Data-driven Prediction Model Research Articles

Evaporating temperature estimation of refrigeration systems based on vibration data-driven soft sensors

The evaluation of the operating conditions of refrigeration compressors once installed in household appliances is challenging due to the need to install pressure transducers, a process which requires system evacuation and refrigerant reintroduction. In addition, changes in the piping modify the characteristics of the original product. This paper proposes a soft-sensing technique based on vibration measurements of the compressor surface to predict the evaporating temperature. Different machine learning (ML) techniques are evaluated as data-driven prediction models, namely multilayer perceptron (MLP) neural networks, least squares boosting, generalized additive model, random forest, extreme learning machine, and random vector functional link neural networks. These techniques were applied to data obtained from a test rig designed to emulate compressor operation in a refrigeration system, with an operating envelope from -30°C to -10°C for the evaporating temperature and from 34°C to 54°C for the condensing temperature. The results showed that, with a single vibration measurement point, it was possible to use an MLP technique to estimate the evaporating temperature with a root mean squared error of 1.74°C in a non-intrusive way. For the other prediction techniques, the errors were a bit higher than for the MLP, but the maximum error value was about 2.5°C in all cases.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.

Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration. This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes. Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis. Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis. The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

Understanding the Effects of Forced and Bubble-Induced Convection in Transport-Limited Organic Electrosynthesis

Organic electrosynthesis offers a sustainable path to decarbonize the chemical industry by integrating renewable energy in chemical manufacturing. However, achieving the selectivity and energy efficiency required for industrial applications is challenging due to the inherent mass transport limitations of most electro-organic reactions. Electrochemical reactions rely on transport of reactants to the electrode surface and become mass transport-limited when the reactant diffusion rate is lower than the reaction rate at the electrode. In organic electrosynthesis with several possible products, mass transport limitations can result in decreased selectivity towards the desired product as the various reaction pathways compete. Convection can mitigate mass transport limitations, but fundamental engineering guidelines for selecting and scaling up convection methods in electrochemical systems do not exist. As a result, the impact of convection on industrial-scale complex organic electrochemical processes remain poorly understood. Here we show that the Sherwood number (Sh)—the ratio of convective mass transport to diffusive mass transport—is a crucial metric to characterize mass transport, determine reactor performance, and enable effective scale-up. We investigate the interplay between mass transport and electrochemical reaction rates under convective flows in the context of the electrosynthesis of adiponitrile, one of the largest organic electrochemical processes in the industry. We use experiments and data-driven predictive models to demonstrate that forced liquid convection and bubble-induced convection produce nearly equivalent mass transport conditions when the corresponding Sherwood numbers are equal. This conclusion shows that the Sherwood number characterizes the mass transport condition independent of the underlying convection mechanism. Additionally, we show that the reactor performance scales with the Sherwood number for a given current density and reactant concentration. This scaling enables accurate prediction of performance irrespective of the convection method, and demonstrates that adiponitrile Faradaic efficiencies of >80% can be achieved for current densities of up to 200 mA/cm2 when Sh > 100. Our results provide guidelines for the design and selection of convection methods, from lab to industrial scale, and contribute to the development of more sustainable chemical manufacturing processes.

Spectral prediction method based on the transformer neural network for high-fidelity color reproduction

Color distortion often occurs during transmission and reproduction processes, and existing spectral prediction methods have the disadvantage of low prediction accuracy in halftone reproduction. Addressing this issue, this paper establishes a halftone dataset composed of four-color inks (CMYK) mixtures. Based on this, the transformer network is introduced to model and characterize the spectral features of mixed inks, and a forward color formulation prediction model and a reverse spectral prediction model combining halftone reproduction with spectral sequences are proposed, namely the spectrum-color transformer (SC-Former). Color reproduction quality assessment experiments are conducted using the dataset established in this paper and the international standard Ugra/Fogra Media Wedge V3.0 test set. The experimental results show that the SC-Former model outperforms traditional physical models and data-driven prediction models in terms of color reproduction effects and spectral prediction accuracy. This research contributes to the development of high-fidelity color reproduction techniques.

Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux Over a Large Parameter Space

A unifying and accurate model to predict critical heat flux (CHF) over a wide range of conditions has been elusive since wall boiling research emerged. With the release of the experimental data utilized in the development of the 2006 Groeneveld CHF lookup table (LUT), by far the most extensive public CHF database available to date (nearly 25 000 data points), the development of data-driven prediction models over a large parameter space in simple geometry (vertical, uniformly heated round tubes) can be achieved. Furthermore, the popularization of machine learning techniques to solve regression problems has led to more advanced tools for analyzing large and complex databases. This work compares three machine learning algorithms to predict the CHF database. For each selected regression algorithm (ν-Support vector, Gaussian process, and neural network), a set of optimized hyperparameters is applied. Among the investigated algorithms, the neural network emerges as the most effective, achieving a CHF predicted/measured factor standard deviation of 12.3%, three times less than that of the LUT. In comparison, the Gaussian process regression and the ν-support vector regression achieve a standard deviation of 17.7%, about two times lower than the LUT. Hence, all considered algorithms significantly outperform the LUT prediction performance. The neural network model and training methodology are designed to prevent overfitting, which is confirmed by data analyses of the CHF predictions. Finally, future development directions (including data coverage, transfer learning, and uncertainty quantification) are discussed.

Prediction of segmental motor outcomes in traumatic spinal cord injury: Advances beyond sum scores

Background and objectivesNeurological and functional recovery after traumatic spinal cord injury (SCI) is highly challenged by the level of the lesion and the high heterogeneity in severity (different degrees of in/complete SCI) and spinal cord syndromes (hemi-, ant-, central-, and posterior cord). So far outcome predictions in clinical trials are limited in targeting sum motor scores of the upper (UEMS) and lower limb (LEMS) while neglecting that the distribution of motor function is essential for functional outcomes. The development of data-driven prediction models of detailed segmental motor recovery for all spinal segments from the level of lesion towards the lowest motor segments will improve the design of rehabilitation programs and the sensitivity of clinical trials. MethodsThis study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery of segmental motor scores as the primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for k-nearest neighbor (kNN) matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. The kNN performance was compared to linear and logistic regression models. ResultsWe obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUCwalker = 0.92, AUCself-carer = 0.83) for the kNN algorithm, improving beyond the linear regression task (RMSElinear = 0.98(0.22, 2.57)). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUCwalker = 0.92). We deploy the final historic control model as a web tool for easy user interaction (https://hicsci.ethz.ch/). DiscussionOur approach is the first to provide predictions across all motor segments independent of the level and severity of SCI. We provide a machine learning concept that is highly interpretable, i.e. the prediction formation process is transparent, that has been validated across European and American data sets, and provides reliable and validated algorithms to incorporate external control data to increase sensitivity and feasibility of multinational clinical trials.

Data-driven thermal modeling of in-situ Automated Fiber Placement

This study proposes a data-driven model for thermal history prediction during in-situ Automated Fiber Placement of thermoplastic composites. Temperature data was experimentally collected with fast-response thermocouples placed within carbon fiber AS4/PEEK composite substrates. The temperature for various combinations of hot gas torch temperatures, heat source velocity, and locations through the thickness and width were collected. A feedforward neural network (FNN) was developed to predict the entire 3-dimensional thermal history. The FNN had five input features and one output: the temperature at a given position and a combination of the process parameters. The FNN predictions for data unseen during training are validated for cases of interpolation and extrapolation. As expected, suitable performance was obtained for cases of interpolation, but predictions suffered in extrapolation. The computational efficiency of FNNs makes them an appropriate candidate for on-line thermal history prediction or process optimization, given that they are used within their training range.

An intelligent predictive and optimized wastewater treatment plant.

The intelligent predictive and optimized wastewater treatment plant method represents a ground-breaking shift in how we manage wastewater. By capitalizing on data-driven predictive modeling, automation, and optimization strategies, it introduces a comprehensive framework designed to enhance the efficiency and sustainability of wastewater treatment operations. This methodology encompasses various essential phases, including data gathering and training, the integration of innovative computational models such as Chimp-based GoogLeNet (CbG), data processing, and performance prediction, all while fine-tuning operational parameters. The designed model is a hybrid of the Chimp optimization algorithm and GoogLeNet. The GoogLeNet is a type of deep convolutional architecture, and the Chimp optimization is one of the bio-inspired optimization models based on chimpanzee behavior. It optimizes the operational parameters, such as pH, dosage rate, effluent quality, and energy consumption, of the wastewater treatment plant, by fixing the optimal settings in the GoogLeNet. The designed model includes the process such as pre-processing and feature analysis for the effective prediction of the operation parameters and its optimization. Notably, this innovative approach provides several key advantages, including cost reduction in operations, improved environmental outcomes, and more effective resource management. Through continuous adaptation and refinement, this methodology not only optimizes wastewater treatment plant performance but also effectively tackles evolving environmental challenges while conserving resources. It represents a significant step forward in the quest for efficient and sustainable wastewater treatment practices. The RMSE, MAE, MAPE, and R2 scores for the suggested technique are 1.103, 0.233, 0.012, and 0.002. Also, the model has shown that power usage decreased to about 1.4%, while greenhouse gas emissions have significantly decreased to 0.12% than the existing techniques.

Data-driven predictive modeling of pollen concentration for allergy prevention in Islamabad and Rawalpindi

Data-driven predictive modeling of pollen concentration for allergy prevention in Islamabad and Rawalpindi

Predictive modeling for cell culture in commercial manufacturing of biotherapeutics.

The biopharmaceutical industry continually seeks advancements in the commercial manufacturing of therapeutic proteins, where mammalian cell culture plays a pivotal role. The current work presents a novel data-driven predictive modeling application designed to enhance the efficiency and predictability of cell culture processes in biotherapeutic production. The capability of the cloud-based digital data science application, developed using open-source tools, is demonstrated with respect to predicting bioreactor potency from at-line process parameters over a 5-day horizon. The uncertainty in model's prediction is quantified, providing valuable insights for process control and decision-making. Model validation on unseen data confirms the model's robust generalizability. An interactive dashboard, tailored to process scientist's requirements is also developed to streamline biopharmaceutical manufacturing processes, ultimately leading to enhanced productivity and product quality.

Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity

While data-driven predictive models are a strictly technological construct, they may operate within a social context in which benign engineering choices entail implicit, indirect and unexpected real-life consequences. Fairness of such systems – pertaining both to individuals and groups – is one relevant consideration in this space; algorithms can discriminate people across various protected characteristics regardless of whether these properties are included in the data or discernible through proxy variables. To date, this notion has predominantly been studied for a fixed model, often under different classification thresholds, striving to identify and eradicate undesirable, discriminative and possibly unlawful aspects of its operation. Here, we backtrack on this fixed model assumption to propose and explore a novel definition of cross-model fairness where individuals can be harmed when one predictor is chosen ad hoc from a group of equally well performing models, i.e., in view of utility-based model multiplicity . Since a person may be classified differently across models that are otherwise considered equivalent, this individual could argue for a predictor granting them the most favourable outcome, employing which may have adverse effects on other people. We introduce this scenario with a two-dimensional example and linear classification; then, we present a comprehensive empirical study based on real-life predictive models and data sets that are popular with the algorithmic fairness community; finally, we investigate analytical properties of cross-model fairness and its ramifications in a broader context. Our findings suggest that such unfairness can be readily found in real life and it may be difficult to mitigate by technical means alone as doing so is likely to degrade predictive performance.

High-resolution harvester data for estimating rolling resistance and forest trafficability

AbstractInformation on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient ($$\mu _{RR}$$ μ RR ), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, $$\mu _{RR}$$ μ RR can be computed for harvest areas. This study describes an extensive, high-resolution data on $$\mu _{RR}$$ μ RR collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating $$\mu _{RR}$$ μ RR on steeper slopes, enabling the calculation within a $$-10^{\circ }$$ - 10 ∘ to $$+10^{\circ }$$ + 10 ∘ slope range with a speed range of 0.6–1.2 ms$$^{-1}$$ - 1 . We characterise the variation in $$\mu _{RR}$$ μ RR both between and within 11 test sites harvested during the April-August period. The site mean $$\mu _{RR}$$ μ RR varies from $$\sim$$ ∼ 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of $$\mu _{RR}$$ μ RR within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the $$\mu _{RR}$$ μ RR extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining $$\mu _{RR}$$ μ RR from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.

Predictive modeling of perioperative patient deterioration: combining unanticipated ICU admissions and mortality for improved risk prediction

ObjectiveThis paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes.Materials and MethodsWith less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost.ResultsDuring evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction.DiscussionThe study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies.ConclusionsThis work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Enhancing efficiency in closed agricultural greenhouses: A data-driven predictive model for energy consumption

Abstract Predicting energy consumption in agricultural greenhouses is essential to effectively allocate resources, enhance plant growth, and minimize energy inefficiencies. Various factors affect the energy consumption inside the greenhouse including external climate conditions and internal microclimate. Proper understanding of these factors is crucial for maintaining an ideal growing environment and optimizing energy efficiency. This drives the need to investigate the interaction between these factors and greenhouse energy consumption, encompassing the energy needed for cooling and the supply of water and nutrients. This work aims at developing a dynamic model that predicts the total energy consumption of a closed agricultural greenhouse to improve microclimate control and energy efficiency. The study is conducted within a closed-loop agricultural greenhouse with no natural ventilation. Inside, the air is cooled and continuously circulated without being exchanged with ambient air through a heating, ventilation, and air conditioning (HVAC) system. The data-driven model encompasses external climate parameters such solar radiation, ambient temperature, and relative humidity; along with microclimate parameters such as internal temperature, humidity, and CO2 concentration to predict overall energy consumption. The study examines two machine learning models, deep neural networks (DNN) and extreme gradient boosting (XGBoost), for forecasting energy consumption, and assesses their performance using the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). Results reveal that the DNN model surpasses the XGBoost model, exhibiting a superior predictive performance with an R2 of 80.9%, RMSE of 171.1 kWh and MAE of 130.3 kWh. This study demonstrates its practicality in assisting with energy consumption analyses and identifying inefficient energy usage patterns within closed agricultural greenhouses.

Minimum-energy virtual machine placement using embedded sensors and machine learning

Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines. Our solution is embodied through the use of sensors embedded inside physical server machines, enabling the introduction of new features for sensitive thermal awareness and proactive hot spot avoidance. Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive VM placements that are both energy-aware and thermal-aware. Indeed, experiments carried out on a cluster of physical server machines demonstrate high performance by both the ML models and the placement algorithm (CPP). Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2%, respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.

Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane

This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances.

A novel data-driven reduced-order model for the fast prediction of gas-solid heat transfer in fluidized beds

Computational fluid dynamics − discrete element method (CFD-DEM) emerges as a promising tool to model dense gas–solid two-phase flow in fluidized beds, yet the numerical efficiency is still far from being satisfactory. Accordingly, this work develops a proper orthogonal decomposition (POD) reduced-order model (ROM) based on the CFD-DEM method for the fast prediction of gas–solid flow and heat transfer in a bubbling fluidized bed (BFB). The POD method is employed to decompose particle coordinates and temperature into spatial modes and time evolution coefficients. The radial basis function neural network (RBFNN) and temporal convolutional neural network (TCN) are utilized to predict the time evolution coefficients. Regarding the POD modes, the particle temperature exhibits an obvious monotonic or periodic characteristic compared to the particle coordinates, indicating the potential for long-term prediction. The RBFNN-ROM and TCN-ROM reconstruct the flow field effectively, achieving acceleration ratios of 2000 and 3000, respectively. The ROM developed in this study holds the potential to enable real-time prediction of industrial processes, thus paving the way for the realization of digital twins.

Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process

Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially valued in the aerospace and automotive industries for their robust strength and lightweight characteristics. The densification process during CVI critically influences the final performance, quality, and consistency of these composite materials. Experimentally optimizing the CVI processes is challenging due to the long experimental time and large optimization space. To address these challenges, this work takes a modeling-centric approach. Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework. An uncertainty quantification feature has been embedded within the PiNDiff method, bolstering the model’s reliability and robustness. Through comprehensive numerical experiments involving both synthetic and real-world manufacturing data, the proposed method showcases its capability in modeling densification during the CVI process. This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding, simulation, and optimization of the CVI manufacturing process, particularly when faced with sparse data and an incomplete description of the underlying physics.

Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation

ObjectivesThis study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease. MethodsThe 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics. ResultsCorrelation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average. ConclusionsData-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal–external validations.