Search Results (3,589)

This paper proposes a cooperative localization method based on time-division processing of interferometric measurements, in which the receiver updates the signals from multiple UAVs in separate time slots, thereby reducing spectrum usage and baseband hardware overhead. A Kalman-enhanced tracking loop is designed to achieve high-precision carrier-phase and Doppler estimation under low-SNR conditions. For angle estimation, a time-division update strategy is employed such that the receiver performs full carrier tracking for only one UAV in each time slot, while the carrier phases of the remaining UAVs are extrapolated from the Doppler states estimated in the previous epoch. This avoids the hardware complexity associated with maintaining multiple parallel tracking loops. By fusing the estimated azimuth, elevation, and pseudorange measurements with the master UAV’s high-precision GNSS observations, a factor-graph-based sliding-window cooperative localization algorithm is constructed. Simulation results show that the proposed method improves the RMSE of carrier-phase and Doppler estimation by nearly an order of magnitude compared with the traditional FLL-assisted PLL. The system maintains angle estimation accuracy better than 0.01° within a four-node configuration and achieves centimeter-level ranging accuracy when SNR ≥ 0 dB. In a cooperative flight scenario with one master and three follower UAVs, the method consistently delivers sub-decimeter 3D localization accuracy. Full article

Large Language Model (LLM) agents depend heavily on multiple external tools such as APIs, databases and computational services to perform complex tasks. However, these tool executions create latency and introduce costs, particularly when agents handle similar queries or workflows. Most current caching methods focus on LLM prompt–response pairs or execution plans and overlook redundancies at the tool level. To address this, we designed a multi-level caching architecture that captures redundancy at both the workflow and tool level. The proposed system integrates four key components: (1) hierarchical caching that operates at both the workflow and tool level to capture coarse and fine-grained redundancies; (2) dependency-aware invalidation using graph-based techniques to maintain consistency when write operations affect cached reads across execution contexts; (3) category-specific time-to-live (TTL) policies tailored to different data types, e.g., weather APIs, user location, database queries and filesystem and computational tasks; and (4) session isolation to ensure multi-tenant cache safety through automatic session scoping. We evaluated the system using synthetic data with 2.25 million queries across ten configurations in fifteen runs. In addition, we conducted four targeted evaluations—write intensity robustness from 4 to 30% writes, personalized memory effects under isolated vs. shared cache modes, workflow-level caching comparison and workload sensitivity across five access distributions—on an additional 2.565 million queries, bringing the total experimental scope to 4.815 million executed queries. The architecture achieved 76.5% caching efficiency, reducing query processing time by 13.3× and lowering estimated costs by 73.3% compared to a no-cache baseline. Multi-tenant testing with fifteen concurrent tenants confirmed robust session isolation and 74.1% efficiency under concurrent workloads. Our evaluation used controlled synthetic workloads following Zipfian distributions, which are commonly used in caching research. While absolute hit rates vary by deployment domain, the architectural principles of hierarchical caching, dependency tracking and session isolation remain broadly applicable. Full article

This study tackles the critical challenges of stress evolution and pillar optimization in underground potash mining, with a focus on the 351-stope of Kaiyuan Mining in Laos. Integrating theoretical calculations, large-scale 3D numerical modeling, and an AHP-Fuzzy comprehensive evaluation, we systematically analyze the complex mechanical behaviors of the mining environment. Applying key stratum theory, we reveal the unique mechanism by which overlying hard rock bends without fracturing in carnallite layers under room-and-pillar conditions. Comparative numerical simulations of four pillar-width schemes—involving 8 m rooms with 10 m, 8 m, 6 m, and 4 m pillars—show that reducing pillar width markedly increases vertical stress, triggers exponential roof subsidence, and expands pillar failure zones. Using an AHP-Fuzzy method that incorporates safety, technical, and economic factors, the Simultaneous Backfilling with 8 m Mining and 6 m Pillar Retention is identified as the optimal scheme. This configuration demonstrates superior stability, exhibiting an average pillar stress of 9.3 MPa and only limited plastic failure zones at pillar ends. These findings offer a robust scientific and technical foundation for enhancing the safety, efficiency, and sustainability of underground potash mining operations. Full article

Credit card fraud detection remains a major challenge due to extreme class imbalance and evolving attack patterns. This paper proposes a practical hybrid pipeline that combines conditional tabular generative adversarial networks (CTGANs) for targeted minority-class synthesis with Light Gradient Boosting Machine (LightGBM) for classification. Inspired by symmetry principles in machine learning, we leverage the adversarial equilibrium of CTGAN to generate realistic fraudulent transactions that maintain distributional symmetry with real fraud patterns, thereby preserving the structural and statistical balance of the original dataset. Synthetic fraud samples are merged with real data to form augmented training sets that restore the symmetry of class representation. We evaluate Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) classifiers, and a LightGBM model on a public dataset using stratified 5-fold validation and an independent hold-out test set. Models are compared using sensitivity, precision, F-measure(F₁), and area under the precision–recall curve (PR-AUC), which reflects symmetry between detection and false-alarm trade-offs. Results show that CTGAN-based augmentation yields large and consistent gains across architectures. The best-performing configuration, CTGAN + LightGBM, attains sensitivity = 0.986, precision = 0.982, F₁ = 0.984, and PR-AUC = 0.918 on the test data, substantially outperforming non-augmented baselines and recent methods. These findings indicate that conditional synthetic augmentation materially improves the detection of rare fraud modes while preserving low false-alarm rates, demonstrating the value of symmetry-aware data synthesis in classification under imbalance. We discuss generation-quality checks, risk of distributional shift, and deployment considerations. Future work will explore alternative generative models with explicit symmetry constraints and time-aware production evaluation. Full article

Here, we introduce novel pregelatinized starch-modified silica gels for Portland cement enhancement. The modified SiO₂ gels demonstrate superior mechanical properties compared to pure silica, with optimal starch modification increasing the modulus by 244.8%. Structural characterization reveals that starch alters Si-O bond configurations without covalent bond formation. Applied to Portland cement, the modified gels significantly enhance compressive strength through method-dependent mechanisms. Casting applications show measurable strength improvements, while pressing methods achieve a 42.3% compressive strength increase with superior packing efficiency under confined conditions. The enhancement primarily stems from accelerated C₃S hydration facilitated by the modified silica gels. These findings establish an innovative approach for high-performance cement materials via organic–inorganic composite modification, providing practical formulation guidance across diverse application scenarios. Full article

To facilitate the successful achievement of the goals outlined in the 2030 Agenda for Sustainable Development, it is imperative to accelerate the advancement of green technological innovation effectiveness (GTIE). This study aims to synthesize three types of drivers and seven concurrent driving factors of green technological innovation effectiveness identified in existing theories, constructing a multiple concurrent mechanism model for such effectiveness. The fuzzy-set Qualitative Comparative Analysis (fsQCA) method is employed to identify the configurational conditions leading to high green technological innovation effectiveness. Furthermore, the robustness of these configurations is verified through panel decomposition, while Necessary Condition Analysis (NCA) is applied to test the necessity of the factors within these configurations and to conduct further examination. The results reveal that high green technological innovation effectiveness is driven by three types of multiple concurrent mechanisms: the “Demand–Pull and Technology–Push and Porter Effect-Driven” configuration type, the “Demand–Pull & Technology–Push-Driven” type, and the “Demand–Pull & Porter Effect-Driven” type. This paper’s contributions are threefold. First, it investigates the configurational drivers of green technological innovation effectiveness. Second, it uses Necessary Condition Analysis (NCA) to identify necessary conditions within these multiple concurrent effects, deepening insight into the drivers. Third, it reveals three patterns driving green innovation in industries and proposes corresponding sustainable manufacturing policy recommendations. Full article

Background/Objectives: Breast cancer is one of the most common malignancies, but its heterogeneous molecular subtypes make treatment decision-making complex and patient-specific. Both the pathology reports and the electronic medical record (EMR) play a critical role for an appropriate treatment decision. This study aimed to develop an integrated clinical decision support system (CDSS) that combines a large language model (LLM)-based pathology analysis with deep learning-based treatment prediction to support standardized and reliable decision-making. Methods: Real-world data (RWD) obtained from a cohort of 5015 patients diagnosed with breast cancer were analyzed. Meta-Llama-3-8B-Instruct automatically extracted the TNM stage and tumor size from the pathology reports, which were then integrated with EMR variables. A multi-label classification of 16 treatment combinations was performed using six models, including Decision Tree, Random Forest, GBM, XGBoost, DNN, and Transformer. Performance was evaluated using accuracy, macro/micro-averaged precision, recall, F1 score, and AUC. Results: Using combined LLM-extracted pathology and EMR features, GBM and XGBoost achieved the highest and most stable predictive performance across all feature subset configurations (macro-F1 ≈ 0.88–0.89; AUC = 0.867–0.868). Both models demonstrated strong discrimination ability and consistent recall and precision, highlighting their robustness for multi-label classification in real-world settings. Decision Tree and Random Forest showed moderate but reliable performance (macro-F1 = 0.84–0.86; AUC = 0.849–0.821), indicating their applicability despite lower predictive capability. By contrast, the DNN and Transformer models produced comparatively lower scores (macro-F1 = 0.74–0.82; AUC = 0.780–0.757), especially when using the full feature set, suggesting limited suitability for structured clinical data without strong contextual dependencies. These findings indicate that gradient-boosting ensemble approaches are better optimized for tabular medical data and generate more clinically reliable treatment recommendations. Conclusions: The proposed artificial intelligence-based CDSS improves accuracy and consistency in breast cancer treatment decision support by integrating automated pathology interpretation with deep learning, demonstrating its potential utility in real-world cancer care. Full article

This study analyzes the hydrodynamic performance of a V-type wave dissipation system and amphibious landing equipment under different combined fields using the Reynolds-averaged Navier–Stokes (RANS) method. A three-dimensional numerical wave tank is established to simulate regular waves and validate the performance of an airbag-type floating breakwater. This study evaluates the optimal hydrodynamic performance of a V-type wave dissipation system under various configurations in a wave-only field and subsequently compares the efficacy of the better-performing system across multiple environmental conditions. The results show that the V-type wave dissipation system in the configurations of 30° and 45° angles is more favorable for the flow field and the amphibious landing equipment behind it. Compared to the wave-only condition, the time histories of wave heights under both wave-current and wind-wave conditions present an obvious phase advancement. In the wave-current field, a following current reduces the wave height and shortens the wave period. Conversely, in the wind-wave field, a following wind velocity leads to a certain increase in wave height while exerting minimal impact on the wave period. Compared to the wave-only condition, the peak and trough values of the wave height monitoring points in the combined wind-wave-current field show an increasing trend, with a significant increase in resistance and a shorter resistance period for the amphibious landing equipment behind the V-type wave dissipation system. This study shows that the selected V-type wave dissipation system proves to be more effective in wave-only and wave-current conditions, providing valuable references for the engineering application of this system. Full article

The horizontal-axis tidal turbine is a representative device for harnessing ocean tidal energy, and the structural optimization of its blades is crucial for enhancing the power capture efficiency. In this work, the twist and chord distributions of the blade are determined using an improved Blade Element Momentum (BEM) approach, in which tip and hub loss factors are employed to enhance the modeling accuracy, and these results are employed to construct a parametric model of the original rotor. Due to its simplified assumptions and inability to capture three-dimensional flow effects, computational fluid dynamics (CFD) simulations were carried out to evaluate the hydrodynamic performance and flow analysis of the designed rotor. Further, the orthogonal test method was used to optimize the hydraulic performance of the rotor. Three optimization parameters, namely hub diameter, airfoil type, and maximum airfoil thickness, were set with three levels. Based on the orthogonal design scheme, nine rotor configurations were generated, and their energy capture characteristics and flow fields were subsequently evaluated through numerical simulations. The analysis indicates that the choice of airfoil exerts the strongest impact on the rotor’s energy capture efficiency, while the influences of maximum airfoil thickness and hub diameter follow in descending order. Consequently, the optimized rotor adopts a NACA63-415 airfoil with a reduced maximum thickness of 0.9 T₀ and an intermediate hub diameter of 15%R, achieving a power coefficient of 0.445 at the design tip-speed ratio of 4, corresponding to a 3.08% improvement compared with the original design. Flow field analysis demonstrates that the optimized geometry promotes a more uniform spanwise pressure distribution and effectively suppresses flow separation, thereby enhancing the overall hydrodynamic efficiency. Full article

Jones fractures are Zone 2 fractures of the fifth metatarsal. Biomechanical comparisons of fixation strategies for Jones fractures remain limited by the lack of standardized, head-to-head evaluations across major fixation methods. The purpose of this study was to perform a standardized biomechanical comparison of six fixation configurations representing the three primary surgical techniques for Jones fractures and to examine the mechanical factors underlying differences in early construct stability. A synthetic fifth metatarsal model with a simulated Zone 2 fracture was stabilized using lateral plate fixation with different screw configurations, Kirschner wire fixation with or without tension-band wiring, or intramedullary headless screw fixation. All constructs were tested under displacement-controlled cantilever bending, and the force required to reach 1 mm of fracture site displacement was obtained and construct stiffness was calculated. Plate-based fixation demonstrated the highest resistance to bending deformation, followed by intramedullary screw fixation, whereas Kirschner wire-based constructs exhibited the lowest stability. These differences were explained by variations in load-sharing pathways and effective working length among fixation constructs. The addition of tension-band wiring did not result in a measurable improvement in stability compared with Kirschner wire fixation alone, consistent with the dependence of tension-band mechanisms on active muscle loading not represented in the experimental model. These findings provide a unified biomechanical comparison of commonly used fixation constructs for Jones fractures and clarify the mechanical basis for differences in early construct stability. Full article

The Failure Assessment Diagram (FAD), as a significant method for evaluating the suitability of defective metallic structures, has been subject to considerable debate regarding its applicability in assessing ring welded joints for high-grade steel and large-diameter pipelines. To address this issue, this study first designed and conducted two sets of full-scale pressure-tension tests on large-diameter X80 pipeline ring welded joints, considering factors such as different welding processes, joint configurations, defect dimensions, and locations. Subsequently, three widely adopted failure assessment diagram methodologies—BS 7910, API 579, and API 1104—were selected. Corresponding assessment curves were established based on material performance parameters obtained from the ring weld tests. Finally, predictive outcomes from each assessment method were compared against experimental data to investigate the applicability of failure assessment diagrams for evaluating high-strength, large-diameter, thick-walled ring welds. The research findings indicate that, under the specific material and defect assessment conditions employed in this study, the API 1104 assessment results exhibited significant conservatism (two sets matched). Conversely, the BS 7910 and API 579 assessment results showed a high degree of agreement with the experimental data (eight sets matched), with the BS 7910 assessment providing a relatively higher safety margin compared to API 579. The data from this study provides valuable experimental reference for selecting assessment methods under specific conditions, such as similar materials, defects, and loading patterns. Full article

Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive datasets. This paper presents a system integrated into a downhole toolstring for CCL log acquisition to facilitate dataset construction. Comprehensive preprocessing methods for data augmentation are proposed, and their effectiveness is evaluated using baseline neural network models. Through systematic experimentation across diverse configurations, the contribution of each augmentation method is analyzed. Results demonstrate that standardization, label distribution smoothing (LDS), and random cropping are fundamental prerequisites for model training, while label smoothing regularization (LSR), time scaling, and multiple sampling significantly enhance model generalization capabilities. Incorporating the proposed augmentation methods into the two baseline models results in maximum F1 score improvements of 0.027 and 0.024 for the TAN and MAN models, respectively. Furthermore, applying these techniques yields F1 score gains of up to 0.045 for the TAN model and 0.057 for the MAN model compared to prior studies. Performance evaluation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the existing gaps in data augmentation methodologies for training casing collar recognition models under CCL data-limited conditions, and provides a technical foundation for the future automation of downhole operations. Full article

Optimizing efficient electrode materials that combine high energy density, rapid charge transport, and excellent cycling stability remains a challenge for advanced supercapacitors. Here, we report the synthesis of an innovative copper(I)-based metal–organic framework (MOF), Cu₃(NDI)₃, prepared via a simple solvothermal method using N,N’-bis(3,5-dimethylpyrazol-4-yl)-naphthalene diimide (H₂NDI-H) as a linker. Structural analyses (XRD, FTIR, SEM, EDX, and BET) confirmed the formation of a highly crystalline, porous MOF. Integration of this MOF into laser-induced graphene (LIG) matrices yielded hybrid electrodes with enhanced structural characteristics and electrochemical activity, compared to its only-LIG counterpart. Electrochemical studies (CV, CD, EIS) revealed that the LIG–MOF electrode exhibited the highest performance, delivering a specific capacitance of 4.6 mF cm⁻² at 0.05 mA cm⁻², and an areal energy density of 60.03 μWh cm⁻² at a power density of 1292.17 μW cm⁻², outperforming both LIG and MOF–LIG configurations. This enhancement arises from the synergetic interaction between the conductive LIG network and the redox-active Cu₃(NDI)₃ framework, highlighting the potential of LIG–MOF hybrids as next-generation materials for high-performance supercapacitors. Full article

In this study, the nonlinear and non-monotonic behavior of amperometric bienzyme biosensors employing an enzymatic trigger reaction is investigated analytically and computationally using a two-compartment model comprising an enzymatic layer and an outer diffusion layer. The trigger enzymatic reaction is coupled with a cyclic electrochemical–enzymatic conversion (CEC) process. The model is formulated as a system of reaction–diffusion equations incorporating nonlinear Michaelis–Menten kinetics and interlayer partitioning effects. Exact steady-state analytical solutions for substrate and product concentrations, as well as for the output current, are obtained for specific cases of first- and zero-order reaction kinetics. At the transition conditions, biosensor performance is further analyzed numerically using the finite difference method. The CEC biosensor exhibits the highest signal gain when the first enzyme has low activity and the second enzyme has high activity; however, under these conditions, the response time is the longest. When the first enzyme possesses a higher substrate affinity (lower Michaelis constant) than the second, the biosensor demonstrates severalfold higher current and gain compared to the reverse configuration under identical diffusion limitations. Furthermore, increasing external mass transport resistance or interfacial partitioning can enhance the apparent signal gain. Full article

This paper presents a 3-Bridge LLC resonant converter featuring wide output voltage gain characteristics and a novel control method. To achieve operation within a narrower frequency control range, the proposed converter introduces one additional operational mode compared to the previously suggested 3-bridge topology. The converter is configured to have five distinct operation modes, controlled by the switching patterns of the main switches, to enable wide-range output voltage regulation. In each mode, frequency modulation is employed for output voltage control. Furthermore, a morphing control strategy is utilized to ensure stable output voltage regulation during mode transitions. The validity and practical applicability of the proposed 3-bridge LLC resonant converter with five operation modes are verified through experimental results from a 6 kW prototype. Full article