Automatic Extraction of Ordinary Differential Equations from Data: Sparse Regression Tools for System Identification

Studying nonlinear systems across engineering, physics, economics, biology, and chemistry often hinges upon successfully discovering their underlying dynamics. However, despite the abundance of data in today's world, a complete comprehension of these governing equations often remains elusive, posing a significant challenge. Traditional system identification methods for building mathematical models to describe these dynamics can be time-consuming, error-prone, and limited by data availability. This thesis presents three comprehensive strategies to address these challenges and automate model discovery. The procedures outlined here employ classic statistical and machine learning methods, such as signal filtering, sparse regression, bootstrap sampling, Bayesian inference, and unsupervised learning algorithms, to capture complex and nonlinear relationships in data. Building on these foundational techniques, the proposed processes offer a reliable and efficient approach to identifying models of ordinary differential equations from data, differing from and complementing existing frameworks. The results presented here provide rigorous benchmarking against state-of-the-art algorithms, demonstrating the proposed methods' effectiveness in model discovery and highlighting the potential for discovering governing equations across applications such as weather forecasting, chemical reaction and electrical circuit modelling, and predator-prey dynamics. These methods can aid in solving critical decision-making problems, including optimising resource allocation, predicting system failures, and facilitating adaptive control in various domains. Ultimately, the strategies developed in this thesis are designed to integrate seamlessly into current workflows, thereby promoting data-driven decision-making and enhancing understanding of complex system dynamics

Increasing Beach Recreation Benefits by Using Wetlands to Reduce Contamination

The public swimming beach at Maumee Bay State Park (MBSP) on Lake Erie is often posted for occurrences of unsafe levels of bacteria. The main source of bacteria derives from a drainage ditch that discharges near the beach. We have conducted a comprehensive study to determine the feasibility of using a constructed wetland to filter the ditch water, prior to its entry into Maumee Bay. As part of this study, we administered an on-site non-market valuation survey of beach visitors, in which observed and contingent trips to the beach were used to estimate the potential welfare benefits of the restored wetlands. The data were analyzed using three versions of the multivariate Poisson-lognormal (MPLN) model, a random effects count data model. We conclude version one, with flexible covariance structure and vehicle costs of $0.25 per mile, is the preferred version and use it to estimate an average annual willingness to pay (WTP) of$166 to construct wetlands and improve water quality. The aggregate annual benefit to an estimated 37,300 annual beach visitors is estimated as $6.19 million. The robustness of this estimate to a variety of alternative assumptions is examined.Count data model, Poisson lognormal, on-site sampling, recreation demand, wetland, simulated maximum likelihood, Community/Rural/Urban Development, Environmental Economics and Policy, Public Economics, Q51,

Automatically identifying ordinary differential equations from data

Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary science. Here, we propose a methodology to identify dynamical laws by integrating denoising techniques to smooth the signal, sparse regression to identify the relevant parameters, and bootstrap confidence intervals to quantify the uncertainty of the estimates. We evaluate our method on well-known ordinary differential equations with an ensemble of random initial conditions, time series of increasing length, and varying signal-to-noise ratios. Our algorithm consistently identifies three-dimensional systems, given moderately-sized time series and high levels of signal quality relative to background noise. By accurately discovering dynamical systems automatically, our methodology has the potential to impact the understanding of complex systems, especially in fields where data are abundant, but developing mathematical models demands considerable effort

Bacteriocins: Novel Solutions to Age Old Spore-Related Problems?

peer-reviewedBacteriocins are ribosomally synthesized antimicrobial peptides produced by bacteria, which have the ability to kill or inhibit other bacteria. Many bacteriocins are produced by food grade lactic acid bacteria (LAB). Indeed, the prototypic bacteriocin, nisin, is produced by Lactococcus lactis, and is licensed in over 50 countries. With consumers becoming more concerned about the levels of chemical preservatives present in food, bacteriocins offer an alternative, more natural approach, while ensuring both food safety and product shelf life. Bacteriocins also show additive/synergistic effects when used in combination with other treatments, such as heating, high pressure, organic compounds, and as part of food packaging. These features are particularly attractive from the perspective of controlling sporeforming bacteria. Bacterial spores are common contaminants of food products, and their outgrowth may cause food spoilage or food-borne illness. They are of particular concern to the food industry due to their thermal and chemical resistance in their dormant state. However, when spores germinate they lose the majority of their resistance traits, making them susceptible to a variety of food processing treatments. Bacteriocins represent one potential treatment as they may inhibit spores in the post-germination/outgrowth phase of the spore cycle. Spore eradication and control in food is critical, as they are able to spoil and in certain cases compromise the safety of food by producing dangerous toxins. Thus, understanding the mechanisms by which bacteriocins exert their sporostatic/sporicidal activity against bacterial spores will ultimately facilitate their optimal use in food. This review will focus on the use of bacteriocins alone, or in combination with other innovative processing methods to control spores in food, the current knowledge and gaps therein with regard to bacteriocin-spore interactions and discuss future research approaches to enable spores to be more effectively targeted by bacteriocins in food settings.KE, DF, CH, PC, MR, RR are supported by the Irish Government under the National Development Plan, through the Food Institutional Research Measure, administered by the Department of Agriculture, Fisheries and Food, Ireland (DAFM 13/F/462) to PC and MR, a Science Foundation Ireland (SFI) Technology and Innovation Development Award (TIDA 14/TIDA/2286) to DF, SFI-PI funding (11/PI/1137) to PDC and the APC Microbiome Insitute under Grant Number SFI/12/RC/2273

Re-Evaluating Subsidies for Services That Carry no Positive Externalities: A Benefit-Cost Analysis of Convenience Loop Busing versus Walking

This paper seeks to provide a benefit-cost analysis of continuous loop busing service offered by The University of Toledo in Toledo, Ohio. The on-campus busing service is completely subsidized by the university through student fees and provided as a free service to students with less than one-quarter of students currently using the service. Using a contingent valuation survey, we estimate a lower bound mean willingness to pay (WTP) of about $6 per student user per semester. Multiplying by the student body population leads to a total WTP estimate of approximately$37,000 per semester. When compared solely to labor and fuel costs to operate the loop busing, costs outweighed benefits $41,000 to$37,000. This does not include maintenance costs on the buses, pollution from burning diesel fuel, or the opportunity cost incurred when students are riding instead of walking. There are no positive externalities from Toledo on-campus busing since it’s only a convenience service to shuttle students from parking and from building to building. Students use the service when it carries an artificially low cost of zero dollars, for which they could walk in approximately the same amount of time, with walking being an activity that would provide positive externalities to society through the known health benefits from exercise

Valuing Water Quality As a Functionof Water Quality Measures

This paper incorporates a rich set of physical water quality attributes, as well as site and household characteristics, into a model of recreational lake usage in Iowa. Our analysis shows individuals are responsive to physical water quality measures. Willingness-to-pay estimates are reported based on improvements in these measures.

Bacteriocins: antibiotics in the age of the microbiome

Antibiotics have revolutionised the treatment of infectious disease and improved the lives of billions of people worldwide over many decades. With the rise in antimicrobial resistance (AMR) and corresponding lack of antibiotic development, we find ourselves in dire need of alternative treatments. Bacteriocins are a class of bacterially produced, ribosomally synthesised, antimicrobial peptides that may be narrow or broad in their spectra of activity. Animal models have demonstrated the safety and efficacy of bacteriocins in treating a broad range of infections; however, one of the principal drawbacks has been their relatively narrow spectra when compared with small-molecule antibiotics. In an era where we are beginning to appreciate the role of the microbiota in human and animal health, the fact that bacteriocins cause much less collateral damage to the host microbiome makes them a highly desirable therapeutic. This review makes a case for the implementation of bacteriocins as therapeutic antimicrobials, either alone or in combination with existing antibiotics to alleviate the AMR crisis and to lessen the impact of antibiotics on the host microbiome

Evaluation Of Juvenile Justice Education Programs: What The Numbers Say About Juvenile Recidivism

Each year more than 100,000 juveniles are incarcerated in residential rehabilitative facilities. As part of their course of treatment, educational services are mandated for these incarcerated youth. Programs serving these individuals must provide adequate and appropriate educational programs for these juveniles. With a growing public concern over juvenile delinquency and recidivism, programs are being held accountable for the effectiveness and quality of the programming they offer. In Florida, juvenile justice programs offering educational services are monitored annually by the Juvenile Justice Education Enhancement Program. These programs receive a Quality Assurance (QA) rating as determined by a review team that spends several days in the program reviewing documentation and interviewing youth and program staff. This study proposes to examine any potential relationship between the rating a program receives and how successful youth are in returning to mainstream society and subsequently school. Linear regression analysis is the main statistical method to answer four research questions designed to examine these potential relationship. A total of 177 Moderate and High Risk programs were included in the study and the QA scores they received over a three year were analyzed. Surprisingly, the research and subsequent analysis shows little relationship between educational program quality and success rates for juveniles exiting incarceration. This result may warrant further study as to the additional factors contributing to a youth\u27s re-involvement in the juvenile justice system