Treatment challenge of a cyanobacterium Romeria elegans bloom in a South Australian wastewater treatment plant: a case study.

A bloom of the non-toxic cyanobacterium Romeria elegans in waste stabilisation ponds (WSPs) within Angaston waste water treatment plant (WWTP) has posed an unprecedented treatment challenge for the local water utility. The water from the WSPs is chlorinated for safety prior to reuse on nearby farmland. Cyanobacteria concentrations of approximately 1.2 × 106 cells mL−1 increased the chlorine demand dramatically. Operators continuously increased the disinfectant dose up to 50 mg L−1 to achieve operational guideline values for combined chlorine (0.5-1.0 mg L−1) prior to reuse. Despite this, attempts to achieve targeted combined chlorine residual (CCR) failed. In this study, samples from the waste stabilisation pond at Angaston WWTP were chlorinated over a range of doses. Combined chlorine, disinfection by-product formation, cyanobacteria cell concentration, Escherichia coli inactivation, as well as dissolved organic carbon and free ammonia were monitored. This study shows that, in the occurrence of cyanobacterial blooms, CCR does not directly suggest pathogen removal efficiency and is therefore not an ideal parameter to evaluate the effectiveness of disinfection process in WWTP. Instead, E. coli removal is a more direct and practical parameter for the determination of the efficiency of the disinfection process

Learning interventions in olympic skeleton through the use of physical simulation

The extreme sport of Skeleton faces driver-training constraints. This PhD focuses on the learning effects from virtual environment (VE) training. It was hypothesised that learning and skill-acquisition within the sport of Skeleton could be accelerated through the use of physical simulation and VE training. This has been investigated by linking various perceptual learning paradigms to the design of VE training.A review of previous simulator development and learning intervention research found an apparent lack of task specificity in VE design. This led to initial perceptual learning theories that focused on task specific stimuli cueing. Two on-ice track-testing sessions were conducted. They primarily provided the acquisition of on-track sled dynamic measurements and athlete subjective data, which helped formulate hypotheses around which cueing stimuli was important within a VE training scenario. Following these tests an experiment was conducted to investigate if proprioceptor stimuli had an effect on athlete sliding performance and learning rates. The results from the 5-subject experiment concluded that proprioceptor stimuli during virtual training showed improvement of learning rates and task performance. The findings promoted the development of new VE feature theories where learning was maximised by customising task specific cueing systems.A virtual environment was developed to investigate these theories and a motion cueing experiment was conducted, aimed at identifying if whole-body roll motion led to an increased learning rate. Subject learning rate and performance was evaluated from steer timing error measurements. Subjective feedback was provided which supported the measured results. Three verification methods for were used to investigate the effectiveness of skill transfer from simulated to real-world environment. Subjective and objective measures assessed the effects each VE subsystem had on subjects' ability to perform the task. The system's applicability and validity as a perceptual priming tool was demonstrated and shown; from pre and post VE intervention of athlete improvement comparisons, real-vs-simulated sled dynamics, and subject matter expert opinion. Compelling evidence was presented to suggest that positive transfer of training occurred

Using amplicon sequencing to characterize and monitor bacterial diversity in drinking water distribution systems

Drinking water assessments use a variety of microbial, physical, and chemical indicators to evaluate water treatment efficiency and product water quality. However, these indicators do not allow the complex biological communities, which can adversely impact the performance of drinking water distribution systems (DWDSs), to be characterized. Entire bacterial communities can be studied quickly and inexpensively using targeted metagenomic amplicon sequencing. Here, amplicon sequencing of the 16S rRNA gene region was performed alongside traditional water quality measures to assess the health, quality, and efficiency of two distinct, full-scale DWDSs: (i) a linear DWDS supplied with unfiltered water subjected to basic disinfection before distribution and (ii) a complex, branching DWDS treated by a four-stage water treatment plant (WTP) prior to disinfection and distribution. In both DWDSs bacterial communities differed significantly after disinfection, demonstrating the effectiveness of both treatment regimes. However, bacterial repopulation occurred further along in the DWDSs, and some end-user samples were more similar to the source water than to the postdisinfection water. Three sample locations appeared to be nitrified, displaying elevated nitrate levels and decreased ammonia levels, and nitrifying bacterial species, such as Nitrospira, were detected. Burkholderiales were abundant in samples containing large amounts of monochloramine, indicating resistance to disinfection. Genera known to contain pathogenic and fecal-associated species were also identified in several locations. From this study, we conclude that metagenomic amplicon sequencing is an informative method to support current compliance-based methods and can be used to reveal bacterial community interactions with the chemical and physical properties of DWDSs.Jennifer L.A. Shaw, Paul Monis, Laura S. Weyrich, Emma Sawade, Mary Drikas, Alan J. Coope

Vision Based Moving Object Detection And Tracking

Moving person detection andtracking and detectionusing color features is the initialstep in biometric system and video surveillance. The main purposeof our system is to detect the object, track the object and identify the predominantcolor by using moving or static camera. Here we track object, detect and recognized the object using RGB layer matrix and PCA(Principal Component Analysis) algorithm. This is done by the virtueof “Matlab 2015a”software. To detect the color RGB layer matrix algorithm is used. The famous approach is used to convert RGB frame into HSV( Hue Saturation Value) and extract the pixels value and R,G,B by subtracting reference value minus threshold value. The facial realization system uses PCA. In PCA every image is a sum of products of each quantity time constantsof eigen vectors which are called as eigen faces. Eigen faces obtained from Co-variance matrix