Search Results (8,312)

Ground-penetrating radar (GPR) has applications across many domains, including archaeology, mining, and infrastructure inspection. This review is specifically focused on urban subsurface utility mapping, where accurate detection of buried pipelines, cables, and conduits is critical for excavation safety and infrastructure management. Within this scope, two major barriers are identified: event–utility mismatch and the synthetic–field domain gap. Bibliometric analysis shows increasing reliance on deep learning, yet most methods remain limited to event-level hyperbola detection rather than utility-level inference. In real urban environments, radar responses are often affected by orientation-dependent signatures, clutter, overlapping reflections, and non-utility anomalies, making detected events difficult to map directly to physical infrastructure. In parallel, models trained on synthetic data frequently show limited field generalization because simulated radargrams do not fully reproduce soil heterogeneity, acquisition variability, and system artifacts. The review argues that future progress in urban utility mapping requires a shift toward utility-level reasoning supported by multi-sensor fusion, physics-guided learning, hybrid simulation–field datasets, and uncertainty-aware interpretation. Such advances are essential for making GPR outputs more reliable and actionable in urban engineering practice. Full article

Accurate measurement of the 2D soot spatial distribution is vital for optimizing combustion efficiency and reducing pollutant emissions. While 1D laser extinction (LE) is robust and cost-effective, it provides only line-of-sight integrated information, lacking the spatial resolution required to resolve complex soot topologies. We propose Soot-MixFormer, a hybrid deep learning model designed for the high-fidelity inversion of 2D soot distributions from 1D extinction data. The architecture integrates CNN-based local feature extraction with Transformer-based global dependency modeling. Key innovations include a dynamic decoupled generation head and a Dual-Axial Gated Refinement (DAGR) module coupled with a physical hard constraint layer to ensure mass conservation and physical consistency. Experimental results demonstrate that Soot-MixFormer significantly outperforms baseline MLP and CNN models, achieving a Structural Similarity Index (SSIM) of 0.800 and a Pearson Correlation Coefficient (PCC) of 0.915, and a highly suppressed Root Mean Square Error (RMSE) representing less than 10% relative error in high-concentration zones. Furthermore, the model exhibits exceptional robustness, maintaining a cosine similarity above 0.72 even under 10% simulated measurement noise. The model is highly efficient, with only 0.97 M parameters and a real-time inference speed of ~246 FPS. This study provides a novel, low-cost diagnostic paradigm for real-time, high-accuracy monitoring of soot fields in industrial combustion environments, effectively bridging the gap between simple 1D sensing and complex 2D spatial reconstruction. Full article

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO₂, and NO₂ were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO₂ emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R² values of 0.968 for CO₂ and 0.936 for NO₂, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R² values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO₂ predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems. Full article

Conventional contact-based monitoring of heart rate (HR) presents challenges such as patient discomfort, skin irritation, and poor long-term adherence, motivating the development of contactless, video-based sensing systems. This study proposes a robust hybrid framework combining advanced signal processing with machine learning to enhance HR estimation accuracy from facial video. The methodology integrates a two-stage geometric stabilization pipeline with dense facial tessellation to mitigate motion. Eulerian Video Magnification (EVM) amplifies subtle color variations, followed by chrominance-based roi filtering. Signal recovery utilizes a sliding-window Principal Component Analysis (PCA) for local coherence, followed by Second-Order Blind Identification (SOBI), with a Light Gradient Boosting Machine (LightGBM) classifier employed to automatically identify physiological sources. Validated on the challenging COHFACE dataset, the approach achieves a Mean Absolute Error (MAE) of 1.50 bpm, a Root Mean Square Error (RMSE) of 3.07 bpm, and a Pearson Correlation Coefficient (PCC) of 0.97 on the test set. The method demonstrates robustness across diverse lighting conditions, outperforming traditional algorithms and achieving parity with state-of-the-art deep learning models, while offering an interpretable solution for contactless health monitoring. Full article

Accurate simulation of karst spring discharge is critical for sustainable water resource management, yet it remains a significant challenge due to the inherent complexity, heterogeneity, and non-linearity of karst systems. While machine learning models have been increasingly applied to this problem, standalone algorithms often struggle to simultaneously capture complex temporal dependencies and maintain robust generalization. This study provides a comprehensive comparative assessment of five state-of-the-art machine learning (ML) models for forecasting the daily discharge of the Jósva Spring, located in the World Heritage Aggtelek karst area. The main goal of the study is to determine which modern machine learning approach can most accurately forecast the daily discharge of the Jósva Spring using meteorological data and the discharge of a hydraulically connected upstream spring. This is motivated by the need for a reliable operational prediction tool for complex karst aquifers, the improved water-resource management in a climate-sensitive region, and a lack of comparative studies evaluating multiple ML paradigms on the same karst system. The study also aimed at comparing the predictive performance of five state-of-the-art ML models to identify the most accurate and robust model and to understand the predictability of the karst system by analyzing feature importance, lag effects, and temporal dependencies. Three tree-based ensemble models (Random Forest, XGBoost, and Extra Trees) and two deep learning architectures (a Bidirectional Long Short-Term Memory network, BiLSTM, and a novel Hybrid XGBoost–BiLSTM model) were trained using a five-year (2015–2019) daily dataset comprising rainfall, temperature, and upstream discharge. The modeling framework was designed for synchronous simulation (lead time = 0 days), estimating concurrent downstream discharge using upstream and meteorological measurements from the same time step. A rigorous feature-engineering workflow was implemented based on statistical characterization, correlation analysis, and time-series diagnostics. Models were trained on 80% of the dataset and evaluated on an independent 20% test set. The results demonstrate that the proposed Hybrid XGBoost-BiLSTM model achieved the highest predictive accuracy on the unseen test data (R² = 0.74, NSE = 0.74, RMSE = 716.35 L/min). While the standalone tree-based models, particularly XGBoost (R² = 0.66), also exhibited strong and competitive performance, the hybrid architecture provided a consistent and measurable improvement across all evaluation metrics. The hybrid model’s success is attributed to its synergistic design, which leverages the powerful feature extraction and refinement capabilities of XGBoost to provide a more informative input space for the BiLSTM, thereby enhancing its ability to capture complex temporal dependencies while mitigating overfitting. Feature importance analysis confirmed that upstream discharge at a 3-day lag was the most critical predictor, highlighting the system’s hydraulic connectivity. This research provides clear, evidence-based guidance showing that hybrid machine learning architectures, which integrate the strengths of different modeling paradigms, represent the most effective approach for developing robust and reliable operational prediction tools for complex karst aquifers. Full article

The phenomenon of CO₂ corrosion of downhole tubing is widespread in oil and gas extraction. Currently, there is a lack of applicable prediction methods for the corrosion rates of different metal tubing in the liquid phase CO₂ environment. To address this issue, this paper systematically investigates the anti-corrosion mechanisms and influencing factors of different metal casings and proposes a deep learning model combining convolutional neural networks and long short-term memory networks. Based on laboratory corrosion experimental data, the model extracts spatial features of parameters affecting the corrosion rate through CNN and captures their temporal dependencies through LSTM. This paper builds a pipe corrosion rate prediction model based on the TensorFlow framework and compares the prediction results with those of the traditional D-W empirical model and the SRV machine learning model. The results showed that the CNN-LSTM model maintained high prediction accuracy regardless of high or low chromium content, with R² reaching 0.83 and 0.94 respectively, solving the problem that existing models have difficulty effectively simulating complex corrosion behavior under flowing corrosive media conditions. The model was verified using the remaining wall thickness of the actual application casing in the field, and the accuracy was over 80%. The established prediction method can be extended to predict the corrosion rate of pipes under similar corrosion conditions. Full article

Cross-age face analysis has emerged as an important topic in biometric recognition due to substantial facial appearance variations caused by aging. Nevertheless, most existing approaches primarily focus on face verification (1:1 matching) and frequently rely on explicit age annotations, which limit their applicability in large-scale retrieval scenarios. In this study, large-scale cross-age face retrieval (1:N matching) is investigated, and a Hybrid Metric Learning Framework (HMLF) is proposed to learn age-invariant and retrieval-oriented facial representations without requiring age labels. The proposed framework integrates Additive Angular Margin Loss (ArcFace) with supervised contrastive learning to enhance feature discriminability. Furthermore, a mixed triplet mining strategy is introduced to improve the effectiveness of hard sample selection. A memory bank-based InfoNCE formulation is incorporated to provide a large number of negative samples, and an uncertainty-based adaptive weighting scheme is designed to automatically balance multiple loss components during optimization. To better simulate realistic retrieval scenarios, an extended cross-age retrieval evaluation protocol is established. Extensive experimental results demonstrate that the proposed framework achieves superior retrieval performance across different backbone architectures. The results further provide systematic insights into the influence of backbone design, loss formulation, and optimization strategies on cross-age retrieval accuracy. Full article

Deep-learning-based vulnerability detection has made substantial progress, but two limitations remain prominent. Sequence-based methods linearize source code and thus weaken the explicit modeling of control-flow and data-flow dependencies. Graph-based methods preserve program structure, yet conventional graph neural networks still have difficulty capturing long-range interactions in large code property graphs (CPGs). In addition, standard CPGs usually lack explicit variable semantics and security-critical node roles, which limits their ability to represent vulnerability-relevant program behavior. To address these issues, we propose SV-GEN, a vulnerability detection framework that combines large-language-model-driven semantic enhancement with hybrid sequence-graph learning. The novelty of SV-GEN lies in introducing a semantically enriched code property graph, termed Sem-CPG, which augments conventional CPGs with variable semantic roles and security-oriented node labels, and in coupling this representation with an adaptive fusion mechanism over structural and sequential views. Specifically, we use a large language model as an external semantic annotator to assign variable roles and identify source, sink, and sanitizer nodes, and then encode the resulting Sem-CPG with a Graph Transformer while modeling the code sequence with GraphCodeBERT. A learnable gating module is further used to adaptively fuse the graph-level and sequence-level representations for final prediction. Experiments on Devign, ReVeal, and DiverseVul show that SV-GEN achieves competitive or superior overall performance across benchmarks, with particularly strong improvements on the large and highly imbalanced DiverseVul dataset. Full article

Credit risk assessment under severe class imbalance requires both structured imbalance correction and principled decision rules, yet most studies treat these as independent steps. This study develops a general integrated three-layer framework for credit risk assessment under class imbalance. The first layer introduces Sequential Hybrid Data Oversampling (SHDO), which sequentially applies five complementary oversampling techniques to enrich minority-class representation in mixed-type credit data. The second layer formulates credit approval as a decision-theoretic optimisation problem: a closed-form optimal threshold is derived under asymmetric costs, extended to constrained portfolios via a Lagrangian formulation with Karush–Kuhn–Tucker conditions, and further extended to minimax-robust decision making under estimation uncertainty. The third layer compares eleven classifiers under a unified evaluation protocol with an ablation isolating the effect of SHDO. The framework is empirically validated on the Home Credit Default Risk dataset, which is used as an illustrative case study rather than defining the scope of the contribution. On the held-out test set, XGBoost trained with SHDO achieves the highest minority-class F1 (0.254), while gradient-boosted models collectively attain ROC-AUC values of 0.713–0.750, outperforming classical baselines (0.540–0.620). The ablation confirms that without SHDO, all models exhibit near-zero minority-class recall despite adequate ranking ability. SHAP analysis on XGBoost confirms that the learned risk structure aligns with established creditworthiness determinants. The decision framework converts these probability estimates into analytically justified approval thresholds responsive to economic parameters, institutional constraints, and estimation uncertainty. Full article

This study examines how hybrid AEC education can foster BIM-enabled construction workflow competencies in Sub-Saharan Africa, where digital limitations impact graduate readiness. A quantitative survey of 120 students and graduates yielded 81 valid responses (68% response rate). Data were analysed using descriptive statistics and Principal Component Analysis (KMO = 0.943; Bartlett’s test p < 0.001), revealing components that explained 77.65% of the variance. Respondents strongly supported hybrid learning, emphasising the importance of digital collaboration, curriculum reform, and equitable access to technology. However, institutional barriers such as limited digital readiness and inadequate BIM workflow training were also identified. These findings informed a competency-based framework linking hybrid delivery, digital infrastructure, curriculum modernisation, and learner capabilities to improved workflow proficiency. The study suggests that hybrid AEC education can help close digital skills gaps if supported by staff upskilling, accessible software, and workflow-focused pedagogy. Future work should validate the framework across contexts and track long-term learning outcomes. Full article

Methane (CH₄) is a potent greenhouse gas, and accurately estimating its global budget is essential for climate change mitigation. This review provides a comparative synthesis of top-down, bottom-up, and integrated approaches for quantifying methane emissions and sinks, with a particular focus on the role of remote sensing. Top-down methods, leveraging satellite observations from instruments like GOSAT and TROPOMI within atmospheric inversion frameworks (Bayesian, 4D-Var), provide observationally constrained, spatially integrated fluxes, reducing global budget uncertainty to ±5–10%. However, they face challenges in source attribution and rely heavily on transport model accuracy. Conversely, bottom-up approaches, including process-based models (e.g., CLM, DNDC) and emission inventories (e.g., EDGAR), offer detailed, sector-specific insights but are prone to underestimating emissions from super-emitters and diffuse sources like wetlands, with uncertainties often exceeding ±20–40% for individual sectors. Key persistent discrepancies between the two approaches are largest for natural sources (e.g., a 20–40 Tg yr⁻¹ gap for tropical wetlands). Integrated approaches, which synergize top-down atmospheric constraints with bottom-up inventory data, are emerging as the most robust methodology, effectively narrowing the global budget gap and improving confidence. Recent advancements in satellite missions (e.g., MethaneSAT), machine learning algorithms for plume detection, and high-resolution inversion models are transforming monitoring capabilities. However, challenges remain in harmonizing datasets, representing complex microbial processes in models, and expanding observational coverage in data-scarce tropical regions. This review concludes by outlining a future path centered on hybrid inversion frameworks, AI-driven source attribution, and cross-disciplinary collaboration to deliver the actionable methane budgets needed for effective climate policy. Full article

Solution electrospinning (SES) and melt electrowriting (MEW) are complementary fiber-based fabrication platforms extensively investigated in tissue engineering. SES generates fibers typically ranging from the nanometer to the low-micrometer scale, producing fibrous networks that mimic the native extracellular matrix (ECM) and support key cellular functions. MEW, by contrast, operates solvent-free and enables precise, layer-by-layer deposition of microfibers with well-controlled geometry, conferring the mechanical integrity and open-pore architecture that SES constructs inherently lack. Despite growing interest, the body of peer-reviewed literature reporting original hybrid SES–MEW fabrication and biological outcome data remains limited, with no comprehensive cross-tissue synthesis available to date. This narrative review examines the current state of SES–MEW hybrid strategies across five tissue engineering targets selected for their clinical relevance: skin, vascular grafts, bone, cartilage, cardiac valves, and skeletal muscle. For each application, the architectural rationale, the fabrication approach, and the in vitro and in vivo biological outcomes are discussed in an integrated manner, with attention to how the spatial organization of nano- and microfibers translates into tissue-specific functional responses. A comparative analysis across tissue types highlights both the versatility of hybrid constructs and their persistent limitations, including suture retention values that remain below clinically accepted thresholds in vascular applications, incomplete cellular infiltration through dense nanofibrous layers, and the absence of validated, reproducible scale-up protocols compatible with clinical-grade manufacturing. The review concludes by identifying the most critical open questions in the field, encompassing process standardization, regulatory classification, and the emerging role of machine learning in closed-loop MEW process optimization. This work aims to provide an evidence-based perspective on the current state of hybrid SES–MEW scaffold engineering and the key translational gaps limiting clinical application. Full article

This paper explores the emerging synergy between control theory and machine learning in robotics, focusing on methods that combine model-based strategies with data-driven adaptation. The authors highlight how classical techniques, such as model predictive control and adaptive control, are being enhanced by reinforcement learning, imitation learning, and neural models to address challenges in complex, uncertain environments. Emphasis is placed on real-world platforms (e.g., legged systems, aerial robots, and manipulators) with special attention to advanced domains such as multi-agent systems and coordination. The authors, in addition, establish a taxonomy to categorize these hybrid approaches as “learning-for-control”, “control-for-learning”, or “co-designed architectures”. This paper also reflects upon key open problems, including sim-to-real transfer, safety, and the need for verifiable learning-based controllers, all facets that help to outline a roadmap for future research. Full article

Text summarization involves analyzing large amounts of text, selecting the salient text features, and arranging them coherently. The graph-based TextRank and statistical topic modeling are unsupervised approaches for generating an extractive synopsis. Deep learning models are supervised, data-driven, and pre-trained on a huge corpus of data, making a significant contribution to automatic text summarization systems. Despite grammatical correctness and coherence, deep learning-based summarization systems are prone to factual inconsistency. This has hindered the applicability of transformer-based summarizers, particularly in critical domains where misleading summarization systems can lead to severe consequences due to their significant social impact. This work proposes an ingenious hybrid hierarchical approach that combines unsupervised approaches, such as the TextRank algorithm and Latent Dirichlet Allocation (LDA)-based summaries, with contemporary transformer-based language models. When validated on three benchmark summarization datasets, empirical results prove that our hybrid hierarchical transformer-based approach mitigates the factual inconsistency problem inherent in abstractive summarization. The improved summary consistency score of the abstractive summaries generated with our multilevel hybrid approach, in comparison to the fine-tuned baseline transformer-based language models, increases trust in transformer-based summarizers. Full article

Battery state-of-health (SOH) estimation is essential for the safety and reliability of energy storage systems. However, incomplete measurements due to sensor or communication failures pose significant challenges for accurate prediction. This paper proposes a robust SOH estimation framework using a minimal 5 min observation window to handle high data sparsity in both random and latter-half missing scenarios. Three Deep Learning (DL) architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Transformer—are evaluated for data imputation and SOH estimation against traditional polynomial fitting. Simulation results on the NASA benchmark dataset demonstrate that the proposed LSTM model achieves high accuracy, with an RMSE of 0.8522 on complete data. For imperfect data scenarios, BiLSTM-based imputation effectively suppresses extreme deviations, reducing the Maximum Error (MxE) by 44% (from 14.04 to 7.85) compared to traditional polynomial methods. Furthermore, in challenging terminal missing-data cases, a hybrid LSTM-Transformer strategy maintains physical consistency, achieving a superior RMSE of 1.0026. These findings confirm that the proposed DL-based framework significantly outperforms conventional techniques, providing a robust and reliable solution for real-time battery health monitoring under unpredictable data conditions. Full article