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This new paper aims to extend the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. After feeding back the output signal to the input and introducing one feedback controller in the adding feedback loop, two parts, i.e. unknown aircraft flutter model and unknown feedback controller, exist in this closed-loop aircraft flutter system, simultaneously, whose input and output are all corrupted with external noise. Because of the relations between aircraft flutter model parameters and the unknown aircraft model, direct data–driven identification is proposed to identify that aircraft flutter model, then some identification algorithms and their statistical analysis are given through the authors’ own derivations. As the feedback controller can suppress the aircraft flutter or guarantee the flutter response converge to one desired constant value, the direct data–driven control is applied to design that feedback controller only through the observed data sequence directly. Numerical simulation results have demonstrated the efficiency of the proposed direct data–driven strategy. Generally, during our new information age, direct data–driven strategy is widely applied around our living life.

First, consider one more complex closed loop stochastic aircraft flutter model, whose input–output are all corrupted with external noise. Second, for the identification problem of closed-loop aircraft flutter model parameters, new identification algorithm and some considerations are given to the corresponding direct data–driven identification. Third, to design that feedback controller, existing in that closed-loop aircraft flutter model, direct data–driven control is proposed to design the feedback controller, which suppresses the flutter response actively.

A novel direct data–driven strategy is proposed to achieve the dual missions, i.e. identification and control for closed-loop aircraft flutter test. First, direct data–driven identification is applied to identify that unknown aircraft flutter model being related with aircraft flutter model parameters identification. Second, direct data–driven control is proposed to design that feedback controller.

To the best of the authors’ knowledge, this new paper extends the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. Consider the identification problem of aircraft flutter model parameters within the presented closed loop environment, direct data–driven identification algorithm is proposed to achieve the identification goal. Direct data–driven control is proposed to design the feedback controller, i.e. only using the observed data to design the feedback controller.

The purpose of this paper is to report the empirical findings of a survey aimed to investigate the need to improve cross‐phase learning between design and construction. Through exploring the need to introduce a design‐construction feedback loop, combined with the barriers against its development, an expansion of knowledge surrounding the deficiencies of current practice is provided.

The paper reports the results from an online survey conducted in spring 2011 targeted at experienced personnel in the planning, design, construction and facilities management phases of healthcare infrastructure projects.

The current approach of detecting and correcting errors is significantly hindering the extent to which learning from previous experiences is taking place. It is shown that improved integration between design and construction is required in the form of improved feedback if continuous improvement in the areas of efficiency, quality, value and general learning from previous experiences/projects is to be achieved.

The focused population of this study limits the extent to which the findings can be generalised. However, it is viewed that this context is potentially one of the most complex and unique project participant arrangements to overcome. Therefore if the need and ability to share learning outcomes across such a complex arrangement can be achieved, then it may be easier within traditional arrangements.

The practical implications of moving away from single‐loop learning towards a double‐loop learning approach are provided.

This paper identifies that there is a distinct need for further efforts to be applied in the area of improving feedback between the phases of design and construction.

Briefly reviews the standard Poisson distribution and then examines a set of derivative, modified Poisson distributions for testing hypotheses derived from positive deviation‐amplifying feedback models, which do not lend themselves to ordinary statistically based hypothesis testing. The “reinforcement” or “contagious” Poisson offers promise for a subset of such models, in particular those models with data in the form of rates (rather than magnitudes). The practical difficulty lies in distinguishing reinforcement effects from initial heterogeneity, since both can form negative binomial distributions, with look‐alike data. Illustrates these difficulties, and also opportunities, for various feedback models employing the self‐fulfilling prophecy, and especially for confidence loops, which incorporate particular self‐fulfilling prophecies as part of a larger dynamic process. Describes an actual methodology for testing hypotheses regarding confidence loops with the aid of a “reinforcement” Poisson distribution, as well as its place within sociocybernetics.

Interventions to support gender equality in organisations are often unsuccessful. Stakeholders disagree about the causes and problem definition of gender equality or pay lip service to the principle of gender equality, but fail to implement gender equality in practice. The purpose of this paper is to examine participatory modelling as an intervention method to support stakeholders in: reaching a shared problem definition and analysis of gender inequality; and identifying and implementing policies to tackle gender inequality.

The authors apply participatory modelling in case studies on impediments to women’s careers in two Dutch universities.

This study shows that participatory modelling supported stakeholders’ identification of the self-reinforcing feedback processes of masculinity of norms, visibility of women and networking of women and the interrelatedness between these processes. Causal loop diagrams visualise how the feedback processes are interrelated and can stabilise or reinforce themselves. Moreover, they allow for the identification of possible interventions.

Further testing of the causal loop diagrams by quantifying the stocks and the flows would validate the feedback processes and the estimated effects of possible interventions.

The integration of the knowledge of researchers and stakeholders in a causal loop diagram supported learning about the issue of gender inequality, hereby contributing to transformative change on gender equality.

The originality of the paper lies in the application of participatory modelling in interventions to support gender equality.

Crowdfunding (CF) is a digital-financial innovation that, bypassing credit crisis, bank system rigidities and constraints of the capital market, is allowing new ventures and established companies to get the needed funds to support innovations. After one decade of research, mainly focused on relations between variables and outcomes of the CF campaign, the literature shows methodological lacks about the study of its overall behavior. These reflect into a weak theoretical understanding and inconsistent managerial guidance, leading to a 27% success ratio of campaigns. To bridge this gap, this paper embraces a “complex system” perspective of the CF campaign, able to explore the system's behavior of a campaign over time, in light of its causal loop structure.

By adopting and following the document model building (DMB) methodology, a set of 26 variables and mutual causal relations modeled the system “Crowdfunding campaign” and a data set based on them and crafted to model the “Crowdfunding campaign” with a causal loop diagram. Finally, system archetypes have been used to link the causal loop structure with qualitative trends of CF's behavior (i.e. the raised capital over time).

The research brought to 26 variables making the system a “Crowdfunding campaign.” The variables influence each other, thus showing a set of feedback loops, whose structure determines the behavior of the CF campaign. The causal loop structure is traced back to three system archetypes, presiding the behavior in three stages of the campaign.

The value of this paper is both methodological and theoretical. First, the DMB methodology has been expanded and reinforced concerning previous applications; second, we carried out a causation analysis, unlike the common correlation analysis; further, we created a theoretical model of a “Crowdfunding Campaign” unlike the common empirical models built on CF platform's data.

The purpose of this paper is to identify the input, process and output factors (along with their manifest variables) of small and medium enterprises (SMEs), and to establish cause and effect relationships amongst the factors and sub-factors. Systems thinking, a holistic approach, is used to carry out qualitative analysis of the feedback loops.

A well-structured questionnaire was developed to gather the relevant data to identify the factors affecting the performance of SMEs in a holistic manner. A total of 150 responses were collected during November 2015–March 2016. Factor analysis and path analysis were used to establish causal relationships between input, process and output factors. The systems thinking approach has been used for qualitative analysis.

Feedback loops have been identified amongst input-process-output-input factors and amongst sub-factors. They enabled authors to infer that the managers/owners of SMEs are systems thinkers, if not completely, at least partially. Six negative feedback loops and one positive feedback loop prevail. System behaviour arises out of the interaction of positive and negative feedback loops; it appears that in the long-run, the SMEs attain their target levels. The following inferences are drawn: circular relationships are identified amongst input, processes and organisational performance (OP), modern management tools such as just in times, Kanban have long-term benefits and are perceived as ineffective by small enterprises and formal financing and functional transparency enhances OP.

Systems thinking, a holistic approach, has been used to study the effect of input, process and output factors on one another. Such studies are sparse, especially, in the Indian context. Many studies have been conducted to study the effect of input and of processes on performance such as innovation, information technology, human resource, technology, government regulation on performance of SMEs in a silo but, rarely all together. The qualitative analysis adds value to the research. Many of the outcomes of the research have been largely discussed in Indian print media which indicates the pragmatic approach of the research.

The purpose of this study is to analyze direct current (DC) drive stability, including parameter uncertainty and perturbation in the feedback loop, by computing disk margins.

Although the closed-loop stability analysis of a DC drive has been presented well in the referenced papers, the effect of parameter uncertainty and perturbation in the feedback loop has not yet been discussed well. In this study, the conventional and disk-based stability margins were measured and compared for the nominal parameters of the DC drive. Subsequently, the smallest disk-based margins that destabilize the feedback loop for a given perturbation are computed and compared with normal disk margins.

The disk-based margin offered by the DC drive controlled by the JAYA-PID controller is disk gain margins (DGM) = 8.41 dB and disk phase margin (DPM) = 48.410 and the smallest disk-based margin offered is DGM = 1.51 dB and DPM = 9.950. In addition, the effect of the modeled uncertainty on the disk stability margins was analyzed, and it was observed that the maximum allowable parameter uncertainty with the JAYA controller was 73% of its nominal parameters. The simulation results were validated using an experimental testbed.

This research work is not published anywhere else.

Electrohydraulic servos have been widely applied to the task of precisely positioning heavy loads. Common examples from the military field are radar antenna and rocket engine swivelling drives. In the commercial area large machine tool position controls are a prime example. Even with relatively substantial driving linkages, the inertia of these loads frequently results in low natural frequency of the output load‐driver structure. Very commonly this is combined with extremely small natural damping forces. Natural frequencies from 5 to 20 c.p.s. with damping ratios in the oder of 0·05 critical are typical. This combination of resonance with low damping creates a severe stability and performance problem for the electrohydraulic servo drive. Efforts to deal with this problem have centred on introducing artificial damping. In the past this has been done either by use of a controlled piston by‐pass leakage path or by use of a load force feedback path. The former technique is simple but wasteful with respect to power and inherently involves serious performance compromises. The latter technique can be arranged to be unassailable on theoretical grounds. However, it leads to severe system complication and large incremental hardware requirements. Questions of a reliability penalty are raised. A new technique has been developed which possesses all the performance advantages of load feedback without serious increase in complexity. Called Dynamic Pressure Feedback, this technique involves only a modification of servo valve component. It utilizes for feedback purposes the inherently high load forces developed as piston differential pressures, insuring reliable operation. The pressures needed are already available at the valve. No new hydraulic or electrical connexions are added. The performance advantages adduced for the Dynamic Pressure Feedback Servo Valve have been confirmed in carefully controlled comparative tests on a typical load system. Correspondence of test data with analytical prediction is good. A sufficient number of Dynamic Pressure Feedback Servo Valves have been produced on a pilot production line and installed in several applications in the field to insure producibility and design reliability.

This paper aims to identify the interaction of different intervention strategies implemented in Malaysia towards flattening the curve of COVID-19 cases. Since the outbreak of COVID-19, many approaches were adopted and implemented by the Malaysian government. Some strategies gained quick wins but with negative unintended consequences after execution, whereas other strategies were slow to take effect. Learning from the previous strategies is pivotal to avoid repeating mistakes.

This paper presents the cause, effect of and connection among the implemented COVID-19 intervention strategies using systems thinking through the development of a causal loop diagram. It enables the visualisation of how each implemented strategy interacted with each other and collectively decreased or increased the spread of COVID-19.

The results of this study suggested that it is not only essential to control the spread of COVID-19, but also to prevent the transmission of the virus. The Malaysian experience has demonstrated that both control and preventive strategies need to be in a state of equilibrium. Focusing only on one spectrum will throw off the balance, leaving COVID-19 infection to escalate rapidly.

The developed feedback loops provided policy makers with the understanding of the merits, pitfalls and dynamics of prior implemented intervention strategies before devising other effective intervention strategies to defuse the spread of COVID-19 and prepare the nation for recovery.

The authors developed a pedagogical framework called the game-based structural debriefing (GBSD) to leverage the affordances of video games for teaching systems thinking. By integrating system dynamics visualization tools within a set of debriefing activities, GBSD helps teachers make systems thinking an explicit goal of the gameplay and learning when they use available educational games in the classroom.

This study uses a design-based research methodology with the goals of validating GBSD and investigating the utility of GBSD across different contexts as a design source to develop game-based curriculum. Over the course of 12 months, the authors conducted one focus group interview and three design workshops with participating teachers and master teachers. Between the workshops, the team rapidly iterated the framework, as well as curricular materials, in collaboration with the teachers.

The authors developed a curriculum unit that integrates systems dynamics visualization tools and a video game for middle school life science ecosystem curriculum. The unit was implemented by the three teachers who participated in the co-design. The implementations confirmed the flexibility of the unit because teachers created additional instructional materials that supplemented the GBSD protocol and addressed the unique limitations and needs of their classrooms.

GBSD builds on system dynamics, which is a distinct academic discipline and methodology, and it uses its visualization tools, which are not widely used in the systems thinking educational literature. GBSD is also unique, in that it applies these tools within the debriefing activities developed for an off-the-shelf educational game. This paper illustrates how a design framework can be used to support teachers’ thoughtful integration of games in curriculum development.