3,345 research outputs found
Strategic Coopetition of Global Brands: A Game Theory Approach to ‘Nike + iPod Sport Kit’ Co-branding
No full textCo-branding can be implemented by establishing an agreement of strategic coopetition that allows companies to compete and cooperate simultaneously in order to obtain competitive advantages through operational synergy. With this type of agreement, brands enter markets sharing loyal customers they would be unlikely to reach individually. The main advantages associated with implementation of this form of strategic coopetition are the possibility of jointly communicating brand image, reputation and credibility in a global market where consumers tend to have homogeneous preferences and convergent lifestyles. The strategic coopetition between two global brands, Apple and Nike, through development of the ‘Nike+iPod Sport Kit’ product, serves as a benchmark to illustrate the benefits associated with implementation of coopetitive cooperation agreements. From application of the game theory, simulation of a game of strategic coopetition provided results that confirm global brands obtain benefits, albeit not in equal measure, in terms of adding value to the brand image at a world level.Co-branding; Coopetition; Global brands; Growth of brand value.
Regulation and function of the tonic component of cortical acetylcholine release
No full textRegulation and function of the tonic component of cortical acetylcholine release
Multiple time scales and variable spaces: synaptic neurotransmission in vivo
No full textRegulation and function of the tonic component of cortical acetylcholine release
Delamination detection in aerospace composite panels using convolutional autoencoders
No full textModern aerospace structures demand lightweight design procedures and require scheduled maintenance intervals. Supervised deep learning strategies can allow reliable damage detection provided a large amount of data is available to train. These learning algorithms may face problems in the absence of possible damage scenarios in the training dataset. This class imbalance problem in supervised deep learning may curtail the learning process and can possess issues related to generalization on unseen examples. On the other hand, unsupervised deep learning algorithms like autoencoders can handle such situations in the absence of labeled data. In this study, an aerospace composite panel is interrogated with a circular array of piezoelectric transducers using ultrasonic guided waves in a round-robin fashion. The time-series signals are collected for both the healthy and unhealthy state of the structure and transformed into a time-frequency dataset using continuous wavelet transformation. A convolutional autoencoder algorithm trained on healthy signals is used to identify anomalies in the form of delamination in the structure. The proposed methodology can successfully identify delamination in the structure with good accuracy
Differential diffusive instabilities of miscible two-layer stratifications in porous media and Hele-Shaw cells
No full textIn porous media, a stratification of a given solution on top of another miscible solution can be buoyantly unstable because of an unstable density stratification or because of differential diffusive effects. The former is the well known Rayleigh–Taylor (RT) mechanism wherein the interface is destabilized by the denser solution overlying a less dense one in the gravity field. Whereas the latter is of particular interest in the field of oceanography, when the upper solution is less dense than the lower one with the lower component diffusing faster than the upper one, resulting in a double diffusive (DD) instability. Similarly, a diffusive-layer convection (DLC) instability has also been observed for a stable density stratification with the upper solute diffusing faster than the lower one. Though the literature on differential diffusion effects is pretty vast, very few studies have managed to establish a connection, both qualitatively and quantitatively, between numerical simulations and experimental observations, which is the basis of the present study. We report our findings in a broad parameter range where the instability mechanism could be triggered by an unstable density stratification or due to differential diffusive effects, or even both, resulting in mixed modes
Fault location and parameter estimation on overhead transmission lines using synchronized sampling
No full textVita.The deregulation of the power industry' in the United States along with the increasing demand for power may require the wheeling of large amounts of power between areas that are geographically distant from one another. This has necessitated the development of Wide Area Monitoring Systems (WAMS) that can provide a realtime picture of the state of the system. The d ata so acquired can be used for a variety of control and protection functions, all of which have a more reliable operation of the power system as the ultimate goal. Transmission line fault location and parameter estimation are two such functions. Most existing methods of fault location compute the phasor representation of a set of time-domain samples of the voltage and current from the transmission line, and then apply the phasor to locating the fault. This dissertation proposes and develops a method that solves the equations of the transmission line model in the time-domain directly. The method is tested using data generated from the simulation of a power system using the Electromagnetic Transients Program (EM TP). It is expected that the new method will locate faults more accurately than the phasor-based methods. The accuracy of the param eter values of the transmission line has a direct bearing on the accuracy of the final fault location. This dissertation also develops a method for on-line param eter estimation, using time-domain samples of voltages and currents. The estim ated parameters are then used in the fault location algorithm developed previously to accurately estimate the fault. The techno logy driving the development in the field of WAMS is based on the Global Positioning System of Satellites (GPS). Specialized data acquisition units, located at various points in the system continuously collect the relevant data and pass it along to one or many control centers. The GPS satellites are used to synchronize the system-wide measurements
Interactions of analogs of the 1,4-dihydropyridine tiamdipine in cardiac and smooth muscle.
No full textTwo series of 1,4-dihydropyridines related to tiamdipine, 2-(2-aminoethylthio)methyl-3-carboethoxy-5-carbomethoxy-6- methyl-4-(3-nitrophenyl)-1,4-dihydropyridine, have been evaluated for their pharmacologic and radioligand binding properties in smooth and cardiac muscle. In the tiamdipine series the influence of phenyl ring substitution, 3-Cl, 3-MeO and 3-CF3, was greatly reduced relative to the N-formyl and neutral nifedipine derivatives. Consistent with our previous observations onset and offset of action were greatly reduced by the presence of the amine side chain. In tiamdipine analogs also bearing an asymmetric substituent at C-2, chirality at C-4 was determinant for activity
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations
No full textWith the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite structures is increasing rapidly. The performance of supervised learning algorithms for SHM depends on the amount of labeled and balanced datasets. Apart from this, collecting datasets accommodating all possible damage scenarios is cumbersome, costly, and inaccessible for aerospace applications. In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals. The trained unsupervised feature learner is used for delamination prediction with an anomaly detection philosophy. In the first approach, we have combined dimensionality reduction techniques (principal component analysis and independent component analysis) with a one-class support vector machine. In another approach, we have utilized deep learning-based deep convolutional autoencoders (CAE). These state-of-the-art algorithms are applied on three different guided wave-based experimental datasets. The raw guided wave signals present in the datasets are converted into wavelet-enhanced higher-order representations for training unsupervised feature-learning algorithms. We have also compared different techniques, and it is seen that CAE generates better reconstructions with lower mean squared error and can provide higher accuracy on all the datasets
Evaluating a Reinforcement Learning Approach for Collision Avoidance with Heterogeneous Aircraft
No full textThis paper focuses on the problem of decentralized collision avoidance for Unmanned Aircraft Systems. The considered setting involves drones that can sense the presence of other traffic within a specified sensing area, for example due to remote ID signal broadcasts or visual perception, and take evasive maneuvers according to drone dynamics to ensure a minimum separation. A Reinforcement Learning approach is investigated to adapt the ego drone trajectory in response to the limited observations of the intruders trajectory within a bounded environment. The key aim of the work is on designing evasive maneuvers for ego drone when sharing the airspace with heterogeneous aircraft that have varying sensing capabilities, maneuverability, and risk-awareness. Beside reachability-based techniques, which offer a powerful framework for identifying safe trajectories under worst case actions by other agents, the proposed approach aims at adapting the evasive maneuver to the incoming aircraft behavior. Tests were executed in the simulated environment comparing the results obtained with heterogeneous incoming aircraft with different maneuverability levels and with randomly selected fixed obstacles. The Reinforcement Learning based method performance was also tested adopting different state vector parameters. The results show that the proposed approach can support the implementation of safe collision avoidance services allowing the generation of adaptive maneuvers for Unmanned Aerial System Traffic Management
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