An application of adaptive cluster sampling for estimating total suspended sediment load.

Suspended sediment transport in river for a particular period is a timescale finite population. This population shows natural aggregation tendencies in sediment concentration particularly during floods. Adaptive cluster sampling (ACS) can be potentially conducted for sampling from this rare clustered population and estimating total load. To illustrate the performance of ACS in sediment estimation, a comparative study was carried out in the Gorgan-Rood River, Iran, with around a 5 year daily concentration record. The total sediment loads estimated by ACS were statistically compared to the observed load, estimations of selection at list time (SALT) and conventional sediment rating curve with and without correction factors. The results suggest that none of the sediment rating curves produced accurate estimates, while both ACS and SALT showed satisfactory results at a semi-weekly sampling frequency. The best estimation obtained by the rating curves did not show a percent error better than -40%; however, ACS and SALT underestimated the load at less than 5%. The results of this study suggest ACS could improve river monitoring programs

Effect of adaptive cluster sampling design on accuracy of sediment rating curve estimation.

Adaptive cluster sampling represents a design whereby more samples during high storm hydrographs can be obtained in the field compared to the commonly used calendar-based. To compare the effect of these sampling designs on accuracy of sediment rating curve estimation, we performed a study for Gorgan-Rood River, Iran by synthesizing sample sets from daily records. The load estimates obtained by conventional, two bias-corrected, and logged mean load within discharge classes (LMLWDCs) rating curves were statistically evaluated. All rating curves derived from calendar-based sample sets–except those with a nonparametric correction factor–underestimated the average load from 25 to 76%. Rating curves derived from adaptive sample sets, however, increased the sediment load to as much as 30%. Among this group of rating curves, LMLWDC produced the most accurate results with only 3% overestimation and a coefficient of variation in the order of 14% when the sampling frequency was semiweekly. The more accurate estimates from adaptive sample sets are likely due to the inclusion of more samples from high load periods

Adaptive cluster sampling for a temporal-scale population

Adaptive cluster sampling (ACS) is appropriate for rare clustered populations with localization tendencies. Up to now, it has been used exclusively for investigating spatial-scale problems rather than temporal-scale such as t his study is dealing with, i.e.sediment transport in rivers. Suspended sediment load is carried mostly during relatively short periods coincide with high flows otherwise negligible. In ACS, more samples from critical river stages can be taken with respect to the aggregation tendencies of sediment loads during transport; thus increasing the level of representativeness of samples. Adoption of ACS to this new area needs further verification and adaptation such as definition of the sampling unit, population frame, neighborhood relation, and threshold. In this study, several scenarios were defined for the purpose of evaluating the ACS in sediment estimation. Numerous sample sets were taken from intensive discharge-load records of Sg. Pangsun River, Malaysia. These sample sets are different with respect to initial sample size, neighborhood relation, and discharge threshold. Total suspended sediment loads were then estimated using modified Horvitz-Thompson method. The comparison made between the symmetric neighborhood relation and the forward method suggested in this study showed that the latter could be used instead of the former in sediment studies without losing the accuracy. The findings also suggested the flow duration curve is a useful tool for ranking initial samples in order to determine an optimum discharge threshold

Improved bacteriostatic and anticorrosion effects of polycaprolactone/chitosan coated magnesium via incorporation of zinc oxide

Magnesium has been recognized as a groundbreaking biodegradable biomaterial for implant applications, but its use is limited because it degrades too quickly in physiological solutions. This paper describes the research on the influence of polycaprolactone (PCL)/chitosan (CS)/zinc oxide (ZnO) composite coating (PCL/CS/ZnO) on the corrosion resistance and antibacterial activity of magnesium. The PCL/CS film presented a porous structure with thickness of about 40–50 μm, while after incorporation of ZnO into the PCL/CS, a homogenous film without pores and defects was attained. The ZnO embedded in PCL/CS enhanced corrosion resistance by preventing corrosive ions diffusion in the magnesium substrate. The corrosion, antibacterial, and cell interaction mechanism of the PCL/CS/ZnO composite coating is discussed in this study. In vitro cell culture revealed that the PCL/CS coating with low loaded ZnO significantly improved cytocompatibility, but coatings with high loaded ZnO were able to induce some cytotoxicity osteoblastic cells. It was also found that enhanced antibacterial activity of the PCL/CS/ZnO coating against both Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) bacteria, while less significant antibacterial activity was detected for uncoated Mg and PCL/CS coating. Based on the results, the PCL/CS coatings loaded with low ZnO content may be recommended as a candidate material for biodegradable Mg‐based orthopedic implant applications