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Characterization of brain tumours requires neuropathological expertise and is generally performed by histological evaluation and molecular analysis. One emerging technique to assist pathologists in future tumour diagnostics is multimodal optical spectroscopy. In the current clinical routine, tissue preprocessing with formalin is widely established and suitable for spectroscopic investigations since degradation processes impede the measurement of native tissue. However, formalin fixation results in alterations of the tissue chemistry and morphology for example by protein cross-linking. As optical spectroscopy is sensitive to these variations, we evaluate the effects of formalin fixation on multimodal brain tumour data in this proof-of-concept study. Nonfixed and formalin-fixed cross sections of different common human brain tumours were subjected to analysis of chemical variations using ultraviolet and Fourier-transform infrared microspectroscopy. Morphological changes were assessed by elastic light scattering microspectroscopy in the visible wavelength range. Data were analysed with multivariate data analysis and compared with histopathology. Tissue type classifications deduced by optical spectroscopy are highly comparable and independent from the preparation and the fixation protocol. However, formalin fixation leads to slightly better classification models due to improved stability of the tissue. As a consequence, spectroscopic methods represent an appropriate additional contrast for chemical and morphological information in neuropathological diagnosis and should be investigated to a greater extent. Furthermore, they can be included in the clinical workflow even after formalin fixation.
Energy efficiency optimization techniques for steady state operation of induction machines are the state-of-the-art, and many methods have already been developed. However, many real-world industrial and electric vehicle applications cannot be considered to be in steady state operation. The focus of this contribution is on the efficiency optimization of induction machines in dynamic operation. Online dynamic operation is challenging due to the computational complexity and the required low sample times in an inverter. An offline optimization is therefore conducted to gain knowledge. Based on this offline optimal solution, a simple and easy to implement template based solution is developed. This approach aims at replicating the solution found by the offline optimization by resembling the shape and anticipative characteristics of the optimal flux trajectory. The energy efficiency improvement of the template based solution is verified by simulations and measurements on a test bench and using a real-world drive cycle scenario. For comparison, a model predictive numerical online optimization is investigated too.
Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train The CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labelled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
Bio-Gütesiegel im B-to-B-Marketing – Teil 2/2: Bio ist bereits seit Langem kein Nischenprodukt mehr. Deutschland hat europaweit nicht nur den höchsten Verbrauch, sondern gleichzeitig auch den höchsten Umsatz an Bio-Produkten. Etwa jeder Vierte kauft hierzulande regelmäßig Bio-Lebensmittel. Damit haben es ökologische Produkte bereits jetzt in beinahe jeden deutschen Haushalt geschafft. Basierend auf der Relevanz von ökologischen Produkten im Markt, ergibt sich ein gesteigerter Fokus auf die Beziehungen zwischen Lebensmittelherstellern und Lebensmittelhändlern, der in den letzten Jahren angezogen ist. Unternehmerische Anforderungen konzentrieren sich daher mehr als je zuvor neben dem üblichen B-to-C-Marketing auf das B-to-B-Marketing. Mit den neuen Marktherausforderungen steigen somit die Erwartungen an die Marketingleistungen im B-to-B-Bereich.
Bio-Gütesiegel im B-to-B-Marketing – Teil 1/2: Bio ist bereits seit Langem kein Nischenprodukt mehr. Deutschland hat europaweit nicht nur den höchsten Verbrauch, sondern gleichzeitig auch den höchsten Umsatz an Bio-Produkten. Etwa jeder Vierte kauft hierzulande regelmäßig Bio-Lebensmittel. Damit haben es ökologische Produkte bereits jetzt in beinahe jeden deutschen Haushalt geschafft. Basierend auf der Relevanz von ökologischen Produkten im Markt, ergibt sich ein gesteigerter Fokus auf die Beziehungen zwischen Lebensmittelherstellern und Lebensmittelhändlern, der in den letzten Jahren angezogen ist. Unternehmerische Anforderungen konzentrieren sich daher mehr als je zuvor neben dem üblichen B-to-C-Marketing auf das B-to-B-Marketing. Mit den neuen Marktherausforderungen steigen somit die Erwartungen an die Marketingleistungen im B-to-B-Bereich.
Beyond Selling orientiert sich am komplexen, vornehmlich mittelständisch geprägten Multi-Kanal-Vertrieb. Beyond Selling hat den Anspruch, holistisch zu agieren und will aufzeigen, dass neben vielen bewährten Konzepten bestimmte Aspekte für die erfolgreiche Unternehmensführung zukünftig zunehmend wichtiger werden. So bleiben bspw. eine stringente Buying-Center-Analyse und auch eine treffsichere Formulierung des kundennutzenorientierten Leistungsversprechens im Rahmen des Value-Based-Selling-Konzeptes unabdingbar. Allerdings gilt es auch, den Fokus auf Themen zu legen, die zukünftig deutlich an Bedeutung gewinnen werden
This paper intends to give an insight on how to develop a customer loyalty-focused gamification concept, that will trigger intrinsic motivation and hence strengthen customer loyalty, using the mobility industry as an example. The authors conducted explorative expert interviews to create a cross-industry process chart that guides the generic development of a customer loyalty-focused gamification concept.
Initial Coin Offering (ICO) und damit verbundene Token spielen bei der Unternehmensfinanzierung eine immer bedeutsamere Rolle. Dies gilt insbesondere im Fall von Start-ups, deren Geschäftsmodell auf der Blockchain-Technologie basiert. Dieser Beitrag stellt die verschiedenen Tokenvarianten im Rahmen eines ICO vor und gibt einen Überblick über den aktuellen rechtlichen Hintergrund.
Der hohe Stellenwert von Energiedienstleistungen steht für Energieversorger außer Zweifel. Der Handlungsbedarf bleibt aber nach wie vor immens, erforderliche Change-Prozesse werden erst in wenigen Fällen aktiv und gezielt gestaltet. Dies sind die zentralen Ergebnisse einer gemeinsamen empirischen Studie vom "Reutlinger Energiezentrum für Dezentrale Energiesysteme und Energieeffizienz (REZ) an der Hochschule Reutlingen und der international tätigen Unternehmensberatung kwp consulting group.
This paper explores why and how dominant international social standards used in the fashion industry are prone to implementation failures. A qualitative multiple-case study method was conducted, using purposive sampling to select 13 apparel supply chain actors. Data were collected through on-site semi-structured face-to-face interviews. The findings of the study are interpreted by using core tenets of agency theory. The case study findings clearly highlight why and how multi-tier apparel supply chains fail to implement social standards effectively. As a consequence of substantial goal conflicts and information asymmetries, sourcing agents and suppliers are driven to perform opportunistic behaviors in form of hidden characteristics, hidden intentions, and hidden actions, which significantly harm social standards. Fashion retailers need to empower their corporate social responsibility (CSR) departments by awarding an integrative role to sourcing decisions. Moreover, accurate calculation of orders, risk sharing, cost sharing, price premiums, and especially guaranteed order continuity for social compliance are critical to reduce opportunistic behaviors upstream of the supply chain. The development of social standards is highly suggested, e.g., by including novel metrics such as the assessment of buying practices or the evaluation of capacity planning at factories and the strict inclusion of subcontractors’ social performances. This paper presents evidence from multiple Vietnamese and Indonesian cases involving sourcing agents as well as Tier 1 and Tier 2 suppliers on a highly sensitive topic. With the development of the conceptual framework and the formulation of seven related novel propositions, this paper unveils the ineffectiveness of social standards, offers guidance for practitioners, and contributes to the neglected social dimension in sustainable supply chain management research and accountability literature.