Localized radiative energy transfer from a plasmonic bow-tie nanoantenna to a magnetic thin film stack

Localized radiative energy transfer from a near-field emitter to a magnetic thin film structure is investigated. A magnetic thin film stack is placed in the near-field of the plasmonic nanoantenna to utilize the evanescent mode coupling between the nanoantenna and magnetic thin film stack. A bow-tie nano-optical antenna is excited with a tightly focused beam of light to improve near-field radiative energy transfer from the antenna to the magnetic thin film structure. A tightly focused incident optical beam with a wide angular spectrum is formulated using Richards-Wolf vector field equations. Radiative energy transfer is investigated using a frequency domain 3-D finite element method solution of Maxwell’s equations. Localized radiative energy transfer between the near-field emitter and the magnetic thin film structure is quantified for a given optical laser power at various distances between the near-field emitter and magnetic thin film

Antecedents and Performance Outcomes of Value-Based Selling in Sales Teams: A Multilevel, Systems Theory of Motivation Perspective

Firms are increasingly deploying a value-based selling (VBS) approach in their sales organizations to drive growth for new offerings. However, VBS adoption remains challenging, signaling that leaders need guidance to motivate VBS. Drawing from the systems theory of motivation, we examine motivational mechanisms at two levels—salesperson and sales team—to understand how to motivate, and benefit from, VBS. Using multisource data (i.e., salespeople, managers, archival performance) from 70 sales teams in a U.S.-based manufacturing and services provider, our findings illustrate drivers and outcomes of VBS. Specifically, we uncover a framework of salesperson, leader, customer, and team factors that help explain salesperson motivation for VBS. Importantly, we link VBS to customers’ adoption of new products to support VBS’s role for selling new products. Critical for sales team strategy, our model also integrates a team-level motivational mechanism to provide a comprehensive framework for salesperson and sales team motivations and outcomes

Signal propagation in diffusive molecular communications over a spherical surface

Diffusive molecular communications (DMC) relies on the Brownian motion of dedicated molecules for transmitting information. The signal propagation characteristics in a DMC channel is determined by the geometries of the environment, receiver and the transmitter and their physical and chemical properties, which impose the boundary- and initial conditions on the partial differential equation (PDE) describing the diffusion process. In this work, we investigate the directional signal propagation characteristics for a point source over (or, as a special case, on) a spherical surface, that may be reflective, perfectly absorptive or partially absorptive. We derive the general solution of the PDE for this case, and validate it via particle based simulations. Our results can be employed to determine the signal propagation characteristics in a wide range of practically relevant scenarios, for which we investigate the effect of the system parameters on the directivity of the signal propagation

Neural network based decision fusion for abnormality detection via molecular communications

Abnormality detection is one of the most highly anticipated application areas of Molecular Communication (MC) based nanonetworks. This task entails sensing, detection, and reporting of abnormal changes in a fluid medium that may characterize a disease or disorder using a network of collaborating nanoscale sensors. Existing strategies for such distributed collaborative detection problems require a complete statistical characterization of the underlying communication channel between the sensors and the fusion centre (FC), with the assumption of perfectly-known or accurately estimated channel parameters. This assumption is usually impractical both due to mathematical intractability of the analytical channel models for MC except in a few ideal cases, and the slow and dispersive signal propagation characteristics that make the channel estimation a difficult task even in these ideal cases. This work, for the first time in the literature, proposes to employ a machine learning approach to this task and shows that this approach provides the robustness and flexibility required for practical implementation. We focus on detection based on deep learning, specifically on a feed-forward neural network and a recurrent neural network structure that learn the underlying model from data. This study shows that the proposed decision fusion strategy can perform well without any knowledge of the communication channel

Transformational leadership and market orientation: Implications for the implementation of competitive strategies and business unit performance.

Abstract Drawing on the resource-based view of the firm, particularly the competency-based view of strategy making, the authors develop and test an integrated model of the source-positional advantage-firm performance chain. The model postulates transformational leadership and market orientation as managerial-based and transformational-based competencies, respectively. Such competencies should lead to marketplace positional advantages through competitive strategies such as innovation differentiation, marketing differentiation, and low cost. In turn, these positional advantages contribute to different firm performance metrics, specifically, effectiveness and efficiency. The authors discuss some implications for competitive strategy theory using a resource-(competency-) based perspective, along with managerial implications

One-Pot 3D Printing of Robust Multimaterial Devices

Polymer 3D printing is a broad set of manufacturing methods that permit the fabrication of complex architectures, and, as a result, numerous efforts focus on formulating processible chemistries that produce desirable material behavior in printed parts. However, current resin chemistries typically result in a single fixed set of properties once fully polymerized, a fact that poses significant engineering challenges to obtaining multimaterial devices. As an alternative to single-property materials, we introduce a ternary sequential reaction scheme that exhibits diverse multimaterial properties by profoundly altering the polymer microstructure from within a single resin composition. In this system, the photodosage during 3D printing sets both the shape and extent of conversion for each subsequent reaction. This different polymerization mechanisms of the subsequent stages yield disparate crosslink densities and viscoelastic properties. As a result, our materials possess Young's Moduli spanning over three orders of magnitude (400 kPa < E < 1.6 GPa) with smooth transitions between soft and stiff regions. We successfully pattern a 500x change in modulus in under a millimeter while the sequential assembly of our polymer networks ensures robust interfaces and enhances toughness by 10x compared to the single property materials. Most importantly, the final objects remain stable to UV and thermal aging, a key limitation to applications of previous multimaterial chemistries. We demonstrate the ability to 3D print intricate multimaterial architectures by fabricating a soft, wearable braille display.Comment: 54 pages including supplemental information, 5 main text figure

Developable Rotationally Symmetric Kirigami‐Based Structures as Sensor Platforms

Developable surfaces based on closed‐shape, planar, rotationally symmetric kirigami (RSK) sheets approximate 3D, globally curved surfaces upon (reversible) out‐of‐plane deflection. The distribution of stress and strain across the structure is characterized experimentally and by finite‐element analysis as a function of the material and cut parameters, enabling the integration with strain gauges to produce a wearable, conformal patch that can capture complex, multiaxis motion. Using the patch, real‐time tracking of shoulder joint and muscle behavior is demonstrated. The facile fabrication and unique properties of the RSK structures potentially enable wearable, textile‐integrated joint monitoring for athletic training, wellness, rehabilitation, feedback control for augmented mobility, motion of soft and traditional robotics, and other applications.This work introduces a new paradigm for realizing 2D to curved, 3D, functional surface transformation using rotationally symmetric kirigami as a platform for deploying wearable sensors; here it is demonstrated for real‐time tracking of complex motion of joints within the body and circumventing longstanding tradeoffs in the design of materials, structures, and devices for conformable, wearable electronics.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153082/1/admt201900563-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153082/2/admt201900563.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153082/3/admt201900563_am.pd

Characterization of kinetic and kinematic parameters for wearable robotics

The design process of a wearable robotic device for human assistance requires the characterization of both kinetic and kinematic parameters (KKP) of the human joints. The first step in this process is to extract the KKP from different gait analyses studies. This work is based on the human lower limb considering the following activities of daily living (ADL): walking over ground, stairs ascending/descending, ramp ascending/descending and chair standing up. The usage of different gait analyses in the characterization process, causes the data to have great variations from one study to another. Therefore, the data is graphically represented using Matlab® and Excel® to facilitate its assessment. Finally, the characterization of the KKP performed was proved to be useful in assessing the data reliability by directly comparing all the studies between each other; providing guidelines for the selection of actuator capacities depending on the end application; and highlighting optimization opportunities such as the implementation of agonist-antagonist actuators for particular human joints

Supply chain of innovation and new product development

This paper conceptualizes the supply chain of innovation of a company as its supply chain not related to physical goods exchanges but to R&amp;D commodities exchanges. R&amp;D commodities, being the outcomes of research activities, are for example patents, technologies, research services, studies, projects, etc. Spe- cifically, we focus on the relationship between the activities of purchasing/selling R&amp;D commodities and the propensity of the firm to develop new products; we examine how the position of the firm within its innovation network moderates this relationship. The empirical setting of the research consists of a cross- sectional dataset of 544 biopharmaceutical companies that have signed 1772 R&amp;D agreements in the years 2006–2010. We find firstly, evidence of the supply chain of innovation (as a natural evolution of the well-acknowledged dual-market model of the biopharmaceutical industry). Secondly, we find that the relational embeddedness, coming from innovation network, influences the effect of purchasing and selling R&amp;D commodities on new product development. Supporting our theoretical predictions, this paper offers contributions to the scientific literature on supply chain relationships in new product development