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Yams of the most widely differing nature are produced in textile mills. The production stages necessary for this are carried out with the aid of textile machines. Between these individual textile machines - from cards to spinning machines - sliver cans serve as a rule as transport containers, in which the sensitive sliver material is temporarily stored, and presented to the next production stage.
Bond graph modelling was devised by Professor Paynter at the Massachusetts Institute of Technology in 1959 and subsequently developed into a methodology for modelling multidisciplinary systems at a time when nobody was speaking of object-oriented modelling. On the other hand, so-called object-oriented modelling has become increasingly popular during the last few years. By relating the characteristics of both approaches, it is shown that bond graph modelling, although much older, may be viewed as a special form of object-oriented modelling. For that purpose the new object-oriented modelling language Modelica is used as a working language which aims at supporting multiple formalisms. Although it turns out that bond graph models can be described rather easily, it is obvious that Modelica started from generalized networks and was not designed to support bond graphs. The description of bond graph models in Modelica is illustrated by means of a hydraulic drive. Since VHDL-AMS as an important language standardized and supported by IEEE has been extended to support also modelling of non-electrical systems, it is briefly investigated as to whether it can be used for description of bond graphs. It turns out that currently it does not seem to be suitable.
Multidisciplinary systems are described most suitably by bond graphs. In order to determine unnormalized frequency domain sensitivities in symbolic form, this paper proposes to construct in a systematic manner a bond graph from another bond graph, which is called the associated incremental bond graph in this paper. Contrary to other approaches reported in the literature the variables at the bonds of the incremental bond graph are not sensitivities but variations (incremental changes) in the power variables from their nominal values due to parameter changes. Thus their product is power. For linear elements their corresponding model in the incremental bond graph also has a linear characteristic. By deriving the system equations in symbolic state space form from the incremental bond graph in the same way as they are derived from the initial bond graph, the sensitivity matrix of the system can be set up in symbolic form. Its entries are transfer functions depending on the nominal parameter values and on the nominal states and the inputs of the original model. The sensitivities can be determined automatically by the bond graph preprocessor CAMP-G and the widely used program MATLAB together with the Symbolic Toolbox for symbolic mathematical calculation. No particular program is needed for the approach proposed. The initial bond graph model may be non-linear and may contain controlled sources and multiport elements. In that case the sensitivity model is linear time variant and must be solved in the time domain. The rationale and the generality of the proposed approach are presented. For illustration purposes a mechatronic example system, a load positioned by a constant-excitation d.c. motor, is presented and sensitivities are determined in symbolic form by means of CAMP-G/MATLAB.
Although p27 plays a central role in cell cycle regulation, its role in breast cancer prognosis is controversial. Furthermore, the p27 gene CDKN1B carries a polymorphism with unknown functional relevance. This study was designed to evaluate p27 expression and p27 genotyping with respect to early breast cancer prognosis. 279 patients with infiltrating metastasis-free breast cancer were included in this study. p27 expression was determined in tumor tissue specimens from 261 patients by immunohistochemistry. From 108 patients, the CDKN1B genotype was examined by PCR and subsequent direct sequencing. 55.2% of the tumors were considered p27 positive. p27 expression did not correlate with any of the established parameters except for nodal involvement but significantly correlated to prolonged disease-free survival. In 35% of the tumors analyzed, the CDKN1B gene showed a polymorphism at codon 109 (V109G). The V109G polymorphism correlated with greater nodal involvement. In the node-negative subgroup, V109G correlated significantly with a shortened disease-free survival. In conclusion, the determination of the CDKN1B genotype might be a powerful tool for the prognosis of patients with early breast cancer.
Background: Type 2 diabetes mellitus is associated with increased cardiovascular risk. One laboratory marker for cardiovascular risk assessment is high-sensitivity C-reactive protein (hsCRP).
Methods: This cross-sectional study attempted to analyze the association of hsCRP levels with insulin resistance, β-cell dysfunction and macrovascular disease in 4270 non-insulin-treated patients with type 2 diabetes [2146 male, 2124 female; mean age ±SD, 63.9±11.1years; body mass index (BMI) 30.1±5.5kg/m2; disease duration 5.4±5.6years; hemoglobin A1c (HbA1c) 6.8±1.3%]. It consisted of a single morning visit with collection of a fasting blood sample. Observational parameters included several clinical scores and laboratory biomarkers.
Results: Stratification into cardiovascular risk groups according to hsCRP levels revealed that 934 patients had low risk (hsCRP <1mg/L), 1369 patients had intermediate risk (hsCRP 1–3mg/L), 1352 patients had high risk (hsCRP >3–10mg/L), and 610 patients had unspecific hsCRP elevation (>10mg/L). Increased hsCRP levels were associated with other indicators of diabetes-related cardiovascular risk (homeostatic model assessment, intact proinsulin, insulin, BMI, β-cell dysfunction, all p<0.001), but showed no correlation with disease duration or glucose control. The majority of the patients were treated with diet (34.1%; hsCRP levels 2.85±2.39mg/L) or metformin monotherapy (21.1%; 2.95±2.50mg/L hsCRP). The highest hsCRP levels were observed in patients treated with sulfonylurea (17.0%; 3.00±2.43mg/L).
Conclusions: Our results indicate that hsCRP may be used as a cardiovascular risk marker in patients with type 2 diabetes mellitus and should be evaluated in further prospective studies.
The development of mobile robotic systems is a demanding task regarding its complexity, required resources and skills in multiple fields such as software development, artificial intelligence, mechanical design, electrical engineering, signal processing, sensor technology or control theory. This holds true particularly for soccer playing robots, where additional aspects like high dynamics, cooperation and high physical stress have to be dealt with. In robot competitions such as RoboCup, additional skills in the domains of team, project and knowledge management are of importance.
Purpose – To describe the development of a novel polyether(meth)acrylate-based resin material class for stereolithography with alterable material characteristics.
Design/methodology/approach – A complete overview of details to composition parameters, the optimization and bandwidth of mechanical and processing parameters is given. Initial biological characterization experiments and future application felds are depicted. Process parameters are studied in a commercial 3D systems Viper stereolithography system, and a new method to determine these parameters is described herein.
Findings – Initial biological characterizations show the non-toxic behavior in a biological environment, caused mainly by the (meth)acrylate-based core components. These photolithographic resins combine an adjustable low Young’s modulus with the advantages of a non-toxic (meth)acrylate-based process material. In contrast to the mostly rigid process materials used today in the rapid prototyping industry, these polymeric formulations are able to fulfll the extended need for a soft engineering material. A short overview of sample applications is given.
Practical implications – These polymeric formulations are able to meet the growing demand for a resin class for rapid manufacturing that covers a bandwidth from softer to stiffer materials.
Originality/value – This paper gives an overview about the novel developed material class for stereolithography and should be therefore of high interest to people with interest in novel rapid manufacturing materials and technology.
This paper introduces our robotic system named UGAV (Unmanned Ground-Air Vehicle) consisting of two semi-autonomous robot platforms, an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicles (UAV). The paper focuses on three topics of the inspection with the combined UGV and UAV: (A) teleoperated control by means of cell or smart phones with a new concept of automatic configuration of the smart phone based on a RKI-XML description of the vehicles control capabilities, (B) the camera and vision system with the focus to real time feature extraction e.g. for the tracking of the UAV and (C) the architecture and hardware of the UAV.
The research of autonomous artificial agents that adapt to and survive in changing, possibly hostile environments, has gained momentum in recent years. Many of such agents incorporate mechanisms to learn and acquire new knowledge from its environment, a feature that becomes fundamental to enable the desired adaptation, and account for the challenges that the environment poses. The issue of how to trigger such learning, however, has not been as thoroughly studied as its significance suggest. The solution explored is based on the use of surprise (the reaction to unexpected events), as the mechanism that triggers learning. This thesis introduces a computational model of surprise that enables the robotic learner to experience surprise and start the acquisition of knowledge to explain it. A measure of surprise that combines elements from information and probability theory, is presented. Such measure offers a response to surprising situations faced by the robot, that is proportional to the degree of unexpectedness of such event. The concepts of short- and long-term memory are investigated as factors that influence the resulting surprise. Short-term memory enables the robot to habituate to new, repeated surprises, and to “forget” about old ones, allowing them to become surprising again. Long-term memory contains knowledge that is known a priori or that has been previously learned by the robot. Such knowledge influences the surprise mechanism, by applying a subsumption principle: if the available knowledge is able to explain the surprising event, suppress any trigger of surprise. The computational model of robotic surprise has been successfully applied to the domain of a robotic learner, specifically one that learns by experimentation. A brief introduction to the context of such application is provided, as well as a discussion on related issues like the relationship of the surprise mechanism with other components of the robot conceptual architecture, the challenges presented by the specific learning paradigm used, and other components of the motivational structure of the agent.
This thesis introduces and demonstrates a novel method for learning qualitative models of the world by an autonomous robot. The method makes possible generation of qualitative models that can be used for prediction as well as directing the experiments to improve the model. The qualitative models form the knowledge representation of the robot and consists of qualitative trees and non-deterministic finite automaton. An efficient exploration algorithm that lets the robot collect the most relevant learning samples is also introduced. To demonstrate the use of the methodology, representation and algorithm, two experiments are described. The first experiment is conducted using a mobile robot and a ball, where the robot observes the ball and learns the effect of its actions on the observed attributes of the world. The second experiment is conducted using a mobile robot and five boxes, two non-movable boxes and three movable boxes. The robot experiments actively with the objects and observes the changes in the attributes of the world. The main difference with the two experiments is that the first one tries to learn by observation while the second tries to learn by experimentation. In both experiments the robot learns qualitative models from its actions and observations. Although the primary objective of the robot is to improve itself by being able to predict the outcome of its actions, the models Learned were also used at each step of the learning process to direct the experiments so that the model converges to the final model as quickly as possible.