The importance of flexible customer-side resources in transitioning to a clean energy future is becoming increasingly apparent. Flexible customer-side resources can resolve most issues associated with intelligent and low carbon power grids and, in the process, unlock new value streams for both resource owners and load-serving entities (LSEs) with access to those resources. However, most LSEs with access to numerous flexible customer-side resources often use them for single applications when these resources can provide multiple value streams simultaneously. This dissertation focuses on developing models and frameworks to help LSEs simultaneously capture multiple value streams from customer-side resources within their jurisdiction.
Firstly, a stochastic equivalent battery model (EBM) that provides a simple yet accurate representation of the overall power consumption flexibility associated with a commercial building is proposed. The proposed stochastic EBM combines model-based functional simulations and optimization techniques to quantify the overall flexibility of a commercial building with flexible resources such as heating, ventilation, and air-conditioning (HVAC), electric water heater (EWH), battery, and electric vehicle charging stations. Illustrative case studies showcasing how the proposed model fits into complex resource scheduling problems whose objectives either maximize or minimize some value reflecting the LSE’s intended outcomes are also considered.
Secondly, a stochastic optimization framework is proposed to help an LSE capture value streams involving bulk power system support services from its residential customer-side resources. The specific value streams of interest are energy arbitrage, peak shaving, and market-based frequency regulation, while the customer-side resources are residential HVACs, EWHs, and behind-the-meter (BTM) storage. A resource type-centric clustering method is employed. The proposed framework contains
two parts. The first part involves a day-ahead resource scheduling problem that captures uncertainties in energy prices, regulation prices, and frequency regulation signals. A voltage sensitivity matrix-based approach is proposed to capture the impacts of resource control actions on system voltages. The second part includes
two real-time resource dispatch algorithms capable of eliciting fast responses from the resources to frequency regulation signals from the market operator with minimal voltage violations. The scheduling model and dispatch algorithms are evaluated using a HELICS-based co-simulation platform and real-world market data from New York Independent System Operator (NYISO).
Thirdly, a stochastic optimization framework is proposed to help an LSE capture multiple value streams focused on distribution system operations from its residential customer-side resources. The value streams of interest are peak shaving, energy arbitrage, ramp rate reduction, loss reduction, and voltage management. The framework captures the impact of third-party aggregators on the LSE’s network and includes two dispatch algorithms - decision rule-based dispatch and optimal real-time dispatch.
Finally, a framework to help LSEs compensate owners of customer-side resources for multiple value streams is proposed. The compensation sharing approach classifies the LSE’s realized value into three categories - additive, super-additive and subadditive. The appropriate compensation-sharing mechanism is then defined for each value category. A special component of the compensation sharing mechanism that provides additional social benefits, specifically credit rating improvement, for low and medium-income flexible resource owners is also proposed.
There is little consensus in the literature as to which approach for classification of Whole Genome Shotgun (WGS) sequences is most accurate. In this defense, two of the most popular classification algorithms, Kraken2 and Metaphlan2, were examined using four publicly available datasets. Surprisingly, Kraken2 reported not only more taxa but many more taxa that were significantly associated with metadata. By comparing the Spearman correlation coefficients of each taxa in the dataset against more abundant taxa, it was found that Kraken2, but not Metaphlan2, showed a consistent pattern of classifying low abundance taxa that were highly correlated with the more abundant taxa. Neither Metaphlan2, nor 16S sequences that were available for two of four datasets, showed this pattern. These results suggest that Kraken2 consistently misclassified high abundance taxa into the same erroneous low abundance taxa. These “phantom” taxa have a similar pattern of inference as the high abundance source. Because of the ever-increasing sequencing depths of modern WGS cohorts, these “phantom” taxa will appear statistically significant in statistical models even with a low classification error rate from Kraken2. These findings suggest a novel metric for evaluating classifier accuracy.
Customer churn leads to higher customer acquisition cost, lower volume of service consumption and reduced product purchase. Reducing the outflow of the customers by 5% can double the profit of a typical company. Therefore, it is of significant value for companies to reduce customer outflow. In this dissertation research, we mainly focus on identifying the customers with high chance of attrition and providing valid and trustworthy recommendations to reduce customer churn.
We designed and developed a customer attrition management system that can predict customer churn and yield actionable and measurable recommendations for the decision makers to reduce customer churn. Moreover, reviews from leaving customers reflect their unfulfilled needs, while reviews of active customers show their satisfactory experience. In order to extract the action knowledge from the unstructured customer review data, we thoroughly applied aspect-based sentiment analysis to transform the unstructured review text data into a structured table. Then, we utilized rough set theory, action rule mining and meta-action triggering mechanism on the structured table to generate effective recommendations for reducing customer churn. Lastly, in practical applications, an action rule is regarded as interesting only if its support and confidence exceed the predefined threshold values. If an action rule has a large support and high confidence, it indicates that this action can be applied to a large portion of customers with a high chance. However, there is little research focused on improving the confidence and coverage of action rules. Therefore, we proposed a guided semantic-aided agglomerative clustering algorithm to improve the discovered action rules.
Each year in the United States (U.S), one in five adults experience mental illness and one in six youth ages 6-17 experience a mental disorder (NAMI, 2020). While mental illness can affect individuals at similar rates, minority populations suffer from existent disparities in mental healthcare that have been exacerbated by the impact of COVID-19. Help-seeking behaviors of racial and ethnic minorities in the US have historically been influenced by the lack of trust in the medical system. When experiences of prejudice and discrimination are present in the counseling experience, they lead to damaging outcomes for minorities including misdiagnosis, receipt of less preferred forms of treatment, increased rate of premature termination, and overall dissatisfaction with service delivery in minority clients (Ridley et al., 2010; Rutgers University, 2019). Counselors who do not address biases, assumptions, and their own epistemological views risk operating within the oppressive framework of the dominant culture (Katz, 2014; Owen, 2017; Owen et al., 2018; Sue et al., 1992). Despite the growing support of cultural humility as complementary or even an alternative to cultural competence in counselor multicultural pedagogy, little has been examined about the ways in which this perspective can be enhanced in counselor education programs. Therefore, a standard multiple regression was utilized to examine the impact of intrinsic spirituality, common humanity, and affective empathy on cultural humility in counseling students. Results indicated that common humanity contributed significantly to the prediction of cultural humility accounting for 16% of the variance. Implications, limitations, and recommendations for future research are discussed.
Studies assessing health disparities in the United States primarily compare White and Black individuals without accounting for the heterogeneity within racial groups. The present study utilizes the racial context of origin framework to identify potential mechanisms that can explain differences in health between foreign-born Black (FBB) and US-born Black (USB) individuals. Using self-report questionnaires, this study examined the interactive effects of internalized racism, perceived discrimination, and racial context of origin on physical health and perceived discrimination reactivity. Further, motivation to succeed, belief in meritocracy, shared racial fate, and connection and belonging to the Black race were assessed to discern factors contributing to differential interactions by racial context of origin. Results indicate that internalized racism is negatively associated with physical health for FBB, but not USB. The 3-way interactions of internalized racism, perceived discrimination, and racial context of origin on physical health and perceived discrimination reactivity were not significant. Motivation to succeed, belief in meritocracy, shared racial fate, and connection and belonging to the Black race did not provide insight to differences in the role of racial context of origin in the association between internalized racism and physical health. Exploratory analyses revealed that racial centrality is a promising factor in understanding health differences by racial context of origin. Notable preliminary analyses and group differences are also discussed. These findings contribute to the understanding of racial context of origin and provide insight to race-related variables that may aid in understanding of differences in health by racial context of origin.
The identification of the artist of a painting is also known as art authentication, and the answer to this question is manifest through art gallery exhibition and is reinforced through financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning algorithm on painting images. Art authentication is not always possible since art can be anonymous, forged, gifted, or stolen. Here we show an image only art authentication attribute marker for WikiArt, Rijksmuseum, and ArtFinder galleries. Contributions to the field of art authentication include the identification of a state-of-the-art machine learning algorithm, an extension to this algorithm, standard data sources for art galleries, standard performance measurements, standard combined measurement for accuracy and multi-class cardinality, limits to multi-class cardinality, and application recommendations for the produced models.
Fertility preservation would benefit young males who must undergo treatments that can result in sterilization, such as radiation treatments for cancer. This can be achieved by removing some testicular tissue before treatment and putting it into frozen storage for later use, a process known as cryopreservation. Cryopreservation requires the use of cryoprotective agents (CPAs), such as dimethyl sulfoxide (DMSO), to reduce injury from ice crystal formation. Because DMSO can be toxic at high exposure levels, it is important to determine the exposure time that is necessary to achieve adequate concentrations for freeze protection, without over-exposing the tissue. Mass diffusion models can be used to predict this loading time, but these models depend on property parameters that are often unknown, such as the mass diffusion coefficient for a given CPA in a specific tissue.
To facilitate the development of cryopreservation protocols for testicular tissue, we determined the mass diffusion coefficient for DMSO in thin (~1 mm) tissue sections that were precision cut from feline testes that were discarded from veterinary sterilization procedures. Samples were placed in a custom tube that was mounted on the surface of an Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) and then exposed to DMSO on the opposite side. Spectra were recorded for 60 minutes, and the area of peak centered at 950 cm-1 was determined as a function of time. This time course absorbance data was then fit to an equation developed by Barbari and Fieldson (1993) that considers both mass diffusion and tissue absorption properties. By minimizing the sum of squares, estimates for the DMSO diffusion coefficient were obtained from each time sequence. Samples were analyzed at 22°C and 4°C.
Because of the inherent variability in biological tissues, alginate-gelatin and agarose were also evaluated for their potential as reference standard materials, to facilitate methodology development and training. Alginate compacted to thicknesses of 1.7 ± 0.2 mm resulting in an effective DMSO diffusion coefficient of 4.3 ± 0.3 x 10-6 cm2/s (n=4). Agarose compacted to thicknesses of 1.1 ± 0.1 mm. The effective diffusion coefficient of DMSO in agarose was 9.2 ± 0.2 x 10-6 cm2/s at 22°C (n=9) and 5.6 ± 0.2 x 10-6 cm2/s (n=9) at 4°C. Although alginate and agarose had similar variability in their thicknesses, agarose had much lower within batch and between batch variability than alginate-gelatin for the effective diffusion coefficients and thus is the preferred reference material for ATR-FTIR diffusion studies. Testicular tissue samples compacted to 2.1 ± 0.7 mm. The effective diffusion coefficient was 10.3 ± 4 x 10-6 cm2/s at 22°C and 7.1 ± 5 x 10-6 cm2/s at 4°C. The high variability is likely due to native variability in the testicular tissue samples. However, these nominal values can be used to inform preservation procedure planning.
This dissertation addresses a novel approach to assessing users' interaction tendencies on social media as a basis for personalized interventions that can make the truth louder and mitigate the spread of misinformation. This research leverages users' high and low interaction tendencies to amplify truth by increasing users' interactions with verified posts and decreasing their interactions with unverified posts. For designing personalized interaction-focused interventions, this dissertation presents an Active-Passive (AP) framework and three principles of social media interactions to make the truth louder on social media. This dissertation presents a study including tasks and questionnaires to investigate users' differences in the Active-Passive (AP) framework for utilizing platforms' basic interaction functionalities, such as like, comment, or share buttons. The results show that users use the interaction functionalities differently due to their interaction tendencies; users with high interaction tendencies use more interaction functionalities, and users with low interaction tendencies use less.
This dissertation presents an analysis of participants' responses to the design principles and investigates users' additional sharing functionality usage and preference for platform-based incentives. The results show that users with lower interaction tendencies share verified information more when they receive additional interaction support. Furthermore, due to the interaction tendencies, users exhibit opposite preferences for platform-based incentives that can encourage their participation in making the truth louder. Users with high interaction tendencies prefer incentives that highlight their presence on the platform, and users with low interaction tendencies favor incentives that can educate them about the impact of their participation on their friends and community. This dissertation concludes with a discussion on personalized interaction-focused interventions and provides directions for future research.
Diversity initiatives are often ineffective because they characterize differences at the group-level, and therefore, do not adequately address individuals’ specific identity-related challenges. The purpose of this study is to use a network-based approach to studying identity to provide a comprehensive examination of the wide range of identities that are salient and important for individuals who are members of diverse race and gender groups, namely White and Black men and women at work. Additionally, I apply intersectionality theory to understand how multiple identities are constructed into overall self-concepts at work and more specifically, how individuals perceive intersections between their multiple identities. According to intersectionality theory, I expect that multiple identities will co-exist and subordinate (i.e., historically marginalized) social identities will be more central for women and racial minorities employees as opposed to dominant (i.e., historically non-marginalized) identities. I also integrate job-demands resources theory to develop and test hypotheses concerning the structural relationships between identities (i.e., conflict, compatibility, centrality) and authenticity at work. Specifically, I propose that identity conflict, compatibility, and centrality are identity structures that serve as resources that can enable or constrain authentic self-expression at work. I test these hypotheses across two studies. In summary, this work sheds theoretical and empirical light on the complex nature of multiple identities at work and how diversity initiatives can more effectively address identity dimensions that intersect and affect personal work experiences.
Indoor environmental conditions play a significant role in protecting occupants’ well-being. The thermal characteristics are one of the primary factors of Indoor Environmental Qualities (IEQ) that can influence occupants’ health. In this regard, schedule-based and predefined environmental control is one of the main reasons for the current discomfort and dissatisfaction with the thermal environment. Recent research is attempting to leverage occupants’ demand in the control loop of the buildings to consider the well-being of each individual based on their own physiological properties. These thermal comfort models are called "personalized comfort models". In this regard, studies are trying to utilize skin temperature recorded by infrared thermal cameras for developing personal comfort models through machine learning prediction algorithms. However, some critical gaps in the current methods have limited the application of this platform in real buildings. The contribution of this dissertation is in the three main aspects of literature review, data collection, and model development. This study presents a comprehensive and systematic review of the current machine learning-based personalized thermal comfort studies. In addition, we introduce "Charlotte-ThermalFace", our recently developed dataset, and how it addresses some of the existing gaps in the subject. Charlotte-ThermalFace contains more than 10,000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. Using this dataset, we have developed a personalized comfort model for subjects farther away in a completely non-intrusive method.