Cement-rock interaction : infiltration of a high-pH solution into a fractured granite core

Within the framework of the HPF project (Hyperalkaline Plume in Fractured Rock) at the Grimsel Test Site (Switzerland), a small scale core infiltration experiment was performed at the University of Bern. A high-pH solution was continuously injected, under a constant pressure gradient, into a cylindrical core of granite containing a fracture. This high-pH solution was a synthetic version of solutions characteristic of early stages in the degradation of cement. The interaction between the rock and the solutions was reflected by significant changes in the composition of the injected solution, despite the negligible pH-buffering capacity, and a decrease in the permeability of the rock. Changes in the mineralogy and porosity of the fault gouge filling the fracture were only minor. Within the new LCS (Long-Term Cement Studies) project at Grimsel, new one-dimensional reactive transport modeling using CrunchFlow has been used to improve the interpretation of the experimental results. Dispersive and advective solute transport, adsorption processes and mineral reaction kinetics have been taken into account. The evolution of solution composition is mainly controlled by dissolution/precipitation reactions. Adsorption processes (cation exchange, surface complexation) only play a role in the very early stages of the experiment

Flowers, leaves or both? How to obtain suitable images for automated plant identification

Background: Deep learning algorithms for automated plant identification need large quantities of precisely labelled images in order to produce reliable classification results. Here, we explore what kind of perspectives and their combinations contain more characteristic information and therefore allow for higher identification accuracy. Results: We developed an image-capturing scheme to create observations of flowering plants. Each observation comprises five in-situ images of the same individual from predefined perspectives (entire plant, flower frontal- and lateral view, leaf top- and back side view). We collected a completely balanced dataset comprising 100 observations for each of 101 species with an emphasis on groups of conspecific and visually similar species including twelve Poaceae species. We used this dataset to train convolutional neural networks and determine the prediction accuracy for each single perspective and their combinations via score level fusion. Top-1 accuracies ranged between 77% (entire plant) and 97% (fusion of all perspectives) when averaged across species. Flower frontal view achieved the highest accuracy (88%). Fusing flower frontal, flower lateral and leaf top views yields the most reasonable compromise with respect to acquisition effort and accuracy (96%). The perspective achieving the highest accuracy was species dependent. Conclusions: We argue that image databases of herbaceous plants would benefit from multi organ observations, comprising at least the front and lateral perspective of flowers and the leaf top view

Ecological study of aquatic midges and some related insects with special reference to feeding habits

Die Schweiz ist ein reiches Land. Sie verfügt über viele Millionäre. Der große Reichtum konzentriert sich auf wenige Familien und Personen. In der Schweiz leben aber auch eine halbe Million der Bevölkerung (7,5 Mio.) in Haushalten von Erwerbstätigen, die weniger als das Existenzminimum verdienen. Über 200‘000 Personen sind auf Sozialhilfe angewiesen. Bei den Vermögen und den verfügbaren Einkommen hat sich in den letzten Jahren die Kluft zwischen den obersten und untersten zehn Prozent verschärft. Die Zunahme der sozialen Ungleichheit erhöht die soziale Brisanz, was mehr zu ergründen ist. Die soziale Differenzierung dokumentiert Prozesse der Globalisierung. Sie reproduziert und spezifiziert alte soziale Ungleichheiten. Wichtig ist, dass die Soziale Arbeit das thematisiert und weiter theoretisiert

Productivity and Profitability of a Cotton-based Production System under Organic and Conventional Management in India

The debate on the relative benefits of conventional and organic farming systems is more topical than ever. The achievements of conventional high-input agriculture were largely brought about at the cost of deteriorating soil fertility; furthermore, they were based to a large extent on fossil fuels. Developing more sustainable farming practices on a large scale is of utmost importance. However, information about the performance of agricultural production systems under organic and conventional management in tropical and subtropical regions is largely lacking. This study aimed to assess agronomic and economic data from a long-term farming systems comparison trial under semi-arid conditions in central India. Four two-year crop rotations comprising cotton-soybean-wheat under biodynamic, organic and conventional management were investigated, including one conventional system with and one without transgenic Bt cotton, between 2007 and 2010. Results showed 13% lower yields in organic compared to conventional systems. Yields in cotton, soybean and wheat were on average 14 %, 7% and 15% lower, respectively. However, production costs of organic systems were on average 32% lower than those of conventional systems, which led to similar gross margins in all systems. To our knowledge, this is the first long-term field trial comparing the agronomic and economic performance of organic, conventional and conventional+Bt cotton-based farming systems. The results of our study suggest that organic farming is a promising alternative to conventional farming in cotton-based production systems in central India. The less capital intensive nature of organic systems may be particularly interesting for smallholder farmers as it decreases dependence on loans for farm inputs. Therefore, our findings have the potential to be useful for decision-making and in turn may lead to a redirection of agricultural policies

Electronic structure of intentionally disordered AlAs/GaAs superlattices

We use realistic pseudopotentials and a plane-wave basis to study the electronic structure of non-periodic, three-dimensional, 2000-atom (AlAs)_n/(GaAs)_m (001) superlattices, where the individual layer thicknesses n,m = {1,2,3} are randomly selected. We find that while the band gap of the equivalent (n = m = 2) ordered superlattice is indirect, random fluctuations in layer thicknesses lead to a direct gap in the planar Brillouin zone, strong wavefunction localization along the growth direction, short radiative lifetimes, and a significant band-gap reduction, in agreement with experiments on such intentionally grown disordered superlattices.Comment: 10 pages, REVTeX and EPSF macros, 4 figures in postscript. e-mail to [email protected]

Deep learning in plant phenological research: A systematic literature review

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field

Virtuelle Verkaufsberater in interaktiven Medien : eine experimentelle Untersuchung zur Wirkung von Avataren in interaktiven Medien

Der elektronische Handel, allen voran repräsentiert durch das Internet, erfreut sich eines stetigen Bedeutungszuwachses bei der Vermarktung von Produkten und Dienstleistungen. Dennoch existieren Faktoren, die das Absatzpotenzial dieses Distributionskanals begrenzen. Das anonyme Erscheinungsbild interaktiver Medien wird oft als Ursache für fehlendes Vertrauen der Konsumenten zu den Anbietern gesehen. Die vorliegende Studie untersucht, inwieweit virtuelle, menschenähnliche Figuren (Avatare) sich zur Steigerung des Vertrauens im elektronischen Handel eignen. Ein Online-Experiment unterstützt die postulierten positiven Effekte von Avataren auf das Vertrauen und weitere zentrale Prädispositionen des Kaufverhaltens