Modeling Spatial Soil Water Dynamics in a Tropical Floodplain, East Africa

Analyzing the spatial and temporal distribution of soil moisture is critical for ecohydrological processes and for sustainable water management studies in wetlands. The characterization of soil moisture dynamics and its influencing factors in agriculturally used wetlands pose a challenge in data-scarce regions such as East Africa. High resolution and good-quality time series soil moisture data are rarely available and gaps are frequent due to measurement constraints and device malfunctioning. Soil water models that integrate meteorological conditions and soil water storage may significantly overcome limitations due to data gaps at a point scale. The purpose of this study was to evaluate if the Hydrus-1D model would adequately simulate soil water dynamics at different hydrological zones of a tropical floodplain in Tanzania, to determine controlling factors for wet and dry periods and to assess soil water availability. The zones of the Kilombero floodplain were segmented as riparian, middle, and fringe along a defined transect. The model was satisfactorily calibrated (coefficient of determination; R2 = 0.54–0.92, root mean square error; RMSE = 0.02–0.11) on a plot scale using measured soil moisture content at soil depths of 10, 20, 30, and 40 cm. Satisfying statistical measures (R2 = 0.36–0.89, RMSE = 0.03–0.13) were obtained when calibrations for one plot were validated with measured soil moisture for another plot within the same hydrological zone. Results show the transferability of the calibrated Hydrus-1D model to predict soil moisture for other plots with similar hydrological conditions. Soil water storage increased towards the riparian zone, at 262.8 mm/a while actual evapotranspiration was highest (1043.9 mm/a) at the fringe. Overbank flow, precipitation, and groundwater control soil moisture dynamics at the riparian and middle zone, while at the fringe zone, rainfall and lateral flow from mountains control soil moisture during the long rainy seasons. In the dry and short rainy seasons, rainfall, soil properties, and atmospheric demands control soil moisture dynamics at the riparian and middle zone. In addition to these factors, depths to groundwater level control soil moisture variability at the fringe zone. Our results support a better understanding of groundwater-soil water interaction, and provide references for wetland conservation and sustainable agricultural water management

The combined effects of high-energy shock waves and cytostatic drugs or cytokines on human bladder cancer cells.

The effects of shock waves generated by an experimental Siemens lithotripter in combination with cytostatic drugs or cytokines on several bladder cancer cell lines were examined in vitro. Proliferation after treatment was determined with the 3-4,5-dimethylthiazol-2,5 diphenyl tetrazolium bromide assay. Dose enhancement ratios were calculated for each drug and each shock wave application mode in order to characterise the sensitising effect of shock wave pretreatment. The influence of the time between shock wave and drug treatment as well as the effects of different sequences of shock wave and drug treatment or concomitant treatment were assessed for selected combinations of cell lines and drugs. It was found that shock wave treatment could render certain cell lines more susceptible to subsequent cis-platinum, mitomycin C or actinomycin D incubation. Cell lines sensitive to tumour necrosis factor alpha or interferon alpha were further sensitised to these cytokines by shock wave pretreatment. The enhanced sensitivity to cis-platinum and actinomycin D decreased rapidly during the first hours after shock wave treatment. The antiproliferative effect was most pronounced after concomitant shock wave and drug treatment. The sensitisation to interferon alpha diminishes more slowly after shock wave exposure. From the results presented in this study it is concluded that transient shock wave-induced permeabilisation of cell membrane not only enhances drug efficiency, but also causes damage to cell organelles and alterations in cellular metabolism

Uncertainty Estimation in Instance Segmentation with Star-convex Shapes

Instance segmentation has witnessed promising advancements through deep neural network-based algorithms. However, these models often exhibit incorrect predictions with unwarranted confidence levels. Consequently, evaluating prediction uncertainty becomes critical for informed decision-making. Existing methods primarily focus on quantifying uncertainty in classification or regression tasks, lacking emphasis on instance segmentation. Our research addresses the challenge of estimating spatial certainty associated with the location of instances with star-convex shapes. Two distinct clustering approaches are evaluated which compute spatial and fractional certainty per instance employing samples by the Monte-Carlo Dropout or Deep Ensemble technique. Our study demonstrates that combining spatial and fractional certainty scores yields improved calibrated estimation over individual certainty scores. Notably, our experimental results show that the Deep Ensemble technique alongside our novel radial clustering approach proves to be an effective strategy. Our findings emphasize the significance of evaluating the calibration of estimated certainties for model reliability and decision-making

Boundary element based multiresolution shape optimisation in electrostatics

We consider the shape optimisation of high-voltage devices subject to electrostatic field equations by combining fast boundary elements with multiresolution subdivision surfaces. The geometry of the domain is described with subdivision surfaces and different resolutions of the same geometry are used for optimisation and analysis. The primal and adjoint problems are discretised with the boundary element method using a sufficiently fine control mesh. For shape optimisation the geometry is updated starting from the coarsest control mesh with increasingly finer control meshes. The multiresolution approach effectively prevents the appearance of non-physical geometry oscillations in the optimised shapes. Moreover, there is no need for mesh regeneration or smoothing during the optimisation due to the absence of a volume mesh. We present several numerical experiments and one industrial application to demonstrate the robustness and versatility of the developed approach.We gratefully acknowledge the support provided by the EU commission through the FP7 Marie Curie IAPP project CASOPT (PIAP-GA-2008-230224). K.B. and F.C. thank for the additional support provided by EPSRC through #EP/G008531/1. J.Z. thanks for the support provided by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070) and by the project SPOMECH – Creating a Multidisciplinary R&D Team for Reliable Solution of Mechanical Problems, reg. no. CZ.1.07/2.3.00/20.0070 within the Operational Programme ‘Education for Competitiveness’ funded by the Structural Funds of the European Union and the state budget of the Czech Republic. Special thanks to Andreas Blaszczyk from the ABB Corporate Research Center Switzerland for fruitful discussions and for providing the industrial applications.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jcp.2015.05.01

Online Popularity and Topical Interests through the Lens of Instagram

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201

AC-KBO Revisited

Equational theories that contain axioms expressing associativity and commutativity (AC) of certain operators are ubiquitous. Theorem proving methods in such theories rely on well-founded orders that are compatible with the AC axioms. In this paper we consider various definitions of AC-compatible Knuth-Bendix orders. The orders of Steinbach and of Korovin and Voronkov are revisited. The former is enhanced to a more powerful version, and we modify the latter to amend its lack of monotonicity on non-ground terms. We further present new complexity results. An extension reflecting the recent proposal of subterm coefficients in standard Knuth-Bendix orders is also given. The various orders are compared on problems in termination and completion.Comment: 31 pages, To appear in Theory and Practice of Logic Programming (TPLP) special issue for the 12th International Symposium on Functional and Logic Programming (FLOPS 2014