In this dissertation, I integrate research on role-based coordination with concepts adapted from the team effectiveness literature to theorize how minimal team structures support effective coordination when people do not work together regularly. I argue that role-based coordination among relative strangers can be interpersonally challenging and propose that team scaffolds (minimal team structures that bound groups of roles rather than groups of individuals) may provide occupants with a temporary shared in-group that facilitates interaction. I develop and test these ideas in a multi-method, multi-site field study of a new work structure, called pods, that were implemented in many hospital emergency departments (EDs) and were sometimes designed to function as team scaffolds.

The applications of differential equations are many. However, many differential equations modelling real-world scenarios are very complex and it can be of great difficulty to find an exact solution if one even exists. Thus, it is of importance to be able to approximate solutions of differential equations. Here, a method using neural networks is explored and its performance is compared to that of a numerical method. To illustrate the method, two first order, two second order and two first order systems of ordinary differential equations are explored. The systems are the Lotka-Volterra system and the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model. The first four examples have exact solutions to compare to and the observations are then used as a basis when discussing the results of the systems. The results of the thesis show that while the neural network method takes longer to deliver an approximation, it continuously gives better approximations than the implicit Euler method used for comparison. The main contribution of this thesis is the comparison done of the performances of the neural network method and the implicit Euler method. / Det finns många användningsområden för differentialekvationer. Däremot är många differentialekvationer som modellerar verkligheten komplexa och det kan vara svårt, om inte omöjligt, att hitta en exakt lösning. På grund av detta är det viktigt att ha metoder som kan approximera lösningar till differentialekvationer. Därför undersöks här en metod som använder sig av neurala nätverk. Dess resultat blir sedan jämförda med en numerisk metod. För att illustrera metoden presenteras två ekvationer av första ordningen, två ekvationer av andra ordningen och två system av differentialekvationer. Systemen som undersöks är Lotka-Volterra ekvationerna samt SEIR (Susceptible, Exposed, Infected, Removed) modellen. De första fyra exemplen som undersöks har exakta lösningar att jämföra med och dessa observationer används sedan vid diskussionerna gällande systemen. Resultaten visar att medan metoden som använder neurala nätverkar tar längre tid att exekvera, så ger metoden bättre approximationer än den implicita Euler metoden som användes som jämförelse. Det huvudsakliga bidraget med det här examensarbetet är jämförelsen av hur de två metoderna presterar.

This thesis concerns Social Network Analysis as a mechanism for exploring Community Dynamics. To be able to use the Social Network methodologies, relationships existing between the modeling entities are required. In this thesis, we use two different kinds of relationships: e-mails exchanged and co-authorship of papers. The e-mails exchanged, as an indicator of information exchange in an organization, is used to facilitate the emergence of structure within the organization. In this thesis we demonstrate the effectiveness of using e-mail communication patterns for crisis detection in a hierarchically set organization. We compare the performance of a Social Network based Classifier with some of the traditional classifiers from the data mining framework for inferring this hierarchy. A generic framework for studying dynamic group transformations is presented and the co-authorship of papers, as an indicator of collaboration in an academic institution, is used to study the community behavioral patterns evolving over time. Enron e-mail corpus and the IISc Co-authorship Dataset are utilized for illustrative purposes.

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This master’s thesis is bent on comparison of four newest method of analog frequency filter design. After the introductory part, a description of properties and internal structure of active elements (Second-Generation Current Conveyor), VG-CCII (Variable Gain Current Conveyor), VCG-CCII (Voltage and current gained second generation current conveyor) a CFTA (Current Follower Transconductance Amplifier) is mentioned. This active elements are used in subsequent design. At the next part, process of frequency filters design by generalized method of filter design flowing from the full admittance network, by extending autonomous circuits, design using the signal flow graphs theory and design with synthetic high-order elements are presented. Thirty nine autonomous circuits are derived from full admittance networks with one and two active elements CFTA. Selected autonomous circuits are used for direct design of frequency filter and for filter design by extending autonomous circuits. Produced structures guarantee the possibility of controlling the quality factor Q0 independently of the characteristic frequency f0. Three frequency filters are designed by method using the signal flow graph theory. Active elements CFTA, VG-CCI and VCG-CCII are used. Full multifunctional frequency filter with conveyors VG-CCII and VCG-CCII guarantee the possibility of controlling the quality factor Q0 and the characteristic frequency f0 independently of the each other. In last part, from 2nd to 5th order low-pass filters by the method using synthetic high-order elements are designed. The behaviour of the selected filters has been verified by simulations in the PSpice program for all of the mentioned methods separately.

Numerous vibrating electromechanical systems lack a rigid connection to the inertial frame. An artificial inertial frame can be generated by a shaker, which compensates for vibrations. In this article, we present an encapsulated and perforated unimorph bending plate for this purpose. Vibrations can be compensated up to the first eigenfrequency of the system. As basis for an efficient system simulation and optimization, a new three-port multi-domain network model was developed. An extension qualifies the network for the simulation of the acoustical behavior inside the capsule. Network parameters are determined using finite element simulations. The dynamic behavior of the network model agrees with the finite element simulation results up to the first resonance of the system. The network model was verified by measurements on a laboratory setup, too. Furthermore, the network model could be simplified and was applied to determine the influence of various parameters on the stabilization performance of the plate transducer. The performance of the piezoelectric bending plate for position stabilization had been in addition investigated experimentally by measurements on a macroscopic capsule.