Efficient Prediction Research Articles

Designing optimized geometry and fiber orientation of composites for dynamic tension via artificial intelligence

Machine learning is quickly becoming an invaluable tool for examining large databases to find patterns and predictions based on similar parameters tested in previous studies. A natural application of this subset of artificial intelligence revolves around material data sets where tests are repeated for basic studies (e.g. tension, compression, bending, or shear) and extrapolating the data for tests not yet considered. Dynamic testing and validation with machine learning is examined here, as a significant time and cost is required for understanding larger-sized specimens (cm scale and larger) especially when the samples are composite materials in tension. This is due in part to the challenges involved in ensuring failure at the region of interests for an anisotropic material and minimizing the number of repeated tests. In this study, the data sets were generated artificially via finite element analysis with failure, and a machine learning code applied to infer the shape of the tensile test coupon knowing only the failure and stress field. Then the algorithm was run to generate a library of ideal coupon geometries based on ply angle and area location of failure within the test specimen. The result is a highly efficient prediction algorithm that can create the ideal specimen shape for tensile testing at speeds orders of magnitude faster than comparable finite element codes.

Comparison of Machine Learning Algorithms for Prediction of Textile Effluent Treatment Efficiency Using Anaerobic Process

ABSTRACTThe prediction of pollutants removal efficiency from the generated effluent of a treatment plant is valuable and can reduce the time, sampling and energy required during performance assessment. The present study aims to predict the effect of different input parameters on the treatment efficiency of the developed microbial‐based anaerobic process for textile effluent using machine leaning algorithms. The decolourisation and chemical oxygen demand (COD) reduction of the treated effluent were predicted on the basis of the three different input parameters pH, COD and colour value of the textile wastewater. The effectiveness of different machine learning algorithms, support vector machines (SVM), random forest (RF), gradient boost regressor (GBR), AdaBoost, extreme gradient boosting (XGB) regressor and voting regressor, were evaluated based on the correlation coefficient (R2) value. The results revealed that the RF achieved the highest accuracy for decolourisation (training data R2: ∼0.85 and test data R2: ∼0.84) as well as COD reduction (training data R2: ∼0.87 and test data R2: ∼0.94) compared to the other algorithms. These results were validated experimentally, confirming that RF can be used as a tool to predict the performance efficiency of a microbial‐based treatment system.

Enhanced and Efficient Predictions of Dynamic Ionization through Constant-pH Adiabatic Free Energy Dynamics

Enhanced and Efficient Predictions of Dynamic Ionization through Constant-pH Adiabatic Free Energy Dynamics

Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

Study on the prediction of economic efficiency for a tertiary recycling plant for waste plastics by using the environmental Kuznets’ curve model

Plastic products are items that we use every day around us, and their replacement speed are very fast, so that to recycle waste plastic has become the focus of environmental problems. This study has proposed an optimized circular design for the recycle plant of waste plastic, therefore, and our proposed strategy is to build a new tertiary recycling plant to reduce the total generation amount of the derived solid plastic waste from ordinary and secondary recycling plants and the semi-finished products from secondary recycling plant. Results obtained from a real recycle plant has showed that to recycle the tertiary waste plastic in a tertiary recycling plant, the finished products produced from a secondary recycling plant accounts about 27% of ordinary waste plastic, and the semi-finished products that mainly is scrap hardware accounts about 1% of ordinary waste plastic. Other derived solid plastic waste accounts for 6% of ordinary plastic waste. Therefore, if the ordinary, secondary and tertiary recycle plant can be set all-in-one, it can reduce the total generation amount of derived solid plastic waste from 34% to 6%, without and with a tertiary recycling plant, respectively. It can also increase the operating income of the secondary recycle plant and the investment willingness of the new tertiary recycle plant.

Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study

IntroductionTemporomandibular disorders (TMD) have a high prevalence and complex etiology. The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.MethodsA total of 949 adults who underwent oral examinations were enrolled in our study. 5 different ML algorithms were used for model development and comparison, and feature selection was performed by feature importance ranking and feature decreasing methods. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The precision-recall curve (PR), calibration curve, and decision curve analysis (DCA) further assessed the accuracy and clinical utility of the model.ResultsThe performance of the random forest (RF) model was the best among the 5 ML models. An interpretable RF model was developed with 7 features (gender, malocclusion, unilateral chewing, chewing hard substances, grinding teeth, clenching teeth, and anxiety). The AUCs of the final model on the training set, internal validation set, and external test set were 0.892, 0.854, and 0.857, respectively. Calibration and DCA curves showed high accuracy and clinical applicability of the model.DiscussionAn efficient and interpretable TMD risk prediction model for adults was successfully developed using the ML method. The model not only has good predictive performance, but also enhances the clinical application value of the model through the SHAP method. This model can provide clinicians with a practical and efficient TMD risk assessment tool that can help them better predict and assess TMD risk in adults, supporting more efficient disease management and targeted medical interventions.

Machine learning-enhanced band gaps prediction for low-symmetry double and layered perovskites.

Density functional theory (DFT) calculations are widely used for material property prediction, but their computational cost can hinder the discovery of novel perovskites. This work explores machine learning (ML) as a faster alternative for predicting band gaps in complex perovskites, focusing on low-symmetry double and layered structures. We employ Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost) to predict both direct and indirect band gaps. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) metrics. Our results reveal SVR as the most effective general model for predicting band gaps in both double and layered perovskites. Interestingly, for double perovskites specifically, XGBoost achieves even higher accuracy when incorporating derivative discontinuity as a feature. Feature importance analysis identifies the standard deviation of valence charges ("Valence (std)") as the most critical factor for band gap prediction across all studied perovskites. This research demonstrates the potential of ML for efficient and accurate band gap prediction in complex perovskites, accelerating material discovery efforts.

Comparative Analysis of Machine Learning Algorithms for Heart Attack Prediction

This study aims to develop a robust and efficient prediction model for heart failure detection by using state-of-the-art machine learning and artificial intelligence techniques. The research analyses various patient data that includes demographic and clinical variables to identify patterns and risk factors associated with heart failure. Parameters such as age, gender, cardiovascular indicators, lipid levels, blood glucose and exercise-induced responses were examined to capture the complex interactions that contribute to heart health outcomes. A comprehensive experimental design was used for the study, which included data preprocessing, feature correlation analysis, model training and performance evaluation of a series of machine learning algorithms. The models tested include random forest, gradient boosting (XGBoost), logistic regression, support vector machines (SVM) and neural networks, with a focus on balancing predictive accuracy and computational efficiency. The results show that ensemble methods such as XGBoost and Random Forest provide superior prediction accuracy while being computationally feasible, making them particularly suitable for clinical applications. Neural network architectures, in particular RNN and FNN (post-SHAP), also achieved high accuracy but were associated with significantly higher computational costs. Simpler models such as decision trees and logistic regression were computationally efficient but delivered lower performance metrics than the ensemble methods. These results underscore the potential of XGBoost and Random Forest as optimal models for integrating AI-assisted heart failure diagnostics into real-time medical decision-making processes and offer a compelling balance between precision and practicality in a clinical context.

Safety of non-invasive brain stimulation in patients with implants: a computational risk assessment.

Non-invasive brain stimulation (NIBS) methodologies, such as transcranial electric (tES) are increasingly employed for therapeutic, diagnostic, or research purposes. The concurrent presence of active/passive implants can pose safety risks, affect the NIBS delivery, or generate confounding signals. A systematic investigation is required to understand the interaction mechanisms, quantify exposure, assess risks, and establish guidance for NIBS applications. We used measurements, simplified generic, and detailed anatomical modeling to: (i) systematically analyze exposure conditions with passive and active implants, considering local field enhancement, exposure dosimetry, tissue heating and neuromodulation, capacitive lead current injection, low-impedance pathways between electrode contacts, and insulation damage; (ii) identify risk metrics and efficient prediction strategies; (iii) quantify these metrics in relevant exposure cases and (iv) identify worst case conditions. Various aspects including implant design, positioning, scar tissue formation, anisotropy, and frequency were investigated. At typical tES frequencies, local enhancement of dosimetric exposure quantities can reach up to one order of magnitude for deep brain stimulation (DBS) and stereoelectroencephalography implants (more for elongated passive implants), potentially resulting in unwanted neuromodulation that can confound results but is still 2-3 orders of magnitude lower than active DBS. Under worst-case conditions, capacitive current injection in the active implants' lead can produce local exposures of similar magnitude as the passive field enhancement, while capacitive pathways between contacts are negligible. Above 10 kHz, applied current magnitudes increase, necessitating consideration of tissue heating. Furthermore, capacitive effects become more prominent, leading to current injection that can reach DBS-like levels. Adverse effects from abandoned/damaged leads in direct electrode vicinity cannot be excluded. Safety related concerns of tES application in the presence of implants are systematically identified and explored, resulting in specific and quantitative guidance and establishing basis for safety standards. Furthermore,several methods for reducing risks are suggested while acknowledging the limitations(see Sec. 4.5).

DFT-based accurate and efficient bandgap prediction of CsSnI3-xBrx and parameter optimization for enhanced perovskite solar cell performance

This article analyzes the bandgap (Eg) of tin-based metal halide perovskites CsSnI3-xBrx (x = 0, 1, 2, 3) using density functional theory (DFT). For the first time, the study evaluates various exchange-correlation (XC) families—LDA, GGA, MGGA, and HSE—along with key DFT parameters such as pseudopotentials (PP), K-point densities, and basis sets. The investigation identifies the optimal combination of parameters that most accurately matches experimental Eg values for different mole fractions. The study reports average deviations from experimental Eg values of 6.48% for LDA, 8.08% for GGA, 3.44% for MGGA, and 3.07% for HSE. While HSE shows the smallest deviation, MGGA demonstrates better consistency across mole fractions with lower computational costs. Additionally, the band structure and Eg trends for each mole fraction are examined, providing valuable insights for future computational studies on perovskite materials.

Pruned tree-structured temporal convolutional networks for quality variable prediction of industrial process

In real industrial processes, the rapid and accurate acquisition of quality variables is essential. Therefore, this paper proposes a pruned tree-structured temporal convolutional network (PT-TCN) for efficient and accurate variables prediction. First, a novel tree network is developed, utilizing dilated causal convolution blocks as nodes to avoid the loss of local information. Each node extracts distinct local information, and by concatenating all tree nodes, the network can capture a comprehensive range of temporal scales. Then, to avoid the increased complexity caused by the tree structure, we design an online two-stage pruning strategy to compress the tree network without introducing additional computations. During the training process, blocks are initially pruned based on the correlation assessment between quality variables and tree nodes. Subsequently, weight normalization layers are employed to evaluate the importance of output channels in blocks, thereby enabling intra-block channel pruning. The effectiveness of PT-TCN is verified on Tennessee Eastman benchmark process. In addition, experiments on the real zinc flotation process demonstrate that the proposed PT-TCN improves in R2 and MAE by 1.32% and 1.26% respectively in predicting quality variables, and it can reduce 91.8% parameters of the initial tree-structured TCN without sacrificing accuracy.

Integrating Data Imputation and Augmentation with Interpretable Machine Learning for Efficient Strength Prediction of Fly Ash-Based Alkali-Activated Concretes

Fly ash-based alkali-activated concrete (AAC) is renowned for its superior mechanical performance and sustainability, presenting an attractive alternative to traditional Portland cement concrete. Despite these advantages, the broad compositional range of AACs presents challenges in precisely tailoring material properties. In this context, machine learning (ML) offers promising prospects to streamline and fast-track the development of advanced materials design strategies by predicting mechanical properties from compositional variations. Effective ML model development, however, hinges on the availability of a comprehensive, high-quality dataset. Previous studies often relied on literature-derived datasets, which typically include outliers, noise, and missing values, potentially leading to biased predictions. Moreover, limited dataset sizes could undermine the robustness of the models. Traditional ML methods applied to AACs also tend to lack interpretability. To address these issues, this paper utilizes several data imputation methods and Generative Adversarial Networks (GANs) for data augmentation, effectively doubling the dataset size. Following this, ML algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Neural Networks (NNs) are leveraged to predict compressive strength. The NN model, especially when enhanced by k-nearest neighbors (kNN) imputation (k = 5), demonstrated superior predictive accuracy compared to RF and XGBoost models. Further, SHAP (SHapley Additive exPlanations) analysis reveals key determinants of compressive strength, such as water content, SiO2, and curing conditions. Visualizations such as SHAP violin and river flow plots further elucidated feature contributions and property distributions. Overall, this study provides a robust framework for exploring composition-strength relationships in AACs, advancing the design of these environment-friendly materials.

Hypersonic glide vehicle trajectory prediction based on frequency enhanced channel attention and light sampling-oriented MLP network

Hypersonic Glide Vehicles (HGVs) are advanced aircraft that can achieve extremely high speeds (generally over 5 Mach) and maneuverability within the Earth's atmosphere. HGV trajectory prediction is crucial for effective defense planning and interception strategies. In recent years, HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency. However, it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models. To solve this problem, we propose a new deep learning framework (FECA-LSMN) for efficient HGV trajectory prediction. The model first uses a Frequency Enhanced Channel Attention (FECA) module to facilitate the fusion of different HGV trajectory features, and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network (LSMN) based on simple MLP-based structures to extract long/short-term HGV trajectory features for accurate trajectory prediction. Also, we employ a new data normalization method called reversible instance normalization (RevIN) to enhance the prediction accuracy and training stability of the network. Compared to other popular trajectory prediction models based on LSTM, GRU and Transformer, our FECA-LSMN model achieves leading or comparable performance in terms of RMSE, MAE and MAPE metrics while demonstrating notably faster computation time. The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network. The RevIN data normalization technique outperforms traditional min-max normalization as well.

Refined transmission line ice-cover prediction model and optimization study

Abstract This study aims to develop a fine-grained transmission line ice-cover prediction model and conduct an optimization study. Ice cover is one of the important factors leading to transmission line accidents, so accurately predicting the ice cover of transmission lines is crucial to ensure the safe operation of power grids. In this paper, an efficient ice-cover prediction model is constructed based on advanced machine learning algorithms and optimization methods and is validated and optimized with a large amount of experimental data. The results show that the proposed model has high accuracy and prediction capability in ice cover prediction, which provides important support for the safe operation of transmission lines.

Data-Supported Prediction of Surface Settlement Behavior on Opencast Mine Dumps Using Satellite-Based Radar Interferometry Observations

To ensure the safe repurposing of post-mining landscapes, understanding and managing geotechnical risks, particularly ground movements such as settlements on opencast mining dump surfaces, is critical. Satellite-based radar interferometry (InSAR) technology offers highly detailed data on vertical ground movements with a high spatial and temporal resolution. By combining a data-driven approach, using InSAR-generated high-resolution datasets, with model-driven methods such as inverse modeling and classic time–settlement models, the efficient monitoring and prediction of opencast mine dump settlements can be achieved. This dual approach—leveraging advanced data analysis tools and precise modeling—yields valuable insights into spatial settlement behavior. In particular, classic time–settlement models are applied to the InSAR data through least square regression and Taylor approximation. The integration of both approaches enables the more robust, data-validated forecasts of key geotechnical indicators, such as the time to settlement stabilization and the expected maximum settlement over large areas. An application at a mine in central Germany illustrates the method by generating spatial predictions of the settlement behavior over more than 200 ha. In general, the results provide a comprehensive dataset for investigating other factors influencing the settlement behavior of opencast mine dumps.

Analytical model for CO2-water displacement with rate-dependent phase permeability for geological storage

Analytical modelling is an effective tool for predicting reservoir behaviour under high uncertainties, like CO2 storage in aquifers, where multiple simulation runs are necessary for stochastic modelling and risk assessment. This paper formulates the Buckley-Leverett problem with x-dependent fractional flow. We derive a new analytical model for displacement of brine by CO2 accounting for (i) rate-dependent phase permeability during radial flows and (ii) radial flows of Forchheimer’s high-rate gas injection; this analytical model is also valid for (iii) linear flows during flooding in micro heterogeneous or composite cores and (iv) CO2-water flow upscaling in reservoir where layering is perpendicular to flux direction. An exact solution of the flow equation is based on the observation that the flux of each phase conserves along the characteristic trajectories. We discuss S-shape fractional flow function, which is typical for reservoir rocks. The solution includes formulae for phase saturations and fluxes and trajectories of the displacement CO2-water front and of the forward and rear mixture zone boundaries. Fast analytical model can be used for multivariate sensitivity study and sweep efficiency prediction.

Predicting Public Violent Crime Using Register and OpenStreetMap Data: A Risk Terrain Modeling Approach Across Three Cities of Varying Size

The aim of the current study is to estimate whether spatial data on place features from OpenStreetMap (OSM) produce results similar to those when employing register data to predict future violent crime in public across three Swedish cities of varying sizes. Using violent crime in public as an outcome, four models for each city are produced using a Risk Terrain Modeling approach. One using spatial data on place features from register data and one from OSM, one model with prior violent crime excluded and one with prior crime included. The results show that several place features are significantly associated with violent crime in public independent of using register or OSM data as input. While models using register data seem to produce more accurate and efficient predictions than OSM data for the two smaller cities, the difference for the largest city is negligible indicating that the models provide similar results. As such, OSM place feature data may be of value when predicting the spatial distribution of future violent crime in public and provide results similar to those when using register data, at least when employed in larger compared to smaller cities. Possibilities, limitations, and avenues for future research when using OSM data in place-based criminological research are discussed.

Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates

ABSTRACTThis paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease.

Constructing a novel double Z-type junction BaTiO3/ZnIn2S4/ZnTiO3 by a new chemical reaction for antibiotics degradation, H2 production, and CO2 reduction

At present, only single charge transfer channel is available in the study of BaTiO3/ZnIn2S4 heterojunction. In order to overcome this bottleneck, this paper adopts a novel chemical reaction to promote the transformation of ZnIn2S4 into ZnTiO3, thereby creating a novel triphase junction BaTiO3/ZnIn2S4/ZnTiO3 and a new transfer channel. This result leads to the photodegradation efficiencies of the levofloxacin and the lomefloxacin attaining 89 % and 92 % for the light irradiation of 30 minutes. Moreover, the removal efficiencies of the total organic carbon and the chemical oxygen demand reach 65 % and 84 % in the levofloxacin degradation, while these are 63 % and 81 % in the lomefloxacin degradation. Further, the H2 production rate attains 2.47 mmol/g/h, while the CO2 reduction rate to CO also reach 27.43 μmol/g/h. The accurate rate achieves 97.05 % for the prediction of the photocatalytic efficiency based on the Back Propagation neural network. The development toxicity indexes of the intermediate products reduce gradually to indicate the final solution is environmentally friendly. Therefore, the BaTiO3/ZnIn2S4/ZnTiO3 has a potential application in the sewage purification and the clean energy generation.

Fractional-Order Modeling and Stochastic Dynamics Analysis of a Nonlinear Rubbing Overhung Rotor System

To study the nonlinear dynamic behavior and system stability of a rubbing overhung rotor with viscoelastic and memory-effect damping and random uncertain parameters, this paper introduces a fractional-order modeling and stochastic dynamic analysis method for the nonlinear overhung rotor system with frictional impact faults. Firstly, the dynamic equations of the overhung rotor considering friction effect and fractional damping effect are established based on the transfer matrix method and fractional order derivative. Then, the time-domain response of the fractional-order dynamic equations is solved by combining the Runge–Kutta method and the continuous fractional expansion, and the steady-state response characteristics of different fractional damping are analyzed in the deterministic case. Finally, to analyze the response of the system under the effect of stochastic parameters, the sparse grid-based PCE metamodel of the system response is developed. Statistical moments, probability distributions, and sensitivity indices of the response of stochastic systems are revealed. The results of this paper provide a theoretical basis for efficient and accurate prediction of the stochastic response of nonlinear rubbing overhung rotor systems.