Analytical modeling and dimensionless characteristics of open wet clutches in consideration of gravity

Forschung im Ingenieurwesen - Wet clutches are widely used in power transmission, but lack of the fact of an energy loss in open state condition. The flow conditions in the fluid flow of an open...     

Engineered Ferritin Nanoparticles for the Bioluminescence Tracking of Nanodrug Delivery in Cancer

The identification of a highly sensitive method to check the delivery of administered nanodrugs into the tumor cells is a crucial step of preclinical studies aimed to develop new nanoformulated cures, since it allows the real therapeutic potential of these devices to be forecast. In the present work, the ability of an H‐ferritin (HFn) nanocage, already investigated as a powerful tool for cancer therapy thanks to its ability to actively interact with the transferrin receptor 1, to act as an efficient probe for the monitoring of nanodrug delivery to tumors is demonstrated. The final formulation is a bioluminescent nanoparticle, where the luciferin probe is conjugated on nanoparticle surface by means of a disulfide containing linker (Luc‐linker@HFn) which is subjected to glutathione‐induced cyclization in tumor cell cytoplasm. The prolonged imaging of luciferase+ tumor models, demonstrated by an in vitro and an in vivo approach, associated with the prolonged release of luciferin into cancer cells by disulfide bridge reduction, clearly indicates the high efficiency of Luc‐linker@HFn for drug delivery to the tumor tissues.     

Markovian agent modeling swarm intelligence algorithms in wireless sensor networks

Wireless Sensor Networks (WSN) are large networks of tiny sensor nodes that are usually randomly distributed over a geographical region. The network topology may vary in time in an unpredictable manner due to many different causes. For example, in order to reduce power consumption, battery operated sensors undergo cycles of sleeping–active periods; additionally, sensors may be located in hostile environments increasing their likelihood of failure; furthermore, data might also be collected from a range of sources at different times. For this reason multi-hop routing algorithms used to route messages from a sensor node to a sink should be rapidly adaptable to the changing topology. Swarm intelligence has been proposed for this purpose, since it allows the emergence of a single global behavior from the interaction of many simple local agents. Swarm intelligent routing has been traditionally studied by resorting to simulation. The present paper aims to show that the recently proposed modeling technique, known as Markovian Agent Model (MAM), is suited for implementing swarm intelligent algorithms for large networks of interacting sensors. Various experimental results and quantitative performance indices are evaluated to support this claim. The validity of this approach is given a further proof by comparing the results with those obtained by using a WSN discrete event simulator.     

Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search

International patent corpus is a gigantic source containing today about 80 million of documents. Every patent is manually analyzed by patent officers and then classified by a specific code called Patent Class (PC). Cooperative Patent Classification CPC is the new classification system introduced since January 2013 in order to standardize the classification systems of all major patent offices. Like keywords for papers, PCs point to the core of the invention, describing concisely what they contain inside. Most of patents strategies are based on PC as filter for results therefore the selection of relevant PCs is often a primary and crucial activity. This task is considered particularly challenging and only few tools have been specially developed for this purpose. The most efficient tools are provided by patent offices of EPO and WIPO.This paper analyzes their PCs search strategy (mainly based on keyword-based engines) in order to identify main limitations in terms of missing relevant PCs (recall) and non-relevant results (precision). Patents have been processed by KOM, a semantic patent search tool developed by the authors. Unlike all other PC search tools, KOM uses semantic parser and many knowledge bases for carrying out a conceptual patent search. Its functioning is described step by step through a detailed analysis pointing out the benefits of a concept-based search vis-à-vis a keyword-based search. An exemplary case is proposed dealing with CPCs describing the sterilization of contact lenses. Comparison could be likewise conducted on other PCs such as International (IPC), European (ECLA) or United States (USPC) patent classification codes.     

Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

The fusion of inertial and visual data is widely used to improve an object??s pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object??s absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial/monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor.