PublicationsJournal Papers

The lack of a common executable modeling framework that integrates systems engineering, software design, and other engineering domains is a major impediment to seamless product development processes. Our research aims to overcome this system-software modeling gap by integrating computational, software-related, and model execution capabilities into OPM-based conceptual modeling, resulting in a holistic unified executable quantitative-qualitative modeling framework. The gap is overcome via a Methodical Approach to Executable Integrative Modeling—MAXIM, an extension of OPM ISO 19450:2015, a standardization approvement given on 2015. We present the principles of MAXIM and demonstrate its operation within OPCloud—a web-based collaborative conceptual OPM modeling framework. As a proof-of-concept, a model of an Airbus civil aircraft landing gear braking system is constructed and executed. Using MAXIM, engineers from five domains can collaborate at the very early phase of the system development and jointly construct a unified model that fuses qualitative and quantitative aspects of the various disciplines. This case study illustrates an important first step towards satisfying the critical and growing need to integrate systems engineering with software computations into a unified framework that enables a smooth transition from high-level architecting to detailed, discipline-oriented design. Such a framework is a key to agile yet robust future development of software-intensive systems.

We investigated the competence of in- and pre-service chemistry teachers and teacher mentors in designing sustainability- and systems-oriented online tasks for their students. Using a dedicated rubric, we evaluated their assessment knowledge (AK) as reflected in the tasks they had developed. The rubric is based on four attributes: integration of sustainability and chemistry, diversity of thinking skills, the variety of system aspects, and diversity of visual representations. Implementing a qualitative case study approach, we tracked the professional development of three purposefully sampled teachers in addition to using the rubric to score their tasks. Combining the rubric scorings and the qualitative investigation via feedback questionnaire revealed new insights. Besides the teachers’ content and pedagogical knowledge, the case studies’ context and relevance to the teachers were found central to their ability to assess learning. This research contributes to the theoretical understanding of AK of teachers with different backgrounds and professional experiences. The methodological contribution stems from the analysis of self-developed tasks based on a designated rubric, which should be further validated.

As science and technology create an ecosystem that is becoming increasingly more knowledge-intensive, complex, and interconnected, the next generation science standards include systems thinking and systems modeling among 21^st skills that should be fostered. We examined the effect of an online cross-disciplinary learning process on the development of systems thinking and modeling skills among engineering students and engineering and science teachers. The study, which used quantitative and qualitative tools, included 55 participants who performed four food-related learning assignments and created conceptual models in Object-Process Methodology. Their responses to online assignments were analyzed along with their perceptions, captured via a reflection questionnaire. The online learning process in this study effectively enhanced the systems thinking and modeling skills of all learners, including those with no relevant background. One main conclusion that extends beyond online learning was that imparting the basics of systems thinking and conceptual modeling skills can be achieved even within a short period of time – less than one semester. The contribution of the study is the formation of theoretical and practical frameworks for the integration of cross-disciplinary model-based systems engineering online assignments into engineering and science curricula.

As part of the design, development, and deployment of a massive open online course (MOOC) on model-based systems engineering, we introduced MORTIF—Modeling with Real-Time Informative Feedback, a new learning-by-doing feature that enables the learner to model, receive detailed feedback, and resubmit improved solutions. We examined the pedagogical usability of MORTIF by investigating characteristics of participants working with it, and their perceived contribution, preferred question type, and learning style. The research included 295 participants and applied the mixed-methods approach, using MOOC server data and online questionnaires. Analyzing 12,095 submissions, we found increasing frequency of using the model resubmitting option. Students ranked MORTIF as the highest of six question types in terms of preference and perceived contribution level. Nine learning style categories were identified and classified based on students’ verbal explanations regarding their preference of MORTIF over the other question types. MORTIF has been effective in promoting meaningful learning, supporting our hypothesis that the combination of active learning with real-time informative feedback is a learning mode that students eagerly embrace and benefit from. The benefits we identified for using MORTIF include active learning, provision of meaningful immediate feedback to the learner, the option to use the feedback on the spot and resubmitting an improved model, and its suitability for a variety of learning styles.

The term “healthcare system” is commonly and loosely used to describe the existing disconnected, inefficient, ineffective, and expensive approach to health management, including disease prevention, diagnosis, and treatment. The unfortunate reality is a complex array of proprietary enterprises, from individual and group medical practices, hospitals, and medical centers to networks of affiliated centers and practices. These range from small to huge in both size and complexity, all attempting to use technology and best practices to deliver evidence-based care to a variety of patient populations with varied economic means and accessibility. Simply put, even without delving into technological, administrative, and financial realms, clearly, a healthcare enterprise exists, but it falls far short of a true healthcare system.

A recent article in the IEEE Systems Journal bluntly notes: “The definition and characteristics of systems have eluded recognition and understanding for a very long time, as different people refer to the concept of system in various ways,” adding that one survey of experts used “100 definitions of system and formed assumptions and hypotheses about the different worldviews represented by different groups of definitions.” [1]

The consequences of not understanding a true systems approach to healthcare and biomedical research plague the health endeavor today, as evidenced by the ongoing COVID-19 pandemic. From a systems perspective, the COVID-19 pandemic has fostered confusion as to what is and what is not a systemic intervention even while also offering useful insights into how the situation might be improved.

Systems thinking, grounded in systems engineering principles, has been utilized by “high hazard” enterprises to deal with the identification and prevention of catastrophic events in mission-critical situations, e.g., a nuclear reactor core meltdown or prevention of aviation accidents. Systems thinking and engineering have improved transportation and distribution systems and banking operations, benefiting many. Even today’s automobiles are themselves elaborate systems capable of transporting their occupants in comfort and safety, sometimes without a driver!

Despite decades of discussion, the application of systems thinking and design principles to health and science remains elusive at best. In some microcosms, success has been achieved by limiting the scope of the size and complexity of the endeavor. Yet, to be effective, a systems approach to health and science must encompass the entirety of healthcare and biomedical research–the people, processes, policies, and technologies, and the many stakeholders, each with their own agendas and vested interests. Ways that healthcare and biomedical research currently affect each other and how they should in the future can be improved through enhanced systems development.

OPCloud is a Web-based collaborative software environment for model-based systems engineering (MBSE) used for creating conceptual models in Object-Process Methodology, OPM, ISO 19450:2005. As we have been designing and developing OPCloud, we faced several challenges, mostly stemming from the unique development environment. OPCloud is a high-end, cloud-based tool. Software of this kind is developed by commercial companies, be they large established ones or small startups. In contrast, OP Cloud is developed in an academic environment at a technological university. As such, it involves a variety of people contributing to its development, each having a different objective, capabilities, and commitment level. In this report, we describe our experiences of a three-year project of OPCloud software design and development. To this end, we have adopted an agile development methodology, involving regular weekly meetings of all the development stakeholders and monthly product deployment to be delivered to the commercial company customer. We describe how we engaged the diverse population of developers, including faculty, post-doctoral fellows, academic researchers, graduate and undergraduate students, and dedicated developers, in the software development process.

Modern complex systems include products and services that comprise many interconnected
pieces of integrated hardware and software, which are expected to serve humans interacting
with them. As technology advances, expectations of a smooth, flawless system operation grow.
Model-based systems engineering, an approach based on conceptual models, copes with this challenge.
Models help construct formal system representations, visualize them, understand the design,
simulate the system, and discover design flaws early on. Modeling tools can benefit tremendously
from querying capabilities that enable gaining deep insights into system aspects that direct model
observations do not reveal. Querying mechanisms can unveil and explain cause-and-effect phenomena,
identify central components, and estimate impacts or risks associated with changes. Being
connected networks of system elements, models can be effectively represented as graphs, to which
queries are applied. Capitalizing on established graph-theoretic algorithms to solve a large variety of
problems can elevate the modeling experience to new levels. To utilize this rich set of capabilities,
one must convert the model into a graph and store it in a graph database with no significant loss of
information. Applying the appropriate algorithms and translating the query response back to the
original intelligible and meaningful diagrammatic and textual model representation is most valuable.
We present and demonstrate a querying approach of converting Object-Process Methodology (OPM)
ISO 19450 models into graphs, storing them in a Neo4J graph database, and performing queries
that answer complex questions on various system aspects, providing key insights into the modeled
system or phenomenon and helping to improve the system design.

Modern systems comprise hardware and software components that together provide value through enabling the functionality that the system is intended to provide. Systems engineering (SE) and software engineering (SwE) are therefore interdependent, tightly coupled, and complementary activities that must be carefully aligned and coordinated throughout the system development process. Yet, these two disciplines have historically grown quite separated from each other, with too little interaction and mutual learning. In this work, we develop and evaluate Object-Process Programming (OPP) as a proof-of-concept for a common framework that integrates SE and SwE based on ISO 19450— Object-ProcessMethodology. The ability of designers to use the same paradigm for engineering the software, the hardware, and the system as a whole, using the same concepts and principles and the same design environment, described and discussed in this work, is a major step toward the integration and streamlining of engineering new systems that feature significant hardware and software components. To evaluate OPP, we established a focus group and conducted an experiment in which participants were asked to develop systems using OPP. Overall, the results were positive in terms of usability and understandability. In particular, the language and the environment were far superior in comparison to textual languages. OPP will contribute to the continuous endeavor to bridge the gap between SE and SwE by providing a seamless, easy-to-learn environment. Non-technical stakeholders can also benefit from OPP by improving their communication with technical stakeholders. The ideas under lying OPP have already served to augment OPM with computational capabilities.

Models have traditionally been mostly either prescriptive, expressing the function, structure and behavior of a system-to-be, or descriptive, specifying a system so it can be understood and analyzed. In this work, we offer a third kind—diagnostic models. We have built a model for assessing potential pediatric failure to thrive (FTT) during the perinatal stage. Although FTT is commonly found in young children and has been studied extensively, the exact etiology is often not clear. The ideal solution is for a pediatrician to input pertinent data and information in a single tool in order to obtain some assessment on the
potential etiology. We present FTTell—an executable model-based medical knowledge aggregation and diagnosis tool, in which the qualitative considerations and quantitative parameters of the problem are modeled using a Methodical Approach to Executable Integrative Modeling (MAXIM)—an extended version of Object-Process Methodology (OPM) ISO 19450, focusing on the perinatal stage. The efficacy of the tool is demonstrated on three real-life cases, and the tool’s diagnosis outcomes may be compared with and critiqued by a domain expert.