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We develop a multigrid, multiple time stepping scheme to reduce computational efforts for calculating complex stress interactions in a strike-slip 2D planar fault for the simulation of seismicity. The key elements of the multilevel solver are separation of length scale, grid-coarsening, and hierarchy. In this study the complex stress interactions are split into two parts: the first with a small contribution is computed on a coarse level, and the rest for strong interactions is on a fine level. This partition leads to a significant reduction of the number of computations. The reduction of complexity is even enhanced by combining the multigrid with multiple time stepping. Computational efficiency is enhanced by a factor of 10 while retaining a reasonable accuracy, compared to the original full matrix-vortex multiplication. The accuracy of solution and computational efficiency depend on a given cut-off radius that splits multiplications into the two parts. The multigrid scheme is constructed in such a way that it conserves stress in the entire half-space.
In order to provide best control of the regeneration process for each individual patient, the release of protein drugs administered during surgery may need to be timely adapted and/or delayed according to the progress of healing/regeneration. This study aims to establish a multifunctional implant system for a local on-demand release, which is applicable for various types of proteins. It was hypothesized that a tubular multimaterial container kit, which hosts the protein of interest as a solution or gel formulation, would enable on-demand release if equipped with the capacity of diameter reduction upon external stimulation. Using devices from poly(epsilon-caprolactone) networks, it could be demonstrated that a shape-memory effect activated by heat or NIR light enabled on-demand tube shrinkage. The decrease of diameter of these shape-memory tubes (SMT) allowed expelling the payload as demonstrated for several proteins including SDF-1 alpha, a therapeutically relevant chemotactic protein, to achieve e.g. continuous release with a triggered add-on dosing (open tube) or an on-demand onset of bolus or sustained release (sealed tube). Considering the clinical relevance of protein factors in (stem) cell attraction to lesions and the progress in monitoring biomarkers in body fluids, such on-demand release systems may be further explored e.g. in heart, nerve, or bone regeneration in the future.
Fluid force microscopy combines the positional accuracy and force sensitivity of an atomic
force microscope (AFM) with nanofluidics via a microchanneled cantilever. However, adequate
loading and cleaning procedures for such AFM micropipettes are required for various
application situations. Here, a new frontloading procedure is described for an AFM micropipette
functioning as a force- and pressure-controlled microscale liquid dispenser. This frontloading
procedure seems especially attractive when using target substances featuring high
costs or low available amounts. Here, the AFM micropipette could be filled from the tip side
with liquid from a previously applied droplet with a volume of only a few μL using a short
low-pressure pulse. The liquid-loaded AFM micropipettes could be then applied for experiments
in air or liquid environments. AFM micropipette frontloading was evaluated with the
well-known organic fluorescent dye rhodamine 6G and the AlexaFluor647-labeled antibody
goat anti-rat IgG as an example of a larger biological compound. After micropipette usage,
specific cleaning procedures were tested. Furthermore, a storage method is described, at
which the AFM micropipettes could be stored for a few hours up to several days without drying
out or clogging of the microchannel. In summary, the rapid, versatile and cost-efficient
frontloading and cleaning procedure for the repeated usage of a single AFM micropipette is
beneficial for various application situations from specific surface modifications through to
local manipulation of living cells, and provides a simplified and faster handling for already
known experiments with fluid force microscopy.
Fluid force microscopy combines the positional accuracy and force sensitivity of an atomic force microscope (AFM) with nanofluidics via a microchanneled cantilever. However, adequate loading and cleaning procedures for such AFM micropipettes are required for various application situations. Here, a new frontloading procedure is described for an AFM micropipette functioning as a force-and pressure-controlled microscale liquid dispenser. This frontloading procedure seems especially attractive when using target substances featuring high costs or low available amounts. Here, the AFM micropipette could be filled from the tip side with liquid from a previously applied droplet with a volume of only a few mu L using a short low-pressure pulse. The liquid-loaded AFM micropipettes could be then applied for experiments in air or liquid environments. AFM micropipette frontloading was evaluated with the well-known organic fluorescent dye rhodamine 6G and the AlexaFluor647-labeled antibody goat anti-rat IgG as an example of a larger biological compound. After micropipette usage, specific cleaning procedures were tested. Furthermore, a storage method is described, at which the AFM micropipettes could be stored for a few hours up to several days without drying out or clogging of the microchannel. In summary, the rapid, versatile and cost-efficient frontloading and cleaning procedure for the repeated usage of a single AFM micropipette is beneficial for various application situations from specific surface modifications through to local manipulation of living cells, and provides a simplified and faster handling for already known experiments with fluid force microscopy.
Anthropogenic changes in climate, land use, and disturbance regimes, as well as introductions of non-native species can lead to the transformation of many ecosystems. The resulting novel ecosystems are usually characterized by species assemblages that have not occurred previously in a given area. Quantifying the ecological novelty of communities (i.e., biotic novelty) would enhance the understanding of environmental change. However, quantification remains challenging since current novelty metrics, such as the number and/or proportion of non-native species in a community, fall short of considering both functional and evolutionary aspects of biotic novelty. Here, we propose the Biotic Novelty Index (BNI), an intuitive and flexible multidimensional measure that combines (a) functional differences between native and non-native introduced species with (b) temporal dynamics of species introductions. We show that the BNI is an additive partition of Rao's quadratic entropy, capturing the novel interaction component of the community's functional diversity. Simulations show that the index varies predictably with the relative amount of functional novelty added by recently arrived species, and they illustrate the need to provide an additional standardized version of the index. We present a detailed R code and two applications of the BNI by (a) measuring changes of biotic novelty of dry grassland plant communities along an urbanization gradient in a metropolitan region and (b) determining the biotic novelty of plant species assemblages at a national scale. The results illustrate the applicability of the index across scales and its flexibility in the use of data of different quality. Both case studies revealed strong connections between biotic novelty and increasing urbanization, a measure of abiotic novelty. We conclude that the BNI framework may help building a basis for better understanding the ecological and evolutionary consequences of global change.
A multidimensional and analytical perspective on Open Educational Practices in the 21st century
(2022)
Participatory approaches to teaching and learning are experiencing a new lease on life in the 21st century as a result of the rapid technology development. Knowledge, practices, and tools can be shared across spatial and temporal boundaries in higher education by means of Open Educational Resources, Massive Open Online Courses, and open-source technologies. In this context, the Open Education Movement calls for new didactic approaches that encourage greater learner participation in formal higher education. Based on a representative literature review and focus group research, in this study an analytical framework was developed that enables researchers and practitioners to assess the form of participation in formal, collaborative teaching and learning practices. The analytical framework is focused on the micro-level of higher education, in particular on the interaction between students and lecturers when organizing the curriculum. For this purpose, the research reflects anew on the concept of participation, taking into account existing stage models for participation in the educational context. These are then brought together with the dimensions of teaching and learning processes, such as methods, objectives and content, etc. This paper aims to make a valuable contribution to the opening up of learning and teaching, and expands the discourse around possibilities for interpreting Open Educational Practices.
Despite advanced seismological techniques, automatic source characterization for microseismic earthquakes remains difficult and challenging since current inversion and modelling of high-frequency signals are complex and time consuming. For real-time applications such as induced seismicity monitoring, the application of standard methods is often not fast enough for true complete real-time information on seismic sources. In this paper, we present an alternative approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain full moment tensor and spatial location of microseismic sources. Specifically, a multibranch neural network algorithm is trained to encapsulate the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations. The learning-based model allows rapid inversion (within a fraction of second) once input data are available. A key advantage of the algorithm is that it can be trained using synthetic seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on synthesized and example recorded small magnitude (M <= 1.6) earthquakes at the Hellisheioi geothermal field in the Hengill area, Iceland. For the examined events, the model achieves excellent performance and shows very good agreement with the inverted solutions determined through standard methodology. In this study, we seek to demonstrate that this approach is viable for microseismicity real-time estimation of source parameters and can be integrated into advanced decision-support tools for controlling induced seismicity.
A multi-reference study of the byproduct formation for a ring-closed dithienylethene photoswitch
(2015)
Photodriven molecular switches are sometimes hindered in their performance by forming byproducts which act as dead ends in sequences of switching cycles, leading to rapid fatigue effects. Understanding the reaction pathways to unwanted byproducts is a prerequisite for preventing them. This article presents a study of the photochemical reaction pathways for byproduct formation in the photochromic switch 1,2-bis-(3-thienyl)-ethene. Specifically, using single-and multi-reference methods the post-deexcitation reaction towards the byproduct in the electronic ground state S-0 when starting from the S-1-S-0 conical intersection (CoIn), is considered in detail. We find an unusual low-energy pathway, which offers the possibility for the formation of a dyotropic byproduct. Several high-energy pathways can be excluded with high probability.
A multi-reference study of the byproduct formation for a ring-closed dithienylethene photoswitch
(2015)
Photodriven molecular switches are sometimes hindered in their performance by forming byproducts which act as dead ends in sequences of switching cycles, leading to rapid fatigue effects. Understanding the reaction pathways to unwanted byproducts is a prerequisite for preventing them. This article presents a study of the photochemical reaction pathways for byproduct formation in the photochromic switch 1,2-bis-(3-thienyl)-ethene. Specifically, using single- and multi-reference methods the post-deexcitation reaction towards the byproduct in the electronic ground state S0 when starting from the S1–S0 conical intersection (CoIn), is considered in detail. We find an unusual low-energy pathway, which offers the possibility for the formation of a dyotropic byproduct. Several high-energy pathways can be excluded with high probability.