Search Results (8,024)

Due to long-term burial underground, oil and gas pipelines are susceptible to external surface corrosion influenced by time and soil conditions, which can lead to leakage and burst failures. Pipeline failure not only results in significant economic losses but also has catastrophic impacts on human safety and the environment. Therefore, modeling and analyzing the corrosion failure of these pipelines is of critical practical importance to ensure their safe operation during service. Addressing the insufficient research on correlation effects in current reliability evaluations of corroded pipelines, this paper proposes a calculation method for the failure probability of corroded oil and gas pipelines that considers the influence of two-layer correlations. Taking a specific segment of the Shaanxi–Beijing pipeline as a case study, the Monte Carlo sampling algorithm is employed to calculate the impact of two-layer correlations and the quantity of defect on the pipeline’s failure probability. Furthermore, a sensitivity analysis of the correlation coefficients is conducted. The results indicate that the influence of defect correlation on pipeline failure probability is significantly more pronounced than that of random variable correlation. The probabilities of pinhole leakage and burst failure decrease as the correlation coefficient between defects increases, while they increase with the number of defects. Random variable correlation exhibits no impact on pinhole leakage probability; however, the burst failure probability decreases with an increasing correlation coefficient between wall thickness and pipe diameter, but increases as the correlation between initial defect length and depth grows. Furthermore, the correlation coefficient between axial and radial defect growth rates exerts a bidirectional effect on burst failure probability: during the first 25 years of the prediction period, the failure probability increases with the correlation coefficient, whereas it subsequently decreases after approximately 25 years. These findings are applicable to the reliability evaluation of oil and gas pipelines containing multiple corrosion defects, providing valuable technical references for ensuring safe operation and the steady supply of energy resources. Full article

This study presents a heterogeneous integration micro-assembly process and circuit board packaging solution for GaN Schottky Barrier Diode (SBD) rectifiers, and innovatively constructs a hierarchical reinforcement learning strategy for optimizing SBD RF parameters. By establishing an optimization framework with the goal of efficiency in the load-input power two-dimensional space, a dual-layer optimization mechanism is employed: the high-level strategy dynamically selects optimization regions and parameter combinations, while the low-level strategy implements specific parameter adjustments. This approach effectively addresses the challenges of device parameter modeling and circuit design. Experimental data shows that the efficiency error for the SBD1 rectifier remains stable within 2%, and the average error for SBD2 is reduced to 1.5%. This method enables efficient and accurate optimization of RF parameters, providing a reliable technical pathway for the engineering application of Wireless Power Transmission systems. Full article

With the widespread adoption of anonymity networks such as Tor, I2P, and JonDonym, reliably classifying darknet traffic remains challenging due to feature redundancy and severe class imbalance in encrypted flows. Existing approaches often rely on static feature-selection strategies and generic oversampling methods, which limit robustness and may distort traffic semantics. This study proposes an adaptive classification framework integrating Adaptive Weighted Feature Aggregation (AWFA) for reliability-aware feature selection and Traffic-Aware SMOTE (TA-SMOTE) for semantically constrained perturbations of packet-size and timing features while preserving flow-level structure. The framework is evaluated on a two-layer hierarchy comprising browser-level (L1) and application-level (L2) classification. At the L2, the proposed AWFA and TA-SMOTE pipeline attains a macro-F1 score of 73.81%, significantly exceeding PCA-based reduction and traditional RF-based selection with SMOTE. At the browser level (L1), macro-F1 rises from 91.58% to 96.09% while reducing the feature space from 84 to 40 attributes, highlighting both performance improvements and structural efficiency gains. Additional semantic validation confirms that the balancing process preserves the statistical and structural characteristics of genuine darknet traffic. These results indicate that reliability-aware feature aggregation and traffic-aware balancing provide a practical, trustworthy approach to modern darknet traffic classification. Full article

As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability. Adversarial example purification is attractive for such deployments because it removes input perturbations without modifying the already deployed models. However, most existing purification methods are inherently goal-free: denoising-based approaches apply blind heuristic operators, while reconstruction-based methods rely on stochastic sampling guided by natural image priors. These methods typically suppress perturbations at the cost of weakening semantic details or inducing structural distortions. To address this limitation, we propose a novel goal-directed purification framework, termed adversarial energy field optimization for adversarial example purification (AEFOP). AEFOP formulates purification as a constrained optimization problem by defining a learnable adversarial energy which quantifies how far an input deviates from the benign region. This allows adversarial examples to be explicitly pushed from high-energy regions toward low-energy benign regions along an interpretable descent trajectory. Specifically, we build an adversarial energy network and optimize the energy field via a two-stage strategy: adversarial energy field shaping, which enforces distance-like energy behavior and correct gradient directions, and task-driven energy field calibration, which unrolls the descent process to calibrate the field with classification-consistency and semantic-preservation objectives. Extensive experiments across multiple attack scenarios demonstrate that AEFOP achieves superior purification accuracy and high visual quality while requiring only a few gradient steps during inference, offering a practical and efficient robustness layer for vision-based AI services in education. Full article

Three-dimensional printed polymers produced using Fused Deposition Modelling (FDM) exhibit directional microstructures resulting from filament paths, layer interfaces, and cellular infill, leading to mechanical and tribological responses distinct from those of homogeneous bulk materials. This study presents a comparative tribomechanical evaluation of polypropylene (PP) bulk inserts and 3D-printed polyethylene terephthalate glycol (PETG) inserts with a 30% hexagonal infill, relevant for robotic gripping applications. Progressive scratch tests were performed under loads from 5 to 100 N (150 N for PP), and profilometry was applied to quantify groove morphology, ridge formation, and displaced-volume ratios. An elasto-plastic conical indentation model was used to derive indentation pressures and elastic–plastic transition radii from groove geometry. The PETG inserts exhibited heterogeneous groove depth, intermittent ridge tearing, and friction fluctuations associated with the internal infill structure, consistent with previous findings on anisotropy and architecture-dependent behaviour in additively manufactured polymers. In contrast, bulk PP demonstrated smoother friction profiles and more stable plastic flow under increasing loads. Two functional indices—specific frictional work and ridge-to-trace volumetric ratio—are introduced to support material selection for robotic gripping systems. The results show that local contact mechanics in 3D-printed inserts are governed by print-induced structural features and can be effectively evaluated through a scratch-based elasto-plastic analysis. The methods and results presented in this work support the rational selection and design of polymer inserts for robotic gripper fingertips. The proposed scratch-based elasto-plastic evaluation framework enables manufacturers and automation engineers to compare 3D-printed and conventional materials based on friction stability, wear response, and deformation resistance. This approach can be directly applied to optimise gripping performance in industrial handling, packaging, and collaborative robotics. Full article

To address the issues of suboptimal recovery performance, low timeliness, and poor economic efficiency associated with traditional fault recovery methods following large-scale power outages in active distribution networks (ADNs) caused by extreme weather, this paper proposes a dynamic fault recovery strategy for ADNs based on a two-layer hybrid algorithm under extreme ice and snow conditions. First, a line fault rate model considering the thermal effect of current under extreme ice and snow conditions is constructed, and an information entropy-based typical scenario screening method is introduced to filter the fault scenarios. Second, a photovoltaic (PV) output model and a time-varying load model under the influence of extreme ice and snow conditions are established. Subsequently, a multi-objective dynamic fault recovery model is formulated, incorporating island partitioning and integration constraints based on the concept of single-commodity flow, alongside tightened relaxation constraints. To achieve an accurate and rapid solution for the fault recovery model, a two-layer hybrid algorithm is proposed. This algorithm combines an outer-layer improved binary grey wolf optimizer (IBGWO) and an inner-layer second-order cone relaxation (SOCR) algorithm to solve the discrete and continuous decision variables within the model, respectively. Finally, the effectiveness and superiority of the proposed method are verified using the PG&E 69-bus and IEEE 123-bus systems. Full article

Reliable analytical methods are essential for the assessment, effective quality control, and guarantee of consistent and reproducible performance of chemical profiles of non-psychoactive low-THC Cannabis sativa L. samples and their products. An integrated analytical approach was applied for the first time to evaluate low-THC C. sativa products on the Serbian legal market using chemometrics combined with five complementary techniques: ultraviolet–visible spectroscopy (UV–Vis), high-performance thin-layer chromatography (HPTLC), portable Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR) and gas chromatography–mass spectrometry (GC–MS). HPTLC rapidly differentiated key cannabinoids with R_F at 0.39 and 0.61, while GC–MS enabled comprehensive identification of major cannabinoids (CBG and CBD). Spectroscopic fingerprints provided characteristic UV–Vis absorption maximum (215, 235, and 275 nm), Raman (1700, 1550, 1517, 1224, 1096 cm⁻¹) and FTIR marker bands (615, 1059, 1288, 1620, 2932 cm⁻¹), supporting robust monitoring. Principal component analysis (PCA) across all five techniques revealed two major distinct sample clusters and identified the most influential analytical signals. The combined separation, spectroscopic, and multivariate approach is proven to be effective for systematic cannabinoid content assessment, authentication, and chemical profiling within a process-oriented context, thus enabling effective quality control in the cultivation process by targeting compounds of interest. Full article

As water resources are becoming increasingly scarce in the North China Plain, irrigation strategies that simultaneously improve grain yield and reduce irrigation water input are needed for winter wheat (Triticum aestivum L.) production. Current irrigation decision rules are based either on fixed soil moisture thresholds or on evapotranspiration (ET)-based ratios applied uniformly across the growing season, limiting their flexibility for growth stage-specific irrigation management. In this study, a multi-objective simulation optimization framework was developed to jointly optimize soil moisture lower control limits (irrigation trigger thresholds) and evapotranspiration-based irrigation replenishment ratios across key winter wheat growth stages. The framework integrated the AquaCrop-OSPy crop model with the PyFAO56 soil moisture balance, irrigation scheduling model and the NSGA-II evolutionary optimization algorithm. A field experiment was conducted during the 2024–2025 growing season in Laoling City, Shandong Province, China, employing a four-dense–one-sparse strip cropping pattern with two irrigation treatments: T1 (subsurface sprinkler irrigation) and T2 (shallow subsurface drip irrigation). The AquaCrop-OSPy model was calibrated and validated using measured canopy cover, aboveground biomass, grain yield, and soil moisture content in the 0–60 cm soil layer. Simulated canopy cover and grain yield showed good agreement with observations, with the coefficient of determination (R²) ranging from 0.87 to 0.94. For grain yield, the normalized root mean square error (NRMSE) ranged from 2.24% to 3.75%, and the root mean square error (RMSE) ranged from 0.29 to 0.54 t·ha⁻¹. For aboveground biomass, R² was 0.99, while RMSE ranged from 1.02 to 1.11 t·ha⁻¹, and NRMSE ranged from 14.25% to 15.49%. The PyFAO56 irrigation strategy model simulated average root-zone soil-moisture dynamics with satisfactory accuracy, with an R² of 0.86 and an RMSE of 5%. Multi-objective optimization (maximizing yield while minimizing irrigation volume) generated 23 Pareto-optimal irrigation strategies, with irrigation volumes ranging from 51 to 128 mm, corresponding yields ranging from 9.8 to 10.8 t·ha⁻¹, and irrigation water use efficiency (IWUE) ranging from 0.08 to 0.19 t·ha⁻¹·mm⁻¹. Correlation analysis within the Pareto set indicated that soil-moisture control lower limits during the regreening–jointing stage and higher soil-moisture control lower limits during the flowering–maturity stage were key controlling factors for achieving high yields and irrigation water use efficiency. The Entropy-Weighted Ranked Minimum Distance method identified an optimal irrigation scheme involving two irrigations (one at the end of the jointing stage and another at the beginning of the grain filling stage) involving an irrigation depth of 75 mm, achieving a simulated yield of 10.4 t·ha⁻¹ and an IWUE of 0.16 t·ha⁻¹·mm⁻¹. The proposed AquaCrop-PyFAO56-NSGA-II framework provides a flexible, process-based workflow for jointly optimizing irrigation control thresholds and evapotranspiration-based irrigation replenishment ratios across different winter wheat growth stages. Under the monitored conditions of the 2024–2025 wet season, the framework identified a two-irrigation strategy that balanced grain yield and irrigation input. This study should, therefore, be regarded as a proof-of-concept evaluation conducted in a well-instrumented single-site field setting rather than as a universally transferable recommendation. Because model calibration, within-season validation, and optimization were all based on one wet growing season at one site, the derived stage-specific thresholds, Pareto front, and S5 recommendation are most applicable to hydro-climatic conditions similar to the study year and should be further tested across contrasting year-types and locations before broader extrapolation. Full article

Background/Objectives: This study aimed to develop bilayer tablets using hot-melt pneumatic extrusion (HMPE)-based 3D printing for the integrated treatment of allergic rhinitis and asthma. The formulation combined levocetirizine dihydrochloride (immediate release) and montelukast sodium (delayed release) within a single dosage form to provide a sequential-release formulation strategy relevant to the intended pharmacological roles of the two drugs. Distinct polymer matrices were selected for each drug layer to ensure mechanical robustness, stability, and appropriate release characteristics. Methods: The printed tablets were systematically characterized by mechanical testing, differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD), and in vitro dissolution. Drug content uniformity was evaluated in accordance with USP <905>. Results: The tablets satisfied USP standards for content uniformity and exhibited sufficient mechanical strength for handling and packaging. DSC and PXRD analyses indicated amorphization of levocetirizine within the polymer matrix, while the amorphous state of the raw montelukast used in this study was retained after printing. In vitro dissolution tests demonstrated immediate release of levocetirizine in acidic medium (pH 1.2) and delayed release of montelukast at intestinal pH (6.8), thereby achieving the intended dual-phase release profile. Conclusions: These findings demonstrate the feasibility of fabricating an HMPE-based 3D-printed bilayer tablet integrating immediate-release levocetirizine and delayed-release montelukast, with reproducible dual-phase release and drug-specific solid-state and performance characteristics within a single oral dosage form. Full article

As Global Navigation Satellite Systems (GNSSs) underpin safety-critical infrastructure, their vulnerability to sophisticated spoofing attacks poses severe physical layer security risks. To address the limitations of existing single-antenna defense mechanisms, this paper proposes a rigorous instantaneous spoofing detection framework utilizing a novel “one-primary-two-auxiliary” three-antenna configuration. By embedding the rigid baseline length as a hard geometric constraint into the Integer Least Squares (ILS) model, we derive a specialized constrained LAMBDA algorithm that significantly shrinks the ambiguity search space. A rigorous hypothesis testing mechanism is established based on the Sum of Squared Residuals (SSR), analytically deriving the detection threshold from the central Chi-square distribution and analyzing the sensitivity via the non-central parameter. Through conducting field experiments using commercial receivers and professional GNSS signal simulators, the proposed method was validated using both single-satellite spoofing and full-constellation spoofing scenarios. Results demonstrate that the system achieves a Minimum Detectable Deviation (MDD) of spatial direction as low as $0.33^{\circ }$ and maintains an empirical detection rate of >99% with a negligible false alarm rate. Notably, the method exhibits instantaneous response capabilities, effectively identifying both single-satellite and full-constellation spoofing attacks within a single epoch without requiring prior attitude information or external aiding. Full article

This paper investigates a tri-objective green flexible job shop rescheduling problem under urgent order insertion and multi-speed machining conditions, where makespan, total energy consumption, and total tool wear are jointly optimized. First, an event-driven freezing mechanism is introduced, in which completed and ongoing operations are fixed, while only the rescheduling window composed of waiting operations and urgent-order operations is re-optimized. On this basis, two rescheduling strategies, namely complete rescheduling and deferred rescheduling, are designed and compared. Second, to improve the solution capability in complex dynamic environments, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) with a three-layer encoding scheme is proposed. The algorithm incorporates hybrid initialization, tabu-guided crossover, simulated annealing mutation, and critical-path-based variable neighborhood search. Experimental results show that the proposed method performs well in energy consumption optimization and tool wear control, while effectively improving the diversity and distribution quality of the Pareto solution set. Further analysis indicates that deferred rescheduling generally outperforms complete rescheduling, while the original-orders-first and urgents-first strategies exhibit different strengths in convergence, solution quality, and objective optimization. The proposed study provides an effective modeling and optimization framework for multi-objective green rescheduling problems and offers theoretical support for production scheduling decisions that need to balance production efficiency, energy saving, and tool-related cost control in practical manufacturing systems. Full article

Airborne gravity survey serves as a rapid remote sensing technique for mapping subsurface mineral target and geological structure over large areas. The raw gravity data contains significant noise corrupted by airflow and the flight platform’s attitude. The Kalman Filter (KF) is an effective method for airborne gravity data denoising, but its processing accuracy is highly dependent on the empirical parameters. The multi-scale CNN-LSTM-attention adaptive Kalman Filter (MSC-LA-AKF) method is proposed to obtain high precision gravity data, which combines the multi-scale CNN (MSC), bidirectional long short-term memory (Bi-LSTM) and attention mechanism for adaptively estimating the parameters of KF. The multi-scale CNN uses convolution kernel of varying sizes to extract signal features at different scales. The Bi-LSTM combines two LSTM layers in opposite directions to extract the signal features at bidirectional time series, and can effectively identify time-varying noise signals. A multi-head attention mechanism with four attention heads ($H=4$) is incorporated into the output feature layer of the Bi-LSTM to adaptively calculate weights for different features and optimize the parameters of the KF. The simulated data tests demonstrate that the MSC-LA-AKF achieves notably higher denoising accuracy than both the finite impulse response (FIR) and wavelet filters, with detailed quantitative comparisons provided in the experimental section. The proposed method is applied to real airborne gravity data, and effectively removes noise signals and enhances the geological interpretation of gravity maps. Full article

Underwater object detection plays a crucial role in the domain of marine engineering. Due to blur, uneven illumination and noise in underwater images, generic object detectors often fail to accurately detect underwater targets. Existing underwater object detection methods generally neglect the enhancement and refinement of multi-scale features, limiting further improvements in detection accuracy. In response to these challenges, we propose the Feature Enhancement and Alignment Pyramid Network (FEAPN), a novel underwater object detection framework. FEAPN consists of two key innovations. First, the Adaptive Feature Refinement Module (AFRM) is developed to adaptively enhance contextual features from complex backgrounds. Second, the Dual-path Feature Alignment Module (DFAM) is designed to align multi-scale features, utilizing cross-layer information to optimize feature representation. Extensive experiments demonstrate that FEAPN achieves state-of-the-art performance. Specifically, FEAPN achieves a 2.4% mAP improvement over the baseline and outperforms the current leading underwater detector by 1.2% mAP. Furthermore, the effectiveness of each component is validated through ablation studies. Full article

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article

Urban ecological recreation space (UERS), as a crucial component of urban blue–green infrastructure, plays a pivotal role in supporting daily recreational activities and enhancing urban ecological resilience. However, existing equity studies often focus on supply–demand matching outcomes while neglecting the structural allocation of green space provision. Against this backdrop, this study constructs a dual-layer analytical framework of “structural supply–supply–demand matching” and introduces a quality factor to improve the Gaussian two-step floating catchment area method (G2SFCA). Focusing on Urumqi as an empirical case, the accessibility and equity of its UERS are analyzed. The results indicate: 1. The accessibility of UERS exhibits a “core–periphery” differentiation, with the old urban area demonstrating higher accessibility levels in terms of structural supply. However, due to the competitive effects of high population density, its accessibility advantage in the supply–demand matching layer is significantly diminished. 2. Population competition amplifies spatial imbalances, resulting in significantly higher inequality at the supply–demand matching layer than at the structural supply layer. 3. After considering the quality factors of UERS, its fairness has improved, which is more pronounced in the supply–demand matching layer. Optimizing the quality of UERS in high-density built-up areas contributes to the enhancement of fairness. This study emphasizes that UERS accessibility should be understood as a coupled outcome of structural supply and competitive redistribution. The proposed dual-layer framework provides a more comprehensive basis for diagnosing spatial inequalities and formulating differentiated blue–green infrastructure planning strategies. Full article