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- Creators: Arizona State University
Description
Since its inception in 1973, the Endangered Species Act has been met with both praise and criticism. More than 40 years later, the Act is still polarizing, with proponents applauding its power to protect species and critics arguing against its perceived ineffectiveness and potential mismanagement. Recovery plans, which were required by the 1988 amendments to the Act, play an important role in organizing efforts to protect and recover species under the Act. In 1999, in an effort to evaluate the process, the Society for Conservation Biology commissioned an independent review of endangered species recovery planning. From these findings, the SCB made key recommendations for how management agencies could improve the recovery planning process, after which the Fish and Wildlife Service and the National Marine Fisheries Service redrafted their recovery planning guidelines. One important recommendation called for recovery plans to make threats a primary focus, including organizing and prioritizing recovery tasks for threat abatement. Here, I seek to determine the extent to which SCB recommendations were incorporated into these new guidelines, and if, in turn, the recommendations regarding threats manifested in recovery plans written under the new guidelines. I found that the guidelines successfully incorporated most SCB recommendations, except those that addressed monitoring. As a result, recent recovery plans have improved in their treatment of threats, but still fail to adequately incorporate threat monitoring. This failure suggests that developing clear guidelines for monitoring should be an important priority in future ESA recovery planning.
ContributorsTroyer, Caitlin (Author) / Gerber, Leah (Thesis advisor) / Minteer, Ben (Committee member) / Guston, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
There has been considerable advancement in the algae research field to move algae production for biofuels and bio-products forward to become commercially viable. However, there is one key element that humans cannot control, the natural externalities that impact production. An algae cultivation system is similar to agricultural crop farming practices. Algae are grown on an area of land for a certain time period with the aim of harvesting the biomass produced. One of the advantages of using algae biomass is that it can be used as a source of energy in the form of biofuels. Major advances in algae research and development practices have led to new knowledge about the remarkable potential of algae to serve as a sustainable source of biofuel. The challenge is to make the price of biofuels from algae cost-competitive with the price of petroleum-based fuels. The scope of this research was to design a concept for an automated system to control specific externalities and determine if integrating the system in an algae cultivation system could improve the algae biomass production process. This research required the installation and evaluation of an algae cultivation process, components selection and computer software programming for an automated system. The results from the automated system based on continuous real time monitored variables validated that the developed system contributes insights otherwise not detected from a manual measurement approach. The implications of this research may lead to technology that can be used as a base model to further improve algae cultivation systems.
ContributorsPuruhito, Emil (Author) / Sommerfeld, Milton (Thesis advisor) / Gintz, Jerry (Thesis advisor) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014
Description
This paper examines how equity analysts' roles as information intermediaries and monitors affect corporate liquidity policy and its associated value of cash, providing new evidence that analysts have a direct impact on corporate liquidity policy. Greater analyst coverage (1) reduces information asymmetry between a firm and outside shareholders and (2) enhances the monitoring process. Consistent with these arguments, analyst coverage increases the value of cash, thereby allowing firms to hold more cash. The cash-to-assets ratio increases by 5.2 percentage points when moving from the bottom analyst-coverage decile to the top decile. The marginal value of $1 of corporate cash holdings is $0.93 for the bottom analyst-coverage decile and $1.83 for the top decile. The positive effects remain robust after a battery of endogeneity checks. I also perform tests employing a unique dataset that consists of public and private firms, as well as a dataset that consists of public firms that have gone private. A public firm with analyst coverage can hold approximately 8% more cash than its private counterpart. These findings constitute new evidence on the real effect of analyst coverage.
ContributorsChang, Ching-Hung (Author) / Bates, Thomas (Thesis advisor) / Bharath, Sreedhar (Committee member) / Lindsey, Laura (Committee member) / Arizona State University (Publisher)
Created2012
How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review
Description
Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD.
This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.
This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.
ContributorsDeb, Ranadeep (Author) / Ogras, Umit Y. (Thesis advisor) / Shill, Holly (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2019
Description
Safe, readily available, and reliable sources of water are an essential component of any municipality’s infrastructure. Phoenix, Arizona, a southwestern city, has among the highest per capita water use in the United States, making it essential to carefully manage its reservoirs. Generally, municipal water bodies are monitored through field sampling. However, this approach is limited spatially and temporally in addition to being costly. In this study, the application of remotely sensed reflectance data from Landsat 7’s Enhanced Thematic Mapper Plus (ETM+) and Landsat 8’s Operational Land Imager (OLI) along with data generated through field-sampling is used to gain a better understanding of the seasonal development of algal communities and levels of suspended particulates in the three main terminal reservoirs supplying water to the Phoenix metro area: Bartlett Lake, Lake Pleasant, and Saguaro Lake. Algal abundances, particularly the abundance of filamentous cyanobacteria, increased with warmer temperatures in all three reservoirs and reached the highest comparative abundance in Bartlett Lake. Prymnesiophytes (the class of algae to which the toxin-producing golden algae belong) tended to peak between June and August, with one notable peak occurring in Saguaro Lake in August 2017 during which time a fish-kill was observed. In the cooler months algal abundance was comparatively lower in all three lakes, with a more even distribution of abundance across algae classes. In-situ data from March 2017 to March 2018 were compared with algal communities sampled approximately ten years ago in each reservoir to understand any possible long-term changes. The findings show that the algal communities in the reservoirs are relatively stable, particularly those of the filamentous cyanobacteria, chlorophytes, and prymnesiophytes with some notable exceptions, such as the abundance of diatoms, which increased in Bartlett Lake and Lake Pleasant. When in-situ data were compared with Landsat-derived reflectance data, two-band combinations were found to be the best-estimators of chlorophyll-a concentration (as a proxy for algal biomass) and total suspended sediment concentration. The ratio of the reflectance value of the red band and the blue band produced reasonable estimates for the in-situ parameters in Bartlett Lake. The ratio of the reflectance value of the green band and the blue band produced reasonable estimates for the in-situ parameters in Saguaro Lake. However, even the best performing two-band algorithm did not produce any significant correlation between reflectance and in-situ data in Lake Pleasant. Overall, remotely-sensed observations can significantly improve our understanding of the water quality as measured by algae abundance and particulate loading in Arizona Reservoirs, especially when applied over long timescales.
ContributorsRussell, Jazmine Barkley (Author) / Neuer, Susanne (Thesis advisor) / Fox, Peter (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Created2018
Description
In rural and urban areas of Nigeria, dependence on groundwater is increasing since the population is growing and high quality, treated municipal water is scarce. Municipal drinking water is often compromised because of old and leaking distribution pipes. About 58% of the water consumed in Lagos State, Nigeria, comes from residential wells. However, a majority of residential wells are shallow wells that are constructed relatively close to septic tanks or pit latrines and are therefore subject to contamination. In certain parts of Africa, there is high potential of severe epidemic if water quality is not improved. With increasing reliance on groundwater, a need exists to monitor the quality of groundwater. This thesis develops a plan for a monitoring program for residential wells in Lagos State, Nigeria. The program focuses on ways by which owners can maintain reasonably good water quality, and on the role of government in implementing water quality requirements. In addition, this thesis describes a survey conducted in various areas of Lagos State to assess community awareness of the importance of groundwater quality and its impact on individuals and the community at large. The survey shows that 30% to 40% of the households have located their wells and septic tanks in the same general area. Various templates have been created to help the staff of a future monitoring program team to effectively gather information during site characterization. A "Questions and Answers" leaflet has been developed to educate citizens about the need for monitoring residential wells.
ContributorsTalabi, Omogbemiga Adepitan (Author) / Edwards, David (Thesis advisor) / Hild, Nicholas (Committee member) / Olson, Larry (Committee member) / Arizona State University (Publisher)
Created2010
Description
Testing and verification is an essential procedure to assert a system adheres to some notion of safety. To validate such assertions, monitoring has provided an effective solution to verifying the conformance of complex systems against a set of properties describing what constitutes safe behavior. In authoring such properties, Temporal Logic (TL) has become a widely adopted specification language in many monitoring applications because of its ability to formally capture time-critical behaviors of reactive systems. This broad acceptance into the verification community and others, however, has naturally led to a lack of TL-based requirement elicitation standards as well as increased friction in tool interoperability. In this thesis, I propose a standardization of TL-based requirement languages through the development of a Formal Requirements Toolkit (FoRek): a modular, extensible, and maintainable collection of TL parsers, translators, and interfaces. To this end, six propositional TL languages are supported in addition to their appropriate past-time variants to provide a framework for a variety of applications using TL as a specification language. Furthermore, improvements to the Pythonic Formal Requirements Language (PyFoReL) tool are performed in addition to a formal definition on the structure of a PyFoReL program. And lastly, to demonstrate the results of this work, FoRek is integrated into an offline monitor to showcase its intended use and potential applications into other domains.
ContributorsAnderson, Jacob W (Author) / Fainekos, Georgios GF (Thesis advisor) / Pedrielli, Giulia GP (Thesis advisor) / Xu, Zhe ZX (Committee member) / Arizona State University (Publisher)
Created2023
Description
Microbial Potentiometric Sensors (MPS) utilize endemic biofilms to generate a signal using a measurable potentiometric difference, without the use of cleaning, maintenance, and reagents of conventional sensor monitoring methods. These advantages are suitable for monitoring bioreactions in water distribution systems, soils, and wastewater treatment. In controlled fermentation processes, monitoring seeks to avoid contamination and degradation, which results in loss of productivity. MPS have yet to be applied to monitor the fermentation of milk to yogurt. This study examined the feasibility of using MPS technology to monitor the progress of milk fermentation in real-time with a bench-scale model bioreactor. Signal data obtained by the MPS was analyzed and assessed for the ability to model and predict the time of complete fermentation. Analysis of complete fermentation times in conjunction with pH and MPS signal values found characteristics indicative of complete fermentation. The method detection limit was assessed to inform of the method’s capacity to distinguish complete fermentation time. A sensitivity analysis was conducted to develop a more robust method for predicting complete fermentation time. At this proof-of-concept scale, MPS successfully performed in this capacity to monitor bioreaction conditions continuously. MPS captured information as fermentation progressed, was completed, and as the yogurt product naturally began to decay. Analysis of the data obtained with the technology found predictions of complete fermentation time within a two hour range, with further assessment in the sensitivity analysis narrowing this timeframe to less than 45 minutes. This study revealed the challenges in precisely predicting complete fermentation;however, advancement of a robust analytical method and demonstration of technical feasibility promotes further MPS technology applications that seek to monitor conditions in real-time to preserve health and production.
ContributorsDieter, Lucien Andres (Author) / Hristovski, Kirl D. (Thesis advisor) / Olson, Larry W. (Committee member) / Ünver, Olcay (Committee member) / Arizona State University (Publisher)
Created2021
Description
Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms, this dissertation investigates whether and how IT-enabled monitoring systems can mitigate moral hazard and reshape the labor demand and supply by providing detailed information about workers’ effort. In the first chapter, I propose and demonstrate that monitoring records can substitute for reputation signals such that they attract more qualified inexperienced workers to enter the marketplace. Specifically, only the effort-related reputation information is substituted by monitoring but the capability-related reputation information. In line with this, monitoring can lower the entry barrier for inexperienced workers on platforms. In the second chapter, I investigate if there is home bias for local workers when employers make the hiring decisions. I further show the existence of home bias from employers and it is primarily driven by statistical inference instead of personal “taste”. In the last chapter, I examine if females tend to have a stronger avoidance of monitoring than males. With the combination of the observational data and experimental data, I find that there is a gender difference in avoidance of monitoring and the introduction of the monitoring system increases the gender wage gap due to genders differences in such willingness-to-pay for the avoidance of monitoring. These three studies jointly contribute to the literature on the online platforms, gig economy and agency theory by elucidating the critical role of IT-enabled monitoring.
ContributorsLiang, Chen, Ph.D (Author) / Gu, Bin (Thesis advisor) / Hong, Yili (Thesis advisor) / Chen, Peiyu (Committee member) / Arizona State University (Publisher)
Created2019