A data-driven fuzzy approach to simulate the critical shear stress of mixed cohesive/non-cohesive sediments

Purpose

The critical shear stress of cohesive and mixed cohesive/non-cohesive sediments is affected by multiple interacting physical, chemical and biological parameters. There are various mathematical approaches in the scientific literature for computing critical shear stress. However, processes that influence sediment stability are still not fully understood, and available formulas differ considerably. These discrepancies in the literature arise from random system behaviour (natural variability of the sediments), different definitions of the critical shear stress, different measurement techniques and different model frameworks (scope of the parameters, undisturbed versus artificial sediment samples). While analytical approaches fail to address the involved uncertainties, fuzzy logic-based models integrate uncertainty and imprecision.

Materials and methods

With this in mind, a data-driven neuro-fuzzy model (ANFIS) was used to determine the critical shear stress based on sediment characteristics such as wet bulk density and grain size distribution. In order to select model predictors systematically, an automated stepwise regression algorithm was applied. The database for this analysis consisted of 447 measurements of the critical shear stress originating from 64 undisturbed sediment samples.

Results and discussion

The study identified clay content as the primarily controlling variable for erosion resistance. Depending on the characteristics of the sampling location, the bulk density was also selected as a model predictor. In comparison to analytical models that are available in the scientific literature, the fuzzy model achieved higher correlation coefficients between measured and predicted data.

Conclusions

The neuro-fuzzy-model includes uncertainties of input variables and their interactions directly. Thus, it provides a reliable method for the prediction of erosion thresholds of cohesive/non-cohesive mixtures. It was also shown that this approach requires fewer measured variables as well as fewer assumptions than the models it was compared to.

     

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R² = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R² < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).     

Emerging viral zoonoses: frameworks for spatial and spatiotemporal risk assessment and resource planning

Spatial epidemiological tools are increasingly being applied to emerging viral zoonoses (EVZ), partly because of improving analytical methods and technologies for data capture and management, and partly because the demand is growing for more objective ways of allocating limited resources in the face of the emerging threat posed by these diseases. This review documents applications of geographical information systems (GIS), remote sensing (RS) and spatially-explicit statistical and mathematical models to epidemiological studies of EVZ.Landscape epidemiology uses statistical associations between environmental variables and diseases to study and predict their spatial distributions. Phylogeography augments epidemiological knowledge by studying the evolution of viral genetics through space and time. Cluster detection and early warning systems assist surveillance and can permit timely interventions. Advanced statistical models can accommodate spatial dependence present in epidemiological datasets and can permit assessment of uncertainties in disease data and predictions. Mathematical models are particularly useful for testing and comparing alternative control strategies, whereas spatial decision-support systems integrate a variety of spatial epidemiological tools to facilitate widespread dissemination and interpretation of disease data. Improved spatial data collection systems and greater practical application of spatial epidemiological tools should be applied in real-world scenarios.     

Chronic toxicity of contaminated sediments on reproduction and histopathology of the crustacean <Emphasis Type="Italic">Gammarus fossarum</Emphasis> and relationship with the chemical contamination and in vitro effects

Purpose  

The aim of the present study was to investigate possible relationships between the sediment contaminants and the occurrence of intersex in situ. Two of the studied sediments were from polluted sites with increased occurrence of intersex crustaceans (Lake Pilnok, black coal mining area in the Czech Republic, inhabited by the crayfish Pontastacus leptodactylus population with 18% of intersex; creek Lockwitzbach in Germany with Gammarus fossarum population with about 7% of intersex).     

A mathematical model linking tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth

To date, models for simulating sap flow dynamics in individual trees with a direct link to stem diameter variation include only the diameter fluctuation driven by a change in stem water storage. This paper reports results obtained with a comprehensive flow and storage model using whole-tree leaf transpiration as the only input variable. The model includes radial stem growth based on Lockhart's equation for irreversible cell expansion. It was demonstrated that including growth is essential to obtaining good simulation results. To model sap flow dynamics, capacitance of storage tissues was assumed either constant (i.e., electrical analogue approach) or variable and dependent on the water content of the respective storage tissue (i.e., hydraulic system approach). These approaches resulted in different shapes for the desorption curve used to calculate the capacitance of storage tissues. Comparison of these methods allowed detection of specific differences in model simulation of sap flow at the stem base (F(stem)) and stem diameter variation (D). Sensitivity analysis was performed to select a limited subset of identifiable parameters driving most of the variability in model predictions of F(stem) and D Both the electrical analogue and the hydraulic system approach for the flow and storage model were successfully calibrated and validated for the case of a young beech tree (Fagus sylvatica L.). Use of an objective model selection criterion revealed that the flow and storage model based on the electrical analogue approach yielded better predictions.     

Identification of fatty acid esters of pectenotoxin-2 seco acid in blue mussels (Mytilus edulis) from Ireland

Pectenotoxins from marine dinoflagellates of the genus Dinophysis are rapidly hydrolyzed by many shellfish to give pectenotoxin-2 seco acid, which isomerizes to 7-epi-pectenotoxin-2 seco acid. Three series of fatty acid esters of pectenotoxin-2 seco acid (PTX-2 seco acid) and 7-epi-PTX-2 seco acid were detected by LC-MS analysis of extracts from blue mussels (Mytilus edulis) from Ireland. The locations of the fatty acid ester linkages were identified by a combination of LC-MSn in positive- and negative-ion modes, LC-MS analysis of the products from reaction of the esters with sodium periodate, and NMR analysis of purified samples of the two most abundant ester derivatives. The 37-O-acyl esters of PTX-2 seco acid were the most abundant, followed by the corresponding 11-O-acyl esters, accompanied by low levels of the 33-O-acyl esters. The most abundant fatty acid esters in the fractionated sample were, in order, the 16:0, 22:6, 14:0, 16:1, 18:4, and 20:5 fatty acids, although a wide array of other PTX-2 seco acid fatty acid esters were also present at low levels.     

The use of <Emphasis Type="Italic">Vp1</Emphasis> in real time RT-PCR to select for pre-harvest sprouting tolerance in triticale

In spite of the availability of laboratory and field tests there is still a major problem to select pre-harvest sprouting (PHS) tolerant triticale varieties in a reliable, field-independent way. One approach to minimize the influence of environmental conditions and physio-morphological traits on PHS detection is using molecular genetic tools. The ‘viviparous’ Vp1 gene has been repeatedly described to play an important role in dormancy in wheat. A quantitative RT-PCR assay based on the expression of the Vp1 gene has been developed. Specific primers were designed for detecting Vp1 in both wheat and triticale. The expression levels of Vp1 were normalized using reference genes and relatively quantified with the comparative C_t-method. However, the first results indicate that the achieved Vp1 expression levels at 50 days post anthesis are not useful to select for PHS tolerance, both in wheat and triticale. This negative outcome so far is possibly due to the existence of several splicing events or to the late assaying moment in the kernel development, when Vp1 expression is found to be low.     

Global marine yield halved as fishing intensity redoubles

There is widespread concern and debate about the state of global marine resources and the ecosystems supporting them, notably global fisheries, as catches now generally stagnate or decline. Many fisheries are not assessed by standard stock assessment methods including many in the world's most biodiverse areas. Though simpler methods using widely available catch data are available, these are often discounted largely because data on fishing effort that contributed to the changes in catches are mostly not considered. We analyse spatial and temporal patterns of global fishing effort and its relationship with catch to assess the status of the world's fisheries. The study reveals that fleets now fish all of the world's oceans and have increased in power by an average of 10‐fold (25‐fold for Asia) since the 1950s. Significantly, for the equivalent fishing power expended, landings from global fisheries are now half what they were a half‐century ago, indicating profound changes to supporting marine environments. This study provides another dimension to understand the global status of fisheries.     

Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses

The use of camera vision to automatically detect head blight (scab) on wheat ears could provide information about the severity of this dangerous disease and help meet future food traceability requirements. Fusarium spp. is dangerous for both human and animal consumption and the ability to monitor symptom location and severity before the harvested product is further processed or stored could help determine whether the grain is fit for human/animal consumption, for bio-conservation, or is completely unusable.To generate various infection levels, field trials were conducted in 2008 and 2009 using wheat varieties with differing levels of susceptibility to the disease; plots were artificially infected with a spore suspension. A color (red, green, and blue) and a multispectral (red, infrared) camera system with real-time image analysis software were developed and compared to detect disease symptoms in the plots.The chlorophyll defect of the infected wheat ears was classified against the image background by setting binarization thresholds. The result was a black and white image. Single pixels or tiny clusters of pixels not belonging to the symptoms were eliminated by setting an area threshold. For both systems, a linear correlation was found between the camera and the visually detected disease levels of the wheat ears in the plots.In the non-infected control plots without disease symptoms, the multispectral system accurately measured “no disease” even though the digital color system detected too much infection (i.e., a false positive). The multispectral system showed a superior calibration capacity. While the color system had to calibrate for each variety, the multispectral system used only one calibration step before starting the measurements.