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Hybrid Physics-Inspired Machine Learning Framework for Predictive Maintenance of Forklift Chains: Leveraging Sensor Data Characteristics
No full textBridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. The discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks
Fabrication of Lattice-Like Waveguides in Planar Cyclic Olefin Copolymers
No full textThis work demonstrates the femtosecond laser-based fabrication of lattice-like waveguides in planar cyclic olefin copolymers. An overview of the fabrication process is provided and waveguiding in the C-band is proven via optical near-field analysis
How to deal with the negative effects of inflated central bank balance sheets?
No full textDue to the massive purchases of securities in the last 15 years central banks incur substantial losses likely to persist for several years. On the other hand, the banking sector gains large profits from interest payments on their excess reserves holdings. Central banks and fiscal authorities must now bear the flip side consequences of their bond purchase programs. Populist demands to limit bank profits by drastically increasing minimum reserve ratios in the Eurosystem are creating new severe problems. Instead, a consistent and faster
normalisation of central bank balance sheets would be desirable. Central banks should also no longer be central players in government bond markets to restore the lost boundaries between fiscal and monetary policy
Revealing patterns in major depressive disorder with machine learning and networks
No full textMajor depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks
Konflikte und Verhandlungsmanagement im Vertrieb
No full text„Never give without taking“ ist das Verhandlungsmotto von Ludger Schneider-Störmann. Verhandlun-gen in gesättigten Märkten mit hartem Wettbewerb sind konfliktreich. Der Autor beschreibt gängige Konflikttypen und erläutert alle Facetten für ein erfolgreiches Management von Online- und Face-to-Face-Verhandlungen. Zudem gibt er Anleitungen für eine perfekte Vorbereitung und Durchführung. Das Buch richtet sich an Studierende der Betriebswirtschaftslehre, des Wirtschaftsingenieurwesens sowie des Vertriebs und an alle Studierenden mit Interesse am Vertrieb. Es eignet sich auch hervorragend für die Praxis
Rheological Investigation of Highly Filled Copper(II) Oxide Nanosuspensions to Optimize Precursor Particle Content in Reductive Laser-Sintering
No full textIn this article, the particle concentration of finely dispersed copper(II) oxide nanosuspensions as precursors for reductive laser sintering (RLS) is optimized on the basis of rheological investigations. For this metallization process, a smooth, homogeneous and defect-free precursor layer is a prerequisite for adherent and reproducible copper structures. The knowledge of the rheological properties of an ink is crucial for the selection of a suitable coating technology as well as for the adjustment of the ink formulation. Different dilutions of the nanosuspension were examined for their rheological behavior by recording flow curves. A strong shear thinning behavior was found and the viscosity decreases exponentially with increasing dilution. The viscoelastic behavior was investigated by a simulated doctor blade coating process using three-interval thixotropy tests. An overshoot in viscosity is observed, which decreases with increasing thinning of the precursor. As a comparison to these results, doctor blade coating of planar glass and polymer substrates was performed to prepare precursor layers for reductive laser sintering. Surface morphology measurements of the resulting coatings using laser scanning microscopy and rheological tests show that homogeneous precursor layers with constant thickness can be produced at a particle–solvent ratio of 1.33. A too-high particle content results in an irregular coating layer with deep grooves and a peak-to-valley height Sz of up to 7.8 μm. Precise dilution control allows the fabrication of smooth surfaces with a Sz down to 1.5 μm
Overall alkaline water electrolysis over active, stable, low loading iridium catalysts sputtered on nickel foam
No full textMicrostructure of highly effective platinum–iridium alloys as catalysts for hydrogen peroxide decomposition
No full textAdvanced power quality measurement techniques at the electric power grid of astronomical observatories
No full textResilience-oriented management control systems: a systematic review of the relationships between organizational resilience and management control systems
No full textOrganizations regularly face serious challenges due to pandemics, recessions, and financial crises. One reason some organizations cope better than others may be that their management control systems (MCSs) more effectively foster organizational resilience. Despite considerable literature on MCSs and organizational resilience, there is a lack of research on the impact of an MCS’s use on organizational resilience. This study examines and bridges the literatures on MCSs and organizational resilience to illuminate how organizations can better cope with adversity. To identify potential relationships between management controls, MCSs, and organizational resilience, we systematically review the literature and perform a content analysis. We examine the relationships between organizational resilience measures, capabilities, and management controls. We propose the use of resilience-oriented management controls and discuss whether organizations can increase their resilience by building resilience-oriented MCSs. Based on Simons’s levers of control framework and Duchek’s capability-based conceptualization of organizational resilience, we develop a conceptual organizational resilience/MCS framework. Our study reveals relationships and gaps between the literatures on MCSs and organizational resilience and proposes avenues for future research. Our findings suggest that resilience-oriented MCSs are beneficial to organizational resilience