Constant force grinding controller for robots based on SAC optimal parameter finding algorithm.

Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

Climate change may shift metapopulations towards unstable source-sink dynamics in a fire-killed, serotinous shrub.

Climate change, with warming and drying weather conditions, is reducing the growth, seed production, and survival of fire-adapted plants in fire-prone regions such as Mediterranean-type ecosystems. These effects of climate change on local plant demographics have recently been shown to reduce the persistence time of local populations of the fire-killed shrub Banksia hookeriana dramatically. In principle, extinctions of local populations may be partly compensated by recolonization events through long-distance dispersal mechanisms of seeds, such as post-fire wind and bird-mediated dispersal, facilitating persistence in spatially structured metapopulations. However, to what degree and under which assumptions metapopulation dynamics might compensate for the drastically increased local extinction risk remains to be explored. Given the long timespans involved and the complexity of interwoven local and regional processes, mechanistic, process-based models are one of the most suitable approaches to systematically explore the potential role of metapopulation dynamics and its underlying ecological assumptions for fire-prone ecosystems. Here we extend a recent mechanistic, process-based, spatially implicit population model for the well-studied fire-killed and serotinous shrub species B. hookeriana to a spatially explicit metapopulation model. We systematically tested the effects of different ecological processes and assumptions on metapopulation dynamics under past (1988-2002) and current (2003-2017) climatic conditions, including (i) effects of different spatio-temporal fires, (ii) effects of (likely) reduced intraspecific plant competition under current conditions and (iii) effects of variation in plant performance among and within patches. In general, metapopulation dynamics had the potential to increase the overall regional persistence of B. hookeriana. However, increased population persistence only occurred under specific optimistic assumptions. In both climate scenarios, the highest persistence occurred with larger fires and intermediate to long inter-fire intervals. The assumption of lower intraspecific plant competition caused by lower densities under current conditions alone was not sufficient to increase persistence significantly. To achieve long-term persistence (defined as >400 years) it was necessary to additionally consider empirically observed variation in plant performance among and within patches, that is, improved habitat quality in some large habitat patches (≥7) that could function as source patches and a higher survival rate and seed production for a subset of plants, specifically the top 25% of flower producers based on current climate conditions monitoring data. Our model results demonstrate that the impacts of ongoing climate change on plant demographics are so severe that even under optimistic assumptions, the existing metapopulation dynamics shift to an unstable source-sink dynamic state. Based on our findings, we recommend increased research efforts to understand the consequences of intraspecific trait variation on plant demographics, emphasizing the variation of individual traits both among and within populations. From a conservation perspective, we encourage fire and land managers to revise their prescribed fire plans, which are typically short interval, small fires, as they conflict with the ecologically appropriate spatio-temporal fire regime for B. hookeriana, and likely as well for many other fire-killed species.

Critically appraised topic: the use of vaccination to control the spread of foot-and-mouth disease in Australian livestock in the event of an incursion.

With recent outbreaks of foot-and-mouth disease (FMD) in Indonesia and Bali, industry, government and public concern for its incursion into Australia is increasing. The potential impact of an outbreak on the agricultural industry and national economy could be devastating. To date, research conducted in relation to FMD in Australia predominantly concerns simulations and models performed to predict various outcomes. This project critically appraises the current literature regarding the simulated use of vaccination and its effectiveness for controlling the spread of FMD in Australia in the event of an outbreak. Findings from 10 modelling studies suggest that vaccination is effective at controlling the size and duration of an outbreak (under certain conditions), however, there is less clarity about cost-effectiveness.

The Influence of Spray Cooling Parameters on Workpiece Residual Stress of Turning GH4169.

To effectively reduce residual stresses in GH4169 workpieces, thus enhancing fatigue strength and operational lifespan, this study investigates the influence of spray cooling parameters on surface residual stresses during GH4169 turning in spray cooling conditions, utilizing both simulation and experimental approaches. A simulation model of residual stresses was established using finite element analysis when GH4169 was cut in spray cooling. The effects of spray pressure and flow rate on residual tensile stresses were analyzed. The analysis reveals that with increasing spray pressure, residual tensile stresses show a decreasing trend, gradually stabilizing. Conversely, with an increasing spray flow rate, residual tensile stresses initially decrease and then increase. The turning experiments of GH4169 were conducted under different spray parameters. After the experiment, the workpiece was sectioned and analyzed for residual stresses using X-ray diffraction instrumentation. The value residual stress measured closely matched those of simulation, with a relative error within 6%, validating the accuracy of the simulation model and confirming the appropriateness of parameter settings. These results contribute to the further promotion of spray cooling technology and facilitate the rational selection of spray parameters.

Design, Fabrication, Characterization, and Simulation of AlN-Based Piezoelectric Micromachined Ultrasonic Transducer for Sonar Imaging Applications.

To address the requirements of sonar imaging, such as high receiving sensitivity, a wide bandwidth, and a wide receiving angle, an AlN PMUT with an optimized ratio of 0.6 for the piezoelectric layer diameter to backside cavity diameter is proposed in this paper. A sample AlN PMUT is designed and fabricated with the SOI substrate-based bulk MEMS process. The characterization test result of the sample demonstrates a -6 dB bandwidth of approximately 500 kHz and a measured receiving sensitivity per unit area of 1.37 V/µPa/mm2, which significantly surpasses the performance of previously reported PMUTs. The -6 dB horizontal angles of the AlN PMUT at 300 kHz and 500 kHz are measured as 68.30° and 54.24°, respectively. To achieve an accurate prediction of its characteristics when being packaged and assembled in a receive array, numerical simulations with the consideration of film stress are conducted. The numerical result shows a maximum deviation of ±7% in the underwater receiving sensitivity across the frequency range of 200 kHz to 1000 kHz and a deviation of about 0.33% in the peak of underwater receiving sensitivity compared to the experimental data. By such good agreement, the simulation method reveals its capability of providing theoretical foundation for enhancing the uniformity of AlN PMUTs in future studies.

Testing a Simulation Model for the Response of Tomato Fruit Quality Formation to Temperature and Light in Solar Greenhouses.

Temperature and light are the key factors affecting the formation of tomato fruit quality in greenhouse cultivation. However, there are few simulation models that examine the relationship between tomato fruit quality formation and temperature and light. In this study, a model was established that investigated the relationships between soluble sugar (SSC), organic acid content (OAC), and SSC/OAC and the cumulative product of thermal effectiveness and photosynthetically active radiation (TEP) during the fruit-ripening period in a solar greenhouse. The root mean square error (RMSE) values were calculated to compare the consistency between the simulated and measured values, and the RMSE values for SSC, OAC, and SSC/OAC were 0.09%, 0.14%, and 0.358, respectively. The combined weights of quality indicators were obtained using the analytic hierarchy process (AHP) and entropy weighting method, ranking as SSC > OAC > SSC/OAC > CI > lycopene > Vc > fruit firmness. The comprehensive fruit quality evaluation value was obtained using the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) and a simulation model between comprehensive tomato fruit quality and TEP was explored. This study could accurately simulate and quantify the accumulation of tomato fruit quality during fruit ripening in response to environmental conditions in a solar greenhouse.

Cumulative sum learning curve for cordocentesis among maternal-fetal medicine fellows in a low-cost simulation model.

OBJECTIVE: To determine the individual learning curves for cordocentesis in a low-cost simulator for maternal-fetal medicine (MFM) fellows. METHODS: This observational, descriptive, educational, and prospective study was performed from July through November 2022. After an introductory course based on a standardized technique for cordocentesis, each second-year MFM fellow who accepted to participate in the study performed this procedure using a low-cost simulation model, and experienced operators supervised the cordocenteses. Learning curves were then created using cumulative sum analysis (CUSUM). RESULTS: Seven second-year MFM fellows with no previous experience in cordocentesis accepted to participate in the study. A total of 2676 procedures were assessed. On average, residents performed 382 ± 70 procedures. The mean number of procedures to achieve proficiency was 369 ± 70, the overall success rate was 84.16%, and the corresponding failure rate was 15.84%. At the end of the study, all fellows were considered competent in cordocentesis. One fellow required 466 attempts to achieve competency, performing a total of 478 procedures, but the resident with the fewest attempts to reach competency required 219 procedures, completing 232 procedures. Some of the most frequent reasons for failed attempts included not reaching the indicated point for vascular access (20.99%) and being unable to retrieve the sample (69.10%). CONCLUSION: CUSUM analysis to assess learning curves, in addition to using low-cost simulation models, helped to appraise individualized learning, allowing an objective demonstration of competency for cordocentesis among MFM fellows.

Species Diversity and Habitat Fragmentation Per Se: The Influence of Local Extinctions and Species Clustering.

AbstractAnthropogenic fragmentation of habitat is considered to be a critical factor contributing to the decline of species. However, a general consensus on the degree to which habitat loss and what has been called "habitat fragmentation per se" contribute to the loss of species diversity has not yet emerged. For empirical and theoretical reasons the topic has recently attracted renewed attention, thus reviving the "single large or several small" (SLOSS) debate. To study the effect of fragmentation per se, we use a spatially explicit and continuous, competitively neutral simulation model with immigration from a regional pool. The model accounts for the influence of ecological drift and intrafragment species clustering (due to limited dispersal) on local (plot) and global (landscape) diversity. We find that fragmentation increases global diversity but decreases local diversity, prominently so if fragments become more isolated. Cluster formation is a key mechanism reducing local diversity. By adding external disturbance events that lead to the occasional extinction of entire communities in habitat fragments, we show that the combined effect of such extinctions and cluster formation can create nonlinear interactive effects of fragmentation and fragment isolation on diversity patterns. We conclude that while in most cases fragmentation will decrease local and increase landscape diversity, universal predictions concerning the SLOSS debate should be taken with care.

Social network analysis reveals the failure of between-farm movement restrictions to reduce Salmonella transmission.

An increasing number of countries are investigating options to stop the spread of the emerging zoonotic infection Salmonella (S.) Dublin, which mainly spreads among bovines and with cattle manure. Detailed surveillance and cattle movement data from an 11-year period in Denmark provided an opportunity to gain new knowledge for mitigation options through a combined social network and simulation modeling approach. The analysis revealed similar network trends for non-infected and infected cattle farms despite stringent cattle movement restrictions imposed on infected farms in the national control program. The strongest predictive factor for farms becoming infected was their cattle movement activities in the previous month, with twice the effect of local transmission. The simulation model indicated an endemic S. Dublin occurrence, with peaks in outbreak probabilities and sizes around observed cattle movement activities. Therefore, pre- and post-movement measures within a 1-mo time-window may help reduce S. Dublin spread.

The effect of avian eggshell membrane structure on microbial penetration: A simulation study.

Avian eggshells exhibit excellent antimicrobial properties. In this study, we conducted simulation experiments to explore the defense mechanisms of eggshell membranes with regards to their physical features. We developed a mathematical model for the movement of microorganisms and estimated their penetration ratio into eggshell membranes based on several factors, including membrane thickness, microbial size, directional drift, and attachment probability to membrane fibers. These results not only suggest that an eggshell membrane with multiple layers and low porosity indicates high antimicrobial performance, but also imply that the fibrous network structure of the membrane might contribute to effective defense. Our simulation results aligned with experimental findings, specifically in measuring the penetration time of Escherichia coli through the eggshell membrane. We briefly discuss the significance and limitations of this pilot study, as well as the potential for these results, to serve as a foundation for the development of antimicrobial materials.

Analysis of numerical modeling of steady-state modes of methane-hydrogen mixture transportation through a compressor station to reduce CO2 emissions.

This study presents a mathematical model to evaluate the performance of gas pipelines during hydrogen injection in a gas pipeline-compressor station. The developed model presents the calculation of methane-hydrogen mixture (CH4/H2) transportation through the compressor station, where the compensation of pressure drops in the mass and energy balance takes place. Simultaneously, in the operation of the centrifugal blower system of gas compressor stations, the emissions of CO2 are considered, considering the mixing of gas media and the compression of CH4/H2. This mathematical model is realized for the pipeline transportation of hydrogen, at which the principle of mixture expansion occurs. The aim is to solve the problem of CO2 emissions at compressor stations. The optimization procedure has been formulated using a system of nonlinear algebraic equalities. The research focuses on the adaptation of existing gas transportation systems to CH4/H2 transportation and the impact of environmental risks on the operation of compressor station equipment. In this case, it is possible to determine the quantitative amount of hydrogen that can be added to natural gas. By solving the problem of finding the inner point of sets using the system of nonlinear algebraic equalities, it is possible to obtain the control parameters for safety control of technological modes of CH4/H2 mixture transportation. The study findings reveal that the consumption of gas charger and hydrogen was 50.67 and 0.184 kg/s, respectively, and the estimated efficiency resulting from the modified turbine design was 75.1 percent. These results indicate that the equipment operates more efficiently when hydrogen is being transported. The numerical analytical results indicated in this study hold practical significance for design applications. It will assist in identifying and evaluating the restrictions that may develop during the technological, operational, and design stages of decision-making.

Design and fabrication of silicone cleft lip simulation model for personalized surgical training.

PURPOSE OF THE STUDY: To elucidate the design and fabrication methodologies employed in creating a personalized cleft lip simulation model, primarily intended for enhancing surgical training and diverse applications. The study further sought to assess the viability of integrating this simulation model into undergraduate oral experiments and instructional settings. STUDY DESIGN: Facial data from individuals with cleft lip conditions were acquired using a scanner. Subsequent stages involved reverse engineering and the utilization of 3D printing technology to generate a cleft lip silicone simulation model. The molding process entailed injecting silicone into a polylactic acid mold. The study enrolled 53 undergraduate students majoring in dentistry, who were randomly assigned to either a control or experimental group. A dedicated instructor guided each group independently, employing a combination of multiple-choice tests and surveys to gauge real-time evaluations and discern inter-group disparities. RESULTS AND CONCLUSIONS: We successfully designed and produced a personalized cleft lip simulation model, demonstrating notable efficacy in the context of cleft lip experimental teaching. Statistical analysis revealed a significant difference (P < 0.05) in the scores of the experimental group students on multiple-choice questions pertaining to cleft lip surgical procedures. Survey outcomes indicated that the experimental group students exhibited higher confidence levels in cleft lip surgery, as reflected from their responses to relevant questions, compared to the traditional group students. These differences were statistically significant (P < 0.05). The simulation model developed in this study emerges as a reliable and cost-effective training and teaching tool for cleft lip surgery.

A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality.

BACKGROUND AND OBJECTIVE: Magnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. METHODS: We propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. RESULTS: The experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. CONCLUSIONS: We developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.

Change of spermatophyte family diversity in distribution patterns with climate change in China.

The global climate is undergoing extraordinary changes, profoundly influencing a variety of ecological processes. Understanding the distribution patterns and predicting the future of plant diversity is crucial for biodiversity conservation in the context of climate change. However, current studies on predictive geographic patterns of plant diversity often fail to separate the effects of global climate change from other influencing factors. In this study, we developed a spatial simulation model of spermatophyte family diversity (SSMSFD) based on data collected from 200 nature reserves covering approximately 1,500,000 km2, where direct anthropogenic disturbances to plant diversity and the surrounding environment are absent. We predicted the spermatophyte family diversity for all provinces in China in 2020, 2040, and 2080, considering the impacts of global climate change. On average, China currently exhibits 118 plant families per 25 km2, with a decreasing trend from southeast to northwest. When considering only the effects of global climate change, excluding direct anthropogenic disturbances, our results indicate that under the Shared Socioeconomic Path Scenarios (SSPs) 245 and 585, spermatophyte family diversity is projected to slowly increase in most Chinese provinces from 2021 to 2080. Notably, the increase is more pronounced under SSPs585 compared to SSPs245. Global climate change has a positive effect on plant diversity, in contrast to the negative impact of anthropogenic disturbances that often lead to declines in plant diversity. This research highlights the contrasting outcomes of future plant diversity under the sole influence of global climate change and the significant negative effects of anthropogenic disturbances on diversity.

[Simulation model of tumor-treating fields].

Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm 3. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.

Simulations of Transients in a Four-Pole Magnetic Bearing with Permanent Magnets.

This paper presents the design of and transient time simulations for a four-pole magnetic bearing with permanent magnets. The usage of permanent magnets reduces the consumption of electric energy in comparison to a traditional active magnetic bearing. Permanent magnets are installed in the yoke of the stator core to limit the cross-coupling of the magnetic flux generated by the windings. The first part of this paper presents the design of the magnetic bearing and its finite-element model, while the second part describes the field-circuit indirectly coupled finite-element model for the transient time simulation. The presented simulation model was used to calculate the transient response for the rotor lifting from the starting position, the step change in the rotor position and the change in the rotor position under an external impact force applied along the y-axis.

Student training in administering inferior alveolar nerve block anesthesia with a simple manufactured simulation model.

OBJECTIVE: To compare the effectiveness of administering inferior alveolar nerve blocks (IANB) for the first time with or without a previous practical lesson using a simple manufactured simulator. METHODS: This was a study designed to compare students' first administration of IANB anesthesia during 2022, with or without a previous practical lesson. Students were randomized into two groups. The experimental group attended both theoretical lessons and a practical lesson with a simulator device, while the control group attended only theoretical lessons. The theoretical lesson included usual contents such as anatomy, physiology, technical maneuvers for administering anesthesia, tips, and complications, and the practical lesson consisted of the administration of anesthesia using a simple manufactured simulator. After students applied their first IANB, its efficacy, and their answers to a questionnaire on a 5-point Likert scale were recorded. Statistical analysis consisted of the Chi-square test (p < 0.05). RESULTS: The study recorded 60 anonymous surveys. The main difference observed was in instrument handing (p < 0.05), and there was a tendency in the ease of recognition of the anatomical marks used for the technique-pterygomandibular raphe and coronoid notch of the mandible (p = 0.08 and 0.11, respectively). No difference in success was observed (p > 0.05). Self-confidence and personal feelings did not differ statistically. All students agreed strongly or partially that training with the simulator model was helpful. CONCLUSION: Students who used simple manufactured simulators achieved better outcomes for instrument handling, and possibly for identification of anatomical landmarks, than those who received only theoretical lessons.

Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021.

BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.

Predicting consequences of COVID-19 control measure de-escalation on nosocomial transmission and mortality: a modelling study in a French rehabilitation hospital.

INTRODUCTION: Infection control measures are effective for nosocomial COVID-19 prevention but bear substantial health-economic costs, motivating their "de-escalation" in settings at low risk of SARS-CoV-2 transmission. Yet consequences of de-escalation are difficult to predict, particularly in light of novel variants and heterogeneous population immunity. AIM: To estimate how infection control measure de-escalation influences nosocomial COVID-19 risk. METHODS: An individual-based transmission model was used to simulate SARS-CoV-2 outbreaks and control measure de-escalation in a French long-term care hospital with multi-modal control measures in place (testing and isolation, universal masking, single-occupant rooms). Estimates of COVID-19 case fatality rates (CFRs) from reported outbreaks were used to quantify excess COVID-19 mortality due to de-escalation. RESULTS: In a population fully susceptible to infection, de-escalating both universal masking and single rooms resulted in hospital-wide outbreaks of 114 (95% CI: 103-125) excess infections, compared with five (three to seven) excess infections when de-escalating only universal masking or 15 (11-18) when de-escalating only single rooms. When de-escalating both measures and applying CFRs from the first wave of COVID-19, excess patient mortality ranged from 1.57 (1.41-1.71) to 9.66 (8.73-10.57) excess deaths/1000 patient-days. By contrast, when applying CFRs from subsequent pandemic waves and assuming susceptibility to infection among 40-60% of individuals, excess mortality ranged from 0 (0-0) to 0.92 (0.77-1.07) excess deaths/1000 patient-days. CONCLUSIONS: The de-escalation of bundled COVID-19 control measures may facilitate widespread nosocomial SARS-CoV-2 transmission. However, excess mortality is probably limited in populations at least moderately immune to infection and given CFRs resembling those estimated during the 'post-vaccine' era.

Modeling Fatigue in Manual and Robot-Assisted Work for Operator 5.0.

Fatigue, and many other human performance factors, impact worker wellbeing, and thus production quality and efficiency. Adopting the Industry 5.0 perspective, we propose that integrating human performance models into wider industrial system models can improve modeling accuracy and lead to superior outcomes. Integrating our Worker Fatigue Model as part of their industrial system architect model allowed Airbus, a leading aircraft manufacturer, to more accurately predict system performance as a function of the workforce makeup, which could be a combination of human workers and robots, or a combination of highly experienced and less experienced workers. Our approach demonstrates the importance and value of including human performance models in trade studies for introducing robots on the shop floor, and can be used to include various aspects of human performance in industrial system models to address specific task requirements or different levels of automation.