Narrowband Organic Light‐Emitting Diodes for Fluorescence Microscopy and Calcium Imaging

Fluorescence imaging is an indispensable tool in biology, with applications ranging from single‐cell to whole‐animal studies and with live mapping of neuronal activity currently receiving particular attention. To enable fluorescence imaging at cellular scale in freely moving animals, miniaturized microscopes and lensless imagers are developed that can be implanted in a minimally invasive fashion; but the rigidity, size, and potential toxicity of the involved light sources remain a challenge. Here, narrowband organic light‐emitting diodes (OLEDs) are developed and used for fluorescence imaging of live cells and for mapping of neuronal activity in Drosophila melanogaster via genetically encoded Ca²⁺ indicators. In order to avoid spectral overlap with fluorescence from the sample, distributed Bragg reflectors are integrated onto the OLEDs to block their long‐wavelength emission tail, which enables an image contrast comparable to conventional, much bulkier mercury light sources. As OLEDs can be fabricated on mechanically flexible substrates and structured into arrays of cell‐sized pixels, this work opens a new pathway for the development of implantable light sources that enable functional imaging and sensing in freely moving animals.     

Salsa dance learning evaluation and motion analysis in gamified virtual reality environment

Multimedia Tools and Applications - Learning couple dance such as salsa is challenging as it requires to understand and assimilate all the dance skills (guidance, rhythm, style) correctly. Salsa is...     

Marginal Abatement Cost Curves for Water Scarcity Mitigation under Uncertainty

Water Resources Management - As water scarcity and drought become more common, planning to avoid their consequences becomes crucial. Measures to prevent the impact of new climate conditions are...     

Automated industrial IoT-device integration using the OpenPnP reference architecture

Distributed control systems are currently evolving towards industrial Internet of Things (IoT) systems communicating fully using Internet protocols. This creates opportunities for streamlining costly commissioning processes, which today require substantial manual work for installing, configuring, and integrating thousands of actuators and sensors. The vision of “plug-and-produce” control systems has been pursued for more than 15 years, but existing approaches fell short regarding configuration tasks and vendor neutrality. This paper introduces the standards-based IoT reference architecture OpenPnP, which allows largely automating the configuration and integration tasks of industrial commissioning processes. The architecture includes a number of design and technology decisions and the required implementation can be scaled down to resource-constrained industrial devices. This paper demonstrates how OpenPnP can reduce configuration and integration efforts up to 90% in typical settings, while potentially scaling well up to tens of thousands of communicated signals. Practitioners can orient their implementations towards OpenPnP, therefore potentially enabling “plug-and-produce” in many thousands of control systems.     

Screw and Lie group theory in multibody dynamics

Screw and Lie group theory allows for user-friendly modeling of multibody systems (MBS), and at the same they give rise to computationally efficient recursive algorithms. The inherent frame invariance of such formulations allows to use arbitrary reference frames within the kinematics modeling (rather than obeying modeling conventions such as the Denavit–Hartenberg convention) and to avoid introduction of joint frames. The computational efficiency is owed to a representation of twists, accelerations, and wrenches that minimizes the computational effort. This can be directly carried over to dynamics formulations. In this paper, recursive \(O ( n ) \) Newton–Euler algorithms are derived for the four most frequently used representations of twists, and their specific features are discussed. These formulations are related to the corresponding algorithms that were presented in the literature. Two forms of MBS motion equations are derived in closed form using the Lie group formulation: the so-called Euler–Jourdain or “projection” equations, of which Kane’s equations are a special case, and the Lagrange equations. The recursive kinematics formulations are readily extended to higher orders in order to compute derivatives of the motions equations. To this end, recursive formulations for the acceleration and jerk are derived. It is briefly discussed how this can be employed for derivation of the linearized motion equations and their time derivatives. The geometric modeling allows for direct application of Lie group integration methods, which is briefly discussed.     

The Impact of Geographic Distance on Online Social Interactions

Online social networking services entice millions of users to spend hours every day interacting with each other. The focus of this work is to explain the effect that geographic distance has on online social interactions and, simultaneously, to understand the interplay between the social characteristics of friendship ties and their spatial properties. We analyze data from a large-scale online social network, Tuenti, with about 10 million active users: our sample includes user profiles, user home locations and online social interactions among Tuenti members. Our findings support the idea that spatial distance constraints whom users interact with, but not the intensity of their social interactions. Furthermore, friendship ties belonging to denser connected groups tend to arise at shorter spatial distances than social ties established between members belonging to different groups. Finally, we show that our findings mostly do not depend on the age of the users, although younger users seem to be slightly more constrained to shorter geographic distances. Augmenting social structure with geographic information adds a new dimension to social network analysis and a large number of theoretical investigations and practical applications can be pursued for online social systems, with many promising outcomes. As the amount of available location-based data is increasing, our findings and results open the door to future possibilities: researchers would benefit from these insights when studying online social services, while developers should be aware of these additional possibilities when building systems and applications related to online social platforms.     

The Role of Network Topology and the Spatial Distribution and Structure of Knowledge in Regional Innovation Policy: A Calibrated Agent-Based Model Study

Recently, new regional innovation policy paradigms emerged that transcend a long-lived dispute on whether either regional specialization, diversification, or rather related variety is most conducive to regional innovativeness. This includes ‘smart specialization’ in which regions are deliberately specialized and connected following technological relatedness, ‘gatekeepers’ in which there are pipelines between dense regional networks, and a ‘regional mix’ of knowledge that allows sustained ‘branching’. We develop and use an agent-based model to study the conjecture that, to stimulate (supra)regional innovativeness, social planners need to consider both, in conjunction, the mix of technological knowledge in regions and the (inter)regional innovation network topology. We use this agent-based model to evaluate the performance of and study the internal mechanisms of these new policy paradigms in a variety of scenarios. To increase the external validity of our findings, we calibrate the knowledge graph searched by the agents in our model to the OECD patent database. We confirm that access to related variety is important, yet that, on top of that, access to incidentally related knowledge is crucial to prevent high-level lock-in and ensure long term technological progress. Moreover, we find that networks with regional gatekeepers are particularly innovative, because these gatekeepers form ‘knowledge hubs’, create short paths to potential partners, and enlarge the total pool of knowledge. In case agents have few relationships, we find exceptionally high performance for the gatekeeper network in combination with regional diversification. The smart specialization network is a solid second option, although it lacks access to incidentally related knowledge and thus will ultimately fall behind whenever agents have relatively few relationships. The study elaborates on specific scenarios to reveal intricacies in the relationship between knowledge distribution, network topology, and the structure of interrelationships between knowledge fields.     

How do investors decide? An interdisciplinary review of decision-making in crowdfunding

Crowdfunding is on the rise: its volume grew 1000% in only three years and is about to outpace worldwide venture capital spending. A quickly growing body of research is exploring the emerging crowdfunding phenomenon. While the literature offers a detailed and comprehensive picture of decision-making for traditional startup financing or bank loans, it does not provide a holistic understanding of decisions to invest in crowdfunded ventures. Many individual studies investigate isolated factors that influence investor decision-making in crowdfunding campaigns without integrating the findings regarding those influences. A comprehensive view of the relevant decision-making factors is necessary to build future research on and for practitioners to gain a better understanding of how investors choose. We conduct an interdisciplinary literature review to examine which factors influence the investment decisions in crowdfunding. From an analysis of 68 articles, we construct a comprehensive framework of relevant influence factors. Even though prior research covers many factors, others have received scant attention. Especially investors’ cognitive features and the context in which the investment decision is made seem to strongly influence decisions but are scarcely researched. In addition, most reviewed studies focus more on individual factors and campaign success than underlying decision processes. To highlight novel factors of crowdfunding investment decisions, we compare decision-making in traditional investments, such as venture capital and bank loans, to crowdfunding. Our findings offer new avenues for research toward understanding how the shift induced by crowdfunding changes our choices and actions. The analysis should support the endeavor to build better theories and provide a basis for further social and technological development.     

Dry Processing and Recycling of Thick Nacre–Mimetic Nanocomposites

Bioinspired nanocomposites with high levels of reinforcement hold great promise for future, green lightweight, and functional engineering materials, but they suffer from slow, tedious, and nonscalable preparation routes, that typically only lead to very thin films. A rapid and facile dry powder processing technique is introduced to generate bioinspired nanocomposite materials at high fractions of reinforcements (50 wt%) and with millimeter scale thickness. The process uses powder drying of vitrimer-coated nanoplatelets (nanoclay and MXene) from aqueous solution and subsequent hot-pressing. As a method of choice in industrial lightweight composite materials engineering, hot-pressing underscores a high potential to translate this approach to actual products. The use of the vitrimer chemistry with temperature-activated bond shuffling is important to facilitate smooth integration into the nanocomposite design, leading to layered nacre-inspired nanocomposites with nanoscale hard/soft order traced by X-ray diffraction and excellent mechanical properties investigated using flexural tests. Recycling by grinding and hot-pressing is possible without property loss. The compatibility with existing composite processing techniques, scalable thickness and dimensions, and recyclability open considerable opportunities for translating bioinspired nanocomposites to real-life applications.