Search Results (85,641)

The urban heat island effect is well recognized and has been quantified using ground observations within and outside urban areas. Earth Observation has further revealed small-scale local spatial differences, especially in urban surface temperatures, that have been shown to be highly correlated with differences in the urban fabric. However, surface temperatures do not directly translate to human-experienced temperatures, and hence high-resolution air temperature data is of high relevance. However, air temperature is not easily measured from space, and seldom do ground measurements allow for small-scale differences to be quantified to a satisfactory degree. In the present study, we assessed the added value of an air temperature product downscaled using machine learning compared to the high-resolution reanalysis model that formed its foundation. The downscaled product was developed using satellite data, local observations from privately owned weather stations, and high-resolution reanalysis. The comparison focused on Denmark’s four largest urban areas and examined the two data product’s ability to describe the urban heat island effect at the city scale as well as intra-city differences in air temperatures. Both data products show similar urban heat island effects at the city scale, while the downscaled product shows greater intra-city variance in air temperature, with patterns that are somewhat correlated with both urban density and urban green spaces. Generally, the downscaling product offers city planners a better data basis for evaluating where to prioritize contingency and mitigation measures within the urban space. Full article

The highly competitive nature of the online food delivery (OFD) market faces a serious retention problem, with acquiring new users typically being much more expensive than retaining existing users. Traditional prediction methods that rely primarily upon static transactional metrics such as recency and frequency are often unable to capture the psychological ‘disconfirmation’ which occurs prior to churn. To fill this gap, this study proposes a framework based on Expectation-Confirmation Theory (ECT). Unsupervised K-Means clustering was employed to classify a simulated and filtered dataset with 1500 customer records containing behaviour, geography, etc. This framework also couples sentiment analysis from BERT, allowing it to identify psychological “silent” attrition. Heterogeneous cohorts, which exhibit different psychological antecedents (utilitarian versus hedonic), were identified. The empirical results of our analyses demonstrated that Random Forest Classifiers with segment-specific features outperform baseline transactional models (F1 = 0.76) with an F1 Score of 0.89. The visual analytic interface developed provides a holistic view of the consumption process than traditional prediction models, including prescriptive, automated segment-based mitigation strategies. Our findings contradict the assumption that the “frequency–loyalty” model applies to all users. High-frequency discretionary users are found to be elastic in terms of retention and will experience significant churn. By utilising the automated action log, managers can plan targeted, highly efficient retention strategies rather than blanket discounting approaches. Full article

The steady operation of underground gas storage (UGS) is significant for securing national energy. However, long-term cyclic injection-production operation causes the dynamic changes in formation stress, potentially leading to fault reactivation and slippage. This could affect the seal performance of the fault zone and cause disastrous consequences. In this paper, a mechanical analysis model for fault slip is constructed to study the dynamic seal performance in response to long-term injection-production cycles. An intelligent approach is proposed to predicate the fault slip value based on machine learning algorithms. It can realize long-term prediction of fault slip value under a new condition of injection-production operation. The study shows that (1) formation pressure tends to accumulate near the fault zone due to the low permeability, and the interface of the reservoir layer, cap layer, and fault zone is the seal weak position of UGS; (2) the response of fault slip is driven by the injection-production rate and the reservoir pressure. There is a significant coupling relationship between the fault slip value and the accumulated injection gas volume; (3) the intelligent prediction approach can capture the nonlinear dynamic characteristics of slip tendency accurately, and it exhibits good prediction performance and generalization ability under the new operating condition. This study effectively assesses the dynamic risk for fault slip of depleted hydrocarbon reservoir UGS during the long-term injection-production procedure. It provides an effective technical approach for fault slip tendency analysis and injection-production process optimization, which is important for the sustainable operation of UGS reducing the risk of seal failure and supporting gas storage security. Full article

In highway widening projects, the wet–dry cycling effect of weakly expansive soil fill under seasonal groundwater fluctuations exacerbates differential settlement. This study establishes a three-dimensional numerical model for a widened road with weakly expansive soil, based on a redeveloped numerical method and actual engineering projects. Through multi-scenario numerical simulations, the influence patterns and weighting factors of widening methods, road height, and water level on differential settlement were clarified. Three safety levels for differential settlement were defined using 6 cm and 12 cm as thresholds. A prediction model based on support vector machines was established to determine the combined threshold limits of key parameters under different differential settlement boundaries. The control effectiveness of sand replacement, water-blocking layers, and wicking geotextiles was comparatively evaluated: sand replacement reduces differential settlement by approximately 70% on average and is applicable to all scenarios; water-blocking layers reduce settlement by about 50% and are more suitable for bilateral widening or unilateral widening of low embankments; wicking geotextiles are unsuitable for controlling differential settlement in high-water-level areas. Selection principles for control methods under different conditions were proposed based on engineering requirements, and field tests validated the effectiveness of the proposed solutions. Full article

Predictive maintenance aims to monitor equipment conditions through data-driven analysis and estimate failure in advance, which enables the scheduling of maintenance prior to equipment breakdown. In this work, a deep-learning neural network is used to predict the condition of the belt-drive system. A combined Convolutional Neural Network with Bi-directional Long Short-Term Memory (CNN-BiLSTM) model is assigned for processing the operational parameters along with vibrational signals to predict belt-drive system conditions in two separate binary classifications: faulty or healthy and unbalanced or balanced conditions. The data flow in the CNN-BiLSTM model initiates with the CNN to extract the features from the vibration signals and performs essential pattern detection. Consequently, the BiLSTM’s role is to capture long-term temporal relationships that cannot be captured by the CNN alone. To predict the targeted conditions, a fully connected layer with a classifier is built at the BiLSTM outputs. For efficient model training, the data is preprocessed through denoising, augmentation, and normalization. Additionally, hyperparameter tuning is conducted to explore different model configurations and select the optimal ones on the basis of relevant performance. An ablation study is conducted to investigate the use of CNN and BiLSTM models individually, confirming that combining both components is essential for accurate classification. Moreover, the cross-validation technique is used to assess the proposed model’s generality by organizing unseen data across rotational speeds, which also depicts robust performance under varying operating conditions. The key added value of this study lies in integrating deep-learning techniques to address a knowledge gap by using raw vibrational signals to establish intelligent monitoring systems, which represents a new scientific contribution to the predictive maintenance of belt-drive systems. Full article

With the rapid advancement of autonomous driving technology and the commercialization of Human–Machine Interface (HMI) services, camera-based systems for external environment perception are being extensively deployed. While comprehensive camera systems enhance safety and convenience, they simultaneously raise serious privacy concerns by collecting facial and biometric information of Vulnerable Road Users (VRUs) and passengers. Although facial anonymization technology has emerged as a key solution, the field currently faces a fundamental challenge: the absence of unified performance evaluation criteria. Existing studies employ disparate evaluation metrics, making objective inter-model comparison and performance verification difficult. This study proposes quantitative evaluation metrics and corresponding evaluation criteria that enable systematic and objective assessment of facial anonymization model performance. Through large-scale experiments, we developed quantitative evaluation metrics encompassing facial landmark variations, visual similarity, and re-identification prevention capability, and derived specific threshold values based on statistical methodologies. Furthermore, to validate the proposed evaluation criteria, we conducted systematic empirical assessments using models that adopt different technical approaches. The validation experiments showed that the evaluation criteria proposed in this study can be applied across models with distinct technical characteristics. This research is expected to contribute to resolving the heterogeneous evaluation criteria issues in existing studies by providing unified evaluation criteria. It may also support the development of privacy protection technologies in autonomous driving environments. Full article

In the context of the rapid advancement of financial technology, the issue of credit card default has become increasingly salient, emerging as one of the crucial risks that financial institutions are eagerly addressing. Traditional credit card default risk prediction models predominantly rely on statistical correlations for feature selection. This approach not only makes it challenging to uncover the genuine causal relationships between variables but also leads to limitations in prediction accuracy and interpretability. To overcome these limitations, this paper presents a novel credit card default risk prediction model that integrates causal feature screening, interaction feature construction, and interpretability enhancement. Initially, by leveraging the information value (IV) and eXtreme gradient boosting (XGBoost), we perform initial feature dimensionality reduction. Subsequently, we introduce the Peter Clark algorithm (PC) augmented with perturbation enhancement and bootstrap sampling to identify a stable set of causal features. Building on this foundation, we proceed to construct higher-order interaction features to bolster the model’s nonlinear modeling capacity. These causal features and their interaction counterparts are then fed into a variety of mainstream machine learning models for training and evaluation purposes. Furthermore, on the basis of the causal feature set identified via the PC algorithm, we construct a causal path diagram. We also incorporate the causal forest double machine learning (causal forest DML) method to estimate the causal effects of features. Additionally, we design a counterfactual explanation mechanism to aid in analyzing the direction and magnitude of the impact of variable interventions on default probability. Empirical tests conducted using four typical credit datasets reveal the following findings: (1) the introduction of causal features generally enhances the model’s performance in terms of the F1 score, area under the curve (AUC), and geometric mean (G-mean). This improvement is especially pronounced in models that are highly reliant on feature quality, such as logistic regression (LR). (2) Causal features offer significant advantages in terms of model interpretability, stability, and compliance, thereby presenting a new research paradigm for credit risk prevention and control in high-risk financial scenarios. Full article

Unsaturated polyester resin (UPR)–quartz composites have become increasingly important in structural, sanitary, and architectural applications. However, their manufacturing processes still rely heavily on empirical knowledge. This review compiles recent developments in materials science, curing kinetics, and digital manufacturing, outlining a pathway toward data-driven, adaptive production of quartz-filled thermosets. The chemical and physical fundamentals of UPR polymerization are summarized, including the influence of initiator systems, filler characteristics, and thermal management on network formation. Challenges associated with highly filled formulations—such as viscosity control, dispersion, shrinkage, and exothermic peak prediction—are discussed in detail. Recent advances in digital twins (DTs) and artificial intelligence (AI) are reviewed, demonstrating how physics-based simulations, machine learning models, and hybrid mechanistic–data-driven approaches improve the prediction of rheology, curing behavior, and quality outcomes in thermoset polymer processes. A practical application example demonstrates the prediction of peak time in quartz–UPR composites using Random Forest and Gradient Boosting ensemble models. Two prediction scenarios are evaluated: Scenario A with gel time by Leave-One-Out cross-validation, and Scenario B without gel time, representing post-mixing and pre-process prediction contexts, respectively. Stratified bootstrap augmentation improves Gradient Boosting in both scenarios. Principal component analysis confirms that the curing process is governed by three independent physical dimensions: curing reactivity, thermal environment and resin thermal state. Full article

Emotion classification is a fundamental task in affective computing, with applications in human–computer interaction, mental health monitoring, and social media analysis. Although most existing methods formulate it as a flat classification problem, emotional expressions are inherently structured and grounded in semantic roles such as the emotion cue, stimulus, experiencer, and target. However, the relative contribution of these roles to emotion inference has not been systematically examined. Unlike prior models, we propose RolEmo, a role-aware framework for emotion classification that explicitly incorporates semantic role information. The framework employs a controlled role-masking strategy to analyze the contribution of individual roles, augments textual representations with external commonsense knowledge to capture implicit affective context, and applies supervised contrastive learning to structure the embedding space by bringing emotionally similar instances closer while separating opposing ones. We evaluate RolEmo on three benchmark datasets annotated with semantic roles. Experimental results demonstrate that RolEmo outperforms the strongest baseline across three datasets by up to $16.4\%$, $25.8\%$, and $23.2\%$ in the Full Text, Only Role, and Without Role settings, respectively. The analysis further indicates that the cue and stimulus roles provide the most reliable signals for emotion classification, with their removal causing performance drops of up to $6.2\%$ in macro f1-score, while experiencer and target roles exhibit more variable effects. These findings highlight the importance of structured semantic modeling and commonsense reasoning for robust and interpretable emotion understanding. Full article

Wildfires are increasingly complex and geographically dynamic phenomena that require timely and context-sensitive decision support across the management cycle. Artificial intelligence (AI) has been widely applied to wildfire detection, prediction, and remote sensing; however, a systemic understanding of how AI methods are structurally integrated into decision-support architectures remains limited. The present configurational mapping review, reported in alignment with PRISMA-ScR guidance, examines AI applications in rural wildfire management between 2020 and 2024. Using a configurational framework, explicit scope–algorithm–vector relations are mapped, identifying how specific AI paradigms are operationalised through technological infrastructures to support decision-relevant functions. A total of 27 articles were included, from which 168 scope–algorithm–vector triplets were extracted and analysed descriptively. The results reveal a concentration of applications in detection and evolution prediction tasks, predominantly supported by machine learning methods and remote sensing platforms. Explicitly linked configurations to action-oriented or prescriptive decision functions are less frequently documented. The findings contribute to a structured mapping of AI deployment patterns in wildfire management and provide a conceptual basis for future research addressing integrative and action-oriented system design. Full article

Accurate extraction of crop planting structures is important for crop area and yield estimation, but complex and fragmented cropping patterns with overlapping phenology in the Hetao Irrigation District hinder reliable crop discrimination. This study proposes a hierarchical workflow that integrates vegetation masking with multi-source feature optimization for crop mapping. First, dual-temporal Sentinel-2 imagery (May and August) is used to generate a vegetation region-of-interest(ROI) mask via Otsu thresholding applied to the Normalized Difference Vegetation Index (NDVI), combined with pixel-wise maximum-value fusion to reduce phenology-driven omissions and background interference. Second, within the vegetation mask, Sentinel-2 spectral, vegetation-index, and texture features are combined with Sentinel-1 synthetic aperture radar (SAR) backscatter and SAR texture features to construct a multi-source feature set. Random Forest(RF) feature-importance ranking is used to select an effective feature subset, and four classifiers (RF, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and convolutional neural network (CNN)) are compared under the same training/validation setting. The vegetation extraction achieves an overall accuracy of 91% (Kappa = 0.80). Using Sentinel-2 features only, the optimized subset with CNN attains the best performance (overall accuracy = 95%, Kappa = 0.93). Adding Sentinel-1 SAR texture features provides an additional improvement (overall accuracy = 96%, Kappa = 0.94), particularly for classes prone to confusion in fragmented plots. Area proportions derived from the final map are consistent with statistical yearbook data (percentage errors: maize 3.45%, sunflower 2.66%, wheat 0.11%, tomato 0.92%) under the study conditions. This workflow supports practical crop-structure monitoring in complex irrigation districts. Full article

Due to challenges such as inadequate lighting, water scattering, high density of small objects, and complex object morphology in underwater environments, traditional YOLO11 models face difficulties including interference from complex backgrounds, weak perception of small objects, and insufficient feature extraction when applied underwater. This paper proposes an improved MIP-YOLO11 model for underwater object detection based on the YOLO11 framework. First, a MCEA module is designed in the backbone network to replace the basic CBS convolution module. Through a lightweight multi-branch convolutional structure, the perception ability for small objects, object edges, contours, and morphological features in underwater scenes are enhanced without significantly increasing computational overhead. Second, an IMCA module based on the coordinate attention mechanism is introduced at the end of the backbone network to replace the C2PSA module, reducing the number of model parameters while maintaining detection accuracy. Finally, the Bottleneck module in C3k2 is improved by incorporating a PConv and a dual residual connection mechanism, thereby expanding the receptive field and enhancing the efficiency of complex feature extraction. Experimental results demonstrate that MIP-YOLO11 significantly outperforms the traditional YOLO11 in underwater environments. P and R are improved by 2.5% and 4.1%, respectively. Moreover, the mAP0.5 and mAP0.5:0.95 metrics are increased by 4.2% and 7.5%, respectively. The improved model achieves a good balance between high accuracy and light weight, and can provide a more reliable underwater object detection scheme for AUV underwater detection and other application scenarios. Full article

Background and Objectives: Rapid diagnosis is fundamental to acute ischemic stroke management; however, access to neuroradiological expertise remains limited. This scoping review maps the diagnostic accuracy, workflow impact, and cost-effectiveness of leading AI platforms (Brainomix, Aidoc, RapidAI, and Viz.ai), characterizing industry and peer-reviewed metrics. Materials and Methods: Following PRISMA-ScR guidelines, we searched PubMed, Cochrane Library, and HTA repositories for studies (2019–2025). Using a PICO-based framework, 29 studies were included for thematic mapping of the technological landscape. Results: Twenty-nine studies were included. Platforms show high proximal LVO sensitivity (78–97%), while performance for distal/MVO and posterior circulation occlusions was more variable. RapidAI is frequently mapped using historical perfusion trial parameters; however, volumetric discrepancies with platforms like Viz.ai indicate outputs are not interchangeable. Brainomix shows extensive validation for automated NCCT ASPECTS in triage. Aidoc demonstrates operational advantages via worklist prioritization, while. Viz.ai is associated with door-to-puncture time reductions (11–25 min). Economically, cost-effectiveness is driven by improved functional outcomes and expanded access to thrombectomy, rather than labor substitution. Conclusions: AI platforms function as diagnostic safety nets and workflow optimizers. Reported roles, such as perfusion-centric analysis (RapidAI) or workflow coordination (Viz.ai), reflect current research trends rather than definitive technological superiority. Institutional selection should consider these evidence clusters alongside local validation and specific clinical priorities. Full article

New, highly integrated, disruptive aircraft concepts are being devised to reduce aviation’s environmental footprint, but their performance is oftentimes challenging for the aircraft designer to assess. Furthermore, these novel aircraft often introduce new risks, such as noise, that cannot be addressed quickly by available methods. Overall, in the pursuit of more environmental friendly aircraft configurations and the lack of methods to design such aircraft, aircraft-level trade-offs between noise and performance are challenging. The present study aims to close this gap by using a machine learning-based approach for one unconventional aircraft to investigate usability in the early stages of aircraft design. Based on overflight noise measurements, noise models for this aircraft are created with different approaches and base models. The single-output models show good performance, with mean absolute errors around 1 dB, good rank correlations and R² scores above 0.9. Support vector regression provides reasonably good agreement from experiments requiring only a small effort to set up; Neural Networks achieve better performance, but increased effort is required to obtain the model. Full article

This work proposes a hybrid Uzbek sentiment analysis model (sometimes referred to as tonality analysis in the local literature) that integrates contextual text representations with named-entity information from an NER module and emoji-based emotional cues that are common in short online messages. To provide a comprehensive baseline comparison, we evaluate seven approaches—SVM, LSTM, mBERT, XLM-RoBERTa-base, mDeBERTa-v3, LaBSE, and the proposed hybrid model—covering both classical machine learning and modern multilingual transformer architectures for low-resource sentiment tasks. The overall pipeline begins with Uzbek-specific text normalization to reduce noise from informal spellings, transliteration variants, and inconsistent apostrophe usage. In parallel, the system performs explicit emoji extraction to capture affective signals that are often expressed non-verbally in social media texts. Next, we construct three complementary feature streams: a context encoder for sentence-level semantics, NER-driven entity features that encode entity mentions and types, and an emotion module that models emoji priors and their interaction with contextual meaning. These streams are fused into a unified representation and fed to a final classifier to predict sentiment polarity. Experiments on an Uzbek test set demonstrate that the hybrid model reaches an F1-score of 0.92, consistently outperforming text-only baselines. The results indicate that entity-aware and emoji-informed features improve robustness under sarcasm/irony, mixed sentiment with multiple targets, and orthographic noise, making the approach suitable for social media analytics, public opinion monitoring, customer feedback triage, and recommendation-oriented text mining. Full article