Anti-Digoxigenin polyclonal antibody

The resistance of ordinary potato digging shovels can increase dramatically when used in a clay soil because of the adhesion between the soil and shovel. In this paper, a new type of bionic potato digging shovel was designed to decrease adhesion. The bionic structural elements, i.e., scalelike units (S-U) were applied to the potato digging shovel with inspiration from pangolin scales. The discrete element method (DEM) considered cohesion was used to simulate the drag reduction performance in clayey soil conditions. An ordinary plane shovel (O-P-S) was used for comparison. Three indicators (total force, draft force and compressive force) were used to characterize the drag reduction performance. The effect of the design variables of the bionic structures (length [l] and height [h]) and the transversal and longitudinal arrangement spacing (S1 and S2) of the structures on the drag reduction performance were analyzed. The results showed that the drag reduction performance of the bionic shovels with suitable parameters was better than that of the O-P-S. The best bionic sample labeled as a bionic prototype had a 22.26% drag reduction rate during the soil bin test and a 14.19% drag reduction rate during the field test compared to the O-P-S.

An approach engaging Vertical Electrical Sounding (VES) and remote sensing data was carried out with a view to developing groundwater potential and aquifer vulnerability maps of the study area. One hundred and one (101) depth sounding data were acquired using Schlumberger array, with half maximum current electrode separation (AB/2) of 100 m. The VES were quantitatively interpreted using partial curve matching and computer aided iteration to determine the geoelectrical parameters of each station. The remote sensing data were processed using the application of Geographic Information System-based multi-criteria technique ArcGIS software. Eight (8) parameters namely lineament density, drainage density, slope, transmissivity, hydraulic conductivity, coefficient of anisotropy, aquifer thickness and resistivity were used to produce the groundwater potential model while five (5) parameters namely, lineament density, slope, longitudinal conductance, hydraulic conductivity and thickness of layer overlying the delineated aquifer were also used to produce the vulnerability model. The final output of overlay parameters for estimating the groundwater potential gave an index that ranged from 1-5. The zone categorised as low groundwater potential covered about 80% of the area. The majority of the area falls within low (about 80%) vulnerability and low groundwater potential rating while being relatively protected from potential contaminants infiltrating from the surface. The prediction accuracy of the groundwater potential model was established via existing hand-dug well correlation analysis.