20005 research outputs found
Sort by
PLS-SEM and reflective constructs: a response to recent criticism and a constructive path forward
No full textThis article addresses criticisms asserting that reflective construct measurement and its associated evaluation criteria are unsuitable for partial least squares structural equation modeling (PLS-SEM). More specifically, critics contend that reflective measurement models correspond exclusively to common factor models, a premise that is both inaccurate and misleading. Reflective measurement models represent theoretically grounded and conceptualized constructs. Statistical methods such as common factor model estimation, composite model estimation, and sum score regression enable researchers to estimate method-specific proxies that serve as approximations for theoretically established conceptual constructs in empirical research. These proxies vary depending on the statistical models and assumptions inherent to each method. In this context, it is important to highlight that the use of reflective evaluation criteria is not restricted to common factor models. When applied to composite model estimation, it does not compromise the validity of the results. Moreover, this article advocates for embracing the complementary strengths of diverse SEM methods within a multimethod approach, rather than positioning one method in opposition to another. We believe that this contribution provides critical insights and guidance, fostering advancements in SEM methodology, and its practical applications
MBSE framework for developing data-driven passenger services in aircraft cabins
No full textThe aircraft cabin plays a crucial role in airline differentiation strategies, particularly when introducing novel, data-driven services. These services aim to enhance the passenger experience during the flight and to improve cabin crew efficiency in order to reduce workload and ensure continued growth of airline revenue. Digitalization and extensive exchange of information across the entire aircraft transport system have emerged as key enablers for these services. The development of aircraft and aircraft systems that realize these services is characterized by a multi-level development process. Various development levels are considered to initially identify the functions of an aircraft in the air transport system, refine its systems and break them down into their components until a level of detail is reached that allows the implementation of the component functions. In addition to the high complexity, a major challenge in this development is to ensure traceability and consistency across the various development levels. Consequently, Model-Based Systems Engineering (MBSE) is increasingly applied in aviation to address these challenges. While numerous MBSE frameworks and methodologies exist, they often overlook the specific requirements of aviation's multi-level development process. Hence, this paper introduces an MBSE framework tailored to the development of novel, data-driven passenger services, along with the corresponding aircraft systems, across multiple development levels. The framework ensures seamless model-based information flow across all levels by providing a development workflow, which encompasses various viewpoints at each development stage and incorporates aviation-specific regulatory and operational considerations. Additionally, given the digitalized nature of these services and the resulting interconnected systems, relevant cybersecurity information is captured within certain viewpoints at each development level, thereby ensuring the development of secure systems
Metallic bipolar plate production through additive manufacturing: contrasting MEX/M and PBF-LB/M approaches
No full textAdditive manufacturing (AM) technologies have witnessed remarkable advancements, offering opportunities to produce complex components across various industries. This paper explores the potential of AM for fabricating bipolar plates (BPPs) in fuel cell or electrolysis cell applications. BPPs play a critical role in the performance and efficiency of such cells, and conventional manufacturing methods often face limitations, particularly concerning the complexity and customization of geometries. The focus here lies in two specific AM methods: the laser powder bed fusion of metals (PBF-LB/M) and material extrusion of metals (MEX/M). PBF-LB/M, tailored for high-performance applications, enables the creation of highly complex geometries, albeit at increased costs. On the other hand, MEX/M excels in rapid prototyping, facilitating the swift production of diverse geometries for real-world testing. This approach can facilitate the evaluation of geometries suitable for mass production via sinter-based manufacturing processes. The geometric deviations of different BPPs were identified by evaluating 3D scans. The PBF-LB/M method is more suitable for small features, while the MEX/M method has lower deviations for geometrically less complex BPPs. Through this investigation, the limits of the capabilities of these AM methods became clear, knowledge that can potentially enhance the design and production of BPPs, revolutionizing the energy conversion and storage landscape and contributing to the design of additive manufacturing technologies
Vintage antennas: a 3D-printed future
No full textThis paper presents the "reflector-horn array,"a novel configuration of the classical horn-reflector antenna developed for the 2024 Student Design Competition on "mmWave Dual-Band 3D-Printed Antenna Design."The proposed array is composed of two truncated reflector antennas. The antenna is 3D-printed and silver-coated. It achieves a 54.9% height reduction compared to the traditional design, with an average gain of 27.35 dB and a mean aperture efficiency of 69.28% over a 40.6% bandwidth, which was validated by the measurements. Compared to the classical reflector antenna, the only drawback is a 10.55 dB increase in the side lobe level
OMIBONE: Omics-driven computer model of bone regeneration for personalized treatment
No full textTreatment of bone fractures are standardized according to the AO classification, which mainly refers to the mechanical stabilization required in a given situation but neglect individual differences due to patient's healing potential or accompanying diseases. Specially in elderly or immune-compromised patients, the complexity of individual constrains on a biological as well as mechanical level are hard to account for. Here, we introduce a novel framework that allows to predict bone regeneration outcome using combined proteomic and mechanical analyses in a computer model. The framework uses Ingenuity Pathway Analysis (IPA) software to link protein changes to alterations in biological processes and integrates these in an Agent-Based Model (ABM) of bone regeneration. This combined framework allows to predict bone formation and the potential of an individual to heal a given fracture setting. The performance of the framework was evaluated by replicating the experimental setup of a mouse femur fracture stabilized with an intramedullary pin. The model was informed by serum derived proteomics data. The tissue formation patterns were compared against experimental data based on x-ray and histology images. The results indicate the framework potential in predicting an individual's bone formation potential and hold promise as a concept to enable personalized bone healing predictions for a chosen fracture fixation
Why less is sometimes more: using Boolean literals to solve 2048
No full textExplaining and understanding AI-generated policies of control problems is crucial for the acceptance of such policies. Based on an optimisation challenge that took place at GECCO 2024, we describe different solutions for optimising generated policies for the well-known game 2048. At the same time, these generated policies aim to be simpler to understand and, thus, their decisions explainable in contrast to current solutions for 2048, as, e.g., neural network-based models. Our approach uses only Boolean expressions in the policy, and the optimisation shows that such a policy has advantages compared to more complex policy variants. The optimisation generates better results in a shorter time than for non-Boolean expressions. Additionally, the Boolean policies are smaller in size and can be reduced even more when applying existing techniques for term rewriting and simplification. These simplifications, again, may aid in understanding the policy's decision
Lineare Regressionsmodelle
No full textIn kartellrechtlichen Fragestellungen werden regelmäßig ökonomische Analysen herangezogen. Sofern dafür Datenanalysen eingesetzt werden, geschieht dies mit ökonometrischen Methoden und in vielen Fällen mit einem Regressionsmodell. Das Ziel eines Regressionsmodells ist es, eine oder mehrere endogene (erklärte bzw. abhängige) Variablen durch eine oder – in der Regel – mehrere exogene (erklärende bzw. unabhängige) Variablen zu erklären. Weit verbreitet sind lineare Regressionsmodelle, deren Parameter mit der gewöhnlichen Methode der kleinsten Quadrate (Ordinary Least Squares, OLS) empirisch geschätzt werden. Sofern ein anderes Modell verwendet wird, weist dessen Form oft Gemeinsamkeiten mit einem linearen Regressionsmodell auf. Deshalb sind grundlegende Kenntnisse des einfachen bzw. multiplen linearen Regressionsmodells auch zum Verständnis anderer Regressionsmodelle hilfreich. Der Beitrag erläutert die Funktionsweise von linearen Regressionsmodellen und einige damit verbundene Begriffe. Dafür wird zunächst das einfache lineare Regressionsmodell erläutert und anschließend das multiple lineare Regressionsmodell. Danach werden einzelne wesentliche Aspekte für die Verlässlichkeit der gewonnenen Ergebnisse skizziert
A quasi-polynomial time algorithm for multi-arrival on tree-like multigraphs
No full textPropp machines, or rotor-router models, are a classic tool to simulate random systems in forms of Markov chains by deterministic systems. To this end, the nodes of the Markov chain are replaced by switching nodes, which maintain a queue over their outgoing arcs, and a particle sent through the system traverses the top arc of the queue which is then moved to the end of the queue and the particle arrives at the next node. A key question to answer about such systems is whether a single particle can reach a particular target node, given as input an initial configuration of the queues at all switching nodes. This question was introduced by Dohrau et al. (2017) under the name of Arrival.
A major open question is whether Arrival can be solved in polynomial time, as it is known to lie in NP ∩co-NP; yet the fastest known algorithm for general instances takes subexponential time (Gärtner et al., ICALP 2021).
We consider a generalized version of Arrival introduced by Auger et al. (RP 2023), which requires routing multiple (potentially exponentially many) particles through a rotor graph. The Multi-Arrival problem is to determine the particle configuration that results from moving all particles from a given initial configuration to sinks. Auger et al. showed that for path-like rotor graphs with a certain uniform rotor order, the problem can be solved in polynomial time.
Our main result is a quasi-polynomial-time algorithm for Multi-Arrival on tree-like rotor graphs for arbitrary rotor orders. Tree-like rotor graphs are directed multigraphs which can be obtained from undirected trees by replacing each edge by an arbitrary number of arcs in either or both directions. For trees of bounded contracted height, such as paths, the algorithm runs in polynomial time and thereby generalizes the result by Auger et al.. Moreover, we give a polynomial-time algorithm for Multi-Arrival on tree-like rotor graphs without parallel arcs
Fast perfekt: regression-based refinement of fast simulation
No full textThe availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression to refine the output of fast simulation that employs residual neural networks. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of existing domain knowledge, and introduces minimal computational overhead to production