A Survey of the State of Research on Augmented Reality from a Business Perspective using Porter’s Value Chain

In recent years, augmented reality (AR) technology has been able to demonstrate more and more impressively the potential it brings for companies and their valueadding activities, and this even though acceptance of the technology in society is only just beginning. Due to this, our work aims to bring a comprehensive overview of AR deployment opportunities based on the value chain, forcing a symbiosis of potential demonstration and acceptance promotion. For our investigation, we consider the most important peer-reviewed papers on the state of research on augmented reality from a business perspective and provide a comprehensive overview of the different possible uses of AR within a company, structured according to Porter’s value chain, as well as an outlook on future research on the expansion and further development of AR systems. Based on this, we formulate research gaps for future work on AR in the context presented

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Image enhancement as a problem-oriented process of optimizing visual appearances to provide easier-toprocess input to automated image processing techniques is an area that will consistently be a companion to computer vision despite advances in image acquisition and its relevance continues to grow. For our systematic literature review, we consider the major peer-reviewed journals and conference papers on the state of the art in machine learning-based computer vision approaches for image enhancement. We describe the image enhancement methods relevant to our work and introduce the machine learning models used. We then provide a comprehensive overview of the different application areas and formulate research gaps for future scientific work on machine learning based computer vision approaches for image enhancement based on our result

Material Classification with a Transfer Learning based Deep Model on an imbalanced Dataset using an epochal Deming-Cycle-Methodology

This work demonstrates that a transfer learning-based deep learning model can perform unambiguous classification based on microscopic images of material surfaces with a high degree of accuracy. A transfer learning-enhanced deep learning model was successfully used in combination with an innovative approach for eliminating noisy data based on automatic selection using pixel sum values, which was refined over different epochs to develop and evaluate an effective model for classifying microscopy images. The deep learning model evaluated achieved 91.54% accuracy with the dataset used and set new standards with the method applied. In addition, care was taken to achieve a balance between accuracy and robustness with respect to the model. Based on this scientific report, a means of identifying microscopy images could evolve to support material identification, suggesting a potential application in the domain of materials science and engineering

Surgical and Oncological Outcomes After Preoperative FOLFIRINOX Chemotherapy in Resected Pancreatic Cancer : An International Multicenter Cohort Study

Background. Preoperative FOLFIRINOX chemotherapy is increasingly administered to patients with borderline resectable (BRPC) and locally advanced pancreatic cancer (LAPC) to improve overall survival (OS). Multicenter studies reporting on the impact from the number of preoperative cycles and the use of adjuvant chemotherapy in relation to outcomes in this setting are lacking. This study aimed to assess the outcome of pancreatectomy after preoperative FOLFIRINOX, including predictors of OS.Methods. This international multicenter retrospective cohort study included patients from 31 centers in 19 European countries and the United States undergoing pancreatectomy after preoperative FOLFIRINOX chemotherapy (2012-2016). The primary end point was OS from diagnosis. Survival was assessed using Kaplan-Meier analysis and Cox regression.Results. The study included 423 patients who underwent pancreatectomy after a median of six (IQR 5-8) preoperative cycles of FOLFIRINOX. Postoperative major morbidity occurred for 88 (20.8%) patients and 90-day mortality for 12 (2.8%) patients. An R0 resection was achieved for 243 (57.4%) patients, and 259 (61.2%) patients received adjuvant chemotherapy. The median OS was 38 months (95% confidence interval [CI] 34-42 months) for BRPC and 33 months (95% CI 27-45 months) for LAPC. Overall survival was significantly associated with R0 resection (hazard ratio [HR] 1.63; 95% CI 1.20-2.20) and tumor differentiation (HR 1.43; 95% CI 1.08-1.91). Neither the number of preoperative chemotherapy cycles nor the use adjuvant chemotherapy was associated with OS.Conclusions. This international multicenter study found that pancreatectomy after FOLFIRINOX chemotherapy is associated with favorable outcomes for patients with BRPC and those with LAPC. Future studies should confirm that the number of neoadjuvant cycles and the use adjuvant chemotherapy have no relation to OS after resection.Peer reviewe

A Fundamental Overview of SOTA-Ensemble Learning Methods for Deep Learning: A Systematic Literature Review

The rapid growth in popularity of Deep Learning (DL) continues to bring more use cases and opportunities, with methods rapidly evolving and new fields developing from the convergence of different algorithms. For this systematic literature review, we considered the most relevant peer-reviewed journals and conference papers on the state of the art of various Ensemble Learning (EL) methods for application in DL, which are also expected to give rise to new ones in combination. The EL methods relevant to this work are described in detail and the respective popular combination strategies as well as the individual tuning and averaging procedures are presented. A comprehensive overview of the various limitations of EL is then provided, culminating in the final formulation of research gaps for future scholarly work on the results, which is the goal of this thesis. This work fills the research gap for upcoming work in EL for by proving in detail and making accessible the fundamental properties of the chosen methods, which will further deepen the understanding of the complex topic in the future and, following the maxim of ensemble learning, should enable better results through an ensemble of knowledge in the future

Material Classification with a Transfer Learning based Deep Model on an imbalanced Dataset using an epochal Deming-Cycle-Methodology

This work demonstrates that a transfer learning-based deep learning model can perform unambiguous classification based on microscopic images of material surfaces with a high degree of accuracy. A transfer learning-enhanced deep learning model was successfully used in combination with an innovative approach for eliminating noisy data based on automatic selection using pixel sum values, which was refined over different epochs to develop and evaluate an effective model for classifying microscopy images. The deep learning model evaluated achieved 91.54% accuracy with the dataset used and set new standards with the method applied. In addition, care was taken to achieve a balance between accuracy and robustness with respect to the model. Based on this scientific report, a means of identifying microscopy images could evolve to support material identification, suggesting a potential application in the domain of materials science and engineering.&nbsp

Leitfähige Tenside mit anorganischen Kopfgruppen

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Stress-dependent dilated cardiomyopathy in mice with cardiomyocyte-restricted inactivation of cyclic GMP-dependent protein kinase I

Aims: Cardiac hypertrophy is a common and often lethal complication of arterial hypertension. Elevation of myocyte cyclic GMP levels by local actions of endogenous atrial natriuretic peptide (ANP) and C-type natriuretic peptide (CNP) or by pharmacological inhibition of phosphodiesterase-5 was shown to counter-regulate pathological hypertrophy. It was suggested that cGMP-dependent protein kinase I (cGKI) mediates this protective effect, although the role in vivo is under debate. Here, we investigated whether cGKI modulates myocyte growth and/or function in the intact organism. Methods and results: To circumvent the systemic phenotype associated with germline ablation of cGKI, we inactivated the murine cGKI gene selectively in cardiomyocytes by Cre/loxP-mediated recombination. Mice with cardiomyocyte-restricted cGKI deletion exhibited unaltered cardiac morphology and function under resting conditions. Also, cardiac hypertrophic and contractile responses to β-adrenoreceptor stimulation by isoprenaline (at 40 mg/kg/day during 1 week) were unaltered. However, angiotensin II (Ang II, at 1000 ng/kg/min for 2 weeks) or transverse aortic constriction (for 3 weeks) provoked dilated cardiomyopathy with marked deterioration of cardiac function. This was accompanied by diminished expression of the $[Ca^{2+}]_i$-regulating proteins SERCA2a and phospholamban (PLB) and a reduction in PLB phosphorylation at Ser16, the specific target site for cGKI, resulting in altered myocyte $Ca^{2+}_i$ homeostasis. In isolated adult myocytes, CNP, but not ANP, stimulated PLB phosphorylation, $Ca^{2+}_i$-handling, and contractility via cGKI. Conclusion: These results indicate that the loss of cGKI in cardiac myocytes compromises the hypertrophic program to pathological stimulation, rendering the heart more susceptible to dysfunction. In particular, cGKI mediates stimulatory effects of CNP on myocyte $Ca^{2+}_i$ handling and contractility