming. The integration of the two methods provides a complete sampling-based approach to the problem. Implementation of the approach is simplified by making use of a specific architecture denoted as an H-block. Practical suggestions are given for reducing computational loads within the H-block for real-time applications. As an example, the method is applied to the control of a stochastic pendulum model having unknown mass, length, initial position and velocity, and unknown sign of its dc gain. Simulation results indicate that active controllers based on the described method can systematically improve closed-loop performance with respect to other more common stochastic control approaches.

Observer-based distributed adaptive fault-tolerant containment control of multi-agent systems with general linear dynamics.

PubMed

Ye, Dan; Chen, Mengmeng; Li, Kui

2017-11-01

In this paper, we consider the distributed containment control problem of multi-agent systems with actuator bias faults based on observer method. The objective is to drive the followers into the convex hull spanned by the dynamic leaders, where the input is unknown but bounded. By constructing an observer to estimate the states and bias faults, an effective distributed adaptive fault-tolerant controller is developed. Different from the traditional method, an auxiliary controller gain is designed to deal with the unknown inputs and bias faults together. Moreover, the coupling gain can be adjusted online through the adaptive mechanism without using the global information. Furthermore, the proposed control protocol can guarantee that all the signals of the closed-loop systems are bounded and all the followers converge to the convex hull with bounded residual errors formed by the dynamic leaders. Finally, a decoupled linearized longitudinal motion model of the F-18 aircraft is used to demonstrate the effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Probabilistic Multi-Person Tracking Using Dynamic Bayes Networks

NASA Astrophysics Data System (ADS)

Klinger, T.; Rottensteiner, F.; Heipke, C.

2015-08-01

Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.

A simple electrostatic switch important in the activation of type I protein kinase A by cyclic AMP.

PubMed

Vigil, Dominico; Lin, Jung-Hsin; Sotriffer, Christoph A; Pennypacker, Juniper K; McCammon, J Andrew; Taylor, Susan S

2006-01-01

Cyclic AMP activates protein kinase A by binding to an inhibitory regulatory (R) subunit and releasing inhibition of the catalytic (C) subunit. Even though crystal structures of regulatory and catalytic subunits have been solved, the precise molecular mechanism by which cyclic AMP activates the kinase remains unknown. The dynamic properties of the cAMP binding domain in the absence of cAMP or C-subunit are also unknown. Here we report molecular-dynamics simulations and mutational studies of the RIalpha R-subunit that identify the C-helix as a highly dynamic switch which relays cAMP binding to the helical C-subunit binding regions. Furthermore, we identify an important salt bridge which links cAMP binding directly to the C-helix that is necessary for normal activation. Additional mutations show that a hydrophobic "hinge" region is not as critical for the cross-talk in PKA as it is in the homologous EPAC protein, illustrating how cAMP can control diverse functions using the evolutionarily conserved cAMP-binding domains.

Self-expressive Dictionary Learning for Dynamic 3D Reconstruction.

PubMed

Zheng, Enliang; Ji, Dinghuang; Dunn, Enrique; Frahm, Jan-Michael

2017-08-22

We target the problem of sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing framework poses the estimation of 3D structure as the problem of dictionary learning, where the dictionary is defined as an aggregation of the temporally varying 3D structures. Given the smooth motion of dynamic objects, we observe any element in the dictionary can be well approximated by a sparse linear combination of other elements in the same dictionary (i.e. self-expression). Our formulation optimizes a biconvex cost function that leverages a compressed sensing formulation and enforces both structural dependency coherence across video streams, as well as motion smoothness across estimates from common video sources. We further analyze the reconstructability of our approach under different capture scenarios, and its comparison and relation to existing methods. Experimental results on large amounts of synthetic data as well as real imagery demonstrate the effectiveness of our approach.

Insect outbreak shifts the direction of selection from fast to slow growth rates in the long-lived conifer Pinus ponderosa.

PubMed

de la Mata, Raul; Hood, Sharon; Sala, Anna

2017-07-11

Long generation times limit species' rapid evolution to changing environments. Trees provide critical global ecosystem services, but are under increasing risk of mortality because of climate change-mediated disturbances, such as insect outbreaks. The extent to which disturbance changes the dynamics and strength of selection is unknown, but has important implications on the evolutionary potential of tree populations. Using a 40-y-old Pinus ponderosa genetic experiment, we provide rare evidence of context-dependent fluctuating selection on growth rates over time in a long-lived species. Fast growth was selected at juvenile stages, whereas slow growth was selected at mature stages under strong herbivory caused by a mountain pine beetle ( Dendroctonus ponderosae ) outbreak. Such opposing forces led to no net evolutionary response over time, thus providing a mechanism for the maintenance of genetic diversity on growth rates. Greater survival to mountain pine beetle attack in slow-growing families reflected, in part, a host-based life-history trade-off. Contrary to expectations, genetic effects on tree survival were greatest at the peak of the outbreak and pointed to complex defense responses. Our results suggest that selection forces in tree populations may be more relevant than previously thought, and have implications for tree population responses to future environments and for tree breeding programs.

How predation shapes the social interaction rules of shoaling fish

PubMed Central

Rosén, Emil; Ioannou, Christos C.; Rogell, Björn; Perna, Andrea; Ramnarine, Indar W.; Kolm, Niclas

2017-01-01

Predation is thought to shape the macroscopic properties of animal groups, making moving groups more cohesive and coordinated. Precisely how predation has shaped individuals' fine-scale social interactions in natural populations, however, is unknown. Using high-resolution tracking data of shoaling fish (Poecilia reticulata) from populations differing in natural predation pressure, we show how predation adapts individuals' social interaction rules. Fish originating from high predation environments formed larger, more cohesive, but not more polarized groups than fish from low predation environments. Using a new approach to detect the discrete points in time when individuals decide to update their movements based on the available social cues, we determine how these collective properties emerge from individuals' microscopic social interactions. We first confirm predictions that predation shapes the attraction–repulsion dynamic of these fish, reducing the critical distance at which neighbours move apart, or come back together. While we find strong evidence that fish align with their near neighbours, we do not find that predation shapes the strength or likelihood of these alignment tendencies. We also find that predation sharpens individuals' acceleration and deceleration responses, implying key perceptual and energetic differences associated with how individuals move in different predation regimes. Our results reveal how predation can shape the social interactions of individuals in groups, ultimately driving differences in groups' collective behaviour. PMID:28855361

Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG

PubMed Central

O'Sullivan, James A.; Power, Alan J.; Mesgarani, Nima; Rajaram, Siddharth; Foxe, John J.; Shinn-Cunningham, Barbara G.; Slaney, Malcolm; Shamma, Shihab A.; Lalor, Edmund C.

2015-01-01

How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroencephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (≈60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at ∼200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain–computer interfaces. PMID:24429136

Knowns and unknowns of plasma membrane protein degradation in plants.

PubMed

Liu, Chuanliang; Shen, Wenjin; Yang, Chao; Zeng, Lizhang; Gao, Caiji

2018-07-01

Plasma membrane (PM) not only creates a physical barrier to enclose the intracellular compartments but also mediates the direct communication between plants and the ever-changing environment. A tight control of PM protein homeostasis by selective degradation is thus crucial for proper plant development and plant-environment interactions. Accumulated evidences have shown that a number of plant PM proteins undergo clathrin-dependent or membrane microdomain-associated endocytic routes to vacuole for degradation in a cargo-ubiquitination dependent or independent manner. Besides, several trans-acting determinants involved in the regulation of endocytosis, recycling and multivesicular body-mediated vacuolar sorting have been identified in plants. More interestingly, recent findings have uncovered the participation of selective autophagy in PM protein turnover in plants. Although great progresses have been made to identify the PM proteins that undergo dynamic changes in subcellular localizations and to explore the factors that control the membrane protein trafficking, several questions remain to be answered regarding the molecular mechanisms of PM protein degradation in plants. In this short review article, we briefly summarize recent progress in our understanding of the internalization, sorting and degradation of plant PM proteins. More specifically, we focus on discussing the elusive aspects underlying the pathways of PM protein degradation in plants. Copyright © 2018 Elsevier B.V. All rights reserved.

Risk assessment and predator learning in a changing world: understanding the impacts of coral reef degradation.

PubMed

Chivers, Douglas P; McCormick, Mark I; Allan, Bridie J M; Ferrari, Maud C O

2016-09-09

Habitat degradation is among the top drivers of the loss of global biodiversity. This problem is particularly acute in coral reef system. Here we investigated whether coral degradation influences predator risk assessment and learning for damselfish. When in a live coral environment, Ambon damselfish were able to learn the identity of an unknown predator upon exposure to damselfish alarm cues combined with predator odour and were able to socially transmit this learned recognition to naïve conspecifics. However, in the presence of dead coral water, damselfish failed to learn to recognize the predator through alarm cue conditioning and hence could not transmit the information socially. Unlike alarm cues of Ambon damselfish that appear to be rendered unusable in degraded coral habitats, alarm cues of Nagasaki damselfish remain viable in this same environment. Nagasaki damselfish were able to learn predators through conditioning with alarm cues in degraded habitats and subsequently transmit the information socially to Ambon damselfish. Predator-prey dynamics may be profoundly affected as habitat degradation proceeds; the success of one species that appears to have compromised predation assessment and learning, may find itself reliant on other species that are seemingly unaffected by the same degree of habitat degradation.

Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

NASA Astrophysics Data System (ADS)

Cui, Guozeng; Xu, Shengyuan; Ma, Qian; Li, Yongmin; Zhang, Zhengqiang

2018-05-01

In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Four Chemical Trends Will Shape the Next Decade's Directions in Perfluoroalkyl and Polyfluoroalkyl Substances Research.

PubMed

Kotthoff, Matthias; Bücking, Mark

2018-01-01

Per- and polyfluoroalkyl substances (PFAS) represent a versatile group of ubiquitously occurring chemicals of increasing regulatory concern. The past years lead to an ever expanding portfolio of detected anthropogenic PFAS in numerous products encountered in daily life. Yet no clear picture of the full range of individual substance that comprise PFAS is available and this challenges analytical and engineering sciences. Authorities struggle to cope with uncertainties in managing risk of harm posed by PFAS. This is a result of an incomplete understanding of the range of compounds that they comprise in differing products. There are analytical uncertainties identifying PFAS and estimating the concentrations of the total PFAS load individual molecules remain unknown. There are four major trends from the chemical perspective that will shape PFAS research for the next decade. Mobility: A wide and dynamic distribution of short chain PFAS due to their high polarity, persistency and volatility.Substitution of regulated substances: The ban or restrictions of individual molecules will lead to a replacement with substitutes of similar concern.Increase in structural diversity of existing PFAS molecules: Introduction of e.g., hydrogens and chlorine atoms instead of fluorine, as well as branching and cross-linking lead to a high versatility of unknown target molecules.Unknown "Dark Matter": The amount, identity, formation pathways, and transformation dynamics of polymers and PFAS precursors are largely unknown. These directions require optimized analytical setups, especially multi-methods, and semi-specific tools to determine PFAS-sum parameters in any relevant matrix.

Four Chemical Trends Will Shape the Next Decade's Directions in Perfluoroalkyl and Polyfluoroalkyl Substances Research

PubMed Central

Kotthoff, Matthias; Bücking, Mark

2018-01-01

Per- and polyfluoroalkyl substances (PFAS) represent a versatile group of ubiquitously occurring chemicals of increasing regulatory concern. The past years lead to an ever expanding portfolio of detected anthropogenic PFAS in numerous products encountered in daily life. Yet no clear picture of the full range of individual substance that comprise PFAS is available and this challenges analytical and engineering sciences. Authorities struggle to cope with uncertainties in managing risk of harm posed by PFAS. This is a result of an incomplete understanding of the range of compounds that they comprise in differing products. There are analytical uncertainties identifying PFAS and estimating the concentrations of the total PFAS load individual molecules remain unknown. There are four major trends from the chemical perspective that will shape PFAS research for the next decade. Mobility: A wide and dynamic distribution of short chain PFAS due to their high polarity, persistency and volatility.Substitution of regulated substances: The ban or restrictions of individual molecules will lead to a replacement with substitutes of similar concern.Increase in structural diversity of existing PFAS molecules: Introduction of e.g., hydrogens and chlorine atoms instead of fluorine, as well as branching and cross-linking lead to a high versatility of unknown target molecules.Unknown “Dark Matter”: The amount, identity, formation pathways, and transformation dynamics of polymers and PFAS precursors are largely unknown. These directions require optimized analytical setups, especially multi-methods, and semi-specific tools to determine PFAS-sum parameters in any relevant matrix. PMID:29675408

Gaussian process model for extrapolation of scattering observables for complex molecules: From benzene to benzonitrile

NASA Astrophysics Data System (ADS)

Cui, Jie; Li, Zhiying; Krems, Roman V.

2015-10-01

We consider a problem of extrapolating the collision properties of a large polyatomic molecule A-H to make predictions of the dynamical properties for another molecule related to A-H by the substitution of the H atom with a small molecular group X, without explicitly computing the potential energy surface for A-X. We assume that the effect of the -H →-X substitution is embodied in a multidimensional function with unknown parameters characterizing the change of the potential energy surface. We propose to apply the Gaussian Process model to determine the dependence of the dynamical observables on the unknown parameters. This can be used to produce an interval of the observable values which corresponds to physical variations of the potential parameters. We show that the Gaussian Process model combined with classical trajectory calculations can be used to obtain the dependence of the cross sections for collisions of C6H5CN with He on the unknown parameters describing the interaction of the He atom with the CN fragment of the molecule. The unknown parameters are then varied within physically reasonable ranges to produce a prediction uncertainty of the cross sections. The results are normalized to the cross sections for He — C6H6 collisions obtained from quantum scattering calculations in order to provide a prediction interval of the thermally averaged cross sections for collisions of C6H5CN with He.

Mental simulation of routes during navigation involves adaptive temporal compression

PubMed Central

Arnold, Aiden E.G.F.; Iaria, Giuseppe; Ekstrom, Arne D.

2016-01-01

Mental simulation is a hallmark feature of human cognition, allowing features from memories to be flexibly used during prospection. While past studies demonstrate the preservation of real-world features such as size and distance during mental simulation, their temporal dynamics remains unknown. Here, we compare mental simulations to navigation of routes in a large-scale spatial environment to test the hypothesis that such simulations are temporally compressed in an adaptive manner. Our results show that simulations occurred at 2.39x the speed it took to navigate a route, increasing in compression (3.57x) for slower movement speeds. Participant self-reports of vividness and spatial coherence of simulations also correlated strongly with simulation duration, providing an important link between subjective experiences of simulated events and how spatial representations are combined during prospection. These findings suggest that simulation of spatial events involve adaptive temporal mechanisms, mediated partly by the fidelity of memories used to generate the simulation. PMID:27568586

Architecture for reactive planning of robot actions

NASA Astrophysics Data System (ADS)

Riekki, Jukka P.; Roening, Juha

1995-01-01

In this article, a reactive system for planning robot actions is described. The described hierarchical control system architecture consists of planning-executing-monitoring-modelling elements (PEMM elements). A PEMM element is a goal-oriented, combined processing and data element. It includes a planner, an executor, a monitor, a modeler, and a local model. The elements form a tree-like structure. An element receives tasks from its ancestor and sends subtasks to its descendants. The model knowledge is distributed into the local models, which are connected to each other. The elements can be synchronized. The PEMM architecture is strictly hierarchical. It integrated planning, sensing, and modelling into a single framework. A PEMM-based control system is reactive, as it can cope with asynchronous events and operate under time constraints. The control system is intended to be used primarily to control mobile robots and robot manipulators in dynamic and partially unknown environments. It is suitable especially for applications consisting of physically separated devices and computing resources.

We can't all be supermodels: the value of comparative transcriptomics to the study of non-model insects

PubMed Central

Oppenheim, Sara J; Baker, Richard H; Simon, Sabrina; DeSalle, Rob

2015-01-01

Insects are the most diverse group of organisms on the planet. Variation in gene expression lies at the heart of this biodiversity and recent advances in sequencing technology have spawned a revolution in researchers' ability to survey tissue-specific transcriptional complexity across a wide range of insect taxa. Increasingly, studies are using a comparative approach (across species, sexes and life stages) that examines the transcriptional basis of phenotypic diversity within an evolutionary context. In the present review, we summarize much of this research, focusing in particular on three critical aspects of insect biology: morphological development and plasticity; physiological response to the environment; and sexual dimorphism. A common feature that is emerging from these investigations concerns the dynamic nature of transcriptome evolution as indicated by rapid changes in the overall pattern of gene expression, the differential expression of numerous genes with unknown function, and the incorporation of novel, lineage-specific genes into the transcriptional profile. PMID:25524309

Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation.

PubMed

Zhao, Shancen; Zheng, Pingping; Dong, Shanshan; Zhan, Xiangjiang; Wu, Qi; Guo, Xiaosen; Hu, Yibo; He, Weiming; Zhang, Shanning; Fan, Wei; Zhu, Lifeng; Li, Dong; Zhang, Xuemei; Chen, Quan; Zhang, Hemin; Zhang, Zhihe; Jin, Xuelin; Zhang, Jinguo; Yang, Huanming; Wang, Jian; Wang, Jun; Wei, Fuwen

2013-01-01

The panda lineage dates back to the late Miocene and ultimately leads to only one extant species, the giant panda (Ailuropoda melanoleuca). Although global climate change and anthropogenic disturbances are recognized to shape animal population demography their contribution to panda population dynamics remains largely unknown. We sequenced the whole genomes of 34 pandas at an average 4.7-fold coverage and used this data set together with the previously deep-sequenced panda genome to reconstruct a continuous demographic history of pandas from their origin to the present. We identify two population expansions, two bottlenecks and two divergences. Evidence indicated that, whereas global changes in climate were the primary drivers of population fluctuation for millions of years, human activities likely underlie recent population divergence and serious decline. We identified three distinct panda populations that show genetic adaptation to their environments. However, in all three populations, anthropogenic activities have negatively affected pandas for 3,000 years.

Ubiquitin Utilizes an Acidic Surface Patch to Alter Chromatin Structure

PubMed Central

Debelouchina, Galia T.; Gerecht, Karola; Muir, Tom W.

2016-01-01

Ubiquitylation of histone H2B, associated with gene activation, leads to chromatin decompaction through an unknown mechanism. We used a hydrogen-deuterium exchange strategy coupled with nuclear magnetic resonance spectroscopy to map the ubiquitin surface responsible for its structural effects on chromatin. Our studies revealed that a previously uncharacterized acidic patch on ubiquitin comprising residues Glu16 and Glu18 is essential for decompaction. These residues mediate promiscuous electrostatic interactions with the basic histone proteins, potentially positioning the ubiquitin moiety as a dynamic “wedge” that prevents the intimate association of neighboring nucleosomes. Using two independent cross-linking strategies and an oligomerization assay, we also showed that ubiquitin-ubiquitin contacts occur in the chromatin environment and are important for the solubilization of the chromatin polymers. Our work highlights a novel, chromatin-related aspect of the “ubiquitin code”, and sheds light on how the information rich ubiquitin modification can orchestrate different biochemical outcomes using different surface features. PMID:27870837

Observer-based robust finite time H∞ sliding mode control for Markovian switching systems with mode-dependent time-varying delay and incomplete transition rate.

PubMed

Gao, Lijun; Jiang, Xiaoxiao; Wang, Dandan

2016-03-01

This paper investigates the problem of robust finite time H∞ sliding mode control for a class of Markovian switching systems. The system is subjected to the mode-dependent time-varying delay, partly unknown transition rate and unmeasurable state. The main difficulty is that, a sliding mode surface cannot be designed based on the unknown transition rate and unmeasurable state directly. To overcome this obstacle, the set of modes is firstly divided into two subsets standing for known transition rate subset and unknown one, based on which a state observer is established. A component robust finite-time sliding mode controller is also designed to cope with the effect of partially unknown transition rate. It is illustrated that the reachability, finite-time stability, finite-time boundedness, finite-time H∞ state feedback stabilization of sliding mode dynamics can be ensured despite the unknown transition rate. Finally, the simulation results verify the effectiveness of robust finite time control problem. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Active vibration control for piezoelectricity cantilever beam: an adaptive feedforward control method

NASA Astrophysics Data System (ADS)

Zhu, Qiao; Yue, Jun-Zhou; Liu, Wei-Qun; Wang, Xu-Dong; Chen, Jun; Hu, Guang-Di

2017-04-01

This work is focused on the active vibration control of piezoelectric cantilever beam, where an adaptive feedforward controller (AFC) is utilized to reject the vibration with unknown multiple frequencies. First, the experiment setup and its mathematical model are introduced. Due to that the channel between the disturbance and the vibration output is unknown in practice, a concept of equivalent input disturbance (EID) is employed to put an equivalent disturbance into the input channel. In this situation, the vibration control can be achieved by setting the control input be the identified EID. Then, for the EID with known multiple frequencies, the AFC is introduced to perfectly reject the vibration but is sensitive to the frequencies. In order to accurately identify the unknown frequencies of EID in presence of the random disturbances and un-modeled nonlinear dynamics, the time-frequency-analysis (TFA) method is employed to precisely identify the unknown frequencies. Consequently, a TFA-based AFC algorithm is proposed to the active vibration control with unknown frequencies. Finally, four cases are given to illustrate the efficiency of the proposed TFA-based AFC algorithm by experiment.

Dynamic Failure of Materials: A Review

DTIC Science & Technology

2010-08-01

stress states . It is currently unknown how well the current trend of multi-scale modeling will impact dynamic failure; however... stress can exist in the necked region after a neck is formed due to the nonuniformity of the necked region. This triaxial stress state is extremely...7 into 8, the effective stress intensity factor (Keff) can be determined in terms of the stress intensity factor (K). Because the onset of

Factors influencing the spatial and temporal dynamics of engelmann spruce mortality during a spruce beetle outbreak on the Markagunt Plateau, Utah

Treesearch

R. Justin DeRose; James N. Long

2012-01-01

Host conditions are known to influence spruce beetle population levels, but whether they influence the spatial and temporal patterns of beetle-caused mortality during an outbreak is unknown. Using dendrochronological techniques, we quantified the spatiotemporal dynamics of a modern (late 1980s through the early 2000s) spruce beetle outbreak in Engelmann spruce on the...

Fast vesicle transport is required for the slow axonal transport of synapsin.

PubMed

Tang, Yong; Scott, David; Das, Utpal; Gitler, Daniel; Ganguly, Archan; Roy, Subhojit

2013-09-25

Although it is known that cytosolic/soluble proteins synthesized in cell bodies are transported at much lower overall velocities than vesicles in fast axonal transport, the fundamental basis for this slow movement is unknown. Recently, we found that cytosolic proteins in axons of mouse cultured neurons are conveyed in a manner that superficially resembles diffusion, but with a slow anterograde bias that is energy- and motor-dependent (Scott et al., 2011). Here we show that slow axonal transport of synapsin, a prototypical member of this rate class, is dependent upon fast vesicle transport. Despite the distinct overall dynamics of slow and fast transport, experimentally induced and intrinsic variations in vesicle transport have analogous effects on slow transport of synapsin as well. Dynamic cotransport of vesicles and synapsin particles is also seen in axons, consistent with a model where higher-order assemblies of synapsin are conveyed by transient and probabilistic associations with vesicles moving in fast axonal transport. We posit that such dynamic associations generate the slow overall anterogradely biased flow of the population ("dynamic-recruitment model"). Our studies uncover the underlying kinetic basis for a classic cytosolic/soluble protein moving in slow axonal transport and reveal previously unknown links between slow and fast transport, offering a clearer conceptual picture of this curious phenomenon.

Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

PubMed

Li, Zhijun; Su, Chun-Yi

2013-09-01

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

The Martian: Examining Human Physical Judgments across Virtual Gravity Fields.

PubMed

Ye, Tian; Qi, Siyuan; Kubricht, James; Zhu, Yixin; Lu, Hongjing; Zhu, Song-Chun

2017-04-01

This paper examines how humans adapt to novel physical situations with unknown gravitational acceleration in immersive virtual environments. We designed four virtual reality experiments with different tasks for participants to complete: strike a ball to hit a target, trigger a ball to hit a target, predict the landing location of a projectile, and estimate the flight duration of a projectile. The first two experiments compared human behavior in the virtual environment with real-world performance reported in the literature. The last two experiments aimed to test the human ability to adapt to novel gravity fields by measuring their performance in trajectory prediction and time estimation tasks. The experiment results show that: 1) based on brief observation of a projectile's initial trajectory, humans are accurate at predicting the landing location even under novel gravity fields, and 2) humans' time estimation in a familiar earth environment fluctuates around the ground truth flight duration, although the time estimation in unknown gravity fields indicates a bias toward earth's gravity.

Solution to the SLAM problem in low dynamic environments using a pose graph and an RGB-D sensor.

PubMed

Lee, Donghwa; Myung, Hyun

2014-07-11

In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detected using a variety of moving object detection algorithms. The changes in the environments then cause groups of false loop closing when the same moved objects are observed for a while, which means that conventional SLAM algorithms produce incorrect results. To address this problem, we propose a novel SLAM method that handles low dynamic environments. The proposed method uses a pose graph structure and an RGB-D sensor. First, to prune the falsely grouped constraints efficiently, nodes of the graph, that represent robot poses, are grouped according to the grouping rules with noise covariances. Next, false constraints of the pose graph are pruned according to an error metric based on the grouped nodes. The pose graph structure is reoptimized after eliminating the false information, and the corrected localization and mapping results are obtained. The performance of the method was validated in real experiments using a mobile robot system.

Medical qualification of a commercial spaceflight participant: not your average astronaut.

PubMed

Jennings, Richard T; Murphy, David M F; Ware, David L; Aunon, Serena M; Moon, Richard E; Bogomolov, Valery V; Morgun, Valeri V; Voronkov, Yuri I; Fife, Caroline E; Boyars, Michael C; Ernst, Randy D

2006-05-01

Candidates for commercial spaceflight may be older than the typical astronaut and more likely to have medical problems that place them at risk during flight. Since the effects of microgravity on many medical conditions are unknown, physicians have little guidance when evaluating and certifying commercial spaceflight participants. This dynamic new era in space exploration may provide important data for evaluating medical conditions, creating appropriate medical standards, and optimizing treatment alternatives for long-duration spaceflight. A 57-yr-old spaceflight participant for an ISS mission presented with medical conditions that included moderately severe bullous emphysema, previous spontaneous pneumothorax with talc pleurodesis, a lung parenchymal mass, and ventricular and atrial ectopy. The medical evaluation required for certification was extensive and included medical studies and monitoring conducted in analogue spaceflight environments including altitude chambers, high altitude mixed-gas simulation, zero-G aircraft, and high-G centrifuge. To prevent recurrence of pneumothorax, we performed video-assisted thoracoscopic pleurodesis, and to assess lung masses, several percutaneous or direct biopsies. The candidate's 10-d mission was without incident. Non-career astronauts applying for commercial suborbital and orbital spaceflight will, at least in the near future, challenge aerospace physicians with unknowns regarding safety during training and flight, and highlight important ethical and risk-assessment problems. The information obtained from this new group of space travelers will provide important data for the evaluation and in-flight treatment of medical problems that space programs have not yet addressed systematically, and may improve the medical preparedness of exploration-class missions.

Global dynamics and diffusion in triaxial galactic models

NASA Astrophysics Data System (ADS)

Papaphilippou, Y.

We apply the Frequency Map Analysis method to the 3--dimensional logarithmic galactic potential in order to clarify the dynamical behaviour of triaxial power--law galactic models. All the fine dynamical details are displayed in the complete frequency map, a direct representation of the system's Arnol'd web. The influence of resonant lines and the extent of the chaotic zones are directly associated with the physical space of the system. Some new results related with the diffusion of galactic orbits are also discussed. This approach reveals many unknown dynamical features of triaxial galactic potentials and provides strong indications that chaos should be an innate characteristic of triaxial configurations.

Spacecraft Orbit Anomaly Representation Using Thrust-Fourier-Coefficients with Orbit Determination Toolbox

NASA Astrophysics Data System (ADS)

Ko, H.; Scheeres, D.

2014-09-01

Representing spacecraft orbit anomalies between two separate states is a challenging but an important problem in achieving space situational awareness for an active spacecraft. Incorporation of such a capability could play an essential role in analyzing satellite behaviors as well as trajectory estimation of the space object. A general way to deal with the anomaly problem is to add an estimated perturbing acceleration such as dynamic model compensation (DMC) into an orbit determination process based on pre- and post-anomaly tracking data. It is a time-consuming numerical process to find valid coefficients to compensate for unknown dynamics for the anomaly. Even if the orbit determination filter with DMC can crudely estimate an unknown acceleration, this approach does not consider any fundamental element of the unknown dynamics for a given anomaly. In this paper, a new way of representing a spacecraft anomaly using an interpolation technique with the Thrust-Fourier-Coefficients (TFCs) is introduced and several anomaly cases are studied using this interpolation method. It provides a very efficient way of reconstructing the fundamental elements of the dynamics for a given spacecraft anomaly. Any maneuver performed by a satellite transitioning between two arbitrary orbital states can be represented as an equivalent maneuver using an interpolation technique with the TFCs. Given unconnected orbit states between two epochs due to a spacecraft anomaly, it is possible to obtain a unique control law using the TFCs that is able to generate the desired secular behavior for the given orbital changes. This interpolation technique can capture the fundamental elements of combined unmodeled anomaly events. The interpolated orbit trajectory, using the TFCs compensating for a given anomaly, can be used to improve the quality of orbit fits through the anomaly period and therefore help to obtain a good orbit determination solution after the anomaly. Orbit Determination Toolbox (ODTBX) is modified to adapt this technique in order to verify the performance of this interpolation approach. Spacecraft anomaly cases are based on either single or multiple low or high thrust maneuvers and the unknown thrust accelerations are recovered and compared with the true thrust acceleration. The advantage of this approach is to easily append TFCs and its dynamics to the pre-built ODTBX, which enables us to blend post-anomaly tracking data to improve the performance of the interpolation representation in the absence of detailed information about a maneuver. It allows us to improve space situational awareness in the areas of uncertainty propagation, anomaly characterization and track correlation.

Online matching with queueing dynamics.

DOT National Transportation Integrated Search

2016-12-01

We consider a variant of the multiarmed bandit problem where jobs queue for service, and service rates of different servers may be unknown. We study algorithms that minimize queue-regret: the (expected) difference between the queue-lengths obtained b...

Output-Feedback Control of Unknown Linear Discrete-Time Systems With Stochastic Measurement and Process Noise via Approximate Dynamic Programming.

PubMed

Wang, Jun-Sheng; Yang, Guang-Hong

2017-07-25

This paper studies the optimal output-feedback control problem for unknown linear discrete-time systems with stochastic measurement and process noise. A dithered Bellman equation with the innovation covariance matrix is constructed via the expectation operator given in the form of a finite summation. On this basis, an output-feedback-based approximate dynamic programming method is developed, where the terms depending on the innovation covariance matrix are available with the aid of the innovation covariance matrix identified beforehand. Therefore, by iterating the Bellman equation, the resulting value function can converge to the optimal one in the presence of the aforementioned noise, and the nearly optimal control laws are delivered. To show the effectiveness and the advantages of the proposed approach, a simulation example and a velocity control experiment on a dc machine are employed.

A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

PubMed

Wang, Ning; Sun, Jing-Chao; Er, Meng Joo; Liu, Yan-Cheng

2016-05-01

In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.

Spatiotemporal neural network dynamics for the processing of dynamic facial expressions.

PubMed

Sato, Wataru; Kochiyama, Takanori; Uono, Shota

2015-07-24

The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150-200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300-350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual-motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions.

Spatiotemporal neural network dynamics for the processing of dynamic facial expressions

PubMed Central

Sato, Wataru; Kochiyama, Takanori; Uono, Shota

2015-01-01

The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150–200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300–350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual–motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions. PMID:26206708

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Quantum critical probing and simulation of colored quantum noise

NASA Astrophysics Data System (ADS)

Mascarenhas, Eduardo; de Vega, Inés

2017-12-01

We propose a protocol to simulate the evolution of a non-Markovian open quantum system by considering a collisional process with a many-body system, which plays the role of an environment. As a result of our protocol, the environment spatial correlations are mapped into the time correlations of a noise that drives the dynamics of the open system. Considering the weak coupling limit, the open system can also be considered as a probe of the environment properties. In this regard, when preparing the environment in its ground state, a measurement of the dynamics of the open system allows to determine the length of the environment spatial correlations and therefore its critical properties. To illustrate our proposal we simulate the full system dynamics with matrix-product-states and compare this to the reduced dynamics obtained with an approximated variational master equation.

Exploration for Agents with Different Personalities in Unknown Environments

NASA Astrophysics Data System (ADS)

Doumit, Sarjoun; Minai, Ali

We present in this paper a personality-based architecture (PA) that combines elements from the subsumption architecture and reinforcement learning to find alternate solutions for problems facing artificial agents exploring unknown environments. The underlying PA algorithm is decomposed into layers according to the different (non-contiguous) stages that our agent passes in, which in turn are influenced by the sources of rewards present in the environment. The cumulative rewards collected by an agent, in addition to its internal composition serve as factors in shaping its personality. In missions where multiple agents are deployed, our solution-goal is to allow each of the agents develop its own distinct personality in order for the collective to reach a balanced society, which then can accumulate the largest possible amount of rewards for the agent and society as well. The architecture is tested in a simulated matrix world which embodies different types of positive rewards and negative rewards. Varying experiments are performed to compare the performance of our algorithm with other algorithms under the same environment conditions. The use of our architecture accelerates the overall adaptation of the agents to their environment and goals by allowing the emergence of an optimal society of agents with different personalities. We believe that our approach achieves much efficient results when compared to other more restrictive policy designs.

Investigations Into Internal and External Aspects of Dynamic Agent-Environment Couplings

NASA Astrophysics Data System (ADS)

Dautenhahn, Kerstin

This paper originates from my work on `social agents'. An issue which I consider important to this kind of research is the dynamic coupling of an agent with its social and non-social environment. I hypothesize `internal dynamics' inside an agent as a basic step towards understanding. The paper therefore focuses on the internal and external dynamics which couple an agent to its environment. The issue of embodiment in animals and artifacts and its relation to `social dynamics' is discussed first. I argue that embodiment is linked to a concept of a body and is not necessarily given when running a control program on robot hardware. I stress the individual characteristics of an embodied cognitive system, as well as its social embeddedness. I outline the framework of a physical-psychological state space which changes dynamically in a self-modifying way as a holistic approach towards embodied human and artificial cognition. This framework is meant to discuss internal and external dynamics of an embodied, natural or artificial agent. In order to stress the importance of a dynamic memory I introduce the concept of an `autobiographical agent'. The second part of the paper gives an example of the implementation of a physical agent, a robot, which is dynamically coupled to its environment by balancing on a seesaw. For the control of the robot a behavior-oriented approach using the dynamical systems metaphor is used. The problem is studied through building a complete and co-adapted robot-environment system. A seesaw which varies its orientation with one or two degrees of freedom is used as the artificial `habitat'. The problem of stabilizing the body axis by active motion on a seesaw is solved by using two inclination sensors and a parallel, behavior-oriented control architecture. Some experiments are described which demonstrate the exploitation of the dynamics of the robot-environment system.

The abundance and diversity of antibiotic resistance genes in the atmospheric environment of composting plants.

PubMed

Gao, Min; Qiu, Tianlei; Sun, Yanmei; Wang, Xuming

2018-07-01

Composting is considered to reduce the introduction of antimicrobial resistance genes (ARGs) into the environment through land application of manure; however, the possible pollution of ARGs in the atmospheric environment of composting plants is unknown. In this study, 29 air samples including up- and downwind, composting, packaging, and office areas from 4 composting plants were collected. Dynamic concentrations of 22 subtypes of ARGs, class 1 integron (intl1), and 2 potential human pathogenic bacteria (HPB), and bacterial communities were investigated using droplet digital PCR and 16S rRNA gene sequencing, respectively. In this study, intl1 and 22 subtypes of ARGs (except tetQ) were detected in air of composting, packaging, office, and downwind areas. The highest concentration of 15 out of 22 subtypes of ARGs was detected in the packaging areas, and intl1 also had the maximum average concentration of 10 4  copies/m 3 , with up to (1.78 ± 0.49) × 10 -2 copies/16S rRNA copy. Non-metric multi-dimensional scaling of ARGs, potential HPBs, and bacterial components all indicated that the bioaerosol pollutant pattern in packaging areas was most similar to that in composting areas, followed by office, downwind, and upwind areas. The co-occurrence between ARGs and bacterial taxa assessed by Procrustes test, mantel test, and network analysis implied that aerosolized ARG fragments from composting and packaging areas contributed to the compositions of ARG aerosols in office and downwind areas. The results presented here show that atmoshperic environments of composting plants harbor abundant and diverse ARGs, which highlight the urgent need for comprehensive evaluation of potential human health and ecological risks of composts during both production as well as land application. Copyright © 2018 Elsevier Ltd. All rights reserved.

Molecular characterization of microbial population dynamics during sildenafil citrate degradation.

PubMed

De Felice, Bruna; Argenziano, Carolina; Guida, Marco; Trifuoggi, Marco; Russo, Francesca; Condorelli, Valerio; Inglese, Mafalda

2009-02-01

Little is known about pharmaceutical and personal care products pollutants (PPCPs), but there is a growing interest in how they might impact the environment and microbial communities. The widespread use of Viagra (sildenafil citrate) has attracted great attention because of the high usage rate, the unpredictable disposal and the unknown potential effects on wildlife and the environment. Until now information regarding the impact of Viagra on microbial community in water environment has not been reported. In this research, for the first time, the genetic profile of the microbial community, developing in a Viagra polluted water environment, was evaluated by means of the 16S and 18S rRNA genes, for bacteria and fungi, respectively, amplified by polymerase chain reaction (PCR) and separated using the denaturing gradient gel electrophoresis (DGGE) technique. The DGGE results revealed a complex microbial community structure with most of the population persisting throughout the experimental period. DNA sequences from bands observed in the different denaturing gradient gel electrophoresis profiles exhibited the highest degree of identity to uncultured bacteria and fungi found previously mainly in polluted environmental and treating bioreactors. Biotransformation ability of sildenafil citrate by the microbial pool was studied and the capability of these microorganisms to detoxify a polluted water ecosystem was assessed. The bacterial and fungal population was able to degrade sildenafil citrate entirely. Additionally, assays conducted on Daphnia magna, algal growth inhibition assay and cell viability determination on HepG2 human cells showed that biotransformation products obtained from the bacterial growth was not toxic. The higher removal efficiency for sildenafil citrate and the lack of toxicity by the biotransformation products obtained showed that the microbial community identified here represented a composite population that might have biotechnological relevance to retrieve sildenafil citrate contaminated sites.

Contemporary group estimates adjusted for climatic effects provide a finer definition of the unknown environmental challenges experienced by growing pigs.

PubMed

Guy, S Z Y; Li, L; Thomson, P C; Hermesch, S

2017-12-01

Environmental descriptors derived from mean performances of contemporary groups (CGs) are assumed to capture any known and unknown environmental challenges. The objective of this paper was to obtain a finer definition of the unknown challenges, by adjusting CG estimates for the known climatic effects of monthly maximum air temperature (MaxT), minimum air temperature (MinT) and monthly rainfall (Rain). As the unknown component could include infection challenges, these refined descriptors may help to better model varying responses of sire progeny to environmental infection challenges for the definition of disease resilience. Data were recorded from 1999 to 2013 at a piggery in south-east Queensland, Australia (n = 31,230). Firstly, CG estimates of average daily gain (ADG) and backfat (BF) were adjusted for MaxT, MinT and Rain, which were fitted as splines. In the models used to derive CG estimates for ADG, MaxT and MinT were significant variables. The models that contained these significant climatic variables had CG estimates with a lower variance compared to models without significant climatic variables. Variance component estimates were similar across all models, suggesting that these significant climatic variables accounted for some known environmental variation captured in CG estimates. No climatic variables were significant in the models used to derive the CG estimates for BF. These CG estimates were used to categorize environments. There was no observable sire by environment interaction (Sire×E) for ADG when using the environmental descriptors based on CG estimates on BF. For the environmental descriptors based on CG estimates of ADG, there was significant Sire×E only when MinT was included in the model (p = .01). Therefore, this new definition of the environment, preadjusted by MinT, increased the ability to detect Sire×E. While the unknown challenges captured in refined CG estimates need verification for infection challenges, this may provide a practical approach for the genetic improvement of disease resilience. © 2017 Blackwell Verlag GmbH.

A genetic analysis of post-weaning feedlot performance and profitability in Bonsmara cattle.

PubMed

van der Westhuizen, R R; van der Westhuizen, J; Schoeman, S J

2009-02-25

The aim of this study was to identify factors influencing profitability in a feedlot environment and to estimate genetic parameters for and between a feedlot profit function and productive traits measured in growth tests. The heritability estimate of 0.36 for feedlot profitability shows that this trait is genetically inherited and that it can be selected for. The genetic correlations between feedlot profitability and production and efficiency varied from negligible to high. The genetic correlation estimate of -0.92 between feed conversion ratio and feedlot profitability is largely due to the part-whole relationship between these two traits. Consequently, a multiple regression equation was developed to estimate a feed intake value for all performance-tested Bonsmara bulls, which were group fed and whose feed intakes were unknown. These predicted feed intake values enabled the calculation of a post-weaning growth or feedlot profitability value for all tested bulls, even where individual feed intakes were unknown. Subsequently, a feedlot profitability value for each bull was calculated in a favorable economic environment, an average economic environment and in an unfavorable economic environment. The high Pearson and Spearman correlations between the estimate breeding values based on the average economic environment and the other two environments suggested that the average economic environment could be used to calculate estimate breeding values for feedlot profitability. It is therefore not necessary to change the carcass, weaned calf or feed price on a regular basis to allow for possible re-rankings based on estimate breeding values.

Population and Environment

PubMed Central

de Sherbinin, Alex; Carr, David; Cassels, Susan; Jiang, Leiwen

2009-01-01

The interactions between human population dynamics and the environment have often been viewed mechanistically. This review elucidates the complexities and contextual specificities of population-environment relationships in a number of domains. It explores the ways in which demographers and other social scientists have sought to understand the relationships among a full range of population dynamics (e.g., population size, growth, density, age and sex composition, migration, urbanization, vital rates) and environmental changes. The chapter briefly reviews a number of the theories for understanding population and the environment and then proceeds to provide a state-of-the-art review of studies that have examined population dynamics and their relationship to five environmental issue areas. The review concludes by relating population-environment research to emerging work on human-environment systems. PMID:20011237

Mycobacterium ulcerans Ecological Dynamics and Its Association with Freshwater Ecosystems and Aquatic Communities: Results from a 12-Month Environmental Survey in Cameroon

PubMed Central

Garchitorena, Andrés; Roche, Benjamin; Kamgang, Roger; Ossomba, Joachim; Babonneau, Jérémie; Landier, Jordi; Fontanet, Arnaud; Flahault, Antoine

2014-01-01

Background Mycobacterium ulcerans (MU) is the agent responsible for Buruli Ulcer (BU), an emerging skin disease with dramatic socioeconomic and health outcomes, especially in rural settings. BU emergence and distribution is linked to aquatic ecosystems in tropical and subtropical countries, especially to swampy and flooded areas. Aquatic animal organisms are likely to play a role either as host reservoirs or vectors of the bacilli. However, information on MU ecological dynamics, both in space and time, is dramatically lacking. As a result, the ecology of the disease agent, and consequently its mode of transmission, remains largely unknown, which jeopardizes public health attempts for its control. The objective of this study was to gain insight on MU environmental distribution and colonization of aquatic organisms through time. Methodology/Principal Findings Longitudinal sampling of 32 communities of aquatic macro-invertebrates and vertebrates was conducted from different environments in two BU endemic regions in Cameroon during 12 months. As a result, 238,496 individuals were classified and MU presence was assessed by qPCR in 3,084 sample-pools containing these aquatic organisms. Our study showed a broad distribution of MU in all ecosystems and taxonomic groups, with important regional differences in its occurrence. Colonization dynamics fluctuated along the year, with the highest peaks in August and October. The large variations observed in the colonization dynamics of different taxonomic groups and aquatic ecosystems suggest that the trends shown here are the result of complex ecological processes that need further investigation. Conclusion/Perspectives This is the largest field study on MU ecology to date, providing the first detailed description of its spatio-temporal dynamics in different aquatic ecosystems within BU endemic regions. We argue that coupling this data with fine-scale epidemiological data through statistical and mathematical models will provide a major step forward in the understanding of MU ecology and mode of transmission. PMID:24831924

Controls on Water Storage, Mixing and Release in a Nested Catchment Set-up with Clean and Mixed Physiographic Characteristics

NASA Astrophysics Data System (ADS)

Pfister, L.; McDonnell, J.; Hissler, C.; Martínez-Carreras, N.; Klaus, J.

2015-12-01

With catchment water storage being only rarely determined, storage dynamics remain largely unknown to date. However, storage bears considerable potential for catchment inter-comparison exercises, as well as it is likely to have an important role in regulating catchment functions. Catchment comparisons across a wide range of environments and scales will help to increase our understanding of relationships between storage dynamics and catchment processes. With respect to the potential of catchment storage for bringing new momentum to catchment classification and catchment processes understanding we currently investigate spatial and temporal variability of dynamic storage in a nested catchment set-up (16 catchments) of the Alzette River basin (Luxembourg, Europe), covering a wide range of geological settings, catchment areas, contrasted landuse, and hydro-meteorological and tracer series. We define catchment storage as the total amount of water stored in a control volume, delimited by the catchment's topographical boundaries and depth of saturated and unsaturated zones. Complementary storage assessments (via input-output dynamics of natural tracers, geographical sounding, groundwater level measurements, soil moisture measurements, hydrometry) are carried out for comparison purposes. In our nested catchment set-up we have (1) assessed dependencies between geology, catchment permeability and winter runoff coefficients, (2) calculated water balance derived catchment storage and mixing potential and quantified how dynamic storage differs between catchments and scales, and (3) examined how stream baseflow dD (as a proxy for baseflow transit time) and integrated flow measures (like the flow duration curve) relate to bedrock geology. Catchments with higher bedrock permeability exhibited larger storage capacities and eventually lower average winter runoff coefficients. Over a time-span of 11 years, all catchments re-produced the same winter runoff coefficients year after year, regardless of their bedrock geology, permeability and winter season storage filling ratios. Ultimately, catchment organisation in our area of interest (i.e. geology, permeability, flowpath length) appeared to have a strong control on winter runoff coefficients, catchment storage and subsequently baseflow dD.

Metagenomic ventures into outer sequence space.

PubMed

Dutilh, Bas E

Sequencing DNA or RNA directly from the environment often results in many sequencing reads that have no homologs in the database. These are referred to as "unknowns," and reflect the vast unexplored microbial sequence space of our biosphere, also known as "biological dark matter." However, unknowns also exist because metagenomic datasets are not optimally mined. There is a pressure on researchers to publish and move on, and the unknown sequences are often left for what they are, and conclusions drawn based on reads with annotated homologs. This can cause abundant and widespread genomes to be overlooked, such as the recently discovered human gut bacteriophage crAssphage. The unknowns may be enriched for bacteriophage sequences, the most abundant and genetically diverse component of the biosphere and of sequence space. However, it remains an open question, what is the actual size of biological sequence space? The de novo assembly of shotgun metagenomes is the most powerful tool to address this question.

Adaptive control of an exoskeleton robot with uncertainties on kinematics and dynamics.

PubMed

Brahmi, Brahim; Saad, Maarouf; Ochoa-Luna, Cristobal; Rahman, Mohammad H

2017-07-01

In this paper, we propose a new adaptive control technique based on nonlinear sliding mode control (JSTDE) taking into account kinematics and dynamics uncertainties. This approach is applied to an exoskeleton robot with uncertain kinematics and dynamics. The adaptation design is based on Time Delay Estimation (TDE). The proposed strategy does not necessitate the well-defined dynamic and kinematic models of the system robot. The updated laws are designed using Lyapunov-function to solve the adaptation problem systematically, proving the close loop stability and ensuring the convergence asymptotically of the outputs tracking errors. Experiments results show the effectiveness and feasibility of JSTDE technique to deal with the variation of the unknown nonlinear dynamics and kinematics of the exoskeleton model.

Dynamic metrology and data processing for precision freeform optics fabrication and testing

NASA Astrophysics Data System (ADS)

Aftab, Maham; Trumper, Isaac; Huang, Lei; Choi, Heejoo; Zhao, Wenchuan; Graves, Logan; Oh, Chang Jin; Kim, Dae Wook

2017-06-01

Dynamic metrology holds the key to overcoming several challenging limitations of conventional optical metrology, especially with regards to precision freeform optical elements. We present two dynamic metrology systems: 1) adaptive interferometric null testing; and 2) instantaneous phase shifting deflectometry, along with an overview of a gradient data processing and surface reconstruction technique. The adaptive null testing method, utilizing a deformable mirror, adopts a stochastic parallel gradient descent search algorithm in order to dynamically create a null testing condition for unknown freeform optics. The single-shot deflectometry system implemented on an iPhone uses a multiplexed display pattern to enable dynamic measurements of time-varying optical components or optics in vibration. Experimental data, measurement accuracy / precision, and data processing algorithms are discussed.

Dynamic SLA Negotiation in Autonomic Federated Environments

NASA Astrophysics Data System (ADS)

Rubach, Pawel; Sobolewski, Michael

Federated computing environments offer requestors the ability to dynamically invoke services offered by collaborating providers in the virtual service network. Without an efficient resource management that includes Dynamic SLA Negotiation, however, the assignment of providers to customer's requests cannot be optimized and cannot offer high reliability without relevant SLA guarantees. We propose a new SLA-based SERViceable Metacomputing Environment (SERVME) capable of matching providers based on QoS requirements and performing autonomic provisioning and deprovisioning of services according to dynamic requestor needs. This paper presents the SLA negotiation process that includes on-demand provisioning and uses an object-oriented SLA model for large-scale service-oriented systems supported by SERVME. An initial reference implementation in the SORCER environment is also described.

Adaptive governance of riverine and wetland ecosystem goods and services

EPA Science Inventory

Adaptive governance and adaptive management have developed over the past quarter century in response to institutional and organizational failures, and unforeseen changes in natural resource dynamics. Adaptive governance provides a context for managing known and unknown consequenc...

Avoidance of Affect Mediates the Effect of Invalidating Childhood Environments on Borderline Personality Symptomatology in a Non-Clinical Sample

ERIC Educational Resources Information Center

Sturrock, Bonnie A.; Francis, Andrew; Carr, Steven

2009-01-01

The aim of this study was to test the Linehan (1993) proposal regarding associations between invalidating childhood environments, distress tolerance (e.g., avoidance of affect), and borderline personality disorder (BPD) symptoms. The sample consisted of 141 non-clinical participants (51 men, 89 women, one gender unknown), ranging in age from 18 to…

Why Some Teachers Easily Learn to Use a New Virtual Learning Environment: A Technology Acceptance Perspective

ERIC Educational Resources Information Center

Rienties, Bart; Giesbers, Bas; Lygo-Baker, Simon; Ma, Hoi Wah Serena; Rees, Roger

2016-01-01

After a decade of virtual learning environments (VLEs) in higher education, many teachers still use only a minimum of its affordances. This study looked at how academic staff interacted with a new and unknown VLE in order to understand how technology acceptance and support materials influence (perceived and actual) task performance. In an…

The Effect of Highly Scaffolded versus General Instruction on Students' Exploratory Behavior and Arousal

ERIC Educational Resources Information Center

Blikstein, Paulo; Gomes, July Silveira; Akiba, Henrique Teruo; Schneider, Bertrand

2017-01-01

Technology is changing the way students interact with knowledge, and open-ended activities are one of the main types of tasks that students engage with in technology-rich environments. However, the amount of guidance needed to promote learning in these environments remains unknown. We explore this issue by focusing on the effects of step-by-step…

Entropic Lattice Boltzmann Methods

DTIC Science & Technology

2001-12-10

model of fluid dynamics in one dimension, first considered by Renda et al. in 1997 [14]. Here the geometric picture involves a four dimensional polytope...convention of including constant terms in an extra column of the matrix, using the device of appending 1 to the column vector of unknowns. In general, there...we apply the entropic lattice Boltzmann method to a simple five-velocity model of fluid dynamics in one dimension, first considered by Renda et al

Neighboring extremals of dynamic optimization problems with path equality constraints

NASA Technical Reports Server (NTRS)

Lee, A. Y.

1988-01-01

Neighboring extremals of dynamic optimization problems with path equality constraints and with an unknown parameter vector are considered in this paper. With some simplifications, the problem is reduced to solving a linear, time-varying two-point boundary-value problem with integral path equality constraints. A modified backward sweep method is used to solve this problem. Two example problems are solved to illustrate the validity and usefulness of the solution technique.

Modeling of load lifting process with unknown center of gravity position

NASA Astrophysics Data System (ADS)

Kamanin, Y. N.; Zhukov, M. I.; Panichkin, A. V.; Redelin, R. A.

2018-03-01

The article proposes a new type of lifting beams that allows one to lift loads where the position of the center of gravity is unknown beforehand. The benefit of implementing this type of traverse is confirmed by the high demand for this product from the industrial enterprises and lack of their availability on the market. In conducted studies, the main kinematic and dynamic dependencies of the load lifting process with an unknown position of the center of gravity were described allowing for design and verification calculations of the traverse with flexible slings and an adjustable bail to be carried out. The obtained results can be useful to engineers and employees of enterprises engaged in the design and manufacturing of the lifting equipment and scientists doing research in “Carrying and lifting machines”.

Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments

PubMed Central

Seaton, Daniel D; Krishnan, J

2016-01-01

Cell cycle progression is carefully coordinated with a cell’s intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments. PMID:26741131

Sampling-based real-time motion planning under state uncertainty for autonomous micro-aerial vehicles in GPS-denied environments.

PubMed

Li, Dachuan; Li, Qing; Cheng, Nong; Song, Jingyan

2014-11-18

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees (CL-RBT), which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints.

Sampling-Based Real-Time Motion Planning under State Uncertainty for Autonomous Micro-Aerial Vehicles in GPS-Denied Environments

PubMed Central

Li, Dachuan; Li, Qing; Cheng, Nong; Song, Jingyan

2014-01-01

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees (CL-RBT), which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints. PMID:25412217

Metabolic gene regulation in a dynamically changing environment.

PubMed

Bennett, Matthew R; Pang, Wyming Lee; Ostroff, Natalie A; Baumgartner, Bridget L; Nayak, Sujata; Tsimring, Lev S; Hasty, Jeff

2008-08-28

Natural selection dictates that cells constantly adapt to dynamically changing environments in a context-dependent manner. Gene-regulatory networks often mediate the cellular response to perturbation, and an understanding of cellular adaptation will require experimental approaches aimed at subjecting cells to a dynamic environment that mimics their natural habitat. Here we monitor the response of Saccharomyces cerevisiae metabolic gene regulation to periodic changes in the external carbon source by using a microfluidic platform that allows precise, dynamic control over environmental conditions. We show that the metabolic system acts as a low-pass filter that reliably responds to a slowly changing environment, while effectively ignoring fast fluctuations. The sensitive low-frequency response was significantly faster than in predictions arising from our computational modelling, and this discrepancy was resolved by the discovery that two key galactose transcripts possess half-lives that depend on the carbon source. Finally, to explore how induction characteristics affect frequency response, we compare two S. cerevisiae strains and show that they have the same frequency response despite having markedly different induction properties. This suggests that although certain characteristics of the complex networks may differ when probed in a static environment, the system has been optimized for a robust response to a dynamically changing environment.

On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach.

PubMed

Baigzadehnoe, Barmak; Rahmani, Zahra; Khosravi, Alireza; Rezaie, Behrooz

2017-09-01

In this paper, the position and force tracking control problem of cooperative robot manipulator system handling a common rigid object with unknown dynamical models and unknown external disturbances is investigated. The universal approximation properties of fuzzy logic systems are employed to estimate the unknown system dynamics. On the other hand, by defining new state variables based on the integral and differential of position and orientation errors of the grasped object, the error system of coordinated robot manipulators is constructed. Subsequently by defining the appropriate change of coordinates and using the backstepping design strategy, an adaptive fuzzy backstepping position tracking control scheme is proposed for multi-robot manipulator systems. By utilizing the properties of internal forces, extra terms are also added to the control signals to consider the force tracking problem. Moreover, it is shown that the proposed adaptive fuzzy backstepping position/force control approach ensures all the signals of the closed loop system uniformly ultimately bounded and tracking errors of both positions and forces can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Bayesian refinement of protein structures and ensembles against SAXS data using molecular dynamics

PubMed Central

Shevchuk, Roman; Hub, Jochen S.

2017-01-01

Small-angle X-ray scattering is an increasingly popular technique used to detect protein structures and ensembles in solution. However, the refinement of structures and ensembles against SAXS data is often ambiguous due to the low information content of SAXS data, unknown systematic errors, and unknown scattering contributions from the solvent. We offer a solution to such problems by combining Bayesian inference with all-atom molecular dynamics simulations and explicit-solvent SAXS calculations. The Bayesian formulation correctly weights the SAXS data versus prior physical knowledge, it quantifies the precision or ambiguity of fitted structures and ensembles, and it accounts for unknown systematic errors due to poor buffer matching. The method further provides a probabilistic criterion for identifying the number of states required to explain the SAXS data. The method is validated by refining ensembles of a periplasmic binding protein against calculated SAXS curves. Subsequently, we derive the solution ensembles of the eukaryotic chaperone heat shock protein 90 (Hsp90) against experimental SAXS data. We find that the SAXS data of the apo state of Hsp90 is compatible with a single wide-open conformation, whereas the SAXS data of Hsp90 bound to ATP or to an ATP-analogue strongly suggest heterogenous ensembles of a closed and a wide-open state. PMID:29045407

Four chemical trends will shape the next decade's directions in Perfluoroalkyl and Polyfluoroalkyl substances research

NASA Astrophysics Data System (ADS)

Kotthoff, Matthias; Bücking, Mark

2018-04-01

Per- and polyfluoroalkyl substances (PFAS) represent a versatile group of ubiquitously occurring chemicals of increasing regulatory concern. The past years lead to an ever expanding portfolio of detected anthropogenic PFAS in numerous products encountered in daily life. Yet no clear picture of the full range of individual substance that comprise PFAS is available and this challenges analytical and engineering sciences. Authorities struggle to cope with uncertainties in managing risk of harm posed by PFAS.This is a result of an incomplete understanding of the range of compounds that they comprise in differing products. There are analytical uncertainties identifying PFAS and estimating the concentrations of the total PFAS loadindividual molecules remain unknown. There are four major trends from the chemical perspective that will shape PFAS research for the next decade. 1.Mobility: A wide and dynamic distribution of short chain PFAS due to their high polarity, persistency and volatility. 2.Substitution of regulated substances: The ban or restrictions of individual molecules will lead to a replacement with substitutes of similar concern. 3.Increase in structural diversity of existing PFAS molecules: Introduction of e.g. hydrogens and chlorine atoms instead of fluorine, as well as branching and cross-linking lead to a high versatility of unknown target molecules. 4. Unknown “Dark Matter”: The amount, identity, formation pathways, and transformation dynamics of polymers and PFAS precursors are largely unknown. These directions require optimized analytical setups, especially multi-methods, and semi-specific tools to determine PFAS-sum parameters in any relevant matrix.

Gaussian process model for extrapolation of scattering observables for complex molecules: From benzene to benzonitrile

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Jie; Krems, Roman V.; Li, Zhiying

2015-10-21

We consider a problem of extrapolating the collision properties of a large polyatomic molecule A–H to make predictions of the dynamical properties for another molecule related to A–H by the substitution of the H atom with a small molecular group X, without explicitly computing the potential energy surface for A–X. We assume that the effect of the −H →−X substitution is embodied in a multidimensional function with unknown parameters characterizing the change of the potential energy surface. We propose to apply the Gaussian Process model to determine the dependence of the dynamical observables on the unknown parameters. This can bemore » used to produce an interval of the observable values which corresponds to physical variations of the potential parameters. We show that the Gaussian Process model combined with classical trajectory calculations can be used to obtain the dependence of the cross sections for collisions of C{sub 6}H{sub 5}CN with He on the unknown parameters describing the interaction of the He atom with the CN fragment of the molecule. The unknown parameters are then varied within physically reasonable ranges to produce a prediction uncertainty of the cross sections. The results are normalized to the cross sections for He — C{sub 6}H{sub 6} collisions obtained from quantum scattering calculations in order to provide a prediction interval of the thermally averaged cross sections for collisions of C{sub 6}H{sub 5}CN with He.« less

Dynamic Geometry as a Context for Exploring Conjectures

ERIC Educational Resources Information Center

Wares, Arsalan

2018-01-01

The purpose of this paper is to provide examples of "non-traditional" proof-related activities that can explored in a dynamic geometry environment by university and high school students of mathematics. These propositions were encountered in the dynamic geometry environment. The author believes that teachers can ask their students to…

Cine CT technique for dynamic airway studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ell, S.R.; Jolles, H.; Keyes, W.D.

1985-07-01

The advent of cine CT scanning with its 50-msec data acquisition time promises a much wider range of dynamic CT studies. The authors describe a method for dynamic evaluation of the extrathoracic airway, which they believe has considerable potential application in nonfixed upper-airway disease, such as sleep apnea and stridor of unknown cause. Conventional CT is limited in such studies by long data acquisition time and can be used to study only prolonged maneuvers such as phonation. Fluoroscopy and digital subtraction studies are limited by relatively high radiation dose and inability to image all wall motions simultaneously.

Controlling quantum memory-assisted entropic uncertainty in non-Markovian environments

NASA Astrophysics Data System (ADS)

Zhang, Yanliang; Fang, Maofa; Kang, Guodong; Zhou, Qingping

2018-03-01

Quantum memory-assisted entropic uncertainty relation (QMA EUR) addresses that the lower bound of Maassen and Uffink's entropic uncertainty relation (without quantum memory) can be broken. In this paper, we investigated the dynamical features of QMA EUR in the Markovian and non-Markovian dissipative environments. It is found that dynamical process of QMA EUR is oscillation in non-Markovian environment, and the strong interaction is favorable for suppressing the amount of entropic uncertainty. Furthermore, we presented two schemes by means of prior weak measurement and posterior weak measurement reversal to control the amount of entropic uncertainty of Pauli observables in dissipative environments. The numerical results show that the prior weak measurement can effectively reduce the wave peak values of the QMA-EUA dynamic process in non-Markovian environment for long periods of time, but it is ineffectual on the wave minima of dynamic process. However, the posterior weak measurement reversal has an opposite effects on the dynamic process. Moreover, the success probability entirely depends on the quantum measurement strength. We hope that our proposal could be verified experimentally and might possibly have future applications in quantum information processing.

The resonant system: Linking brain-body-environment in sport performance☆.

PubMed

Teques, Pedro; Araújo, Duarte; Seifert, Ludovic; Del Campo, Vicente L; Davids, Keith

2017-01-01

The ecological dynamics approach offers new insights to understand how athlete nervous systems are embedded within the body-environment system in sport. Cognitive neuroscience focuses on the neural bases of athlete behaviors in terms of perceptual, cognitive, and motor functions defined within specific brain structures. Here, we discuss some limitations of this traditional perspective, addressing how athletes functionally adapt perception and action to the dynamics of complex performance environments by continuously perceiving information to regulate goal-directed actions. We examine how recent neurophysiological evidence of functioning in diverse cortical and subcortical regions appears more compatible with an ecological dynamics perspective, than traditional views in cognitive neuroscience. We propose how athlete behaviors in sports may be related to the tuning of resonant mechanisms indicating that perception is a dynamic process involving the whole body of the athlete. We emphasize the important role of metastable dynamics in the brain-body-environment system facilitating continuous interactions with a landscape of affordances (opportunities for action) in a performance environment. We discuss implications of these ideas for performance preparation and practice design in sport. © 2017 Elsevier B.V. All rights reserved.

Quantitative real-time imaging of glutathione

USDA-ARS?s Scientific Manuscript database

Glutathione plays many important roles in biological processes; however, the dynamic changes of glutathione concentrations in living cells remain largely unknown. Here, we report a reversible reaction-based fluorescent probe—designated as RealThiol (RT)—that can quantitatively monitor the real-time ...

Model Mismatch Paradigm for Probe based Nanoscale Imaging

NASA Astrophysics Data System (ADS)

Agarwal, Pranav

Scanning Probe Microscopes (SPMs) are widely used for investigation of material properties and manipulation of matter at the nanoscale. These instruments are considered critical enablers of nanotechnology by providing the only technique for direct observation of dynamics at the nanoscale and affecting it with sub Angstrom resolution. Current SPMs are limited by low throughput and lack of quantitative measurements of material properties. Various applications like the high density data storage, sub-20 nm lithography, fault detection and functional probing of semiconductor circuits, direct observation of dynamical processes involved in biological samples viz. motor proteins and transport phenomena in various materials demand high throughput operation. Researchers involved in material characterization at nanoscale are interested in getting quantitative measurements of stiffness and dissipative properties of various materials in a least invasive manner. In this thesis, system theoretic concepts are used to address these limitations. The central tenet of the thesis is to model, the known information about the system and then focus on perturbations of these known dynamics or model, to sense the effects due to changes in the environment such as changes in material properties or surface topography. Thus a model mismatch paradigm for probe based nanoscale imaging is developed. The topic is developed by presenting physics based modeling of a particular mode of operation of SPMs called the dynamic mode operation. This mode is modeled as a forced Lure system where a linear time invariant system is in feedback with an unknown static memoryless nonlinearity. Tools from averaging theory are used to tame this complex nonlinear system by approximating it as a linear system with time varying parameters. Material properties are thus transformed from being parameters of unknown nonlinear functions to being unknown coefficients of a linear plant. The first contribution of this thesis deals with real time detection and reduction of spurious areas in the image which are also known as probe-loss areas. These areas become severely detrimental during high speed operations. The detection strategy is based on thresholding of a distance measure, which captures the difference between sensor models in absence and presence of probe-loss. A switching gain control strategy based on the output of a Kalman Filter is used to reduce probe-loss areas in real time. The efficacy of this technique is demonstrated through experimental results showing increased image fidelity at scan rates that are 10 times faster than conventional scan rates. The second contribution of this thesis deals with developing multi-frequency input excitation strategy and deriving a bias compensated adaptive parameter estimation strategy to determine the instantaneous equivalent cantilever model. This is used to address the challenge of quantitative imaging at high bandwidth operation by relating the estimated plant coefficients to conservative and dissipative components of tip-sample interaction. The efficacy of the technique is demonstrated for quantitative material characterization of a polymer sample, resulting in material information not previously obtainable during dynamic mode operation. This information is obtained at speeds which are two orders faster than existing techniques. Quantitative verification strategies for the accuracy of estimated parameters are presented. The third contribution of this thesis deals with developing real time tractable models and characterization methodology for an electrostatically actuated MEMS cantilever with an integrated solid state thermal sensor. Appropriate modeling assumptions are made to delineate various nonlinear forces on the cantilever viz. electrostatic force, tip-sample interaction force and capacitive coupling. Experimental strategy is presented to measure the thermal sensing transfer function from DC-100kHz. A quantitative match between experimental and simulated data is obtained for the large range nonlinearities and small signal dynamics.

Persistent Factors Facilitating Excellence in Research Environments

ERIC Educational Resources Information Center

Kalpazidou Schmidt, Evanthia; Graversen, Ebbe Krogh

2018-01-01

The study presented here identifies robust and time-invariant features that characterise dynamic and innovative research environments. It takes as its point of departure the results of an empirical study conducted in 2002 which identified the common characteristics of 15 dynamic and innovative public research environments, and focusses on their…

A novel algorithm for fast grasping of unknown objects using C-shape configuration

NASA Astrophysics Data System (ADS)

Lei, Qujiang; Chen, Guangming; Meijer, Jonathan; Wisse, Martijn

2018-02-01

Increasing grasping efficiency is very important for the robots to grasp unknown objects especially subjected to unfamiliar environments. To achieve this, a new algorithm is proposed based on the C-shape configuration. Specifically, the geometric model of the used under-actuated gripper is approximated as a C-shape. To obtain an appropriate graspable position, this C-shape configuration is applied to fit geometric model of an unknown object. The geometric model of unknown object is constructed by using a single-view partial point cloud. To examine the algorithm using simulations, a comparison of the commonly used motion planners is made. The motion planner with the highest number of solved runs, lowest computing time and the shortest path length is chosen to execute grasps found by this grasping algorithm. The simulation results demonstrate that excellent grasping efficiency is achieved by adopting our algorithm. To validate this algorithm, experiment tests are carried out using a UR5 robot arm and an under-actuated gripper. The experimental results show that steady grasping actions are obtained. Hence, this research provides a novel algorithm for fast grasping of unknown objects.

Possible stimuli for strength and power adaptation : acute metabolic responses.

PubMed

Crewther, Blair; Cronin, John; Keogh, Justin

2006-01-01

The metabolic response to resistance exercise, in particular lactic acid or lactate, has a marked influence upon the muscular environment, which may enhance the training stimulus (e.g. motor unit activation, hormones or muscle damage) and thereby contribute to strength and power adaptation. Hypertrophy schemes have resulted in greater lactate responses (%) than neuronal and dynamic power schemes, suggesting possible metabolic-mediated changes in muscle growth. Factors such as age, sex, training experience and nutrition may also influence the lactate responses to resistance exercise and thereafter, muscular adaptation. Although the importance of the mechanical and hormonal stimulus to strength and power adaptation is well recognised, the contribution of the metabolic stimulus is largely unknown. Relatively few studies for example, have examined metabolic change across neuronal and dynamic power schemes, and not withstanding the fact that those mechanisms underpinning muscular adaptation, in relation to the metabolic stimulus, remain highly speculative. Inconsistent findings and methodological limitations within research (e.g. programme design, sampling period, number of samples) make interpretation further difficult. We contend that strength and power research needs to investigate those metabolic mechanisms likely to contribute to weight-training adaptation. Further research is also needed to examine the metabolic responses to different loading schemes, as well as interactions across age, sex and training status, so our understanding of how to optimise strength and power development is improved.

Dynamic response of desert wetlands to abrupt climate change

PubMed Central

Springer, Kathleen B.; Manker, Craig R.; Pigati, Jeffrey S.

2015-01-01

Desert wetlands are keystone ecosystems in arid environments and are preserved in the geologic record as groundwater discharge (GWD) deposits. GWD deposits are inherently discontinuous and stratigraphically complex, which has limited our understanding of how desert wetlands responded to past episodes of rapid climate change. Previous studies have shown that wetlands responded to climate change on glacial to interglacial timescales, but their sensitivity to short-lived climate perturbations is largely unknown. Here, we show that GWD deposits in the Las Vegas Valley (southern Nevada, United States) provide a detailed and nearly complete record of dynamic hydrologic changes during the past 35 ka (thousands of calibrated 14C years before present), including cycles of wetland expansion and contraction that correlate tightly with climatic oscillations recorded in the Greenland ice cores. Cessation of discharge associated with rapid warming events resulted in the collapse of entire wetland systems in the Las Vegas Valley at multiple times during the late Quaternary. On average, drought-like conditions, as recorded by widespread erosion and the formation of desert soils, lasted for a few centuries. This record illustrates the vulnerability of desert wetland flora and fauna to abrupt climate change. It also shows that GWD deposits can be used to reconstruct paleohydrologic conditions at millennial to submillennial timescales and informs conservation efforts aimed at protecting these fragile ecosystems in the face of anthropogenic warming. PMID:26554007

Solvation of magnesium dication: molecular dynamics simulation and vibrational spectroscopic study of magnesium chloride in aqueous solutions.

PubMed

Callahan, Karen M; Casillas-Ituarte, Nadia N; Roeselová, Martina; Allen, Heather C; Tobias, Douglas J

2010-04-22

Magnesium dication plays many significant roles in biochemistry. While it is available to the environment from both ocean waters and mineral salts on land, its roles in environmental and atmospheric chemistry are still relatively unknown. Several pieces of experimental evidence suggest that contact ion pairing may not exist at ambient conditions in solutions of magnesium chloride up to saturation concentrations. This is not typical of most ions. There has been disagreement in the molecular dynamics literature concerning the existence of ion pairing in magnesium chloride solutions. Using a force field developed during this study, we show that contact ion pairing is not energetically favorable. Additionally, we present a concentration-dependent Raman spectroscopic study of the Mg-O(water) hexaaquo stretch that clearly supports the absence of ion pairing in MgCl(2) solutions, although a transition occurring in the spectrum between 0.06x and 0.09x suggests a change in solution structure. Finally, we compare experimental and calculated observables to validate our force field as well as two other commonly used magnesium force fields, and in the process show that ion pairing of magnesium clearly is not observed at higher concentrations in aqueous solutions of magnesium chloride, independent of the choice of magnesium force field, although some force fields give better agreement to experimental results than others.

Dynamic response of desert wetlands to abrupt climate change

USGS Publications Warehouse

Springer, Kathleen; Manker, Craig; Pigati, Jeffrey S.

2015-01-01

Desert wetlands are keystone ecosystems in arid environments and are preserved in the geologic record as groundwater discharge (GWD) deposits. GWD deposits are inherently discontinuous and stratigraphically complex, which has limited our understanding of how desert wetlands responded to past episodes of rapid climate change. Previous studies have shown that wetlands responded to climate change on glacial to interglacial timescales, but their sensitivity to short-lived climate perturbations is largely unknown. Here, we show that GWD deposits in the Las Vegas Valley (southern Nevada, United States) provide a detailed and nearly complete record of dynamic hydrologic changes during the past 35 ka (thousands of calibrated 14C years before present), including cycles of wetland expansion and contraction that correlate tightly with climatic oscillations recorded in the Greenland ice cores. Cessation of discharge associated with rapid warming events resulted in the collapse of entire wetland systems in the Las Vegas Valley at multiple times during the late Quaternary. On average, drought-like conditions, as recorded by widespread erosion and the formation of desert soils, lasted for a few centuries. This record illustrates the vulnerability of desert wetland flora and fauna to abrupt climate change. It also shows that GWD deposits can be used to reconstruct paleohydrologic conditions at millennial to submillennial timescales and informs conservation efforts aimed at protecting these fragile ecosystems in the face of anthropogenic warming.

Dynamic Domains in Data Production Planning

NASA Technical Reports Server (NTRS)

Golden, Keith; Pang, Wanlin

2005-01-01

This paper discusses a planner-based approach to automating data production tasks, such as producing fire forecasts from satellite imagery and weather station data. Since the set of available data products is large, dynamic and mostly unknown, planning techniques developed for closed worlds are unsuitable. We discuss a number of techniques we have developed to cope with data production domains, including a novel constraint propagation algorithm based on planning graphs and a constraint-based approach to interleaved planning, sensing and execution.

Controlling Herds of Cooperative Robots

NASA Technical Reports Server (NTRS)

Quadrelli, Marco B.

2006-01-01

A document poses, and suggests a program of research for answering, questions of how to achieve autonomous operation of herds of cooperative robots to be used in exploration and/or colonization of remote planets. In a typical scenario, a flock of mobile sensory robots would be deployed in a previously unexplored region, one of the robots would be designated the leader, and the leader would issue commands to move the robots to different locations or aim sensors at different targets to maximize scientific return. It would be necessary to provide for this hierarchical, cooperative behavior even in the face of such unpredictable factors as terrain obstacles. A potential-fields approach is proposed as a theoretical basis for developing methods of autonomous command and guidance of a herd. A survival-of-the-fittest approach is suggested as a theoretical basis for selection, mutation, and adaptation of a description of (1) the body, joints, sensors, actuators, and control computer of each robot, and (2) the connectivity of each robot with the rest of the herd, such that the herd could be regarded as consisting of a set of artificial creatures that evolve to adapt to a previously unknown environment. A distributed simulation environment has been developed to test the proposed approaches in the Titan environment. One blimp guides three surface sondes via a potential field approach. The results of the simulation demonstrate that the method used for control is feasible, even if significant uncertainty exists in the dynamics and environmental models, and that the control architecture provides the autonomy needed to enable surface science data collection.

Proceedings: Shrubland ecosystem dynamics in a changing environment

Treesearch

Jerry R. Barrow; E. Durant McArthur; Ronald E. Sosebee; Robin J. Tausch

1996-01-01

This proceedings contains 50 papers including an overview of shrubland ecosystem dynamics in a changing environment and several papers each on vegetation dynamics, management concerns and options, and plant ecophysiology as well as an account of a Jornada Basin field trip. Contributions emphasize the impact of changing environmental conditions on vegetative composition...

Dragging in a Dynamic Geometry Environment through the Lens of Variation

ERIC Educational Resources Information Center

Leung, Allen

2008-01-01

What makes Dynamic Geometry Environment (DGE) a powerful mathematical knowledge acquisition microworld is its ability to visually make explicit the implicit dynamism of thinking about mathematical geometrical concepts. One of DGE's powers is to equip us with the ability to retain the background of a geometrical configuration while we can…

Young Children Reasoning about Symmetry in a Dynamic Geometry Environment

ERIC Educational Resources Information Center

Ng, Oi-Lam; Sinclair, Nathalie

2015-01-01

In this paper, we investigate children's learning of reflectional symmetry in a dynamic geometry environment. Through a classroom-based intervention involving two 1-h lessons, we analyse the changes in the children's thinking about reflectional symmetry: first, they developed dynamic and embodied ways of thinking about symmetry after working with…

Dynamic geometry as a context for exploring conjectures

NASA Astrophysics Data System (ADS)

Wares, Arsalan

2018-01-01

The purpose of this paper is to provide examples of 'non-traditional' proof-related activities that can explored in a dynamic geometry environment by university and high school students of mathematics. These propositions were encountered in the dynamic geometry environment. The author believes that teachers can ask their students to construct proofs for these propositions.

Self-Organizing Distributed Architecture Supporting Dynamic Space Expanding and Reducing in Indoor LBS Environment

PubMed Central

Jeong, Seol Young; Jo, Hyeong Gon; Kang, Soon Ju

2015-01-01

Indoor location-based services (iLBS) are extremely dynamic and changeable, and include numerous resources and mobile devices. In particular, the network infrastructure requires support for high scalability in the indoor environment, and various resource lookups are requested concurrently and frequently from several locations based on the dynamic network environment. A traditional map-based centralized approach for iLBSs has several disadvantages: it requires global knowledge to maintain a complete geographic indoor map; the central server is a single point of failure; it can also cause low scalability and traffic congestion; and it is hard to adapt to a change of service area in real time. This paper proposes a self-organizing and fully distributed platform for iLBSs. The proposed self-organizing distributed platform provides a dynamic reconfiguration of locality accuracy and service coverage by expanding and contracting dynamically. In order to verify the suggested platform, scalability performance according to the number of inserted or deleted nodes composing the dynamic infrastructure was evaluated through a simulation similar to the real environment. PMID:26016908

[Situational awareness: you won't see it unless you understand it].

PubMed

Graafland, Maurits; Schijven, Marlies P

2015-01-01

In dynamic, high-risk environments such as the modern operating theatre, healthcare providers are required to identify a multitude of signals correctly and in time. Errors resulting from failure to identify or interpret signals correctly lead to calamities. Medical training curricula focus largely on teaching technical skills and knowledge, not on the cognitive skills needed to interact appropriately with fast-changing, complex environments in practice. The term 'situational awareness' describes the dynamic process of receiving, interpreting and processing information in such dynamic environments. Improving situational awareness in high-risk environments should be part of medical curricula. In addition, the flood of information in high-risk environments should be presented more clearly and effectively. It is important that physicians become more involved in this regard.

Accurate distance determination of nucleic acids via Förster resonance energy transfer: implications of dye linker length and rigidity.

PubMed

Sindbert, Simon; Kalinin, Stanislav; Nguyen, Hien; Kienzler, Andrea; Clima, Lilia; Bannwarth, Willi; Appel, Bettina; Müller, Sabine; Seidel, Claus A M

2011-03-02

In Förster resonance energy transfer (FRET) experiments, the donor (D) and acceptor (A) fluorophores are usually attached to the macromolecule of interest via long flexible linkers of up to 15 Å in length. This causes significant uncertainties in quantitative distance measurements and prevents experiments with short distances between the attachment points of the dyes due to possible dye-dye interactions. We present two approaches to overcome the above problems as demonstrated by FRET measurements for a series of dsDNA and dsRNA internally labeled with Alexa488 and Cy5 as D and A dye, respectively. First, we characterize the influence of linker length and flexibility on FRET for different dye linker types (long, intermediate, short) by analyzing fluorescence lifetime and anisotropy decays. For long linkers, we describe a straightforward procedure that allows for very high accuracy of FRET-based structure determination through proper consideration of the position distribution of the dye and of linker dynamics. The position distribution can be quickly calculated with geometric accessible volume (AV) simulations, provided that the local structure of RNA or DNA in the proximity of the dye is known and that the dye diffuses freely in the sterically allowed space. The AV approach provides results similar to molecular dynamics simulations (MD) and is fully consistent with experimental FRET data. In a benchmark study for ds A-RNA, an rmsd value of 1.3 Å is achieved. Considering the case of undefined dye environments or very short DA distances, we introduce short linkers with a propargyl or alkenyl unit for internal labeling of nucleic acids to minimize position uncertainties. Studies by ensemble time correlated single photon counting and single-molecule detection show that the nature of the linker strongly affects the radius of the dye's accessible volume (6-16 Å). For short propargyl linkers, heterogeneous dye environments are observed on the millisecond time scale. A detailed analysis of possible orientation effects (κ(2) problem) indicates that, for short linkers and unknown local environments, additional κ(2)-related uncertainties are clearly outweighed by better defined dye positions.

Human-Robot Teams for Unknown and Uncertain Environments

NASA Technical Reports Server (NTRS)

Fong, Terry

2015-01-01

Man-robot interaction is the study of interactions between humans and robots. It is often referred as HRI by researchers. Human-robot interaction is a multidisciplinary field with contributions from human-computer interaction, artificial intelligence.

State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications

NASA Astrophysics Data System (ADS)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is presented. The developed theory is used to estimate vertical tire forces and predict tripped rollovers in situations involving road bumps, potholes, and lateral unknown force inputs. To estimate the tire-road friction coefficients at each individual tire of the vehicle, algorithms to estimate longitudinal forces and slip ratios at each tire are proposed. Subsequently, tire-road friction coefficients are obtained using recursive least squares parameter estimators that exploit the relationship between longitudinal force and slip ratio at each tire. The developed approaches are evaluated through simulations with industry standard software, CARSIM, with experimental tests on a Volvo XC90 sport utility vehicle and with experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed approaches can reliably estimate the vehicle parameters and state variables needed for effective ESC and rollover prevention applications.

Vision-based navigation in a dynamic environment for virtual human

NASA Astrophysics Data System (ADS)

Liu, Yan; Sun, Ji-Zhou; Zhang, Jia-Wan; Li, Ming-Chu

2004-06-01

Intelligent virtual human is widely required in computer games, ergonomics software, virtual environment and so on. We present a vision-based behavior modeling method to realize smart navigation in a dynamic environment. This behavior model can be divided into three modules: vision, global planning and local planning. Vision is the only channel for smart virtual actor to get information from the outside world. Then, the global and local planning module use A* and D* algorithm to find a way for virtual human in a dynamic environment. Finally, the experiments on our test platform (Smart Human System) verify the feasibility of this behavior model.

Single cell genomic study of Dehalococcoidites in deep sea sediments of Peru Margin 1230

NASA Astrophysics Data System (ADS)

Kaster, A.; Meyer-Blackwell, K.; Spormann, A. M.

2013-12-01

Dehalogenating Chloroflexi, such as Dehalococcoidites Dhc were originally discovered as the key microorganisms mediating reductive dehalogenation of the prevalent groundwater contaminants tetrachloroethene and trichloroethene. Molecular and genomic studies on their key enzymes for energy conservation, reductive dehalogenases rdh, have provided evidence for ubiquitous horizontal gene transfer. A pioneering study by Futagami et al. discovered novel putative rdh phylotypes in sediments from the Pacific, revealing an unknown and surprising abundance of rdh genes in pristine habitats. The frequent detection of Dhc-related 16S rRNA genes from these environments implied the occurrence of dissimilatory dehalorespiration in marine subsurface sediments, however, pristine Dhc could never be linked to this activity. Despite being ubiquitous in those environments, metabolic life style or ecological function of Dhc in the absence of anthropogenic contaminants is still completely unknown. We therefore analyzed a non-contaminated deep sea sediment sample of the Peru Margin 1230 site by a single cell genomic (SGC) approach. We present for the first time data on three single Dhc cells, helping to elucidate their role in the poorly understood oligotrophic marine sub-surface environment.

The human newborn's umwelt: Unexplored pathways and perspectives.

PubMed

André, Vanessa; Henry, Séverine; Lemasson, Alban; Hausberger, Martine; Durier, Virginie

2018-02-01

Historically, newborns, and especially premature newborns, were thought to "feel nothing." However, over the past decades, a growing body of evidence has shown that newborns are aware of their environment, but the extent and the onset of some sensory capacities remain largely unknown. The goal of this review is to update our current knowledge concerning newborns' perceptual world and how ready they are to cope with an entirely different sensory environment following birth. We aim to establish not only how and when each sensory ability arises during the pre-/postbirth period but also discuss how senses are studied. We conclude that although many studies converge to show that newborns are clearly sentient beings, much is still unknown. Further, we identify a series of internal and external factors that could explain discrepancies between studies, and we propose perspectives for future studies. Finally, through examples from animal studies, we illustrate the importance of this detailed knowledge to pursue the enhancement of newborns' daily living conditions. Indeed, this is a prerequisite for assessing the effects of the physical environment and routine procedures on newborns' welfare.

Connectivity of microbial populations in coral reef environments: microbiomes of sediment, fish and water

NASA Astrophysics Data System (ADS)

Biddle, J.; Leon, Z. R.; McCargar, M.; Drew, J.

2016-12-01

The benthic environments of coral reefs are heavily shaped by physiochemical factors, but also the ecological interactions of the animals and plants in the reef ecosystem. Microbial populations may be shared between the ecosystem of sediments, seagrasses and reef fish, however it is unknown to what degree. We investigated the potential connections between the microbiomes of sediments, seagrass blades and roots (Syringodium isoetifolium), Surgeonfish (A. nigricauda, Acanthurinae sp. unknown, C. striatus) and Parrotfish (C. spinidens) guts in reef areas of Fiji. We contrasted these with sediment samples from the Florida Keys and ocean water microbiomes from the Atlantic, Pacific and Indian Oceans. In general, we see a higher diversity of sediment microbial communities in Fiji compared to the Florida Keys. However, many of the same taxa are shared in these chemically similar environments, whereas the ocean water environments are completely distinct with few overlapping groups. We were able to show connectivity of a core microbiome between seagrass, fish and sediments in Fiji, including identifying a potential environmental reservoir of a surgeonfish symbiont, Epulopiscum. Finally, we show that fish guts have different microbial populations from crop to hindgut, and that microbial populations differ based on food source. The connection of these ecosystems suggest that the total microbiome of these environments may vary as their animal inhabitants shift in a changing ocean.

Usability testing of a mobile robotic system for in-home telerehabilitation.

PubMed

Boissy, Patrick; Brière, Simon; Corriveau, Hélène; Grant, Andrew; Lauria, Michel; Michaud, François

2011-01-01

Mobile robots designed to enhance telepresence in the support of telehealth services are being considered for numerous applications. TELEROBOT is a teleoperated mobile robotic platform equipped with videoconferencingcapabilities and designed to be used in a home environment to. In this study, learnability of the system's teleoperation interface and controls was evaluated with ten rehabilitation professionals during four training sessions in a laboratory environment and in an unknown home environment while performing the execution of a standardized evaluation protocol typically used in home care. Results show that the novice teleoperators' performances on two of the four metrics used (number of command and total time) improved significantly across training sessions (ANOVAS, p<0.05) and that performance in these metrics in the last training session reflected teleoperation abilities seen in the unknown home environment during navigation tasks (r=0,77 and 0,60). With only 4 hours of training, rehabilitation professionals were able learn to teleoperate successfully TELEROBOT. However teleoperation performances remained significantly less efficient then those of an expert. Under the home task condition (navigating the home environment from one point to the other as fast as possible) this translated to completion time between 350 seconds (best performance) and 850 seconds (worse performance). Improvements in other usability aspects of the system will be needed to meet the requirements of in-home telerehabilitation.

Modelling Mathematics Teachers' Intention to Use the Dynamic Geometry Environments in Macau: An SEM Approach

ERIC Educational Resources Information Center

Zhou, Mingming; Chan, Kan Kan; Teo, Timothy

2016-01-01

Dynamic geometry environments (DGEs) provide computer-based environments to construct and manipulate geometric figures with great ease. Research has shown that DGEs has positive impact on student motivation, engagement, and achievement in mathematics learning. However, the adoption of DGEs by mathematics teachers varies substantially worldwide.…

Accommodating the Instrumental Genesis Framework within Dynamic Technology Environments

ERIC Educational Resources Information Center

Hegedus, Stephen J.; Moreno-Armella, Luis

2010-01-01

In certain digital environments, "hot-spots" are key infrastructural pieces that allow the dynamic construction and re-construction of mathematical figures. We shall discuss their existence with respect to what we call user-environment co-actions, describing how they are sustainable bi-directional processes that have the potential to ground and…

Dynamic and Interactive Mathematics Learning Environments: The Case of Teaching the Limit Concept

ERIC Educational Resources Information Center

Martinovic, Dragana; Karadag, Zekeriya

2012-01-01

This theoretical study is an attempt to explore the potential of the dynamic and interactive mathematics learning environments (DIMLE) in relation to the technological pedagogical content knowledge (TPACK) framework. DIMLE are developed with intent to support learning mathematics through free exploration in a less constrained environment. A…

A unifying view of synchronization for data assimilation in complex nonlinear networks

NASA Astrophysics Data System (ADS)

Abarbanel, Henry D. I.; Shirman, Sasha; Breen, Daniel; Kadakia, Nirag; Rey, Daniel; Armstrong, Eve; Margoliash, Daniel

2017-12-01

Networks of nonlinear systems contain unknown parameters and dynamical degrees of freedom that may not be observable with existing instruments. From observable state variables, we want to estimate the connectivity of a model of such a network and determine the full state of the model at the termination of a temporal observation window during which measurements transfer information to a model of the network. The model state at the termination of a measurement window acts as an initial condition for predicting the future behavior of the network. This allows the validation (or invalidation) of the model as a representation of the dynamical processes producing the observations. Once the model has been tested against new data, it may be utilized as a predictor of responses to innovative stimuli or forcing. We describe a general framework for the tasks involved in the "inverse" problem of determining properties of a model built to represent measured output from physical, biological, or other processes when the measurements are noisy, the model has errors, and the state of the model is unknown when measurements begin. This framework is called statistical data assimilation and is the best one can do in estimating model properties through the use of the conditional probability distributions of the model state variables, conditioned on observations. There is a very broad arena of applications of the methods described. These include numerical weather prediction, properties of nonlinear electrical circuitry, and determining the biophysical properties of functional networks of neurons. Illustrative examples will be given of (1) estimating the connectivity among neurons with known dynamics in a network of unknown connectivity, and (2) estimating the biophysical properties of individual neurons in vitro taken from a functional network underlying vocalization in songbirds.

Continuous quantum measurement in spin environments

NASA Astrophysics Data System (ADS)

Xie, Dong; Wang, An Min

2015-08-01

We derive a stochastic master equation (SME) which describes the decoherence dynamics of a system in spin environments conditioned on the measurement record. Markovian and non-Markovian nature of environment can be revealed by a spectroscopy method based on weak continuous quantum measurement. On account of that correlated environments can lead to a non-local open system which exhibits strong non-Markovian effects although the local dynamics are Markovian, the spectroscopy method can be used to demonstrate that there is correlation between two environments.

Structure, Dynamics and Environment of Galaxies

NASA Astrophysics Data System (ADS)

Combes, Francoise; Terlevich, Roberto

This contribution presents a summary of the discussion on structure, dynamics and environment of Galaxies, held on Friday May 26 evening, after the Sherry/Coffee interval and the oral presentation of two dozen posters papers.

Insect outbreak shifts the direction of selection from fast to slow growth rates in the long-lived conifer Pinus ponderosa

PubMed Central

Sala, Anna

2017-01-01

Long generation times limit species’ rapid evolution to changing environments. Trees provide critical global ecosystem services, but are under increasing risk of mortality because of climate change-mediated disturbances, such as insect outbreaks. The extent to which disturbance changes the dynamics and strength of selection is unknown, but has important implications on the evolutionary potential of tree populations. Using a 40-y-old Pinus ponderosa genetic experiment, we provide rare evidence of context-dependent fluctuating selection on growth rates over time in a long-lived species. Fast growth was selected at juvenile stages, whereas slow growth was selected at mature stages under strong herbivory caused by a mountain pine beetle (Dendroctonus ponderosae) outbreak. Such opposing forces led to no net evolutionary response over time, thus providing a mechanism for the maintenance of genetic diversity on growth rates. Greater survival to mountain pine beetle attack in slow-growing families reflected, in part, a host-based life-history trade-off. Contrary to expectations, genetic effects on tree survival were greatest at the peak of the outbreak and pointed to complex defense responses. Our results suggest that selection forces in tree populations may be more relevant than previously thought, and have implications for tree population responses to future environments and for tree breeding programs. PMID:28652352

How predation shapes the social interaction rules of shoaling fish.

PubMed

Herbert-Read, James E; Rosén, Emil; Szorkovszky, Alex; Ioannou, Christos C; Rogell, Björn; Perna, Andrea; Ramnarine, Indar W; Kotrschal, Alexander; Kolm, Niclas; Krause, Jens; Sumpter, David J T

2017-08-30

Predation is thought to shape the macroscopic properties of animal groups, making moving groups more cohesive and coordinated. Precisely how predation has shaped individuals' fine-scale social interactions in natural populations, however, is unknown. Using high-resolution tracking data of shoaling fish ( Poecilia reticulata ) from populations differing in natural predation pressure, we show how predation adapts individuals' social interaction rules. Fish originating from high predation environments formed larger, more cohesive, but not more polarized groups than fish from low predation environments. Using a new approach to detect the discrete points in time when individuals decide to update their movements based on the available social cues, we determine how these collective properties emerge from individuals' microscopic social interactions. We first confirm predictions that predation shapes the attraction-repulsion dynamic of these fish, reducing the critical distance at which neighbours move apart, or come back together. While we find strong evidence that fish align with their near neighbours, we do not find that predation shapes the strength or likelihood of these alignment tendencies. We also find that predation sharpens individuals' acceleration and deceleration responses, implying key perceptual and energetic differences associated with how individuals move in different predation regimes. Our results reveal how predation can shape the social interactions of individuals in groups, ultimately driving differences in groups' collective behaviour. © 2017 The Authors.

Groundwater mixing at fracture intersections triggers massive iron-rich microbial mats

NASA Astrophysics Data System (ADS)

Bochet, O.; Le Borgne, T.; Bethencourt, L.; Aquilina, L.; Dufresne, A.; Pédrot, M.; Farasin, J.; Abbott, B. W.; Labasque, T.; Chatton, E.; Lavenant, N.; Petton, C.

2017-12-01

While most freshwater on Earth resides and flows in groundwater systems, these deep subsurface environments are often assumed to have little biogeochemical activity compared to surface environments. Here we report a massive microbial mat of iron-oxidizing bacteria, flourishing 60 meters below the surface, far below the mixing zone where most microbial activity is believed to occur. The abundance of microtubular structures in the mat hinted at the prevalence of of Leptothrix ochracea, but metagenomic analysis revealed a diverse consortium of iron-oxidizing bacteria dominated by unknown members of the Gallionellaceae family. This deep biogeochemical hot spot formed at the intersection of bedrock fractures, which maintain redox gradients by mixing water with different residence times and chemical compositions. Using measured fracture properties and hydrological conditions we developed a quantitative model to simulate the reactive zone where such deep hot spots could occur. While seasonal fluctuations are generally thought to decrease with depth, we found that meter-scale changes in water table level moved the depth of the reactive zone hundreds of meters because the microaerophilic threshold for ironoxidizers is highly sensitive to changes in mixing rates at fracture intersections. These results demonstrate that dynamic microbial communities can be sustained deep below the surface in bedrock fractures. Given the ubiquity of fractures at multiple scales in Earth's subsurface, such deep hot spots may strongly influence global biogeochemical cycles.

Shuttle payload bay dynamic environments: Summary and conclusion report for STS flights 1-5 and 9

NASA Technical Reports Server (NTRS)

Oconnell, M.; Garba, J.; Kern, D.

1984-01-01

The vibration, acoustic and low frequency loads data from the first 5 shuttle flights are presented. The engineering analysis of that data is also presented. Vibroacoustic data from STS-9 are also presented because they represent the only data taken on a large payload. Payload dynamic environment predictions developed by the participation of various NASA and industrial centers are presented along with a comparison of analytical loads methodology predictions with flight data, including a brief description of the methodologies employed in developing those predictions for payloads. The review of prediction methodologies illustrates how different centers have approached the problems of developing shuttle dynamic environmental predictions and criteria. Ongoing research activities related to the shuttle dynamic environments are also described. Analytical software recently developed for the prediction of payload acoustic and vibration environments are also described.

From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction.

PubMed

Hellyer, Peter John; Clopath, Claudia; Kehagia, Angie A; Turkheimer, Federico E; Leech, Robert

2017-08-01

In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional "task negative" activity that compensates for "task positive", sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic "task-negative" patterns of activity (e.g., the default mode network).

Dynamic access control model for privacy preserving personalized healthcare in cloud environment.

PubMed

Son, Jiseong; Kim, Jeong-Dong; Na, Hong-Seok; Baik, Doo-Kwon

2015-01-01

When sharing and storing healthcare data in a cloud environment, access control is a central issue for preserving data privacy as a patient's personal health data may be accessed without permission from many stakeholders. Specifically, dynamic authorization for the access of data is required because personal health data is stored in cloud storage via wearable devices. Therefore, we propose a dynamic access control model for preserving the privacy of personal healthcare data in a cloud environment. The proposed model considers context information for dynamic access. According to the proposed model, access control can be dynamically determined by changing the context information; this means that even for a subject with the same role in the cloud, access permission is defined differently depending on the context information and access condition. Furthermore, we experiment the ability of the proposed model to provide correct responses by representing a dynamic access decision with real-life personalized healthcare system scenarios.

Perceptions of the Effectiveness of System Dynamics-Based Interactive Learning Environments: An Empirical Study

ERIC Educational Resources Information Center

Qudrat-Ullah, Hassan

2010-01-01

The use of simulations in general and of system dynamics simulation based interactive learning environments (SDILEs) in particular is well recognized as an effective way of improving users' decision making and learning in complex, dynamic tasks. However, the effectiveness of SDILEs in classrooms has rarely been evaluated. This article describes…

Investigating the Problem Solving Competency of Pre Service Teachers in Dynamic Geometry Environment

ERIC Educational Resources Information Center

Haja, Shajahan

2005-01-01

This study investigated the problem-solving competency of four secondary pre service teachers (PSTs) of University of London as they explored geometry problems in dynamic geometry environment (DGE) in 2004. A constructivist experiment was designed in which dynamic software Cabri-Geometre II (hereafter Cabri) was used as an interactive medium.…

Chemical Clarification Methods for Confined Dredged Material Disposal.

DTIC Science & Technology

1983-07-01

foot second per metre cubic yards 0.7645549 cubic metres Farenheit degrees 5/9 Celsius degrees or Kelvins* feet 0.3048 metres feet per minute 0.3048...unknown in freshwater environments, use zero S.G. = specific gravity of solids; use 2.67 if unknown Wt. H20 [(weight of wet sample and dish, g...62.4 lb/ft v = average velocity, ft/sec Ps= absolute viscosity, 2.36 x 10-5 at 60F The duration t of the mixing is determined by t =L (6) v The net

A methodology for the generation of the 2-D map from unknown navigation environment by traveling a short distance

NASA Technical Reports Server (NTRS)

Bourbakis, N.; Sarkar, D.

1994-01-01

A technique for generation of a 2-D space map by traveling a short distance is described. The space to be mapped can be classified as: (1) space without obstacles, (2) space with stationary obstacles, and (3) space with moving obstacles. This paper presents the methodology used to generate a 2-D map of an unknown navigation space. The ability to minimize the redundancy during traveling and maximize the confidence function for generation of the map are advantages of this technique.

Computer network defense through radial wave functions

NASA Astrophysics Data System (ADS)

Malloy, Ian J.

The purpose of this research is to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has been devastating to geopolitical regions in that they are severely difficult for a civilian to avoid triggering given the unknown position of a landmine. Thus, the importance of understanding a logic bomb is relevant and has corollaries to quantum mechanics as well. The research synthesizes quantum logic phase shifts in certain respects using the Dynamic Data Exchange protocol in software written for this work, as well as a C-NOT gate applied to a virtual quantum circuit environment by implementing a Quantum Fourier Transform. The research focus applies the principles of coherence and entanglement from quantum physics, the concept of expert systems in artificial intelligence, principles of prime number based cryptography with trapdoor functions, and modeling radio wave propagation against an event from unknown parameters. This comes as a program relying on the artificial intelligence concept of an expert system in conjunction with trigger events for a trapdoor function relying on infinite recursion, as well as system mechanics for elliptic curve cryptography along orbital angular momenta. Here trapdoor both denotes the form of cipher, as well as the implied relationship to logic bombs.

Analysis of a homemade Edison tinfoil phonograph.

PubMed

Sagers, Jason D; McNeese, Andrew R; Lenhart, Richard D; Wilson, Preston S

2012-10-01

Thomas Edison's phonograph was a landmark acoustic invention. In this paper, the phonograph is presented as a tool for education in acoustics. A brief history of the phonograph is outlined and an analogous circuit model that describes its dynamic response is discussed. Microphone and scanning laser Doppler vibrometer (SLDV) measurements were made on a homemade phonograph for model validation and inversion for unknown model parameters. SLDV measurements also conclusively illustrate where model assumptions are violated. The model elements which dominate the dynamic response are discussed.

Application of Ensemble Kalman Filter in Power System State Tracking and Sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yulan; Huang, Zhenyu; Zhou, Ning

2012-05-01

Ensemble Kalman Filter (EnKF) is proposed to track dynamic states of generators. The algorithm of EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF can effectively track generator dynamic states using disturbance data.

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

Laird, Phil

1992-01-01

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

New Flutter Analysis Technique for Time-Domain Computational Aeroelasticity

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Lung, Shun-Fat

2017-01-01

A new time-domain approach for computing flutter speed is presented. Based on the time-history result of aeroelastic simulation, the unknown unsteady aerodynamics model is estimated using a system identification technique. The full aeroelastic model is generated via coupling the estimated unsteady aerodynamic model with the known linear structure model. The critical dynamic pressure is computed and used in the subsequent simulation until the convergence of the critical dynamic pressure is achieved. The proposed method is applied to a benchmark cantilevered rectangular wing.

Robust approximation-free prescribed performance control for nonlinear systems and its application

NASA Astrophysics Data System (ADS)

Sun, Ruisheng; Na, Jing; Zhu, Bin

2018-02-01

This paper presents a robust prescribed performance control approach and its application to nonlinear tail-controlled missile systems with unknown dynamics and uncertainties. The idea of prescribed performance function (PPF) is incorporated into the control design, such that both the steady-state and transient control performance can be strictly guaranteed. Unlike conventional PPF-based control methods, we further tailor a recently proposed systematic control design procedure (i.e. approximation-free control) using the transformed tracking error dynamics, which provides a proportional-like control action. Hence, the function approximators (e.g. neural networks, fuzzy systems) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. The proposed control design leads to a robust yet simplified function approximation-free control for nonlinear systems. The closed-loop system stability and the control error convergence are all rigorously proved. Finally, comparative simulations are conducted based on nonlinear missile systems to validate the improved response and the robustness of the proposed control method.

Formation tracker design of multiple mobile robots with wheel perturbations: adaptive output-feedback approach

NASA Astrophysics Data System (ADS)

Yoo, Sung Jin

2016-11-01

This paper presents a theoretical design approach for output-feedback formation tracking of multiple mobile robots under wheel perturbations. It is assumed that these perturbations are unknown and the linear and angular velocities of the robots are unmeasurable. First, adaptive state observers for estimating unmeasurable velocities of the robots are developed under the robots' kinematics and dynamics including wheel perturbation effects. Then, we derive a virtual-structure-based formation tracker scheme according to the observer dynamic surface design procedure. The main difficulty of the output-feedback control design is to manage the coupling problems between unmeasurable velocities and unknown wheel perturbation effects. These problems are avoided by using the adaptive technique and the function approximation property based on fuzzy logic systems. From the Lyapunov stability analysis, it is shown that point tracking errors of each robot and synchronisation errors for the desired formation converge to an adjustable neighbourhood of the origin, while all signals in the controlled closed-loop system are semiglobally uniformly ultimately bounded.

Presentations: Adverse Outcome Pathways for Abnormal Phenotypes

EPA Science Inventory

Birth defects affect many infants and the etiology for most are unknown. Although environmental factors are known to influence pregnancy outcome, thousands of chemicals, present in the environment, are untested for developmental toxicity potential. Application of computational p...

G-Consistent Subsets and Reduced Dynamical Quantum Maps

NASA Astrophysics Data System (ADS)

Ceballos, Russell R.

A quantum system which evolves in time while interacting with an external environ- ment is said to be an open quantum system (OQS), and the influence of the environment on the unperturbed unitary evolution of the system generally leads to non-unitary dynamics. This kind of open system dynamical evolution has been typically modeled by a Standard Prescription (SP) which assumes that the state of the OQS is initially uncorrelated with the environment state. It is here shown that when a minimal set of physically motivated assumptions are adopted, not only does there exist constraints on the reduced dynamics of an OQS such that this SP does not always accurately describe the possible initial cor- relations existing between the OQS and environment, but such initial correlations, and even entanglement, can be witnessed when observing a particular class of reduced state transformations termed purity extractions are observed. Furthermore, as part of a more fundamental investigation to better understand the minimal set of assumptions required to formulate well defined reduced dynamical quantum maps, it is demonstrated that there exists a one-to-one correspondence between the set of initial reduced states and the set of admissible initial system-environment composite states when G-consistency is enforced. Given the discussions surrounding the requirement of complete positivity and the reliance on the SP, the results presented here may well be found valuable for determining the ba- sic properties of reduced dynamical maps, and when restrictions on the OQS dynamics naturally emerge.

[Epidemiological characteristics of violence against women in the Federal District, Brazil, 2009-2012].

PubMed

Silva, Lídia Ester Lopes da; Oliveira, Maria Liz Cunha de

2016-01-01

to describe the epidemiological characteristics of cases of violence against women reported in the Federal District, Brazil, 2009-2012. this was a descriptive study of cases of violence against women aged 18- 59 registered on the National Notifiable Diseases System (Sinan). 1,924 cases of violence against women were registered, the perpetrators of which were identified as unknown (25.7%) or spouses (19.0%) of the victims; violence mainly occurred against women of brown skin color (25.0%) and in the domestic environment (38.5%); regarding violence type, physical violence (46.8%) by force (48.0%) stood out, whereby the genitals (15.7%) and the head (12.9%) were the most affected regions. physical violence in domestic environments by unknown aggressors was the main type of violence among the reported cases; shortcomings were identified in recording reported cases, showing the need to improve system quality and train health workers involved.

Training a Network of Electronic Neurons for Control of a Mobile Robot

NASA Astrophysics Data System (ADS)

Vromen, T. G. M.; Steur, E.; Nijmeijer, H.

An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input-output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.

Organizational Strategy and Business Environment Effects Based on a Computation Method

NASA Astrophysics Data System (ADS)

Reklitis, Panagiotis; Konstantopoulos, Nikolaos; Trivellas, Panagiotis

2007-12-01

According to many researchers of organizational theory, a great number of problems encountered by the manufacturing firms are due to their ineffectiveness to respond to significant changes of their external environment and align their competitive strategy accordingly. From this point of view, the pursuit of the appropriate generic strategy is vital for firms facing a dynamic and highly competitive environment. In the present paper, we adopt Porter's typology to operationalise organizational strategy (cost leadership, innovative and marketing differentiation, and focus) considering changes in the external business environment (dynamism, complexity and munificence). Although simulation of social events is a quite difficult task, since there are so many considerations (not all well understood) involved, in the present study we developed a dynamic system based on the conceptual framework of strategy-environment associations.

Dynamic analysis of multirigid-body system based on the Gauss principle

NASA Astrophysics Data System (ADS)

Lilov, L.; Lorer, M.

Two different approaches can be used for solving the basic dynamic problem in the case of a multirigid body system. The first approach is based on the derivation of the nonlinear equations of motion of the mechanical system, while the second approach is concerned with the direct derivation of the unknown accelerations. Using the Gauss principle, the accelerations can be determined by using the condition for the minimum of a functional. The present investigation is concerned with an algorithm for a dynamical study of a multibody system on the basis of the Gauss principle. The system may contain an arbitrary number of closed loops. The main purpose of the proposed algorithm is the investigation of the dynamics of industrial manipulators, robots, and similar mechanisms.

Students' Views about the Problem Based Collaborative Learning Environment Supported by Dynamic Web Technologies

ERIC Educational Resources Information Center

Ünal, Erhan; Çakir, Hasan

2017-01-01

The purpose of this study was to design a problem based collaborative learning environment supported by dynamic web technologies and to examine students' views about this learning environment. The study was designed as a qualitative research. Some 36 students who took an Object Oriented Programming I-II course at the department of computer…

Dynamic but Prosaic: A Methodology for Studying E-Learning Environments

ERIC Educational Resources Information Center

Whitworth, Andrew

2006-01-01

This paper develops a critical methodology which could be applied to the study and use of e-learning environments. The foundations are, first, an ontological appreciation of environments as multiple, dynamic and interactive: this is based on the environmental theories of Vladimir Vernadsky. The next step is then into epistemology, and here use is…

Non-equilibrium quantum phase transition via entanglement decoherence dynamics.

PubMed

Lin, Yu-Chen; Yang, Pei-Yun; Zhang, Wei-Min

2016-10-07

We investigate the decoherence dynamics of continuous variable entanglement as the system-environment coupling strength varies from the weak-coupling to the strong-coupling regimes. Due to the existence of localized modes in the strong-coupling regime, the system cannot approach equilibrium with its environment, which induces a nonequilibrium quantum phase transition. We analytically solve the entanglement decoherence dynamics for an arbitrary spectral density. The nonequilibrium quantum phase transition is demonstrated as the system-environment coupling strength varies for all the Ohmic-type spectral densities. The 3-D entanglement quantum phase diagram is obtained.

Structured crowding and its effects on enzyme catalysis.

PubMed

Ma, Buyong; Nussinov, Ruth

2013-01-01

Macromolecular crowding decreases the diffusion rate, shifts the equilibrium of protein-protein and protein-substrate interactions, and changes protein conformational dynamics. Collectively, these effects contribute to enzyme catalysis. Here we describe how crowding may bias the conformational change and dynamics of enzyme populations and in this way affect catalysis. Crowding effects have been studied using artificial crowding agents and in vivo-like environments. These studies revealed a correlation between protein dynamics and function in the crowded environment. We suggest that crowded environments be classified into uniform crowding and structured crowding. Uniform crowding represents random crowding conditions created by synthetic particles with a narrow size distribution. Structured crowding refers to the highly coordinated cellular environment, where proteins and other macromolecules are clustered and organized. In structured crowded environments the perturbation of protein thermal stability may be lower; however, it may still be able to modulate functions effectively and dynamically. Dynamic, allosteric enzymes could be more sensitive to cellular perturbations if their free energy landscape is flatter around the native state; on the other hand, if their free energy landscape is rougher, with high kinetic barriers separating deep minima, they could be more robust. Above all, cells are structured; and this holds both for the cytosol and for the membrane environment. The crowded environment is organized, which limits the search, and the crowders are not necessarily inert. More likely, they too transmit allosteric effects, and as such play important functional roles. Overall, structured cellular crowding may lead to higher enzyme efficiency and specificity.

Laser speckle contrast imaging of collateral blood flow during acute ischemic stroke

PubMed Central

Armitage, Glenn A; Todd, Kathryn G; Shuaib, Ashfaq; Winship, Ian R

2010-01-01

Collateral vasculature may provide an alternative route for blood flow to reach the ischemic tissue and partially maintain oxygen and nutrient support during ischemic stroke. However, much about the dynamics of stroke-induced collateralization remains unknown. In this study, we used laser speckle contrast imaging to map dynamic changes in collateral blood flow after middle cerebral artery occlusion in rats. We identified extensive anastomatic connections between the anterior and middle cerebral arteries that develop after vessel occlusion and persist for 24 hours. Augmenting blood flow through these persistent yet dynamic anastomatic connections may be an important but relatively unexplored avenue in stroke therapy. PMID:20517321

Bursting dynamics in Rayleigh-Bénard convection

NASA Astrophysics Data System (ADS)

Dan, Surajit; Ghosh, Manojit; Nandukumar, Yada; Dana, Syamal K.; Pal, Pinaki

2017-06-01

We report bursting dynamics in a parametrically driven Rayleigh-Bénard convection (RBC) model of low Prandtl-number fluids with free-slip boundary conditions. A four dimensional RBC model [P. Pal, K. Kumar, P. Maity, S.K. Dana, Phys. Rev. E 87, 023001 (2013)] is used for this study. The dynamical system shows pitchfork, Hopf and gluing bifurcations near the onset of RBC of low Prandtl-number fluids. Around the bifurcation points, when the Rayleigh number of the system is slowly modulated periodically, two unknown kinds of bursting appears, namely, Hopf/Hopf via pitchfork bifurcation and Hopf/Hopf via gluing bifurcation besides the conventional Hopf/Hopf (elliptical) and pitchfork/pitchfork bursting.

Testing for the 'predictability' of dynamically triggered earthquakes in The Geysers geothermal field

NASA Astrophysics Data System (ADS)

Aiken, Chastity; Meng, Xiaofeng; Hardebeck, Jeanne

2018-03-01

The Geysers geothermal field is well known for being susceptible to dynamic triggering of earthquakes by large distant earthquakes, owing to the introduction of fluids for energy production. Yet, it is unknown if dynamic triggering of earthquakes is 'predictable' or whether dynamic triggering could lead to a potential hazard for energy production. In this paper, our goal is to investigate the characteristics of triggering and the physical conditions that promote triggering to determine whether or not triggering is in anyway foreseeable. We find that, at present, triggering in The Geysers is not easily 'predictable' in terms of when and where based on observable physical conditions. However, triggered earthquake magnitude positively correlates with peak imparted dynamic stress, and larger dynamic stresses tend to trigger sequences similar to mainshock-aftershock sequences. Thus, we may be able to 'predict' what size earthquakes to expect at The Geysers following a large distant earthquake.

Testing for the ‘predictability’ of dynamically triggered earthquakes in Geysers Geothermal Field

USGS Publications Warehouse

Aiken, Chastity; Meng, Xiaofeng; Hardebeck, Jeanne L.

2018-01-01

The Geysers geothermal field is well known for being susceptible to dynamic triggering of earthquakes by large distant earthquakes, owing to the introduction of fluids for energy production. Yet, it is unknown if dynamic triggering of earthquakes is ‘predictable’ or whether dynamic triggering could lead to a potential hazard for energy production. In this paper, our goal is to investigate the characteristics of triggering and the physical conditions that promote triggering to determine whether or not triggering is in anyway foreseeable. We find that, at present, triggering in The Geysers is not easily ‘predictable’ in terms of when and where based on observable physical conditions. However, triggered earthquake magnitude positively correlates with peak imparted dynamic stress, and larger dynamic stresses tend to trigger sequences similar to mainshock–aftershock sequences. Thus, we may be able to ‘predict’ what size earthquakes to expect at The Geysers following a large distant earthquake.

A reconfigurable computing platform for plume tracking with mobile sensor networks

NASA Astrophysics Data System (ADS)

Kim, Byung Hwa; D'Souza, Colin; Voyles, Richard M.; Hesch, Joel; Roumeliotis, Stergios I.

2006-05-01

Much work has been undertaken recently toward the development of low-power, high-performance sensor networks. There are many static remote sensing applications for which this is appropriate. The focus of this development effort is applications that require higher performance computation, but still involve severe constraints on power and other resources. Toward that end, we are developing a reconfigurable computing platform for miniature robotic and human-deployed sensor systems composed of several mobile nodes. The system provides static and dynamic reconfigurability for both software and hardware by the combination of CPU (central processing unit) and FPGA (field-programmable gate array) allowing on-the-fly reprogrammability. Static reconfigurability of the hardware manifests itself in the form of a "morphing bus" architecture that permits the modular connection of various sensors with no bus interface logic. Dynamic hardware reconfigurability provides for the reallocation of hardware resources at run-time as the mobile, resource-constrained nodes encounter unknown environmental conditions that render various sensors ineffective. This computing platform will be described in the context of work on chemical/biological/radiological plume tracking using a distributed team of mobile sensors. The objective for a dispersed team of ground and/or aerial autonomous vehicles (or hand-carried sensors) is to acquire measurements of the concentration of the chemical agent from optimal locations and estimate its source and spread. This requires appropriate distribution, coordination and communication within the team members across a potentially unknown environment. The key problem is to determine the parameters of the distribution of the harmful agent so as to use these values for determining its source and predicting its spread. The accuracy and convergence rate of this estimation process depend not only on the number and accuracy of the sensor measurements but also on their spatial distribution over time (the sampling strategy). For the safety of a human-deployed distribution of sensors, optimized trajectories to minimize human exposure are also of importance. The systems described in this paper are currently being developed. Parts of the system are already in existence and some results from these are described.

Visual Environments for CFD Research

NASA Technical Reports Server (NTRS)

Watson, Val; George, Michael W. (Technical Monitor)

1994-01-01

This viewgraph presentation gives an overview of the visual environments for computational fluid dynamics (CFD) research. It includes details on critical needs from the future computer environment, features needed to attain this environment, prospects for changes in and the impact of the visualization revolution on the human-computer interface, human processing capabilities, limits of personal environment and the extension of that environment with computers. Information is given on the need for more 'visual' thinking (including instances of visual thinking), an evaluation of the alternate approaches for and levels of interactive computer graphics, a visual analysis of computational fluid dynamics, and an analysis of visualization software.

SLAM algorithm applied to robotics assistance for navigation in unknown environments.

PubMed

Cheein, Fernando A Auat; Lopez, Natalia; Soria, Carlos M; di Sciascio, Fernando A; Pereira, Fernando Lobo; Carelli, Ricardo

2010-02-17

The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI). In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents. The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface. The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.

Esterified sago waste for engine oil removal in aqueous environment.

PubMed

Ngaini, Zainab; Noh, Farid; Wahi, Rafeah

2014-01-01

Agro-waste from the bark of Metroxylon sagu (sago) was studied as a low cost and effective oil sorbent in dry and aqueous environments. Sorption study was conducted using untreated sago bark (SB) and esterified sago bark (ESB) in used engine oil. Characterization study showed that esterification has successfully improved the hydrophobicity, buoyancy, surface roughness and oil sorption capacity of ESB. Sorption study revealed that water uptake of SB is higher (30 min static: 2.46 g/g, dynamic: 2.67 g/g) compared with ESB (30 min static: 0.18 g/g, dynamic: 0.14 g/g). ESB, however, showed higher oil sorption capacity in aqueous environment (30 min static: 2.30 g/g, dynamic: 2.14) compared with SB (30 min static: 0 g/g, dynamic: 0 g/g). ESB has shown great poTENTial as effective oil sorbent in aqueous environment due to its high oil sorption capacity, low water uptake and high buoyancy.

Dynamic assessment of urban economy-environment-energy system using system dynamics model: A case study in Beijing.

PubMed

Wu, Desheng; Ning, Shuang

2018-07-01

Economic development, accompanying with environmental damage and energy depletion, becomes essential nowadays. There is a complicated and comprehensive interaction between economics, environment and energy. Understanding the operating mechanism of Energy-Environment-Economy model (3E) and its key factors is the inherent part in dealing with the issue. In this paper, we combine System Dynamics model and Geographic Information System to analyze the energy-environment-economy (3E) system both temporally and spatially, which explicitly explore the interaction of economics, energy, and environment and effects of the key influencing factors. Beijing is selected as a case study to verify our SD-GIS model. Alternative scenarios, e.g., current, technology, energy and environment scenarios are explored and compared. Simulation results shows that, current scenario is not sustainable; technology scenario is applicable to economic growth; environment scenario maintains a balanced path of development for long term stability. Policy-making insights are given based on our results and analysis. Copyright © 2018 Elsevier Inc. All rights reserved.

Gene-culture coevolution in the age of genomics

PubMed Central

Richerson, Peter J.; Boyd, Robert; Henrich, Joseph

2010-01-01

The use of socially learned information (culture) is central to human adaptations. We investigate the hypothesis that the process of cultural evolution has played an active, leading role in the evolution of genes. Culture normally evolves more rapidly than genes, creating novel environments that expose genes to new selective pressures. Many human genes that have been shown to be under recent or current selection are changing as a result of new environments created by cultural innovations. Some changed in response to the development of agricultural subsistence systems in the Early and Middle Holocene. Alleles coding for adaptations to diets rich in plant starch (e.g., amylase copy number) and to epidemic diseases evolved as human populations expanded (e.g., sickle cell and G6PD deficiency alleles that provide protection against malaria). Large-scale scans using patterns of linkage disequilibrium to detect recent selection suggest that many more genes evolved in response to agriculture. Genetic change in response to the novel social environment of contemporary modern societies is also likely to be occurring. The functional effects of most of the alleles under selection during the last 10,000 years are currently unknown. Also unknown is the role of paleoenvironmental change in regulating the tempo of hominin evolution. Although the full extent of culture-driven gene-culture coevolution is thus far unknown for the deeper history of the human lineage, theory and some evidence suggest that such effects were profound. Genomic methods promise to have a major impact on our understanding of gene-culture coevolution over the span of hominin evolutionary history. PMID:20445092

Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

PubMed

Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

2014-07-01

In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Probabilistic double guarantee kidnapping detection in SLAM.

PubMed

Tian, Yang; Ma, Shugen

2016-01-01

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features' positions and the robot's posture. Simulation results demonstrate the validity and accuracy of the proposed method.

Regularity in an environment produces an internal torque pattern for biped balance control.

PubMed

Ito, Satoshi; Kawasaki, Haruhisa

2005-04-01

In this paper, we present a control method for achieving biped static balance under unknown periodic external forces whose periods are only known. In order to maintain static balance adaptively in an uncertain environment, it is essential to have information on the ground reaction forces. However, when the biped is exposed to a steady environment that provides an external force periodically, uncertain factors on the regularity with respect to a steady environment are gradually clarified using learning process, and finally a torque pattern for balancing motion is acquired. Consequently, static balance is maintained without feedback from ground reaction forces and achieved in a feedforward manner.

Experimental Evaluation Methodology for Spacecraft Proximity Maneuvers in a Dynamic Environment

DTIC Science & Technology

2017-06-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA DISSERTATION EXPERIMENTAL EVALUATION METHODOLOGY FOR SPACECRAFT PROXIMITY MANEUVERS IN A DYNAMIC...29, 2014 – June 16, 2017 4. TITLE AND SUBTITLE EXPERIMENTAL EVALUATION METHODOLOGY FOR SPACECRAFT PROXIMITY MANEUVERS IN A DYNAMIC ENVIRONMENT 5...LEFT BLANK ii Approved for public release. Distribution is unlimited. EXPERIMENTAL EVALUATION METHODOLOGY FOR SPACECRAFT PROXIMITY MANEUVERS IN A

Signatures of a quantum dynamical phase transition in a three-spin system in presence of a spin environment

NASA Astrophysics Data System (ADS)

Álvarez, Gonzalo A.; Levstein, Patricia R.; Pastawski, Horacio M.

2007-09-01

We have observed an environmentally induced quantum dynamical phase transition in the dynamics of a two-spin experimental swapping gate [G.A. Álvarez, E.P. Danieli, P.R. Levstein, H.M. Pastawski, J. Chem. Phys. 124 (2006) 194507]. There, the exchange of the coupled states |↑,↓> and |↓,↑> gives an oscillation with a Rabi frequency b/ℏ (the spin-spin coupling). The interaction, ℏ/τSE with a spin-bath degrades the oscillation with a characteristic decoherence time. We showed that the swapping regime is restricted only to bτSE≳ℏ. However, beyond a critical interaction with the environment the swapping freezes and the system enters to a Quantum Zeno dynamical phase where relaxation decreases as coupling with the environment increases. Here, we solve the quantum dynamics of a two-spin system coupled to a spin-bath within a Liouville-von Neumann quantum master equation and we compare the results with our previous work within the Keldysh formalism. Then, we extend the model to a three interacting spin system where only one is coupled to the environment. Beyond a critical interaction the two spins not coupled to the environment oscillate with the bare Rabi frequency and relax more slowly. This effect is more pronounced when the anisotropy of the system-environment (SE) interaction goes from a purely XY to an Ising interaction form.

A New Fractal Model of Chromosome and DNA Processes

NASA Astrophysics Data System (ADS)

Bouallegue, K.

Dynamic chromosome structure remains unknown. Can fractals and chaos be used as new tools to model, identify and generate a structure of chromosomes?Fractals and chaos offer a rich environment for exploring and modeling the complexity of nature. In a sense, fractal geometry is used to describe, model, and analyze the complex forms found in nature. Fractals have also been widely not only in biology but also in medicine. To this effect, a fractal is considered an object that displays self-similarity under magnification and can be constructed using a simple motif (an image repeated on ever-reduced scales).It is worth noting that the problem of identifying a chromosome has become a challenge to find out which one of the models it belongs to. Nevertheless, the several different models (a hierarchical coiling, a folded fiber, and radial loop) have been proposed for mitotic chromosome but have not reached a dynamic model yet.This paper is an attempt to solve topological problems involved in the model of chromosome and DNA processes. By combining the fractal Julia process and the numerical dynamical system, we have finally found out four main points. First, we have developed not only a model of chromosome but also a model of mitosis and one of meiosis. Equally important, we have identified the centromere position through the numerical model captured below. More importantly, in this paper, we have discovered the processes of the cell divisions of both mitosis and meiosis. All in all, the results show that this work could have a strong impact on the welfare of humanity and can lead to a cure of genetic diseases.

New insights into flavivirus biology: the influence of pH over interactions between prM and E proteins

NASA Astrophysics Data System (ADS)

Oliveira, Edson R. A.; de Alencastro, Ricardo B.; Horta, Bruno A. C.

2017-11-01

Diseases caused by flaviviruses, such as dengue and zika, are globally recognized as major threats. During infection, a critical point in their replicative cycle is the maturation step, which occurs throughout the cellular exocytic pathway. This step is a pH-dependent process that involves the modification of the viral envelope by converting prM (pre-membrane) into M (membrane) proteins with the release of a "pr peptide". After this reaction, the pr peptides remain bound to the viral envelope while the virions cross the acidic trans-Golgi network, and are released only at neutral pH after secretion of the virus particles. Despite this current knowledge, the molecular basis of the flavivirus maturation step is largely unknown. Here, based on the crystal structure of the dengue pr-E complex ("pr peptide" bound to virus envelope protein) and using molecular dynamics simulations, we found that the pH shift from acidic to neutral yields considerable structural changes in the system. Dynamic cross correlation maps and root mean square deviation analyses revealed that the pr-E junction is clearly unstable under neutral pH. Secondary structure analysis also revealed that the fusion loop region, present in the E protein, is sensitive to pH and tends to unstructure at a neutral environment. Moreover, we found that five residues present in the E protein, Gly102, His244, Thr70, Thr68 and Asn67 are critical to confer stability to the pr-E complex while inside the Golgi apparatus. This work brings details about the dynamical behavior of the pr-E system, helps to better understand the flavivirus biology and may also be of use in the development of novel antiviral strategies.

The need for speed: escape velocity and dynamical mass measurements of the Andromeda galaxy

NASA Astrophysics Data System (ADS)

Kafle, Prajwal R.; Sharma, Sanjib; Lewis, Geraint F.; Robotham, Aaron S. G.; Driver, Simon P.

2018-04-01

Our nearest large cosmological neighbour, the Andromeda galaxy (M31), is a dynamical system, and an accurate measurement of its total mass is central to our understanding of its assembly history, the life-cycles of its satellite galaxies, and its role in shaping the Local Group environment. Here, we apply a novel approach to determine the dynamical mass of M31 using high-velocity Planetary Nebulae, establishing a hierarchical Bayesian model united with a scheme to capture potential outliers and marginalize over tracers unknown distances. With this, we derive the escape velocity run of M31 as a function of galactocentric distance, with both parametric and non-parametric approaches. We determine the escape velocity of M31 to be 470 ± 40 km s-1 at a galactocentric distance of 15 kpc, and also, derive the total potential of M31, estimating the virial mass and radius of the galaxy to be 0.8 ± 0.1 × 1012 M⊙ and 240 ± 10 kpc, respectively. Our M31 mass is on the low side of the measured range, this supports the lower expected mass of the M31-Milky Way system from the timing and momentum arguments, satisfying the H I constraint on circular velocity between 10 ≲ R/ kpc < 35, and agreeing with the stellar mass Tully-Fisher relation. To place these results in a broader context, we compare them to the key predictions of the ΛCDM cosmological paradigm, including the stellar-mass-halo-mass and the dark matter halo concentration-virial mass correlation, and finding it to be an outlier to this relation.

The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions

PubMed Central

Jacoby, Richard; Peukert, Manuela; Succurro, Antonella; Koprivova, Anna; Kopriva, Stanislav

2017-01-01

In their natural environment, plants are part of a rich ecosystem including numerous and diverse microorganisms in the soil. It has been long recognized that some of these microbes, such as mycorrhizal fungi or nitrogen fixing symbiotic bacteria, play important roles in plant performance by improving mineral nutrition. However, the full range of microbes associated with plants and their potential to replace synthetic agricultural inputs has only recently started to be uncovered. In the last few years, a great progress has been made in the knowledge on composition of rhizospheric microbiomes and their dynamics. There is clear evidence that plants shape microbiome structures, most probably by root exudates, and also that bacteria have developed various adaptations to thrive in the rhizospheric niche. The mechanisms of these interactions and the processes driving the alterations in microbiomes are, however, largely unknown. In this review, we focus on the interaction of plants and root associated bacteria enhancing plant mineral nutrition, summarizing the current knowledge in several research fields that can converge to improve our understanding of the molecular mechanisms underpinning this phenomenon. PMID:28974956

We can't all be supermodels: the value of comparative transcriptomics to the study of non-model insects.

PubMed

Oppenheim, Sara J; Baker, Richard H; Simon, Sabrina; DeSalle, Rob

2015-04-01

Insects are the most diverse group of organisms on the planet. Variation in gene expression lies at the heart of this biodiversity and recent advances in sequencing technology have spawned a revolution in researchers' ability to survey tissue-specific transcriptional complexity across a wide range of insect taxa. Increasingly, studies are using a comparative approach (across species, sexes and life stages) that examines the transcriptional basis of phenotypic diversity within an evolutionary context. In the present review, we summarize much of this research, focusing in particular on three critical aspects of insect biology: morphological development and plasticity; physiological response to the environment; and sexual dimorphism. A common feature that is emerging from these investigations concerns the dynamic nature of transcriptome evolution as indicated by rapid changes in the overall pattern of gene expression, the differential expression of numerous genes with unknown function, and the incorporation of novel, lineage-specific genes into the transcriptional profile. © 2014 The Authors. Insect Molecular Biology published by John Wiley & Sons Ltd on behalf of The Royal Entomological Society.

Modularity of mind and the role of incentive motivation in representing novelty.

PubMed

Anselme, Patrick

2012-07-01

Animal and human brains contain a myriad of mental representations that have to be successfully tracked within fractions of a second in a large number of situations. This retrieval process is hard to explain without postulating the massive modularity of cognition. Assuming that the mind is massively modular, it is then necessary to understand how cognitive modules can efficiently represent dynamic environments-in which some modules may have to deal with change-induced novelty and uncertainty. Novelty of a stimulus is a problem for a module when unknown, significant stimuli do not satisfy the module's processing criteria-or domain specificity-and cannot therefore be included in its database. It is suggested that the brain mechanisms of incentive motivation, recruited when faced with novelty and uncertainty, induce transient variations in the domain specificity of cognitive modules in order to allow them to process information they were not prepared to learn. It is hypothesised that the behavioural transitions leading from exploratory activity to habit formation are correlated with (and possibly caused by) the organism's ability to counter novelty-induced uncertainty.

Phosphorylation of an HP1-like protein is a prerequisite for heterochromatin body formation in Tetrahymena DNA elimination.

PubMed

Kataoka, Kensuke; Noto, Tomoko; Mochizuki, Kazufumi

2016-08-09

Multiple heterochromatic loci are often clustered into a higher order nuclear architecture called a heterochromatin body in diverse eukaryotes. Although phosphorylation of Heterochromatin Protein 1 (HP1) family proteins regulates heterochromatin dynamics, its role in heterochromatin bodies remains unknown. We previously reported that dephosphorylation of the HP1-like protein Pdd1p is required for the formation of heterochromatin bodies during the process of programmed DNA elimination in the ciliated protozoan Tetrahymena Here, we show that the heterochromatin body component Jub4p is required for Pdd1p phosphorylation, heterochromatin body formation, and DNA elimination. Moreover, our analyses of unphosphorylatable Pdd1p mutants demonstrate that Pdd1p phosphorylation is required for heterochromatin body formation and DNA elimination, whereas it is dispensable for local heterochromatin assembly. Therefore, both phosphorylation and the following dephosphorylation of Pdd1p are necessary to facilitate the formation of heterochromatin bodies. We suggest that Jub4p-mediated phosphorylation of Pdd1p creates a chromatin environment that is a prerequisite for subsequent heterochromatin body assembly and DNA elimination.

Counteraction of urea-induced protein denaturation by trimethylamine N-oxide: a chemical chaperone at atomic resolution.

PubMed

Bennion, Brian J; Daggett, Valerie

2004-04-27

Proteins are very sensitive to their solvent environments. Urea is a common chemical denaturant of proteins, yet some animals contain high concentrations of urea. These animals have evolved an interesting mechanism to counteract the effects of urea by using trimethylamine N-oxide (TMAO). The molecular basis for the ability of TMAO to act as a chemical chaperone remains unknown. Here, we describe molecular dynamics simulations of a small globular protein, chymotrypsin inhibitor 2, in 8 M urea and 4 M TMAO/8 M urea solutions, in addition to other control simulations, to investigate this effect at the atomic level. In 8 M urea, the protein unfolds, and urea acts in both a direct and indirect manner to achieve this effect. In contrast, introduction of 4 M TMAO counteracts the effect of urea and the protein remains well structured. TMAO makes few direct interactions with the protein. Instead, it prevents unfolding of the protein by structuring the solvent. In particular, TMAO orders the solvent and discourages it from competing with intraprotein H bonds and breaking up the hydrophobic core of the protein.

Pathogenesis of tuberculosis and other mycobacteriosis.

PubMed

Cardona, Pere-Joan

2018-01-01

The evolution between Mycobacterium tuberculosis infection and active tuberculosis is multifactorial and involves different biological scales. The synthesis of ESAT-6 or the induction of alveolar macrophage necrosis are key, but to understand it, it is necessary to consider the dynamics of endogenous and exogenous reinfection, drainage of lung parenchyma and respiratory mechanics, local fibrosis processes and blood supply. Paradoxically, the immune response generated by the infection is highly protective (90%) against active tuberculosis, although as it is essentially based on the proliferation of Th1 lymphocytes, it cannot prevent reinfection. Severe immunosuppression can only explain 10% of active tuberculosis cases, while the remainder are attributable to comorbidities, a proinflammatory environment and an unknown genetic propensity. The pathogenic capacity of environmental mycobacteria is discrete, linked to deficits in the innate and acquired immune response. The ability to generate biofilms and the ability of M. ulcerans to generate the exotoxin mycolactone is remarkable. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Exact docking flight controller for autonomous aerial refueling with back-stepping based high order sliding mode

NASA Astrophysics Data System (ADS)

Su, Zikang; Wang, Honglun; Li, Na; Yu, Yue; Wu, Jianfa

2018-02-01

Autonomous aerial refueling (AAR) exact docking control has always been an intractable problem due to the strong nonlinearity, the tight coupling of the 6 DOF aircraft model and the complex disturbances of the multiple environment flows. In this paper, the strongly coupled nonlinear 6 DOF model of the receiver aircraft which considers the multiple flow disturbances is established in the affine nonlinear form to facilitate the nonlinear controller design. The items reflecting the influence of the unknown flow disturbances in the receiver dynamics are taken as the components of the "lumped disturbances" together with the items which have no linear correlation with the virtual control variables. These unmeasurable lumped disturbances are estimated and compensated by a specially designed high order sliding mode observer (HOSMO) with excellent estimation property. With the compensation of the estimated lumped disturbances, a back-stepping high order sliding mode based exact docking flight controller is proposed for AAR in the presence of multiple flow disturbances. Extensive simulation results demonstrate the feasibility and superiority of the proposed docking controller.

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

PubMed

Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

2018-05-10

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints.

PubMed

Chen, Weisheng

2009-07-01

This paper focuses on the problem of adaptive neural network tracking control for a class of discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints. Two novel state-feedback and output-feedback dynamic control laws are established where the function tanh(.) is employed to solve the saturation constraint problem. Implicit function theorem and mean value theorem are exploited to deal with non-affine variables that are used as actual control. Radial basis function neural networks are used to approximate the desired input function. Discrete Nussbaum gain is used to estimate the unknown sign of control gain. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of control schemes proposed in this paper.

Quadrotor Control in the Presence of Unknown Mass Properties

NASA Astrophysics Data System (ADS)

Duivenvoorden, Rikky Ricardo Petrus Rufino

Quadrotor UAVs are popular due to their mechanical simplicity, as well as their capability to hover and vertically take-off and land. As applications diversify, quadrotors are increasingly required to operate under unknown mass properties, for example as a multirole sensor platform or for package delivery operations. The work presented here consists of the derivation of a generalized quadrotor dynamic model without the typical simplifying assumptions on the first and second moments of mass. The maximum payload capacity of a quadrotor in hover, and the observability of the unknown mass properties are discussed. A brief introduction of L1 adaptive control is provided, and three different L 1 adaptive controllers were designed for the Parrot AR.Drone quadrotor. Their tracking and disturbance rejection performance was compared to the baseline nonlinear controller in experiments. Finally, the results of the combination of L1 adaptive control with iterative learning control are presented, showing high performance trajectory tracking under uncertainty.

Market-Based Coordination of Thermostatically Controlled Loads—Part I: A Mechanism Design Formulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sen; Zhang, Wei; Lian, Jianming

This paper focuses on the coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. Using the mechanism design approach, we propose a market-based coordination framework, which can effectively incorporate heterogeneous load dynamics, systematically deal with user preferences, account for the unknown load model parameters, and enable the real-world implementation with limited communication resources. This paper is divided into two parts. Part I presents a mathematical formulation of themore » problem and develops a coordination framework using the mechanism design approach. Part II presents a learning scheme to account for the unknown load model parameters, and evaluates the proposed framework through realistic simulations.« less

Inverse Analysis of Cavitation Impact Phenomena on Structures

DTIC Science & Technology

2007-07-02

can occur within different types of dynamic water environments of structures. Case study analyses using experimental data are used to demonstrate the...cavitation impact phenomena, and ultimately, with cavitation erosion of structures within turbulent water environments. 02-07-2007 Memorandum Report...of dynamic water environments of structures. Case study analyses using experimental data are used to demonstrate the fundamentals of various aspects

Colloquium: Non-Markovian dynamics in open quantum systems

NASA Astrophysics Data System (ADS)

Breuer, Heinz-Peter; Laine, Elsi-Mari; Piilo, Jyrki; Vacchini, Bassano

2016-04-01

The dynamical behavior of open quantum systems plays a key role in many applications of quantum mechanics, examples ranging from fundamental problems, such as the environment-induced decay of quantum coherence and relaxation in many-body systems, to applications in condensed matter theory, quantum transport, quantum chemistry, and quantum information. In close analogy to a classical Markovian stochastic process, the interaction of an open quantum system with a noisy environment is often modeled phenomenologically by means of a dynamical semigroup with a corresponding time-independent generator in Lindblad form, which describes a memoryless dynamics of the open system typically leading to an irreversible loss of characteristic quantum features. However, in many applications open systems exhibit pronounced memory effects and a revival of genuine quantum properties such as quantum coherence, correlations, and entanglement. Here recent theoretical results on the rich non-Markovian quantum dynamics of open systems are discussed, paying particular attention to the rigorous mathematical definition, to the physical interpretation and classification, as well as to the quantification of quantum memory effects. The general theory is illustrated by a series of physical examples. The analysis reveals that memory effects of the open system dynamics reflect characteristic features of the environment which opens a new perspective for applications, namely, to exploit a small open system as a quantum probe signifying nontrivial features of the environment it is interacting with. This Colloquium further explores the various physical sources of non-Markovian quantum dynamics, such as structured environmental spectral densities, nonlocal correlations between environmental degrees of freedom, and correlations in the initial system-environment state, in addition to developing schemes for their local detection. Recent experiments addressing the detection, quantification, and control of non-Markovian quantum dynamics are also briefly discussed.

Robust Synchronization Schemes for Dynamic Channel Environments

NASA Technical Reports Server (NTRS)

Xiong, Fugin

2003-01-01

Professor Xiong will investigate robust synchronization schemes for dynamic channel environment. A sliding window will be investigated for symbol timing synchronizer and an open loop carrier estimator for carrier synchronization. Matlab/Simulink will be used for modeling and simulations.

A dynamical approach in exploring the unknown mass in the Solar system using pulsar timing arrays

NASA Astrophysics Data System (ADS)

Guo, Y. J.; Lee, K. J.; Caballero, R. N.

2018-04-01

The error in the Solar system ephemeris will lead to dipolar correlations in the residuals of pulsar timing array for widely separated pulsars. In this paper, we utilize such correlated signals, and construct a Bayesian data-analysis framework to detect the unknown mass in the Solar system and to measure the orbital parameters. The algorithm is designed to calculate the waveform of the induced pulsar-timing residuals due to the unmodelled objects following the Keplerian orbits in the Solar system. The algorithm incorporates a Bayesian-analysis suit used to simultaneously analyse the pulsar-timing data of multiple pulsars to search for coherent waveforms, evaluate the detection significance of unknown objects, and to measure their parameters. When the object is not detectable, our algorithm can be used to place upper limits on the mass. The algorithm is verified using simulated data sets, and cross-checked with analytical calculations. We also investigate the capability of future pulsar-timing-array experiments in detecting the unknown objects. We expect that the future pulsar-timing data can limit the unknown massive objects in the Solar system to be lighter than 10-11-10-12 M⊙, or measure the mass of Jovian system to a fractional precision of 10-8-10-9.

Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays.

PubMed

Wang, Min; Ge, Shuzhi Sam; Hong, Keum-Shik

2010-11-01

This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables x(i) as virtual controls α(i), and of the actual control u. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of u and [Formula: see text] . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.

An Energy-Aware Trajectory Optimization Layer for sUAS

NASA Astrophysics Data System (ADS)

Silva, William A.

The focus of this work is the implementation of an energy-aware trajectory optimization algorithm that enables small unmanned aircraft systems (sUAS) to operate in unknown, dynamic severe weather environments. The software is designed as a component of an Energy-Aware Dynamic Data Driven Application System (EA-DDDAS) for sUAS. This work addresses the challenges of integrating and executing an online trajectory optimization algorithm during mission operations in the field. Using simplified aircraft kinematics, the energy-aware algorithm enables extraction of kinetic energy from measured winds to optimize thrust use and endurance during flight. The optimization layer, based upon a nonlinear program formulation, extracts energy by exploiting strong wind velocity gradients in the wind field, a process known as dynamic soaring. The trajectory optimization layer extends the energy-aware path planner developed by Wenceslao Shaw-Cortez te{Shaw-cortez2013} to include additional mission configurations, simulations with a 6-DOF model, and validation of the system with flight testing in June 2015 in Lubbock, Texas. The trajectory optimization layer interfaces with several components within the EA-DDDAS to provide an sUAS with optimal flight trajectories in real-time during severe weather. As a result, execution timing, data transfer, and scalability are considered in the design of the software. Severe weather also poses a measure of unpredictability to the system with respect to communication between systems and available data resources during mission operations. A heuristic mission tree with different cost functions and constraints is implemented to provide a level of adaptability to the optimization layer. Simulations and flight experiments are performed to assess the efficacy of the trajectory optimization layer. The results are used to assess the feasibility of flying dynamic soaring trajectories with existing controllers as well as to verify the interconnections between EA-DDDAS components. Results also demonstrate the usage of the trajectory optimization layer in conjunction with a lattice-based path planner as a method of guiding the optimization layer and stitching together subsequent trajectories.

An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station

NASA Technical Reports Server (NTRS)

Lin, Paul P.; Jules, Kenol

2002-01-01

An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.

Improved Ambient Pressure Pyroelectric Ion Source

NASA Technical Reports Server (NTRS)

Beegle, Luther W.; Kim, Hugh I.; Kanik, Isik; Ryu, Ernest K.; Beckett, Brett

2011-01-01

The detection of volatile vapors of unknown species in a complex field environment is required in many different applications. Mass spectroscopic techniques require subsystems including an ionization unit and sample transport mechanism. All of these subsystems must have low mass, small volume, low power, and be rugged. A volatile molecular detector, an ambient pressure pyroelectric ion source (APPIS) that met these requirements, was recently reported by Caltech researchers to be used in in situ environments.

Nursing education trends: future implications and predictions.

PubMed

Valiga, Theresa M Terry

2012-12-01

This article examines current trends in nursing education and proposes numerous transformations needed to ensure that programs are relevant, fully engage learners, reflect evidence-based teaching practices, and are innovative. Such program characteristics are essential if we are to graduate nurses who can practice effectively in today's complex, ambiguous, ever-changing health care environments and who are prepared to practice in and, indeed, shape tomorrow's unknown practice environments. Copyright © 2012 Elsevier Inc. All rights reserved.

A new reduced-order observer for the synchronization of nonlinear chaotic systems: An application to secure communications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castro-Ramírez, Joel, E-mail: ingcastro.7@gmail.com; Martínez-Guerra, Rafael, E-mail: rguerra@ctrl.cinvestav.mx; Cruz-Victoria, Juan Crescenciano, E-mail: juancrescenciano.cruz@uptlax.edu.mx

2015-10-15

This paper deals with the master-slave synchronization scheme for partially known nonlinear chaotic systems, where the unknown dynamics is considered as the master system and we propose the slave system structure which estimates the unknown states. It introduced a new reduced order observer, using the concept of Algebraic Observability; we applied the results to a Sundarapandian chaotic system, and by means of some numerical simulations we show the effectiveness of the suggested approach. Finally, the proposed observer is utilized for encryption, where encryption key is the master system and decryption key is the slave system.

A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm.

PubMed

Wei, Kun; Ren, Bingyin

2018-02-13

In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human-Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most important issue which must be resolved first in the process of improving robotic manipulator intelligence. Among the path-planning methods, the Rapidly Exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator, especially in a complex environment because of its probability completeness, perfect expansion, and fast exploring speed over other planning methods. However, the existing RRT algorithm has a limitation in path planning for a robotic manipulator in a dynamic unstructured environment. Therefore, an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed. This method that targets a directional node extends and can increase the sampling speed and efficiency of RRT dramatically. A path optimization strategy based on the maximum curvature constraint is presented to generate a smooth and curved continuous executable path for a robotic manipulator. Finally, the correctness, effectiveness, and practicability of the proposed method are demonstrated and validated via a MATLAB static simulation and a Robot Operating System (ROS) dynamic simulation environment as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator. The proposed method not only provides great practical engineering significance for a robotic manipulator's obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of robots' path planning.

A data base and analysis program for shuttle main engine dynamic pressure measurements. Appendix B: Data base plots for SSME tests 901-290 through 901-414

NASA Technical Reports Server (NTRS)

Coffin, T.

1986-01-01

A dynamic pressure data base and data base management system developed to characterize the Space Shuttle Main Engine (SSME) dynamic pressure environment is described. The data base represents dynamic pressure measurements obtained during single engine hot firing tesets of the SSME. Software is provided to permit statistical evaluation of selected measurements under specified operating conditions. An interpolation scheme is also included to estimate spectral trends with SSME power level. Flow dynamic environments in high performance rocket engines are discussed.

A data base and analysis program for shuttle main engine dynamic pressure measurements. Appendix C: Data base plots for SSME tests 902-214 through 902-314

NASA Technical Reports Server (NTRS)

Coffin, T.

1986-01-01

A dynamic pressure data base and data base management system developed to characterize the Space Shuttle Main Engine (SSME) dynamic pressure environment is reported. The data base represents dynamic pressure measurements obtained during single engine hot firing tests of the SSME. Software is provided to permit statistical evaluation of selected measurements under specified operating conditions. An interpolation scheme is included to estimate spectral trends with SSME power level. Flow Dynamic Environments in High Performance Rocket Engines are described.

Membrane perturbing properties of toxin mycolactone from Mycobacterium ulcerans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lopez, Cesar A.; Unkefer, Clifford J.; Swanson, Basil I.

Mycolactone is the exotoxin produced by Mycobacterium ulcerans and is the virulence factor behind the neglected tropical disease Buruli ulcer. The toxin has a broad spectrum of biological effects within the host organism, stemming from its interaction with at least two molecular targets and the inhibition of protein uptake into the endoplasmic reticulum. Although it has been shown that the toxin can passively permeate into host cells, it is clearly lipophilic. Association with lipid carriers would have substantial implications for the toxin’s distribution within a host organism, delivery to cellular targets, diagnostic susceptibility, and mechanisms of pathogenicity. Yet the toxin’smore » interactions with, and distribution in, lipids are unknown. Herein we have used coarse-grained molecular dynamics simulations, guided by all-atom simulations, to study the interaction of mycolactone with pure and mixed lipid membranes. Using established techniques, we calculated the toxin’s preferential localization, membrane translocation, and impact on membrane physical and dynamical properties. The computed water-octanol partition coefficient indicates that mycolactone prefers to be in an organic phase rather than in an aqueous environment. Our results show that in a solvated membrane environment the exotoxin mainly localizes in the water-membrane interface, with a preference for the glycerol moiety of lipids, consistent with the reported studies that found it in lipid extracts of the cell. The calculated association constant to the model membrane is similar to the reported association constant for Wiskott-Aldrich syndrome protein. Mycolactone is shown to modify the physical properties of membranes, lowering the transition temperature, compressibility modulus, and critical line tension at which pores can be stabilized. It also shows a tendency to behave as a linactant, a molecule that localizes at the boundary between different fluid lipid domains in membranes and promotes inter-mixing of domains. This property has implications for the toxin’s cellular access, T-cell immunosuppression, and therapeutic potential.« less

BACTERIAL COMMUNITY DYNAMICS AND ECOTOXICOLOGICAL ASSESSMENT DURING BIOREMEDIATION OF SOILS CONTAMINATED BY BIODIESEL AND DIESEL/BIODIESEL BLENDS.

PubMed

Matos, G I; Junior, C S; Oliva, T C; Subtil, D F; Matsushita, L Y; Chaves, A L; Lutterbach, M T; Sérvulo, E F; Agathos, S N; Stenuit, B

2015-01-01

The gradual introduction of biodiesel in the Brazilian energy landscape has primarily occurred through its blending with conventional petroleum diesel (e.g., B20 (20% biodiesel) and B5 (5% biodiesel) formulations). Because B20 and lower-level blends generally do not require engine modifications, their use as transportation fuel is increasing in the Brazilian distribution networks. However, the environmental fate of low-level biodiesel blends and pure biodiesel (B100) is poorly understood and the ecotoxicological-safety endpoints of biodiesel-contaminated environments are unknown. Using laboratory microcosms consisting of closed reactor columns filled with clay loam soil contaminated with pure biodiesel (EXPB100) and a low-level blend (EXPB5) (10% w/v), this study presents soil ecotoxicity assessement and dynamics of culturable heterotrophic bacteria. Most-probable-number (MPN) procedures for enumeration of bacteria, dehydrogenase assays and soil ecotoxicological tests using Eisenia fetida have been performed at different column depths over the course of incubation. After 60 days of incubation, the ecotoxicity of EXPB100-derived samples showed a decrease from 63% of mortality to 0% while EXPB5-derived samples exhibited a reduction from 100% to 53% and 90% on the top and at the bottom of the reactor column, respectively. The dehydrogenase activity of samples from EXPB100 and EXPB5 increased significantly compared to pristine soil after 60 days of incubation. Growth of aerobic bacterial biomass was only observed on the top of the reactor column while the anaerobic bacteria exhibited significant growth at different column depths in EXPB100 and EXPB5. These preliminary results suggest the involvement of soil indigenous microbiota in the biodegradation of biodiesel and blends. However, GC-FID analyses for quantification of fatty acid methyl esters (FAMEs) and aliphatic hydrocarbons and targeted sequencing of 16S rRNA tags using illumina platforms will provide important insights into the profiles and underlying mechanisms of (bio)diesel biodegradation in soil environments.

Membrane perturbing properties of toxin mycolactone from Mycobacterium ulcerans

DOE PAGES

Lopez, Cesar A.; Unkefer, Clifford J.; Swanson, Basil I.; ...

2018-02-05

Mycolactone is the exotoxin produced by Mycobacterium ulcerans and is the virulence factor behind the neglected tropical disease Buruli ulcer. The toxin has a broad spectrum of biological effects within the host organism, stemming from its interaction with at least two molecular targets and the inhibition of protein uptake into the endoplasmic reticulum. Although it has been shown that the toxin can passively permeate into host cells, it is clearly lipophilic. Association with lipid carriers would have substantial implications for the toxin’s distribution within a host organism, delivery to cellular targets, diagnostic susceptibility, and mechanisms of pathogenicity. Yet the toxin’smore » interactions with, and distribution in, lipids are unknown. Herein we have used coarse-grained molecular dynamics simulations, guided by all-atom simulations, to study the interaction of mycolactone with pure and mixed lipid membranes. Using established techniques, we calculated the toxin’s preferential localization, membrane translocation, and impact on membrane physical and dynamical properties. The computed water-octanol partition coefficient indicates that mycolactone prefers to be in an organic phase rather than in an aqueous environment. Our results show that in a solvated membrane environment the exotoxin mainly localizes in the water-membrane interface, with a preference for the glycerol moiety of lipids, consistent with the reported studies that found it in lipid extracts of the cell. The calculated association constant to the model membrane is similar to the reported association constant for Wiskott-Aldrich syndrome protein. Mycolactone is shown to modify the physical properties of membranes, lowering the transition temperature, compressibility modulus, and critical line tension at which pores can be stabilized. It also shows a tendency to behave as a linactant, a molecule that localizes at the boundary between different fluid lipid domains in membranes and promotes inter-mixing of domains. This property has implications for the toxin’s cellular access, T-cell immunosuppression, and therapeutic potential.« less

Non-equilibrium quantum phase transition via entanglement decoherence dynamics

PubMed Central

Lin, Yu-Chen; Yang, Pei-Yun; Zhang, Wei-Min

2016-01-01

We investigate the decoherence dynamics of continuous variable entanglement as the system-environment coupling strength varies from the weak-coupling to the strong-coupling regimes. Due to the existence of localized modes in the strong-coupling regime, the system cannot approach equilibrium with its environment, which induces a nonequilibrium quantum phase transition. We analytically solve the entanglement decoherence dynamics for an arbitrary spectral density. The nonequilibrium quantum phase transition is demonstrated as the system-environment coupling strength varies for all the Ohmic-type spectral densities. The 3-D entanglement quantum phase diagram is obtained. PMID:27713556

Linear Optics Simulation of Quantum Non-Markovian Dynamics

PubMed Central

Chiuri, Andrea; Greganti, Chiara; Mazzola, Laura; Paternostro, Mauro; Mataloni, Paolo

2012-01-01

The simulation of open quantum dynamics has recently allowed the direct investigation of the features of system-environment interaction and of their consequences on the evolution of a quantum system. Such interaction threatens the quantum properties of the system, spoiling them and causing the phenomenon of decoherence. Sometimes however a coherent exchange of information takes place between system and environment, memory effects arise and the dynamics of the system becomes non-Markovian. Here we report the experimental realisation of a non-Markovian process where system and environment are coupled through a simulated transverse Ising model. By engineering the evolution in a photonic quantum simulator, we demonstrate the role played by system-environment correlations in the emergence of memory effects. PMID:23236588

Population dynamics on heterogeneous bacterial substrates

NASA Astrophysics Data System (ADS)

Mobius, Wolfram; Murray, Andrew W.; Nelson, David R.

2012-02-01

How species invade new territories and how these range expansions influence the population's genotypes are important questions in the field of population genetics. The majority of work addressing these questions focuses on homogeneous environments. Much less is known about the population dynamics and population genetics when the environmental conditions are heterogeneous in space. To better understand range expansions in two-dimensional heterogeneous environments, we employ a system of bacteria and bacteriophage, the viruses of bacteria. Thereby, the bacteria constitute the environment in which a population of bacteriophages expands. The spread of phage constitutes itself in lysis of bacteria and thus formation of clear regions on bacterial lawns, called plaques. We study the population dynamics and genetics of the expanding page for various patterns of environments.

Autonomous Collision-Free Navigation of Microvehicles in Complex and Dynamically Changing Environments.

PubMed

Li, Tianlong; Chang, Xiaocong; Wu, Zhiguang; Li, Jinxing; Shao, Guangbin; Deng, Xinghong; Qiu, Jianbin; Guo, Bin; Zhang, Guangyu; He, Qiang; Li, Longqiu; Wang, Joseph

2017-09-26

Self-propelled micro- and nanoscale robots represent a rapidly emerging and fascinating robotics research area. However, designing autonomous and adaptive control systems for operating micro/nanorobotics in complex and dynamically changing environments, which is a highly demanding feature, is still an unmet challenge. Here we describe a smart microvehicle for precise autonomous navigation in complicated environments and traffic scenarios. The fully autonomous navigation system of the smart microvehicle is composed of a microscope-coupled CCD camera, an artificial intelligence planner, and a magnetic field generator. The microscope-coupled CCD camera provides real-time localization of the chemically powered Janus microsphere vehicle and environmental detection for path planning to generate optimal collision-free routes, while the moving direction of the microrobot toward a reference position is determined by the external electromagnetic torque. Real-time object detection offers adaptive path planning in response to dynamically changing environments. We demonstrate that the autonomous navigation system can guide the vehicle movement in complex patterns, in the presence of dynamically changing obstacles, and in complex biological environments. Such a navigation system for micro/nanoscale vehicles, relying on vision-based close-loop control and path planning, is highly promising for their autonomous operation in complex dynamic settings and unpredictable scenarios expected in a variety of realistic nanoscale scenarios.

Aurally aided visual search performance in a dynamic environment

NASA Astrophysics Data System (ADS)

McIntire, John P.; Havig, Paul R.; Watamaniuk, Scott N. J.; Gilkey, Robert H.

2008-04-01

Previous research has repeatedly shown that people can find a visual target significantly faster if spatial (3D) auditory displays direct attention to the corresponding spatial location. However, previous research has only examined searches for static (non-moving) targets in static visual environments. Since motion has been shown to affect visual acuity, auditory acuity, and visual search performance, it is important to characterize aurally-aided search performance in environments that contain dynamic (moving) stimuli. In the present study, visual search performance in both static and dynamic environments is investigated with and without 3D auditory cues. Eight participants searched for a single visual target hidden among 15 distracting stimuli. In the baseline audio condition, no auditory cues were provided. In the 3D audio condition, a virtual 3D sound cue originated from the same spatial location as the target. In the static search condition, the target and distractors did not move. In the dynamic search condition, all stimuli moved on various trajectories at 10 deg/s. The results showed a clear benefit of 3D audio that was present in both static and dynamic environments, suggesting that spatial auditory displays continue to be an attractive option for a variety of aircraft, motor vehicle, and command & control applications.

A survey of occupational health in the Royal Norvegian Navy.

PubMed

Moen, Bente E; Koefoed, Vilhelm F; Bondevik, Kristin; Haukenes, Inger

2008-01-01

The aim of this article is to describe possible risk factors in the work environment that can affect the health of staff of the Royal Norwegian Navy (RNoN). The article presents the main results from a subproject related to a major surveillance of the health and work environment in this population. The project was performed as a response to general concerns regarding harmful work environment and negative health effects for these employees. In 2002, a questionnaire was sent out to all the employees in RNoN, and they answered during a period of three months. The overall response rate was 58% (n=2265), 2001 men and 250 women (14 unknown sex). 1581 military employees and 580 civilians participated (104 unknown). Mean age was 38 (range 18-70). Questions about years at work, exposure to chemical, physical and ergonomic hazards were developed for this particular study. Questions about allergy, asthma, hand eczema, hearing loss, cardiovascular disease, diabetes mellitus and cancer were asked. Musculoskeletal disorders were obtained by a standardized instrument. Exposure to noise, heavy lifting, twisted work positions and work close to antennas and communication equipment occurred often in this population. The most commonly reported diseases that might be work related were hand eczema, hearing loss and low back pain. The results indicate the presence of several possible risk factors to health related to the work environment in this population. The project gives a basis for further action regarding the Health Safety and Environment work within RNoN.

The dynamics of perception and action.

PubMed

Warren, William H

2006-04-01

How might one account for the organization in behavior without attributing it to an internal control structure? The present article develops a theoretical framework called behavioral dynamics that integrates an information-based approach to perception with a dynamical systems approach to action. For a given task, the agent and its environment are treated as a pair of dynamical systems that are coupled mechanically and informationally. Their interactions give rise to the behavioral dynamics, a vector field with attractors that correspond to stable task solutions, repellers that correspond to avoided states, and bifurcations that correspond to behavioral transitions. The framework is used to develop theories of several tasks in which a human agent interacts with the physical environment, including bouncing a ball on a racquet, balancing an object, braking a vehicle, and guiding locomotion. Stable, adaptive behavior emerges from the dynamics of the interaction between a structured environment and an agent with simple control laws, under physical and informational constraints. ((c) 2006 APA, all rights reserved).

Novel Mycobacterium tuberculosis complex pathogen, M. mungi.

PubMed

Alexander, Kathleen A; Laver, Pete N; Michel, Anita L; Williams, Mark; van Helden, Paul D; Warren, Robin M; Gey van Pittius, Nicolaas C

2010-08-01

Seven outbreaks involving increasing numbers of banded mongoose troops and high death rates have been documented. We identified a Mycobacterium tuberculosis complex pathogen, M. mungi sp. nov., as the causative agent among banded mongooses that live near humans in Chobe District, Botswana. Host spectrum and transmission dynamics remain unknown.

The Impact of Children's Static versus Dynamic Conceptions of People on Stereotype Formation.

ERIC Educational Resources Information Center

Levy, Sheri R.; Dweck, Carol S.

1999-01-01

Two studies examined sixth graders' personality theories on stereotype formation. Compared to those with malleable views, children with fixed views of personality made more extreme trait ratings of schools characterized positively or negatively, generalized judgments to an unknown student, perceived greater within-school similarity and…

The Development of Executive Functions and Early Mathematics: A Dynamic Relationship

ERIC Educational Resources Information Center

Van der Ven, Sanne H. G.; Kroesbergen, Evelyn H.; Boom, Jan; Leseman, Paul P. M.

2012-01-01

Background: The relationship between executive functions and mathematical skills has been studied extensively, but results are inconclusive, and how this relationship evolves longitudinally is largely unknown. Aim: The aim was to investigate the factor structure of executive functions in inhibition, shifting, and updating; the longitudinal…

Filopodial dynamics and growth cone stabilization in Drosophila visual circuit development

PubMed Central

Özel, Mehmet Neset; Langen, Marion; Hassan, Bassem A; Hiesinger, P Robin

2015-01-01

Filopodial dynamics are thought to control growth cone guidance, but the types and roles of growth cone dynamics underlying neural circuit assembly in a living brain are largely unknown. To address this issue, we have developed long-term, continuous, fast and high-resolution imaging of growth cone dynamics from axon growth to synapse formation in cultured Drosophila brains. Using R7 photoreceptor neurons as a model we show that >90% of the growth cone filopodia exhibit fast, stochastic dynamics that persist despite ongoing stepwise layer formation. Correspondingly, R7 growth cones stabilize early and change their final position by passive dislocation. N-Cadherin controls both fast filopodial dynamics and growth cone stabilization. Surprisingly, loss of N-Cadherin causes no primary targeting defects, but destabilizes R7 growth cones to jump between correct and incorrect layers. Hence, growth cone dynamics can influence wiring specificity without a direct role in target recognition and implement simple rules during circuit assembly. DOI: http://dx.doi.org/10.7554/eLife.10721.001 PMID:26512889

Virtual acoustic environments for comprehensive evaluation of model-based hearing devices.

PubMed

Grimm, Giso; Luberadzka, Joanna; Hohmann, Volker

2018-06-01

Create virtual acoustic environments (VAEs) with interactive dynamic rendering for applications in audiology. A toolbox for creation and rendering of dynamic virtual acoustic environments (TASCAR) that allows direct user interaction was developed for application in hearing aid research and audiology. The software architecture and the simulation methods used to produce VAEs are outlined. Example environments are described and analysed. With the proposed software, a tool for simulation of VAEs is available. A set of VAEs rendered with the proposed software was described.

Antibiotic resistance genes and residual antimicrobials in cattle feedlot surface soil

USDA-ARS?s Scientific Manuscript database

Cattle feedlot soils receive manure containing both antibiotic residues and antibiotic resistant bacteria. The fates of these constituents are largely unknown with potentially serious consequences for increased antibiotic resistance in the environment. Determine if common antimicrobials (tetracycl...

Pharmaceuticals and Hormones in the Environment

EPA Science Inventory

Some of the earliest initial reports from Europe and the United States demonstrated that a variety of pharmaceuticals and hormones could be found in surface waters, source waters, drinking water, and influents and effluents from wastewater treatment plants (WWTPs). It is unknown...

A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks.

PubMed

Shang, Fengjun; Jiang, Yi; Xiong, Anping; Su, Wen; He, Li

2016-11-18

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed. When the unknown node is inside positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is outside positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The development of ecological environment in China based on the system dynamics method from the society, economy and environment perspective.

PubMed

Guang, Yang; Ge, Song; Han, Liu

2016-01-01

The harmonious development in society, economy and environment are crucial to regional sustained boom. However, the society, economy and environment are not respectively independent, but both mutually promotes one which, or restrict mutually complex to have the long-enduring overall process. The present study is an attempt to investigate the relationship and interaction of society, economy and environment in China based on the data from 2004 to 2013. The principal component analysis (PCA) model was employed to identify the main factors effecting the society, economy and environment subsystems, and SD (system dynamics) method used to carry out dynamic assessment for future state of sustainability from society, economy and environment perspective with future indicator values. Sustainable development in China was divided in the study into three phase from 2004 to 2013 based competitive values of these three subsystems. According to the results of PCA model, China is in third phase, and the economy growth is faster than the environment development, while the social development still maintained a steady and rapid growth, implying that the next step for sustainable development in China should focus on society development, especially the environment development.

Dynamic nuclear polarization methods in solids and solutions to explore membrane proteins and membrane systems.

PubMed

Cheng, Chi-Yuan; Han, Songi

2013-01-01

Membrane proteins regulate vital cellular processes, including signaling, ion transport, and vesicular trafficking. Obtaining experimental access to their structures, conformational fluctuations, orientations, locations, and hydration in membrane environments, as well as the lipid membrane properties, is critical to understanding their functions. Dynamic nuclear polarization (DNP) of frozen solids can dramatically boost the sensitivity of current solid-state nuclear magnetic resonance tools to enhance access to membrane protein structures in native membrane environments. Overhauser DNP in the solution state can map out the local and site-specific hydration dynamics landscape of membrane proteins and lipid membranes, critically complementing the structural and dynamics information obtained by electron paramagnetic resonance spectroscopy. Here, we provide an overview of how DNP methods in solids and solutions can significantly increase our understanding of membrane protein structures, dynamics, functions, and hydration in complex biological membrane environments.

Issues of Dynamic Coalition Formation Among Rational Agents

DTIC Science & Technology

2002-04-01

approaches of forming stable coalitions among rational agents. Issues and problems of dynamic coalition environments are discussed in section 3 while...2.2. 2.1.2 Coalition Algorithm, Coalition Formation Environment and Model Rational agents which are involved in a co-operative game (A,v) are...publicly available simulation environment for coalition formation among rational information agents based on selected classic coalition theories is, for

The New Learning Ecology of One-to-One Computing Environments: Preparing Teachers for Shifting Dynamics and Relationships

ERIC Educational Resources Information Center

Spires, Hiller A.; Oliver, Kevin; Corn, Jenifer

2012-01-01

Despite growing research and evaluation results on one-to-one computing environments, how these environments affect learning in schools remains underexamined. The purpose of this article is twofold: (a) to use a theoretical lens, namely a new learning ecology, to frame the dynamic changes as well as challenges that are introduced by a one-to-one…

Helping to combat chronic wasting disease

USGS Publications Warehouse

,

2003-01-01

Chronic wasting disease (CWD) is a disease of the nervous system that results in distinctive brain lesions. CWD affects elk, white-tailed deer, and mule deer, but has not been documented in livestock or humans. The origins of the disease, as well as the modes of transmission, remain unknown. Infected deer and elk appear robust and healthy in the early stages of CWD; clinical signs might not show for years. Mortality typically occurs within months after the appearance of clinical signs. The route of transmission is unknown; likely routes include direct transmission between infected and noninfected animals and infected animals contaminating local environments.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaur, S.

With the increased release of numerous chemical substances into the biosphere, careful assessment of health effects of polluted environment must be made for maintaining and enhancing the quality of human life on this earth. Significant number of malformed children are born each year. Sixty-five to 70% of all birth defects have an unknown etiology. More than one-third of early human conception and up to 15% of recognized pregnancies are terminated by spontaneous abortion. The extent of the effect of environmental pollution on human reproductive performance is for the most part unknown. Of the approximately five million chemicals in existence, humansmore » could be expose to a sufficient quantity of an estimated 53,000 for toxicity to be of potential problem. Methods that do not require autopsy or surgery such as semen analysis would be attractive for assessing the effect of environmental toxicology on quality of human life. Therefore, the present study was conducted to observe the effects of heavily polluted environment of industrial area of Ludhiana and relatively clean, pollution free environment of Chandigarh on the human semen quality. It was believed that the function of the male reproductive system may often be the most sensitive to toxic effects.« less

Applying Utility Functions to Adaptation Planning for Home Automation Applications

NASA Astrophysics Data System (ADS)

Bratskas, Pyrros; Paspallis, Nearchos; Kakousis, Konstantinos; Papadopoulos, George A.

A pervasive computing environment typically comprises multiple embedded devices that may interact together and with mobile users. These users are part of the environment, and they experience it through a variety of devices embedded in the environment. This perception involves technologies which may be heterogeneous, pervasive, and dynamic. Due to the highly dynamic properties of such environments, the software systems running on them have to face problems such as user mobility, service failures, or resource and goal changes which may happen in an unpredictable manner. To cope with these problems, such systems must be autonomous and self-managed. In this chapter we deal with a special kind of a ubiquitous environment, a smart home environment, and introduce a user-preference-based model for adaptation planning. The model, which dynamically forms a set of configuration plans for resources, reasons automatically and autonomously, based on utility functions, on which plan is likely to best achieve the user's goals with respect to resource availability and user needs.

Developmental temperature affects the expression of ejaculatory traits and the outcome of sperm competition in Callosobruchus maculatus.

PubMed

Vasudeva, R; Deeming, D C; Eady, P E

2014-09-01

The outcome of post-copulatory sexual selection is determined by a complex set of interactions between the primary reproductive traits of two or more males and their interactions with the reproductive traits of the female. Recently, a number of studies have shown the primary reproductive traits of both males and females express phenotypic plasticity in response to the thermal environment experienced during ontogeny. However, how plasticity in these traits affects the dynamics of sperm competition remains largely unknown. Here, we demonstrate plasticity in testes size, sperm size and sperm number in response to developmental temperature in the bruchid beetle Callosobruchus maculatus. Males reared at the highest temperature eclosed at the smallest body size and had the smallest absolute and relative testes size. Males reared at both the high- and low-temperature extremes produced both fewer and smaller sperm than males reared at intermediate temperatures. In the absence of sperm competition, developmental temperature had no effect on male fertility. However, under conditions of sperm competition, males reared at either temperature extreme were less competitive in terms of sperm offence (P(2)), whereas those reared at the lowest temperature were less competitive in terms of sperm defence (P(1)). This suggests the developmental pathways that regulate the phenotypic expression of these ejaculatory traits are subject to both natural and sexual selection: natural selection in the pre-ejaculatory environment and sexual selection in the post-ejaculatory environment. In nature, thermal heterogeneity during development is commonplace. Therefore, we suggest the interplay between ecology and development represents an important, yet hitherto underestimated component of male fitness via post-copulatory sexual selection. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.