Smart Query Answering for Marine Sensor Data

We review existing query answering systems for sensor data. We then propose an extended query answering approach termed smart query, specifically for marine sensor data. The smart query answering system integrates pattern queries and continuous queries. The proposed smart query system considers both streaming data and historical data from marine sensor networks. The smart query also uses query relaxation technique and semantics from domain knowledge as a recommender system. The proposed smart query benefits in building data and information systems for marine sensor networks

Economics of Dairy Manure Management in Iowa

Dairy producers have many duties to carry out in their dairy operations, one of which is storing, handling and applying manure. Manure handling can be a significant cost on most dairies even when considering the value of the nutrients in the manure. Seldom do producers consider their costs of handling and applying manure on a per hundredweight (cwt) of milk sold basis like they do with many of their other costs. This project encompassed surveys of manure management systems and nutrient management practices on 22 Iowa Dairy Farms, including economic costs and returns. Key findings are that the cost of storing, hauling and applying manure averaged $306.13 per cow or$1.33 per / cwt. If we subtract the nutrient value of the manure applied (using book values and assuming perfect utilization) the net cost of storing, hauling and applying manure averaged $104.10 per cow or$$0.45 per / cwt. The tie stall barns had a cumulative cost per cwt. of$1.42; the mattress/waterbed barns had a cumulative cost per cwt. of $1.50; the 2 stage sand systems had a cumulative cost per cwt. of$0.97; the 1 stage sand systems had a cumulative cost per cwt. of $1.44; and the dried manure solid barns had a cumulative cost per cwt. of$1.24

Automated Data Quality Assessment of Marine Sensors

The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance/Quality Control (QA/QC) is necessary to ensure that the data collected is fit for purpose. Current automated QA/QC approaches provide assessments based upon hard classifications of the gathered data; often as a binary decision of good or bad data that fails to quantify our confidence in the data for use in different applications. We propose a novel framework for automated data quality assessments that uses Fuzzy Logic to provide a continuous scale of data quality. This continuous quality scale is then used to compute error bars upon the data, which quantify the data uncertainty and provide a more meaningful measure of the data’s fitness for purpose in a particular application compared with hard quality classifications. The design principles of the framework are presented and enable both data statistics and expert knowledge to be incorporated into the uncertainty assessment. We have implemented and tested the framework upon a real time platform of temperature and conductivity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. Results indicate that the error bars generated from the Fuzzy QA/QC implementation are in good agreement with the error bars manually encoded by a domain expert

Implementing Risk Management Decisions that Optimize Nutrient Value of Dairy Manure while Minimizing Related Risk

Nutrients present in manure are increasingly receiving attention for environmental, production, and financial reasons. Dairy producers continue to strive for better ways and educational opportunities to improve profits by evaluating fertilizer and value of manure to their operation and to protect the environment. These farming decisions which help producers stay economically viable also support and stimulate their local economy, which promotes a more vital rural community. Utilizing 22 dairy nutrient management surveys, 14 on-farm workshops, 10 small group on-farm assessment workshops, one video, and individual producer visits, producers were able to make informed decisions using tools and knowledge gained to control risks associated with manure nutrients during handling, storage, and application

BCL-3 loss sensitises colorectal cancer cells to DNA damage by targeting homologous recombination

The proto-oncogene BCL-3 is upregulated in a subset of colorectal cancers (CRC), where it has been shown to enhance tumour cell survival. However, although increased expression correlates with poor patient prognosis, the role of BCL-3 in determining therapeutic response remains largely unknown. In this study, we use combined approaches in multiple cell lines and pre-clinical mouse models to investigate the function of BCL-3 in the DNA damage response. We show that suppression of BCL-3 increases γH2AX foci formation and decreases homologous recombination in CRC cells, resulting in reduced RAD51 foci number and increased sensitivity to PARP inhibition. Importantly, a similar phenotype is seen in Bcl3-/- mice, where Bcl3-/- mouse crypts also exhibit sensitivity to DNA damage with increased γH2AX foci compared to wild type mice. Additionally, Apc.Kras-mutant x Bcl3-/- mice are more sensitive to cisplatin chemotherapy compared to wild type mice. Taken together, our results identify BCL-3 as a regulator of the cellular response to DNA damage and suggests that elevated BCL-3 expression, as observed in CRC, could increase resistance of tumour cells to DNA damaging agents including radiotherapy. These findings offer a rationale for targeting BCL-3 in CRC as an adjunct to conventional therapies and suggest that BCL-3 expression in tumours could be a useful biomarker in stratification of rectal cancer patients for neo-adjuvant chemoradiotherapy

Time-series prediction of shellfish farm closure: A comparison of alternatives

Shellfish farms are closed for harvest when microbial pollutants are present. Such pollutants are typically present in rainfall runoff from various land uses in catchments. Experts currently use a number of observable parameters (river flow, rainfall, salinity) as proxies to determine when to close farms. We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall. Time-series event prediction consists of two steps: (i) feature extraction, and (ii) prediction. A number of data mining challenges exist for these scenarios: (i) which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?, (ii) The farm closure events occur infrequently and this leads to a class imbalance problem; the question is what is the best way to deal with this problem? In this paper we have analysed and compared different combinations of balancing methods (under-sampling and over-sampling), feature extraction methods (cluster profile, curve fitting, Fourier Transform, Piecewise Aggregate Approximation, and Wavelet Transform) and learning algorithms (neural network, support vector machine, k-nearest neighbour, decision tree, and Bayesian Network) to predict closure events accurately considering the above data mining challenges. We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall, given the above data mining challenges

A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality

Online automated quality assessment is critical to determine a sensor’s fitness for purpose in real-time applications. A Dynamic Bayesian Network (DBN) framework is proposed to produce probabilistic quality assessments and represent the uncertainty of sequentially correlated sensor readings. This is a novel framework to represent the causes, quality state and observed effects of individual sensor errors without imposing any constraints upon the physical deployment or measured phenomenon. It represents the casual relationship between quality tests and combines them in a way to generate uncertainty estimates of samples. The DBN was implemented for a particular marine deployment of temperature and conductivity sensors in Hobart, Australia. The DBN was shown to offer a substantial average improvement (34%) in replicating the error bars that were generated by experts when compared to a fuzzy logic approach

Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration

This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail