Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 87-89). / Substituting non-renewable energy for renewable energy plays an important role for our sustainability, which is the common goal for human beings. However, several strategies by governments and companies exist to make this shift, because the priority of each strategy mainly depends on the relative costs and their regulations, which makes this shift complicated. This paper describes a model of the common causal loop diagram and applies it to three cases. Additionally, by building stock and flow model, the future dynamics are simulated by System Dynamics. Based on the casual loop diagram analysis, the renewable shift makes three phases. The first phase is making an ecosystem of renewables initiated by political support or guideline such establishing a low generation cost and making the power grid system flexible enough to accept renewables. The second phase is pushing the energy mix by private investment to capture the economic benefit including reducing electric bills with low-cost renewable energy, the merit of reputation and sustainability of business. The third phase aims at meeting the political target of the energy mix by political strategies, such as tax exemptions, subsidies and obligations for companies. Stock and flow model of System Dynamics is applied for the future of the Japanese renewable shift cases to illustrate which compositions of the casual loop are the key causes for dynamics. At first, the relative cost triggers the renewable shift not only for companies but also for utilities. After that, the difference of the energy mix of a company and its target decides how much the energy mix increases each year. These two factors decide the intensiveness of investment of a company, even though the relative cost is not an internal factor. Also, the capacity mix of a utility deals with the speed of the renewable shift. / by Masahiro Yamada. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 57-59). / With the advent of machine learning, organizations today collect and process data at an unprecedented scale. This has led to rapid growth in innovation across industries, but also poses numerous challenges around maintaining user privacy. Specifically, in the field of healthcare and genomics where data is highly sensitive. Unlike credit cards or passwords, one's genomic information cannot be modified at will and has the ability to uniquely identify the individual. The objective of this thesis is to develop an easily configurable framework that would allow organizations to collaborate and advance genomic research without directly sharing user data with each other. This thesis includes the development of a privacy preserving framework for federated learning on genomic datasets that are distributed across organizational silos. PAGe (Privacy Aware Genomics) has been open-sourced and has a low barrier to entry. A packaged runtime environment is available that includes popular bioinformatics tools and machine learning libraries. Experimental setup is controlled through configuration files, allowing users to easily terminate, restart or reproduce results. Finally, there is an in depth evaluation of the framework using Type 2 Diabetes disease risk prediction as a case study with the 1000 genomes dataset as input. / by Yashashree Kokje. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 / Cataloged from the official version of thesis. Page 180 blank. / Includes bibliographical references (pages 171-179). / Digital twins are receiving considerable attention as a cutting-edge technology that will transform the oil and gas industry. Powered by a digital thread that connects data across the product lifecycle, a digital twin virtually mirrors or emulates processes, assets, and projects in real-time to generate highly valuable insights. Promises of value creation, delivering optimized production, increased reliability, improved safety, and enhanced foresight, are now driving oil and gas operators to realize their potential. Despite these claimed and expected benefits, realized value is often hard to quantify and explicitly link to digital twin technology. And in addition, consistency in definition and availability of a reference architecture is lacking, resulting in a gap of a standard approach in implementing this technology. This thesis attempts to investigate and summarize the digital twin's enabling technologies (e.g., model-based systems engineering, network infrastructure, the Internet of Things (IoT), and automation) and provide insight into industry digital twin applications in use today. Modeling a simple production facility demonstrates that digital twins have the potential to improve the prediction and mitigation of facility failures leading to overall higher availability and improved financial outlooks for projects. The simulation results of a highly robust and integrated digital twin used on an offshore, deepwater facility showed an improved NPV of $211 million over 27 years. With enhanced upstream capabilities enabled by a digital twin, considerations to reducing daily physical inspection requirements are made more feasible. However, as costs for offshore personnel decrease, the cost of software development and maintenance will increase sharply. Oil and gas assets are more enabled to be monitored and controlled remotely through this increased rigor and oversight provided by the digital twin platform. From the perspective of a three-component digital twin framework consisting of modeling and analytics, enablement technology, and data, a digital twin can provide value from a virtual proxy to a fully autonomous system. / by Mollie B. LeBlanc. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 86-88). / Given an uncertainty-based world with changing stakeholder needs, stakeholder objectives, operating environments, and technologies, there is a paradigm shift in systems engineering from systems built to last to systems built to evolve. This is further observed when developing a product/system with a tension between confidence in requirements and the ability to respond to requirements. This natural tension of uncertainties requires a framework for balancing the need-space and solution-space between acquisition managers & chief engineers and warfighters. Embedding flexibility in a product or system is a method to foster evolvability which can sustain value delivery to its stakeholders in a feasible time and cost-effective way after the product or system has been fielded. This research and subsequent findings develop a foundational framework that was tested and analyzed from past military acquisitions through anonymous surveys and voluntary interviews. This uncertainty-based strategy framework is intended to help guide the tailorable traditional pathway acquisition process during the material solution analysis phase and can provide valuable insight into guiding the correct acquisition strategy quadrant for system design. / by Donald K. Lew Jr. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 / Cataloged from the official version of thesis. "September 2020." / Includes bibliographical references (pages 81-89). / The primary role of universities in strengthening economic development is in accelerating innovation and entrepreneurship. The framework Universities as Engines of Economic Development codifies a set of practices that collectively illustrate how universities can more effectively accelerate innovation, and contribute to sustainable economic development. This qualitative interdisciplinary study complements this systemic framework by illustrating with eleven case studies how these practices of knowledge exchange in research, education, and catalyzing innovation, are applied across different types of universities and circumstances. Results show how universities invigorate discoveries, enhance the training of future researchers, and improve direct diffusion of knowledge exchange by supporting investigator-driven mechanisms that solve societal challenges through interdisciplinary collaborations--among scholars, industry, and government--while influencing education, science policy and practice. Students are educated through integrated curriculum, real-world projects and independent learning to scale up tech ventures by leveraging the resources of local ecosystems and providing access to university intellectual property, tangible research products, incubators, labs, funding and professional entrepreneurial networks. These case studies reveal how universities can accelerate sustainable prosperity through innovation and help stakeholders reach their full potential. / by Juan Cristóbal García Sánchez. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 / Cataloged from the official version of thesis. "September 2020." / Includes bibliographical references (pages 121-129). / This thesis investigates analytic and data-mining methods that can be used for the management of petroleum brownfields, specifically as it applies to the surveillance, analysis, & optimization of gas lifted oil wells. Building on the output of validated physics-based models, this thesis investigates a range of analytic methods which may be used to determine a probable depth of gas lift injection of wells without pressure gauges, and finds that the Random Forest method coupled with a k-means clustering algorithm can offer good results. Additionally, this thesis shows how a pan matrix profile may be used to efficiently identify patterns (motifs) in the real time pressure signatures of wells. Understanding of the motifs are assessed through a physics-based model, providing a useful tool for engineers to perform surveillance of large well count areas, which are typical for brownfields. / by Ben Partington. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 85-88). / Insulin Regimen refers to instructions prescribed by a clinician indicating the kind of insulin to take (long-acting, short-acting, etc), how much insulin to take (dosage) and how often to take it (frequency). Determining the daily insulin regimen for diabetic patients is more of an art than a science. Clinicians who care for diabetic patients carefully assess the patient's blood glucose levels, medical history and symptoms before prescribing insulin medication. The challenge for clinicians is often in accessing the historical insulin regimen prescribed to patients, which is hidden in unstructured clinical notes. The reason that is a problem is that the individual clinician is unable to draw on the wisdom that might exist in collective experience. Additionally, having access to a patient's historical insulin regimen can help identify patient groups with distinct insulin regimen patterns, analyze total and average daily insulin consumption of different patient groups, discover patient groups showing variation in their insulin regimen, etc. In this thesis, we treat insulin regimen extraction from clinical notes as an information extraction problem and explore machine learning methods focused on extracting this information from prescription lists available in outpatient clinical notes. We explore two n-gram models - Logistic Regression and Conditional Random Field and analyze their performance. We also explore models using contextual word representations from the domain specific pretrained language models, character level embeddings and auxillary features constructed from external knowledge sources and analyze their performance. We find that our final Multi Layer Perceptron method using contextual word representations gives a micro averaged F1 score of 0.98 and is able to detect patterns that go undetected by n-gram models. We then apply a rule based post processing system to convert the extracted insulin regimen into a normalized timeseries format. We analyze the extracted insulin regimen information and find that, in most cases, prescription lists in clinical notes contain an accurate account of the current insulin regimen prescribed to patients. However, supporting insulin regimen information such as patient specific glycemic targets, basal-bolus insulin ratio, etc are available only in the narrative text in clinical notes. We also examine the data to find patient samples with interesting insulin regimen patterns such as those changing from a long-short to combined insulin regimen and vice versa from our extracted dataset. / by Monisha Pushpanathan. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 150-159). / High impact digital innovations present opportunities for organizations to transform their business capabilities to adapt for future sustainability. To adopt new platforms offered by disruptive technologies, organizations must alter or retire existing business models, create and develop new competencies, and build an agile business culture. An organization's failure to respond to evolving digital initiatives will inevitably lead to a loss of competitive advantage and even obsoletion. Undertaking and managing transformative digital solutions may seem risky, but the alternative is riskier. This thesis explores the opportunities associated with integrating digital technologies into established oil and gas (O&G) organizations where transformation will be exceedingly difficult. Investing in the right technologies that fit the organizational size, competencies, and culture is critical for the success of adopted digital initiatives. Case studies reviewing digital investment portfolios within the O&G industry are presented to evaluate the investment size, capabilities, and realized value creation associated with digital integration on design and operations. A systems approach was employed to understand the barriers and limitations to digital integration in the following areas: data value chain and workflows, data architecture standardization, and end-to-end lifecycle integration, with emphasis on O&G drilling and completion operations. Additionally, a business strategy roadmap was created to recommend realized value opportunities for a digital investment portfolio to succeed in this constantly evolving marketplace. / by Michael Trevathan. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 59-61). / Agile product development promises improved productivity, fast development cycles, and high employee satisfaction. Large-scale agile development frameworks (e.g., SAFe, Sage, Scrum [at symbol] Scale, Spotify, LeSS) adapt agile principles for programs where multiple teams must work together to build complex products and services. In this research, we explore how large-scale agile organizations manage cross-team interfaces and dependencies. We reviewed existing frameworks and interviewed fourteen individuals from six different organizations. We learned that many large-scale agile practices act as coordination mechanisms. Large-scale agile programs use these coordination mechanisms to: (1) reduce the quantity and complexity of cross-team interfaces; (2) identify interfaces up front; (3) surface interfaces as they emerge in development; (4) manage interfaces during development; and (5) build a shared understanding between teams. Experienced practitioners consider how agile roles, events, artifacts, and other mechanisms contribute to coordination in each of these areas. While large-scale agile frameworks provide recommended practices, we suggest programs should adapt these approaches to fit their specific needs. Future research may help to evolve large-scale agile practices by further exploring: product, process and program architecture, coordination mechanisms and effectiveness, and leadership and accountability. / by Lisa Crofoot. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 196-207). / access with(out) judgment takes the reader on a learning expedition by means of reading, writing, and doodling. Since learning is accessible and actionable for any reader, this text prioritizes opportunities for research-driven practice - the acquisition, contemplation, integration, synthesis, and expression of knowledge on the part of the reader - over descriptions of research that is of convention with traditional academic publications. Research for this text includes, but is not limited to, analyses of and references to 60+ years of neuroscience research, 130+ years of psychology research, and a few centuries of history and lessons learned from the western education system (and its impact on non-western nations). Content within this text incorporates several millennia of philosophy of knowledge, philosophy of logic, and systems of knowledge as captured in written texts and as deduced through means of scientific study (such as through anthropology and linguistics). This text is organized as a collection of access points for a self-directed learning experience. This non-traditional format responds to and complements the state of modern-day technology, whence readers can access a global information system. access with(out) judgment encourages readers to explore multi-dimensional learning pathways driven by their individual sense of curiosity. / by Dai Lin. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program