Understanding the Impact of Future Social Self-Concepts on Newcomer Adjustment

The purpose of this study is to investigate the impacts of future social self-concepts on newcomer short-term adjustment. Employing a qualitative longitudinal design based on interviews, this work aims to uncover how the shape of identities before and three weeks after entry, as well as the identity processes between them, impact adjustment success. This is important because adjustment is a precursor for job outcomes, such as performance, satisfaction, and intentions to remain. In the first part, the relevance of identities for job outcomes is carved out and major theoretical contributions to identity and socialization are identified and presented. The thesis then discusses a fitting methodology for studying identity and describes key methodological choices. Three newcomers participated in the narrative-based interviews. The first interview was conducted shortly before the second interview three weeks after organizational entry. The interviews were audio-recorded, transcribed, and coded employing an abductive coding procedure. The results support the view that identity plays a key role in newcomer socialization and illustrate currently discussed identity processes. The complexity of self-concept phenomena involved in newcomer socialization calls for further research efforts. Keywords: Newcomer socialization; Newcomer adjustment; Self-concept; Possible selves; Identity partnership.The purpose of this study is to investigate the impacts of future social self-concepts on newcomer short-term adjustment. Employing a qualitative longitudinal design based on interviews, this work aims to uncover how the shape of identities before and three weeks after entry, as well as the identity processes between them, impact adjustment success. This is important because adjustment is a precursor for job outcomes, such as performance, satisfaction, and intentions to remain. In the first part, the relevance of identities for job outcomes is carved out and major theoretical contributions to identity and socialization are identified and presented. The thesis then discusses a fitting methodology for studying identity and describes key methodological choices. Three newcomers participated in the narrative-based interviews. The first interview was conducted shortly before the second interview three weeks after organizational entry. The interviews were audio-recorded, transcribed, and coded employing an abductive coding procedure. The results support the view that identity plays a key role in newcomer socialization and illustrate currently discussed identity processes. The complexity of self-concept phenomena involved in newcomer socialization calls for further research efforts. Keywords: Newcomer socialization; Newcomer adjustment; Self-concept; Possible selves; Identity partnership

Human myocardial protein pattern reveals cardiac diseases

Proteomic profiles of myocardial tissue in two different etiologies of heart failure were investigated using high performance liquid chromatography (HPLC)/Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Right atrial appendages from 10 patients with hemodynamically significant isolated aortic valve disease and from 10 patients with isolated symptomatic coronary heart disease were collected during elective cardiac surgery. As presented in an earlier study by our group (Baykut et al., 2006), both disease forms showed clearly different pattern distribution characteristics. Interesting enough, the classification patterns could be used for correctly sorting unknown test samples in their correct categories. However, in order to fully exploit and also validate these findings there is a definite need for unambiguous identification of the differences between different etiologies at molecular level. In this study, samples representative for the aortic valve disease and coronary heart disease were prepared, tryptically digested, and analyzed using an FT-ICR MS that allowed collision-induced dissociation (CID) of selected classifier masses. By using the fragment spectra, proteins were identified by database searches. For comparison and further validation, classifier masses were also fragmented and analyzed using HPLC-/Matrix-assisted laser desorption ionization (MALDI) time-of-flight/time-of-flight (TOF/TOF) mass spectrometry. Desmin and lumican precursor were examples of proteins found in aortic samples at higher abundances than in coronary samples. Similarly, adenylate kinase isoenzyme was found in coronary samples at a higher abundance. The described methodology could also be feasible in search for specific biomarkers in plasma or serum for diagnostic purposes

Erweiterung eines phänomenologischen Lidar-Sensormodells durch identifizierte physikalische Effekte

Die voranschreitende Entwicklung im Bereich des hochautomatisierten Fahrens lässt den Wunsch nach einer simulationsbasierten Entwicklung der Fahrfunktionen und insbesondere deren Sicherheitsüberprüfung entstehen. Dies ist darauf zurückzuführen, dass sich durch eine Simulation beliebige Testszenarien mit geringem Aufwand generieren lassen. Das Testen in der Praxis ist hingegen aufwendiger und zeitintensiver. Auch ist eine Simulation wesentlich wirtschaftlicher als millionen Stunden von Testfahrten im realen Straßenverkehr zu generieren. Für eine möglichst praxisnahe Simulation der Fahrfunktionen sind realitätsnahe Modelle der eingesetzten Sensoren unumgänglich. Zu den für das hochautomatisierte Fahren zur Verfügung stehenden Sensoren gehören unter anderem Lidar-Sensoren. Sie lassen sich beispielsweise zur Umwelterfassung oder Abstandsmessung verwenden. Lidar-Sensoren basieren auf einem optischen Messprinzip, das auf das Aussenden und anschließende Messen der Laufzeit von zurückreflektierten Lichtstrahlen setzt. Ziel dieser Arbeit ist die Erweiterung des am Fachgebiet Fahrzeugtechnik der TU Darmstadt in Entwicklung befindlichen phänomenologischen Modells eines Lidar-Sensors. Dazu werden identifizierte physikalische Effekte in Versuchen parametrisiert und anschließend in das Sensormodell implementiert. Weiterhin erfolgen erste Vergleiche zwischen Modell und Realität. Die zu implementierenden physikalischen Effekte umfassen das Strahlmuster und die Strahlaufweitung von verschiedenen im Automobilbereich genutzen Sensoren. Ferner sind auch weitere sensorspezifische Eigenschaften berücksichtigt. Die untersuchten Sensoren sind der Ibeo Lux 2010, der Velodyne VLP32 und VLP16 und der Valeo Scala. Die Strahlaufweitung und das Strahlmuster sowie die Intensität, als Messgröße der Verlodyne Sensoren, und die Echopulsweite, als Messgröße der Ibeo und Valeo Sensoren, werden mit Hilfe einer Infrarotkamera untersucht und durch Versuche parametrisiert. Als wichtigstes Ergebnis der Versuche lässt sich festhalten, dass die gemessene Form und Größe von Strahlmuster und Strahlaufweitung von den Herstellerangaben abweichen. Dies führt dazu, dass eine Modellbildung auf Basis der Herstellerangaben unter Umständen nicht ausreichend ist. Die untersuchten physikalischen Effekte sind in das am Fachgebiet bestehende Sensormodell auf Basis der Versuchsergebnisse des Ibeo Lux 2010 Sensors integriert. Dazu zählen Strahlaufweitung, Strahlmuster und weitere sensorspezifische Parameter. Auch sind erste Ansätze bzgl. des Signalrauschens, der Signaldämpfung durch den Abstand und der Simulation des Spannungsverlaufs im Empfänger zur Berechnung der Echopulsweite implementiert. Die Ergebnisse der ersten Validierung des erweiterten Sensormodells im Bezug auf die Strahlaufweitung zeigen, dass diese mit steigender Entfernung an Einfluss gewinnt. Das ideale Sensormodell ist somit bei steigendem Abstand zwischen Objekt und Sensor unpräziser als das erweiterte Modell

Enhancing composition control of alloy nanoparticles from gas aggregation source by in operando optical emission spectroscopy

The use of multicomponent targets allows the gas‐phase synthesis of a large variety of alloy nanoparticles (NPs) via gas aggregation sources. However, the redeposition of sputtered material impacts the composition of alloy NPs, as demonstrated here for the case of AgAu alloy NPs. To enable NPs with tailored Au fractions, in operando control over the composition of the NPs is in high demand. We suggest the use of optical emission spectroscopy as a versatile diagnostic tool to determine and control the composition of the NPs. A strong correlation between operating pressure, intensity ratio of Ag and Au emission lines, and the obtained NP compositions is observed. This allows precise in operando control of alloy NP composition obtained from multicomponent targets

In Situ Laser Light Scattering for Temporally and Locally Resolved Studies on Nanoparticle Trapping in a Gas Aggregation Source

Gas phase synthesis of nanoparticles (NPs) via magnetron sputtering in a gas aggregation source (GAS) has become a well-established method since its conceptualization three decades ago. NP formation is commonly described in terms of nucleation, growth, and transport alongside the gas stream. However, the NP formation and transport involve complex non-equilibrium processes, which are still the subject of investigation. The development of in situ investigation techniques such as UV–Vis spectroscopy and small angle X-ray scattering enabled further insights into the dynamic processes inside the GAS and have recently revealed NP trapping at different distances from the magnetron source. The main drawback of these techniques is their limited spatial resolution. To understand the spatio-temporal behavior of NP trapping, an in situ laser light scattering technique is applied in this study. By this approach, silver NPs are made visible inside the GAS with good spatial and temporal resolution. It is found that the argon gas pressure, as well as different gas inlet configurations, have a strong impact on the trapping behavior of NPs inside the GAS. The different gas inlet configurations not only affect the trapping of NPs, but also the size distribution and deposition rate of NPs

Interpretable machine learning for real estate market analysis

Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle

Genetic programming for iterative numerical methods

We introduce GPLS (Genetic Programming for Linear Systems) as a GP system that finds mathematical expressions defining an iteration matrix. Stationary iterative methods use this iteration matrix to solve a system of linear equations numerically. GPLS aims at finding iteration matrices with a low spectral radius and a high sparsity, since these properties ensure a fast error reduction of the numerical solution method and enable the efficient implementation of the methods on parallel computer architectures. We study GPLS for various types of system matrices and find that it easily outperforms classical approaches like the Gauss–Seidel and Jacobi methods. GPLS not only finds iteration matrices for linear systems with a much lower spectral radius, but also iteration matrices for problems where classical approaches fail. Additionally, solutions found by GPLS for small problem instances show also good performance for larger instances of the same problem