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Institute
In this paper, we evaluate the application of Bayesian Optimization (BO) to discrete event simulation (DES) models. In a first step, we create a simple model, for which we know the optimal set of parameter values in advance. We implement the model in SimPy, a framework for DES written in Python. We then interpret the simulation model as a black box function subject to optimization. We show that it is possible to find the optimal set of parameter values using the open source library GPyOpt. To enhance our evaluation, we create a second and more complex model. To better handle the complexity of the model, and to add a visual component, we build the second model in Simio, a commercial off-the-shelf simulation modeling tool. To apply BO to a model in Simio, we use the Simio API to write an extension for optimization plug-ins. This extension encapsulates the logic of the BO algorithm, which we deployed as a web service in the cloud.
The fact that simulation models are black box functions with regard to their behavior and the influence of their input parameters makes them an apparent candidate for Bayesian Optimization (BO). Simulation models are multivariable and stochastic, and their behavior is to a large extent unpredictable. In particular, we do not know for sure which input parameters to adjust to maximize (or minimize) the model’s outcome. In addition, the complex models can take a substantial amount of time to run.
Bayesian Optimization is a sequential and self-learning algorithm to optimize black box functions similar to as we find them in simulation models: they contain a set of parameters for which we want to identify the optimal set, they are expensive to evaluate, and they exhibit stochastic noise. BO has proven to efficiently optimize black box functions from varius disciplines. Among those, and most notably, it is successfully applied in machine learning algorithms to optimize hyperparameters.
Sustainability Research 2019
(2020)
Seitdem die Vereinten Nationen die Verwirklichung eines weltweiten nachhaltigen Entwicklungsprozesses propagiert haben, hat sich eine umfangreiche Nachhaltigkeitsforschung (Sustainability Research) herausgebildet. Es wird untersucht, wie die drei Komponenten der Nachhaltigkeit, die soziale, die ökologische und die ökonomische Nachhaltigkeit, umgesetzt werden können. Aus der Vielzahl der Forschungsbeiträge zum Thema Nachhaltigkeit wird im vorliegenden Band eine Auswahl präsentiert, die im Einflussbereich der Herausgeber der Schriftenreihe "Lingener Studien zu Management und Technik" bis zum Jahr 2019 entstand.
Fridays Lectures for Future
(2020)
Die Bewegung "Fridays for Future" hat der Umwelt- und der Nachhaltigkeitsdiskussion neuen Schwung gegeben. Schüler demonstrieren für mehr Umweltschutz. Im Mittelpunkt steht die CO2-Reduzierung als Gegenmaßnahme zum weltweit erkennbaren Klimawandel und seinen Folgen. Zur Unterstützung der Diskussionen um verstärkten Umwelt- und Klimaschutz sowie mehr Nachhaltigkeit werden in diesem Band "Fridays Lectures for Future" fünfzehn Lektionen zum Themenbereich Umwelt und Nachhaltigkeit vorgestellt. Es handelt sich um eine Auswahl von Themen, die selbstverständlich nicht das ganze, sehr umfangreiche Problemfeld widerspiegeln können. Es bleibt die Hoffnung, dass die ausgewählten Themenbereiche für den interessierten Leser Ansporn zu einem intensiven Selbststudium sind, um sich weitere Themen zu erschließen.
Events are intangible services and services marketing thus plays a considerable role within event management education. The marketing mix with its “4 Ps” (product, price, promotion, place) is an essential element of many event management curricula. Most educational institutions also reflect the development (and related discussions) towards the existence of “7 Ps” – adding personnel, physical facilities and process management (Meffert/ Bruhn 2009) – or even “8 Ps” – adding physical environment, purchasing process, packaging and participation(Burke/ Resnick 2000) – within the service marketing domain.
Artificial intelligence will change our lives permanently - both at work and in our private lives. But how does machine learning actually work? The authors explore this question in their English-language textbook. They teach the necessary basics for the use of support vector machines, for example, through linear programming, the Lagrange multiplier, kernels and the SMO algorithm. They also cover neural networks, evolutionary algorithms, and Bayesian networks. Definitions are highlighted in the book and assignments invite readers to think along. The textbook is aimed at students of computer science, engineering and natural sciences, especially in the fields of robotics, artificial intelligence, and mathematics.
Als Jane mit ihrer kleinen Schwester tobt, schießt ihr plötzlich ein starker Schmerz in Nacken und Kopf. So weit nicht ungewöhnlich und eine Indikation für Physiotherapie. Doch als die junge Studentin erzählt, dass der Kopfschmerz pulsierend ist, wird ihr Therapeut hellhörig und stellt die entscheidenden Fragen.
Objective
To identify assessment tools used to evaluate patients with temporomandibular disorders (TMD) considered to be clinically most useful by a panel of international experts in TMD physical therapy (PT).
Methods
A Delphi survey method administered to a panel of international experts in TMD PT was conducted over three rounds from October 2017 to June 2018. The initial contact was made by email. Participation was voluntary. An e-survey, according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES), was posted using SurveyMonkey for each round. Percentages of responses were analysed for each question from each round of the Delphi survey administrations.
Results
Twenty-three experts (completion rate: 23/25) completed all three rounds of the survey for three clinical test categories: 1) questionnaires, 2) pain screening tools and 3) physical examination tests. The following was the consensus-based decision regarding the identification of the clinically most useful assessments. (1) Four of 9 questionnaires were identified: Jaw Functional Limitation (JFL-8), Mandibular Function Impairment Questionnaire (MFIQ), Tampa Scale for Kinesiophobia for Temporomandibular disorders (TSK/TMD) and the neck disability index (NDI). (2) Three of 8 identified pain screening tests: visual analog scale (VAS), numeric pain rating scale (NRS) and pain during mandibular movements. (3) Eight of 18 identified physical examination tests: physiological temporomandibular joint (TMJ) movements, trigger point (TrP) palpation of the masticatory muscles, TrP palpation away from the masticatory system, accessory movements, articular palpation, noise detection during movement, manual screening of the cervical spine and the Neck Flexor Muscle Endurance Test.
Conclusion
After three rounds in this Delphi survey, the results of the most used assessment tools by TMD PT experts were established. They proved to be founded on test construct, test psychometric properties (reliability/validity) and expert preference for test clusters. A concordance with the screening tools of the diagnostic criteria of TMD consortium was noted. Findings may be used to guide policymaking purposes and future diagnostic research.