3,065 research outputs found
Strategy Tripod Perspective on the Determinants of Airline Efficiency in A Global Context: An Application of DEA and Tobit Analysis
The airline industry is vital to contemporary civilization since it is a key player in the globalization process: linking regions, fostering global commerce, promoting tourism and aiding economic and social progress. However, there has been little study on the link between the operational environment and airline efficiency. Investigating the amalgamation of institutions, organisations and strategic decisions is critical to understanding how airlines operate efficiently.
This research aims to employ the strategy tripod perspective to investigate the efficiency of a global airline sample using a non-parametric linear programming method (data envelopment analysis [DEA]). Using a Tobit regression, the bootstrapped DEA efficiency change scores are further regressed to determine the drivers of efficiency. The strategy tripod is employed to assess the impact of institutions, industry and resources on airline efficiency. Institutions are measured by global indices of destination attractiveness; industry, including competition, jet fuel and business model; and finally, resources, such as the number of full-time employees, alliances, ownership and connectivity. The first part of the study uses panel data from 35 major airlines, collected from their annual reports for the period 2011 to 2018, and country attractiveness indices from global indicators. The second part of the research involves a qualitative data collection approach and semi-structured interviews with experts in the field to evaluate the impact of COVID-19 on the first part’s significant findings.
The main findings reveal that airlines operate at a highly competitive level regardless of their competition intensity or origin. Furthermore, the unpredictability of the environment complicates airline operations. The efficiency drivers of an airline are partially determined by its type of business model, its degree of cooperation and how fuel cost is managed. Trade openness has a negative influence on airline efficiency. COVID-19 has toppled the airline industry, forcing airlines to reconsider their business model and continuously increase cooperation. Human resources, sustainability and alternative fuel sources are critical to airline survival. Finally, this study provides some evidence for the practicality of the strategy tripod and hints at the need for a broader approach in the study of international strategies
Direct measurement of coating thermal noise in the AEI 10m prototype
A thermal noise interferometer for the characterization of thermal noise in high reflectivity mirrors has been commissioned and first direct measurements of coating thermal noise have been performed. This serves as an important step in the improvement of current and future gravitational wave detectors
Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics
It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been
emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations
Parasitic extraction of a power management integrated circuit PCB
Abstract. In this master’s thesis parasitic extraction of a power management integrated circuit was established and evaluated using Ansys Q3D. From PCB the S21 parameter was extracted between two nodes from output and input to efficiently show the parasitic properties of the PCB. Extraction was done over frequencies from 100 kHz to 100 MHz. This was done using multiple different settings for the extraction to find out the optimal settings in terms of accuracy and time to solution.
An evaluation module PCB was designed for the power management integrated circuit using Altium. In this design the best practices for PCB layout design were utilized to get the performance as good as possible. Some of the PCB design choices were evaluated with Ansys Q3D to make an informed decision of the better design choice.
A measurement setup was established and validated by using a known component to ensure the setup is working as expected. The PCB was measured without components except the ones needed for the experiment. Measurements were taken with S21 shunt-through method with spectrum analyser with built-in network option, external vector signal generator and external pre-amplifier to get more dynamic range.
The output and input were evaluated with and without a capacitor to get a broader understanding of the modelling accuracy. A case with two capacitors was tested. These models were compared with a measurement result to evaluate the accuracy of the tools and methods. It was noticed that with simple geometries the different extraction options do not significantly affect the extraction accuracy. At the same time, the time to solution varies greatly which leads to the use of the simpler extraction settings to save time. When comparing the simulation with measurement the best average error was 3.3 % and the worst 34.3 %. The simulations matched the measurements best when a capacitor was placed and worst with open termination with no components. The model accuracies obtained in this thesis reflect what has been seen in previous studies in terms of frequency range and deviation from measured results.Parasiittisten ominaisuuksien ekstraktointi tehonhallinta piirilevyltä. Tiivistelmä. Tässä diplomityössä parasiittisten ominaisuuksien ekstraktointityövaihe luotiin, sekä sen suorituskyky arvioitiin käyttäen Ansys Q3D ohjelmaa. Piirilevyltä ekstraktoitiin S21 parametri kahden solmun väliltä tulo- ja lähtöpuolelta käyttäen 100 kHz–100 MHz taajuusaluetta. Tällä tavoin saatiin tehokkaasti esitettyä piirilevyn parasiittisten ominaisuuksien muodostama impedanssi. Tämä tehtiin käyttäen useita eri asetuksia, joita on saatavilla ohjelmistossa. Nämä asetukset vaikuttavat eri tavoilla ekstraktoinnin tarkkuuteen. Näitä tuloksia vertailemalla löydettiin tarkkuuden ja simulointiajan suhteen optimaaliset asetukset, joilla tehdä ekstraktointi.
Työtä varten suunniteltiin piirilevy tehonhallinta integroidulle piirille käyttäen Altium ohjelmaa. Tässä suunnittelussa käytettiin hyviä käytänteitä, jotta piirilevyn suorituskyvystä saataisiin mahdollisimman hyvä. Jotkin suunnitteluvalinnoista perustuvat Q3D:llä saatuihin tuloksiin, jotta voitiin valita useista vaihtoehdoista paras.
Mittauksia varten suunniteltiin ja toteutettiin mittausjärjestelmä, jonka toiminta varmennettiin mittaamalla tunnetun komponentin impedanssi ja vertaamalla sitä valmistajan antamaan dataan. Valmistetulta piirilevyltä mitattiin käyttäen vain niitä komponentteja, jotka olivat merkittäviä tutkimukselle. Mittaukset tehtiin käyttäen S21 shunt-through menetelmää käyttämällä spektrianalysaattoria, jossa on sisäänrakennettu verkkoanalysointioptio. Tämän kanssa käytettiin ulkoista vektorisignaaligeneraattoria ja ulkoista esivahvistinta, jotta saataisiin enemmän dynaamista aluetta.
Vertailuun valittiin piirin ulos- ja sisääntuloverkot kondensaattorilla ja ilman, jotta saataisiin laajempi käsitys mallinnuksen tarkkuudesta. Myös kahden kondensaattorin tapaus käsiteltiin. Näitä mallinnuksella saatuja tuloksia verrattiin mittaamalla saatuihin tuloksiin. Työssä huomattiin, että tässä sovelluksessa, jossa on yksinkertaisia geometrioita, eri ekstraktointi vaihtoehdot eivät vaikuttaneet tarkkuuteen huomattavasti. Ekstraktointiin kulunut aika vaihteli huomattavasti joidenkin vaihtoehtojen välillä, jonka takia valittiin yksinkertaisempi mallinnustapa, jotta säästettäisiin aikaa. Verrattaessa simuloituja ja mitattuja tuloksia, huomattiin että paras keskiarvoinen virhe oli 3,3 % ja huonoin 34,3 %. Simuloinnit vastasivat mittauksia parhaiten, kun tarkasteltiin tapauksia, joissa oli käytössä yksi kondensaattori ja huonoin, kun käytettiin avointa terminointia. Tässä työssä saadut tulokset vastaavat hyvin aikaisemmissa tutkimuksissa saatuja tuloksia sekä taajuusalueen puolesta, että eron mittauksen ja simuloinnin välillä
2017 GREAT Day Program
SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation
Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors.
To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach.
A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk.
The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D
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