Cloud Computing allows companies to scale seamlessly, providing a broad range of state-of-the-art services. Another promise is to free users from the operational and administrative burdens [1]. However, with the advent of cloud-native applications [2], this promise becomes questionable – especially when DevOps [3] principles are used during development. Experience from practice shows that teams often struggle dealing with both the infrastructure, finding the right architecture, and implementing business logic. When working in decentralized teams, things are even worse, as standardization across teams cannot be assumed. To tackle those issues, automation by means of techniques such as Infrastructure-as-code [4] help to ease to cope with infrastructural concerns. However, when working with several teams in a decentralized manner, operational overhead is still there. Organizations struggle with standardization of infrastructure code and there is no clear centralized visibility for security-related concerns within the development lifecycle. To address these issues, we propose two things: First, building up a Platform Team [5], which serves as an organizational structure for continuous delivery. A Platform Teams can be the size of a typical small DevOps Team and support the whole organization with standardized security-hardened modules. Second, an Ops-Platform is needed that is operated by the Platform Team to centrally provide and maintain those modules. Other Dev-Teams can then consume those modules. In this paper, we report insights from the implementation of this approach in practice. We find out that developers are 75% less focused on operations by using such a platform and name specific success factors.

This study demonstrates the need for novel gas engine control systems for com-
bined heat and power plants, also known as cogeneration power plants, connected to natural
gas grids. Hydrogen addition to natural gas grids in a range of up to 5% by volume is already
permitted throughout Europe. This offers the possibility to reduce carbon dioxide emissions
of end consumers connected to public natural gas grids and contributes to climate protec-
tion. However, conventional engine controls are not designed for natural gas/hydrogen mixture
operation. We tested fuels with up to 30% hydrogen by volume using a commercial six-cylinder
spark ignition engine, designed for natural gas or biogas operation in power plants. With engine
settings according to usual cogeneration operation, nitrogen oxide emissions increased expo-
nentially with increasing hydrogen amounts. We demonstrate that the usual approach of using
the lower heating value of the fuel mixture to regulate the engine is unable to accommodate the
hydrogen induced changes. For this reason, we developed a mathematical model to determine
the nitrogen oxide emissions based on boost pressure and power output. The idea behind this
novel approach is to regulate the engine based on emissions, regardless of the fuel gas. In this
work the approach for this virtual sensor is described and its performance demonstrated.

Purpose: Magnetic separation of microbes can be an effective tool for pathogen identification and diagnostic applications to reduce the time needed for sample preparation. After peptide functionalization of superparamagnetic iron oxide nanoparticles (SPIONs) with an appropriate interface, they can be used for the separation of sepsis-associated yeasts like Candida albicans. Due to their magnetic properties, the magnetic extraction of the particles in the presence of an external magnetic field ensures the accumulation of the targeted yeast.

Materials and methods: In this study, we used SPIONs coated with 3-aminopropyltriethoxysilane (APTES) and functionalized with a peptide originating from GP340 (SPION-APTES-Pep). For the first time, we investigate whether this system is suitable for the separation and enrichment of Candida albicans, we investigated its physicochemical properties and by thermogravimetric analysis we determined the amount of peptide on the SPIONs. Further, the toxicological profile was evaluated by recording cell cycle and DNA degradation. The separation efficiency was investigated using Candida albicans in different experimental settings, and regrowth experiments were carried out to show the use of SPION-APTES-Pep as a sample preparation method for the identification of fungal infections.

Conclusion: SPION-APTES-Pep can magnetically remove more than 80% of the microorganism and with a high selective host-pathogen distinction Candida albicans from water-based media and about 55% in blood after 8 minutes processing without compromising effects on the cell cycle of human blood cells. Moreover, the separated fungal cells could be regrown without any restrictions.

This study investigates the potential benefits and drawbacks of adding hydrogen to natural
gas grids on stationary cogeneration plants. Fuel blended with up to 30% hydrogen by
volume was tested using a commercial six-cylinder spark ignition engine designed for pure
natural gas operation without modifications to the engine. In line with normal practice for
cogeneration plant engines, the power output, the lower heating value of the air/fuel
mixture, the ignition timing and the engine speed were held constant. Results show that
increasing hydrogen concentration led to an earlier peak cylinder pressure, indicating
significantly accelerated combustion. As a result, peak pressures were up to 39% higher
than with natural gas and up to 10% of fuel burned before top dead center. Despite this,
thermal efficiency improved up to 6%. Cycle-by-cycle variation decreased by half, indi-
cating reduced misfires on account of hydrogen. However, nitrogen oxide emissions
increased exponentially with increasing hydrogen amounts. Our findings suggest that
hydrogen-enriched natural gas is a promising fuel for stationary cogeneration plants, but
modifications to engine control settings are necessary to ensure optimal performance and
compliance with nitrogen oxide emission regulations. These modifications might include
adjustments to the mixture control system and ignition timing.

Industrial requirements to design high quality products in shorter and shorter times impose the use of numerical models
to estimate the geometrical deviations of these products, which are assemblies, starting from the geometrical deviations
of their components. Numerical models may support this estimation activity, thus reducing the time to market and the
design costs.
The free-body model is an interesting numerical method to translate the tolerance chain into a static problem solved by
algebraic or graphical procedures by using the free-body diagrams of force analysis. This work presents a free-body
model that is able to deal with dimensional and geometrical tolerances that involve translations of the features to which
the tolerances are applied. Moreover, it validates the free-body model for tolerance analysis of rigid parts by defining and
solving a case study through numerical and experimental activities. The present work uses a case study to demonstrate
the effectiveness of the free-body model by underlining that the experimental results obtained are very close to those
from the numerical model.

In recent years the interest in finding new shape memory polymers has increased. Withal, fewer studies approached the use of regular materials such as polyethene terephthalate (PET), a widely available material. The research investigated the shape memory characteristics of regular PET 3D printed samples in two testing cycles. The recovery temperature was determined through dynamical mechanical, followed by tensile testing and heat treatment of the specimens. The results show PET has good shape memory properties, recording a slight increase in shape recovery during the second testing cycle without affecting the sample integrity. The thermal analysis of the recovered material shows that heat treatment time or/and temperature need to be improved to stabilise the PET material structure.

Dielectric properties for nanocomposites with metallic fillers inside a polymer matrix were determined using CST STUDIO SUITE—Electromagnetic field simulation software followed by the free-space Nicolson–Ross–Weir procedure. The structure is randomly generated to simulate the intrinsic non-uniformity of real nanomaterials. Cubic insertions were equated to corresponding spherical particles in order to provide either the same volume index or the same exterior surface index. The energy concentration around the inserts and within the entire material was determined as useful information in practice in order to design materials tailored to avoid exceeding the field/temperature limit values. The paper successfully associated the dialectic measurements with the results from the computer simulations, which are mainly based on energetic effects in electromagnetic applications. The experimental results are comparable with the software simulation in terms of precision. The conclusions outline the practical applications of the method for both electromagnetic shielding and microwave domain/telecommunications applications.

Cast iron components have a good strength to weight ratio. This leads to their frequent use in the wind industry. The design of cast iron components is currently based on the use of individual simulation tools and material data that is common to all components. In order to better exploit the lightweight potential of cast iron components, it is necessary to link the simulation software tools and thus take into account local material properties already in the design phase. This is described in this paper using the example of a large casting for the wind industry

The world-wide plastics pollution into nature enters the human food chain as degradation products, influencing environmental habitat and health. Micro- and nanoplastics as foreign bodies contaminating beverages and foods on the cellular level are likely to have a long-term impact on nutrition growth, public and personal health. Nanoparticles and their spatial location are generally difficult to detect in biological specimens, especially plastic particles due to their analogous hydrocarbon-based matrix. Reliable analysis methods for the discovery of micro- and nanoplastic particles in entities are not fully established yet. We are therefore developing a correlative workflow for detecting plastics residuals in cellular tissue. We determined that light microscopy (LM) is usable for a coarse estimation of the contamination grade of plant samples. Due to its optical diffraction limit, LM rather detects nanoparticle patches than single particles, shown with fluorescent particles. Scanning electron microscopy (SEM) instead provides higher resolution, especially for single particle detection. EM sample staining is still required to depict polymerous nanoparticle specimens. Using LM and SEM data in a CLEM approach with TESCAN CORAL, discovery of contaminated plant areas is possible with single nanoparticle resolution. It reveals that nanoparticles seem to be tightly attached to the plant material. It indicates further that basic food cleaning procedures might be insufficient for particle removal. The workflow circumvents correlative sample 3D-topography issues. But insights into the plant matrix is best possible with z-information. Therefore, we additionally use focused ion beam (FIB) to determine inclusions of micro- and nanoplastics in the tissue/cellular matrix, investigating the optimal procedural approach as model for biomaterial systems. Procedural correlative microscopy provides a promising analysis method for the semiquantitative analysis of micro- and nanoparticles plant contaminations.