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Within this work, we report the results of nuclear inelastic scattering experiments of the low-spin phase of the iron(II) mononuclear SCO complex Fe[HBpz3]2 and density functional theory based calculations performed on a model molecule of the complex. We show that the calculated partial density of vibrational states based on the structure of a single iron(II) center which is linked by three pyrazole rings to borat is in good accordance with the experimentally obtained 57Fe-pDOS and assign the molecular vibrations to the prominent optical phonons.
We present new results on standard basis computations of a 0-dimensional ideal I in a power series ring or in the localization of a polynomial ring over a computable field K. We prove the semicontinuity of the “highest corner” in a family of ideals, parametrized by the spectrum of a Noetherian domain A. This semicontinuity is used to design a new modular algorithm for computing a standard basis of I if K is the quotient field of A. It uses the computation over the residue field of a “good” prime ideal of A to truncate high order terms in the subsequent computation over K. We prove that almost all prime ideals are good, so a random choice is very likely to be good, and whether it is good is detected a posteriori by the algorithm. The algorithm yields a significant speed advantage over the non-modular version and works for arbitrary Noetherian domains. The most important special cases are perhaps A = ℤ and A = k[t], k any field and t a set of parameters. Besides its generality, the method differs substantially from previously known modular algorithms for A = ℤ, since it does not manipulate the coefficients. It is also usually faster and can be combined with other modular methods for computations in local rings. The algorithm is implemented in the computer algebra system SINGULAR and we present several examples illustrating its power.
Nanostructured tantalum (Ta)-based dental implants have recently attracted significant attention thanks to their superior biocompatibility and bioactivity as compared to their titanium-based counterparts. While the biological and chemical aspects of Ta implants have been widely studied, their mechanical features have been investigated more rarely. Additionally, the mechanical behavior of these implants and, more importantly, their plastic deformation mechanisms are still not fully understood. Accordingly, in the current research, molecular dynamics simulation as a powerful tool for probing the atomic-scale phenomena is utilized to explore the microstructural evolution of pure polycrystalline Ta samples under tensile loading conditions. Various samples with an average grain size of 2–10 nm are systematically examined using various crystal structure analysis tools to determine the underlying deformation mechanisms. The results reveal that for the samples with an average grain size larger than 8 nm, twinning and dislocation slip are the main sources of any plasticity induced within the sample. For finer-grained samples, the activity of grain boundaries—including grain elongation, rotation, migration, and sliding—are the most important mechanisms governing the plastic deformation. Finally, the temperature-dependent Hall–Petch breakdown is thoroughly examined for the nanocrystalline samples via identification of the grain boundary dynamics.
In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided.
In this paper we consider the stochastic primitive equation for geophysical flows subject to transport noise and turbulent pressure. Admitting very rough noise terms, the global existence and uniqueness of solutions to this stochastic partial differential equation are proven using stochastic maximal
-regularity, the theory of critical spaces for stochastic evolution equations, and global a priori bounds. Compared to other results in this direction, we do not need any smallness assumption on the transport noise which acts directly on the velocity field and we also allow rougher noise terms. The adaptation to Stratonovich type noise and, more generally, to variable viscosity and/or conductivity are discussed as well.
Municipal wastewater is an interesting source of phosphorus and several processes for the recovery of phosphorus from this source have been described. These processes yield magnesium ammonium phosphate (MAP), a valuable fertilizer. In these processes, pH shifts and the addition of chemicals are used to influence the species distribution in the solution such as to finally obtain the desired product and to prevent the co-precipitation of salts of heavy metal ions. Elucidating these species distributions experimentally is a challenging and cumbersome task. Therefore, in the present work, a thermodynamic model was developed that can be used for predicting the species distributions in the various steps of the recovery process. The model combines the extended Debye-Hückel equation for the prediction of activity coefficients with dissociation constants and solubility product data from the literature and contains no parameters that need to be adjusted to process data. The model was successfully tested by comparison to experimental data for the Stuttgart process from the literature and used for analyzing the different process steps. Furthermore, it was demonstrated how the model can be used for optimizing the process.
In recent decades, academia has addressed a wide range of research topics in the field of ethical decision-making. Besides a great amount of research on ethical consumption, also the domain of ethical investments increasingly moves in the focus of scholars. While in this area most research focuses on whether socially or environmentally sustainable businesses outperform traditional investments financially or investigates the character traits as well as other socio-demographic factors of ethical investors, the impact of sustainable corporate conduct on the investment intentions of private investors still requires further research. Hence, we conducted two studies to shed more light on this highly relevant topic. After discussing the current state of research, in our first empirical study, we explore whether besides the traditional triad of risk, return, and liquidity, also sustainability exerts a significant impact on the willingness to invest. As hypothesized, we find that sustainability shows a clear and decisive impact in addition to the traditional factors. In a consecutive study, we investigate deeper into the sustainability-willingness to invest link. Here, our results show that improved sustainability might not pay off in terms of investment attractiveness, however and conversely, it certainly harms to conduct business in a non-sustainable manner, which cannot even be compensated by an increased return.
As a consequence of the real estate market crash after 2008, large investors invested a significant amount of wealth into single-family houses to construct a portfolio of rental dwellings, whose income is securitized in the capital. In some local housing markets, these investors own remarkable numbers of single-family houses. Furthermore, their trading activities have resulted in a new investment strategy, which exacerbates property wealth concentration and polarization. This new investment strategy and its portfolio optimization inspire curiosity about its influence on housing markets. This paper first aims to find an optimal portfolio strategy by employing an expected utility optimization from the terminal wealth, which adopts a stochastic model that includes a variety of economic states to estimate house prices. Second, it aims to analyze the effect of large investors on the housing market. The results show the investment strategies of large investors depend on the balance among economic state, maintenance cost, rental income, interest rate and investment willingness of large investors to housing and their effect depends on the state of the economy.