Systems Engineering Research Articles

A novel system architecture of intelligent adaptive cruise control for safety aspects

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

Developing methods to identify resilience and improve communication about diagnosis in pediatric primary care.

Communication underlies every stage of the diagnostic process. The Dialog Study aims to characterize the pediatric diagnostic journey, focusing on communication as a source of resilience, in order to ultimately develop and test the efficacy of a structured patient-centered communication intervention in improving outpatient diagnostic safety. In this manuscript, we will describe protocols, data collection instruments, methods, analytic approaches, and theoretical frameworks to be used in to characterize the patient journey in the Dialog Study. Our approach to characterization of the patient journey will attend to patient and structural factors, like race and racism, and language and language access, before developing interventions. Our mixed-methods approach is informed by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework (which describes the sociotechnical system underpinning diagnoses within the broader context of multiple interactions with different care settings over time) and the Safety II framework (which seeks to understand successful and unsuccessful adaptations to ongoing changes in demand and capacity within the healthcare system). We will assess the validity of different methods to detect diagnostic errors along the diagnostic journey. In doing so, we will emphasize the importance of viewing the diagnostic process as the product of communications situated in systems-of-work that are constantly adapting to everyday challenges.

Harnessing AI for Enhanced Searching in Digital Libraries: Transforming Research Practices

The application of Artificial Intelligence (AI) technologies in digital libraries is changing the paradigm in which users search and interact with digital content. This article is focused on how the use of AI technologies improves the search in digital libraries, demonstrating the benefits of such technologies in overcoming the challenges associated with formulation of query, information retrieval, and end user usage. Most search engine systems face the problems of user queries, which are inherent in online searches. Many user queries are vague, imprecise or ambiguous with regards to the context and as a result lead to less than satisfactory search results. AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) solve this challenge effectively as they enhance comprehension of queries, contextual understanding, and relevance of the results returned. With the capabilities of NLP incorporated in search engines, search queries which are in the form of natural language can be understood and executed which leads to efficiency in targeting the information needed. Comprehensive restructuring of standard information systems is used by computer learning algorithms for searches by inference and instills usage in users. This study also examines the place of AI in query formulation and the different ways it can be employed; fully automated, semi-automated and/or manually so that the users are less faced with the challenges that are usually present during the query formulation process. Such increases in the level of difficulty and scope of information retrieval suggest that search AI improved the scenario.

Testing Time Optimization Method for IEEE 802.15.4z Ultra-wideband Integrated Circuits

IEEE 802.15.4z-compliant ultra-wideband (UWB) devices are becoming ever more popular in contemporary radio engineering systems. Such systems are capable of precisely measuring distances (with their accuracy expressed in centimeters), are immune to interference, offer low latency and transmit data in an energy-efficient manner. Widespread adoption of UWB technology has triggered significant demand for testing integrated circuits these systems rely on, prompting the development of new testing methods to meet the ever increasing requirements in terms of testing speed and reliability. The same applies to sensitivity tests, in the course of which up to 2000 different packets may be received. The process of generating and analyzing such a large number of packets is time consuming. Furthermore, if multiple devices need to be tested simultaneously, the duration of the test will be multiplied accordingly. In such a context, the article investigates the lead time required to generate 2000 UWB packets using conventional methods and proposes a novel approach to significantly reduce packet generation time and improve testing efficiency.

A heterogeneous model quantization and similarity matching approach based on structural features for MBSE

With the development of Model-Based Systems Engineering (MBSE), model reuse technology is an important method to improve the modeling efficiency and credibility, and the research difficulties and hotspots in the field of complex system simulation. However, model reuse still faces challenges due to the existence of text structure bias and feature mismatch. To this end, this paper presents a structure-based model similarity quantization algorithm to obtain the similarities among models. In the first stage, the properties of the structure between the models are obtained by the structural vectorization method, and different module data are accounted. In the second stage, weights are assigned to the module data using the AHP–CRITIC assignment method. The experiment is based on the circuit amplifier model as a case study, and the experimental results verify the feasibility of structure-based model similarity quantification.

Resilience Assessment of Deep Excavation Engineering Systems Based on Bayesian Network

As a key component of infrastructure, the construction safety of deep excavation is particularly important. In order to improve the safety and stability of Deep excavation construction, research on the risk resistance and rapid recovery capabilities of Deep excavation projects is carried out. By introducing the concept of resilience, we analyze the key factors that affect the safety and resilience of Deep excavation projects, use Bayesian networks to establish a resilience model that expresses the causal relationship between factors, and learn the parameters of the network model through data samples collected through surveys; by adjusting the network Analyze the relationship between each factor and system resilience at each node, and clarify the measures that should be taken and key control objects, thereby improving the system resilience and ensuring that normal construction can be carried out safely and orderly. Research shows that this model can be used to quantify the safety toughness value of deep excavation and reason and analyze the impact of various factors on the toughness value, thereby improving risk management and decision support capabilities.

Experimental and numerical analysis of L-shaped concrete-filled steel tube stub columns

This paper reports experimental and numerical studies on the cross-sectional compression resistance and behaviour of a novel L-shaped concrete-filled steel tube system. The novel composite system is proposed to eliminate concave corners and enhance the confinement of the outer steel tube to the concrete infill. A testing programme was firstly conducted, including nine tensile steel coupon tests, six compressive concrete cube tests and ten stub column tests. Upon testing, all the ten L-shaped concrete-filled steel tube stub column specimens were found to fail by local buckling. Finite-element models were then developed in the finite-element analysis software ABAQUS and validated against test results. The validated models were then employed to perform parametric studies to obtain an additional set of 48 numerical data. Test and numerical results were used to perform a design analysis, in which the suitability of the design rules in the European code EN 1994–1-1, the American specification ANSI/AISC 360 and the Chinese code GB 50936 for the developed L-shaped concrete-filled steel tube stub column system was assessed. The analysis results reveal that both EN 1994–1-1 and GB 50936 yield unsafe compression resistance predictions for the studied L-shaped concrete-filled steel tube stub columns, owing to the improper consideration of the contributions of the outer steel tube and the concrete infill to the overall capacity, while ANSI/AISC 360 offers safe design with high accuracy. The research findings are expected to contribute to the safe and wide use of the proposed novel L-shaped concrete-filled steel tube system in structural engineering and also to the further development of the current international design standards.

Prediction of inlet gas volume fraction of rotary vane pump under variable operational conditions with 1DCNN-TL model utilizing vibration signals

The Rotary vane pump (RVP) exhibits promising potential in the lubricating oil system of aircraft engines. The entrainment of air into the lubricating oil flowing through the pump affects the suction and discharge flow rates of the RVP. Therefore, understanding the inlet gas volume fraction (IGVF) of the RVP is crucial to assess its capability to deliver sufficient lubricating oil for ensuring the safe operation of aircraft engines. This study presents a one-dimensional convolutional neural networks (1DCNN)-based transfer learning (TL) (1DCNN-TL) prediction model to predict IGVF for the RVP using experimental data from gas-liquid two-phase flow in the RVP. The model is pre-trained using the vibration signals under a specific differential pressure between the pump inlet and outlet to enable predictions of the IGVF under varying differential pressures. The 1DCNN serves as pre-trained model for analyzing the outcomes of different transfer tasks. The effect of different frozen layer numbers on training time and prediction accuracy of the neural network is explored. Subsequently, the influence of varied source domain data on prediction accuracy is investigated, revealing that for optimal TL results, stable condition data at 200 kPa should be preferred as the source domain. Then the 1DCNN-TL prediction model of IGVF of RVP under variable operational conditions is determined. The results indicate varying iteration convergence rates of the pre-trained model under a differential pressure of 200 kPa across different transfer tasks, with a final goodness of fit exceeding 0.958 after fine-tuning the model. The model demonstrates the high prediction accuracy and applicability, which can contribute to improving the reliability and safety of aircraft engines.

Human-powered vehicles as a way to abate transport-related greenhouse gas emissions: Part 2 - A virtual prototype for emissions and performance analyses

Velomobiles, despite their potential for efficient transportation, remain relatively unknown compared to electric cars, receiving limited attention in academic research and public awareness. This paper highlights their untapped potential and underlines public interest in their adoption, provided a velomobile meeting customer requirements is available. Leveraging the Model Based System Engineering (MBSE) methodology, this work presents a structured approach to design, focusing on customer needs translation and digital prototype validation. This iterative process ensures velomobiles meet performance criteria while minimizing environmental impact. The lack of standardized testing for the energy consumption of Human-Powered Vehicles (HPVs) is addressed by proposing an analysis of consumption under diverse operating conditions, giving information on the environmental assessments. This study underscores velomobiles’ potential as a sustainable transportation solution and provides a framework for their design, validation, and optimization.

Innovating Pharmaceuticals: The Rise of 3D printing in Drug Delivery.

Additionally known as three-dimensional (3D) printing, additive manufacturing has made major advancements possible in the fields of engineering, business, the arts, education, and medical. Thanks to recent developments, it is now possible to print three-dimensional to create complex, useful living tissues, biocompatible substances, cells, and supporting structures are combined. Regenerative medicine is utilizing 3D bioprinting. Additive manufacturing technology, or 3D printing, has been labelled the “next big thing” and is predicted to overtake cell phones in popularity. 3D printers use digital templates to produce actual, three-dimensional items. Adding layers to a print, commonly referred to as additive manufacturing, allows for the use of more than a hundred different materials, including nylon, metal, and plastic. Applications for 3D printing can be found in many different industries, such as industrial design, manufacturing, dental, automotive, aerospace, civil engineering, education, jewellery, footwear, and geographic information systems. It has shown to be a simple and affordable solution for a variety of use cases. Using computer-aided design tools and programming, three-dimensional printing is a sophisticated technique that adds material to a base surface to create three-dimensional things. Additive layer manufacturing, also referred to as 3D printing, is the technique of creating three-dimensional things by depositing or solidifying material one layer at a time. Using a computer-aided design module, pharmaceutical components are organized in a three-dimensional pattern. Afterwards, the constituents are converted into a machine-readable format resembling the surface of a three-dimensional dosage form. 3D printing has been used for jewelry, shoe-making, architecture, engineering & construction, the automotive industry as well as the aerospace field, dentistry and medicine, plus geographic information systems (GIS), civil engineering and education. After that stage of the process is completed, the surface transferred to the machine is then printed in different layers. Bioprinting is an interdisciplinary domain that integrates additive manufacturing with biology and material sciences to manufacture threedimensional structures representative of living organisms. The ability to create biological tissues and organs has attracted considerable attention in biomedical research owing to the rising demand for personalized medicine. This scenario propelled bioprinting forward which received much interest thus triggering comprehensive research efforts by various players such as companies, universities as well as research institutes. The goal of this book is to provide a thorough analysis of the complex and rapidly evolving field of bioprinting by critically analyzing and evaluating the existing scientific literature.

Generative Artificial Intelligence Systems and the Challenges in Latin American Education

This article examines the transformative role of Generative Artificial Intelligence (GAI) in education, exploring diverse disciplines such as engineering, medicine, programming, and information systems. GAI emerges as a revolutionary force, promising to fundamentally change educational practices. The PRISMA 2020 methodology (Page et al., 2021) was used to evaluate the relevant scientific literature. The systematic review identified research that applied specific criteria in the analysis of IAG in education. The selected papers covered a variety of disciplines, providing a comprehensive view of the impact of IAG in education. The results show a diversity of approaches and institutional responses to IAG in education. From significant changes in teaching practices to the identification of specific opportunities in disciplines such as computer science and engineering, GAI stands out as a catalyst for transformation. The adaptation of tertiary education, the need for specific skills to work with GAI, and ethical responses emerge as key themes. It focuses on the need for a deeper and more consensual understanding of the role of GAI in education. Concrete research-supported opportunities are highlighted, but ethical limitations and concerns are also underscored. Collaboration, reflection, and adaptation to open educational practices are proposed as approaches to address the challenges and fully exploit the benefits of IAG in education. This comprehensive analysis contributes to understanding the current and future landscape of GAI in education, providing valuable guidelines for educators, institutions, and educational technology developers.

Thermal Energy Resource Potentials of the Kara-Bogaz-Gol Gulf as a “Solar Pond”

Introduction. The use of environmentally-friendly engineering systems including solar energy technologies makes it possible to reduce energy costs and therefore to lower production costs and anthropogenic stress on the environment.Aim of the Study. The authors used innovative techniques to assess the thermal resource potential of solar radiation, to analyze the salt deposits of the Kara-Bogaz-Gol Gulf as thermal accumulators for the development, introduction and use of solar thermal technologies and to justify the technical and economic feasibility of their use in engineering systems in the Kara-Bogaz-Gol Gulf (Caspian region).Meterials and Methods. The study design is based on systematic theoretical calculations of the gross, technical, economic and ecological potentials of solar radiation taking into account environmental conditions. For calculating there were used the methods of mathematical modeling of heat and mass transfer processes in active solar energy systems when converting solar energy into thermal energy in the salty reservoir of the Kara-Bogaz-Gol Gulf as a “solar pond”.Results. There have been assessed the solar energy characteristics for the introduction of various engineering storage systems and technologies. There have been determined the results of energy storage on the reservoir salt surface during the day: in winter – 1 009.0 W/m2 per day, in summer – 1 574.7 W/m2 per day. It has been proven that the potential of solar energy conversion into thermal energy varies from 40 to 70% depending on the season. Aaccording to theoretical calculations, the solar pond efficiency in winter is 11.4% and in summer – 14.6%. In summer, there was measured the average temperature on the salt surface of the reservoir bottom, it ranges from 55.04 to 79.8 ºC, in winter from 20.0 to 25.6 ºC.Discussion and Conclusion. The results obtained can be used for strengthening energy security, developing energy systems and producing autonomous thermal power devices based on solar energy that will reduce the energy consumption of fossil fuels and improve the environmental situation in the region. The materials of the article can be used in preparing design estimates and feasibility study for developing various solar energy systems and technologies in the Caspian region.

Surrogate model-based cognitive digital twin for smart remote maintenance of fusion reactor: modeling and implementation

Abstract A remote maintenance robot system (RMRS) plays a critical role in safeguarding the fusion energy experimental device’s security and stability. State-of-the-art intelligent technology such as cognitive digital twins (CDT) is widely considered capable of improving complex equipment’s performance and reducing management burden using a visualized system. However, the CDT virtual space cannot mirror the RMRS which is a kind of flexible multi-body system in real-time and with high fidelity. Therefore, we propose a cognitive digital twin (CDT) modeling method based on a surrogate model for the RMRS. Firstly, model-based system engineering (MBSE) is leveraged to build a structural modular architecture, which can decrease the modeling complexity of CDT and increase the modeling efficiency. Then, the surrogate models are self-learning within the CDT physical space, which reconstructs the RMRS’s real-time dynamic performances and endows CDT with cognitive capabilities. Finally, after integrating the CDT system, a smart decision-making plan that compensates for the operation error is generated for RMRS’s accurate control. We take a China Fusion Engineering Test Reactor (CFETR) multi-purpose overload robot (CMOR) as an example to demonstrate the implementation process. According to the results, CDT can achieve real-time (230 ms time delay) high-fidelity (5 mm control error) monitoring and accurate control, and CMOR conducts smart maintenance based on the simulation results. This method improves the efficiency of RM and provides solutions for high-duty cycle time of CFETR, it can also be applied to other tokamak fusion energy devices.

Recent Progress of Corrosion Prevention Method of Magnesium Alloy

Magnesium (Mg) and its alloys have received much attention in the aerospace, transportation, automotive industry, and military equipment fields due to their unique chemical and physical properties, such as their low density and high specific strength, particularly as the lightest structural metal materials, with the opportunity to achieve the design of lighter engineering systems. With the continuous improvement of processing technology, the application scope of magnesium alloy is rapidly expanding, and market demand is increasing. However, because of its significant electronegativity (2.37 V) and loose naturally formed oxide coating, magnesium has low corrosion resistance in comparison to other structural metal elements, severely limiting its large‐scale use. This review summarizes several typical anticorrosion methods for magnesium alloys, including chemical conversion coating treatment, anodic oxide film treatment, micro‐arc oxidation treatment, laser surface treatment, ion implantation, physical vapor deposition, and superhydrophobic coating. In most cases, the corrosion resistance of magnesium and its alloys has improved, but it has a certain degree of environmental damage. It is hoped that this review will contribute to further developing magnesium alloy materials in the field of preservative coating.

Simultaneous spectral illumination of microplates for high-throughput optogenetics and photobiology.

The biophysical characterization and engineering of optogenetic tools and photobiological systems has been hampered by the lack of efficient methods for spectral illumination of microplates for high-throughput analysis ofaction spectra. Current methods to determine action spectraonly allow the sequential spectral illumination of individual wells. Here we present the open-source RainbowCap-system, which combines LEDs and optical filters in a standard 96-well microplate format for simultaneous and spectrally defined illumination. The RainbowCap provides equal photon flux for each wavelength, with the output of the LEDs narrowed by optical bandpass filters. We validatedthe RainbowCap for photoactivatable G protein-coupled receptors (opto-GPCRs) and enzymes for the control of intracellular downstream signaling. The simultaneous, spectrally defined illumination provides minimal interruption during time-series measurements, while resolving 10 nm differences in the action spectra of optogenetic proteins under identical experimental conditions. The RainbowCap is also suitable for studying the spectral dependence of light-regulated gene expression in bacteria, which requires illumination over several hours. In summary, the RainbowCap provides high-throughput spectral illumination of microplates, while its modular, customizable design allows easy adaptation to a wide range of optogenetic and photobiological applications.

Cellular Nanointerface of Vertical Nanostructures: Impact of Size‐Modulated Nanopillar Arrays on Neuronal Morphology, Maturation, and Synapse Formation

Investigating the cellular mechanisms facilitated by highly ordered nanostructures in vitro neuronal networks is crucial for advancing biomedical research. However, the intricate effects of these nanostructures on cellular behavior remain unclear. Herein, we explore how variations in nanopillar (NP) array dimensions, defined by the spacing‐to‐diameter (S/D) ratio, influence cortical neuronal responses—including cellular morphology, mechanosensing, neuronal maturation, and synapse formation. NP arrays with a low S/D ratio accelerate neurite protrusion and enhance neuronal maturation. These topography‐driven changes are attributed to the degree of cell confinement on NPs, which can be visualized using focused ion beam scanning microscopy. Furthermore, the synaptic density in cortical neurons declines as the S/D ratio decreases, with neurons preferring to form synapses along the NPs. By utilizing correlative light and electron microscopy, the spatial organization of these synapses is mapped relative to the NP geometries, revealing specialized synaptic regions with remarkable clarity. This approach to designing high‐aspect‐ratio nanostructures with various S/D ratios holds broad applications in materials engineering, offering insights into nervous system engineering and neural‐interfacing bioelectronics.

Prognostics and Health Management for Electrified Aircraft Propulsion: State of the Art and Challenges

Abstract In recent years, the aviation industry has witnessed a transformative wave of innovation in electrified aircraft propulsion (EAP), driven by sustainability and efficiency goals. Integration of novel electrical subsystems, including high-voltage power electronics, motors/generators, and energy storage devices, has introduced intricate complexities. In this context, an intensified focus on Prognostics and Health Management (PHM) is imperative, considering the heightened reliability needs in a transportation propulsion application. This paper analyzes the current state-of-the-art in PHM applicable to various EAP systems and components crucial for the functioning of electric aircraft. Typical fault modes and fault management strategies are analyzed at various levels of systems hierarchy. An integral aspect of our investigation involves the identification of critical gaps within existing PHM frameworks, guiding the research agenda for enhanced reliability and performance. Moreover, the distributed nature and complexity of EAP systems underscore the importance of Model-Based Systems Engineering (MBSE). We advocate for the exploration of MBSE not only to inform the design of PHM solutions but also to facilitate certification and Verification and Validation activities. Additionally, the paper offers insights into existing tools and simulation software capable of integrating traditional gas turbine modules with electric subsystems, as well as simulating various faulty conditions in EAP relevant to PHM development. Key gaps in these tools are emphasized, drawing attention to areas that require further refinement and development. This comprehensive exploration aims to pave the way for future advancements in PHM tailored for the unique challenges posed by EAP systems.

Application of improved fish school algorithm in variable frequency speed control system

In order to solve the problems of long adjustment time and poor stability of the commonly used speed current variable frequency negative feedback speed control system in engineering, the author proposes an improved fish school algorithm application method in variable frequency speed control systems. A fish swarm algorithm optimized based on the arena method is applied to a variable frequency speed control system, and the optimization algorithm is applied to the speed control system to screen the PI parameters that meet the requirements of the speed control system. The simulation results show that compared with manual parameter tuning, the controller parameters optimized by the improved fish school algorithm have better control performance. In the absence of overshoot starting, the starting time was shortened by 0.21s, and the system response speed was improved. When a sudden load of 6 N m is applied, the speed drop is reduced by 3 r · min−1, and the recovery time is shortened by 0.15 s, resulting in stronger anti-interference performance of the system. Conclusion: The new algorithm shortens the control time and improves the robustness of the system.

Powering Through the Turn: Finding Time for Concept Exploration Before Industry Stagnation

This study examines how the tension between exploration and exploitation affects early-stage development within the engineering teams of large corporations. Using survey data collected from over 900 system engineers and managers, it was observed that exploration decreased as an organization’s market growth declined, but dire market projections prompted a refocus on exploration. In addition, engineers routinely desire more concept exploration time than they perceive that they have available. The authors argue that engineering teams should more intentionally consider their innovation strategy, and that companies with stagnant market growth should invest in concept exploration before they get to a period of market decline.

A Demonstration of the Risk-Informed NEI 18-04 Design Evaluation Model for the Modular High-Temperature Gas Reactor

Several advanced reactor designs are now under active development in the United States and elsewhere, promising sustainable solutions to the growing world energy needs. The designs currently being considered are quite diverse and different from the more established light water reactor technology that has dominated the operating commercial nuclear landscape. While advanced reactor concepts were first explored in the dawn of the nuclear age, they are now being reconsidered under the light of modern needs, and specifically, for their flexible operating conditions and inherent safety characteristics. In response, the U.S. Nuclear Regulatory Commission staff is moving forward with development of 10 CFR Part 53 rulemaking, which is a more risk-informed, technology-agnostic framework for licensing and regulating such new designs. The nuclear industry response to this regulatory initiative resulted in the technical report by the Nuclear Energy Institute, NEI 18-04 Revision 1, which provides an implementation roadmap of the risk-informed approach when defining the safety case for a new plant design. The implementation of this safety case may be a nontrivial exercise for an actual reactor design. This paper provides a demonstration of performing such an analysis for a representative advanced reactor. Public information from the General Atomics high-temperature gas reactor design was considered in this demonstration. The analysis workflow was facilitated with the FPoliSolutions’ proprietary Risk-Informed System Engineering (RISE) digital platform, a product that was presented in previous publications. RISE is one application of FPoli’s enterprise digital platform, which was created to facilitate orchestration of complex workflows leveraging recent technologies developed at national laboratories, such as Idaho National Laboratory’s RAVEN and EMRALD frameworks. The analysis described in the paper includes the selection and classifications of events, the integration of probabilistic risk analysis artifacts, and event modeling simulations for consequence evaluations. The results are then used for system, structures, and components safety classification and a synthesis of the safety case for the design in line with the frequency-consequence targets presented in NEI 18-04. The purpose of the analysis, as framed in RISE, is to readily produce outputs and views that can aid users and regulators in making risk-informed decisions to demonstrate their plant safety case.