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The transformation to an Industry 4.0, which is in general seen as a solution to increasing market challenges, is forcing companies to radically change their way of thinking and to be open to new forms of cooperation. In this context, the opening-up of the innovation process is widely seen as a necessity to meet these challenges, especially for small and medium enterprises (SMEs). The aim of the study therefore is to analyze how cooperation today can be characterized, how this character has changed since the establishment of the term Industry 4.0 at Hanover Fair in 2011 and which cooperation strategies have proven successful. The analysis consists of a quantitative, secondary data analysis that includes country-specific data from 35 European countries of 2010 and 2016 collected by the European Commission and the OECD. The research, focusing on the secondary sector, shows that multinational enterprises MNEs still tend to cooperate more than SMEs, with a slight overall trend towards protectionism. Nevertheless, there is a clear tendency towards the opening-up of SMEs. In this regard, especially universities, competitors and suppliers have become increasingly attractive as cooperation partners for SMEs.
Traditional Western philosophy, cognitive science and traditional HCI frameworks approach the term digital and its implications with an implicit dualism (nature/cul-ture, theory /practice, body/mind, human/machine). What lies between is a feature of our postmodern times, in which different states, conditions or positions merge and co-exist in a new, hybrid reality, a “continuous beta” (Mühlenbeck & Skibicki, 2007) version of becoming .Post-digitality involves the physical dimensions of spatio-temporal engagements. This new ontological paradigm reconceptualizes digital technology through the ex-perience of the human body and its senses, thus emphasizing form-taking, situation-al engagement and practice rather than symbolic, disembodied rationality. This rais-es two questions in particular: how to encourage curiosity, playfulness, serendipity, emergence, discourse and collectivity? How to construct working methods without foregrounding and dividing the subject into an individual that already takes posi-tion? This paper briefly outlines the rhizomatic framework that I developed within my PhD research. This attempts to overcome two prevailing tendencies: first, the one-sided view of scientific approaches to knowledge acquisition and the pure-ly application-oriented handling of materials, technologies and machines; second, the distanced perception of the world. In contrast, my work involves project-driven alchemic curiosity and doing research through artistic design practice. This means thinking through materials, technologies and machinic interactions. Now, at the end of this PhD journey, 10 interdisciplinary projects have emerged from this ontological queer-paradigm that is post-digital–crafting 4.0. Below I illustrate this approach and its outcomes.
Production and marketing of cereal grains are some of the main activities in developing countries to ensure food security. However, the food gap is complicated further by high postharvest loss of grains during storage. This study aimed to compare low‐cost modified‐atmosphere hermetic storage structures with traditional practice to minimize quantitative and qualitative losses of grains during storage. The study was conducted in two phases: in the first phase, seven hermetic storage structures with or without smoke infusion were compared, and one selected structure was further validated at scaled‐up capacity in the second phase.
In modern fruit processing technology, non-destructive quality measuring techniques aresought for determining and controlling changes in the optical, structural, and chemical properties of theproducts. In this context, changes inside the product can be measured during processing. Especiallyfor industrial use, fast, precise, but robust methods are particularly important to obtain high-qualityproducts. In this work, a newly developed multi-spectral imaging system was implemented andadapted for drying processes. Further it was investigated if the system could be used to link changesin the surface spectral reflectance during mango drying with changes in moisture content andcontents of chemical components. This was achieved by recovering the spectral reflectance frommulti-spectral image data and comparing the spectral changes with changes of the total soluble solids(TSS), pH-value and the relative moisture contentxwbof the products. In a first step, the camera wasmodified to be used in drying, then the changes in the spectra and quality criteria during mangodrying were measured. For this, mango slices were dried at air temperatures of 40–80◦C and relativeair humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectralimaging system. The quality criteria were then predicted from spectral data. It could be shown thatthe newly developed multi-spectral imaging system can be used for quality control in fruit drying.There are strong indications as well, that it can be employed for the prediction of chemical qualitycriteria of mangoes during drying. This way, quality changes can be monitored inline during theprocess using only one single measuring device.
What drives entrepreneurial action to create a lasting impact? The creation of new ventures that aim at having an impact beyond their financial performance face additional challenges: achieving economic sustainability and at the same time addressing social or environmental issues. Little is known on how these new hybrid organizations, aiming for multiple impact dimensions, manage to be congruent with their blended values. A dataset of 4,125 early-stage ventures is used to gain insights into how blended values are converted into financial, social and environmental impacts, giving shape to different types of hybrid organizations. Our findings suggest new hybrid organizations might opt to sacrifice financial impact to achieve social impact, yet this is not the case when they aim to generate environmental or sustainable impact. Therefore, the tensions and sacrifices related to holding blended values are not homogeneous across all types of new hybrid organizations.
We provide an overview of the ongoing discussions on the objectives of the energy transition in the form of a conceptual framework, intending to facilitate the search for the most viable options for a successful transformation of the energy system. For this purpose, we examine the development of energy policy goals in Germany in the past and present, whereby we give an overview of objectives and assessment approaches from politics, economics, and science. Moreover, we then merge the different views into a common framework and analyze the central conflict between the wholeness of a hypothetical target circle and the simplification in favor of a hypothetical target point in more detail.
In this article, we give the construction of new four-dimensional signal constellations in the Euclidean space, which represent a certain combination of binary frequency-shift keying (BFSK) and M-ary amplitude-phase-shift keying (MAPSK). Description of such signals and the formulas for calculating the minimum squared Euclidean distance are presented. We have developed an analytic building method for even and odd values of M. Hence, no computer search and no heuristic methods are required. The new optimized BFSK-MAPSK (M = 5,6,···,16) signal constructions are built for the values of modulation indexes h =0.1,0.15,···,0.5 and their parameters are given. The results of computer simulations are also provided. Based on the obtained results we can conclude, that BFSK-MAPSK systems outperform similar four-dimensional systems both in terms of minimum squared Euclidean distance and simulated symbol error rate.
This work presents a new concept to implement the elliptic curve point multiplication (PM). This computation is based on a new modular arithmetic over Gaussian integer fields. Gaussian integers are a subset of the complex numbers such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this arithmetic is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of secure hardware implementations, which are robust against attacks. Furthermore, an area-efficient coprocessor design is proposed with an arithmetic unit that enables Montgomery modular arithmetic over Gaussian integers. The proposed architecture and the new arithmetic provide high flexibility, i.e., binary and non-binary key expansions as well as protected and unprotected PM calculations are supported. The proposed coprocessor is a competitive solution for a compact ECC processor suitable for applications in small embedded systems.
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level.
Mapping of tree seedlings is useful for tasks ranging from monitoring natural succession and regeneration to effective silvicultural management. Development of methods that are both accurate and cost-effective is especially important considering the dramatic increase in tree planting that is required globally to mitigate the impacts of climate change. The combination of high-resolution imagery from unmanned aerial vehicles and object detection by convolutional neural networks (CNNs) is one promising approach. However, unbiased assessments of these models and methods to integrate them into geospatial workflows are lacking. In this study, we present a method for rapid, large-scale mapping of young conifer seedlings using CNNs applied to RGB orthomosaic imagery. Importantly, we provide an unbiased assessment of model performance by using two well-characterised trial sites together containing over 30,000 seedlings to assemble datasets with a high level of completeness. Our results showed CNN-based models trained on two sites detected seedlings with sensitivities of 99.5% and 98.8%. False positives due to tall weeds at one site and naturally regenerating seedlings of the same species led to slightly lower precision of 98.5% and 96.7%. A model trained on examples from both sites had 99.4% sensitivity and precision of 97%, showing applicability across sites. Additional testing showed that the CNN model was able to detect 68.7% of obscured seedlings missed during the initial annotation of the imagery but present in the field data. Finally, we demonstrate the potential to use a form of weakly supervised training and a tile-based processing chain to enhance the accuracy and efficiency of CNNs applied to large, high-resolution orthomosaics.