Search Results (15,472)

Understanding how transport conditions and the built environment shape urban CO₂ emissions is critical for low-carbon urban development. This study analyses CO₂ emission intensity across fifty major Chinese cities using integrated ODIAC emissions, VIIRS night-time lights, traffic performance indicators, built-environment morphology, population/POI structure, and socioeconomic controls. We develop a GeoAI workflow that couples XGBoost modelling with SHAP interpretation, congestion-based city grouping, and 1 km grid-level GNNWR to map intra-urban spatial non-stationarity. The global model identifies night-time light intensity as the strongest predictor, followed by population density and building density. SHAP results reveal pronounced nonlinearities, with high sensitivity at low–medium levels and diminishing marginal effects as activity and density increase. Although transport indicators are less influential in the aggregate model, their roles differ across congestion regimes: in low-congestion cities, emissions align more consistently with overall activity intensity, whereas in high-congestion cities they respond more strongly to population distribution, motorisation, and built-form intensity, with less stable relationships. Grid-level GNNWR further shows that key mechanisms are spatially uneven within cities, with local effects concentrating in specific cores and corridors or fragmenting across multiple subareas. These findings demonstrate that emission drivers are context-dependent across and within cities. Accordingly, uncongested cities may gain more from activity-related energy-efficiency measures, while highly congested cities may require congestion-sensitive land-use planning, spatial-structure optimisation, and motorisation control. Integrating explainable GeoAI with regime differentiation and spatial heterogeneity mapping provides actionable evidence for targeted low-carbon planning. Full article

The increasing digitization of critical infrastructure and the increasing use of Industrial Internet of Things (IIoT) systems are leading to a significant increase in the exposure of operating systems to cyber threats. The integration of information (IT) and operational (OT) layers, characteristic of today’s industrial environments, results in an increase in the complexity of system architecture and the number of security events that require ongoing analysis. Under such conditions, classic approaches to monitoring and responding to incidents prove insufficient, especially in the context of systems with high reliability and business continuity requirements. The aim of this article is to analyze the possibilities of using Large Language Models (LLMs) in the protection of industrial IoT systems operating in critical infrastructure. The paper analyzes the architecture of industrial automation systems and identifies classes of cyber threat scenarios characteristic of IIoT environments, including availability disruptions, degradation of system operation, manipulation of process data, and supply-chain-based attacks. On this basis, the potential roles of large language models in security monitoring processes are examined, particularly with respect to incident interpretation, correlation of heterogeneous data sources, and contextual analysis under operational constraints. The experimental evaluation demonstrates that, when compared to a rule-based baseline, the LLM-based approach provides consistently improved classification of incident impact and attack vectors across IT, DMZ, and OT segments, while maintaining a low rate of unsupported responses. These results indicate that large language models can complement existing industrial IoT security mechanisms by enhancing context-aware analysis and decision support rather than replacing established detection and monitoring systems. Full article

Artificial intelligence (AI) is rapidly transforming medical diagnostics by allowing for early, accurate, and data-driven clinical decision-making. This review provides an overview of how machine learning (ML), deep learning, and emerging multimodal foundation models have been used in diagnostic procedures across imaging, pathology, molecular analysis, physiological monitoring, and electronic health record (EHR)-integrated decision-support systems. We have discussed the basic computational foundations of supervised, unsupervised, and reinforcement learning and have also shown the importance of data curation, validation metrics, interpretability methods, and feature engineering. The use of AI in many different applications has shown that it can find abnormalities and integrate some features from multi-omics and imaging, which has shown improvements in prognostic modeling. However, concerns about data heterogeneity, model drift, bias, and strict regulatory guidelines still remain and are yet to be addressed in this field. Looking forward, future advancements in federated learning, generative AI, and low-resource diagnostics will pave the way for adaptable and globally accessible AI-assisted diagnostics. Full article

Decision-making under uncertainty, especially when dealing with incomplete or linguistically described data, remains a significant challenge in various fields of science and industry. The increasing complexity of real-world problems necessitates the development of mathematical models and data processing techniques that effectively address uncertainty and incompleteness. Aggregators play a key role in solving these problems, particularly in fuzzy systems, where they constitute fundamental tools for decision-making, data analysis, and information fusion. Aggregation functions have been extensively studied and applied in many fields of science and engineering. Recent research has explored their usefulness in fuzzy control systems, highlighting both their advantages and limitations. One promising approach is the use of ordered fuzzy numbers (OFNs), which can represent directional tendencies in data. Previous studies have introduced the property of direction sensitivity and the corresponding determinant parameter, which enables the analysis of correspondence between OFNs and facilitates inference operations. The aim of this paper is to examine existing aggregate functions for fuzzy set numbers and assess their suitability within OFNs. By analyzing the properties, theoretical foundations, and practical applications of these functions, we aim to identify a suitable aggregation operator that complies with the principles of OFN while ensuring consistency and efficiency in decision-making based on fuzzy structures. This paper introduces a novel aggregation approach that preserves the expected mathematical properties while incorporating the directional components inherent to OFN. The proposed method aims to improve the robustness and interpretability of fuzzy reasoning systems under uncertainty. Full article

Rolling bearing fault diagnosis is critical for the reliable operation of rotating machinery. However, many existing deep learning-based methods rely on complex signal preprocessing and lack interpretability. This paper proposes an adversarial autoencoder (AAE)-based framework that integrates adaptive, data-driven signal decomposition directly into a neural network. A convolutional autoencoder is employed to extract latent representations while preserving temporal resolution, enabling encoder channels to be interpreted as nonlinear signal components. A channel attention mechanism adaptively reweights these components, and a classifier acts as a discriminator to enhance class separability. The model is trained in an end-to-end manner by jointly optimizing reconstruction and classification objectives. Experiments on three benchmark datasets demonstrate that the proposed method achieves high diagnostic accuracy (99.64 ± 0.29%) without additional signal preprocessing and outperforms several representative deep learning-based methods. Moreover, the learned representations exhibit interpretable characteristics analogous to classical envelope demodulation, confirming the effectiveness and interpretability of the proposed approach. Full article

This study addresses the issues of sparse observations from buoys in the tropical Indian Ocean and systematic biases in reanalysis products by proposing a daily-mean wind speed reconstruction framework that integrates multi-source meteorological fields. This study also considers the impact of different source domains on model pre-training, with the goal of providing reliable data support for wind energy assessment. The model was pre-trained using data from the Americas and tropical Pacific buoys as the source domain and then fine-tuned on Indian Ocean buoys as the target domain. Using annual leave-one-out cross-validation, we evaluated the model’s performance against uncorrected ERA5 and CCMP data while comparing three deep reconstruction models. The results demonstrate that deep models significantly reduce reanalysis bias: the RMSE decreases from approximately 1.00 m/s to 0.88 m/s, while R² improves by approximately 8.9% and 7.1% compared to ERA5/CCMP, respectively. The Branch CNN–Transformer outperforms standalone LSTM or CNN models in overall accuracy and interpretability, with transfer learning yielding directional gains for specific wind conditions in complex topography and monsoon zones. The 20-year wind energy data reconstructed using this model indicates wind energy densities 60–150 W/m² higher than in the reanalysis data in open high-wind zones such as the southern Arabian Sea and the Somali coast. This study not only provides a pathway for constructing high-precision wind speed databases for tropical Indian Ocean wind resource assessment but also offers precise quantitative support for delineating priority development zones for offshore wind farms and mitigating near-shore engineering risks. Full article

The widespread application of robots in complex and dynamic environments demands that Visual SLAM is both robust and accurate. However, dynamic objects, varying illumination, and environmental complexity fundamentally challenge the static world assumptions underlying traditional SLAM methods. This review provides a comprehensive investigation into the mathematical foundations of V-SLAM and systematically analyzes the key optimization techniques developed for dynamic environments, with particular emphasis on advances since 2020. We begin by rigorously deriving the probabilistic formulation of V-SLAM and its basis in nonlinear optimization, unifying it under a Maximum a Posteriori (MAP) estimation framework. We then propose a taxonomy based on how dynamic elements are handled mathematically, which reflects the historical evolution from robust estimation to semantic modeling and then to deep learning. This framework provides detailed analysis of three main categories: (1) robust estimation theory-based methods for outlier rejection, elaborating on the mathematical models of M-estimators and switch variables; (2) semantic information and factor graph-based methods for explicit dynamic object modeling, deriving the joint optimization formulation for multi-object tracking and SLAM; and (3) deep learning-based end-to-end optimization methods, discussing their mathematical foundations and interpretability challenges. This paper delves into the mathematical principles, performance boundaries, and theoretical controversies underlying these approaches, concluding with a summary of future research directions informed by the latest developments in the field. The review aims to provide both a solid mathematical foundation for understanding current dynamic V-SLAM techniques and inspiration for future algorithmic innovations. By adopting a math-first perspective and organizing the field through its core optimization paradigms, this work offers a clarifying framework for both understanding and advancing dynamic V-SLAM. Full article

Daily runoff series exhibit high complexity and significant fluctuations, which often lead to large prediction errors and limit the scientific basis of water resource scheduling and management. This study proposes a runoff prediction framework that incorporates upstream–downstream hydrological correlation information and integrates Improved Empirical Wavelet Transform (IEWT), SHAP-based interpretable feature selection, Improved Population-Based Training (IPBT), and the Modern Temporal Convolutional Network (ModernTCN) to enhance forecasting accuracy and model robustness. First, IEWT is employed to perform multi-scale decomposition of the daily runoff sequence, extracting structural features at different temporal scales. Then, upstream–downstream hydrological correlation information is introduced, and the SHAP method is used to evaluate the importance of multi-source basin features, eliminating redundant variables to improve input quality and training efficiency. Finally, IPBT is applied to optimize ModernTCN hyperparameters, thereby constructing a high-performance forecasting model. Case studies at the Hankou station demonstrate that the proposed IPBT-IEWT-SHAP-ModernTCN model significantly outperforms benchmark methods such as LSTM, iTransformer, and TCN in terms of accuracy, stability, and generalization. Specifically, the model achieves a root mean square error of 342.14, a mean absolute error of 251.01, and a Nash–Sutcliffe efficiency of 0.9992. These results indicate that the proposed method can effectively capture the nonlinear correlation characteristics between upstream and downstream hydrological processes, thus providing an efficient and widely adaptable framework for daily runoff prediction and scientific water resources management. Full article

Farmland shelterbelts are crucial elements in safeguarding agricultural ecological security and sustainable development, with their precise extraction being vital for regional ecological monitoring and precision agriculture management. However, constrained by their narrow linear distribution, complex farmland backgrounds, and spectral confusion issues, traditional remote sensing methods encounter significant challenges in terms of accuracy and generalization capability. In this study, six representative deep learning semantic segmentation models—U-Net, Attention U-Net (AttU_Net), ResU-Net, U²-Net, SwinUNet, and TransUNet—were systematically evaluated for farmland shelterbelt extraction using high-resolution Gaofen-6 imagery. Model performance was assessed through four-fold cross-validation and independent test set validation. The results indicate that convolutional neural network (CNN)-based models show overall better performance than Transformer-based architectures; on the independent test set, the best-performing CNN model (U-Net) achieved a Dice Similarity Coefficient (DSC) of 91.45%, while the lowest DSC (88.86%) was obtained by the Transformer-based TransUNet model. Among the evaluated models, U-Net demonstrated a favorable balance between accuracy, stability, and computational efficiency. The trained U-Net was applied to large-scale farmland shelterbelt mapping in the study area (Alar City, Xinjiang), achieving a belt-level visual accuracy of 95.58% based on 385 manually interpreted samples. Qualitative demonstrations in Aksu City and Shaya County illustrated model transferability. This study provides empirical guidance for model selection in high-resolution agricultural remote sensing and offers a feasible technical solution for large-scale and precise farmland shelterbelt extraction. Full article

Pollen identification is critical for melissopalynology (honey authentication), ecological monitoring, and allergen tracking, yet manual microscopic analysis remains labor-intensive, subjective, and error-prone when multiple grains overlap in realistic samples. Existing automated approaches often fail to address multi-grain scenarios or lack systematic comparison across classical and deep learning paradigms, limiting their practical deployment. This study proposes a subinstance-based classification framework combining YOLOv12n object detection for grain isolation, independent classification via classical machine learning (ML), convolutional neural networks (CNNs), or Vision Transformers (ViTs), and majority voting aggregation. Five classical classifiers with systematic feature selection, three CNN architectures (ResNet50, EfficientNet-B0, ConvNeXt-Tiny), and three ViT variants (ViT-B/16, ViT-B/32, ViT-L/16) are evaluated on four datasets (full images vs. isolated grains; raw vs. CLAHE-preprocessed) for four berry pollen species (Ribes nigrum, Ribes uva-crispa, Lonicera caerulea, and Amelanchier alnifolia). Stratified image-level splits ensure no data leakage, and explainable AI techniques (SHAP, Grad-CAM++, and gradient saliency) validate biological interpretability across all paradigms. Results demonstrate that grain isolation substantially improves classical ML performance (F1 from 0.83 to 0.91 on full images to 0.96–0.99 on isolated grains, +8–13 percentage points), while deep learning excels on both levels (CNNs: F1 = 1.000 on full images with CLAHE; ViTs: F1 = 0.99). At the instance level, all paradigms converge to near-perfect discrimination (F1 ≥ 0.96), indicating sufficient capture of morphological information. Majority voting aggregation provides +3–5% gains for classical methods but only +0.3–4.8% for deep models already near saturation. Explainable AI analysis confirms that models rely on biologically meaningful cues: blue channel moments and texture features for classical ML (SHAP), grain boundaries and exine ornamentation for CNNs (Grad-CAM++), and distributed attention across grain structures for ViTs (gradient saliency). Qualitative validation on 211 mixed-pollen images confirms robust generalization to realistic multi-species samples. The proposed framework (YOLOv12n + SVC/ResNet50 + majority voting) is practical for deployment in honey authentication, ecological surveys, and fine-grained biological image analysis. Full article

Efficient phosphorus recovery is of great significance for sustainable wastewater management and resource recycling. While chemical precipitation is widely used, its effectiveness under complex multi-factor conditions remains challenging to predict and optimize. This study compiled a multidimensional dataset from recent experimental literature, encompassing key operational parameters (reaction time, temperature, pH, stirring speed) and dosages of three metal precipitants (Al³⁺, Ca²⁺, Fe³⁺) to systematically evaluate and benchmark phosphorus recovery performance across these distinct systems, six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gaussian Process Regression (GPR), Elastic Net, Artificial Neural Network (ANN), and Partial Least Squares Regression (PLSR)—were developed and cross-validated. Among them, the GPR model exhibited superior predictive accuracy and robustness. ( = 0.69, = 0.54). Beyond achieving high-fidelity predictions, this study advances the field by integrating interpretability analysis with Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP). These analyses identified distinct controlling factors across systems: reaction time and pH for aluminum, Ca²⁺ dosage and alkalinity for calcium, and phosphorus loading with stirring speed for iron. The revealed factor-specific mechanisms and synergistic interactions (e.g., among pH, metal dose, and mixing intensity) provide actionable insights that transcend black-box prediction. This work presents an interpretable Machine Learning (ML) framework that offers both theoretical insights and practical guidance for optimizing phosphorus recovery in multi-metal systems and enabling precise control in wastewater treatment operations. Full article

Background/Objectives: The early and accurate identification of impacted teeth in the maxilla is critical for effective dental treatment planning. Traditional diagnostic methods relying on manual interpretation of radiographic images are often time-consuming and subject to variability. Methods: This study presents a deep learning-based approach for automated classification of impacted maxillary canines using panoramic radiographs. A comparative evaluation of four pre-trained convolutional neural network (CNN) architectures—ResNet50, Xception, InceptionV3, and VGG16—was conducted through transfer learning techniques. In this retrospective single-center study, the dataset comprised 694 annotated panoramic radiographs sourced from the archives of a university dental hospital, with a mildly imbalanced representation of impacted and non-impacted cases. Models were assessed using accuracy, precision, recall, specificity, and F1-score. Results: Among the tested architectures, VGG16 demonstrated superior performance, achieving an accuracy of 99.28% and an F1-score of 99.43%. Additionally, a prototype diagnostic interface was developed to demonstrate the potential for clinical application. Conclusions: The findings underscore the potential of deep learning models, particularly VGG16, in enhancing diagnostic workflows; however, further validation on diverse, multi-center datasets is required to confirm clinical generalizability. Full article

Precision agriculture has increasingly adopted controlled agricultural microclimates, particularly smart greenhouses, as a strategy to enhance crop yields while maintaining environmental conditions within suitable ranges for each crop. Among the variables that govern the water balance in these systems, air humidity plays a critical role; therefore, accurate humidity forecasting is essential for implementing timely control actions that support productivity levels. However, greenhouse conditions are frequently perturbed by extreme weather events, which lead to nonlinear and non-stationary humidity dynamics. In this context, the aim of this study was to design an optimized evolving fuzzy inference system for humidity forecasting that can adapt to changing and unforeseen situations in agricultural microclimates. A prototyping-based methodology was followed, including phases of communication, quick planning, modeling and quick design, construction of the prototype, and deployment. A hybrid genetic algorithm was used to optimize the parameters of an evolving Mamdani-type fuzzy inference system, extended to handle missing values in online data streams. Thirty independent optimization runs were performed, and the best configuration achieved a mean squared error of 1.20 × 10⁻² in humidity forecasting using one minute of data for three months. The resulting model showed high interpretability, with an average number of 1.35 rules, tolerance for missing values, imputing 2% of the data, and robustness to sudden changes in the data stream with a p-value of 0.01 for the Augmented Dickey–Fuller test at alpha = 0.05. In general, the optimized evolving fuzzy inference system obtained an effectiveness rate greater than 90% and demonstrated adaptability to extreme weather conditions, suggesting its applicability to other phenomena with similar characteristics. Full article

The inconsistency of system parameters inevitably emerges due to reasons such as modeling imprecision, manufacturing error, and aging process. Due to the inconsistency between nominal models and real-world conditions, controllers designed accordingly frequently fail to maintain performance guarantees during physical deployment. This phenomenon exemplifies the open sim-to-real gap problem. To address this limitation, we develop an equilibrium drift restriction strategy (EDR) to reduce the steady-state error due to the system inconsistency. We first present an example to show the reason why some existing controllers cannot counteract the system inconsistency when the equilibrium is not at the origin. Then, a control strategy is proposed by using the EDR method to reduce the induced steady-state error. Both intuitive interpretation and theoretical analysis demonstrate how EDR reduces steady-state deviations. Simulation results of a common pendulum system are provided to demonstrate that the restriction mitigates the impact of parameter inconsistency. A comparison with the popular Q-learning method is also presented. The results show that the EDR method can serve as a simple but effective tool to improve the steady-state performance of existing controllers. This paper offers a fresh perspective for exploring the control functions with specific properties in the realm of related controller research. Full article

Neuroinflammation is a hallmark of both acute and chronic neurodegenerative diseases and is driven, in part, by activated glial cells, including microglia. A key regulator of this inflammatory response is the NLRP3 inflammasome, an immune sensor that can be triggered by diverse, unrelated stimuli such as pathogen-associated molecular patterns, cellular stress, and mitochondrial dysfunction. Despite progress in targeting NLRP3-mediated immune activation, many drug candidates fail, potentially due to the limited availability of physiologically relevant disease models. The SIM-A9 murine microglial cell line, established in 2014, has emerged as a widely used model for studying neuroinflammation; however, its proteome has not yet been systemically characterized. In this study, we investigated the proteomic landscape of SIM-A9 microglia treated with classical pro-inflammatory stimuli, including lipopolysaccharide (LPS) and extracellular ATP and nigericin (NG), to induce NLRP3 inflammasome activation. Using complementary proteomic approaches, we quantified 4903 proteins and observed significant enrichment of proteins associated with immune and nervous system processes. Differentially expressed proteins were consistent with an activated microglial phenotype, including the upregulation of proteins involved in NLRP3 inflammasome signaling. To our knowledge, this is the first comprehensive proteomic analysis of SIM-A9 microglia. These findings provide a foundational resource that may enhance the interpretation and design of future studies using SIM-A9 cells as a model of neuroinflammation. Full article