A Novel Implementation of a Flexible Robotic Fin Actuated by Shape Memory Alloy

In this paper,study of a novel flexible robotic-fin actuated by Shape Memory Alloy (SMA) is presented.The developed robotic fin is capable of implementing various 3-Dimensional (3D) motions,which plays an important role in robot propulsion and maneuverability.Firstly,the morphological and mechanics parameters of a real pectoral fin from a carp are investigated.Secondly,a detailed design of the flexible pectoral fin driven by SMA is presented according to the previous morphological and mechanics analyses.Thirdly,a simplified theoretical model on the SMA fin plate is derived.The thermodynamics of the SMA plate and the relationship between curvature and phase transformation are analyzed.Finally,several simulations and model experiments are conducted according to the previous analyses.The results of the experiments are useful for the control of the robotic fin.The experimental results reveal that the SMA actuated fin ray has a good actuating performance.     

Solvent entropy-driven searching for protein modeling examined and tested in simplified models.

Solvent entropy is a force to consider in protein folding and protein design but is difficult to model. It is investigated here in the context of the hp model: Two types of residues, hydrophobic and hydrophilic, are modeled on a lattice. Nine chains and two- and three-dimensional simulations are compared. We show that considering solvent entropy alone, efficient folding of lattice chains (identification of the native fold) can be achieved by an entropy-driven simulation on its own. Moreover, in a detailed comparison over a wide range of parameters, entropy-guided searching outperforms an energy-driven search in the model. The combination of energy- and entropy-driven search yields the most efficient searching. It is compared in detail with the above results, indicating also how this solvent shell model may advantageously be implemented in more complex protein modeling simulations.     

Evaluation of Tools for Modeling Manufacturing Systems Design with Multiple Levels of Detail

This paper presents an investigation of simulation packages regarding their ability to model business processes related to manufacturing systems. Three simulation packages are investigated: VS7, SIMAN/CINEMA IV, and SIMFACTORY II.5. These packages are evaluated with regard to their capabily of modeling problems related to the manufacturing systems design (MSD) framework, which involves different levels of detail: the conceptual modeling level and the detailed design level. The investigation is based on a case study related to manufacturing systems. The main objective of this investigation is to examine the manufacturing simulation packages and their ability to offer variable detail modeling. Research findings suggest that no simulation environment offers sufficiently flexible facilities for the variable detailed modeling of manufacturing systems design. The paper proposes a method for systems entity classification to increase the levels of detail in an effective manner without duplication of data collection and model building efforts.     

Monitoring futility and efficacy in phase II trials with Bayesian posterior distributions—A calibration approach

A multistage single arm phase II trial with binary endpoint is considered. Bayesian posterior probabilities are used to monitor futility in interim analyses and efficacy in the final analysis. For a beta‐binomial model, decision rules based on Bayesian posterior probabilities are converted to “traditional” decision rules in terms of number of responders among patients observed so far. Analytical derivations are given for the probability of stopping for futility and for the probability to declare efficacy. A workflow is presented on how to select the parameters specifying the Bayesian design, and the operating characteristics of the design are investigated. It is outlined how the presented approach can be transferred to statistical models other than the beta‐binomial model.     

A novel application of capnography during controlled human exposure to air pollution

Background

A detailed contrast bolus propagation model is essential for optimizing bolus-chasing Computed Tomography Angiography (CTA). Bolus characteristics were studied using bolus-timing datasets from Magnetic Resonance Angiography (MRA) for adaptive controller design and validation.

Methods

MRA bolus-timing datasets of the aorta in thirty patients were analyzed by a program developed with MATLAB. Bolus characteristics, such as peak position, dispersion and bolus velocity, were studied. The bolus profile was fit to a convolution function, which would serve as a mathematical model of bolus propagation in future controller design.

Results

The maximum speed of the bolus in the aorta ranged from 5–13 cm/s and the dwell time ranged from 7–13 seconds. Bolus characteristics were well described by the proposed propagation model, which included the exact functional relationships between the parameters and aortic location.

Conclusion

The convolution function describes bolus dynamics reasonably well and could be used to implement the adaptive controller design.     

Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module

Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.     

A simple model of cortical culture growth: burst property dependence on network composition and activity

This paper describes large-scale simulations of growth, network formation, and behavior in cultures of dissociated cortical cells. A neuron model that incorporates synaptic facilitation/depression and neurite outgrowth/retraction was used to construct virtual cultures of 10,000 cells whose spiking behavior and evolution were investigated in closed-loop simulations. This approach allows us to perform detailed analysis of the effects of model parameters on burst shape and timing, their changes, and the interrelationship among these behaviors, gross network structure, and model parameters. We examined the effects of two parameters—network composition (fraction of excitatory cells) and neuron excitability (activity level corresponding to neurite outgrowth equilibrium)—on network structure and behavior. Our results suggest that much of the burst shape and timing observed in vitro can be explained by a model that includes only closed-loop neurite outgrowth and dynamic synapses; features such as LTP/LTD, random connectivity, long-distance connections, and detailed neurite topology are not necessary.     

Macro approach and fuzzy modeling of entrapped biocatalyst

The interactions between a strain of Saccharomyces cerevisiae and an alginate matrix are investigated to ascertain the main factors affecting the bioreaction evolution. During the tests several parameters (glucose, ethanol, calcium ion and biomass concentration, pH, and alginate bed diameter) were evaluated, coupled with microscopic investigation inside the beads to determine the spatial biomass distribution. A detailed analysis of macro parameters and a correlation among them are proposed using a fuzzy algorithm. A global two-step fuzzy model results in which biomass distribution inside the beads is represented as a hidden parameter.     

A functionally based model for hydrolysis of cellulose by fungal cellulase

A new functionally based kinetic model for enzymatic hydrolysis of pure cellulose by the Trichoderma cellulase system is presented. The model represents the actions of cellobiohydrolases I, cellobiohydrolase II, and endoglucanase I; and incorporates two measurable and physically interpretable substrate parameters: the degree of polymerization (DP) and the fraction of beta-glucosidic bonds accessible to cellulase, F(a) (Zhang and Lynd, 2004). Initial enzyme-limited reaction rates simulated by the model are consistent with several important behaviors reported in the literature, including the effects of substrate characteristics on exoglucanase and endoglucanase activities; the degree of endo/exoglucanase synergy; the endoglucanase partition coefficient on hydrolysis rates; and enzyme loading on relative reaction rates for different substrates. This is the first cellulase kinetic model involving a single set of kinetic parameters that is successfully applied to a variety of cellulosic substrates, and the first that describes more than one behavior associated with enzymatic hydrolysis. The model has potential utility for data accommodation and design of industrial processes, structuring, testing, and extending understanding of cellulase enzyme systems when experimental date are available, and providing guidance for functional design of cellulase systems at a molecular scale. Opportunities to further refine cellulase kinetic models are discussed, including parameters that would benefit from further study.     

De novo design of a two-stranded coiled-coil switch peptide

The properties and characteristics shared by amyloid fibrils formed from disease and non-disease associated proteins that are unrelated in sequence and structure offer the prospect that model systems can be used to systematically assess the factors that predispose a native protein to form amyloid fibrils. Based on a de novo design approach, we recently reported a unique switch peptide model system, ccbeta, that forms a three-stranded coiled-coil structure at low temperatures and which can be easily converted to amyloid fibrils by increasing the temperature. To simplify the system further, we describe here the redesign of a two-stranded ccbeta coiled-coil variant and its detailed analysis by a variety of biophysical methods. Compared with the original design, the characteristics of the peptide make it even simpler to elucidate and validate fundamental principles of amyloid fibril-formation.     

Global bifurcations of a periodically forced nonlinear oscillator

The effects of periodic pulsatile stimulation on a simple mathematical model of biological oscillations, called the radial isochron clock (RIC), are investigated as a function of stimulus frequency and amplitude. This system can be reduced to a two parameter, one-dimensional circle map. Numerical and topological methods are used to give a very detailed picture of the observed bifurcations over the complete range of parameters. The bifurcations are generic for a class of models which generalize the RIC.     

Swing phase simulation and design of above knee prostheses

A detailed dynamic model of the stump-prosthesis system for an above knee amputee was developed. The model was used to examine the influence of controls and design parameters on the limb system performance during the swing phase of gait. The model duplicated the clinically known fact that hydraulic knee controllers allow the amputee to change walking speed while mechanical knee controllers limit the amputee to a single walking speed. Contrary to current practice, the simulations suggest that light weight prosthesis designs do not perform as well as heavier designs. A simple design based on a constant friction knee is shown to yield good overall performance.     

Modeling of nitrogen in river water using a detailed and a simplified model

To model catchment surface water quantity and quality, different model types are available. They vary from detailed physically based models to simplified conceptual and empirical models. The most appropriate model type for a certain application depends on the project objectives and the data availability. The detailed models are very useful for short-term simulations of representative events. They cannot be used for long-term statistical information or as a management tool. For those purposes, more simplified (conceptual or meta-) models must be used. In this study, nitrogen dynamics are modeled in a river in Flanders. Nitrogen sources from agricultural leaching and domestic point sources are considered. Based on this input, concentrations of ammonium (NH4-N) and nitrate (NO3-N) in the river water are modeled in MIKE 11 by taking into consideration advection and dispersion and the most important biological and chemical processes. Model calibration was done on the basis of available measured water quality data. To this detailed model, a more simplified model was calibrated with the objective to more easily yield long-term simulation results that can be used in a statistical analysis. The results show that the conceptual simplified model is 1800 times faster than the MIKE 11 model. Moreover the two models have almost the same accuracy. The detailed models are recommended for short-term simulations unless there are enough data for model input and model parameters. The conceptual simplified model is recommended for long-term simulations.     

Riparian forest indicators of potential future stream condition

Large wood in streams can play an extraordinarily important role in influencing the physical structure of streams and in providing habitat for aquatic organisms. Since wood is continually lost from streams, predicting the future input of wood to streams from riparian forests is crucial to assessing or managing stream ecosystems. Unfortunately, regional monitoring protocols have no established capacity to provide this information. The goal of this research is to propose one or more methods that could meet this need. This goal is pursued by using stream wood delivery models to aid in the design of a monitoring method. Two questions are asked. First, does simpler data change model predictions of future contributions of wood from riparian ecosystems to the stream? The answers to this first question enable monitoring design to be tailored to details affecting estimates of future stream condition. These answers are important, because more detailed data is typically more costly. Second, which metrics, if any, correlate well with model predictions? If such metrics can be identified, then these measures can serve as effective indicators of ecosystem function directly, without using ecosystem models.These questions were addressed by collecting highly detailed field observations of riparian forests from 109 forested riparian sites in the Coast Range, Willamette Valley, and western Cascades of northwestern Oregon. Detailed and simplified versions of these data were used in models that forecast the potential of riparian forests to provide wood to the stream. Model predictions with less detailed data typically provided answers different than did predictions made with more detailed data. Thus, ecosystem assessments requiring these types of model predictions would benefit from more detailed data. In contrast, riparian metrics easily observed in the field (e.g. number of basal area of trees) or derived from remotely sensed imagery (e.g. number or height of canopy trees) were well correlated with model predictions of potential stream wood recruitment. When direct model predictions or model scenario analyses are not required, these metrics can serve as effective indicators of the potential of riparian forests to provide wood to the future stream network.     

A Process Chaining Approach toward Product Design for Environment

This article presents an approach toward product design for environment (DfE) at the level that integrates environmental hazard analysis with models of transformation processes. As a complementary analysis tool to life-cycle assessment (LCA), this method would support detailed design decisions through modeling of a "process chain" for a subset of the product's life cycle. The building blocks for this approach are a set of unit process models that can convert process and design parameters into estimates for energy utilization, production scrap, and ancillary waste flows. These values for quantity of environmental releases can be integrated using a multicriiteria environmental hazard evaluation methodology that can estimate the "qualrty" of environmental releases. Finally, the waste information can be used to support a design model that can link design parameters to material, process, and operational parameter selection. A case study illustrating printed circuit board (PCB) assembly is presented to show process chain implementation in manufacturing applications.     

QSAR study on dual SET and NET reuptake inhibitors: an insight into the structural requirement for antidepressant activity

Monoamine transporters have emerged as important drug targets with a multitude of therapeutic potentials for their inhibitors. With the purpose of designing new chemical entities with enhanced inhibitory potencies against norepinephrine and serotonin transporters, the QSAR study carried out on N-arylmethylpiperidinamine derivatives as known inhibitors of these transporters is presented. The developed model was validated by standard QSAR parameters and through a detailed structural analysis on how it reproduces and explains the differences in the experimentally known activity data. The model showed a good correlative and predictive ability having a squared cross validated correlation co-efficient of 0.716 and 0.700 respectively for SET and NET inhibition. The squared conventional correlation coefficient was found to be 0.731 for SET antagonism and 0.777 for norepinephrine reuptake inhibition. The study confirmed that the serotonin reuptake inhibitory activity exhibited by the series is largely explained by steric factors of substituents emphasizing the role of size and shape of the inhibitors in making effective inhibitor-SET binding interactions whereas substituent lipophilicity was found to govern inhibitor-NET interaction chemistry. A detailed comparative investigation was made between the two models and the insights gleaned from the study could be usefully employed to design inhibitors with a much more enhanced potency and selectivity.     

QSAR study on dual SET and NET reuptake inhibitors: An insight into the structural requirement for antidepressant activity

Monoamine transporters have emerged as important drug targets with a multitude of therapeutic potentials for their inhibitors. With the purpose of designing new chemical entities with enhanced inhibitory potencies against norepinephrine and serotonin transporters, the QSAR study carried out on N-arylmethylpiperidinamine derivatives as known inhibitors of these transporters is presented. The developed model was validated by standard QSAR parameters and through a detailed structural analysis on how it reproduces and explains the differences in the experimentally known activity data. The model showed a good correlative and predictive ability having a squared cross validated correlation co-efficient of 0.716 and 0.700 respectively for SET and NET inhibition. The squared conventional correlation coefficient was found to be 0.731 for SET antagonism and 0.777 for norepinephrine reuptake inhibition. The study confirmed that the serotonin reuptake inhibitory activity exhibited by the series is largely explained by steric factors of substituents emphasizing the role of size and shape of the inhibitors in making effective inhibitor-SET binding interactions whereas substituent lipophiliCIT000y was found to govern inhibitor-NET interaction chemistry. A detailed comparative investigation was made between the two models and the insights gleaned from the study could be usefully employed to design inhibitors with a much more enhanced potency and selectivity.     

Microalgae bulk growth model with application to industrial scale systems

The scalability of microalgae growth systems is a primary research topic in anticipation of the commercialization of microalgae-based biofuels. To date, there is little published data on the productivity of microalgae in growth systems that are scalable to commercially viable footprints. To inform the development of more detailed assessments of industrial-scale microalgae biofuel processes, this paper presents the construction and validation of a model of microalgae biomass and lipid accumulation in an outdoor, industrial-scale photobioreactor. The model incorporates a time-resolved simulation of microalgae growth and lipid accumulation based on solar irradiation, species specific characteristics, and photobioreactor geometry. The model is validated with 9 weeks of growth data from an industrially-scaled outdoor photobioreactor. Discussion focuses on the sensitivity of the model input parameters, a comparison of predicted microalgae productivity to the literature, and an analysis of the implications of this more detailed growth model on microalgae biofuels lifecycle assessment studies.