Bergische Universität Wuppertal
  • Wuppertal, NRW, Germany
Recent publications
Deep learning is a powerful tool, which is becoming increasingly popular in financial modeling. However, model validation requirements such as SR 11-7 pose a significant obstacle to the deployment of neural networks in a bank’s production system. Their typically high number of (hyper-)parameters poses a particular challenge to model selection, benchmarking and documentation. We present a simple grid based method together with an open source implementation and show how this pragmatically satisfies model validation requirements. We illustrate the method by learning the option pricing formula in the Black–Scholes and the Heston model.
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further analysis. Typically, these workflows use distributed storage and computing resources. A straightforward setup of storage media would be low-cost tape storage and higher-cost disk storage. The large, infrequently accessed input data are stored on tape storage. The smaller, frequently accessed derived data is stored on disk storage. In a best-case scenario, the large input data is only accessed very infrequently and in a well-planned pattern. However, practice shows that often the data has to be processed continuously and unpredictably. This can significantly reduce tape storage performance. A common approach to counter this is storing copies of the large input data on disk storage. This contribution evaluates an approach that uses cloud storage resources to serve as a flexible cache or buffer, depending on the computational workflow. The proposed model is explored for the case of continuously processed data. For the evaluation, a simulation tool was developed, which can be used to analyse models related to storage and network resources. We show that using commercial cloud storage can reduce on-premises disk storage requirements, while maintaining an equal throughput of jobs. Moreover, the key metrics of the model are discussed, and an approach is described, which uses the simulation to assist with the decision process of using commercial cloud storage. The goal is to investigate approaches and propose new evaluation methods to overcome future data challenges.
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
Hydrochar has potential applications in soil improvement and heavy metal remediation. Hydrochar would undergo the process of aging when introduced into the soil, altering its properties. However, recent studies have focused mainly on the artificial aging of hydrochar, which could not reveal the cumulative effect of multiple environmental factors. Therefore, the periodical monitoring of the property and sorption behavior of hydrochar after amending soils is necessary to better understand the multifaceted mechanisms associated with the natural aging of hydrochar. This study selected the sludge-derived hydrochar (SLHC) as a typical hydrochar and applied a 16-month rice–wheat–rice rotation to mimic the natural aging of hydrochar, focusing on changing properties and cadmium (Cd) sorption and literature contrast between aging strategies and biochar types. The porosity, O abundance, and ash content of 16-month aged SLHC increased by 37%, 47%, and 8.5%, respectively, facilitating Cd sorption due to surface complexation, pore sorption, and precipitation. The sorption percentage of Cd to SLHC was in the range of 11–14% for SLHC-A0 and increased to 17–31% for SLHC-A4 and 20–32% for SLHC-A16 after natural aging. The natural aging of SLHC induced by ash content played an essential role in Cd sorption site heterogeneity. Linear regression analysis showed that aging strategies on sorption behavior significantly differed between biochars. Thus, studies involving natural aging with multiple environmental factors are preferred over those involving chemical or biological aging. Future studies should continue to explore the mechanisms of natural aging-induced heavy metal sorption between hydrochar and pyrochar. These results improve insights to appraise the potential of SLHC as soil amendments to alleviate the adverse effects of heavy metal contamination and provide an essential basis for researchers and staff in soil management and environmental prevention. Graphical Abstract
Globally, the valorisation of food waste into digestate through the process of anaerobic digestion is becoming increasingly popular. As a result, a large amount of food-waste digestate will need to be properly utilised. The utilisation of anaerobic digestion for fertiliser and alternative uses is essential to obtain a circular bioeconomy. The review aims to examine the environmental management of food-waste digestate, the value of digestate as a fertiliser and soil conditioner, and the emerging uses and improvements for post-anaerobic digestion reuse of digestate. Odour emissions, contaminants in food waste, emission and leaching of nutrients into the environment, and the regulations, policies, and voluntary initiatives of anaerobic digestion are evaluated in the review. Food-waste digestate can provide essential nutrients, carbon, and bio-stimulants to soils and increase yield. Recently, promising research has shown that digestates can be used in hydroponic systems and potentially replace the use of synthetic fertilisers. The integration of anaerobic digestion with emerging uses, such as extraction of value-added products, algae cultivation, biochar and hydrochar production, can further reduce inhibitory sources of digestate and provide additional economic opportunities for businesses. Moreover, the end-product digestate from these technologies can also be more suitable for use in soil application and hydroponic use.
We extend a recently developed modeling approach which allows to calculate dynamic moduli of filled elastomers using the experimental surface tensions of the material’s components as input. The approach combines a morphology generator, mimicking the post-mixing filler flocculation, with a finite-element-like dynamic mechanical analysis. The minimum length scale is set by the size of the non-breakable filler aggregates, whereas the maximum length scale is in the μm range. In this work we focus on binary polymer mixtures containing high amounts of filler. The polymers are natural and styrene-butadiene rubber, while the filler type is variable. We study the interdependence between the filler surface tension and the post-mixing dispersion of the components, in particular the relative enrichment or depletion of one or the other polymer with filler, and how this couples to the dynamic moduli of the respective compounds. The algorithm ties the chemical identity of the components, via the dispersive and polar parts of the surface tensions, to the amplitude and frequency dependence of the storage and loss moduli, allowing fast numerical screening for desired mechanical properties in filled elastomers.
Rice is an important food crop that is susceptible to arsenic (As) contamination under paddy soil conditions depending on As uptake characteristics of the rice genotypes. Here we unveiled the significance of eighteen (fine and coarse) rice genotypes against As accumulation/tolerance, morphological and physiological response, and antioxidant enzymes-enabled defense pathways. Arsenic significantly affected rice plant morphological and physiological attributes, with relatively more impacts on fine compared to coarse genotypes. Grain, shoot, and root As uptake were lower in fine genotypes (0.002, 0.020, and 0.032 mg pot-1 DW, respectively) than that of coarse (0.031, 0.60, and 1.2 mg pot-1 DW, respectively). Various biochemical (pigment contents, hydrogen peroxide, lipid peroxidation) and defense (antioxidant enzymes) plant parameters indicated that the fine genotypes, notably Kainat and Basmati-385, possessed the highest As tolerance. Arsenic-induced risk indices exhibited greater hazard quotient (up to 1.47) and carcinogenic risk (up to 0.0066) for coarse genotypes compared to the fine ones, with the greatest risk for KSK-282. This study elaborates the pivotal role of genotypic variation among rice plants in As accumulation, which is crucial for mitigating the associated human health risk. Further research is required on molecular aspects, e.g., genetic sequencing, to examine rice genotypes variation in defense mechanisms to As contamination.
Biodegradation of microplastics (MPs) in contaminated biowastes has received big scientific attention during the past few years. The aim here is to study the impacts of livestock manure biochar (LMBC) on the biodegradation of polyhydroxyalkanoate microplastics (PHA-MPs) during composting, which have not yet been verified. LMBC (10% wt/wt) and PHA-MPs (0.5% wt/wt) were added to a mixture of pristine cow manure and sawdust for composting, whereas a mixture without LMBC served as the control (CK). The maximum degradation rate of PHA-MPs (22–31%) was observed in the thermophilic composting stage in both mixtures. LMBC addition significantly (P < 0.05) promoted PHA-MPs degradation and increased the carbon loss and oxygen loading of PHA-MPs compared to CK. Adding LMBC accelerated the cleavage of C–H bonds and oxidation of PHA-MPs, and increased the O–H, CO and C–O functional groups on MPs. Also, LMBC addition increased the relative abundance of dominant microorganisms (Firmicutes, Proteobacteria, Deinococcus-Thermus, Bacteroidetes, Ascomycota and Basidiomycota) and promoted the enrichment of MP-degrading microbial biomarkers (e.g., Bacillus, Thermobacillus, Luteimonas, Chryseolinea, Aspergillus and Mycothermus). LMBC addition further increased the complexity and connectivity between dominant microbial biomarkers and PHA-MPs degradation characteristics, strengthened their positive relationship, thereby accelerated PHA-MPs biodegradation, and mitigated the potential environmental and human health risk. These findings provide a reference point for reducing PHA-MPs in compost and safe recycling of MPs contaminated organic wastes. However, these results should be validated with other composting matrices and conditions.
Models of the operational energy system analysis map partial aspects of the energy system in high temporal and spatial resolution. In this contribution different power grid focused scenarios are considered and results from four different models are compared for an exemplary, rural low voltage grid. These scenarios include the expansion of photovoltaic (PV) power generation and measures to address resulting grid limit violations with conventional grid expansion and the installation of a voltage regulating distribution transformer (VRDT). The model comparison should provide the optimization potentials and recommendations for the use of the models. The models ZuBer (University of Wuppertal), Pandapower Pro (University of Kassel), Energy Agents (University of Duisburg-Essen), and GridSim (FfE) are represented in these scenarios. After description of the scenario definition, data basis and the individual methodological approaches of the models, the results of the power flow calculation and further calculation results are compared. Finally, the optimization potential for the individual models is derived and a recommendation is made for each model as to which scenario it is particularly suitable for.
Passive infrared thermography (IRT) has already been applied in several approaches for high-resolution and non-contact imaging of microbial hot spots and hot moments on soil sample surfaces. The technique has only been used on homogenized disturbed samples to characterize the in-situ heterogeneity of heat development. In this study, undisturbed top- and subsoils samples from two forest sites were used in a substrate-induced approach to capture surface heat production using passive IRT with homogeneously applied glucose and water. The soil sample surface temperature was measured at 10-minute intervals and a spatial resolution of 0.17 mm per pixel. The soil samples were incubated for five days during passive IRT measurements under controlled ambient conditions with a relative air humidity >95% and constant ambient air temperature of 20 °C. Soil sample surface characterization was done by using active IRT for soil moisture approximation and surface structure, digital photography to estimate soil organic carbon (SOC) contents from soil color parameters, and zymography to get an indicator of initial microbial activity. In a first step, surface temperature dynamics were characterized using mathematical and geostatistical methods concerning microbial hot spots and hot moments. The characterization of hot moments was performed using a Gaussian curve fit. The spatial information of the hot spots was described using geostatistical semivariance. In a second step, a stepwise forward regression using sample surface properties was performed to find explanatory variables for surface heat production. Finally, a hierarchical k-means clustering was applied to the sequential thermal images and transferred to the other spatial datasets for deeper insights into small-scale variations in heat production and to also consider the temporal perspective. With an ANOVA combined with Tukey’s HSD post hoc test, significant differences between the cluster groups were calculated. This study showed that the temperature difference between averaged glucose- and water-treated sample surface temperature increased up to 0.2 K with a maximum hot moment duration of nearly 46 h. The variance in the samples could not be mapped, since the maximum spatial variability of the sample surface temperature could not be represented in the sample sections. The regression analysis revealed significant influences by soil moisture content, approximated SOC content, as well as initial β-glucosidase activity in the subsoil, which could be confirmed by the k-means cluster analysis. There was an indication that soil brightness (SOC contents) was influencing heat production due to the varying soil-borne substrate availability. The initial β-glucosidase activity showed that the initial biotic state of the soil contributed to the temperature increase concerning the provision of energy for microbial activity. In conclusion, passive IRT is a helpful mapping tool for the spatio-temporal characterization of hot spots and hot moments. In this context, additional mapping techniques such as zymography help to gain insights into the process level of heat generation by microbial activity.
Business model innovation (BMI) is often complementary to technological innovation and offers novel and sustainable value creation opportunities. Enabling BMI through policy is difficult, however, and not yet well understood in practice or theory. We build on the quickly evolving literature on policy mixes to develop a theoretical model which explains how policy strategies and instruments shape the conditions for BMI. We derive the model inductively by studying the emergence of an off-grid renewable energy BMI in sub-Saharan Africa which proposes to actively create sustainable development in rural areas as opposed to merely increase energy access, drawing from 61 interviews with companies and industry experts as well as policy documents across six African countries. Our model has three core theoretical implications. First, focusing on policy strategies, policy instruments and their respective interactions, we uncover a set of mechanisms that explain how policy mix elements combine to create conducive conditions for BMI. Second, we shed light on the role of multiple objectives and goals within a policy mix for fostering BMI, which, if balanced appropriately, can create a productive tension between support and constraints. Third, we suggest the distinction between sector-specific and society-wide policy mixes as an analytical tool to study these tensions in policy mix research.
Theories on gender bias argue that women in academia benefit less from their academic achievements than men do; women, as a result, show lower rates of success in becoming tenured professors. Based on longitudinal data from CVs of virtually all psychologists in German academia, we analyze factors that lead to a first permanent professorship in German psychology departments. We find no overall gender differences in getting a tenured position when considering all psychologists and holding research productivity and other observable factors constant. Among currently tenured professors, women show a 32% higher chance of having gotten tenure than men. Interaction effects reveal that women's publishing or signaling investments are not devalued when they try to obtain tenure. We particularly find that women benefit more from their scholarly publications than men do. Hence, we find no support for gender bias or devaluation of women's academic achievements.
High-grade non-muscle-invasive bladder cancer (HG NMIBC) patients are at high risk (HR) of progression to muscle-invasion. Bladder-preserving therapies for this patient subgroup are limited, and additional treatments are desirable. Recently, enfortumab vedotin, targeting cancer-associated NECTIN4, has been approved for the treatment of advanced urothelial carcinoma. However, data on the expression of NECTIN4 and its therapeutic potential for HR NMIBC are scarce. Here, NECTIN4 was immunohistochemically analyzed in urothelial HG NMIBC by studying cohorts of carcinoma in situ (CIS)/T1HG (N = 182 samples), HG papillary tumors from mixed-grade lesions (mixed TaHG) (N = 87) and papillary HG tumors without a history of low-grade disease (pure TaHG/T1HG) (N = 98) from overall 225 patients. Moreover, inter-lesional NECTIN4 heterogeneity in multifocal HG NMIBC tumors was determined. A high prevalence of NECTIN4 positivity was noted across HG NMIBC subgroups (91%, N = 367 samples), with 77% of samples showing moderate/strong expression. Heterogenous NECTIN4 levels were observed between HG NMIBC subgroups: non-invasive areas of CIS/T1HG and pure TaHG/T1HG samples showed NECTIN4 positivity in 96% and 99%, with 88% and 83% moderate/strong expressing specimens, respectively, whereas significantly lower NECTIN4 levels were detected in mixed TaHG lesions (72% positivity, 48% of samples with moderate/strong NECTIN4 expression). Moreover, higher NECTIN4 heterogeneity was observed in patients with multifocal mixed TaHG tumors (22% of patients) compared to patients with multifocal CIS/T1HG and pure TaHG/T1HG tumors (9% and 5%). Taken together, NECTIN4-directed antibody–drug conjugates might be promising for the treatment of HR NMIBC patients, especially for those exhibiting CIS/T1HG and pure TaHG/T1HG tumors without a history of low-grade disease.
During the last decade, the body of research investigating teachers’ implicit attitudes toward different student groups, particularly toward ethnic minority groups, has been consistently increasing. The Implicit Association Test (IAT) is the main test that is used for this research purpose. However, what has been neglected so far is the investigation of implicit expectations about ethnic minority students in terms of their academic performance. Thus, the aim of the current study was to employ the Relational Responding Task (RRT) in the educational context as this test allowed us to assess stereotypical expectations in a more holistic way than other implicit measures would allow. We administered the IAT to assess implicit attitudes and the RRT to assess implicit expectations in a sample of teachers and preservice teachers in Italy. We also administered a questionnaire to assess explicit prejudice. For 93 teachers and preservice teachers who worked on all three measures, their implicit attitudes toward and their implicit expectations about ethnic minority students were negative, whereas their explicit prejudice was low. We did not find any correlations between the different measures. The findings are discussed in terms of the reasons for the zero correlations, and the usefulness of considering an implicit measure for teacher expectations in future research is highlighted. In this vein, we might obtain deeper insight into the role that teachers play in the disadvantages ethnic minority students experience in school.
In this work, we demonstrate the feasibility of manufacturing an iron-based oxide-dispersion-strengthened (ODS) PM2000 composite material with the chemical composition of Fe20Cr4.5Al0.5Ti + 0.5Y 2 O 3 (in wt.%) via the advanced directed energy deposition (DED) process of high-speed laser cladding (HSLC). The characteristic high solidification rates of HSLC processes allow the successful dispersion of nano-scaled yttrium-based oxides in the ferritic stainless steel matrix. The effective suppression of nano-particle agglomeration during the melting stage, which is frequently observed in conventional DED processes of ODS materials, is reflected by smaller dispersoid sizes and corresponding higher hardness of manufactured specimen compared to DED-manufactured counterparts.
Radicals and their precursors play a central role in the chemical transformations occurring in indoor air and on indoor surfaces. Such species include OH, HO2, peroxy radicals, nitrous acid, reactive chlorine species, NO3, N2O5, Criegee intermediates, and glyoxal and methylglyoxal. Recent advances on instrumental analysis and modeling studies have demonstrated the need for a wider range of measurements of radical species and their precursors in indoor air. This work reviews measurement techniques and provides considerations for indoor measurements of several radicals and their precursors. Techniques to determine the actinic flux are also presented owing to the relevance of photolytically-initiated processes indoors. This review is also intended to provide pointers for those wanting to learn more about measurements of radicals indoors.
Recent (de-)globalization tendencies and rising protectionist measures has created new interest in studying the effects of unilateral and world-wide tariffs. This paper contributes to this issue by taking into account that international transactions in goods and services increasingly take the form of foreign direct investment. We look at the effects of import tariffs in the context of a two-region DSGE model with both an exporting and an FDI sector. We find that the tariff jumping effect on FDI is largely outweighed by a cost effect if the tariff is imposed on all imports. This holds in the case of both tariffs imposed unilaterally and worldwide import tariffs. Our analysis confirms the aggregate positive welfare effects of a unilateral tariff, but also shows inefficiencies resulting from consumption and production distortions. This leads to lower GDP and real wages through the investment channel. However, governments can generate a tariff jumping effect by exempting imports of multinationals from tariffs. This reduces negative growth effects but also lowers welfare gains since there are less tariff revenues to support consumption. In the case of a world-wide tariff, exempting imports of multinationals reduces negative welfare effects.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
5,287 members
Matthias Ehrhardt
  • Department of Mathematik und Informatik
Michael Grosche
  • School of Education
Oliver Passon
  • School of Mathmatics and Natural Sciences
Thomas Riedl
  • Faculty of Electrical, Information and Media Engineering
Gaußstr. 20, 42119, Wuppertal, NRW, Germany
Head of institution
Prof. Dr. Lambert T. Koch