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BIM-Enabled Facility Management Framework and Simulation
Full text linkThis research offers a comprehensive investigation into developing and validating a standardized Building Information Modeling for Facility Management (BIM-FM) framework and simulation, supported by an empirical case study of the Long and Kimmy Nguyen Engineering Building. The primary objective of this study is to bridge the gap between theoretical BIM-FM frameworks and their practical application within the Architecture, Engineering, and Construction (AEC) industry to facilitate a more integrated and efficient approach to facility operation. The study adopts a mixed-methods research methodology that combines a qualitative literature review with a quantitative computational simulation. The qualitative phase focuses on a systematic review of existing BIM-FM frameworks and their implementation within the AEC sector. This serves to identify key performance indicators (KPIs) and industry requirements critical for BIM-FM framework development and integration, such as the Facility Condition Index (FCI), Maintenance Efficiency Index (MEI), and Replacement Efficiency Index (REI). The quantitative phase focuses on developing a computational simulation as a primary tool for empirically validating the proposed framework using the KPIs applied to a specific real-world context through a case study. This is to offer a robust platform for assessing BIM-FM strategies and providing quantifiable evidence of their utility and relevance. The proposed framework developed in the study is driven by insights from the literature review. It encompasses three main phases: (1) identifying best practices and gaps, (2) analyzing and comparing existing frameworks, and (3) formulating the framework's logic and workflow. This structured approach attempts to tackle the identified industry needs and challenges within the AEC industry. Furthermore, the research simulation development chapter details using Unity as a platform for operating the proposed simulation logic. The simulation integrates several core components to facilitate realistic presentations of facility operations and deepen the understanding of BIM-FM integration and predictive performance in long-term facility operation strategies. A core aspect of the research is the case study, focusing on the Long and Kimmy Nguyen Engineering Building at George Mason University. The case study selection adheres to criteria that ensure alignment with industry standards and contains diverse assets. This study also develops a BIM model of the facility and integrates it into the Unity simulation for accurate operation and performance assessment. The simulation results show that the proposed BIM-FM framework outperformed traditional FM and BIM-FM systems across key performance metrics. It maintained superior facility conditions, with a mean FCI of 0.794, and showed a lower FCI decrease over time than its counterparts, emphasizing its effectiveness in facility upkeep. Its mean MEI of 0.160 and REI of 6.03 were also notably higher, which shows greater maintenance and replacement efficacy. The framework's strategic approach also resulted in more consistent deferred maintenance work orders and higher average facility conditions, showcasing effective maintenance scheduling. Financially, the system demonstrated improved operational efficiency, saving an average of approximately \$91,424.94 annually, translating into over \$3.75 million in net savings over its lifecycle. Sensitivity analysis underscored its financial robustness and adaptability to economic changes, highlighting its long-term viability for FM operations. This application of a real-world facility provides critical insights into the framework's effectiveness and challenges encountered compared to other traditional systems, offering a comprehensive evaluation of its adaptability and versatility across a broad spectrum of FM requirements. In conclusion, this study contributes to the body of knowledge by offering a standardized BIM-FM framework that integrates qualitative insights with quantitative validations using a proposed novel FM simulation. This, coupled with a detailed real-world case study, highlights the potential of standardized BIM-FM systems to improve the facility operation process and provide a more integrated and efficient approach aligned with the current industry needs and standards
LARGE-SCALE MICROTASK PROGRAMMING
Full text linkMicrotask programming decontextualizes work into self-contained microtasks, reducing the context necessary to onboard onto a software project and the barriers necessary to contribute. At the same time, it may reduce the time to market for software by increasing parallelism. A number of prior systems have explored approaches for microtasking program- ming work, devising workflows through which large and complex programming tasks are decomposed into self-contained microtasks which may be completed in minutes. However, existing approaches have important limitations, impeding their ability to scale to real- world programs and crowds. (1) Current approaches o↵er limited support for parallelism and conflict management. (2) Current approaches ensure quality by assigning feedback and management responsibility to the client or developer requesting the work. (3) Current approaches remain highly limited in the programming activities and software domains that they support.To make microtask programming more scalable, I designed and implemented novel techniques to address parallelism and conflict management limitations by re-envisioning the microtask scope and adapting behavior-driven development to o↵er contributors new opportunities for feedback. In contrast to existing approaches which rely on feedback from either a client or manager which are only available after a microtask is completed, I developed new techniques that enable developers to receive initial feedback within the microtask itself. To assess these techniques, I conducted four studies. These studies demonstrated that crowd workers can use the techniques to build a functioning microservice backend with minimal defects, that the time to onboard can be drastically reduced while increasing project velocity, and that, in an industrial context, the techniques enable more fluid assignments between developer teams
The Economic Effect of Covid-19 on Metropolitan Statistical Areas
Full text linkThe COVID-19 pandemic created an economic crisis never seen before in our globally interconnected society. A highly infectious respiratory disease coupled with lockdown measures and consumer spending habit changes had severe impacts on key sectors of the United States. This research assessed the economic impact and government response of the COVID-19 pandemic on Metropolitan Statistical Areas. Multiple analyses were conducted in this research to assess the pandemic influenced economic impact. They are Getis-Ord* Hot Spot Analysis, Negative Binomial Regression, Ordinary Least Squares Regression, and a prediction of excess unemployment using the Farrington Surveillance Algorithm. This research identified economic changes experienced by MSAs and what influenced those changes. Characteristics of MSAs vulnerable to extended unemployment times and excess unemployment are high populations of retail jobs, high levels of income inequality, high education rate, and high vaccination rate. Surprisingly beneficial characteristics of MSAs to excess unemployment included high populations of construction jobs and healthcare workers. Finally, this research identified that PPP loan assistance did not appear to be targeted at jobs and companies in the most vulnerable industries like retail and food services
Systemic Inflammation and Structural Violence: An Examination of Periodontal Disease Among Individuals From Colonial Peru
Full text linkThis thesis examines bioarchaeological patterns of periodontal disease in the 16th and 17th century Peru during a period of extreme political and socioeconomic instability involving Spanish subjugation of Andean peoples. A growing number of paleopathological studies have begun characterizing the biocultural effects of conquest in the Colonial Andes. Unfortunately, periodontal disease has remained virtually unstudied. Here, this thesis provides the first characterization of periodontal disease severity in Colonial Peru to assess this marker of diet, oral health, and systemic inflammation. The central hypothesis of this thesis predicts that periodontal disease prevalence and severity was greater in the Colonial Muchik town of Mórrope (a setting of heightened economic and biological stress) compared to their Muchik neighbors to the south in Eten (an economically stable community). Established protocols measuring CEJ-alveolar margin distances were used as a measure of periodontitis prevalence/absence and alveolar margin morphology/porosity/recession to assess severity. Sixty-four adults from Mórrope and 115 adults from Eten were examined. Data were analyzed using odds ratio (ÔR) analysis. A higher crude prevalence of periodontitis is observed in Eten (59% vs 36% in Mórrope, which was statistically significant (ÔR=0.38; p=0.01). A statistically significant difference of severity was not observed (ÔR=0.61; p=0.01). Severe expressions of periodontitis were observed in both samples (55% Mórrope and 44% Eten). These unexpected findings were interpreted in the contexts of their archaeological settings, inflammatory disease biology, patterns of other pathological conditions, and isotopic variation to suggest (1) periodontal disease was widespread and severe in these two communities, and; (2) that the extremely high prevalence of antemortem tooth loss and alveolar remodeling in Mórrope obscures documentation of potentially extreme periodontal disease in that community. This prevents straightforward comparisons between these communities and highlights new questions and opportunities in studying periodontitis in historic Peru. This findings are also considered in terms of structural violence, colonial subjugation, and systemic inflammation as embodied in expressions of periodontal disease
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 5938.01
Full text link“The intriguing field of exoplanets continuously amazes scientists around the world. The TESS mission intended to help discover new possible exoplanet candidates, but there is so much data it is difficult to deeply research each one. Our ground-based research intends to help with that problem by thoroughly analyzing our target TOI 5938.01. We plate-solved the observation data taken from GMU using Python code. We then used AstroImageJ to create a light curve and perform a NEB analysis. From here we interpreted our results from the NEB analysis, light curve plot and seeing profile with credible past data to conclude there is a transit. TOI 5938.01 provides a plethora of data concluding it is extremely likely it is an exoplanet.
COMPUTATIONAL MODELING OF THE SOCIAL COMPLEXITY IN GÖBEKLI TEPE AND EARLIEST NEOLITHIC COMMUNITIES
No full textFor years, we believed that the transformation of human communities from simple nomadichunter-gatherer societies to complex sedentary societies occurred due to the expansion of cultivation and the rise of agriculture. Accepting this perspective as premise to years of archaeological research limited the field in investigating alternative explanations to the significant socio-cultural transition which potentially led to years of collaborative efforts in the Mesopotamian region of West Asia. Göbekli Tepe (pronunciation: gow·beh·klee.teh·puh), a pre-pottery Neolithic site in Turkey, requires such alternative explanations for the Neolithic Revolution. Since the site was discovered, various excavations and analyses have been conducted. However, all such investigations include traditional archaeological methods and expectations. This research proposes a multidisciplinary theory of the development of the social complexity of Göbekli Tepe and the contemporary communities in the Urfa region prior to the rise of agriculture. Understanding the interplay of factors resulting in group identity and social cohesion shed light on the reasons that motivated the collective action of hunter-gatherer societies of the region to construct this communal space for ritualistic purposes. This research, rooted in complex system theory, pioneers a novel exploration of the nonlinear dynamics of humanhuman and human-environment interactions among Neolithic communities. Utilizing a multi-method computational approach, it delves into group identity and social cohesion, departing from prior studies confined to single quantitative methods. The study employs quantitative and qualitative socio-cultural and environmental data to construct an artificial society, unveiling nuanced factors shaping interactions. Through agent-based modeling and social network analysis, it sheds light on the dynamics of trust, cooperation, leadership emergence, belief propagation, and universal narratives, offering insights into historical and contemporary societal behaviors. The model underwent continuous testing during implementation to ensure replicability of observed data, with a conceptualization involving three phases: agent-environmental dynamics, agent behavior, and analysis and interpretation of outcomes. Phase I focuses on agent dynamics during hunting and gathering, addressing survival, prosperity, and subsistence patterns, utilizing diverse data sources for model design. Phase II introduces sociopsychological elements like trust and cooperation, enhancing complexity and discovery pace. In phase III, results are analyzed to reveal patterns and insights on Göbekli Tepe’s dynamics, including group identity, social cohesion, and belief propagation. Social network analysis quantifies structural cohesion, identifies leaders, explores group identity factors, and compares parameters affecting subsistence patterns, combining socio-cultural factors influencing decision-making and adaptive behavior. Justified results include (i) demonstration of a consistent shift from hunting to gathering in simulated societies, showcasing adaptability and resilience, with insights into potential agricultural emergence from extended wild grain gatherings; (ii) improved understanding of the essential role of trust and cooperation in community formation among hunter-gatherers, impacting network connectivity and emphasizing their influence on the construction of Göbekli Tepe and early human societies; (iii) novel findings that suggest belief propagation in Neolithic communities, indicating a shared belief system’s crucial role in fostering collective identity and influencing the emergence of leaders, contributing to monumental construction at Göbekli Tepe; and, (iv) exploration of the development of universal narratives, demonstrating that trust thresholds influence narrative attachment growth, providing insights into symbolic motivations behind hunter-gatherer monumental structures and emphasizing storytelling’s pivotal role in fostering extensive cooperation. This dissertation’s research, grounded in complex system theory, innovatively integrates computational methods to explore group identity and social cohesion among Neolithic communities, surpassing prior studies relying on single quantitative methods. The consistent patterns across experiments demonstrate the adaptive resilience of simulated societies transitioning to gathering. Trust emerges as a crucial factor influencing cooperation, social cohesion, and group identity, with implications for modern societal dynamics and policymaking. The study also delves into the emergence of leaders and belief propagation dynamics, offering insights relevant to historical and contemporary societal behaviors. This interdisciplinary computational approach advances scientific understanding, with potential applications in anthropology, archaeology, and policy-making. Collaboration with the Göbekli Tepe archaeological team enhances study validity, fostering international research partnerships and future discoveries
COMPREHENSIVE APPROACHES TO THE CAPTURE, IDENTIFICATION, AND QUANTITATIVE ANALYSIS OF ANTIMICROBIAL PEPTIDES AND OTHER PEPTIDES OF INTEREST FROM BIOLOGICAL SOURCES
No full textABSTRACTCOMPREHENSIVE APPROACHES TO THE CAPTURE, IDENTIFICATION, AND QUANTITATIVE ANALYSIS OF ANTIMICROBIAL PEPTIDES AND OTHER PEPTIDES OF INTEREST FROM BIOLOGICAL SOURCES Amaal Altalhi, Ph.D. George Mason University, 2024 Dissertation Director: Dr. Dr. Barney Bishop The emergence of antibiotic-resistant pathogens necessitates the development of novel antimicrobial agents. This dissertation focuses on the synthesis and characterization of magnetic iron (III) oxide particles (IOPs) incorporating amphipathic cross-linked polymers, specifically N-methacryloyl-6-aminohexanoic acid (MA6AHA) and N-isopropylacrylamide/methacrylic acid (NIPMAm/MAA). These IOPs are evaluated for their stability and effectiveness in capturing antimicrobial peptides (AMPs) from biological sources, using American alligator plasma as a model system. The captured peptides were analyzed using tandem mass spectrometry (LC-MS/MS), employing electron-transfer/higher-energy collision dissociation (EThcD) fragmentation techniques, and data analysis was conducted with PEAKS Xpro software. Results demonstrated that the functionalized IOPs efficiently captured a variety of peptides, including potential AMPs, highlighting their potential utility in discovering novel bioactive peptides. This study contributes to the broader research effort in combating antibiotic resistance and exploring the reptilian host-peptidome
DATA-DRIVEN STRATEGIES FOR IMPROVED HEALTHCARE DECISION MAKING: FROM KNOWLEDGE DISCOVERY TO RISK STRATIFICATION
No full textThe health sector has undergone a remarkable transformation thanks to the advances in machine learning and data science techniques. These advancements have allowed researchers and policymakers to find novel solutions to complex issues involving disease diagnosis, precision medicine, policy planning, administrative processes, and many others. However, integrating data-driven techniques in the health sector poses unique challenges. Limited access to high-resolution data and the need for precise outcomes are significant hurdles to leveraging these techniques effectively, which is further exacerbated by privacy concerns. This dissertation investigates the complex challenges within the health domain and proposes novel ways to overcome them using data-driven techniques. The primary focus is on maximizing the utility of the available data, with each chapter addressing unique issues across different data levels (aggregated and individual) while considering the implications for decision-making and policy development. Central to this dissertation is the recognition of the complex nature of health and the numerous factors influencing health outcomes, including individual behaviors, community structure, and environmental impacts. A holistic analysis of all these dimensions is crucial for addressing health needs and making effective decisions. At the aggregated level, we use spatio-temporal analyses to explore the geographic disparities and socio-ecological risk factors across populations from broader areas such as counties. By understanding the emerging health trends and identifying disparities at the county level, policymakers can tailor the necessary interventions to mitigate the disparities effectively.To study individual-level health outcomes, we utilize Electronic Health Records (EHRs), focusing on the risk prediction of conditions like substance use disorder (SUD), opioid use disorder (OUD), and diabetes. We find that leveraging large language models (LLMs) to represent and model EHRs while addressing fairness and bias concerns enables a pathway to more accurate decision-making. Lastly, we highlight a method for extrapolating individual-level data by merging information from multiple aggregated data sources. This technique, often referred to as population synthesis, facilitates timely access to data. We explore synthetic data generation techniques such as copula and maximum entropy for this purpose. In conclusion, the findings from the dissertation demonstrate the potential of data science and machine learning in enhancing health outcomes. Data-driven approaches hold promise for advancing health initiatives and fostering healthier communities by allowing better resource allocation, risk assessment, and evidence-based policy development
SERVICE NETWORK OPTIMIZATION TO GUIDE DECISIONS ON INFRASTRUCTURE INVESTMENT
Full text linkInterrelated infrastructures, such as manufacturing, supply chain, renewable energy andsmart grid, are critical for achieving long-term organizational and societal goals and enabling future growth. Deciding on infrastructure portfolio investment is a complex problem, given the uncertainty in future supply and demand, the rapid emergence of new technologies, and non-trivial operational interactions among the infrastructure components. Today, models and systems supporting stakeholders in infrastructure investment decisions either (1) express the investment model in high-level financial terms, which fails to accurately express the underlying operational system behavior over the investment time horizon, or (2) are hard- wired to a siloed domain-specific investment problem, which does not take into account interactions with interrelated infrastructures across the silos and inhibits the widespread adoption and re-usability of these models. Thus, both accurate and flexible investment decision models and systems are needed to recommend investment alternatives and guide stakeholders in making Pareto-optimal trade-offs between competing performance indicators such as total cost of ownership, carbon emissions and quality of service. This dissertation is driven by the need to overcome the aforementioned gap of investment decisions made in silos, as opposed to accounting for the synergistic value of strongly interdependent infrastructures. More specifically, the key contributions of this dissertation are as follows. First, designed and developed are formal predictive Analytic Models (AM) for both steady-state and tran- sient Service Networks. These models express metrics, capacity, and demand constraints over a specified time horizon as functions of fixed and controllable parameters, representing investment choices and precise operational settings throughout investment periods. Second, developed is a modular, extensible repository of investment component models, such as pumps, renewable energy sources, water and energy storage, Reverse Osmosis plants, transformers, energy contracts and electric and gas boilers, renewable energy certificates (RECs) and carbon offsets. Third, designed and developed are Decision Guidance Systems for both steady-state and transient models for investment in Service Networks. These systems optimize performance metrics and analyze Pareto-optimal trade-offs between different financial, environmental, and quality-of-service investment objectives leveraging a mixed-integer linear programming solver. As a specialization in the domain of Energy and Sustainability, developed is the Green Assessment and Decision Guidance Tool (GADGET.) Finally, a case study is conducted to provide recommendations to George Mason Uni- versity’s stakeholders on the most cost-effective approach to achieve its carbon neutrality goals by 2040. GADGET provides recommendations for Pareto-optimal operational settings and investment choices related to the integration of renewable energy sources and related infrastructures with existing systems
A Comparative Evaluation of Kits for Seminal Fluid Detection
Full text linkSeminal fluid detection kits are commonly used within the forensic science field to determine whether an unknown sample contains semen. The ability to correctly identify seminal fluid in any criminal cases involving bodily fluids is of the utmost importance. The ABACard p30 kit is most commonly used in forensic DNA laboratories to detect semen. This study “A Comparative Evaluation of Kits for Seminal Fluid Detection” will compare the ABACard p30, RSID - Semen, and SERATEC Semiquant PSA kit to the Bluestar Identi-PSA kit which is newer to the field. Bluestar, ABA, and SERATEC kits test for the presence of prostate specific antigen (PSA) which is the protein present in semen. On the other hand, RSID - Semen detection kit tests for semenogelin which is the protein that is found in ejaculated semen and is responsible for sperm immobilization in the seminal coagulum. To evaluate the specificity of these kits, breastmilk, saliva, and blood will also be tested in duplicate. Labor intensity, cost, and sensitivity will also all be compared between the four kits. Sensitivity will be evaluated by testing a series of dilutions of semen in duplicate from 1:10 to 1:100,000. It is expected that all four kits will be equally effective (creating true positives) in detecting semen, however, there will be differences in labor intensity and cost. It is also anticipated that a potential false positive with the breastmilk will happen since previous research has reported the detection of prostate specific antigen or semenogelin in breastmilk. The result of a positive test from breastmilk would pose the question of whether said kit would be deemed to be as reliable as stated. It is expected to conclude that between the four kits: Bluestar Identi-PSA, ABACard p30, RSID - Semen and SERATEC Semiquant PSA, each will yield different results when it comes to specificity and sensitivity