Simulation Model For Prediction Research Articles

Accuracy and robustness of a plant-level cabbage yield prediction system generated by assimilating UAV-based remote sensing data into a crop simulation model

In-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha–1 (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha–1 higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.

Numerical simulation and intelligent prediction of a 500 t/d municipal solid waste incinerator

After classifying municipal solid waste (MSW), understanding how changes in its characteristics affect the incineration process becomes essential. Optimizing and adjusting operating parameters is crucial to ensure the smooth operation of MSW incinerators. In this study, we developed a three-dimensional numerical simulation model for a 500 t/d MSW incinerator, based on a two-fluid model that accounts for grate motion. We examined the effects of load and moisture on the combustion process. Using the results from the numerical simulation and on-site operational data, we established a machine learning model to predict incinerator conditions based on load, waste particle size, and moisture content. The average relative errors for the training and test sets were approximately 0.33 % and 0.44 %, respectively. Finally, we compared the influence of different operating parameters on flue gas temperature. Our numerical simulation model and intelligent prediction method offer valuable guidance for optimizing existing MSW incinerators and designing new ones.

Predictive Modelling and Functional Group of Clay Soil Treated with Steel Slag and Calcium Carbide Residue

Swell-shrink subgrade soils like clay are rich in sulphate and exhibit a high rate of volume change by swelling and shrinking during wet and dry cycles, respectively, leading to heaves, cracks, and differential settlement of pavement layers. The aim of this study was to establish predictive simulation models and functional group of clay soil treated with steel slag and calcium carbide residue. Physical and geotechnical tests (such as California bearing ratio (CBR), compaction characteristics, and consistency parameters) were carried out on the unstabilised and stabilised clay soil. Using SPSS statistical software (version 23), multiple linear regression analysis was applied to the experimental data that relate CBR to compaction parameters and additive contents (steel slag and calcium carbide residues). The generated models gave a reliable coefficient of determination, R2 , of 0.990, and 0.998, which can be used to successfully predict both unsoaked and soaked CBR of the stabilised clay soil, respectively. Fourier transform infrared spectroscopy (FTIR) analysis was examined on the clay soil, the additives, and the stabilised clay soils. The FTIR analysis showed weak peaks at 1115 and 1103 cm-1 for all the stabilised clay soils, and this revealed that both additives drastically reduced the sulphate content in the clay soil, which consequently reduced the expansion, cracking, and disintegration rates. As a result, the clay soil's chemical bonding was enhanced through physico- chemical mechanisms.

Anti-IgG Doped Melanin Nanoparticles Functionalized Quartz Tuning Fork Immunosensors for Immunoglobulin G Detection: In Vitro and In Silico Study.

The quartz tuning fork (QTF) is a promising instrument for biosensor applications due to its advanced properties such as high sensitivity to physical quantities, cost-effectiveness, frequency stability, and high-quality factor. Nevertheless, the fork's small size and difficulty in modifying the prongs' surfaces limit its wide use in experimental research. Our study presents the development of a QTF immunosensor composed of three active layers: biocompatible natural melanin nanoparticles (MNPs), glutaraldehyde (GLU), and anti-IgG layers, for the detection of immunoglobulin G (IgG). Frequency shifts of QTFs after MNP functionalization, GLU activation, and anti-IgG immobilization were measured with an Asensis QTF F-master device. Using QTF immunosensors that had been modified under optimum conditions, the performance of QTF immunosensors for IgG detection was evaluated. Accordingly, a finite element method (FEM)-based model was produced using the COMSOL Multiphysics software program (COMSOL License No. 2102058) to simulate the effect of deposited layers on the QTF resonance frequency. The experimental results, which demonstrated shifts in frequency with each layer during QTF surface functionalization, corroborated the simulation model predictions. A modelling error of 0.05% was observed for the MNP-functionalized QTF biosensor compared to experimental findings. This study validated a simulation model that demonstrates the advantages of a simulation-based approach to optimize QTF biosensors, thereby reducing the need for extensive laboratory work.

Integration of Sentinel-1A SAR data with crop simulation model for rice yield prediction in Udham Singh Nagar, Uttarakhand

Integration of Sentinel-1A SAR data with crop simulation model for rice yield prediction in Udham Singh Nagar, Uttarakhand

Optimizing healthcare resource allocation through data-driven demographic and psychographic analysis

This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, and geospatial data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics and machine learning unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Challenges such as data quality, bias, and ethical considerations underscore the complexity of healthcare resource allocation. However, emerging innovations, including artificial intelligence, precision medicine, and value-based care models, offer transformative potential for optimizing resource allocation and improving patient outcomes. Looking ahead, the integration of innovative technologies and patient-centered care models promises to reshape healthcare resource allocation, emphasizing equity, efficiency, and patient engagement. By navigating challenges thoughtfully and embracing future opportunities, healthcare systems can enhance resource allocation strategies to better meet the evolving needs of patients and communities. This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, administrative databases, geospatial data, and social determinants of health data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics, machine learning, spatial analysis, and simulation modeling unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Keywords: Healthcare Resource Allocation, Demographic Analysis, Psychographic Analysis, Data Sources, Advanced Analysis Techniques, Future Directions.

A quantitative study of the effects of physical properties and external loads on the liquefaction of saturated sand based on an intelligent numerical cyclic triaxial test system

ABSTRACT Soil liquefaction would cause significant damage to the safety of cargo transportation. The aim of this article is to conduct a quantitative study of the influence of the main physical characteristic parameters of saturated sand and external loads on its liquefaction. On the basis of the physical cyclic triaxial test (CTT), the finite element simulation model and PSO-BP neural network prediction model and importance analysis model were optimised in this study. Based on this, an innovative intelligent numerical CTT system for saturated sand was constructed. The research results indicate the influence of external load, effective internal friction Angle and plasticity index on the liquefaction of saturated sand is significant, and the average weight is 40.15%, 29.15% and 25.05%, respectively. In this paper, the relevant research provides a theoretical basis for effective control of sand liquefaction and provides new ideas and feasible solutions for subsequent research on sand liquefaction.

Ethics Perspective of Quality Accounting Information and Future Certainty: A Pattern of Rationally Perceived Decision

Purpose: This research tested the distinctive capability of detecting misleading information when real earnings have positively stimulated the smooth movement of the market price. Methodology/approach: This causal research used the primary data from the investor community in 2022, which amounted to 3.242 respondents for running SEM and endogeneity testing; FGD has been the fittest validation process for capturing the rationality models. Findings: High consistency of integrity-characteristic played a crucial role in designing a prudent accounting treatment which indicates the ethics code related to highly sustainable business during an uncertain future global economy. Inevitably, a measurement of trust in high earning quality pointed out that this quality measurement is to protect this investment decision in a highly secured, safe area. Practical implications: One centralized platform of financial reporting services should support the attractiveness of obedience and compliance as an incomparable advantage of positive earnings management. High-prospected firms should be assessed by high-quality accounting information; the delisting procedure has been prioritized firmly for low-prospect ones. Originality/value: This research explores the pattern of predictive simulation modeling as the existence of game theory, adapted from simplex-linear programming; this rationality model has strengthened as a fundamental requirement to illustrate high future certainty.

Investigation of experimental and numerical analysis of thermal load of WEDM on all metals through process parameters on wire electrode temperature

The thermal load on wire electrode in a wire electrical discharge machining (WEDM) is the most important factor in predicting wire electrode breakage. Thermal failure of wire electrode may be expected when the maximum temperature of the wire crosses a critical value. The maximum temperature of wire electrode is a function of process parameters of the machine and hence the failure depends upon what settings we choose to run WEDM process. In this investigation, simulation is done using finite-difference thermal model for continuous pulse without clustering of the spark. The model is used for determining distribution of the temperature throughout the length of wire electrode and analyzed the significance of various factors on wire breakage. The outcome of the present simulation model is validated by comparing it with the works of other researchers followed by its validation through experiments. A parametric study is also done to determine the effect of input process parameters and their interaction on the temperature of the wire. For finding the interaction effect, analysis of variance analysis is carried out followed by developing an empirical model. The predictive performance of our simulation model and developed regression models is assessed by comparing them with experimental value. The regression model is found to be closer, with an average percentage deviation of −2.55% on the experimental dataset, compared to 2.64% for the simulation model. During the validation test, the minimum and maximum deviations are 0.08% and 0.20% for simulation versus regression model predictions, −3.31% and −1.65% for experimental versus predicted results, and 1.82% and 3.40% for simulation versus experimental results, respectively. It is observed through this study that temperature of wire electrode mainly depends on the intensity of sparks per unit area which is a function of the input parameters of the WEDM.

A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction

Ground settlement prediction during mechanized tunneling is of paramount importance and remains a challenging research topic. Typically, two paradigms are existing: a physics-driven approach utilizing numerical simulation models for prediction, and a data-driven approach employing machine learning techniques to learn mappings between influencing factors and the settlement. To integrate the advantages of both approaches and to assimilate the data from different sources, we propose a multi-fidelity deep operator network (DeepONet) framework, leveraging the recently developed operator learning methods. The presented framework comprises of two components: a low-fidelity subnet that captures the fundamental ground settlement patterns obtained from finite element simulations, and a high-fidelity subnet that learns the nonlinear correlation between numerical models and real engineering monitoring data. A pre-processing strategy for causality is adopted to consider the spatio-temporal characteristics of the settlement during tunnel excavation. The results show that the proposed method can effectively capture the physical information provided by the numerical simulations and accurately fit measured data (R2 around 0.9) as well. Notably, even when dealing with very limited noisy monitoring data (with a 50% error), the proposed model remains robust, achieving satisfactory results with R2>0.8. In comparison, the R2 score obtained by pure simulation-based prediction is only 0.2. The utilization of transfer learning significantly reduces the training time from 20 min to within 30 s, showcasing the potential of our method for real-time settlement prediction during tunnel construction.

Assessment of Greenhouse Gas Emissions from Heavy-Duty Trucking in a Non-Containerized Port through Simulation-Based Methods

Seaports are perceived as multimodal hubs of the logistics supply chain where various transport modes intersect to exchange goods shipped by vessels. Increasing trade and capacity constraints are making this area a major contributor to GHG emissions. National and regional decision-makers perceive port sustainability as a concern while planning GHG mitigation projects. However, to plan and conduct successful GHG management programs, it is critical to first develop an appropriate assessment approach that fits well with the operating and geographical context of the given port. For heavy-duty trucking activities taking place within such ports, several models and methodologies for assessing GHG emissions are available, but their generalization is challenging for many reasons, notably because of the specific features of traffic within the port. Therefore, this paper presents an assessment model for heavy-duty trucking emissions within a non-containerized port based on an in-depth study of the traffic per port zone and on parameters drawn from several real data sources. The GHG model based on road traffic profiles by zone is implemented in a simulation model for emission evaluation and prediction. The output shows the pattern of GHG emissions by zone and provides an outlook on how decision-makers could achieve a GHG reduction plan.

Identifying Occurrences of the Cnidarian Physalia physalis in Social Media Data

The Portuguese man-of-war (Physalia physalis), though beautiful, poses a risk to the population due to its potential to cause severe burns. Tracking their occurrences can prevent accidents through alerts to the population and predictive simulation models. However, traditional sources do not always provide records of their sightings. On the other hand, previous studies indicate that social media can be an effective source of information for conservation science. This work uses natural language processing and computer vision to obtain machine learning models to classify data extracted from Instagram. Such models can be used as part of an automated Extract-Transform-Load process to build a database on occurrences of Physalia physalis on the Brazilian coast. In preparation for training the models, we collected and manually labeled Instagram posts in order to distinguish the ones about the animal from other subjects, such as ships and tattoos. Given the nature of the problem, the spatial and temporal information associated with the sightings are essential for biologists. Thus, the absence or nonvalidity of such data is often used as a rationale to reject the post. However, the same criteria may not be suitable for training machine learning models to classify new posts automatically. The main goal of this article is to highlight the importance of choosing appropriate labels to train both text and image models, as well as to take into consideration the rejection criteria of the biologist before using a classification model. An experimental study is presented to show the effect of unquestioning adoption of labels given by a specialist, compared to labels adapted for machine learning training.

Revolutionizing turnaround management with innovative strategies: Reducing ramp-up durations post-maintenance

Revolutionizing turnaround management through innovative strategies is essential for enhancing operational efficiency and minimizing downtime in various industries, particularly in manufacturing, oil and gas, and chemical processing. This paper examines effective approaches to reduce ramp-up durations following maintenance activities, which are often characterized by significant operational disruptions and lost productivity. By implementing structured methodologies and leveraging advanced technologies, organizations can streamline their turnaround processes, ensuring a swift return to optimal production levels. Key strategies discussed include the adoption of predictive maintenance techniques, which utilize data analytics and machine learning to forecast equipment failures and schedule maintenance proactively. This approach not only minimizes unexpected breakdowns but also allows for better planning of turnaround activities. Additionally, the integration of digital tools, such as mobile applications and cloud-based platforms, facilitates real-time communication and collaboration among teams, enabling quicker decision-making and response times during the ramp-up phase. Moreover, the paper explores the significance of workforce training and cross-functional collaboration in improving turnaround outcomes. Empowering employees with the necessary skills and fostering a culture of teamwork enhances operational readiness and efficiency during critical phases. Furthermore, the utilization of simulation modeling can provide valuable insights into workflow processes, allowing organizations to identify bottlenecks and optimize resource allocation during turnarounds. Case studies from various industries highlight the successful implementation of these innovative strategies, showcasing significant reductions in ramp-up durations and enhanced overall performance. The findings emphasize the importance of continuous improvement and adaptability in turnaround management, encouraging organizations to embrace a proactive mindset and invest in technology-driven solutions. In conclusion, revolutionizing turnaround management through innovative strategies offers a pathway to minimize ramp-up durations post-maintenance, ultimately leading to improved operational efficiency and cost savings. This paper advocates for a holistic approach, combining predictive maintenance, digital tools, workforce engagement, and simulation modeling to achieve sustainable improvements in turnaround performance.

A computational investigation of DMSO/water separation through functionalized GO multilayer nanosheet membrane using molecular dynamics simulation and deep neural network model for membrane performance prediction

In this molecular dynamics (MD) simulation study, the separation of dimethyl sulfoxide (DMSO) from water was investigated using multilayer functionalized graphene oxide (GO) membranes. The GO nanosheets were modified with chemical groups (-F, -H) to alter their properties. The study analyzed the influence of pressure and functional groups on the separation rate. Additionally, a deep neural network (DNN) model was developed to predict membrane behavior under different conditions in water treatment processes. Results revealed that the fluorine-functionalized membrane exhibited higher permeation compared to the hydrogen-functionalized one, with potential of mean force (PMF) analysis indicating higher energy barriers for water molecules passing through the hydrogen-functionalized membrane. The study used density profile, water density map analysis, and radial distribution function (RDF) analysis to understand water and DMSO molecule interactions. The diffusion coefficient of water molecules was also calculated, showing higher diffusion in the fluorine-functionalized system. Overall, the findings suggest that functionalized GO membranes are effective for DMSO-water separation, with the fluorine-functionalized membrane showing superior performance. The DNN model accurately predicts membrane behavior, contributing to the optimization of membrane separation systems.

Assessment of an Open-Source Aircraft Noise Prediction Model Using Approach Phase Measurements

An open-source simulation model for aircraft noise prediction is presented and validated using backpropagated noise measurements for a state-of-the-art engine and aircraft. The validation is focused on approach procedures and was performed using ground-based noise measurements that were taken at 17 recording stations for a total of 18 consecutive flights carried out during the morning of 8 April 2021. The flights were performed using two A321neo aircraft with LEAP-1A engines. It is demonstrated that the presented noise model provides a satisfactory estimation of the source noise for varying approach configurations and flight conditions. Configurations using a greater number of high-lift devices are particularly well predicted in the mid- and high-frequency regions, whereas the lower configuration settings show greater spectral deviations, which are partly attributed to measurement uncertainties caused by the increased aircraft–microphone distance. The model can predict the overall mean total sound intensity level within a 2 dB accuracy for all configurations, while the average predicted level at each microphone differs by less than 3 dB from the measurement average, for all cases except one. Variation in aircraft speed showed to have a strong impact on the predicted total noise, which matches the well-recognized sixth-power Mach number far-field sound intensity scaling law for airframe noise models, while the measurements indicated a less significant dependency. This is mainly due to installation effects and noise reduction measures that are not included in the models. Nevertheless, the variations in the spectra of the predicted and measured noise showed similar patterns.

Modeling the transport of fecal coliform in ntanwaogba creek, influenced by variations in micronutrient deposition, velocity and dispersion coefficient

The study of Micronutrients in Ntanwaogba Creek were thoroughly carried out to monitor its rates of deposition at different numerous discharge location sites in the study environment, this was imperative because the rates of biological waste discharge at regular interval, based on this factor, it was necessary to conduct a comprehensive investigation of their rate of concentration at different station point of discharge. This implies that the rate of dispersions from the contaminant influenced constant discharge of waste in the creek, and based on these factors, it was determined that such comprehensive research was required. Micronutrients act as a substrate for microbial growth, but the speed at which they are injected into the rill affects how quickly they move through the system. In order to determine the effects of these two parameters on the migration rate of faecal coliform at different point sources of discharge, the study observed different growth rate at different station point in the study location. This observed condition indicates that the pollutants had a range of development speeds, including both slow and fast, which was enabled by these considerations. The system discovered that lower velocities have an effect on velocity rates with higher concentrations, and that accumulation with micronutrients increased their concentration. However, the concentration rates varied depending on the dominant characteristics of the transport under pressure at various points of discharge. In the simulation, these two parameters were used to determine the various pressure rates at different station points. Unquestionably, the study has depicted the effects of these two parameters' pressures on the movement of faecal coliform in a range of figures that correspond to the several point sources of discharge looked at. The speeds recorded at various station locations represented the pressure rates at various rates of concentration in the research environment. It has established the scope of the influence of rill flow velocity and the variance in micronutrient deposition at various point sources. On the basis of model simulation prediction results, also, the dispersions at various point sources were evaluated. Both parameters showed correlations for the best fits when the predicted and experimental values were compared for model validation.

A low-order simulation model for wind turbine infrasound prediction

Wind energy is one of the fastest-growing renewable energy technologies. One of the environmental concerns associated with wind energy development is noise emission from wind energy facilities, especially the low-frequency noise and infrasound that allegedly raises public health concerns. In order to analyze the noise emission from wind turbines and to aid future wind energy development, a numerical simulation tool for the prediction of low-frequency noise and infrasound emission from wind turbines is developed. The simulation tool is a low-order model developed by combining aerodynamic noise theories and basic working principles of wind turbines. 2-D airfoil aerodynamics calculation is coupled with a subroutine that solves Ffowcs Williams-Hawkings (FW-H) equation in time domain. By omitting unsteady aeroacoustics mechanisms, the low-order simulation model is capable of computing infrasound emissions from multiple wind turbines simultaneously. By utilizing the low-order simulation model, the effects of wind directions, wind turbine locations, and phases of wind turbine rotations on the consequent aerodynamic infrasound are studied. Results of aerodynamic infrasound computation imply that wind farm configuration or wind turbine phase adjustment can help reduce the noise level at certain locations, which makes the low-order simulation model a useful tool to aid wind farm siting and controlling.

Transport model simulation prediction and environmental risk assessment of pyrethroid insecticides in urban artificial lakes caused by rainfall: A case study of Cloud Mountain Park in subhumid climate zone

Pyrethroid insecticides are among urban parks' most widely used and harmful insecticides. The advanced prediction method is the key to studying the pollution and diffusion risk of plant conservation insecticides in parks. A two-dimensional advection-dispersion model was established for the North Lake of Cloud Mountain Park in the subhumid area of Hebei Province. The temporal and spatial distribution of lambda-cyhalothrin pollution required by plant growth in artificial lakes under different rainfall intensities and the time of water renewal after rainfall was simulated and predicted. According to the model efficiency (E: 0.98), mean absolute error (MAE: 0.016–0.064 cm), and root mean square error (RMSE: 0.014–0.041 cm), the prediction results showed that the model fits well. The results showed that the concentration of lambda-cyhalothrin in the artificial lake was positively correlated with the increase in rainfall intensity. Under the three scenarios of moderate rain, heavy rain, and rainstorm, the variation of total pollutants into the lake over time conformed to the first-order dynamic equation (R2＞0.97), and the cumulative rates were 0.013 min−1, 0.019 min−1 and 0.022 min−1, respectively. Under light rain, the accumulation rate of lambda-cyhalothrin showed a double-linear relationship, which was in accordance with the second-order kinetic equation (R2＞0.97). The rapid accumulation rate of early-stage rainfall was 0.0024 min−1, and the slow accumulation rate of late-stage rainfall was 0.0019 min−1. The human health risk assessment predicted by the simulation was lower than the hazard value (Rtgn(a-1): 9.65 E−11–1.12 E−10 a-1). However, the potential risk value to aquatic species was higher (RQ: 0.33–23.05). In addition, the increase in rainfall intensity has no significant effect on the acceleration of water renewal time. The two-dimensional dispersion model of pollutants driven by water dynamics provided relevant examples for evaluating the impact of runoff on pesticide scour in parks and supplied scientific support for improving the management of artificial lakes in urban parks.

The influence of websites user engagement on the development of digital competitive advantage and digital brand name in logistics startups

Logistics startups gradually rely on digital marketing strategies to acquire a competitive advantage. The main aim of Logistics startups is to increase their digital brand name and user engagement in order to acquire a competitive advantage. To the completion of this target, various digital marketing strategies could be implemented to ensure a differentiating factor. A three-stage data-driven methodology was adopted to evaluate the contribution between the parameters and to reflect strategies that can be presented to improve the website's user engagement and digital brand name. The first part of the study collects data from nine logistics startups’ websites over a period of 180 days. The second part of the study employs Fuzzy Cognitive Mapping (FCM) to develop an exploratory diagnostic model that visually depicts the cause-and-effect relationships between the metrics under consideration. In the last part of the study, a predictive simulation model has been created to present the intercorrelation between the examined metrics and to present possible optimization strategies. According to the findings of this study, Logistics startups’ websites must be developed with fewer web pages and need to be focused on the customers’ target. Additionally, in contradiction with other industries’ websites, logistics startups must maintain a steady flow of digital advertisements to optimize brand name and profit.

Grading Threads. Exploiting Viscous Thread Instability for the additive fabrication of Functionally Graded Structures via sensor-adaptive robotic control

We present a novel computational fabrication method for the production of Functionally Graded Structures (FGS) via robotic control of Viscous Thread Instability (VTI). Of interest in several fields and at different scales of application, the fabrication of FGS is often relying on offline fabrication workflows and on stable material conditions. By introducing partial control in the process of spatial deposition of an extruded clay thread in a state of instability, our method extends the design and fabrication possibilities of VTI to the production of FGS. Traditionally exploited for the industrial production of not-graded two-dimensional nonwoven textiles or for surface treatments in design-related 3d printing applications, we frame VTI as the main design and fabrication driver for the computational fabrication of functionally graded clay volumetric structures. Without relying on predictive physical simulation models, our method relies on feedback information provided by sensing equipment in combination with an industrial 6 axis robotic manipulator integrated with a numerically controlled clay extruder. The sensed information is used to retroactively update the inputs of a computational model programmed to guide the robotic additive fabrication of user-defined functional volumetric gradients. We illustrate the main design- and fabrication-related parameters and a set of material experiments designed to validate the accuracy of our model. We present a set of fabricated outputs to illustrate the flexibility of the model to accommodate a variety of design intentions and, finally, we discuss its potential for further research involving cross-scalar and trans-disciplinary applications.