Reliability Prediction Method Research Articles

Feature selection and response prediction on a suspension bridge due to wind effect by machine learning

This research introduces an efficient machine learning approach for predicting the structural health of long-span suspension bridges, focusing on their responses to dynamic environmental load like earthquakes and wind. Unlike traditional analytical methods that rely on simplifications, this study utilizes real-world data from anemometers and accelerometers to enhance prediction accuracy and efficiency. Also instead of using whole signals, the study uses fewer time frame signal. The main contribution of this paper is the application of support vector regression (SVR), optimized through Observer-Teacher-Learner-Based-Optimization (OTLBO) for parameter tuning. The optimization process is further refined using Multi-Objective Observer-Teacher-Learner-Based-Optimization (MOOTLBO), targeting feature selection and dimension reduction to improve model performance significantly. Hardanger Bridge serves as a case study, demonstrating the model's capability to accurately predict acceleration responses to wind. By inputting wind sensor data and analyzing acceleration outputs, the enhanced SVR model, through OTLBO and MOOTLBO, shows remarkable predictive accuracy, validated against six performance indices. This methodological advancement suggests that the machine learning-based model could potentially supplant traditional finite element models, offering a more adaptable, efficient, and accurate tool for structural health monitoring (SHM). This advancement addresses critical discrepancies in SHM systems, such as data gaps or sensor malfunctions, by providing a reliable prediction method that ensures the ongoing safety and integrity of bridge structures.

A novel hybrid method for predicting maglev train-induced environmental vibrations combining field measurement, 2.5D FEM modelling and multi-objective optimization

The maglev system has been developed rapidly in recently years since its advantage in mass transportation, ride comfort and energy efficiency, which also caused an increasing concern on the maglev train-induced environmental vibrations. This paper presents a novel hybrid framework for predicting high-speed maglev train-induced environmental vibrations combining field measurement, 2.5D FEM simulation and multi-objective optimization. First, a maglev train-guideway-soil coupled model considering guideway irregularities was proposed to calculate the ground vibration using 2.5D FEM. Then, the guideway irregularity spectrum and soil damping are inverted by the optimization process using the NSGA3 genetic algorithm, in which the differences of measured and calculated vertical vibrations of measuring points are chosen as objective functions. With the inverted guideway irregularity spectrum and soil damping, the ground vibrations were calculated using the 2.5D FEM model and compared with the field measurements. It was found that the inverted PSD spectrum of guideway irregularity generally decreases as the spatial frequency increases, exhibiting a similar trend as the measured ones. The results obtained by the proposed hybrid prediction method agree well with the field measurement in both time domain and frequency domain. The prediction error for the vertical vibration level of all measuring points induced by five maglev train passing are mainly less than 10 %, indicating that the proposed hybrid method provided a reliable and effective method for vibration prediction combining field measurement and numerical simulation.

Nomogram for predicting diabetes insipidus following endoscopic transsphenoidal surgery in pituitary adenomas.

Postoperative diabetes insipidus (DI) frequently complicates endoscopic transsphenoidal surgery (TSS) in pituitary adenoma (PA) patients, yet reliable predictive methods for DI risk remain lacking. This study aims to identify risk factors associated with DI following endoscopic transsphenoidal resection of PA and to develop a predictive nomogram for assessing postoperative DI risk. This study involved 600 PA patients underwent endoscopic TSS at Shandong Provincial Hospital from 2021 to 2023. Among these patients, 82 developed postoperative DI while 518 did not. The cohort was randomly divided into training (n = 360) and validation (n = 240) groups at 6:4 ratios by R software. Clinical parameters and radiographic features were evaluated using univariable and multivariable logistic regression to construct a predictive nomogram for post-endoscopic TSS DI risk. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis. Subgroup analysis was used to evaluate the model's ability to discriminate between transient and permanent DI. Univariable and multivariable logistic regression analyses on the training group identified several independent risk factors for post-endoscopic TSS DI, including maximum tumor diameter, Knosp grade, Esposito grade, recurrent PA, and pituitary stalk deviation angle. A nomogram was developed based on these factors, demonstrating robust predictive accuracy with ROC areas under curve of 0.840 for the training group and 0.815 for the validation group. Calibration plots indicated excellent agreement between predicted and observed probabilities of postoperative DI. DCA curves highlighted the nomogram's efficacy in guiding clinical decision-making. Subgroup analysis showed that the model was able to discriminate between transient and permanent DI, and the AUC was 0.652 (95% CI 0.525-0.794). This study presents a nomogram designed to predict postoperative DI risk in patients undergoing endoscopic TSS for PA. Internal and external validations underscored the model's high accuracy, calibration, and clinical utility. Simultaneously, the model can also assess the development risk of permanent DI. This predictive tool offers clinicians valuable support in identifying high-risk DI patients, optimizing postoperative care strategies, and tailoring treatment plans to improve patient outcomes.

A Reliable Prediction Method to Forecast Pile Bearing Capacity Using Classic NB Base Hybrid Schemes

A Reliable Prediction Method to Forecast Pile Bearing Capacity Using Classic NB Base Hybrid Schemes

ECG-based epileptic seizure prediction: Challenges of current data-driven models.

Up to a third of patients with epilepsy fail to achieve satisfactory seizure control. A reliable method of predicting seizures would alleviate psychological and physical impact. Dysregulation in heart rate variability (HRV) has been found to precede epileptic seizures and may serve as an extracerebral predictive biomarker. This study aims to identify the preictal HRV dynamics and unveil the factors impeding the clinical application of ECG-based seizure prediction. Thirty-nine adult patients (eight women; median age: 38, [IQR = 31, 56.5]) with 252 seizures were included. Each patient had more than three recorded epileptic seizures, each at least 2 hours apart. For each seizure, one hour of ECG prior to seizure onset was analyzed and 97 HRV features were extracted from overlapping three-minute windows with 10s stride. Two separate patient-specific experiments were performed using a support vector machine (SVM). Firstly, the separability of training data was examined in a non-causal trial. Secondly, the prediction was attempted in pseudo-prospective conditions. Finally, visualized HRV data, clinical metadata, and results were correlated. The mean receiver operating characteristic (ROC) area under the curve (AUC) for the non-causal experiment was 0.823 (±0.12), with 208 (82.5%) seizures achieving an improvement over chance (IoC) classification score (p < 0.05, Hanley & McNeil test). In pseudo-prospective classification, the ROC-AUC was 0.569 (±0.17), and 86 (49.4%) seizures were classified with IoC. Off-sample optimized SVMs failed to improve performance. Major limiting factors identified include non-stationarity, variable preictal duration and dynamics. The latter is expressed as both inter-seizure onset zone (SOZ) and intra-SOZ variability. The pseudo-prospective preictal classification achieving IoC in approximately half of tested seizures suggests the presence of genuine preictal HRV dynamics, but the overall performance does not warrant clinical application at present. The limiting factors identified are often overlooked in non-causal study designs. While current deterministic prediction methods prove inadequate, probabilistic approaches may offer a promising alternative. Many patients with epilepsy suffer from uncontrollable seizures and would greatly benefit from a reliable seizure prediction method. Currently, no such system is available to meet this need. Previous studies suggest that changes in the electrocardiogram (ECG) precede seizures by several minutes. In our work, we evaluated whether variations in heart rate could be used to predict epileptic seizures. Our findings indicate that we are still far from achieving results suitable for clinical application and highlight several limiting factors of present seizure prediction approaches.

A novel operational reliability and state prediction method based on degradation hidden Markov model with random threshold

AbstractBearings are widely used as a common mechanical component and significantly impacts the reliability and safety of various engineering equipment. In the field of large‐scale equipment such as wind turbines, statistical data for reliability and state prediction of bearings is usually limited, leading to the inapplicability of traditional statistical methods. Besides, individual differences commonly exist among bearings, and the appropriate failure threshold for a specific bearing is difficult to be determined due to the individual uncertainty, which may induce prediction errors. To address these issues, a novel operational reliability and state prediction method based on random threshold degradation hidden Markov model was proposed in the study. The traditional hidden Markov model was improved by considering the effect of performance degradation on state transition probability matrix. Moreover, random failure thresholds of performance degradation were employed to describe individual differences between bearings. The operational reliability and state of bearings was obtained by comprehensive analysis of prediction results under different failure thresholds. Verification of the proposed operational reliability and state prediction method were conducted using PHM2012 bearing operation data. The results underscored the effectiveness of the proposed method in achieving a comparatively high accuracy in operational reliability prediction without the prerequisite of an exact failure threshold, when contrasted with several established reliability and life prediction techniques.

Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study

BackgroundPredicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain radiographs and patient data and to determine its accuracy.MethodsThis retrospective study utilized anteroposterior pelvic radiographs of consecutive patients with hip OA who underwent primary unilateral total hip arthroplasty. Radiographs diagnosed with Kellgren–Lawrence (KL) grade 0–2 were extracted from 361 patients and 1697 images. This AI model was developed to predict whether OA would progress from KL grade 0–2 to KL grade ≥ 3 within n years (n = 3, 4, 5). A gradient-boosting decision tree approach was utilized according to feature extractions obtained by a convolutional neural network from radiographs and patient data (height, body weight, sex, age, and KL grade given by an orthopedic surgeon) with five-fold cross-validation. The model performance was assessed using accuracy, specificity, sensitivity, and the area under the receiver operating characteristic curve (AUC).ResultsThe mean accuracy, specificity, sensitivity, and AUC of our prediction model were, respectively, 81.8%, 88.0%, 66.7%, and 0.836 for 3 years; 79.8%, 85.0%, 71.6%, and 0.836 for 4 years; and 78.5%, 80.4%, 76.9%, and 0.846 for 5 years.ConclusionsThe proposed AI model performed adequately in predicting hip OA progression and may be clinically applicable with additional datasets and validation.

Multi-relation spatiotemporal graph residual network model with multi-level feature attention: A novel approach for landslide displacement prediction

Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters. While most existing prediction methods focus on time-series forecasting for individual monitoring points, there is limited research on the spatiotemporal characteristics of landslide deformation. This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention (MFA-MRSTGRN) that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion. This model integrates internal seepage factors as data feature enhancements with external triggering factors, allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset. The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules: multi-level feature attention, temporal-residual decomposition, spatial multi-relational graph convolution, and spatiotemporal fusion prediction. This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets, facilitating adaptive exploration of the evolving multi-relational, multi-dimensional spatiotemporal complexities in landslides. When applying this model to predict the displacement of the Liangshuijing landslide, we demonstrate that the MFA-MRSTGRN model surpasses traditional models, such as random forest (RF), long short-term memory (LSTM), and spatial temporal graph convolutional networks (ST-GCN) models in terms of various evaluation metrics including mean absolute error (MAE = 1.27 mm), root mean square error (RMSE = 1.49 mm), mean absolute percentage error (MAPE = 0.026), and R-squared (R2 = 0.88). Furthermore, feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models. This research provides an advanced and reliable method for landslide displacement prediction.

Integrating active demand into the distribution system using metaheuristic techniques

AbstractIntegrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.

Prediction of Power Equipment Emergency Repair Based on Adaptive Neural Network Fuzzy Inference Method

If the emergency repair prediction of power equipment is only made from the perspective of historical spare parts inventory data, it cannot reflect the impact of disaster-causing elements and disaster evolution on the demand for emergency repair spare parts in the future. Therefore, this paper aims to propose a reliable power equipment emergency repair prediction method, and constructs a demand prediction method for emergency repair spare parts of power equipment based on scenario analysis. Constructing a power equipment repair system through intelligent reasoning methods to improve the efficiency of power equipment repair. This article comprehensively uses methods such as literature analysis, model inference, and case simulation verification, this paper innovatively combines the adaptive neural network fuzzy inference system with expert experience. This paper validates the superiority of the prediction method constructed in this paper through comparative analysis. The results show that with the increase of the amount of data, the prediction accuracy of the method proposed in this paper will be improved, which can provide a reference for the subsequent emergency repair prediction of power equipment.

Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing energy management and extending the lifespan of batteries across various industries. However, the raw capacity data of these batteries is often noisy and exhibits complex nonlinear degradation patterns, especially due to capacity regeneration phenomena during operation, making precise RUL prediction a significant challenge. Although various deep learning-based methods have been proposed, their performance relies heavily on the availability of large datasets, and satisfactory prediction accuracy is often achievable only with extensive training samples. To overcome this limitation, we propose a novel method that integrates sequence decomposition algorithms with an optimized neural network. Specifically, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw capacity data, effectively mitigating the noise from capacity regeneration. Subsequently, Particle Swarm Optimization (PSO) is used to fine-tune the hyperparameters of the Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based prediction model was extensively tested on eight lithium-ion battery datasets from NASA and CALCE, demonstrating robust generalization capability, even with limited data. The experimental results indicate that the CEEMDAN-PSO-BiGRU model can reliably and accurately predict the RUL and capacity of lithium-ion batteries, providing a promising and reliable method for RUL prediction in practical applications.

Investigation of interfacial bond strength of circular CFST columns based on ordinary and inorganic polymers through advanced machine learning methods

Concrete-filled steel tube (CFST) is a combination of materials, in which the interfacial bond-slip behavior is the prerequisite to reflect the synergistic loading of external steel tube and core concrete. The inorganic polymer concrete within the concrete-filled steel tube (IPCFST) compensates for the drying shrinkage of the concrete and enhances the adhesion at the interface between the steel tube and the concrete. However, the current code is too simple for the bond strength of CFSTs, and the existing theoretical calculation methods are not universally applicable. This paper aims to develop a practical artificial neural network tool for predicting the interfacial bond strength of ordinary CFSTs and IPCFSTs. An efficient prediction model for the bond strength of CFST interfaces was developed using a radial basis function network (RBFNN), with concrete strength, steel tube size, bond length and other factors as the main parameters. Bond performance test data was collected for a sample database of 322 circular CFST columns, including 56 ordinary CFSTs and 263 IPCFSTs. The results show that the Artificial Neural Network (ANN) algorithm can effectively address the bond strength problem of CFST structures and improve the accuracy and density of prediction in the comparative analyses with the measured values and the results of the semi-theoretical formulations. Based on the prediction results of ANN model, a sensitivity analysis of the parameters using Garson's algorithm shows that the diameter-to-thickness ratio and concrete strength significantly affect bond strength for both ordinary CFSTs and IPCFSTs. This study provides a more reliable prediction method and technical support for the engineering design of CFST structures.

A generative adversarial learning strategy for spatial inspection of compaction quality

Reliable prediction methods play a crucial role in enhancing the compaction quality and implementing the intelligent compaction (IC). The predictive performance of contemporary data-driven models is significantly influenced by the quantity and quality of training data. The scarcity and high dimensionality of field compaction data may lead to overfitting issues in prediction models. In this study, an innovative methodology, spatial trajectory condition generative adversarial networks (STC-GAN), is proposed to synthesize IC datasets by considering the construction procedures. The dataset expansion is driven both by the non-attentional algorithm and the engineering practice. The augmented dataset is used to train the extreme gradient boosting (XGBoost) for estimating the compaction quality. The model accuracy is consistently enhanced by adopting the STC-GAN augmented datasets. The optimal model is achieved after a 2.5-fold expansion by performing the sensitivity study. The proposed STC-GAN-PSO-XGBoost is a physical-based intelligent model, enhancing the reliability of data augmentation and the compaction evaluation model. This method is beneficial to minimize the amount of field tests, enabling the continuous evaluation of compaction quality for IC.

Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature

Water quality assessment and prediction play crucial roles in ensuring the sustainability and safety of freshwater resources. This study aims to enhance water quality assessment and prediction by integrating advanced machine learning models with XAI techniques. Traditional methods, such as the water quality index, often require extensive data collection and laboratory analysis, making them resource-intensive. The weighted arithmetic water quality index is employed alongside machine learning models, specifically RF, LightGBM, and XGBoost, to predict water quality. The models' performance was evaluated using metrics such as MAE, RMSE, R2, and R. The results demonstrated high predictive accuracy, with XGBoost showing the best performance (R2 = 0.992, R = 0.996, MAE = 0.825, and RMSE = 1.381). Additionally, SHAP were used to interpret the model's predictions, revealing that COD and BOD are the most influential factors in determining water quality, while electrical conductivity, chloride, and nitrate had minimal impact. High dissolved oxygen levels were associated with lower water quality index, indicative of excellent water quality, while pH consistently influenced predictions. The findings suggest that the proposed approach offers a reliable and interpretable method for water quality prediction, which can significantly benefit water specialists and decision-makers.

SCARF: A new algorithm for continuous prediction of biomass dynamics using machine learning and Landsat time series

We developed the SCARF (Spatial Mismatch and Systematic Prediction Error Corrected cAscade Random Forests) algorithm for continuous prediction of biomass dynamics using machine learning and Landsat Time Series (LTS). Our approach addresses the challenges posed by the cloudy subtropical forests in southern China, where monitoring biomass dynamics is notoriously difficult. To derive spectral-temporal features from the LTS, we applied the Continuous Change Detection and Classification (CCDC) algorithm (Zhu and Woodcock, 2014). Subsequently, we employed the cascade random forests machine learning algorithm for biomass prediction. This new approach corrects the spatial mismatch effects between plots and Landsat pixels as well as the systematic prediction errors in the machine learning model. As a result, it substantially enhances biomass prediction accuracy, with a coefficient of determination (R2) of 0.83 and a root mean square error (RMSE) of 6.27 Mg ha-1. In comparison, the commonly used random forests approach yields an R2 of 0.47 and RMSE of 8.52 Mg ha-1. Additionally, it provides reliable spatial prediction beyond the model-training area, achieving an R2 of 0.79 and an RMSE of 6.62 Mg ha-1. Furthermore, we demonstrate that modeling five different forest age groups separately further improves prediction accuracies, resulting in an increased R2 of 0.87 and a reduced RMSE of 3.65 Mg ha-1. A comparison of the allometric model prediction from the field plots and those from the SCARF model revealed a strong agreement, indicating that this approach can provide a temporally continuous prediction of biomass dynamics. Our study presents a robust method for continuous, reliable, and explicit spatiotemporal prediction of biomass dynamics in cloudy subtropical forests using LTS.

Experimental study on low-cycle fatigue performance of high-strength bolted shear connections

In strong earthquakes, low-cycle fatigue fractures may occur in the bolted connections of steel braced frames. Ensuring a high low-cycle fatigue life for high-strength bolted connections is of paramount significance in enhancing the overall safety and collapse resistance of steel structures. Despite substantial research on plate fatigue failure within bolted connections, there remains a research gap regarding low-cycle fatigue failure of high-strength bolts in these connections. This study aims to address the aforementioned shortcomings by conducting 34 sets of low-cycle fatigue tests on high-strength bolted connections. The test specimens all experienced low-cycle shear fatigue fracture at the threaded or unthreaded portion of the bolt shank as the dominant failure mode. Residual deformations were observed in the holes of connection plates. Based on the test data, ultimate shear capacity of a single shear connection is around 0.61Fy (tensile yield bearing capacity of bolt) when the shear force passes through the unthreaded portion and drops to 0.54Fy for the shear force at the thread. The untreated Q355 (minimum yield strength of 355 MPa) steel surface has an average anti-slip coefficient of around 0.37, while milling reduces it to approximately 0.30. The influence patterns of loading amplitude, bolt material, minimum plate thickness, bolt diameter, shear force position and connection type on low-cycle fatigue life are given by range analysis. Among them, variance analysis shows that loading amplitude and bolt material are the primary influencing factors. The correlation coefficient between loading amplitude and low-cycle fatigue life is −0.92, indicating a strong negative correlation. The degradation amount and rate of anti-slip capacity and shear capacity of bolted connections are also investigated. To propose reliable life prediction method, three models were used to establish the relationship between dimensionless shear deformation and low-cycle fatigue life of 10.9-grade high-strength bolted connections. It is not appropriate to use the fatigue categories defined in current standards to predict the low-cycle shear fatigue life of high-strength bolted connections.

Determination of Propeller-Rudder-Hull Interaction Coefficients in Ship Manoeuvring Prediction

Abstract The assessment of ship manoeuvring properties is a crucial part of the process of ship design and is usually first carried out during the model test phase of the project. According to the International Maritime Organisation (IMO), the manoeuvrability of the ship can be assessed on the basis of the standard trial manoeuvres. In order to do this, free running model tests or captive model tests are used, in conjunction with a mathematical model of ship motion; this is considered to be a reliable prediction method. In recent years, numerical-based methods have also been widely used in ship hydrodynamics and constantly improving computing power and more accurate fluid dynamics models have made the simulation of more complex cases possible. The study presented in this paper focuses on the determination of propeller-rudder-hull interaction coefficients based on the Mathematical Modelling Group (MMG) standard method in ship manoeuvring prediction. The identification of the parameters uses both captive model tests and a simplified numerical method, as well as regression formulas. The results of 35° turning and 10°/10° zig-zag manoeuvres, obtained with the use of each prediction method, are then compared. The test case used in the study is the container type cargo ship equipped with a single propeller and rudder. The model scale, for which the referenced model tests were carried out, is equal to 1:25 and a NACA 0020 rudder profile was used. This research highlights the advantages and disadvantages of each presented prediction method and their potential for future improvement.

Simulation of Electrical Conductivity of Porous Composite Electrodes for Solid Oxide Cells Using a Monte Carlo 3D Equivalent Electronic Circuit Networks

Solid oxide cells (SOCs) show high potential in energy conversion applications necessary for the decarbonization of the economy. Their advantage consists in ability to operate at high temperatures, reaching up to 900 °C. Firstly, it results in accelerated electrode reactions kinetics, secondly, in favorable thermodynamic conditions decreasing the equilibrium voltage of the water splitting. As a result, SOCs are attractive candidates for both efficient electrolysis and fuel cell applications, offering a robust solution in the pursuit of clean energy. However, the elevated operational temperature also introduces material challenges that hinder the competitiveness of SOCs.Porous gas diffusion electrodes are used to accomplish the desired electrode reactions. Considering conventional exclusively electron-conductive electrode materials, triple phase boundary (TPB) is required for the reactions to take place – a simultaneous contact of electron-conductive, ion-conductive and gaseous phase. While the electrode and electrolyte phases are solid, the gaseous phase is represented by open porosity in the electrode body. TPB is located exclusively at the contact area of electrode and electrolyte components in the case of single-phase electrodes, limiting the electrode electrocatalytic performance. To avoid the limitation by TPB length, composite electrodes are frequently used. Due to the involvement of the ionically conductive phase, they exhibit improved electrocatalytic capabilities.Electrolyte phase, however, usually exhibits 4 orders of magnitude lower electrical conductivity than the electron-conductive phase, while gaseous phase is electrically non-conductive. Thus, an improper phase composition and morphology of the composites can lead to a significant drop in their electrical conductivity and thus in the cell performance. Simple and reliable method for the conductivity value prediction is up to now missing. At the same time, experimental determination is both expensive and time-consuming. The goal of this study was to develop simple, fast and reliable method for prediction of electrical conductivity of the porous electron-ion conductor composites.The key feature of the presented approach lies in the minimal requirements for input parameters. Only electrode phase composition and bulk electrical conductivity of each phase are required. Based on the input composition, the model generates a 3D artificial specimen, a domain discretized into cubic voxels each assigned randomly to a specific phase. The resulting structure is substituted with an equivalent circuit network with electric elements corresponding to the electrochemical properties of the material components: specific resistivity of electrical conductors and polarization resistance / capacitance of electrochemical reaction at material interface. In-silica electrochemical impedance spectroscopy measurement is performed by solving charge balance following Kirchhoff’s current law for multiple frequency values. Resulting impedance spectrum is fitted in order to obtain Ohmic and polarization resistance and the capacitance of the artificial specimen. This procedure is averaged over a large number of artificial specimens in a Monte Carlo manner.Conventionally used oxygen electrode composite material lanthanum strontium manganite (LSM) mixed with yttria-stabilized zirconia (YSZ) was used to produce validation dataset. LSM:YSZ powder mixtures of compositions between 1:0 and 0:1, chosen to produce samples of various degree of LSM percolation, were homogenized. The mixtures were fired at 1150 °C. Electrical conductivity of the pellets was determined at temperatures between 600 and 800 °C using electrochemical impedance spectroscopy.Experimental data obtained were confronted to the model results. The model demonstrated very good accuracy for a porosity value of up to 55%. Significant error was observed in the porosity range between 55% and 68%. Finally, the model failed to generate an artificial specimen with a porosity of 75%. As it was found, the limited applicability of the model for the materials characteristic for high porosity was caused by the coalescence of the void phase. This shortcoming of the model was solved by implementing morphological parameter describing degree of void phase coalescence in the electrode structure. Due to this modification, model allows to gain valuable information on the microstructure of the studied composite material on the base of the experimentally determined conductivity data.This work introduces a novel modelling approach with minimal amount of input parameters, streamlining the prediction of electrical conductivity of porous electron-ion conductor composites. This simplified yet effective methodology holds great promise for efficiently characterizing and optimizing materials for energy conversion applications, offering a valuable tool for advancing research in the field.This publication was supported by the project "The Energy Conversion and Storage", funded as project No. CZ.02.01.01/00/22_008/0004617 by Programme Johannes Amos Commenius, call Excellent Research.

Long Non-Coding H19 in Lymphocytes: Prognostic Value in Acute Ischemic Stroke Patients.

Acute ischemic stroke (AIS) is a cerebrovascular disease that seriously affects the physical and mental health and quality of life of patients. However, there is a lack of reliable prognostic prediction methods. The main objective of this study was to investigate the prognostic value of long non-coding RNA (lncRNA) H19 in lymphocytes of patients with AIS, and to construct a prognostic prediction model for AIS including lncRNA H19 in lymphocytes, which would provide new ideas for the prognostic evaluation of AIS. Poor prognosis was defined when the patient's modified Rankin scale (mRS) score at 3 months after AIS onset was greater than 2. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the level of lncRNA H19 in lymphocytes. Spearman correlation analysis revealed a positive correlation between lncRNA H19 and mRS score at 3 months after AIS onset (r = 0.1977, p = 0.0032), while lncRNA H19 was negatively correlated with white blood cells counts, lymphocytes counts, and neutrophils counts. Logistic regression analysis identified lncRNA H19 as an independent predictor of poor prognosis (OR = 3.062 [1.69-5.548], p < 0.001). Moreover, a nomogram prediction model incorporating lncRNA H19 in lymphocytes demonstrated effective discrimination, calibration, and clinical applicability in predicting AIS outcomes. The findings suggest that lncRNA H19 in lymphocytes could be a valuable prognostic indicator and a potential pharmacological target for AIS patients, and might be a novel pathway for enhanced prognostic evaluation and targeted therapeutic strategies.

Prediction of Flash Point of Materials Using Bayesian Kernel Machine Regression Based on Gaussian Processes With LASSO‐Like Spike‐and‐Slab Hyperprior

ABSTRACTThe determination of flash points is a critical aspect of chemical safety, essential for assessing explosion hazards and fire risks associated with flammable solutions. With the advent of new chemical blends and the increasing complexity of chemical waste management, the need for accurate and reliable flash point prediction methods has become more pronounced. This study introduces a novel predictive approach using Bayesian kernel machine regression (BKMR) with Gaussian process priors, designed to meet the growing demand for precise flash point estimation in the context of chemical safety. The BKMR model, underpinned by Bayesian statistics, offers a comprehensive framework that not only quantifies prediction uncertainty but also enhances interpretability amidst experimental data variability. Our comparative analysis reveals that BKMR surpasses traditional predictive models, including support vector machines, kernel ridge regression, and Gaussian process regression, in terms of accuracy and reliability across multiple metrics. By elucidating the intricate interactions between molecular features and flash point properties, the BKMR model provides profound insights into the chemical dynamics that influence flash point determinations. This study signifies a methodological leap in flash point prediction, offering a valuable tool for chemical safety analysis and contributing to the development of safer chemical handling and storage practices.