Search Results (7,577)

Oil shale in situ conversion provides an important pathway for developing medium- to deep-buried, low-grade, and thin oil shale resources. Among the available approaches, the in situ conversion technology triggered by the topochemical reaction method, hereafter referred to as the TSA method, induces local oxidation reactions of pyrolysis residuals, fixed carbon, and reactive organic matter through preheating and oxygen-containing gas injection. The released in-formation heat then supports continued kerogen cracking and reaction-front propagation. This review summarizes the TSA method from a process-oriented perspective, linking reaction mechanisms, engineering controls, geochemical process identification, pilot tests, economic–environmental constraints, and scale-up evaluation. Existing studies indicate that the TSA method has formed a technical chain involving reaction initiation, heat/reaction-front propagation, oil and gas recovery, and process monitoring. Pilot tests provide evidence for operational feasibility, but not yet for full commercial feasibility. Thermal simulation results show that oil and gas generation and expulsion become significant above ~350 °C, and that 375–425 °C can be used as an important reference window for temperature control rather than a fixed optimum for all oil shale reservoirs. Geochemical indicators can provide complementary constraints for identifying reaction progress, especially when calibrated with produced oil and gas. Further development should focus on fracture-network control, heat-transfer enhancement, oxygen-supply regulation, multi-well coordination, equipment reliability, economic evaluation, groundwater protection, and CO₂ emission accounting. These issues are critical for advancing the TSA method toward larger-scale, low-carbon, and well-regulated application. Full article

This study presents a systematic review of Artificial Intelligence (AI) in vehicular bridge engineering, covering design, monitoring, and lifecycle decision support. The objective is to identify, classify, and critically analyze the main AI methods applied across the bridge lifecycle, including Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANNs), and Optimization Algorithms (OAs). The review follows the PRISMA 2020 framework to ensure transparency and reproducibility, considering publications from 2018 to 2026. The results show that AI applications span the entire bridge lifecycle; however, current research is predominantly concentrated in Structural Health Monitoring (SHM), damage detection, inspection, and predictive maintenance, while design-oriented applications—such as optimization, surrogate modeling, and structural analysis—remain comparatively less developed. Importantly, SHM data serve as a key input for data-driven modeling, enabling design optimization, reliability assessment, and lifecycle decision support. Classical ML methods remain effective for structured datasets, whereas DL models, particularly convolutional and recurrent neural networks, dominate image-based and time-series applications. In addition, hybrid physics-informed AI approaches are emerging to improve model reliability and interpretability. The review also identifies key challenges, including data quality limitations, lack of standardized methodologies, limited integration with engineering design codes, and barriers related to trust, expertise, and regulatory frameworks. Overall, the findings highlight a shift toward integrated digital frameworks, including digital twins and multimodal data fusion, to support more reliable monitoring and lifecycle decision-making. This study provides a comprehensive synthesis of current developments and outlines future research directions toward more resilient and intelligent bridge infrastructure systems. Full article

This study provides an empirical assessment of greenhouse gas emissions and the environmental performance of hybrid vehicles in the European Union. The analysis integrates a macro-level examination of nitrous oxide (N₂O) emission trends in EU Member States for road and pipeline transport with a micro-level econometric investigation of emissions generated by the internal combustion engines of hybrid vehicles. The empirical analysis is based on a large sample of hybrid vehicles of different brands and variants, including 1350 observations used to examine the relationship between CO₂ emissions and fuel consumption per 100 km, and 123 observations to analyze nitrogen oxides (NO_x) emissions. CO₂ is assessed as the principal greenhouse gas emitted during vehicle operation, while NO_x (NO and NO₂) is examined as a major regulated atmospheric pollutant relevant to environmental performance. A bibliometric analysis of NO_x-related publications further highlights increasing scientific attention to this pollutant, supporting the relevance of the current study. Results reveal significant heterogeneity across hybrid vehicle models in terms of fuel consumption and NO_x emissions, indicating that environmental performance is strongly influenced by technological design and operational characteristics. Robust multiple regression models (R² = 0.84 for vehicle with low CO₂ emissions, 0.82 for high CO₂ emissions and R² = 0.72 for NO_x emissions) revealed significant correlations between pollutant emissions and fuel consumption, providing valuable tools for predicting emissions and informing environmental policies and hybrid vehicle design. Overall, the findings indicate that hybrid vehicles can contribute to improved environmental performance and lower greenhouse gas emissions relative to conventional vehicles, while their effectiveness depends on model specific characteristics and broader sectoral emission dynamics in the EU. These insights provide evidence for policymakers and industry stakeholders to support the transition toward cleaner vehicle technologies and align climate neutrality targets in the European Union. Full article

This editorial synthesizes the key findings from 17 original research articles featured in the Special Issue on “Intelligent and Integrated Approaches for Efficient Oil and Gas Development.” The collection demonstrates a paradigm shift from purely data-driven methods toward physics-informed, interpretable, and operationally deployable intelligent systems across the upstream lifecycle. Advances span intelligent drilling with real-time model predictive control frameworks achieving sub-20 ms execution times and bottomhole pressure fluctuations below 0.30 MPa; AI-assisted reservoir characterization using multiscale convolutional neural networks, seismic waveform-constrained inversion, and geology-informed transformers that improve sandstone thickness prediction (R² = 0.895) and stratigraphic correlation (F1 = 0.886); production optimization through hybrid decomposition-ensemble models (R² = 0.954) and improved XGBoost (R² = 0.989); and enhanced oil recovery via self-assembled foam systems and polymer injector designs. Fundamental geochemical studies on the Qiongzhusi Formation shale and tight sandstone gas in the Ordos Basin provide critical geological constraints. The editorial identifies persistent challenges, including real-time performance versus physical fidelity, interpretability and uncertainty quantification, multi-scale integration, and generalizability across diverse geological settings. Future directions highlight reinforcement learning for autonomous operations, physics-informed digital twins, generative AI for subsurface scenario modelling, and integration with carbon capture, utilization, and storage. This Special Issue advances the convergence of petroleum engineering, artificial intelligence, and Earth sciences toward intelligent, secure, and sustainable hydrocarbon development. Full article

The design of polygonal pretensioned concrete girders involves a practical conflict among load-carrying capacity, stiffness, ductility, damage control, and material cost. In conventional design, these characteristics are strongly coupled: increasing web thickness may improve stiffness but reduce ductility, while modifying tendon inclination or inflection-point position may improve prestress efficiency but may also induce local stress concentration near tendon-deviation regions. This coupling makes it difficult to identify rational design solutions through trial-and-error procedures alone. To address this problem, this study proposes a mechanism-informed and interpretable design framework for 30 m polygonal pretensioned concrete Bulb-T girders by integrating nonlinear finite-element analysis, surrogate-assisted modeling, multi-objective trade-off evaluation, and SHAP-based feature-attribution analysis. The scientific problem addressed in this study is the insufficient understanding of how tendon geometry and sectional parameters interact to govern structural response, while the applied problem is the lack of transparent design guidance for balancing performance and cost in polygonal pretensioned girders. The results show that girder behavior is controlled by coordinated parameter interactions rather than isolated parameter changes. Tendon inclination, inflection-point location, and web thickness are identified as the dominant variables affecting load-carrying capacity, damage evolution, stiffness–ductility balance, and cost-effectiveness. Compared with the conventional design, the representative optimized design increased the ultimate load-carrying capacity by approximately 26%, reduced the peak concrete damage index by approximately 24%, and increased the structural performance index by approximately 8.5%, lowered the normalized material–cost indicator by approximately 5%, and improved the performance–cost index by approximately 14–15%. These findings indicate that the proposed framework is not a fundamentally new girder form, but an improved interpretable design methodology that converts numerical optimization results into transferable engineering design principles for polygonal pretensioned concrete girders. Full article

To address the challenges of time-dependent error divergence in Strapdown Inertial Navigation Systems (SINS) and the insufficient accuracy of traditional terrain matching algorithms in feature-sparse flat terrain environments, this paper proposes an intelligent terrain-aided navigation method integrating an Improved Osprey Optimization Algorithm (IOOA), Distribution Estimation, and Q-learning. Utilizing terrain information entropy as a robust matching metric, the algorithm establishes a two-phase evolutionary framework comprising Lévy flight-based random search (exploration phase) and elite-guided Gaussian Estimation of Distribution (exploitation phase). By introducing a Q-learning mechanism to adaptively regulate exploration parameters, an intelligent balance between population diversity and convergence speed is achieved. Under a unified computational benchmark, systematic multi-scenario simulations were conducted using datasets from simulated moderately undulating foothill terrain, the Libyan Sahara, and the real Digital Elevation Model (DEM) of the Junggar Basin in Xinjiang, China. Experimental results demonstrate that, compared to traditional TERCOM and mainstream swarm intelligence algorithms, the proposed algorithm drastically reduces positioning errors in the aforementioned complex terrains and significantly enhances matching accuracy. Robustness and real-time performance tests indicate that the algorithm achieves an average single-match processing time of only 0.08 s and maintains error variability as low as ±0.83 m under random perturbations. Furthermore, an ablation study confirms the necessity of the multi-strategy fusion mechanism in suppressing local optima entrapment and non-convergent oscillations. This study validates the engineering feasibility of the algorithm under conditions of low computational dependency, providing an effective technical approach for high-precision autonomous navigation in GPS-denied environments. Full article

Efficient exploitation of evolutionary knowledge while preserving population diversity remains a central challenge in optimization. Existing knowledge-learning evolutionary algorithms primarily rely on successful experiences, overlooking structural information embedded in failed search attempts. This asymmetric learning limits adaptability and may cause premature convergence in high-dimensional landscapes. To address this issue, a failure-aware bidirectional evolutionary knowledge assimilation framework is developed within the honey badger optimization algorithm. Unsuccessful offspring are treated as negative knowledge carriers and transformed through symmetric adversarial reflection, enabling simultaneous extraction of positive and negative structural information. A time-dependent regulation mechanism dynamically adjusts knowledge assimilation intensity across evolutionary phases to balance exploration and exploitation. In addition, a continuous mutation spectrum transition strategy adaptively integrates Cauchy and Gaussian perturbations, facilitating smooth migration from global exploration to local refinement. Comprehensive experiments conducted on the CEC 2017 benchmark suite across 10, 30, and 50 dimensions validate the proposed framework, establishing a novel failure-aware bidirectional evolutionary learning paradigm for knowledge-driven optimization. The results demonstrate that our method achieves statistically significant and consistent performance improvements over classical baseline algorithms. Furthermore, its robustness and cross-domain adaptability are corroborated through successful application to a real-world constrained engineering problem: welded beam design. Full article

This study presents a symmetry-guided, mechanism-informed, and constraint-aware staged evolutionary framework for the structural optimization of a heavy-duty industrial robot with dominant joint flexibility. Unlike conventional sizing strategies that treat transmission compliance as a secondary verification issue, the proposed method incorporates joint-flexibility-induced low-frequency vibration directly into the optimization formulation and organizes the design problem through a symmetric joint-space/Cartesian-space evaluation framework. An equivalent linearized flexible-joint dynamic model is established for the dominant load-bearing joints under the heavy-load operating condition of interest, and three coordinated performance indices are constructed to characterize vibration robustness, end-effector static stiffness, and global velocity-transmission quality under explicit workspace-retention constraints. To improve engineering interpretability, a staged NSGA-II strategy is adopted, in which global link-length variables and local sectional variables are optimized sequentially. The results indicate that the proposed framework increases the minimum first-order vibration frequency, reduces end-effector deformation, and preserves acceptable workspace coverage. More importantly, the optimization process reveals an interpretable asymmetry in structural sensitivity: sectional redistribution, especially in the forearm, contributes more effectively to vibration suppression than direct reduction in the global arm span. The study therefore provides a reusable symmetry-oriented structural redesign methodology for heavy-duty serial manipulators whose low-frequency dynamics are governed primarily by compliant drive chains. Full article

Accurate forecasting of municipal electric vehicle (EV) charging demand is increasingly important for distribution system planning, charging infrastructure management, and demand-side operation. This study proposes a weather-aware Transformer–LSTM hybrid framework for spatio-temporal forecasting of EV charging load across municipal public charging stations. The proposed approach integrates multi-source information within a unified pipeline, including cyclic temporal encodings, multi-lag autoregressive features, rolling statistics, behavioral aggregates, and meteorological variables, while combining a Transformer encoder to capture long-range temporal dependencies with an LSTM decoder to model local sequential dynamics and nonlinear load patterns. The framework was evaluated using 211,324 charging sessions collected from eight New York City municipal charging stations between July 2021 and December 2025. Under controlled benchmarking against Simple RNN, standalone LSTM, and encoder-only Transformer models using identical preprocessing, feature engineering, and training settings, the proposed hybrid model achieved R² = 0.9731, MAE = 62.71 kWh, RMSE = 94.21 kWh, and MAPE = 19.62%. Relative to the standalone Transformer, the proposed model reduced RMSE by 32.6% and MAPE by 34.5%. In addition, the model maintained strong forecasting performance across stations with heterogeneous demand profiles without station-specific retraining and remained robust across seasonal variations. These results demonstrate that the proposed framework provides a reproducible and scalable solution for municipal EV charging load forecasting in real-world urban environments. Full article

Real-time binaural beat synthesis in dynamic acoustic environments is challenged by carrier non-stationarity, interaural phase discontinuities, and processing delay in conventional digital signal processing pipelines. This study proposes a predictive dual-stage neural framework for phase-coherent auditory synthesis under non-stationary acoustic conditions. The framework decouples real-time carrier estimation from phase-coherent signal generation through two specialized modules. An intelligent acoustic sensing module (AI-1) estimates time-varying carrier information across harmonic, fluctuating, and broadband acoustic profiles using a causal neural front-end with an adaptive confidence-driven strategy. A predictive phase-coherent generator (AI-2) then forecasts short-horizon carrier trajectories and drives a discrete-time phase accumulator to maintain continuous phase evolution during binaural beat embedding. Objective evaluation under multiple acoustic profiles and noise conditions shows that the proposed framework maintains strong phase continuity, with a Phase Coherence Factor greater than 0.91, and low artifact levels, with a Signal-to-Artifact Ratio greater than 39.8 dB, under the evaluated conditions. Additional comparisons with conventional DSP baselines, stronger classical F0 estimators, a lightweight neural F0 tracker, and component-wise ablation variants further demonstrate that the performance improvement arises from the combination of adaptive carrier estimation and predictive phase-coherent actuation, rather than from carrier estimation alone. Hardware profiling shows a combined INT8 inference time of 2.4 ms per frame on a resource-constrained Raspberry Pi Zero 2W-class edge device. Importantly, this inference time and the sub-millisecond phase-accumulator resolution should not be interpreted as sub-millisecond end-to-end physical audio latency. The complete system still includes buffering, framing, neural inference, and output processing delay; the proposed method instead reduces effective phase-boundary misalignment through short-horizon predictive compensation. These results support the proposed framework as a lightweight engineering solution for real-time phase-continuous auditory synthesis in dynamic listening environments. The reported PCF and SAR values should be interpreted as signal-level indicators of phase continuity and artifact suppression, rather than as evidence of listener comfort, perceptual preference, or neurophysiological efficacy. Full article

Particle swarm optimization (PSO) has been widely used in engineering optimization because of its simple structure and easy implementation. However, standard PSO and most of its variants mainly learn from the personal best position and the global best position. Thus, they often fail to preserve and reuse population-level knowledge generated during the search process. This problem becomes more evident when the search state changes or the swarm falls into stagnation, at which point useful search information may be ignored or forgotten. To address this issue, this paper proposes a state-adaptive knowledge recall PSO algorithm, termed SKRPSO. It includes three cooperative components. First, a state-aware adaptive aggregation mechanism adjusts the elite knowledge-pool size according to population dispersion and builds a rank-weighted knowledge vector for stable population-level guidance. Second, a stagnation-driven knowledge recall mechanism stores historical knowledge associated with global improvements in a bounded memory buffer and recalls recently successful knowledge with a time-decay preference when stagnation is detected. Third, a knowledge-fusion position update strategy uses current aggregated knowledge during normal search and recalled knowledge under stagnation, balancing local exploitation and stagnation escape. Experiments on the CEC2017 benchmark suite show that, based on 30 independent runs, SKRPSO achieves the best mean error on 22 of 29 functions and the best overall Friedman average rank of 1.431 among all compared algorithms. Engineering design results further indicate stable performance. Full article

The rapid expansion of artificial intelligence has intensified the demand for hardware systems capable of emulating brain-like information processing with high accuracy, energy efficiency, and reliability. Neuromorphic computing based on memristive artificial synapses has emerged as a promising approach to overcome the limitations of conventional von Neumann architectures. Although inorganic and oxide-based synaptic memristors have been widely explored for neuromorphic systems, they often suffer from poor linearity, asymmetric potentiation/depression behavior, limited conductance states, and device variability, which restrict learning accuracy and scalability. In contrast, polymer-based memristors have gained significant attention owing to their intrinsic advantages, including mechanical flexibility, molecular tunability, controllable electronic/ionic transport, low-temperature processability, and compatibility with large-area fabrication. This review critically examines recent advances in polymer—based memristive materials and devices for achieving linear and symmetric artificial synaptic behavior. Polymer synapses are classified into pure polymer, polymer composite, and polymer-hybrid systems through a mechanism to function framework. Rather than providing a general compilation of organic memristor studies, this review analyzes how polymer chemistry, ion-migration control, trap state distribution, redox activity, electrode selection, active layer thickness, and interface engineering govern conductance update linearity, symmetry, and uniformity. Fundamental switching mechanisms, material classifications, device architectures, key synaptic characteristics, and system-level neuromorphic performance, including pattern-recognition applications, are critically discussed. By explicitly linking material and device design to conductance update fidelity, learning accuracy, training convergence, and pattern-recognition reliability, this review provides practical design guidelines and future perspectives for next-generation polymer-based neuromorphic hardware with improved linearity, symmetry, reliability, and scalability. Full article

To address the defects of the traditional egret swarm optimization algorithm (ESOA) in high-dimensional complex optimization problems, such as low optimization accuracy, weak ability to escape from local extrema, rapid decay of population diversity, and insufficient efficiency in the late convergence stage, an improved egret swarm optimization algorithm (IESOA) combining variable-factor weighted learning and adjacent generation dimension crossover strategy is proposed. Firstly, a dynamic change rule of core model parameters (exploration factor ω and exploitation factor μ) is constructed to adaptively adjust with the iteration process, so as to balance global exploration and local exploitation capabilities. Secondly, a multi-individual variable-factor weighted learning mechanism is designed to enable offspring individuals to inherit the position information of following individuals, sub-population optimal individuals, and global optimal individuals simultaneously, avoiding excessively fast assimilation of the population. Furthermore, an adjacent generation dimension crossover strategy is established to update the optimal individual based on the priority principle of absolute dimension difference, fully retaining the historical optimal dimension information. Finally, a preferred mutation reverse learning strategy is integrated to further enhance the local extremum escape ability and convergence accuracy of the algorithm. The IESOA is compared with eight algorithms, including PSO, DE, SBOA, BKA, HHO, DOA, and the original ESOA on CEC2014 and CEC2019 benchmark test suites. The results show that IESOA presents significant advantages in optimization accuracy, convergence speed, and stability. The algorithm is applied to three typical engineering optimization problems: reinforced concrete beam design, welded beam design, and pressure vessel design, which effectively reduces the structural design cost and verifies its application value in practical engineering. Full article

In this paper, we focus on online stochastic optimization problems in which random parameters follow time-varying distributions. In each round t, a decision is obtained from solving the current optimization problem. Then samples are drawn from distributions which are updated after obtaining the decision. The objective and constraint are updated in this process, and the updated problem is used to obtain the next decision. To solve the online stochastic optimization problem, we propose a model-based stochastic augmented Lagrangian method, which is referred to as the MSALM. In each round, we construct model functions for the sample objective and constraint functions based on their properties, which reduce computational complexity. The step size is designed in a dynamic way and decreases as t increases to accelerate convergence. Due to the setting of the online stochastic problem, we use stochastic dynamic regret and constraint violation to measure the performance of our algorithm. Under certain assumptions, we prove that our algorithm’s stochastic dynamic regret and constraint violation have a sublinear bound in terms of the total number of slots T. We design simulation experiments to verify the efficiency of our online algorithm. Its performance is evaluated on a range of information and system engineering problems, including adaptive filtering, online logistic regression, time-varying smart grid energy dispatch, online network resource allocation, and path planning. In addition, in the context of the path planning problem, we integrate our algorithm with supervised learning to demonstrate its enhanced capabilities. The experimental results validate the performance of our new algorithm in practical applications. Full article

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary differentiable prior knowledge into the reverse diffusion dynamics by augmenting the score function with a guidance term derived from a prior potential V(x,t) and weighted by a time-dependent strength γ_t. This formulation subsumes existing mechanisms (classifier guidance, model-based diffusion, physics-informed corrections) as special cases. We analyze the guided path measures, providing an upper bound on the Kullback–Leibler divergence between guided and unguided marginals (Theorem 1), quantifying the inherent trade-off between data fidelity and prior satisfaction. Experiments on synthetic data confirm the predicted dependence on γ_t. On the NASA C-MAPSS turbofan benchmark, we enforce compressor-oriented physical constraints (e.g., speed–pressure consistency, monotonicity) within PGDP; remaining useful life scores are reported only as reference metrics under transparent protocols. A cross-domain study on the NASA IGBT accelerated aging dataset, using the same backbone with a replaced physics module, achieves a 99.98% reduction in monotonicity loss, demonstrating generality across distinct degradation mechanisms. PGDP provides a principled, extensible template for knowledge-informed generative modeling with theoretical guarantees and verifiable physical consistency. Full article