Search Results (17,915)

The primary focus of enhancing the efficiency of operations in the Industry 4.0 setting is Predictive and Preventive Maintenance (PPM). The paper introduces a predictive-maintenance system based on the Unified Namespace (UNS), which involves real-time sensor measurements, photogrammetry, and modelling of a digital twin to improve fault prediction and responsiveness to maintenance. This experiment was conducted over six months in a medium-sized discrete electromechanical production plant equipped with motors, Variable Speed Drives (VSDs), robot/cobots, precision grip systems, pipework systems, Magnemotion/linear motor drives, and a CNC machine. The continuous data, such as high-frequency vibration, temperature, current, and pressure, were monitored and analysed with machine-learning models, including support-vector machines, Gradient Boosting, long-short-term memory, and Random Forest, through which temporal degradation can be predicted. UNS architecture integrated all sensor and imaging data into a vendor-neutral data model through OPC UA to help ensure that all experiments could be integrated consistently and be updated in real time to real digital twins. The suggested system correctly identified mechanical and electrical failures and predicted failures before they really took place. Consequently, machine downtime was reduced by 42.25%, and Mean Time to Repair (MTTR) by 36%, compared to the prior six-month baseline period. These improvements were associated with earlier anomaly detection and digital-twin-supported pre-inspection. Overall, the findings indicate that the integration of UNS with multi-modal sensing and digital-twin technologies may enhance predictive maintenance performance in comparable industrial settings. The framework provides a data-driven, scalable solution to organisations that aim to modernise their maintenance processes, attain greater reliability and better equipment utilisation, as well as enhanced Industry 4.0 preparedness. Full article

This study aimed to investigate the emotional changes in the brain activity of 34 special education teachers using electroencephalography (EEG) signals in response to common problem behaviours observed in students with Autism Spectrum Disorder (ASD), such as self-harm, aggression, tantrums, and stereotyped behaviours. Vignettes with Turkish narration and stimulus videos were used for each behaviour type to trigger emotions. EEG data were collected from the frontal, temporal, parietal, and occipital regions, and subjected to pre-processing steps such as band-pass filtering (0.5–40 Hz) and Independent Component Analysis (ICA), and various spectral and statistical features were extracted. To improve classification performance, feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method, and Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), and Random Forest (RF) algorithms were used for classification. The machine learning techniques used achieved success rates of up to 97.66% F1 score in classifying teachers’ brain activity in response to different behavioural patterns. Teachers showed strong negative emotional responses to self-harm, aggression, and tantrums, while showing less response to the stereotypical behaviours. It is recommended that the study be replicated with different signals and teachers. Full article

Background: Periprosthetic joint infection (PJI) after hip arthroplasty for proximal femur fracture is a severe complication, and early postoperative identification remains challenging. This study developed and validated machine learning (ML) models for the early prediction of 90-day EBJIS 2021 “confirmed” PJI using routinely available perioperative data. Methods: We performed a single-center retrospective study including 1182 consecutive adults undergoing primary hip arthroplasty for proximal femur fracture (2015–2022). Forty-seven perioperative candidate predictors were extracted, including early postoperative laboratory values (postoperative day 1–2 and maxima within 72 h). Six algorithms were trained and compared (logistic regression, random forest, support vector machine, multilayer perceptron, XGBoost, and stacking ensemble) using a stratified 80/20 training–test split with 10-fold cross-validation, grid-search hyperparameter tuning, and class weighting. A sensitivity-prioritizing classification threshold was derived using training data only and applied unchanged to evaluation cohorts. Uncertainty was estimated via 1000 bootstrap iterations. Calibration was assessed using the Brier score and calibration intercept/slope. Temporal validation was conducted in a same-center 2023 cohort (n = 147). Model explainability used SHAP. Results: EBJIS-confirmed 90-day PJI occurred in 58/1182 (4.9%) patients. In held-out testing, the final XGBoost model demonstrated good discrimination (AUC 0.889, 95% CI 0.804–0.960) with good overall calibration (Brier score 0.043). Using a prespecified sensitivity-prioritizing threshold selected in the training set, test-set sensitivity was 100%, specificity 58.5%, PPV 11.4%, and NPV 100%. The stacking ensemble yielded the highest discrimination (AUC 0.937; 95% CI 0.89–0.98). In temporal validation (same-center 2023 cohort; n = 147), model performance remained stable (AUC 0.892; sensitivity 85.7%; NPV 99.1% at the prespecified threshold). Calibration was favorable in the development cohort (Brier 0.041; intercept −0.04; slope 0.96) and in 2023 (Brier 0.038; intercept −0.06; slope 0.94). SHAP identified postoperative C-reactive protein, operative duration, body mass index, ASA class, and serum sodium as the most influential predictors. Conclusions: ML models, particularly XGBoost, supported early postoperative risk stratification for 90-day EBJIS-confirmed PJI after fracture-related hip arthroplasty, with a consistently high NPV and stable calibration in a temporally independent same-center cohort. Prospective multi-center validation and impact evaluation are needed before clinical implementation. Full article

An assessment and prediction system for the quality of public water networks was developed, using Timișoara, Romania, as a case study. This was implemented on a Google Firebase cloud storage system and comprised twelve ML algorithms applied to test samples for drinkability and used in predictions of upcoming samples. The system compares 17 water quality parameters to the World Health Organization and public reports of Timișoara drinking water standards for 804 samples. The system provides real-time data storage, drinkability prediction for the reservoir water system, and rule-based critical water recommendations for elementary treatment in samples. The most accurate and best-calibrated against random forest, gradient boosting, and Logistic Regression algorithms was the decision tree algorithm of the ML models. The experimental findings also determine the regions of the worst and best water quality and propose respective treatment. In contrast to previous research and structures, the paper demonstrates an approved stable solution for smart water monitoring, correlating practical deployment with sophisticated data-based conclusions. The results contribute to enhancing public health, enhancing water management measures, and upscaling the system for larger-scale applications. Full article

This study provides a comprehensive empirical examination of the factors influencing the adoption of smart technologies in the Serbian hotel industry by integrating structural equation modeling (SEM), mediation and multigroup analyses, and machine-learning-based robustness testing. Grounded in the UTAUT framework, the research investigates how perceptual, organizational, and social determinants shape employees’ Behavioural Intention (BI) and actual Use Behaviour (USE). A key theoretical contribution is the introduction of the construct Perceived Spatial Impact of Technology (PST), which captures employees’ perceptions of how smart technologies transform the architectural concept, spatial organization, aesthetics, and functional logic of hotels. Although UTAUT traditionally focuses on users, neither prior studies nor the present one examine these dynamics from the perspective of architects or designers who create hotel spaces. Thus, the findings serve as an initial step from the user viewpoint, while future research should incorporate expert architectural reasoning to better understand how spatial knowledge and design logic intersect with user perceptions. All core UTAUT constructs significantly predict BI and USE, with Performance Expectancy and BI emerging as the strongest predictors across SEM and Random Forest models. PST exerts a fully mediated effect on USE through BI, and multigroup analysis reveals notable differences across job roles, hotel categories, and age groups. Overall, the results highlight that digital transformation in hospitality is not only technological and organizational, but also fundamentally architectural. Full article

Lonicera caerulea L. (blue honeysuckle) is rich in antioxidant polyphenols, and rapid and accurate determination of its polyphenol content is of great significance for functional food quality control. This study proposed a hybrid variable selection strategy designed for high-dimensional small-sample scenarios and developed a quantitative prediction model for polyphenol content based on mid-infrared (MIR) spectroscopy. A total of 191 Lonicera caerulea samples were collected from Northeast China, and 7468-dimensional spectral data were acquired using a Fourier transform infrared spectrometer. Polyphenol reference values were determined by the Folin–Ciocalteu method. Samples were divided into calibration (n = 152) and prediction (n = 39) sets using the SPXY algorithm. Among the 10 preprocessing methods evaluated, MSC combined with Savitzky–Golay first derivative achieved the best performance and was therefore used for subsequent modeling. The proposed hybrid variable selection method (VIP1.0∩RFR30%) intersected PLS variable importance in projection (VIP ≥ 1.0) with the top 30% important variables from random forest regression, selecting 984 key wavelengths and achieving 86.8% dimensionality reduction. A three-stage hyperparameter tuning strategy was implemented across four models (PLS, RFR, SVR, and XGBoost) to validate feature stability and control overfitting. The optimized XGBoost model achieved excellent performance on the independent test set ($R^2$ = 0.92, RMSE = 0.098, RPD = 3.47). Compared with the classical CARS method ($R^2$ = 0.78, RPD = 2.14), $R^2$ improved by 16.3% and RPD improved by 55.2%. The results demonstrate that the proposed hybrid variable selection strategy can effectively address the challenges of high-dimensional MIR spectral data in small-sample modeling, providing a reliable tool for rapid and non-destructive quantitative analysis of polyphenols in Lonicera caerulea. Full article

Background: Invasive non-functional pituitary adenomas (NFPAs) are associated with high recurrence and unfavorable clinical outcomes, yet their underlying molecular mechanisms remain incompletely understood. This study aimed to identify robust biomarkers of invasiveness by integrating transcriptional networks, machine learning, and epigenetic regulation. Methods: RNA sequencing was performed on 32 NFPA samples (15 invasive, 17 non-invasive). Weighted gene co-expression network analysis (WGCNA) was used to identify invasiveness-associated modules, which were validated in public datasets (GSE169498, GSE51618). Candidate genes were prioritized using machine learning, and their epigenetic regulation was studied using DNA methylation datasets (GSE207937, GSE115783). Results: We identified a five-gene signature associated with invasiveness (KIFC3, PNMA3, ARHGAP18, LRRC10B, and KCNC4). All five genes were consistently downregulated in invasive NFPAs (all p < 0.01) and were enriched in oxidative phosphorylation and neuroactive ligand–receptor interaction pathways. A machine learning validation approach (Random Forest followed by forward stepwise logistic regression) showed strong discriminative performance for this signature (mean AUC = 0.919). DNA methylation analyses indicated no robust differences at the genome-wide level or across promoter regions of the core genes; nevertheless, several locus-specific CpG sites (e.g., near KIFC3) showed suggestive methylation changes. Conclusions: Using an integrative multi-omics framework, we identified a novel five-gene signature associated with NFPA invasiveness. The coordinated downregulation of these genes may reflect alterations in cellular energy metabolism and microenvironmental signaling. Although the signature demonstrated promising diagnostic potential, its transcriptional repression is unlikely to be primarily explained by DNA methylation. These findings provide candidate markers and mechanistic hypotheses for understanding invasive NFPA and developing risk-stratification tools. Full article

Coaches in professional football need to estimate how many minutes a player can tolerate in a match before relevant fatigue occurs. This study aimed to develop a framework to translate monitoring information into individualised, minute-based fatigue thresholds. Over four seasons in an elite club, external load (total distance, high-speed running, mechanical work) and heart rate were collected in training. Machine-learning-derived fitness and fatigue indices were computed and combined with 7- and 28-day load variables in a Random Forest regression model predicting match minutes. The trained model was then used to simulate four fatigue conditions by fixing the match-day fatigue index (z-FA_match = 0, −1, −2, −3). In an independent test season, the model showed a mean absolute error of 22.5 min and R² = 0.17 for playing time prediction, with z-FA_match as the most influential predictor. Simulated fatigue thresholds occurred in an ordered way (0 = 57.1, −1 = 64.9, −2 = 84.8, −3 = 84.4) and differed across season period, playing position, overall seasonal minutes, and return-to-play status. Integrating external load with fitness and fatigue indices via machine learning can provide individualised estimates of when players are likely to reach fatigue states, supporting decisions on selection, substitutions, and return-to-play management. Full article

Mangrove ecosystems are important blue carbon systems and play a critical role in understanding carbon cycling and responses to climate change. However, accurate regional estimation of Net Ecosystem Exchange (NEE) remains challenging due to the environmental complexity and spatial heterogeneity. This study combined eddy covariance observations from four mangrove sites along China’s southeastern coast (natural and restored mangrove forests) with multi-source remote sensing and environmental reanalysis data to construct three variable schemes (site observations only, with added vegetation indices, and comprehensive multi-source variables). We compared three machine learning models for daily NEE prediction, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM). The results showed that: (1) Restored and natural mangroves exhibited similar temporal NEE dynamics and consistently functioned as carbon sinks, restored mangrove sites showed greater cross-site variability. Among the study sites, CN-LZR exhibited the strongest cumulative carbon uptake. (2) Scheme 3 combined with the XGBoost algorithm achieved the highest predictive accuracy, reaching an R² of 0.73 across sites. Differences among machine learning models were primarily associated with their ability to capture nonlinear interactions between atmospheric and hydrological variables, with tree-based models outperforming SVM. (3) SHAP analysis indicated that radiation-related variables were the dominant drivers of NEE, while hydrological influences were site-dependent; and (4) Regional upscaling indicated that all sites consistently functioned as long-term carbon sinks, with CN-LZR exhibiting slightly higher daily mean carbon uptake than the other sites. This study presented the first machine learning framework for estimating daily-scale NEE in mangroves, providing methodological and data support for regional carbon flux assessment and blue carbon management. Full article

Strip intercropping of maize and soybean is a key practice for improving land productivity and ensuring food and oil security in the hilly regions of the Loess Plateau. However, complex interspecific interactions generate highly heterogeneous canopy structures, making it difficult for traditional linear models to capture yield variability within mixed pixels. Based on a single-season (2025) field experiment, this study developed a UAV multispectral imagery-based yield estimation framework integrating multiple machine-learning algorithms. Shapley additive explanations (SHAP) and partial dependence plots (PDP) were used to interpret the spectral–yield relationships under different spatial configurations. The predictive performance of linear regression and eight nonlinear algorithms was compared using 20 spectral features. Ensemble learning outperformed linear approaches in all intercropping scenarios. In the maize–soybean 3:2 pattern, the GBDT model delivered the highest accuracy (R² = 0.849; NRMSE = 9.28%), whereas in the 4:2 pattern with stronger shading stress on soybean, the random forest model showed the greatest robustness (R² = 0.724). Interpretation results indicated that yield in monoculture systems was mainly driven by physiological traits characterized by visible-band indices, while yield in intercropping systems was dominated by structural and stress-response traits represented by near-infrared and soil-adjusted vegetation indices. The generated centimeter-scale yield maps revealed clear strip-like spatial variability driven by interspecific competition. Overall, explainable machine learning combined with UAV multispectral data shows promise for within-season yield estimation in intercropping systems and can support spatially differentiated precision management under the sampled conditions. Full article

To comprehensively assess the emissions of flue gas pollutants from municipal solid waste incineration (MSWI) in China and their socioeconomic driving factors, this study employs a bottom-up approach to develop an integrated carbon and air pollutant emission inventory for 1016 MSWI plants in 2024. We apply a Random Forest (RF) model to analyze the underlying drivers. Results indicate that for air pollutants, NO_x has the highest emissions, whereas mercury (Hg) and dioxins (polychlorinated dibenzo-p-dioxins and dibenzofurans, PCDD/Fs) are identified as priority control pollutants due to their high toxicity. Spatially, emissions display a distinct “high in the east, low in the west” pattern, concentrated in eastern coastal provinces, with characteristic pollutants being prominent in specific regions. Meanwhile, among greenhouse gases (GHGs), CO₂ dominates mass emissions, while N₂O exhibits significant global warming potential. Driver analysis reveals that Gross Domestic Product (GDP) and MSWI treatment capacity are key common drivers, showing stable positive and negative contributions, respectively. The number of invention patent applications is specifically and strongly associated with NO_x and heavy metal emissions. This study provides a national-scale integrated quantification of MSWI emissions and a quantitative analysis of their driving mechanisms using RF, offering a critical data foundation and scientific basis for supporting synergistic pollution and carbon reduction. Full article

As a key parameter of the Earth’s ecosystem, soil moisture significantly influences land-atmosphere interactions and has important applications in meteorology, hydrology, and agricultural studies. However, existing passive microwave remote sensing products of soil moisture are limited by their discontinuous temporal coverage and relatively coarse spatial resolution (typically 25–55 km), which cannot meet the requirements for fine-scale applications. This study developed and compared four machine learning-based downscaling approaches to improve the spatiotemporal resolution of MCCA SMAP soil moisture products. The methodology involved establishing complex nonlinear relationships between soil moisture and various high-resolution surface parameters including albedo, evapotranspiration, precipitation, and soil properties. High-resolution soil moisture maps were generated by leveraging the scale-invariant characteristics between soil moisture and surface parameters, followed by comprehensive evaluation using in situ ground observations and triple collocation analysis. The results demonstrated that all downscaling models showed excellent consistency with original MCCA SMAP observations (R > 0.93, RMSE < 0.033 m³ m⁻³), while successfully providing enhanced spatial details. The Random Forest (RF) model exhibited superior performance, showing higher correlation coefficients and lower biases when compared with in situ measurements. Uncertainty analysis revealed relatively low uncertainty levels for all models except Backpropagation Neural Network (BPNN) model. The RF-downscaled products accurately tracked temporal variations of soil moisture and showed good responsiveness to precipitation patterns, demonstrating their potential for fine-scale hydrological applications and regional environmental monitoring. Full article

The chlorophyll content of plant leaves measured by the soil plant analysis development (SPAD) is an important indicator for measuring crop growth status and irrigation effect. The rapid, non-destructive and efficient estimation of crop SPAD values is of great significance to the field management of crops. In this study, the canopy hyperspectral reflectance and SPAD values of winter wheat were obtained, and the spectral curve was changed through four spectral processing methods, including first-order differential (FD), second-order differential (SD), multivariate scattering correction (MSC), and Savitzky–Golay smoothing (SG) to improve the correlation between canopy spectral reflectance and SPAD. Furthermore, to investigate and evaluate the performance of various vegetation indices (VIs) in estimating SPAD values for winter wheat, existing published indices were optimized using random band combinations derived from multiple canopy spectral transformations. The optimized vegetation index was used as the input variable of the model, and six machine learning algorithms, including random forest (RF), long short-term memory network (LSTM), multilayer perceptron (MLP), deep recurrent neural network (Deep-RNN), gated recurrent unit (GRU), and convolutional neural network (CNN), were used to construct the winter wheat SPAD values estimation model, and the model was verified. The experimental results demonstrate that, when utilizing an equivalent number of optimized vegetation indices as input, the GRU-based model achieves higher estimation accuracy compared to other models. Specifically, the coefficient of determination (R²) is improved by 0.12 compared to the RF model, by 0.03 compared to the LSTM model, by 0.12 compared to the MLP model, by 0.02 compared to the Deep-RNN model, and by 0.02 compared to the CNN model. At the same time, the GRU model also has a lower root mean square error (RMSE) and relative error (RE) of 7.37 and 24.90%, respectively. This study provides valuable hyperspectral remote sensing technology support for the implementation of winter wheat SPAD values estimation in the field. Full article

Urban air quality assessment is central to environmental sustainability and public health management. This study presents a structured comparative evaluation of Random Forest (RF), Support Vector Machine (SVM), LSTM, and Bi-LSTM models for pollutant-driven air quality classification under the Indian National Air Quality Index (NAQI) framework defined by CPCB guidelines. To provide a fair comparison, multi-pollutant data of Indian urban monitoring stations were preprocessed, and the class-balancing protocol and validation protocol were combined. RF had highest total accuracy (0.9971) in the held-out set, with Bi-LSTM (0.9615), LSTM (0.9495), and SVM (0.9442) coming next. Although ensemble methods proved to be very separable in line with the threshold-based NAQI structure, Bi-LSTM was more stable when it came to boundary-sensitive switches among the adjacent severity classes. Calibration analysis (multiclass Brier score: 0.08) showed consistent probabilistic behavior and interpretation, and using SHAP showed physically significant pollutant driving factors. The results explain the appropriateness of comparative models in organized AQI classification and present a reproducible assessment framework for the NAQI framework. Full article

Accurate estimation of Body Carcass Fat (BCF) is essential for evaluating the physiological condition of ewes. Traditional assessment via Body Condition Score (BCS) through palpation is inaccurate and subjective. BCF can now be predicted more precisely using objective measurements. This study presents a comparative analysis of eight machine learning (ML) models for predicting BCF in Coopworth ewes, using weight and RGB-image-based body measurements. Four non-linear regression methods and four neural network architectures were evaluated using a dataset of 74 ewes with 13 independent variables. The dataset was partitioned into training (52 ewes), validation (11 ewes), and testing (11 ewes) sets. The Gradient Boosting Regression achieved the highest predictive accuracy with an R² value of 0.9434 using body weight and width, followed by Ensemble Neural Network (R² = 0.9371) using body weight. The findings demonstrate the effectiveness of the Gradient Boosting Regression, Ensemble Neural Network and Random Forest tree-based approaches for morphometric prediction tasks in biological applications. BCF values obtained from image analysis were validated against those derived from computerized tomography (CT), considered the gold standard. These findings highlight the potential of image-guided, ML-driven models for objective, non-invasive, cost-effective assessment of ewe body composition in modern livestock systems. Full article