Search Results (2,393)

Traditional approaches to urban real estate green value assessment rely heavily on single structured data sources. Such methods often provide limited interpretability and fail to capture multidimensional green attributes accurately. To address these limitations, this study constructs a multimodal assessment framework that integrates image, text, and spatial information. A housing price prediction model is developed based on a Multi-Layer Perceptron architecture. Results show that the proposed method is superior to traditional models (such as the Hedonic pricing model, Ridge regression, and eXtreme Gradient Boosting, as well as single-modality control models). The core evaluation metric, mean squared error, reaches 0.0505 ± 0.0021. SHapley Additive exPlanations analysis shows that the text modality provides the largest contribution to model prediction, accounting for 51.45% of the global contribution. However, this dominance reflects the model’s dependence on textual green signals rather than the establishment of causal relationships. The result may also be influenced by marketing language bias and symbolic sustainability signals. The image modality contributes 38.48%, while the spatial modality contributes 10.07%, indicating a complementary relationship among the three modalities. Green premium analysis confirms that the model achieves higher prediction accuracy for high-priced residences and effectively captures differences in green premium across housing price tiers. This study provides a new technical pathway for real estate green value assessment. Full article

Machine learning has become a field of growing interest in asphalt pavement engineering, spanning mix design, material characterization, performance prediction, distress detection, sustainability, quality control, and maintenance planning. However, a lack of transparency can undermine engineering trust, defensibility, and field implementation. This systematic scoping review aims to synthesize explainable artificial intelligence (XAI) and interpretable machine-learning applications for asphalt pavement materials and systems, following the PRISMA-ScR guidelines. Major scientific databases were used to identify relevant peer-reviewed studies, which were screened against a set of inclusion and exclusion criteria and categorized into seven research dimensions. A final library of 163 publications was compiled, comprising 73 core evidence studies and 90 supporting references. The review covers techniques such as SHAP, LIME, partial-dependence analysis, attention mechanisms, surrogate models, sensitivity analysis, symbolic modeling, and physically informed interpretation. The use of XAI in performance prediction, material-property interpretation, and modeling for mix design is well developed, while distress/damage analysis, life cycle sustainability, field validation, uncertainty-aware explanation, maintenance decision support, and human-centered evaluation are still relatively underdeveloped. The main contribution is a five-layer framework linking data provenance, model performance, explanation quality, physical plausibility, and decision utility. The review proposes moving from post hoc feature ranking to validated, physically centered, uncertainty-aware, and engineer-in-the-loop decision support for asphalt XAI. Full article

Chronic pain is increasingly understood as a multidimensional condition in which, in a substantial subgroup of patients, a protective symptom can evolve into a persistent maladaptive disorder of the nervous system, while in others it may remain closely tied to ongoing mechanical or structural factors. Central sensitization (CS) represents a key mechanism underlying this transition, characterized by enhanced neural responsiveness and impaired endogenous pain inhibition, leading to a dissociation between pain and tissue pathology. The aim of this narrative review is to critically discuss current evidence on CS as a mechanism-based explanation for persistent pain, using lumbar disk herniation (LDH) as a clinical model of the radiological-clinical mismatch, and to discuss its direct implications for identifying sensitized phenotypes, multimodal assessment, and rehabilitation strategies. A total of 77 sources published between 2006 and 2026 were synthesized. These reviewed sources demonstrate that identification of the sensitized phenotype requires a multimodal assessment approach combining self-report measures, such as the Central Sensitization Inventory (CSI), with psychophysical methods including quantitative sensory testing (QST) and conditioned pain modulation (CPM). Cognitive-emotional factors are also critical, as postoperative kinesiophobia affects approximately 38.3% of LDH patients and is associated with increased pain intensity and reduced self-efficacy. Management strategies reported in these publications focus on mechanism-based interventions, particularly pain neuroscience education (PNE) and graded, time-contingent exercise, which aim to modify pain-related cognitions and restore endogenous inhibitory processes. These approaches may be supported by adjunctive therapies, including dry needling (DN), electro-dry needling (EDN), centrally acting pharmacological agents (e.g., serotonin–norepinephrine reuptake inhibitors [SNRIs] and gabapentinoids), and psychologically informed treatments such as cognitive behavioral therapy (CBT). While surgical decompression may reduce CS-related symptoms, preoperative sensitization does not necessarily predict poorer outcomes, highlighting the interaction between peripheral and central mechanisms. Adopting a sensitization-informed perspective may encourage a broader integration of contemporary pain models alongside traditional structural views in lumbar disc herniation clinical care. Full article

Green roofs are used as nature-based solutions for urban stormwater management and for improving the thermal performance of buildings. Their hydrological performance depends on structural properties and rainfall characteristics, but the relative importance of these factors has not been fully quantified. Therefore, this study aimed to identify the key variables controlling the hydrological effectiveness of a green roof. A conceptual model of a flat roof representing a typical single-family building in south-eastern Poland was developed in the Storm Water Management Model (SWMM), with a modeled roof area of 232 m² and 100% of the roof surface covered by the green roof LID system. A total of 24,576 simulation cases were analyzed, considering different values of soil thickness, berm height, initial saturation, vegetation-related storage, rainfall duration, rainfall probability, and rainfall temporal distribution. The hydrological response was evaluated using peak runoff reduction and cumulative runoff volume ratio determined at selected times after rainfall. Predictive models based on the eXtreme Gradient Boosting (XGBoost) algorithm were developed, and their interpretation was performed using the SHapley Additive exPlanations (SHAP) method. The main novelty of the study is its application-oriented framework combining SWMM simulations, XGBoost modeling, and SHAP explainability to distinguish the factors controlling peak runoff reduction and delayed runoff release from a green roof. The results showed that peak runoff reduction ranged from 10.97% to 100.00%, with a median of 99.91%, indicating a generally high capacity of the analyzed system to attenuate peak flow. In contrast, the cumulative runoff volume ratio increased over time, with median values rising from 0.05% immediately after rainfall to 7.91% after 24 h, confirming the significant retention and detention potential of the green roof. SHAP analysis revealed that peak runoff reduction was governed primarily by berm height, whereas cumulative runoff volume was controlled mainly by initial substrate saturation. The results confirm that different mechanisms control short-term and long-term green roof performance. Full article

Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning. Full article

Glaucoma, the leading cause of irreversible vision loss, often goes undetected in early stages due to its asymptomatic behaviour. Early diagnosis typically involves visual analysis of the optic disc (OD) in eye fundus images. Machine and deep learning techniques have emerged as valuable tools for automating this process; however, their integration into clinical practice still faces limitations. These challenges include the presence of image regions that are not directly related to glaucoma assessment, such as retinal vasculature, the macula, and background structures, which may introduce irrelevant information and negatively affect classification performance, as well as a general lack of transparency in the decision-making process. This article proposes a methodology that enhances both the accuracy and interpretability of glaucoma detection by focusing solely on the OD region. First, a metaheuristic-based strategy is employed for precise OD detection and cropping, generating an OD-centric dataset with glaucoma-labeled images, which is composed of different public datasets. Four convolutional neural networks (CNNs), namely VGG-19, MobileNet-V2, ResNet-50, and DenseNet-161, are trained on this dataset using transfer learning. To address the need for model explainability, Grad-CAM, Score-CAM, and Eigen-CAM are applied to the trained models to generate post hoc visual explanations of their predictions. The experimental results showed that DenseNet-161 achieved the best overall performance on the assembled public dataset, using an 80%-10%-10% training, validation, and testing split, with a test accuracy of 0.9369 and an AUC of 0.9831. By isolating the OD region and incorporating explainability techniques, the methodology provides a robust and interpretable second opinion, supporting more accurate and efficient glaucoma screening. Full article

Flow-cell ultrasonication of gelatinized rice flour slurries alters cultivar-dependent water solubility, viscosity, and retrogradation of pregelatinized rice flour, properties important for plant-based beverages and convenience foods. We tested whether cultivar-level composition descriptors, amylose, protein, and fiber, can represent cultivar-associated variation in ultrasonication responses while separating process-only prediction, within-domain cultivar representation, and unseen-cultivar transfer. Six rice cultivars were processed across nine amplitude-time combinations and two slurry concentrations. Water solubility index, apparent viscosity at a shear rate of 50 s⁻¹, and setback viscosity were modeled using ElasticNet, partial least squares regression, support vector regression, random forest, and extreme gradient boosting. Three input formulations were compared: process variables alone, process variables plus composition descriptors, and process variables plus cultivar identity. Repeated nested group cross-validation showed insufficient process-only prediction and substantial improvement from composition descriptors. Within-domain validation showed comparable composition-descriptor and cultivar-identity performance under nonlinear algorithms. However, because cultivar identity is undefined for absent cultivars, leave-one-cultivar-out transfer of the composition-descriptor model remained uncertain. Cross-fitted Shapley additive explanations showed predictions used process and composition variables. For the validated cultivar-process domain, this approach can screen cultivar-process combinations for beverage and convenience-food applications, but replacing categorical source identifiers with continuous descriptors requires explicit transfer validation. Full article

Wildfires increasingly contribute to urban particulate matter (PM) exposure, particularly fine particles (PM_2.5), through atmospheric transport processes influenced by meteorological conditions and terrain complexity. This study investigated wildfire impacts on PM₁₀ and PM_2.5 concentrations in Split, Solin, and Kaštela (Croatia) using a terrain-aware wildfire transport framework combined with statistical and machine learning (ML) approaches. Daily PM observations (2016–2024) from three air quality monitoring stations were integrated with meteorological data from six stations, wildfire polygons, and a digital elevation model (DEM). A wildfire influence index accounting for fire size, transport distance, wind conditions, and terrain-modified airflow was evaluated using Ordinary Least Squares (OLSs) regression, Random Forest (RF) modelling, and SHAP (SHapley Additive exPlanations) analysis. Results showed stronger wildfire-related effects for PM_2.5 than for PM₁₀, while meteorological variables remained the dominant predictors of PM variability. RF models improved predictive performance relative to OLS, achieving $R^2$ = 0.474 for PM_2.5 and $R^2$ = 0.416 for PM₁₀. SHAP analysis identified precipitation, temperature, and lagged wildfire transport variables as important predictors. A total of 84 wildfire events were classified as effective wildfires, with most measurable impacts occurring within approximately 30–70 km of monitoring stations, indicating that wildfire impacts on urban air quality in Mediterranean coastal environments are strongly mediated by atmospheric transport and meteorological conditions. The proposed framework demonstrates the potential of explainable and geospatially informed ML for environmental monitoring and wildfire-related urban air quality risk assessment. Full article

Optimizing the injection molding of short-fiber-reinforced thermoplastics (SFRTs) is a persistent challenge due to the complex interplay between processing parameters and final mechanical performance. To address this, we developed and validated a machine learning (ML) pipeline to maximize both the tensile strength and Charpy impact resistance in polyamide 6 with 30% glass fiber (PA6-GF30). Through a designed experimental campaign, we systematically varied four key process parameters—melt temperature (260–300 °C), injection pressure (600–1000 bar), packing pressure (400–800 bar), and cooling time (15–35 s). The resulting dataset was used to train and compare three different regression models: Random Forest (RF), Gradient Boosting (GB), and Support Vector Regression (SVR). Our findings indicate that the Gradient Boosting (GB) algorithm yielded the most reliable predictions, significantly outperforming the other evaluated models. Further analysis using SHAP (Shapley Additive exPlanations) identified packing pressure as the dominant factor influencing tensile strength (contributing approximately 40% to the prediction), while melt temperature emerged as the key driver for impact resistance (around 35% contribution). By integrating our best-performing GB model with a multi-objective genetic algorithm, we identified an optimal set of parameters that simultaneously enhances both mechanical properties. Among the evaluated models (Random Forest, Support Vector Regression, and Gradient Boosting), the Gradient Boosting algorithm achieved the highest predictive accuracy. Compared to the baseline condition (280 °C melt temperature, 800 bar injection pressure, 600 bar packing pressure, 25 s cooling time), experimental validation of these optimized settings demonstrated substantial improvement: tensile strength increased from 145 MPa to 171 MPa (an 18% enhancement), and impact resistance rose from 45 kJ/m² to 55 kJ/m² (a 22% gain). This work establishes that an integrated ML and optimization framework can serve as a transformative approach for high-precision manufacturing of advanced engineering polymers. The primary novelty of this work lies in the development of a fully integrated, bias-free methodological framework that explicitly couples physical interpretability with multi-objective optimization, bridging the critical gap between black-box predictions and actionable industrial insights. Full article

Objective diagnosis in psychiatry remains challenging due to the lack of reliable biological markers and the presence of confounding variables in observational data. While EEG-based machine learning models have shown promising classification performance, their validity remains unclear when confounding factors such as age are not explicitly controlled. In this work, we propose a confound-aware mathematical framework for supervised learning, where classification is formulated as a mapping $f:{\mathbb{R}}^{E\times C\times T}\to \mathcal{Y}$ under the presence of a confounding variable A. Within this formulation, model performance is interpreted as a function of both predictive structure and confound dependence. The proposed framework integrates classification, regression, and feature selection into a unified evaluation pipeline. A central contribution is the Cross-Task Explanation Concordance (CTEC) index, a rank-based metric that quantifies the stability of feature importance across models and predictive tasks. Experimental results on a large-scale EEG dataset (N = 670) demonstrate that deep learning models outperform handcrafted approaches under standard evaluation. However, under confound-controlled settings, handcrafted models show a dual response to confound control: age residualization improves classification by removing feature-level noise (+20.3%), while age-matching collapses performance to chance (balanced accuracy, BA = 0.238) by eliminating demographic separability. Deep learning models retain partial robustness under both conditions. These findings highlight that conventional performance metrics may overestimate model validity in the presence of structured bias. The proposed framework provides a general mathematical approach for evaluating supervised learning models under confounding effects and is applicable to a wide range of data-driven systems beyond EEG. Full article

Background/Objectives: Preoperative differentiation among benign thyroid nodules, follicular variant papillary thyroid carcinoma (FV-PTC), and classical papillary thyroid carcinoma (C-PTC) remains clinically challenging. FV-PTC is particularly difficult to identify due to its substantial sonographic and cytological overlap with both benign nodules and other malignant subtypes, frequently resulting in overtreatment or delayed diagnosis. This study aimed to develop and validate an interpretable multimodal model for accurate three-class discrimination using routine ultrasound images, with a specific focus on improving the preoperative identification of FV-PTC. Methods: This retrospective study included 479 pathologically confirmed thyroid nodules from 462 patients. Conventional ultrasound features and radiomics features extracted from grayscale ultrasound and color Doppler flow imaging were used to construct three predictive models: a Conventional Ultrasound model (conventional ultrasound features only), a Radiomics model (radiomics features only), and an Integrated model (combined features). Each model was trained using four machine learning classifiers. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis, and clinical usefulness was evaluated using decision curve analysis (DCA). Results: The support vector machine (SVM)-based Integrated Model achieved the best overall performance. In the independent testing cohort, the AUCs were 0.853 for FV-PTC, 0.882 for C-PTC and 0.928 for benign nodules. The Integrated Model showed the greatest improvement for FV-PTC, with a ΔAUC of 0.141 compared with the Conventional Ultrasound Model. SHAP (SHapley Additive exPlanations) analysis identified wavelet-HL_gldm_Dependence and wavelet-HH_glcm_InverseVariance as the two most important radiomics predictors in both the Radiomics Model and the Integrated Model, demonstrating robust cross-model stability and high discriminative power. Conclusions: The SVM-based Integrated Model demonstrated promising performance for three-class classification of thyroid nodules and enhanced the preoperative identification of FV-PTC. This approach may provide an interpretable and noninvasive decision-support tool for refining subtype-specific risk stratification and supporting individualized clinical management. Full article

Employee attrition is a significant organizational challenge associated with substantial financial costs and the erosion of institutional knowledge. This study presents an AI-based Management Information System (MIS) that integrates machine learning (ML) models to forecast employee turnover and support technical interpretability for HR decision-making. Using the IBM HR Analytics Dataset comprising 1480 employee records with 38 features, we implemented a rigorous preprocessing pipeline—including Synthetic Minority Over-sampling Technique (SMOTE) applied exclusively within training folds to prevent data leakage, one-hot encoding, Z-score normalization, and mean-value imputation. Four ML classifiers—Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost—were evaluated under a stratified 80/20 split with 5-fold cross-validation. XGBoost achieved the highest performance, attaining an accuracy of 87.83%, a ROC-AUC of 0.94, a PR-AUC of 0.96, and an F1-score of 93.04%, attributed to its sequential boosting mechanism and built-in L1/L2 regularization. Beyond predictive performance, the system incorporates SHapley Additive exPlanations (SHAP) to deliver feature-level transparency, enabling HR professionals to engage in proactive, informed retention interventions while retaining full decision-making authority. Within-dataset comparisons confirm that the proposed framework outperforms prior methods evaluated on the same benchmark; cross-study accuracy comparisons are reported as contextual reference only, given differences in datasets and experimental protocols. The system facilitates human oversight by positioning AI as a decision-support collaborator rather than an autonomous replacement in workforce management. Future work will address real-time deployment, controlled user studies with HR practitioners, and validation with actual organizational HR data. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Mixed ground conditions cause sudden fluctuations in the tunnel boring machine (TBM) advance rate (AR). Accurate forecasting is necessary for tunneling safety. Existing data-driven models, however, often treat the excavation process as an isolated event. They ignore the physical memory effect of rock–machine interactions. They also lack the ability to diagnose abnormal AR drops. To address these issues, an interpretable forecasting framework is proposed. First, a Selection–Processing (SP) system is established to standardize data handling and quantify geological heterogeneity. Second, a Time-Series Structure (TSS) network is developed to construct a one-ring-ahead input block using the current completed-ring state and CCF/PACF-guided historical windows. The framework is validated on the Shenzhen–Dayawan Intercity Line. The optimized GWO-LSTM model achieves high accuracy (R² = 0.977, MAE = 2.15, RMSE = 3.07). Compared with the no-TSS reference scheme, the MAE and RMSE decrease from 2.7081 and 3.6045 to 2.1496 and 3.0724, respectively. Furthermore, Shapley Additive Explanations (SHAP) are applied for ring-by-ring anomaly diagnosis. Local SHAP analysis indicates that both current-state variables and selected lagged variables provide diagnostic information for AR fluctuations. The identified lags are interpreted as project-specific memory indicators rather than universal physical delay constants. This method provides model-based diagnostic clues for associating sudden AR drops with specific operational or geological factors. The proposed framework provides a transparent and practical tool for TBM performance prediction and field diagnosis. Full article

Forest carbon assessment requires methods that capture the combined effects of stand structure, site conditions, carbon pools, operational emissions, and circular-economy processes. This study aimed to develop and optimize hybrid machine learning models for predicting the gross CO₂e (carbon dioxide equivalent) balance of Polish forest stands using measurable stand- and site-related variables. The research was based on a primary dataset describing forest management in major Polish macroregions in 2020–2024. After data cleaning and preprocessing, multiple machine learning algorithms, including ensemble, boosting, neural, and hybrid models, were trained, validated, and tested. Model performance was assessed using standard regression metrics, overfitting diagnostics, learning curves, and SHAP (Shapley Additive Explanations). Most models achieved high predictive accuracy, with six of ten algorithms reaching R² values above 0.90 on the test set. The reduction in strongly correlated variables helped limit multicollinearity and excessive overlap between predictors and the target variable, supporting a more reliable interpretation of model performance. The CatBoost algorithm achieved the highest predictive performance on the test set (R² = 0.948), while also recording the lowest root mean squared error (RMSE = 152.242). However, the Decision Tree demonstrated the weakest generalization performance (R² = 0.806) on the test set. SHAP analysis identified tree height as the most influential predictor, followed by tree age, number of trees, species composition, and selected habitat features. The novelty of the study lies in integrating hybrid machine learning, interpretable modelling, and circular-economy-related carbon balance components into a single framework for rapid and operational forest carbon assessment in Polish forest stands. Full article