Neuro-fuzzy Model Research Articles

Hybrid Neuro-Fuzzy Based Improved Control Design of Electro-Pneumatic Clutch Actuation Control System for Heavy Duty Vehicles

The application of hybrid Neuro-Fuzzy principles of clutch actuation control in enhancing the performance of electro-pneumatic clutch actuation system for heavy duty vehicles is the aim of this presentation. Neuro-Fuzzy is a hybrid design that accommodates the principles of fuzzy Logic and Neural Network in a manner that take advantage of their positive sides. The inability of some heavy-duty vehicles to operate optimally on hilly terrains and which often results in road mishaps are traceable to the inadequacies in conventional clutch actuation control mechanisms. These conventional actuation control techniques provide for routine calibration of clutch actuators after maintenance. Often times, this important requirement of calibration is neglected with attendant ugly consequences. To stem this tide of manual calibration and its obvious defects, an intelligent method of clutch actuation modelled in a hybrid Neuro-Fuzzy control is implemented. Conventional data obtained for errors, speed, torque and power from Mercedes Benz Actros Truck model MP 2, 2031 provided the reference points. These data were embedded into a Simulink block and cascaded into a designed Neuro-Fuzzy Simulink model. Both conventional and Neuro-Fuzzy Simulink model controllers were also simulated. Different percentages of improvements were recorded for piston error, angular speed, engine torque and power respectively. The level and percentage of improvements stood at 0.4821mm or 33.04% decrease for error, increases of 334.1 RPM or 33% for angular speed, 0.0594NM or 33.26% for torque and 2.79W or 16.53% for power respectively.

Development of an expert system for technological support of required roughness when machining hardened steel parts on numerically controlled machines

This work addresses the problem of improving the surface quality of parts of rotation bodies, which are made of hardened steels, in the course of their machining on lathes with numerical program control. The research object was the surface roughness of parts. The methodology involved system analysis and synthesis, artificial neural networks, fuzzy logic, experiment, and processing of experimental results. A decomposition scheme for the expert system structure was developed, which can be used as the basis for formulating requirements to a future expert system for monitoring and prediction of roughness parameters. It was established that the models currently applied to describe the relationship between surface quality and the technological regimes used to ensure the technological level of roughness give a high error of over 20%. The possibility of using models that apply artificial intelligence and contain neural network blocks and decision-making devices based on fuzzy logic is substantiated. It is shown that such a combination makes it possible to customize the system for processing parts of a certain production range, as well as a more correct assessment of the onset of catastrophic wear of cutting tools. The neuro-fuzzy model was confirmed to have an error of less than 10%, which is significantly lower than when using spectral or correlation models. According to the testing results, the proposed expert system for monitoring and prediction of roughness parameters enables a 2.5-fold reduction in the scatter of roughness parameters under an increase in tool wear, compared to without its application. Thus, the proposed system makes it possible to assess the level of cutting tool wear more correctly and determine the onset of its limit state.

Preparation of new green poly (amino amide) based on cellulose nanoparticles for adsorption of Congo red and its adaptive neuro-fuzzy modeling

In this study, a novel green poly(amino amide) nanoparticle based on cellulose nanoparticles (Cell-PAMN) was developed for the efficient adsorption of Congo Red dye. Cellulose nanocrystals obtained from acid hydrolysis of cotton linter were functionalized via Oxa-Michael addition of acrylamide on their surface hydroxyl groups, followed by transamidation with ethylenediamine. The resulting nanoparticles were characterized using FT-IR spectroscopy, SEM, and X-ray diffraction techniques. The as-prepared Cell-PAMN exhibited considerably higher adsorption capacity compared to unmodified cellulose nanoparticles due to the presence of amino and amide functional groups. The adsorption kinetics and the effects of parameters such as contact time and initial dye concentration on the adsorption capacity were investigated. An adaptive Neuro-Fuzzy model was used to study the efficiency of dye removal, accurately predicted the adsorption behavior of Cell-PAMN. The kinetic study results showed that the adsorption process followed a pseudo-second-order kinetic model, with a maximum adsorption capacity of around 40 mg/g. The results demonstrated the potential of the synthesized material for the removal of Congo Red from aqueous solutions, highlighting its applicability in wastewater treatment. This research contributes to the development of sustainable and eco-friendly materials for environmental remediation applications.

Mobile robots control with neuromorphic classifires of environment states

The article proposes to implement mobile robots control systems using neuromorphic classifiers, in which biosimi-lar neurons are built on an original neuro-fuzzy model, trained to display the function of the Izhikevich neuron. Us-ing this approach, neuromorphic classifiers of spatial and spatiotemporal patterns were developed. Such a classi-fiers were used in non-contact supervisory control system for mobile robot based on static and dynamic states of the environment. The experiments performed demonstrated the effectiveness of using the neuromorphic classifi-ers, both in a neural interface when recognizing imaginary supervisory commands of the user, and when solving problems of robot navigation in environments with static and dynamic obstacles. At comparative analysis of navi-gation systems implemented using the neuromorphic classifiers, modular fuzzy logic and the adaptive neuro-fuzzy system ANFIS it was shown that the system with the neuromorphic classifiers give the highest average speed of the robot, as well as the shortest travel time. An experiment with the use of the neuromorphic classifiers in the con-trol system based on dynamic states of the environment showed that robot successfully avoids collisions with per-son.

Neuro-fuzzy prediction model of occupational injuries in mining.

Objectives. This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mineral extraction involving people, machinery and limited workplaces. Methods. A universal model for predicting occupational accidents takes into account influential factors such as organizational aspects, personal and collective protective equipment, on-the-job training and leadership factors. The selected networks achieved a prediction accuracy of >90%. Results. The study successfully identifies potential risks and critical worker groups leading to injuries. The sensitivity analysis provides insights for targeted safety measures and improved organizational practices. Conclusion. This data-driven approach makes a valuable contribution to safety in the mining industry. Implementation of the predictive model can reduce injuries and machine damage, and improve worker well-being.

Optimizing the energy values of solid biofuel through acidic pre-treatment: An evolutionary-based neuro-fuzzy modelling and feature importance analysis

The increasing focus on sustainable energy solutions and eco-friendly fuel alternatives has sparked interest in exploring methods to enhance the energy content of solid biofuels through pre-treatment processes. This study investigates the impact of acidic (H2SO4) pretreatment on the energy content of solid biofuel derived from watermelon peel waste using machine learning methods. The biofuel properties, and calorific value (CV) were determined experimentally for both H2SO4-treated and untreated biofuel samples. Subsequently, an Adaptive neuro-fuzzy inference system (ANFIS) model with Genetic Algorithm (GA) and Grid Partitioning (GP) clustering techniques was developed to predict heating value of the biofuel, with performance evaluation based on root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), Mean Absolute Error (MAE) and R2-values. The decision tree regressor was employed to evaluate the feature importance, predictive power and impact of each input variable on CV prediction based on Gini importance metrics. To establish its superior performance, the hybrid ANFIS-GA-GP model was compared with ordinary-ANFIS and ANN model using same metrics. Results show that acid-pretreatment increased the CV of the solid biofuel by 3.53 MJ/kg (31.83 % improvement) and reduced ash content by 1.77 %. FTIR analysis revealed surface modifications, and a shift in the C-O vibrational stretch, while SEM micrographs displayed denser surfaces and reduced porosity as indicated by a lesser fiber diameter in treated samples. The GP-clustered ANFIS-GA model with a triangular membership function (tri-MF) exhibited superior performance and higher accuracy (RMSE of 0.1309, MAPE of 9.343, MAD of 0.1036, MAE of 0.1110, and R2-value of 0.9773 at model training). Furthermore, a tree decision regressor identified Fixed carbon as the most significant predictor for CV with a Gini importance of 0.988375. This study demonstrates the substantial enhancement in the energy content of biofuel using acidic pre-treatment and insights into a cutting-edge approach of improving the combustion properties of solid biofuel with machine learning models. This significantly contributes to the field of sustainable bioenergy research.

Improving the Quality of Production Management Processes Based on Neural Network and Neuro-Fuzzy Models and Tools

Improving the Quality of Production Management Processes Based on Neural Network and Neuro-Fuzzy Models and Tools

The Method of Evaluation for Complex Systems and Processes Based on Ontological and Neuro-Fuzzy Modeling

The Method of Evaluation for Complex Systems and Processes Based on Ontological and Neuro-Fuzzy Modeling

Heat transfer and erosion control of nanofluid flow with tube inserts: Discrete phase model and adaptive neuro-fuzzy inference system approach

The current research explores the heat transfer and erosion phenomena in a tube employing newly designed inserts. Seven different geometries for the inserts have been developed into three dimensions. The fluid used is water with iron oxide nanoparticles and the flow regime is turbulent. The modeling technique involves a finite volume method with the Eulerian-Lagrangian framework incorporating a particle-scale erosion model. The outcomes indicate that raising the velocity and volume fraction of nanoadditives enhance both the heat transfer and erosion. In comparison to the scenario without fins, the heat transfer can be increased by approximately 8.07, 6.22, 18.03, 53.72, 45.21, 14.74 and 13.08 for the considered cases G1, G2, G3, G4, G5, G6 and G7, respectively. The specific geometric design of the inserts significantly influences the heat transfer and erosion in the tube, with geometry G4 showing the most substantial improvements in the heat transfer (approximately 53 %) and erosion (around 100 %) compared to a smooth tube. Factors such as velocity, particle size, fluid viscosity and insert shape affect the erosion in the tube with water demonstrating higher erosion rates than oil. The introduction of iron oxide nanoparticles leads to 2–3 times increment in the tube wall erosion. The erosion estimation on the tube wall has been performed using an adaptive neuro-fuzzy inference system artificial intelligence model with R2 = 0.981.

Effective design of sustainable energy productivity based on the experimental investigation of the humidification-dehumidification-desalination system using hybrid optimization

Reliable and cost-effective industrial-based sustainable desalination technologies are crucial for efficient and effective desalination of saline water. Seawater desalination emerges as a vital treatment, with humidification-dehumidification identified as a promising technology due to its simple, low-maintenance design and compatibility with renewable energy sources. The present work was developed using a large-scale experimental test rig based on the performance of a humidification-dehumidification system operated by the heat pump. As such, the study presents an advanced approach for optimizing humidification-dehumidification desalination systems using hybrid machine learning optimization techniques. The effectiveness of a neuro-fuzzy model, a decision algorithm based on a boosted tree, and a simple averaging ensemble in enhancing the performance of humidification-dehumidification systems were investigated. Experimental data includes cold water flowrate (L/min), hot water flowrate (L/min), inlet air temperature (°C), inlet cold water temperature (°C), inlet hot water temperature (°C), and inlet air relative humidity (%) as input variables combination and freshwater productivity (L/h), recovery ratio (%) as target variables. Several statistical indicators were used to evaluate the models’ prediction skills; similarly, a 2-dimensional Taylor diagram and error-radial plot were also utilized. In the calibration phase, boosted tree models slightly outperform neuro-fuzzy models, particularly for recovery ratio, demonstrating high accuracy and low bias. However, all models exhibit robust predictive accuracy in the verification phase, especially for recovery ratio, emphasizing their potential in generalizing to unseen data. The result indicated that the boosted tree outperformed others with Nash–Sutcliffe Efficiency = 94 % and 88 % for recovery ratio and freshwater productivity, respectively. The simple averaging ensemble shows marginal improvement with 95 % accuracy for the recovery ratio. Integrating hybrid artificial intelligence optimization with humidification-dehumidification desalination systems marks a transformative step in addressing freshwater shortages. By utilizing cutting-edge machine learning techniques, we achieve a more efficient, accurate, and reliable prediction of desalination performance.

A novel modeling approach on the water–electricity–climate nexus in the context of resource sustainability

AbstractWater and energy have become essential resources and must be wisely managed for a sustainable future. This paper explores the relationship between water consumption and electricity generation in hydropower plants with dams under different climate change scenarios to contribute to the policy perspectives with a new tool and method to sustain the future. Still, as a reliable forecasting tool, the evaluation of the adaptive neuro-fuzzy inference system model has not been tested for forecasting water consumption during electricity generation. Thus, this study uses this modeling approach to generate reliable water consumption estimates based on electricity generation. The operational data of 78 hydroelectric power plants with dams and meteorological parameters were used as input variables, while water consumption was the output parameter in the model. The dataset was randomly divided into training and testing sets, and 85–15% data splitting presented the best-fitted model. The lowest mean average percent error of the hydroelectric power plants' model resulted in 9.59%, and the coefficient of determination of the model was 0.97, which showed that the developed model presented acceptable prediction performance. Various climate change scenarios are applied to analyze the effects of climate parameters on the water consumption of hydropower plants. The annual hydroelectric power plant water consumption and water intensity were estimated between 2,609 million m3 and 4,393 million m3, and 50,768 m3/GWh and 85,487 m3/GWh, respectively, based on climate change scenarios. The study concludes with significant policy suggestions to endorse this approach. Graphical abstract

Adaptive Neuro-Fuzzy Inference System-Based Predictive Modeling of Mechanical Properties in Additive Manufacturing

Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process.

Discussion on adaptive neuro-fuzzy inference system-based models for predicting ultimate bearing capacity of rock-socketed piles

Discussion on adaptive neuro-fuzzy inference system-based models for predicting ultimate bearing capacity of rock-socketed piles

Performance Evaluation of Benzyl Alcohol Oxidation with tert-Butyl Hydroperoxide to Benzaldehyde Using the Response Surface Methodology, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Model.

The adaptive neuro-fuzzy inference system (ANFIS), central composite experimental design (CCD)-response surface methodology (RSM), and artificial neural network (ANN) are used to model the oxidation of benzyl alcohol using the tert-butyl hydroperoxide (TBHP) oxidant to selectively yield benzaldehyde over a mesoporous ceria-zirconia catalyst. Characterization reveals that the produced catalyst has hysteresis loops, a sponge-like structure, and structurally induced reactivity. Three independent variables were taken into consideration while analyzing the ANN, RSM, and ANFIS models: the amount of catalyst (A), reaction temperature (B), and reaction time (C). With the application of optimum conditions, along with a constant (45 mmol) TBHP oxidant amount, (30 mmol) benzyl alcohol amount, and rigorous refluxing of 450 rpm, a maximum optimal benzaldehyde yield of 98.4% was obtained. To examine the acceptability of the models, further sensitivity studies including statistical error functions, analysis of variance (ANOVA) results, and the lack-of-fit test, among others, were employed. The obtained results show that the ANFIS model is the most suited to predicting benzaldehyde yield, followed by RSM. Green chemistry matrix calculations for the reaction reveal lower values of the E-factor (1.57), mass intensity (MI, 2.57), and mass productivity (MP, 38%), which are highly desirable for green and sustainable reactions. Therefore, utilizing a ceria-zirconia catalyst synthesized via the inverse micelle method for the oxidation of benzyl alcohol provides a green and sustainable methodology for the synthesis of benzaldehyde under mild conditions.

Hybrid neuro-fuzzy models for assessing the optimum moisture content of lime cement-treated soil

Hybrid neuro-fuzzy models for assessing the optimum moisture content of lime cement-treated soil

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document}, NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and Radj2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{adj}^2$$\\end{document} values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model’s capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model’s superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models

To enhance energy production, the wind energy industry builds increasingly larger wind turbines (WT) in terms of hub height and rotor diameter. This enlargement results in higher structural loads, which emphasizes the importance of load-reducing WT control alongside the nominal control of power and rotational speed. Generally, it is not possible to directly measure these structural loads, so a controller needs to include their online estimation. Here we extend a baseline WT state estimator in the form of an Extended Kalman Filter (EKF) to include two unaccounted loads. These loads are a change of thrust in the main bearing and a yaw moment in the rotor hub. We incorporate these loads via data-driven local linear neuro-fuzzy models (LLNFM). These LLNFMs can represent nonlinear relationships while maintaining limited complexity. We use alaska/Wind to generate the underlying regression data and to perform the state estimation both in simulation. The regression data consists of nominal WT operation under turbulent conditions for different average wind speeds. We further superimpose the individual pitch angles to excite the WT. The evaluation is performed in a different scenario, using another turbulent wind field and a realistic noisy sensor configuration. While we can estimate the change of thrust in the one per revolution (1P) frequency range, we achieve more precise results for the yaw moment due to the incorporation of sensors with a strong correlation to it. The estimation of the change of thrust and the yaw moment appear to be sufficiently precise for load-reducing WT control in practically relevant frequency ranges.

Neuro-fuzzy model for predicting insulin delivery from crosslinked agar-carbomer hydrogels

This study focuses on the innovation of an inhaled sustained release form of insulin and the development of a neuro-fuzzy model specifically tailored to predict insulin release kinetics from polycondensed agar-carbomer hydrogels. These were synthesized by blending agar and carbomer, incorporating propylene glycol and glycerol, and then cross-linking by polycondensation. The structure and morphology of the hydrogel were analyzed via Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy and Proton Nuclear Magnetic Resonance Spectroscopy. The neuro-fuzzy model, a combination of artificial neural networks and fuzzy logic, employs inputs such as concentrations of crosslinking agents, polycondensation time, and release time, with the output being the rate of insulin release. The model demonstrated a strong correlation with experimental data, highlighting its effectiveness and precision in predicting insulin delivery from hydrogel compositions and temporal parameters. This emphasizes the importance of intelligent modelling for forecasting the kinetic release of therapeutic agents from novel drug delivery systems.

Training neuro-fuzzy using flower pollination algorithm to predict number of COVID-19 cases: situation analysis for twenty countries

Predicting the number of COVID-19 cases offers a reflection of the future, and it is important for the implementation of preventive measures. The numbers of COVID-19 cases are constantly changing on a daily. Adaptive methods are needed for an effective estimation instead of traditional methods. In this study, a novel method based on neuro-fuzzy and FPA is proposed to estimate the number of COVID-19 cases. The antecedent and conclusion parameters of the neuro-fuzzy model are determined by using FPA. In other words, neuro-fuzzy training is carried out with FPA. The number of COVID-19 cases belonging to twenty countries including USA, India, Brazil, Russian, France, UK, Italy, Spain, Argentina, Germany, Colombia, Mexico, Poland, Turkey, Iran, Peru, Ukraine, South Africa, the Netherlands and Indonesia is estimated. Time series is created using the number of COVID-19 cases. Daily, weekly and monthly estimates are realized by utilizing these time series. MSE is used as the error metric. Although it varies according to the example and problem type, the best training error values between 0.000398027 and 0.0286562 are obtained. These best test error values are between 0.0005607 and 0.409867. The best training and test error values are 0.000398027 and 0.0005607, respectively. In addition to FPA, the number of cases is also predicted with the algorithms such as particle swarm optimization, harmony search, bee algorithm, differential evolution and their performances are compared. Success score and ranking are created for all algorithms. The scores of FPA for the daily, weekly and monthly forecast are 71, 77 and 62, respectively. These scores have shown that neuro-fuzzy training based on FPA is successful than other meta-heuristic algorithms for all three prediction types in the short- and medium-term estimation of COVID-19 case numbers.

Feasibility of integrated numerical and Adaptive Neuro-Fuzzy Inference System models in predicting the thermophysical properties of unsteady coupled micropolar–Casson hybrid nanofluids

This article aims to probe postulated phenomena using a paired micropolar and Casson hybrid fluid over a rotating disk. The intension to design a numerical technique (Runge–Kutta fourth-order along with shooting technique) integrated Adaptive Neuro-Fuzzy Inference System (ANFIS) envisioned with a thermal and exponentially space-dependent heat source, nonlinear thermal radiation and entropy production is developed in this phase of work. To ensure that, a nonlinear partial differential equation set of equations has been transformed into an ordinary differential equation by using the proper self-similarity variables. The model's research results, with a few notable outliers, are mostly consistent with those from prior research that was merged into the dataset used to train the ANFIS model. With the impact of active factors, the results are esthetically exhibited for numerous profiles. This displays that with the rise in magnetic field and radiation, the velocity and temperature profiles increase sharply, resulting in a contradiction phenomenon with the decreasing electric field inputs. Also, tilting of vortex viscosity, spin gradient viscosity and microinertia density on the various microrotation components displays inclination. Moreover, ANFIS training was exploited to analyze the approximate solutions for specific scenarios, and the developed ANFIS was evaluated against a testing dataset to emphasize its performance. Due to their longer render, the nanoparticles exploited here are deemed suitable for use in bone implants, iodinated agents for blood imaging and red blood cell stimulation. Thus, the results of this study may be applied to therapeutic anemia therapies.