Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm

Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best-fitting subsurface boundaries. Such algorithms represent the current state-of-the-art in geoelectric modelling. An analysis of the numerical accuracy as a function of the number of degrees of freedom revealed that the adaptive wavelet algorithm outperforms the finite element solver for simple and moderately complex models, whereas the results become comparable for models with high spatial variability of electrical conductivities. The linear dependence of the modelling error and the computing time proved to be model-independent. This feature will allow very efficient computations using large-scale models as soon as our experimental code is optimized in terms of its implementatio

3-D electrical resistivity tomography using adaptive wavelet parameter grids

We present a novel adaptive model parametrization strategy for the 3-D electrical resistivity tomography problem and demonstrate its capabilities with a series of numerical examples. In contrast to traditional parametrization schemes, which are based on fixed disjoint blocks, we discretize the subsurface in terms of Haar wavelets and adaptively adjust the parametrization as the iterative inversion proceeds. This results in a favourable balance of cell sizes and parameter reliability, that is, in regions where the data constrain the subsurface properties well, our parametrization strategy leads to a fine grid, whereas poorly resolved areas are represented only by a few large blocks. This is documented with eigenvalue analyses and by computing model resolution matrices. During the initial iteration steps, only a few model parameters are involved, which reduces the risk that the regularization dominates the inversion. The algorithm also automatically accounts for non-linear effects caused by pronounced conductivity contrasts. Inside conductive features a finer grid is generated than inside more resistive structures. The automated parameter adaptation is computationally efficient, because the coarsening and refinement subroutines have a nearly linear numerical complexity with respect to the number of model parameters. Because our approach is not tightly coupled to electrical resistivity tomography, it should be straightforward to adapt it to other data type

An Infinite Swapping Approach to the Rare-Event Sampling Problem

We describe a new approach to the rare-event Monte Carlo sampling problem. This technique utilizes a symmetrization strategy to create probability distributions that are more highly connected and thus more easily sampled than their original, potentially sparse counterparts. After discussing the formal outline of the approach and devising techniques for its practical implementation, we illustrate the utility of the technique with a series of numerical applications to Lennard-Jones clusters of varying complexity and rare-event character.Comment: 24 pages, 16 figure

Mortality in Recreational Mountain-Biking in the Austrian Alps: A Retrospective Study over 16 Years.

Despite recreational mountain-biking's growing popularity worldwide, the literature on mortality in this leisure sporting activity is scarce. Therefore, the aim of the present study was to investigate the characteristics of fatal accidents as well as resulting dead victims during recreational mountain-biking in the Austrian Alps over the past 16 years. For this purpose, a retrospective study based on Austrian institutional documentation from 2006 to 2021 was conducted. In total, 97 fatalities (1 woman) with a mean age of 55.6 ± 13.9 years were recorded by the Austrian Alpine Police. Of those, 54.6% died due to a non-traumatic (mostly cardio-vascular) and 41.2% due to a traumatic event. Mountain-bikers fatally accidented for non-traumatic reasons frequently belonged to older age classes (p = 0.05) and mostly (73.6%) died during the ascent, whereas traumatic events mainly (70.0%) happened during the descent (p < 0.001). Throughout the examined period, the absolute number of fatalities slightly increased, whereas the mortality index (proportion of deaths/accidented victims) did not (mean value: 1.34 ± 0.56%). Factors such as male sex in general, above average age and uphill riding for non-traumatic accidents, as well as downhill riding for traumatic events, seem to be associated with fatalities during recreational mountain-biking in the Austrian Alps. These results should be considered for future preventive strategies in recreational mountain-biking

Hybrid-learning for social design

Underlying causes of conflict, inequity, and injustice remain deeply entrenched in the lives of people ranging from impoverished villages to overpopulated megalopolises. To help address these complex issues, social design brings together designers from varying disciplines to address the needs of the community. While universities across the world recognize the need to introduce social design pedagogy into their curriculum, many programs remain confined within Western post-graduate education. In response, two multidisciplinary professors initiated a team-taught \u27Design for Social Change\u27 course in an undergraduate design program in Dubai, UAE. Open to students across disciplines, the course followed a hybrid-learning approach to planning, conducting, and evaluating learning activities. The methodology empowered students to determine their project interest, cooperatively build research, and value their diverse skills. This paper introduces the notion of hybrid-learning, collabor-active team-teaching in an interdisciplinary classroom, and applies the methodology to a social design course in the MENA region. This paper has been presented as part of the Tasmeem Exploration Platform during Tasmeem Conference, Doha, 2013

PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models

Markov (state) models (MSMs) and related models of molecular kinetics have recently received a surge of interest as they can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods. In this work we present the opensource Python package PyEMMA (http://pyemma.org) that provides accurate and efficient algorithms for kinetic model construction. PyEMMA can read all common molecular dynamics data formats, helps in the selection of input features, provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for MSMs, hidden Markov models, and several other models. Systematic model validation and error calculation methods are provided. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. We have derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system. Plotting functions to produce a manuscript-ready presentation of the results are available. In this work, we demonstrate the features of the software and show new methodological concepts and results produced by PyEMMA

Photochemistry of Furyl- and Thienyldiazomethanes: Spectroscopic Characterization of Triplet 3-Thienylcarbene

Photolysis (λ \u3e 543 nm) of 3-thienyldiazomethane (1), matrix isolated in Ar or N2 at 10 K, yields triplet 3-thienylcarbene (13) and α-thial-methylenecyclopropene (9). Carbene 13 was characterized by IR, UV/vis, and EPR spectroscopy. The conformational isomers of 3-thienylcarbene (s-E and s-Z) exhibit an unusually large difference in zero-field splitting parameters in the triplet EPR spectrum (|D/hc| = 0.508 cm–1, |E/hc| = 0.0554 cm–1; |D/hc| = 0.579 cm–1, |E/hc| = 0.0315 cm–1). Natural Bond Orbital (NBO) calculations reveal substantially differing spin densities in the 3-thienyl ring at the positions adjacent to the carbene center, which is one factor contributing to the large difference in D values. NBO calculations also reveal a stabilizing interaction between the sp orbital of the carbene carbon in the s-Z rotamer of 13 and the antibonding σ orbital between sulfur and the neighboring carbon—an interaction that is not observed in the s-E rotamer of 13. In contrast to the EPR spectra, the electronic absorption spectra of the rotamers of triplet 3-thienylcarbene (13) are indistinguishable under our experimental conditions. The carbene exhibits a weak electronic absorption in the visible spectrum (λmax = 467 nm) that is characteristic of triplet arylcarbenes. Although studies of 2-thienyldiazomethane (2), 3-furyldiazomethane (3), or 2-furyldiazomethane (4) provided further insight into the photochemical interconversions among C5H4S or C5H4O isomers, these studies did not lead to the spectroscopic detection of the corresponding triplet carbenes (2-thienylcarbene (11), 3-furylcarbene (23), or 2-furylcarbene (22), respectively)

Scalable transactions in the cloud: partitioning revisited

Lecture Notes in Computer Science, 6427Cloud computing is becoming one of the most used paradigms to deploy highly available and scalable systems. These systems usually demand the management of huge amounts of data, which cannot be solved with traditional nor replicated database systems as we know them. Recent solutions store data in special key-value structures, in an approach that commonly lacks the consistency provided by transactional guarantees, as it is traded for high scalability and availability. In order to ensure consistent access to the information, the use of transactions is required. However, it is well-known that traditional replication protocols do not scale well for a cloud environment. Here we take a look at current proposals to deploy transactional systems in the cloud and we propose a new system aiming at being a step forward in achieving this goal. We proceed to focus on data partitioning and describe the key role it plays in achieving high scalability.This work has been partially supported by the Spanish Government under grant TIN2009-14460-C03-02 and by the Spanish MEC under grant BES-2007-17362 and by project ReD Resilient Database Clusters (PDTC/EIA-EIA/109044/2008)

Framework, principles and recommendations for utilising participatory methodologies in the co-creation and evaluation of public health interventions

Background: Due to the chronic disease burden on society, there is a need for preventive public health interventions to stimulate society towards a healthier lifestyle. To deal with the complex variability between individual lifestyles and settings, collaborating with end-users to develop interventions tailored to their unique circumstances has been suggested as a potential way to improve effectiveness and adherence. Co-creation of public health interventions using participatory methodologies has shown promise but lacks a framework to make this process systematic. The aim of this paper was to identify and set key principles and recommendations for systematically applying participatory methodologies to co-create and evaluate public health interventions. Methods: These principles and recommendations were derived using an iterative reflection process, combining key learning from published literature in addition to critical reflection on three case studies conducted by research groups in three European institutions, all of whom have expertise in co-creating public health interventions using different participatory methodologies. Results: Key principles and recommendations for using participatory methodologies in public health intervention co-creation are presented for the stages of: Planning (framing the aim of the study and identifying the appropriate sampling strategy); Conducting (defining the procedure, in addition to manifesting ownership); Evaluating (the process and the effectiveness) and Reporting (providing guidelines to report the findings). Three scaling models are proposed to demonstrate how to scale locally developed interventions to a population level. Conclusions: These recommendations aim to facilitate public health intervention co-creation and evaluation utilising participatory methodologies by ensuring the process is systematic and reproducible

Phosphorylation of SOS1 on tyrosine 1196 promotes its RAC GEF activity and contributes to BCR-ABL leukemogenesis

Son of Sevenless 1 (SOS1) is a dual guanine nucleotide exchange factor (GEF) that activates the small GTPases RAC and RAS. Although the molecular mechanisms of RAS GEF catalysis have been unveiled, how SOS1 acquires RAC GEF activity and what is the physio-pathological relevance of this activity is much less understood. Here we show that SOS1 is tyrosine phosphorylated on Y1196 by ABL. Phosphorylation of Y1196 controls SOS1 inter-molecular interaction, is required to promote the exchange of nucleotides on RAC in vitro and for platelet-derived growth factor (PDGF) activation of RAC- and RAC-dependent actin remodeling and cell migration. SOS1 is also phosphorylated on Y1196 by BCR-ABL in chronic myelogenous leukemic cells. Importantly, in these cells, SOS1 is required for BCR-ABL-mediated activation of RAC, cell proliferation and transformation in vitro and in a xenograft mouse model. Finally, genetic removal of Sos1 in the bone marrow-derived cells (BMDCs) from Sos1fl/flmice and infected with BCR-ABL causes a significant delay in the onset of leukemogenesis once BMDCs are injected into recipient, lethally irradiated mice. Thus, SOS1 is required for full transformation and critically contribute to the leukemogenic potential of BCR-ABL