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We propose the so-called chance constrained programming model of stochastic programming theory to analyze limit and shakedown loads of structures under random strength with a lognormal distribution. A dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit and the shakedown limit. The edge-based smoothed finite element method (ES-FEM) is used with three-node linear triangular elements.
Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.
Die Energiewirtschaft befindet sich in einem starken Wandel, der v. a. durch die Energiewende und Digitalisierung Druck auf sämtliche Marktteilnehmer ausübt. Das klassische Geschäftsmodell des Energieversorgungsunternehmens verändert sich dabei grundlegend. Der kontinuierlich ansteigende Einsatz dezentraler und volatiler Erzeugungsanlagen macht die Identifikation von Flexibilitätspotenzialen notwendig, um weiterhin eine hohe Versorgungssicherheit zu gewährleisten. Dieser Schritt ist nur mit einem hohen Digitalisierungsgrad möglich. Eine funktionale Plattform mit Microservices, die zu Geschäftsprozessen verbunden werden können, wird als Möglichkeit zur Aktivierung der Flexibilität und Digitalisierung der Energieversorgungsunternehmen im Folgenden vorgestellt.
Robust estimators for free surface turbulence characterization: A stepped spillway application
(2020)
Robust estimators are parameters insensitive to the presence of outliers. However, they presume the shape of the variables’ probability density function. This study exemplifies the sensitivity of turbulent quantities to the use of classic and robust estimators and the presence of outliers in turbulent flow depth time series. A wide range of turbulence quantities was analysed based upon a stepped spillway case study, using flow depths sampled with Acoustic Displacement Meters as the flow variable of interest. The studied parameters include: the expected free surface level, the expected fluctuation intensity, the depth skewness, the autocorrelation timescales, the vertical velocity fluctuation intensity, the perturbations celerity and the one-dimensional free surface turbulence spectrum. Three levels of filtering were utilised prior to applying classic and robust estimators, showing that comparable robustness can be obtained either using classic estimators together with an intermediate filtering technique or using robust estimators instead, without any filtering technique.
While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees’ engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown.
The manufacturing share of laser powder bed fusion (L-PBF) increases in industrial application, but still many process steps are manually operated. Additionally, it is not possible to achieve tight dimensional tolerances or low surfaces roughness. Hence, a process chain has to be set up to combine additive manufacturing (AM) with further machining technologies. To achieve a continuous workpiece flow as basis for further industrialization of L-PBF, the paper presents a novel substrate system and its application on L-PBF machines and post-processing. The substrate system consists of a zero-point clamping system and a matrix-like interface of contact pins to be substantially connected to the workpiece within the L-PBF process.
In this paper we present SMART-FACTORY, a setup for a research and teaching facility in industrial robotics that is based on the RoboCup Logistics League. It is driven by the need for developing and applying solutions for digital production. Digitization receives constantly increasing attention in many areas, especially in industry. The common theme is to make things smart by using intelligent computer technology. Especially in the last decade there have been many attempts to improve existing processes in factories, for example, in production logistics, also with deploying cyber-physical systems. An initiative that explores challenges and opportunities for robots in such a setting is the RoboCup Logistics League. Since its foundation in 2012 it is an international effort for research and education in an intra-warehouse logistics scenario. During seven years of competition a lot of knowledge and experience regarding autonomous robots was gained. This knowledge and experience shall provide the basis for further research in challenges of future production. The focus of our SMART-FACTORY is to create a stimulating environment for research on logistics robotics, for teaching activities in computer science and electrical engineering programmes as well as for industrial users to study and explore the feasibility of future technologies. Building on a very successful history in the RoboCup Logistics League we aim to provide stakeholders with a dedicated facility oriented at their individual needs.
In traditional microbial biobutanol production, the solvent must be recovered during fermentation process for a sufficient space-time yield. Thermal separation is not feasible due to the boiling point of n-butanol. As an integrated and selective solid-liquid separation alternative, solvent impregnated resins (SIRs) were applied. Two polymeric resins were evaluated and an extractant screening was conducted. Vacuum application with vapor collection in fixed-bed column as bioreactor bypass was successfully implemented as butanol desorption step. In course of further increasing process economics, fermentation with renewable lignocellulosic substrates was conducted using Clostridium acetobutylicum. Utilization of SIR was shown to be a potential strategy for solvent removal from fermentation broth, while application of a bypass column allows for product removal and recovery at once.
Räumliche Transformation
(2020)
Berücksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen
(2020)
Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen für hohe Kosten. Diese sind häufig auf unpräzise Fehlerbeschreibungen zurückzuführen. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen.
The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.
Background
Osteoporosis is associated with the risk of fractures near the hip. Age and comorbidities increase the perioperative risk. Due to the ageing population, fracture of the proximal femur also proves to be a socio-economic problem. Preventive surgical measures have hardly been used so far.
Methods
10 pairs of human femora from fresh cadavers were divided into control and low-volume femoroplasty groups and subjected to a Hayes fall-loading fracture test. The results of the respective localization and classification of the fracture site, the Singh index determined by computed tomography (CT) examination and the parameters in terms of fracture force, work to fracture and stiffness were evaluated statistically and with the finite element method. In addition, a finite element parametric study with different position angles and variants of the tubular geometry of the femoroplasty was performed.
Findings
Compared to the control group, the work to fracture could be increased by 33.2%. The fracture force increased by 19.9%. The used technique and instrumentation proved to be standardized and reproducible with an average poly(methyl methacrylate) volume of 10.5 ml. The parametric study showed the best results for the selected angle and geometry.
Interpretation
The cadaver studies demonstrated the biomechanical efficacy of the low-volume tubular femoroplasty. The numerical calculations confirmed the optimal choice of positioning as well as the inner and outer diameter of the tube in this setting. The standardized minimally invasive technique with the instruments developed for it could be used in further comparative studies to confirm the measured biomechanical results.
There is a growing body of evidence for the effects of vitamin D on intestinal host-microbiome interactions related to gut dysbiosis and bowel inflammation. This brief review highlights the potential links between vitamin D and gut health, emphasizing the role of vitamin D in microbiological and immunological mechanisms of inflammatory bowel diseases. A comprehensive literature search was carried out in PubMed and Google Scholar using combinations of keywords “vitamin D,” “intestines,” “gut microflora,” “bowel inflammation”. Only articles published in English and related to the study topic are included in the review. We discuss how vitamin D (a) modulates intestinal microbiome function, (b) controls antimicrobial peptide expression, and (c) has a protective effect on epithelial barriers in the gut mucosa. Vitamin D and its nuclear receptor (VDR) regulate intestinal barrier integrity, and control innate and adaptive immunity in the gut. Metabolites from the gut microbiota may also regulate expression of VDR, while vitamin D may influence the gut microbiota and exert anti-inflammatory and immune-modulating effects. The underlying mechanism of vitamin D in the pathogenesis of bowel diseases is not fully understood, but maintaining an optimal vitamin D status appears to be beneficial for gut health. Future studies will shed light on the molecular mechanisms through which vitamin D and VDR interactions affect intestinal mucosal immunity, pathogen invasion, symbiont colonization, and antimicrobial peptide expression.
To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Masked hypertension is known to induce microvascular complications. However, it is unclear whether early microvascular changes are already occurring in young, otherwise healthy adults. We therefore investigated whether retinal microvascular calibers and acute responses to a flicker stimulus are related to masked hypertension. We used the baseline data of 889 participants aged 20–30 years who were taking part in the African Prospective study on the Early Detection and Identification of Cardiovascular Disease and Hypertension. Clinic and 24-h ambulatory blood pressure were measured. The central retinal artery equivalent (CRAE) and central retinal vein equivalent were calculated from fundus images, and retinal vessel dilation was determined in response to flicker light-induced provocation. A smaller CRAE was observed in those with masked hypertension vs. those with normotension (157.1 vs. 161.2 measuring units, P < 0.001). In forward multivariable-adjusted regression analysis, only CRAE was negatively related to masked hypertension [adjusted R² = 0.267, β = −0.097 (95% CI = −0.165; −0.029), P = 0.005], but other retinal microvascular parameters were not associated with masked hypertension. In multivariable logistic regression analyses, masked hypertension [OR = 2.333, (95% CI = 1.316; 4.241), P = 0.004] was associated with a narrower CRAE. In young healthy adults, masked hypertension was associated with retinal arteriolar narrowing, thereby reflecting early microvascular alterations known to predict cardiovascular outcomes in later life.
SHEMAT-Suite: An open-source code for simulating flow, heat and species transport in porous media
(2020)
SHEMAT-Suite is a finite-difference open-source code for simulating coupled flow, heat and species transport in porous media. The code, written in Fortran-95, originates from geoscientific research in the fields of geothermics and hydrogeology. It comprises: (1) a versatile handling of input and output, (2) a modular framework for subsurface parameter modeling, (3) a multi-level OpenMP parallelization, (4) parameter estimation and data assimilation by stochastic approaches (Monte Carlo, Ensemble Kalman filter) and by deterministic Bayesian approaches based on automatic differentiation for calculating exact (truncation error-free) derivatives of the forward code.