System Design Model Research Articles

Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better trade-off between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

Physics-informed machine learning for system reliability analysis and design with partially observed information

Constructing a high-fidelity predictive model is crucial for analyzing complex systems, optimizing system design, and enhancing system reliability. Although Gaussian Process (GP) models are well-known for their capability to quantify uncertainty, they rely heavily on data and necessitate a large representative dataset to establish a high-fidelity predictive model. Physics-informed Machine Learning (PIML) has emerged as a way to integrate prior physics knowledge and machine learning models. However, current PIML methods are generally based on fully observed datasets and mainly suffer from two challenges: (1) effectively utilize partially available information from multiple sources of varying dimensions and fidelity; (2) incorporate physics knowledge while maintaining the mathematical properties of the GP-based model and uncertainty quantification capability of the predictive model. To overcome these limitations, this paper proposes a novel physics-informed machine learning method that incorporates physics prior knowledge and multi-source data by leveraging latent variables through the Bayesian framework. This method effectively utilizes partially available limited information, significantly reduces the need for costly fully observed data, and improves prediction accuracy while maintaining the model property of uncertainty quantification. The developed approach has been demonstrated with two case studies: the vehicle design problem and the battery capacity loss prediction. The case study results demonstrate the effectiveness of the proposed model in complex system design and optimization while propagating uncertainty with limited fully observed data.

Design concept of intelligent integrated control system for neutral beam injection

Abstract Due to the specificity of neutral beam injection (NBI) system, the control of its actual physical system is achieved by the integrated control system (ICS). The current NBI ICS adopts a distributed design structure to balance the system load, which is also the mainstream system design and architecture model of ICS around the world. However, from a practical point of view, the distributed system architecture is not perfect. In the long run, upgrading the system intelligence and automation capabilities is an inevitable choice for the future, so it is necessary to explore ways to transform and upgrade the ICS. At present, Internet of Things (IoT), as an important part of the new generation information technology, can realize the control of everything through data exchange. This is because on the one hand, IoT has wide compatibility and powerful scenario-based capabilities, it not only has the advantages and characteristics of distributed design, but also can pull the NBI subsystems into the same level scenario and lay the foundation for the further construction of digital NBI; on the other hand, the intervention of artificial intelligence makes IoT have some new typical characteristics such as intelligent sensing, ubiquitous connectivity, precise control, digital modeling, real-time analysis and iterative optimization, which is enough to pull the current NBI ICS into a new intelligent control era. Finally, it is worth mentioning that due to its inherent design structure and functional characteristics, ICS tends to be broadly generic, so it is not exclusively used for NBI operation in nuclear fusion, and it can provide some insight into other application areas.

Preferences for Text Messaging Supports During Youth Transition to Adult Mental Health Services: Theory-Informed Modified e-Delphi Study.

For many young people, the transition from child to adult mental health services is a vulnerable time associated with treatment disengagement and illness progression. Providing service information and options to youth, appealing to them, and tailoring to their needs during this period could help overcome systematic barriers to a successful transition. We know little about how SMS text message-based interventions might be leveraged to support the motivational, informational, and behavioral needs of youth during this time. Ascertaining youth preferences for the content and functionality of an SMS text message service could inform prototype development. This study investigated consensus preferences among youth on important content, technology features, and engagement supports to inform a transition-focused SMS text message service. A modified e-Delphi survey design was used to collect demographics, current levels of technology use, importance ratings on message content, preferred technical features, and barriers and enablers to engagement for youth in Canada aged 16-26 years who have accessed mental health services within the past 5 years. Survey items on content were categorized according to the information-motivation-behavioral skills (IMB) model. Survey items on technical features were categorized according to the persuasive system design (PSD) model. A predefined consensus rating matrix and descriptive statistics were used to characterize the sample. The high consensus threshold was 70%. A total of 100 participants, predominantly non-White (n=47, 47%), aged 20-26 years (n=59, 59%), and who had first accessed mental health services between the ages of 13 and 19 years (n=60, 60%), were selected. The majority (n=90, 90%) identified as daily SMS text message users. A high level of consensus on importance ratings was reported in 45% (9/20) of content items based on the IMB model. There were higher levels of consensus on importance ratings related to behavior domain items (3/3, 100%) than information domain items (4/9, 44%) or motivation domain items (2/8, 25%). A high level of consensus on importance ratings was reported in only 19% (4/21) of feature and functionality items based on the PSD model. Among PSD model categories, there was a high level of consensus on importance ratings in 8% (1/12) of the primary task support domain items and 100% (3/3) of the system credibility support domain items. None of the dialogue-support and social-support domain items met the high level of consensus thresholds. In total, 27% (27/100) of youth indicated that the most significant enabler for engaging with a transition-focused SMS text message intervention was the personalization of text messages. Scientists developing next-generation SMS text messaging interventions for this population need to consider how levels of consensus on different features may impact feasibility and personalization efforts. Youth can (and should) play an integral role in the development of these interventions.

How online pro-environmental games affect users’ pro-environmental behavioural intentions? — Insights from Ant Forest

Information technology has created new ways for people to participate in environmental protection. Ant Forest, a pro-environmental game that actively encourages users to take part in offline environmental activities through online gaming content, has gained great popularity. However, research in this area is still limited, and the effect of playing such games on users' pro-environmental intentions in real life and the underlying mechanisms are unknown. Therefore, this research draws from Persuasive Systems Design model and value theory and examines the relationships between design features of Ant Forest, users' values and their pro-environmental behavioural intentions. The research model is empirically tested by survey data from 428 Ant Forest users. Results indicate that users' self-enhancement values, including utilitarian, hedonic and social values, and self-transcendence values, including altruistic and biospheric values, all positively affect their continued gameplay intentions and therefore affect their pro-environmental behavioural intentions. Additionally, the primary task support, social support, dialogue support, and system credibility of Ant Forest have varying positive effects on users’ perceived values.

Forward osmosis for concentrating lithium-enriched brine: From membrane performance to system design

Using concentrated salt-lake brine as the draw solution (DS), forward osmosis (FO) has been proposed as the low carbon footprint concentration technology for extracting water from lithium-enriched brine. From membrane modules to a process, there exist tremendous risks when considering total cost in time and investment for a trial. To address this issue, a bridge between the FO membrane/ module characteristics and a FO system design is highly desirable. This study for the first time developed a system design model using hollow fiber (HF) FO membrane modules for FO concentration of lithium-enriched brine. The concentration polarization along the module, the membrane structural parameters (S), crossflow velocity, and DS concentration were the main factors considered in the model. The effect of all factors on module water flux and total membrane area was systematically investigated for a typical industrial output of 10 k ton of Li2CO3. A master curve of the water flux verse feed concentration for the tailor-made HF modules was utilized as the basis. On a module scale, the structural parameter S is of first importance and indicates requirement of FO membranes with low S. The crossflow velocity has relatively small impact to the membrane area, but not negligible. On a system scale, the empirical equation of the structural parameters and crossflow velocity on the theoretical minimum system membrane area was summarized. The results show that increasing the stages can reduce the number of modules and required DS flow. Increasing DS dilution rate in system design efficiently utilizes the osmotic pressure of DS. The present model serves as a practical solution for process optimization and FO membrane selection. The methodology is valuable for other FO applications using HF FO membranes.

Designing preschool children's educational games for enlightenment through decision analysis methods

As an innovative form of digital online education, educational games have potentially significant impacts on preschool children's enlightenment education. Identifying key factors for effective design is crucial in game development. This study identifies key factors for the design of educational games aimed at preschool children's enlightenment through decision analysis methods. The specific research steps include using SET analysis method to identify gaps in development opportunities, employing the Analytic Hierarchy Process to construct an interactive system design model for enlightenment educational games, calculating the weight value of each factor, and pinpointing the key factors for design practice. Based on this methodology, an enlightenment educational game has been developed, and Fuzzy Comprehensive Evaluation method has been utilized to assess the practical implementation. The approach outlined in this study enables designers to swiftly recognize the critical elements in designing educational games for preschool children's enlightenment, facilitating a responsive design process, achieving effective game designs, and ultimately enhancing preschool children's learning interest and outcomes.

Designing data-informed support for building learning habits in the Japanese K12 context

While building proper learning habits has been said to enhance academic performance, it is challenging to give long-term support for building habits in educational contexts due to the lack of continuous tracing of one’s habitual behaviors. With the accumulation of learning logs and the advancement of Learning Analytics (LA) techniques, this paper illustrates the data-informed support for building learning habits, which involves persuading one to change behaviors. Specifically, we tackled 2 research objectives following the Persuasive Systems Design (PSD) model. First, we defined indicators of learning habits from log data and analyzed 115,340 learning logs of 96 learners from a Japanese junior high school. As a result, the learners’ types of habits could be detected even though some might not be efficient and the stages of habits fluctuated over time. We also identified differences when comparing the learning habits extracted from the log data with those reported in the questionnaire. Second, based on the understanding of the learner profiles, we designed elements of an LA dashboard to support habit-building by applying the design principles from the PSD model. Overall, the learners recognized the feasibility of integrating data-informed support into their daily learning. Therefore, we look forward to the evidence of its effectiveness on the behavior change that can be depicted by the transition between stages of different types of learning habits.

Strategies for Combating Adversarial Information Operations: Theory and Practical Applications

In the contemporary information landscape, the proliferation of disinformation and propaganda poses a significant challenge to societal discourse and democratic processes. This paper proposes a multi-disciplinary approach to combatting adversarial information operations, drawing upon theoretical frameworks and practical applications. Theoretical foundations are established through an examination of the Persuasive System Design (PSD) model (Oinas-Kukkonen, 2013) and its parallels with propaganda tactics. By analyzing the shared flaws and vulnerabilities, insights emerge into the manipulation techniques employed by threat actors in online information spaces. Building upon this theoretical framework, the paper presents a proactive strategy for countering disinformation: the development of Early Warning and Control Systems (EWACS). These systems leverage AI-assisted narrative discovery to monitor the digital information landscape continuously. By identifying emerging threats and inauthentic activity, strategic communicators gain valuable insights for crafting counter-narratives and pre-emptive communication strategies. Key components of the proposed approach include deterrence by denial and resilience-building measures. By shifting the cost-gain calculation of adversaries and enhancing societal resilience, the aim is to create an environment where propagandists face increased challenges in achieving their objectives. This paper emphasizes the importance of collaboration between diverse stakeholders, including governmental organizations, academia, NGOs, and journalists. By harnessing the collective expertise from multiple fields, more effective strategies can be developed to safeguard information integrity and restore public trust. In conclusion, this paper advocates for a convergence of theory and practice in addressing the complex challenges posed by adversarial information operations. By integrating theoretical insights with practical applications, the proposed approach offers a holistic framework for countering disinformation and propaganda in contemporary information environments.

Persuasive Systems Design Trends in Coronary Heart Disease Management: Scoping Review of Randomized Controlled Trials.

Behavior change support systems (BCSSs) have the potential to help people maintain healthy lifestyles and aid in the self-management of coronary heart disease (CHD). The Persuasive Systems Design (PSD) model is a framework for designing and evaluating systems designed to support lifestyle modifications and health behavior change using information and communication technology. However, evidence for the underlying design principles behind BCSSs for CHD has not been extensively reported in the literature. This scoping review aims to identify existing health BCSSs for CHD, report the characteristics of these systems, and describe the persuasion context and persuasive design principles of these systems based on the PSD framework. Using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, 3 digital databases (Scopus, Web of Science, and MEDLINE) were searched between 2010 to 2022. The major inclusion criteria for studies were in accordance with the PICO (Population, Intervention, Comparison, and Outcome) approach. Searches conducted in the databases identified 1195 papers, among which 30 were identified as eligible for the review. The most interesting characteristics of the BCSSs were the predominant use of primary task support principles, followed by dialogue support and credibility support and the sparing use of social support principles. Theories of behavior change such as the Social Cognitive Theory and Self-Efficacy Theory were used often to underpin these systems. However, significant trends in the use of persuasive system features on par with behavior change theories could not be established from the reviewed studies. This points to the fact that there is still no theoretical consensus on how best to design interventions to promote behavior change in patients with CHD. Our results highlight key software features for designing BCSSs for the prevention and management of CHD. We encourage designers of behavior change interventions to evaluate the techniques that contributed to the success of the intervention. Future research should focus on evaluating the effectiveness of the interventions, persuasive design principles, and behavior change theories using research methodologies such as meta-analysis.

Enhancing automated strabismus classification with limited data: Data augmentation using StyleGAN2-ADA.

In this study, we propose a generative data augmentation technique to overcome the challenges of severely limited data when designing a deep learning-based automated strabismus diagnosis system. We implement a generative model based on the StyleGAN2-ADA model for system design and assess strabismus classification performance using two classifiers. We evaluate the capability of our proposed method against traditional data augmentation techniques and confirm a substantial enhancement in performance. Furthermore, we conduct experiments to explore the relationship between the diagnosis agreement among ophthalmologists and the generation performance of the generative model. Beyond FID, we validate the generative samples on the classifier to establish their practicality. Through these experiments, we demonstrate that the generative model-based data augmentation improves overall quantitative performance in scenarios of extreme data scarcity and effectively mitigates overfitting issues during deep learning model training.

Enhancing public transport accessibility: Exploring hybrid car sharing systems for improved connectivity

Transportation networks within a region are vital. They link individuals from their starting points to their destinations. Nonetheless, even highly effective transportation systems may not ensure optimal performance, service quality, or fairness for users unless they are extensively interconnected and accessible to as many people as possible. In this context, we present a planning framework for complementing public transit (PT) systems with other cost-effective mobility systems, such as car-sharing (CS) systems, to improve performance in terms of connectivity and accessibility. These metrics are evaluated by using the suitably introduced Connectivity and Accessibility Index (CAI). Specifically, in the present research, we first introduce a novel methodology for assessing the connectivity and accessibility values of existing PT systems. Then, the proposed approach provides an optimization-based design model for a hybrid CS system comprising both one-way (OW) and two-way (TW) models. To evaluate the capabilities of the proposed approach, a real-world case study of the PT system of the city of Trento (Italy) is evaluated.

Designing a marine systems design specialization track at NTNU

In this paper we describe and discuss the current and future MSc specialization programme in marine systems design at the Norwegian University for Science and Technology (NTNU). We follow the structure of a design process. First, we identify relevant stakeholders. That includes industry, society and the students themselves, and we discuss what are their key needs and requirements for a master level education. Further, we outline what can be considered a conceptual solution for the marine systems design specialization programme´s structure and content. We apply two basic types of learning elements. First, a set of focused topic blocks covering central systems design models and methods to develop the required theoretical competence platform. Second, the students perform a series of creative, 'use case'-oriented collaborative development projects based on CDIO (Conceive, Design, Implement, Operate) principles. We discuss the “educational design solution” in terms of learning objectives vs. achieved results and evaluate the impact both from an academic and an industrial perspective.

Developing an Optimal Model for Water Distribution System Design

Future difficulties related to water utilization in sustainable cities will be influenced by factors such as urban development, climate change, and resource scarcity. Distribution accounts for 80–85% of the entire cost of a water supply system, making it a crucial part of all urban water systems. To increase system reliability, water distribution systems (WDS) are typically designed with the "worst scenario" or "robustness" in mind. Because deterministic assumptions are historically incorrect, a new design methodology that acknowledges uncertainty and provides greater flexibility is needed. To design WDS that are more adaptive, a Genetic Algorithm Flexibility Optimization (GAFO) model is created in Visual C++ and connected to EPANET. In contrast to classical GA optimization, GAFO uses a dynamic decision-making method to maximize a WDS's versatility at the lowest possible cost while taking into account a variety of potential future scenarios. The result is a WDS that may develop a staged implementation strategy that enables a gradual evolution of the WDS over time and follows various future trajectories (changing conditions). The convergence and flexibility of the GAFO model were determined to be good after it was tested on several fictitious scenarios. In comparison to traditional, non-flexible designs, cost reductions of 35% to 72% were achieved. DOI: https://doi.org/10.52783/pst.386

Evaluation and instantiation of a generic process model for early-stage sustainable product-service system design within three manufacturing companies

Although product-service systems (PSS) have been in focus in literature and industry for some years, no generically agreed or applied PSS design process exists thus far, in contrast to the neighbouring fields of product development or service design, respectively. This paper presents the evaluation and instantiation of a recently proposed generic process model (GPM) for early-stage sustainable PSS design in capital goods manufacturing companies. The model was applied in three case studies with an aim to quantitatively and qualitatively evaluate the GPM concerning eight criteria, and study its instantiation process actions to a company-specific model while simultaneously identifying and proposing solutions for challenges to instantiation. Based on observation and feedback received from the companies, the GPM shows promise for widespread applicability, aided by proposed guidelines for instantiating the GPM to company-specific contexts, as also described in this paper. These guidelines aim to facilitate the translation of the GPM into pragmatic models for practitioners in the field of PSS design, thus increasing its chances of successful implementation as well as impact.

INNOVATION IN LEARNING DEVICE FOR COMMERCIAL GRAPHICS COURSE

This article is motivated by the problem of lack of innovation in learning tools for commercial graphic learning. The current situation of adaptive and innovative commercial graphic learning is still low. Strategies need to be selected and implemented in product development studies for Project Based Commercial Graphic Planning learning tools. The method in this paper is descriptive - experimental. The system design model used in Research and Development of learning tools uses the Dick, Carey and Carey model. Learning outcomes products are reviewed by learning design experts, through subject matter experts, learning design experts, and learning media experts. A series of formative evaluation sessions – the review and revision process carried out in the development of this learning product tool. The research results show that the material learning tools implementing a project-based approach have improved learning outcomes that are better than the target. The results of research and development of this model are very satisfying.

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—to assess their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

Analytical approach for the design of composite linings in deep tunnels considering the blasting damaged zone

Tunneling using the drilling and blasting method induces a blasting damaged zone around the tunnel, which affects the tunnel stability as well as the resulting support design. In this article, an analytical model that represents the whole-process interaction between the sequentially installed composite linings and the surrounding rock in the presence of the blasting damaged zone is proposed. First, assuming the blasting damaged zone to be a finite cylinder with decreased mechanical parameters, four ground forms according to the distribution of plastic zone are categorized and solved. Furthermore, the fictitious pressure concept is adopted to represent the three-dimensional effect of tunnel advancement, and thus, the construction process of the tunnel can be simulated. After that, the evolution patterns of the whole-process interaction between the composite linings and ground are distinguished. The critical distances and tunnel displacements between different forms in various interaction stages are identified and solved. Finally, the analytical solutions for tunnel displacement and structural responses can be obtained, based on the displacement compatibility conditions between composite linings and the ground. The analytical model is validated by numerical simulations through several typical examples, and the applicability in actual tunnel support design is verified by field monitoring results. Comparisons with an existing model and the conventional convergence-confinement method illustrate the inherent nature and advantages of the proposed model in the support system design. It is found that the blasting damage effect is significant on the tunnel responses, which should be considered in the support design. The proposed model provides a convenient feasible approach in determining the stiffness and support time of the composite linings for tunnels using the drilling and blasting method.

Can Your Smartphone Save A Life? A Systematic Review of Mobile-Based Interventions For Suicide Prevention

Mobile health (mHealth) apps are handy tools for tackling stigmatized mental health issues, including suicide. Mobile-based interventions for suicide prevention are easily accessible, increase the likelihood of honest reporting on sensitive topics and reduce stigma as compared to face-to-face or traditional interventions. Many mHealth apps for suicide prevention exist. However, the persuasive strategies employed in these apps and their efficacy remains unknown. To address this gap, we reviewed 80 suicide prevention apps available on app stores and in academic journals. We identified different persuasive strategies implemented in these apps using the Persuasive System Design (PSD) model. We also identified current trends within these apps, most and least-dominant implementations of persuasive strategies, effectiveness of apps, evaluation methods, and app content. We found that Personalization (n = 32) and Self-monitoring (n = 29) were the most-dominant strategies and Social Comparison, Social Role were the least-dominant strategies in suicide prevention apps. Based on our findings we discuss three major concerns in developing suicide prevention apps and offer recommendations for mitigating them. Our results show that persuasive strategies are a promising tool that can be used for designing suicide prevention apps. Our conclusions and recommendations will guide future work in suicide prevention app development and enhance the usability, effectiveness, and user-experience of such apps.

Effects of a mobile health intervention on health-related outcomes in Japanese office workers: a pilot study.

The purpose of the current study was to explore the effects of a mobile health (mHealth) intervention based on the Persuasive System Design (PSD) model on health-related outcomes among office workers. The authors conducted a trial that consisted of a 4-week baseline and an 8-week intervention period by reference to 23 office workers in a private research company. The mHealth application was developed to improve these workers' daily step count, decrease their sedentary time, and increase their sleep duration in accordance with the PSD model. The app features included at least 1 principal factor from each of the 4 main categories of the PSD model (primary task support, dialogue support, system credibility support, and social support). The objective health-related variables were measured using a smartwatch (Fitbit Luxe) that was synchronized with the application using the Fitbit Web Application Programming Interface. Subjects used the app, which included self-monitoring, personalized messages, education, and a competition system for users, during the intervention period. Sedentary time exhibited a significant decrease (a median reduction of 14 min/day, p < 0.05) during the intervention period. No significant differences in daily step count and sleep duration were observed between the baseline and intervention periods. This study suggests that the mHealth intervention based on the PSD model was useful for reducing sedentary time among office workers. Given that many previous studies on this topic have not been based on any theories, future studies should investigate the impact of structured selection behavior change theories on health-related outcomes among office workers. Int J Occup Med Environ Health. 2024;37(2):153-64.