Predictive Approach Research Articles

Proactive conformance checking: An approach for predicting deviations in business processes

Modern business processes are subject to an increasing number of external and internal regulations. Compliance with these regulations is crucial for the success of organizations. To ensure this compliance, process managers can identify and mitigate deviations between the predefined process behavior and the executed process instances by means of conformance checking techniques. However, these techniques are inherently reactive, meaning that they can only detect deviations after they have occurred. It would be desirable to detect and mitigate deviations before they occur, enabling managers to proactively ensure compliance of running process instances. In this paper, we propose Business Process Deviation Prediction (BPDP), a novel predictive approach that relies on a supervised machine learning model to predict which deviations can be expected in the future of running process instances. BPDP is able to predict individual deviations as well as deviation patterns. Further, it provides the user with a list of potential reasons for predicted deviations. Our evaluation shows that BPDP outperforms existing methods for deviation prediction. Following the idea of action-oriented process mining, BPDP thus enables process managers to prevent deviations in early stages of running process instances.

Impact of yeast extract and basal salts medium on 1,4-dioxane biodegradation rates and the microorganisms involved in carbon uptake from 1,4-dioxane

Conventional physical and chemical treatment technologies for 1,4-dioxane can be ineffective and consequently attention has focused on bioremediation. Towards this, the current research investigated the impact of basal salts medium (BSM) and yeast extract on 1,4-dioxane biodegradation rates in microcosms with different soil or sediment (agricultural soil, wetland sediment, sediment from an impacted site). Phylotypes responsible for carbon uptake from 1,4-dioxane were determined using stable isotope probing (SIP), both with and without BSM and yeast extract. Further, putative functional genes were investigated using 1) soluble di-iron monooxygenase (SDIMO) based amplicon sequencing, 2) qPCR targeting propane monooxygenase (large subunit, prmA) and 3) a predictive approach (PICRUSt2). The addition of BSM and yeast extract significantly enhanced 1,4-dioxane removal rates the agricultural soil and impacted site sediment microcosms. The phylotypes associated with carbon uptake varied across treatments and inocula. Gemmatimonas was important in the heavy SIP fractions of the wetland sediment microcosms. Unclassified Solirubacteraceae, Solirubrobacter, Pseudonocardia and RB4 were dominant in the heavy SIP fractions of the agricultural soil microcosms. The heavy SIP fractions of the impacted site microcosms were dominated by only two phylotypes, unclassified Burkholderiaceae and oc3299. SDIMO based amplicon sequencing detected three genes previously associated with 1,4-dioxane. The predicted functional gene analysis suggested the importance of propane monooxygenases associated with Solirubrobacter and Pseudonocardia. Overall, more microorganisms were involved in carbon uptake from 1,4-dioxane in both the wetland and agricultural soil microcosms compared to the impacted site sediment microcosms. Many of these microorganisms have not previously been associated with 1,4-dioxane removal.

DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes.

Phages, the natural predators of bacteria, were discovered more than 100years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments are needed to help screen phages quickly for therapeutic use. Traditional computational methods usually ignore the fact that phage-bacteria interactions are achieved by key genes and proteins. Methods for intraspecific prediction are rare since almost all existing methods consider only interactions at the species and genus levels. Moreover, most strains in existing databases contain only partial genome information because whole-genome information for species is difficult to obtain. Here, we propose a new approach for interaction prediction by constructing new features from key genes and proteins via the application of K-means sampling to select high-quality negative samples for prediction. Finally, we develop DeepPBI-KG, a corresponding prediction tool based on feature selection and a deep neural network. The results show that the average area under the curve for prediction reached 0.93 for each strain, and the overall AUC and area under the precision-recall curve reached 0.89 and 0.92, respectively, on the independent test set; these values are greater than those of other existing prediction tools. The forward and reverse validation results indicate that key genes and key proteins regulate and influence the interaction, which supports the reliability of the model. In addition, intraspecific prediction experiments based on Klebsiella pneumoniae data demonstrate the potential applicability of DeepPBI-KG for intraspecific prediction. In summary, the feature engineering and interaction prediction approaches proposed in this study can effectively improve the robustness and stability of interaction prediction, can achieve high generalizability, and may provide new directions and insights for rapid phage screening for therapy.

HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.

Epidemiologic and genetic studies in many complex diseases suggest subgroup disparities (e.g. by sex, race) in disease course and patient outcomes. We consider this from the standpoint of integrative analysis where we combine information from different views (e.g. genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. We propose Heterogeneity in Integration and Prediction (HIP), a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to a subgroup. We apply HIP to proteomics and gene expression data pertaining to chronic obstructive pulmonary disease (COPD) to identify proteins and genes shared by, and unique to, males and females, contributing to the variation in COPD, measured by airway wall thickness. Our COPD findings have identified proteins, genes, and pathways that are common across and specific to males and females, some implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms. HIP accounts for subgroup heterogeneity in multi-view data, ranks variables based on importance, is applicable to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. With the efficient algorithms implemented using PyTorch, this method has many potential scientific applications and could enhance multiomics research in health disparities. HIP is available at https://github.com/lasandrall/HIP, a video tutorial at https://youtu.be/O6E2OLmeMDo and a Shiny Application at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/ for users with limited programming experience.

Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM).

Microplastic pollution poses a significant threat to our environment, necessitating effective predictive modelling approaches for better management and mitigation. In this study, we introduce a pioneering methodology that fuses the power of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) for microplastic predictive modelling. Leveraging a comprehensive dataset, our integrated model exhibits exceptional performance, with an Accuracy of 0.96, Precision of 0.96, Recall of 0.97, and an F1 Score of 0.96. The achieved Accuracy underscores the model's proficiency in distinguishing microplastic and non-microplastic entities, promising robust and reliable predictions. Precision, as a measure of correct positive identifications, demonstrates our model's effectiveness in minimizing false positives, a crucial aspect for environmental monitoring. Moreover, the perfect Recall score signifies the model's ability to detect all relevant microplastic instances, addressing concerns about false negatives. The F1 Score encapsulates this dual proficiency, showcasing a harmonious trade-off between precision and recall. Our research not only advances the field of microplastic prediction but also highlights the potential of synergizing ANN and HMM methodologies for comprehensive environmental assessments. The reported performance metrics underscore the practical applicability of our approach, offering a valuable tool for tackling the pervasive issue of microplastic pollution and fostering proactive environmental stewardship.

YOLOv5-POS: research on cabbage pose prediction method based on multi-task perception technology.

Accurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance. This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem. YOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency.

Integrated Approach for Human Wellbeing and Environmental Assessment Based on a Wearable IoT System: A Pilot Case Study in Singapore.

This study presents the results of the practical application of the first prototype of WEMoS, the Wearable Environmental Monitoring System, in a real case study in Singapore, along with two other wearables, a smart wristband to monitor physiological data and a smartwatch with an application (Cozie) used to acquire users' feedback. The main objective of this study is to present a new procedure to assess users' perceptions of the environmental quality by taking into account a multi-domain approach, considering all four environmental domains (thermal, visual, acoustic, and air quality) through a complete wearable system when users are immersed in their familiar environment. This enables an alternative to laboratory tests where the participants are in unfamiliar spaces. We analysed seven-day data in Singapore using a descriptive and predictive approach. We have found that it is possible to use a complete wearable system and apply it in real-world contexts. The WEMoS data, combined with physiology and user feedback, identify the key comfort features. The transition from short-term laboratory analysis to long-term real-world context using wearables enables the prediction of overall comfort perception in a new way that considers all potentially influential factors of the environment in which the user is immersed. This system could help us understand the effects of exposure to different environmental stimuli thus allowing us to consider the complex interaction of multi-domains on the user's perception and find out how various spaces, both indoor and outdoor, can affect our perception of IEQ.

Hybrid artificial neural networks-based approaches predicting the heat transfer and flow characteristics of manifold microchannels

Aiming at meeting the requirement of high heat flux electronics cooling, the geometric complexity of manifold microchannels as an energy-saving technique has been increasing rapidly. It is well worth exploring optimal prediction models for the heat transfer and flow characteristics of manifold microchannels to reduce computing resources and time costs in the optimization process. In this paper, hybrid prediction approaches based on artificial neural networks are proposed for manifold microchannels. Four optimization algorithms, namely, genetic algorithm, particle swarm optimization algorithm, artificial hummingbird algorithm, and zebra optimization algorithm were adopted to enhance the prediction performance. Initially, all prediction models were trained and tested by a numerical database including six input parameters, and the thermal resistance and pumping power were taken as the target outputs. Compared to the original method, hybrid prediction models show higher accuracy and reliability on the numerical data with regression coefficients beyond 0.987. The prediction model optimized by the artificial hummingbird algorithm shows the best performance with the mean average error for thermal resistance and pumping power decreased by 88.7% and 81.4% respectively. Additionally, the runtime of all prediction models is compared and the model optimized by the zebra optimization algorithm takes the longest time, but is still two orders of magnitude shorter than that of the traditional simulation method. Finally, the generalizability of all prediction models is further validated by a database amassed from six experimental studies on manifold microchannels. The results showed that three hybrid models can provide accurate outputs with regression coefficients beyond 0.84 which differs from the poor prediction of the original method. The model optimized by the zebra optimization algorithm shows superior prediction performance for six individual studies, two fluids, three fin shapes, and wide flow rate conditions. The trained hybrid prediction models in this study perform higher accuracy and can be cost-effective and reliable prediction tools for the rapid optimization process of various manifold microchannel applications.

Exploring novel approaches for fertility and subfertility prediction in dromedary male camels (Camelus dromedarius)

Dromedary bulls exhibit particularly suboptimal semen quality. In this species, standard breeding soundness evaluation (BSE) often cannot catch the subtle difference between fertile and subfertile males. Therefore, the aim was to explore novel diagnostic tools to predict fertility and recognize subfertility in dromedary bulls. We explored the relationship between semen quality, fertility, echotexture parameters, accessory glands volume and total testosterone. Two sets of experiments were performed: in vitro (semen group) and in vivo (natural mating group). Each group was assessed by standard BSE: clinical examination, testis volume, ultrasound analysis of testicular parenchyma (TP) and epididymis. For the first time, images of epididymal head were obtained. Testis volume was not correlated to any parameters studied. Ultrasound TP showed especially low sensitivity (20 % in vitro and 14 % in vivo). The standard BSE was found unsuccessful in assessing subfertility. Therefore, animals were then submitted to advanced BSE: digital image analysis of TP, assessment of accessory glands (prostate (P) and bulbourethral (BB) glands) and serum total testosterone concentration. Upon analysis, significant correlations were found among some echotexture parameters (white pixels, mean grey levels, area and density of hypoechogenic areas) and percentage of viability, head defects, mitochondria potential and fertility. Only white pixels showed strong sensitivity (86 %) and specificity (100 %). BB volume correlated with ejaculate volume. Total testosterone concentration did not correlate with any parameters studied. In conclusion, advanced BSE (digital ultrasound image analysis and accessory glands volume) proved to be a useful tool for predicting semen quality and detecting subfertility in dromedary camels.

A correlation-based approach for fast prediction of the equilibrium position of particles within a spiral microchannel

ABSTRACT As inertial microfluidics is a proven technique for manipulating particles within microchannels, a deep understanding of hydrodynamics forces acting on particles in these systems is crucial for optimal manipulation. However, existing numerical techniques for examining particle equilibrium positions are often time-intensive and require high-memory systems, limiting accessibility due to complexity and cost. To address this issue, this study proposes a method to develop a correlation for fast estimation of particle equilibrium positions. An algorithm is devised to develop the correlation in three main steps: first, a dataset is created by conducting simulations using the finite element method, considering width, height, flow rate, particle size, and curvature radius. The computed distance between the equilibrium position of the particle and the inner channel wall serves as input for the next step where a general form of the correlation is established using Buckingham’s Π-theorem. The unknown coefficients of the correlation are calculated using 80 percent of the dataset from the first step and the least sum squares errors method. Finally, the obtained correlation is validated using the remaining data and previous experimental studies. The proposed correlation reduces the computational burden associated with predicting particle focus positions, with a 3.7 percent mean absolute error.

Pengaruh Dukungan Sosial terhadap Motivasi Belajar Mahasiswa Tingkat Ii

This study examines the relationship between work motivation and psychological well-being (PWB) in internship students in Greater Jakarta. Program policies provide opportunities for students to develop their skills and work experience. This study aims to identify how work motivation, both intrinsic and extrinsic, affects the psychological well-being of internship students. Using a predictive correlational approach, data was collected through a questionnaire that measured the dimensions of work motivation and PWB. The results of the study showed that intrinsic motivation had a significant positive impact on psychological well-being, while excessive extrinsic motivation could have a negative impact. This research emphasizes the importance of creating a supportive work environment to motivate interns, improve their performance, and prepare them for the transition from academia to the world of work. The researcher obtained 103 respondents from various students at the University of Greater Jakarta with 25.2% male and 74.8% female. The data of this study was processed using convenience sampling techniques, using Multidimensional Work Motivation Scale (MWMS) and Psychological Well-Being (PWB) questionnaires. The results of data analysis are known through the Kolmogorov Smirnov test obtained a number of 0.200 > 0.05 for both variables, it was concluded that the residual data was normally distributed.

ASPECTS OF SCIENTIFIC RESEARCH IN UNIVERSAL ENVIRONMENTAL TREATIES: LEGAL AND ECONOMIC ANALYSIS

The article emphasises that aspects of international legal cooperation in the field of scientific research have not yet been the subject of a systematic, comprehensive scientific study either in the context of international law doctrine or in relevant publications. The subject of the study is the legal regulation of scientific research in universal international environmental treaties, with the aim of identifying the specific features of such international legal mechanisms. The paper examines the challenges and paradoxes faced by international environmental law in regulating scientific research as a process, as well as in balancing scientific research with the practical needs for appropriate resources. The research uses hermeneutical, prognostic, comparative and systemic analysis of relevant international treaties as components of the modern system of international law. It applies a complex set of methods, including analysis and synthesis, structural and formal-legal methods, as well as scientific deduction and induction approaches. The combination of these methods provides a thorough examination of how international environmental treaties regulate scientific research. The methodology includes a hermeneutic approach to interpret the language and provisions of various environmental treaties, a predictive approach to forecast potential developments and the outcomes of current regulation, a comparative analysis to identify similarities and differences between different treaties, and a systems analysis to understand how these treaties fit into the broader system of international law. Taken together, these methods provide a reliable basis for assessing the current state and future directions of international legal cooperation in environmental research. The main objective of the article is to reveal the unique characteristics and complexities inherent in the international legal mechanisms governing scientific research under environmental treaties. It aims to highlight the current challenges and paradoxes faced by international environmental law in this context. These include the need to address global environmental problems with often limited and unreliable scientific data, the need to respect and encourage scientific research, and the imperative to balance the search for scientific knowledge with the practical needs of natural resources. The results of the study show that environmental conventions reflect several important challenges. One of the main challenges is to address global environmental issues in the face of a lack of reliable scientific information, while respecting scientific research and its results. Another crucial challenge is to maintain a balance between promoting scientific research and meeting the practical resource needs of humanity. The author posits that international environmental law sources represent an enduring endeavour to achieve equilibrium in the formation of convention bodies. This equilibrium strives to reconcile the sovereign interests of participating states with the necessity of maintaining a high level of expertise within relevant commissions and committees. In conclusion, the paper emphasises the necessity for the continuous development and refinement of legal regulation pertaining to scientific research within the field of international environmental law. This is in order to effectively address the dynamic and complex interplay between scientific advancement and environmental sustainability.

MGCN-PolyA: An Integrated Computational Framework for Predicting Poly(A) Signals with Multiscale-gated Convolutional Networks

Background: The accurate recognition of the polyadenylation signal (PAS) from DNA sequences is essential for understanding gene transcriptional regulation. A variety of machine learning-based computational methods have been developed to predict PAS in recent years; however, their performance and their generalization ability are unsatisfactory. It is highly desirable to design more preferable computational approaches for PAS prediction. Methods: In this work, we developed an integrated framework MGCN-PolyA for PAS prediction across four species, including Homo sapiens, Bos taurus, Mus musculus, and Drosophila melanogaster. MGCN-Poly(A) benefits from the diversity of feature engineering and the effectiveness of the model architecture. We combined features from different perspectives, such as word embedding, One-hot encoding, K-mer frequency, and Enhanced Nucleic Acid Composition (ENAC), which complement each other and provide rich and comprehensive information for model learning. In model architecture, MGCN-Poly(A) leverages a two-channel multi-scale gated convolutional network to effectively learn high-level feature representations at different scales, and then combines the statistical features to predict PAS using random forest algorithm. These designs not only speed up network training, but also improves the generalization ability Results: The benchmarking experiments on the independent test datasets demonstrate that MGCNPolyA outperforms other state-of-the-art algorithms in identifying PAS. MGCN-PolyA has the highest accuracy on all test datasets, and its excellent performance on cross-species validation also demonstrates the robustness of our model. Conclusion: Extracting features from different perspectives is important for PAS recognition, and the integration of DNNs and shallow machine learning algorithms can improve the model performance.

Predictive Maintenance in IoT-Monitored Systems for Fault Prevention

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed.

Predictive Genetic Programming Approaches for Swell-Shrink Soil Compaction

Predictive Genetic Programming Approaches for Swell-Shrink Soil Compaction

Multi-constrained predictive optimal control for spacecraft attitude stabilization and tracking with performance guarantees

This paper investigates a novel predictive control approach for spacecraft attitude stabilization and tracking problems with consideration of performance constraints, angular velocity limitation and control saturation. First, the prescribed performance control (PPC) structure and barrier Lyapunov function (BLF) are utilized to design the cost function of optimal control problem. Subsequently, a multi-constrained predictive optimal controller for spacecraft attitude stabilization and tracking with performance guarantees is devised via exploiting an explicit receding horizon optimization control strategy under actuator saturation. Compared with the existing methods, the major merit of the proposed one lies in the semi-analytic solving scheme of optimal control problems with high computing efficiency and simultaneous handling of multiple constraints without increasing computational burden. Finally, two illustrative examples are employed to validate the effectiveness of the proposed control method.

Research on the Remaining Useful Life Prediction Method of Energy Storage Battery Based on Multimodel Integration.

The remaining useful life (RUL) of lithium-ion batteries (LIBs) needs to be accurately predicted to enhance equipment safety and battery management system design. Currently, a single machine learning approach (including an improved machine learning approach) has poor generalization performance due to stochasticity, and the combined prediction approach lacks sufficient theoretical support at the same time. In this paper, we first analyze the prediction principles and applicability of models such as long and short-term memory networks and random forests, and then propose a method for predicting the RUL of batteries based on the integration of multiple-model, and finally validate the proposed model by using experimental data. The experimental results show that (1) for the proposed model, in the best case, the root-mean-square error (RMSE) does not exceed 0.14%, which has a stronger generalization; (2) for the comparison with the single model used, the average RMSE is reduced by 46.2%, 43.7%, and 80.6%, which has a better fitting performance. These results show that the model has good prediction accuracy and application prospects for predicting the RUL of energy storage batteries.

Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis

Accurate prediction of blood glucose level (BGL) has proven to be an effective way to help in type 1 diabetes management. The choice of input, along with the fundamental choice of model structure, is an existing challenge in BGL prediction. Investigating the performance of different data-driven time series forecasting approaches with different inputs for BGL prediction is beneficial in advancing BGL prediction performance. Limited work has been made in this regard, which has resulted in different conclusions. This paper performs a comprehensive investigation of different data-driven time series forecasting approaches using different inputs. To do so, BGL prediction is comparatively investigated from two perspectives; the model’s approach and the model’s input. First, we compare the performance of BGL prediction using different data-driven time series forecasting approaches, including classical time series forecasting, traditional machine learning, and deep neural networks. Secondly, for each prediction approach, univariate input, using BGL data only, is compared to a multivariate input, using data on carbohydrate intake, injected bolus insulin, and physical activity in addition to BGL data. The investigation is performed on two publicly available Ohio datasets. Regression-based and clinical-based metrics along with statistical analyses are performed for evaluation and comparison purposes. The outcomes show that the traditional machine learning model is the fastest model to train and has the best BGL prediction performance especially when using multivariate input. Also, results show that simply adding extra variables does not necessarily improve BGL prediction performance significantly, and data fusion approaches may be required to effectively leverage other variables’ information.

Artificial Neural Network Approach for Analysis and Prediction of Solar Radiation and Wind Speed in Telecommunication Systems

The use of renewable energy sources in the power grid is essential for maintaining economic growth, especially for many African countries. Therefore, these countries can also install advanced artificial intelligence technology in their airport’s power system to capture renewable energy. The paper aims to propose a real-time power management algorithm for a distributed hybrid renewable energy grid. It continues with the network modeling with the integration of solar and wind power. A multi-objective approach is proposed for the maximization of the spatial spectral efficiency and the energy efficiency as a function of the number of wind turbines and solar photovoltaic panels. Radio parameters for a Mobile Wireless inter-operability Medium Access (WiMAX) technology are considered for the simulation in MATLAB software. The results are quite mitigated but theoretically promising for the integration of green energy into the current telecommunication systems

Human exposure to micro/nano-plastics through vegetables, fruits, and grains – A predictive modelling approach

The emergence of human exposure (HE) to micro/nano-plastics (MN-P) via the food chain is a significant public health concern. This study aimed to evaluate HE from ingesting vegetables, fruits, and grains using linear regression models to analyse MN-P size-concentration relationships and bioaccumulation factors (BF). For Irish adults, the Estimated Daily Intake (EDI) of MN-Ps was calculated, considering potential internalisation in these foods, with a sensitivity analysis addressing variability and uncertainty. The simulated mean (SM) root stomatal diameter in selected plants was 620 nm, indicating the potential uptake of MN-Ps smaller than this size. The SM BF for vegetables was 24.24 for nanoplastics (NP). Limited NP data led to the use of metal nanoparticle (MNP) data, yielding an overall BF of 3.22 for pooled vegetables, fruits, and grains. Potential HE levels of MN-Ps in agricultural soil were simulated at 6.05 × 104 n/kg (SM), with predicted MN-P levels in edible plants at 1.47 × 106 n/kg of food products. The simulated EDI of MN-Ps through all crops was 1.62 × 103 n/kg bw/day, with vegetables contributing the most to MN-P exposure, followed by fruits and grains. Sensitivity parameters are ranked as MN-P abundance in soil > bioaccumulation factor > food consumption.