No description available.

No description available.

No description available.

aus dem Inhalt: „Im Vergleich zur automatisierten Fertigung anderer industrieller Branchen ist das Bauen mit Beton noch immer stark durch handwerkliche Tätigkeiten geprägt. Dazu kommt, dass die endgültigen Eigenschaften von Betonbauteilen stark von den Umgebungsbedingungen während der Herstellung abhängig sind. Dieser Zustand führt zu Ungenauigkeiten und Unsicherheiten bei der Bauwerkserstellung, welche in einem wenig effizienten Materialeinsatz und Störungen im Bauprozess resultieren. Lange Bauzeiten und Wartezeiten bei Störungen sind die Folgen....” / from the content: „Compared to the automated production of other industrial sectors, construction with concrete is still strongly characterized by craftsmanship. In addition, the final properties of concrete components are strongly dependent on the environmental conditions during production. This leads to inaccuracies and uncertainties during construction, which result in inefficient use of materials and disruptions in the construction process. Long construction times and waiting periods in the case of disruptions are the consequences....”

No description available.

In the dynamic landscape of global commerce, the business-to-business (B2B) sector stands out for its significance and changing nature. Traditional values used to evaluate a supplier, such as price and quality, are becoming accompanied by "soft" values, such as cultural and social fit. This change makes the B2B market more intricate and illuminates a fundamental change in how businesses and customers evaluate their relationships. Another factor straining the relationship between companies and customers is prolonged lead times, i.e., when the lead time is longer than usual. Prolonged lead times are an imperative problem for markets and companies, heavily affecting customer retention and relationships, and can result in a loss of business and monetary punishments. Given the changes in how businesses and customers evaluate B2B relationships and the effects of prolonged lead times, companies must evaluate and develop existing customer retention strategies to meet the changing demands. Thus, this thesis explores how B2B companies with long lead times can manage and develop their customer relationships to increase customer retention and decrease customer churn in the long term.  Given the comprehensive scope of the thesis, the literature review assumes a pivotal role. It provides the foundation for answering the purpose and research questions, including relationship marketing, organizational buying behavior, switching behavior, and customer success management. In addition to the literature review, 19 semi-structured interviews were conducted to collect primary data. The primary data was later analyzed through thematic analysis to assist answering the purpose and research questions. A descriptive purpose and deductive and qualitative approach characterize the thesis.  The thesis reached the following conclusions concerning factors affecting the willingness to remain in a long-term B2B relationship: Delivery and track record are standard evaluation criteria for customers evaluating a supplier, heavily affecting the willingness to remain in a long-term B2B relationship. Relationship value dependence is critical in the willingness to remain in a long-term B2B relationship. Relationship development affects commitment, trust, communication, transparency, and customer satisfaction, which are critical factors. Non-task dimensions, such as the degree of innovation and values, are becoming more prevalent concerns for customers evaluating their suppliers. Value in Use monitoring and Value in Use enhancement measures, i.e., value measurements during and after a process, affect the willingness to remain in a long-term B2B relationship. Co-creation is increasingly prioritized and positively affects the willingness to remain in a long-term B2B relationship. / I globala handelns dynamiska landskap sticker B2B-marknaden ut för sin betydelse och förändrande natur. Traditionella värderingar använda för att utvärdera en leverantör, till exempel pris och kvalitet, blir numera en del av en större utvärdering, där mjuka värderingar, till exempel kulturell och social lämplighet, blir allt vanligare. Dessa förändringar gör B2B-marknaden mer invecklad och belyser den fundamentala förändringen i hur företag och kunder utvärderar sin relation. En annan faktor som påverkar förhållandet mellan företag och kunder är förlängda ledtider, det vill säga när ledtiden är längre än vanligt. Förlängda ledtider är ett signifikant problem för marknadsförare och företag som påverkar kundretention och förhållandet, samt kan medföra risken att förlora en affär eller monetära bestraffningar.  Givet förändringarna i hur företag och kunder utvärderar sitt förhållande samt effekterna från förlängda ledtider måste företag utvärdera och utveckla sina befintliga kundretentionsstrategier för att möta kundernas förändrande krav. Således undersöker denna masteruppsats hur B2B-företag med förlängda ledtider kan hantera och utveckla sina kundrelationer för att öka kundretention och minska kundomsättning på lång sikt.  Givet studiens omfattning ges litteraturstudien en betydande roll. Litteraturstudien är basen för besvarandet av syftet och forskningsfrågorna, och inkluderar relationsmarknadsföring, organisatoriskt köpbeteende, bytesbeteende och customer success management. Utöver litteraturstudien genomfördes 19 semi-strukturerade intervjuer för att samla in primärdata, som sedan analyserades med hjälp av tematisk analys för att underlätta besvarandet av syftet och forskningsfrågorna. Uppsatsen karaktäriseras av ett deskriptivt syfte med deduktiv och kvalitativ ansats.   Studien resulterade i följande slutsatser avseende vilka faktorer som påverkar benägenheten att stanna i en långsiktigt B2B-relation: Leverans och historisk framgång är vanligt förekommande utvärderingskriterier för kunder som utvärderar en leverantör med stor påverkan på benägenheten att stanna i en långsiktigt B2B-relation. Beroendet av värdet från förhållandet är kritiskt när det kommer till benägenheten att stanna i en långsiktigt B2B-relation. Utvecklingen av relationen påverkar de kritiska faktorerna engagemang, tillit, kommunikation, transparens och kundnöjdhet. Olika roller inom köpprocessen är unika och mötesdeltagare bör anpassas. Non-task dimensioner, till exempel grad av innovation och värderingar blir alltmer vanligt förekommande kriterier när kunder utvärderar sina leverantörer. Value in Use monitoring och Value in Use enhancement, det vill säga mätningar under och efter en process, påverkar benägenheten att stanna i en långsiktigt B2B-relation. Samskapande blir alltmer prioriterat och påverkar benägenheten att stanna i en långsiktigt B2B-relation positivt.

The calculation of the electron–phonon coupling from first principles is computationally very challenging and remains mostly out of reach for systems with a large number of atoms. Semi-empirical methods, like density functional tight binding (DFTB), provide a framework for obtaining quantitative results at moderate computational costs. Herein, we present a new method based on the DFTB approach for computing electron–phonon couplings and relaxation times. It interfaces with PHONOPY for vibrational modes and DFTB+ to calculate transport properties. We derive the electron–phonon coupling within a non-orthogonal tight-binding framework and apply them to graphene as a test case.

Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

As man arrived, so he will leave: in solitude. In between, through necessity or desire, he associates with others. Yet he resides as he is, alone with his soul. Inseparable yet distinct. He can neglect the soul, allow it to become obscured through the noise of others. It will wither, but not die. He can nourish the soul; grant it all his attention and obscure the man. The man will wither and die. Man and soul can coexist and flourish through the association of mankind. Architecture should celebrate this coexistence. / Master of Architecture

This dissertation focuses on projects where optical techniques were employed to characterize novel materials, developing concepts toward next generation of devices. The materials that I studied included InAsP, InMnSb and InMnAs, and BT-BCN. I have employed several advanced time resolved and magneto-optical techniques to explore unexplored properties of these structures. The first class of the materials were the ternary alloys InAsP. The electron g-factor of InAsP can be tuned, even allowing for g=0, making InAsP an ideal candidate for quantum communication devices. Furthermore, InAsP shows promises for opto-electronics and spintronics, where the development of devices requires extensive knowledge of carrier and spin dynamics. Thus, I have performed time and polarization resolved pump-probe spectroscopy on InAsP with various compositions. The carrier and spin relaxation time in these structures were observed and demonstrated tunability to the excitation wavelengths, composition and temperature. The sensitivity to these parameters provide several avenues to control carrier and spin dynamics in InAsP alloys. The second project focused on the ferromagnetic narrow gap semiconductors InMnAs and InMnSb. The incorporation of Mn can lead to ferromagnetic behavior of InMnAs and InMnSb, and enhance the g-factors, making them ideal candidates for spintronics devices. When grown using Molecular Beam Epitaxy (MBE), the Curie temperature (textit{$T_c$}) of these structures is textless 100 K, however structures grown using Metalorganic Vapor phase Epitaxy (MOVPE) have textit{$T_c$} textgreater 300 K. Magnetic circular dichroism was performed on MOVPE grown InMnAs and InMnSb. Comparison of the experimental results with the theoretical calculations provides a direct method to map the band structure, including the temperature dependence of the spin-orbit split-off band to conduction band transition and g-factors, as well as the estimated sp-d electron/hole coupling parameters. My final project was on the lead-free ferroelectric BT-BCN. Ferroelectric materials are being investigated for high speed, density, nonvolatile and energy efficient memory devices; however, commercial ferroelectric memories typically contain lead, and use a destructive reading method. Reflectometry measurements were used in order to determine the refractive index of BT-BCN with varying thicknesses, which can provide a means to nondestructively read ferroelectric memory through optical methods. / Ph. D.