Advancing the Cyberinfrastructure for Smart Water Metering and Water Demand Modeling

With rapid growth of urban populations and limited water resources, achieving an appropriate balance between water supply capacity and residential water demand poses a significant challenge to water supplying agencies. With the recent emergence of smart metering technology, where water use can be monitored and recorded at high resolution (e.g., observations of water use every 5 seconds), most existing research has been aimed at providing water managers with detailed information about the water use behavior of their consumers and the performance of water using fixtures. However, replacing existing meters with smart meters is expensive, and effectively using data produced by smart meters can be a roadblock for water utilities that lack sophisticated information technology expertise. The research in this dissertation presents low cost, open source cyberinfrastructure aimed at addressing these challenges. Components developed include an open source algorithm for identifying and classifying water end use events from smart meter data, a low cost datalogging and computational device that enables existing water meters to collect high resolution data and compute end use information, and a detailed water demand model that uses end use event information to simulate residential water use at a municipality level. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah. We tested the applicability of the disaggregation algorithm in quantifying water end uses for different meter sizes and types. We tested the datalogging computational device at a residential household and demonstrated collection, disaggregation, and transfer of high resolution flow data and classified events into a secure server. Finally, we demonstrated a water demand model that simulates the detailed water end uses of Logan’s residents using a combination of a set of representative water end use events and monthly billing data. Using the data we collected and the outputs from the model, we demonstrated opportunities for conserving water through improving the efficiency of water using fixtures and promoting behavior changes

Cytotoxic effects on splenic ultrafiltrates upon leukaemic lymphocytes.

Ultrafiltrates from spleen inhibited both DNA synthesis and the proliferation of normal lymphocytes stimulated inculture from both mouse and man without apparent cytotoxicity. However, the same doses of this spleen ultrafiltrate will kill up to two-thirds of the leukaemic lymphoblasts from both mouse and man after 24 h incubation. This unique lymphocytotoxic effect could also be demonstrated on fresh primary cultures of leukaemic lymphocytes and was highly effective on slowly growing established cell lines under crowd culture conditions. Furthermore. ultrafiltrated thymus extract did not affect the DNA synthesis rates of the viability of NC-37 lymphoblasts, which have B cell characteristic. Thymus extract was cytotoxic to Molt cells, which have T cell characteristics

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Variability in Consumption and End Uses of Water for Residential Users

Research Objective/Summary: In most large urban water systems in the US, the residential sector consumes the majority of total supplied fresh water. In a world plagued with increasing water scarcity and climate change stresses, understanding individual home water end-uses is vital to water management and conservation. We studied the end uses of water in residential homes, both indoor and outdoor to find patterns and variations in consumption over time. Results indicate a need for more efficient water fixtures, particularly toilets, and provide an opportunity to promote conservation behavior

An Open-Source, Semisupervised Water End-Use Disaggregation and Classification Tool

Research Objective/Summary: Research on individual household water consumption is vital to water management and conservation approaches. Despite a significant number of papers published on end-use disaggregation tools, reproduction and further study of results on water-use behavior is difficult because the data and code are not easily accessible. In order to fulfill this need for open and reproducible tools, we present a new, semisupervised, non-intrusive water end-use disaggregation and classification tool

Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the Edge

We present a new, open source, computationally capable datalogger for collecting and analyzing high temporal resolution residential water use data. Using this device, execution of water end use disaggregation algorithms or other data analytics can be performed directly on existing, analog residential water meters without disrupting their operation, effectively transforming existing water meters into smart, edge computing devices. Computation of water use summaries and classified water end use events directly on the meter minimizes data transmission requirements, reduces requirements for centralized data storage and processing, and reduces latency between data collection and generation of decision-relevant information. The datalogger couples an Arduino microcontroller board for data acquisition with a Raspberry Pi computer that serves as a computational resource. The computational node was developed and calibrated at the Utah Water Research Laboratory (UWRL) and was deployed for testing on the water meter for a single-family residential home in Providence City, UT, USA. Results from field deployments are presented to demonstrate the data collection accuracy, computational functionality, power requirements, communication capabilities, and applicability of the system. The computational node’s hardware design and software are open source, available for potential reuse, and can be adapted to specific research needs

Insulin sensitizing agent improves clinical pregnancy rate and insulin resistant parameters in polycystic ovarian syndrome patients with acanthosis nigricans: a randomized controlled study

Objective: To investigate the effect of adding metformin to clomiphene citrate (CC) in polycystic ovarian syndrome (PCOS) patients with acanthosis nigricans (AN) who were previously not responding to CC. Material and Methods: A double blinded randomized controlled trial (NCT02562664) included 66 PCOS women with acanthosis nigricans who were CC resistant (at least 3 months). Day 3 follicle stimulating hormone (FSH) level, fasting insulin, fasting glucose and homeostatic model assessment were used to quantify insulin resistance. Participants were randomly assigned to either group I (CC with placebo tablets) or group II (CC with metformin) for three cycles. Insulin resistance parameters as well as clinical pregnancy rate had been evaluated in both groups. The statistical analysis was done using Chi- square and Fischer exact tests. Results: The demographic data was comparable in both groups, however; there was higher cumulative pregnancy rate after three cycles of stimulation in group II (18/33) (54.5%) in comparison with group I (7/33) (21.1%) (P=0.03). There was a significant improvement in the insulin resistance parameters after three months of combining clomiphene citrate with metformin as compared with CC alone. Conclusion: Adding metformin to CC in clomiphene citrate resistant PCOS patients who have acanthosis nigricans improves the pregnancy rate and insulin resistant parameters