Polysulfobetaines in aqueous solution show upper critical solution temperature (UCST) behavior. We investigate here the representative of this class of materials, poly (N,N-dimethyl-N-(3-methacrylamidopropyl) ammonio propane sulfonate) (PSPP), with respect to: (i) the dynamics in aqueous solution above the cloud point as function of NaBr concentration; and (ii) the swelling behavior of thin films in water vapor as function of the initial film thickness. For PSPP solutions with a concentration of 5 wt.%, the temperature dependence of the intensity autocorrelation functions is measured with dynamic light scattering as function of molar mass and NaBr concentration (0–8 mM). We found a scaling of behavior for the scattered intensity and dynamic correlation length. The resulting spinodal temperatures showed a maximum at a certain (small) NaBr concentration, which is similar to the behavior of the cloud points measured previously by turbidimetry. The critical exponent of susceptibility depends on NaBr concentration, with a minimum value where the spinodal temperature is maximum and a trend towards the mean-field value of unity with increasing NaBr concentration. In contrast, the critical exponent of the correlation length does not depend on NaBr concentration and is lower than the value of 0.5 predicted by mean-field theory. For PSPP thin films, the swelling behavior was found to depend on film thickness. A film thickness of about 100 nm turns out to be the optimum thickness needed to obtain fast hydration with H 2 O.

The assessment of natural hazards and risk has traditionally been built upon the estimation of threat maps, which are used to depict potential danger posed by a particular hazard throughout a given area. But when a hazard event strikes, infrastructure is a significant factor that can determine if the situation becomes a disaster. The vulnerability of the population in a region does not only depend on the area’s local threat, but also on the geographical accessibility of the area. This makes threat maps by themselves insufficient for supporting real-time decision-making, especially for those tasks that involve the use of the road network, such as management of relief operations, aid distribution, or planning of evacuation routes, among others. To overcome this problem, this paper proposes a multidisciplinary approach divided in two parts. First, data fusion of satellite-based threat data and open infrastructure data from OpenStreetMap, introducing a threat-based routing service. Second, the visualization of this data through cartographic generalization and schematization. This emphasizes critical areas along roads in a simple way and allows users to visually evaluate the impact natural hazards may have on infrastructure. We develop and illustrate this methodology with a case study of landslide threat for an area in Colombia.

Background: Malaria is an old life-threatening parasitic disease that is still affecting many people, mainly children living in sub-Saharan Africa. Availability of effective antimalarial drugs played a significant role in the treatment and control of malaria. However, recent information on the emergence of P. falciparum parasites resistant to one of the artemisinin-based combination therapies suggests the need for discovery of new drug molecules. Therefore, this study aimed to evaluate the antiplasmodial activity of extracts, fractions and isolated compound from medicinal plants traditionally used in the treatment of malaria in Tanzania. Methods: Dry powdered plant materials were extracted by cold macerations using different solvents. Norcaesalpin D was isolated by column chromatography from dichloromethane root extract of Caesalpinia bonducella and its structure was assigned based on the spectral data. Crude extracts, fractions and isolated compound were evaluated for antiplasmodial activity against chloroquine-sensitive P. falciparum (3D7), chloroquine-resistant P. falciparum (Dd2, K1) and artemisinin-resistant P. falciparum (IPC 5202 Battambang, IPC 4912 Mondolkiri) strains using the parasite lactate dehydrogenase assay. Results: The results indicated that extracts of Erythrina schliebenii, Holarrhena pubescens, Dissotis melleri and C. bonducella exhibited antiplasmodial activity against Dd2 parasites. Ethanolic root extract of E. schliebenii had an IC50 of 1.87 mu g/mL while methanolic and ethanolic root extracts of H. pubescens exhibited an IC50 = 2.05 mu g/mL and IC50 = 2.43 mu g/mL, respectively. Fractions from H. pubescens and C. bonducella roots were found to be highly active against K1, Dd2 and artemisinin-resistant parasites. Norcaesalpin D from C. bonducella root extract was active with IC50 of 0.98, 1.85 and 2.13 mu g/mL against 3D7, Dd2 and IPC 4912-Mondolkiri parasites, respectively. Conclusions: Antiplasmodial activity of norcaesalpin D and extracts of E. schliebenii, H. pubescens, D. melleri and C. bonducella reported in this study requires further attention for the discovery of antimalarial lead compounds for future drug development.

Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images and then to displace the precipitation field to the imminent future (minutes to hours) based on that motion, assuming that the intensity of the features remains constant (“Lagrangian persistence”). In that context, “optical flow” has become one of the most popular tracking techniques. Yet the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (“rainymotion”) for precipitation nowcasting is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). That way, the library may serve as a tool for providing fast, free, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.

Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.