System Modeling Approach Research Articles

On the Dimensions Required for a Molten Salt Zero Power Reactor Operating on Chloride Salts

Molten salt reactors have gained substantial interest in the last years due to their flexibility and their potential for simplified closed fuel cycle operation for massive expansion in low-carbon electricity production, which will be required for a future net-zero society. The importance of a zero-power reactor for the process of developing a new, innovative rector concept, such as that required for the molten salt fast reactor based on iMAGINE technology, which operates directly on spent nuclear fuel, is described here. It is based on historical developments as well as the current demand for experimental results and key factors that are relevant to the success of the next step in the development process of all innovative reactor types. In the systematic modelling and simulation of a zero-power molten salt reactor, the radius and the feedback effects are studied for a eutectic based system, while a heavy metal rich chloride-based system are studied depending on the uranium enrichment accompanied with the effects on neutron flux spectrum and spatial distribution. These results are used to support the relevant decision for the narrowing down of the configurations supported by considerations on cost and proliferation for the follow up 3-D analysis. The results provide for the first time a systematic modelling and simulation approach for a new reactor physics experiment for an advanced technology. The expected core volumes for these configurations have been studied using multi-group and continuous energy Monte-Carlo simulations identifying the 35% enriched systems as the most attractive. This finally leads to the choice of heavy metal rich compositions with 35% enrichment as the reference system for future studies of the next steps in the zero power reactor investigation. An alternative could be the eutectic system in the case the increased core diameter is manageable. The inter-comparison of the different applied codes and approaches available in the SCALE package has delivered a very good agreement between the results, creating trust into the developed and used models and methods.

Reliability analysis for uncertain competing failure degradation system with a change point

Modeling on dependent competing failure process (DCFP) with change points has been widely investigated in recent years. However, in actual engineering fields, the observed data of DCFP systems is often too limited to estimate the parameters and probability distributions by statistic methods, since the fundamental of probability theory will not satisfied, that is, the law of large numbers is no longer valid. Therefore, the reliability assessment for DCFP systems under small sample conditions is a challenge. This article proposes a modeling approach for DCFP systems with a change point that considers the uncertainty caused by small datasets. The system belief reliability is defined as the uncertain measure that the uncertain degradation does not exceed a threshold level and the uncertain external shocks do not cause the system failure. The system belief reliability is obtained by employing uncertainty theory under three different shock patterns. Finally, a numerical example is carried out to show the implementation of the proposed model.

Perancangan Sistem Informasi Reservasi Laboratorium TI UIN Walisongo Bebasis Web

The Information Technology Study Program Laboratory is part of the integrated Laboratory of UIN Walisongo Semarang which will be completed in 2021 with funding from the IsDB (Islamic Development Bank). With the existence of the laboratory, of course, many challenges will be faced. One of them is the mechanism for making laboratory space reservations. In this research, an information technology laboratory reservation information system will be designed using the waterfall method and a system model approach using an object oriented development model. With this approach, it produces a simple reservation information system and can assist in facilitating users in making reservations for IT laboratory space. From the test results, it is known that the interface design and functional system can run according to user expectations.

A conceptual framework for supply chain digitalization using integrated systems model approach and DIKW hierarchy

• Novel approach for decision making in supply chain digitalization is developed. • SCD framework is developed using systems model approach and DIKW hierarchy. • Decision making factors affecting supply chain digitalization (SCD) is identified. • Factors classified under organizational, technological and innovative dimensions. • Validated based on feedback from experts and comparison with existing literature. A robust and dynamic supply chain can be developed using advanced digital technologies, enabling real-time information sharing. This study provides a conceptual framework for effective supply chain digitalization by considering the key decision-making factors and interrelationships. The key decision-making factors affecting the digitalization process are identified from the literature and consultation with industry and academia experts. A conceptual framework is developed using the integrated systems model approach and DIKW (Data, Information, Knowledge, and Wisdom) hierarchy. Further, an integrated decision support system is also developed for effective decision-making on digitalization. The factors identified were also classified under three major dimensions like organizational, technological, and innovative dimensions for effective management. The findings of the study were validated based on feedback from experts and a comparison with existing literature. The study contributes to the knowledge of supply chain digitalization by introducing a novel method and approach. The sequential methodology and the framework developed can support the decision-making process and development of appropriate design for digitalization.

Modeling and simulation of dynamic mechanical systems using electric circuit analogy

Modeling and simulation are prerequisite to analysis and design of engineering systems. Modern engineering systems often are multy disciplinary, i.e., may include blocks from different majors of engineering, such as electrical, mechanical, fluid, etc. Availability of a unified approach for system modeling will make it easy for engineers or researcher from a certain discipline to model the systems from other disciplines. For example, with availability of a unified modeling methodology an electrical engineer will be able to model a system composed of electrical, mechanical and fluid systems. Modeling of complex mechanical systems is not always easy for engineers from other disciplines. On the other hand, it is much easier to establish mathematical model of electric circuits. Furthermore, simulation software is much richer for electric circuits. Therefore, in this paper a methodology is proposed for unifying the modeling of electrical and complex mechanical systems by obtaining electric circuit model of complex mechanic systems. In developing the proposed methodology, analogy between the electrical and mechanical elements have been used as tools. Proposed methodology has been applied to modeling and simulation of a relatively complex mechanical system and benefits accrued from this approach has been discussed. It is further proposed that the approach presented in this paper can be easily extended to modeling of dynamic systems from other engineering disciplines.

My life matters here: Assessing the adjusted identity of domestic migrant workers at intangible cultural heritage tourism businesses in China

In this study, we explore and assess the nature of the double guests and host roles of tourism migrant workers (TMWs), and their effects on the TWSs' adjusted person-place relationships and local identities by employing a systematic modeling approach in the context of the TMWs working in the intangible cultural tourism (ICH) businesses. The research site is fittingly set in Suzhou of east China, a destination whose rich historical and cultural heritages are now co-expressed and co-presented by a migrant workforce that has already outsized the locals. Research findings have shown that the socio-cultural adjustments availed by working in the ICH tourism businesses positively influence the local identities of TMWs, as mediated by their place attachment. This study advances a theoretical understanding of the mechanism of migrant integration in the particular context of cultural tourism development, and shows how tourism can contribute towards healthy, rather than stigmatizing, dialogues pertaining to migrant integration in the society at large.

Modelling the impacts of COVID-19 pandemic on the quality of population-based colorectal cancer screening

COVID-19 pandemic has severely affected regular public health interventions including population-based cancer screening. Impacts of such screening delays on the changes in structure and screening process and the resultant long-term outcomes are unknown. It is therefore necessary to develop a systematic framework to assess theses impacts related to these components of quality. Using population-based cancer screening with fecal immunochemical test (FIT) as an illustration, the main analysis was to assess how various scenarios of screening delays were associated with the capacity for primary screening and full time equivalent (FTE) for colonoscopy and impact long-term outcomes based on a Markov decision tree model on population level. The second analysis was to quantify how the extent of COVID-19 epidemic measured by social distancing index affected capacity and FTE that were translated to delays with an exponential relationship. COVID-19 epidemic led to 25%, 29%, 34%, and 39% statistically significantly incremental risks of late cancer for the delays of 0.5-year, 1-year,1.5-year, and 2-year, respectively compared with regular biennial FIT screening. The corresponding statistically findings of four delayed schedules for death from colorectal cancer (CRC) were 26%, 28%, 29%, and 30%, respectively. The higher social distancing index led to a lower capacity of uptake screening and a larger reduction of FTE, resulting in longer screening delay and longer waiting time, which further impacted long-term outcomes as above. In summary, a systematic modelling approach was developed for demonstrating the strong impact of screening delays caused by COVID-19 epidemic on long-term outcomes illustrated with a Taiwan population-based FIT screening of CRC.

Sustainable Development Goal Drivers in Food Systems

Interacting driving forces in food systems, resulting in cumulative driver effects and synergies induce non-linear processes in multiple directions. This paper critically reviews the discourse on driving forces in food systems and argues that mindset is the primary predictor for food system outcomes. In the epoch of SDGs and the Anthropocene mindset matters more than ever. Transformative narratives are beginning to transcend the dominant social paradigm, which is still driving the food system’s overall trajectory. The psychosocial portrayal of the systemic mindset found in organic food systems presented in this paper “flips the script” and hypothesizes that worldview and paradigm have the most causal linkages with unsustainable driver synergies and reversely the biggest leverage on the mitigation thereof. Borrowing from ecological economics discourses the paper sharpens the driver definition by applying the D.P.S.I.R. analytical tool as a modified diagnostic framework and modelling approach for food systems. This research sheds new light on the nature of drivers of change, which are often portrayed as almighty and inevitable trends shaping food systems. Instead it is proposed that drivers emerge from the actors’ mindset, affecting food system behavior in a non-linear way. Mindset drives reinforcing feedback loops, resulting in vicious and virtuous cycles. These driver motives manifest in subsystems and continue to drive their interaction across food system elements. Mindset acts as an encapsulated input of food systems, all the while responding to feedback and releasing new drivers. This research wants to showcase how the mindset in organic food systems is converging with the trajectories of both SDG and Planetary Boundaries agendas. The connecting thread throughout this paper is a juxtaposition between the scientific community’s emerging consensus of what transformative responses to food system threats and weaknesses should look like on the one hand and the kinds of responses and core tenets that are emblematic of organic food systems on the other hand. By contrasting the two, an astounding degree of compatibility was revealed, which is synthesized into an SDG Driver framework.

Sustainable Development Goal Drivers in Food Systems

Interacting driving forces in food systems, resulting in cumulative driver effects and synergies, induce non-linear processes in multiple directions. This paper critically reviews the discourse on driving forces in food systems and argues that mindset is the primary predictor for food system outcomes. In the epoch of sustainable development goals (SDGs) and the Anthropocene, mindset matters more than ever. Transformative narratives are beginning to transcend the dominant social paradigm, which is still driving the food system's overall trajectory. The psychosocial portrayal of the systemic mindset found in organic food systems presented in this paper “flips the script” and hypothesizes that worldview and paradigm have the most causal linkages with unsustainable driver synergies and reversely the biggest leverage on the mitigation thereof. Borrowing from ecological economics discourses, the paper sharpens the driver definition by applying the DPSIR analytical tool as a modified diagnostic framework and modeling approach for food systems. This research sheds new light on the nature of drivers of change, which are often portrayed as almighty and inevitable trends shaping food systems. Instead, it is proposed that drivers emerge from the actors' mindset, affecting food system behavior in a non-linear way. Mindset drives reinforcing feedback loops, resulting in vicious and virtuous cycles. These driver motives manifest in subsystems and continue to drive their interaction across food system elements. Mindset acts as an encapsulated input of food systems, all the while responding to feedback and releasing new drivers. A transformation framework along leverage points of the food system is presented that features the concept of SDG drivers.

Control- Oriented Linear Fractional Transformation Modelling and H∞ Control of a Two-DOF Mass- Spring- Dashpot Dynamic System

ABSTRACT This paper presents a systematic control-oriented uncertainty modelling approach in the Linear Fractional Transformation (LFT) framework of a Multi-Input-Multi-Output (MIMO) mechanical system for designing an H∞ controller. A compact modelling structure has been formulated by considering the parametric uncertainties and disturbances to implement robust control law to achieve certain performance specifications. A popular mechanical system, namely, Two-Degree-of-Freedom (2DOF) Mass-Spring-Dashpot (MSD) dynamics system, which is highly oscillatory and implements in many classical control techniques have been considered as a candidate system to formulate proposed control-oriented LFT modelling framework and design H∞ controller to verify the effectiveness of the derived modelling structure in a real-time environment. This modelling methodology has been illustrated in the simulation environment and indicating satisfactory robust stability and performance margin of the designed H∞ controller. The proposed control strategy is compared with the standard Model Reference Adaptive Control (MRAC) law to express the effectiveness of the controller.

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. The MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

Modeling the anaerobic digestion of wastewater sludge under sulfate-rich conditions.

Anaerobic digestion (AD) is a biological treatment process to stabilize organic solids and produce biogas. If present, sulfate is reduced to sulfide by anaerobic sulfate-reducing bacteria and the sulfide can be toxic to anaerobic microorganisms. Here, the effect of high initial sulfate concentration on AD of wastewater sludge was investigated using lab-scale batch experiments. Additionally, a systematic mathematical modeling approach was applied for insight into the experimental results. Cumulative biogas and methane production decreased with increasing initial sulfate doses (0-3.300mgSL-1 ). The correlation between the sulfate dose and methane production was consistent with theoretical predictions and model results, indicating no toxic effect of sulfide on methane production. The carbon dioxide content in the biogas decreased linearly with the increasing sulfate dose, which is consistent with the model-predicted behavior of the bicarbonate and hydrogen sulfide buffering system. The examined high sulfate concentrations resulted in no clear negative effects on the COD removal or VSS destruction of the wastewater sludge, indicating negligible inhibition by sulfide toxicity. Even considering the possibility of ferrous sulfide precipitation and the low model estimates of residual sulfide concentration the residual sulfide concentration was higher than reported concentrations that trigger process inhibition. PRACTITIONER POINTS: The effect of sulfate loading on anaerobic digestion of waste activated sludge was characterized. The stoichiometry of sulfate reduction allows accurate prediction of CH4 loss. High sulfate levels (up to 3300mg/L as S) did not affect COD/VSS removal. Sulfide formation increases effluent COD; often misinterpreted as sulfide toxicity. Correcting COD for sulfide's contributions is crucial for results interpretation.

A model transformation approach for multiscale modeling of software architectures applied to smart cities

SummaryModeling and specifying correct software systems is a challenging task that can be supported by providing appropriate modeling abstractions. This article proposes an approach for graphical multiscale modeling of such systems using model transformation techniques. The approach is founded on a guided rule‐based iterative modeling process ensuring controlled transition from a coarse‐grained description to a fine‐grained description. It provides also user‐friendly graphical descriptions by extension of UML notations, hence preserving the common practices from software architectures design. The iterative design process is supported by a set of model transformation rules. The rules manage the refinement process (by adding or removing subsystems or by adding or removing details on a given subsystem) as a model transformation. Our approach is supported by a rule‐based generator that implements the automatic transformation of UML diagrams into Event‐B specifications allowing formal verification of their correctness properties, and relieving software architects of mastering formal techniques. To experiment and validate our approach, we consider a case study dedicated to the smart cities.

Renal Ciliopathies: Sorting Out Therapeutic Approaches for Nephronophthisis.

Nephronophthisis (NPH) is an autosomal recessive ciliopathy and a major cause of end-stage renal disease in children. The main forms, juvenile and adult NPH, are characterized by tubulointerstitial fibrosis whereas the infantile form is more severe and characterized by cysts. NPH is caused by mutations in over 20 different genes, most of which encode components of the primary cilium, an organelle in which important cellular signaling pathways converge. Ciliary signal transduction plays a critical role in kidney development and tissue homeostasis, and disruption of ciliary signaling has been associated with cyst formation, epithelial cell dedifferentiation and kidney function decline. Drugs have been identified that target specific signaling pathways (for example cAMP/PKA, Hedgehog, and mTOR pathways) and rescue NPH phenotypes in in vitro and/or in vivo models. Despite identification of numerous candidate drugs in rodent models, there has been a lack of clinical trials and there is currently no therapy that halts disease progression in NPH patients. This review covers the most important findings of therapeutic approaches in NPH model systems to date, including hypothesis-driven therapies and untargeted drug screens, approached from the pathophysiology of NPH. Importantly, most animal models used in these studies represent the cystic infantile form of NPH, which is less prevalent than the juvenile form. It appears therefore important to develop new models relevant for juvenile/adult NPH. Alternative non-orthologous animal models and developments in patient-based in vitro model systems are discussed, as well as future directions in personalized therapy for NPH.

A Reduced-Order Model for Wirelessly Excited Machine Based on Linear Approximation

Due to the high cost and shortage of rare earth materials, electrically excited synchronous machines (EESMs) become a potential alternative for electric vehicles. With the help of inductive power transfer (IPT), the EESM would easily realize the brushless excitation to improve its reliability. However, the dependence of IPT and EESM leads to significant control challenge, especially considering the inductive load characteristics. When the traditional modeling approaches are applied to the whole system, a complicated 13th-order model is built, which is unfavorable for dynamic analysis. For simplification, this article proposes a systematic and universal reduced-order model approach based on the local linear approximation of resonant voltage/current. A reduced ninth-order model of system is then obtained based on the approach. The frequency domain characteristic of primary-side control is analyzed, and the reduced-order model has high accuracy compared with the full-order one. Finally, the experimental results are given out to verify the effectiveness of the proposed modeling method.

Comparison of Computer Extended Descriptive Geometry (CeDG) with CAD in the Modeling of Sheet Metal Patterns

The emergence of computer-aided design (CAD) has propelled the evolution of the sheet metal engineering field. Sheet metal design software tools include parameters associated to the part’s forming process during the pattern drawing calculation. Current methods avoid the calculation of a first pattern drawing of the flattened part’s neutral surface, independent of the forming process, leading to several methodological limitations. The study evaluates the reliability of the Computer Extended Descriptive Geometry (CeDG) approach to surpass those limitations. Three study cases that cover a significative range of sheet metal systems are defined and the associated solid models and patterns’ drawings are computed through Geogebra-based CeDG and two selected CAD tools (Solid Edge 2020, LogiTRACE v14), with the aim of comparing their reliability and accuracy. Our results pointed to several methodological lacks in LogiTRACE and Solid Edge that prevented to solve properly several study cases. In opposition, the novel CeDG approach for the computer parametric modeling of 3D geometric systems overcame those limitations so that all models could be built and flattened with accuracy and without methodological limitations. As additional conclusion, the success of CeDG suggests the necessity to recover the relevance of descriptive geometry as a key core in graphic engineering.

Demand for animal source of food in Central Java, Indonesia

Consumption of protein was often used as an indicator of household welfare. This study analyzed the impact of price andincome changeon demand foranimal food in rural households in Central Java, using the Quadratic Almost Ideal Demand System model approach. The research data used data from the National Socio-Economic Survey, totalling13, 872 households. The output appeared that a one percent increase in animal food prices reduced the demand for eggs, chicken, beef, fish, and milk by 0.505%, 1.281%, 4.099%, 3.424%, and 1.915%, respectively. The most elastic animal food group is beef, followed by milk, fish, chicken, and eggs with income elasticities of 3.278%, 2.339%, 2.156%, 1.411%, and 0.556%. All animal food is a luxury item except eggs, which are normal goods. Beef is a substitute for fish, chicken, and eggs, while milk is complementary. The price policy is more effective compared to the income policy for rural Central Java. Rural households in Central Java very responsive to changes in price.

Quantile regression on inactivity time.

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a semi-parametric regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer.

Empirical Large-Signal Modeling of mm-Wave FDSOI CMOS Based on Angelov Model

This article presents a systematic empirical modeling approach in fully depleted silicon-on-insulator (FDSOI) CMOS for large-signal simulation in power amplifier applications. The model is constructed from multibias S-parameter measurements up to 67 GHz. The frequency dispersions in transconductance and output conductance are addressed by two independent radio frequency (RF) current sources. Angelov's current formula is modified to adapt to the FDSOI transistors. Load-pull measurements are performed for the large-signal verification in a non-50-Ω environment. The model accurately predicts the nonlinear characteristics of the device under test and the harmonic components. The time-domain waveforms also show excellent agreement to the simulation.

Understanding job stress in The Iranian oil industry: A qualitative analysis based on the work systems model and macroergonomics approach

The aim of this study was to understand why and how working in the oil industry causes job stress. Using the work-systems model as a guiding framework, we conducted 15 in-depth interviews with employees of an oil company located on an island in the Persian Gulf to investigate work system-related stressors. The interviews were audio-taped, and qualitative content data analysis was carried out continuously and simultaneously with data collection over a 12-month period. 1118 meaning codes were extracted which could be understood though fourteen categories of stressor associated with the five themes of the work system. Employees reported the job was stressful primarily because of organizational structural problems and management attributes, although aspects of living and working on the small island were also difficult. These results provide direction for intervention that can dovetail with risk assessment of occupational health of workers.