Search Results (1,481)

The Late Eocene Huizhou-A sandy conglomeratic system in the Pearl River Mouth Basin presents a highly heterogeneous reservoir system shaped by intense synsedimentary fault activity and variable depositional processes. Utilizing 3D seismic interpretation, well log analysis, and core calibration, this study reconstructs the tectono-sedimentary evolution, facies distribution, and diagenetic modifications controlling reservoir quality. Results show that the best reservoir quality is not confined to proximal fan-delta coarse-grained deposits near steep boundary faults, but occurs mainly in fan-delta front and braided-river-delta deposits, especially braided- and turbidite-channel microfacies. These reservoirs benefit from better sorting, favorable grain size, and higher textural maturity, whereas proximal clastic-flow deposits are poorer due to heterogeneity, poor sorting, and compaction. Reservoir quality is also depth-dependent: upper Enping reservoirs are mainly controlled by maturity, while lower Enping reservoirs are more influenced by grain size. Semi-quantitative analysis identifies the 7–11 km transport-distance zone as the optimal fairway for vertically stacked high-quality reservoirs. This approach not only guides exploration and development in the Huizhou Sag but also offers a transferable predictive model for similar steep slope lacustrine rift basins with comparable tectono-sedimentary settings worldwide. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

A compact, low-profile dual-band feed network operating at 37–40 GHz (Ka-band) and 70–86 GHz (E-band) is presented for millimeter-wave backhaul applications. The proposed network integrates a quad-ridged orthomode transducer (OMT) and four ridge-waveguide diplexers into a three-layer all-metal stacked architecture, eliminating the cascaded inter-stage flanges of conventional feed chains and yielding a monolithic-like assembly that is mechanically robust and CNC-friendly for mass production. Stepped-impedance matching stubs in the OMT junction provide broadband matching across the widely separated bands, while compact ridge-waveguide T-junction diplexers, comprising stepped-impedance low-pass filters and rectangular high-pass paths, perform the spectral separation. Back-to-back measurements of the fabricated prototype demonstrate an insertion loss below 0.6 dB across both bands. The measured VSWR at the four output ports remains below 1.5 across both bands, and the port-to-port isolation exceeds 32 dB at the Ka-band and 45 dB at the E-band. The proposed network thus offers a highly integrated, low-loss solution for next-generation dual-band mmWave links. Full article

This study systematically investigates the hot deformation behavior of TC18 alloy under the conditions of deformation temperatures of 720–840 °C and strain rates of 0.001–1 s⁻¹. Based on the stress–strain data obtained under the aforementioned process parameters, a support vector regression (SVR) model was established and further optimized by using a Stacking algorithm to enhance predictive accuracy. Although SVR and Stacking techniques have been applied previously in material constitutive modeling, this paper presents a systematic optimization framework specifically for TC18, integrating comprehensive experimental data, kernel selection, hyperparameter tuning, and Stacking-based model fusion. The polynomial kernel function was identified as optimal, and hyperparameters were tuned via grid search combined with five-fold cross-validation, which is determined as {C = 1000, coef0 = 1, d = 5, ε = 1, γ = 1}. The Stacking-SVR model exhibits significantly improved fitting and generalization performance compared to Poly-SVR, Arrhenius, XGBoost and MLP, with RMSE, MAPE, and R² metrics of 2.7882, 0.0110, and 0.9973 on the training set, and 2.7956, 0.0169, and 0.9982 on the test set, respectively. Additionally, the proportion of samples with relative errors within 5% reaches 98.7% for the training set and 94.83% for the test set. These results indicate that the proposed framework not only possesses extremely high predictive accuracy, but also ensures strong generalization ability and interpretability in practical applications. Full article

This paper presents the design and implementation of an X-band voltage-controlled oscillator (VCO) fabricated in a standard 180-nm CMOS process. To sustain stable oscillation under a constrained power budget, a gm-boosted topology is employed, integrating vertically stacked cross-coupled transistors with a center-tapped transformer to enhance the equivalent negative conductance. The boosting is achieved through two complementary mechanisms: the center-tapped transformer performs an impedance transformation that repurposes the layout parasitic capacitances into transconductance-enhancing elements, while the stacked cross-coupled pair reuses the DC current and suppresses the source-degeneration of a conventional pair, jointly sustaining a robust start-up margin at a low 0.75 V supply. On-wafer measurement results demonstrate a frequency tuning range from 8.78 GHz to 9.13 GHz as the control voltage is swept from 0 V to 1.8 V, with an average VCO gain K_VCO of 447.5 MHz/V. Under a total DC power consumption of 6.9 mW, the oscillator delivers an output power of 4.54 dBm and exhibits a measured phase noise of −103 dBc/Hz at a 1-MHz offset. Full article

Robust manipulation of small, densely stacked objects remains a challenging problem due to severe occlusions and geometric ambiguities, particularly under single-view sensing conditions. When observed using a single RGB-D sensor, adjacent surfaces of featureless cuboid objects, such as Jenga blocks, often merge in depth measurements, making reliable instance separation and pose estimation difficult. This paper presents a strategy-driven perception and manipulation framework for the robotic rearrangement of randomly stacked Jenga blocks under single RGB-D sensor constraints. The proposed approach employs a heightmap-based perception pipeline that integrates color filtering with geometric reasoning to segment individual blocks and estimate manipulation-compatible poses. Beyond perception, the proposed system determines robot actions through a structured manipulation policy consisting of region-wise search for directly executable grasps, grasp candidate evaluation based on accessibility and collision risk, selective local regrasping for workspace reconfiguration, and placement mode selection between direct insertion and sliding-assisted placement. In this framework, controlled grasp-and-release actions are applied only when no directly executable candidate is found within the currently scanned region and a suitable recovery target can be identified, thereby transforming cluttered local arrangements into more executable states without requiring additional sensing modalities. Experimental results, conducted under competition-equivalent conditions, demonstrate a high task success rate of 99.02%, confirming the robustness and reliability of the proposed framework. The results show that strategy-driven manipulation can effectively compensate for perception limitations in single RGB-D sensor environments, enabling stable and efficient pick-and-place operations in dense clutter. Full article

The accurate location of leakage points is a key part of underground gas prevention. To solve the problem of low positioning accuracy for gas extraction pipeline leakage, the gas extraction pipeline leakage experimental system was built, and the multi-source collaborative leakage localization method based on Stacking learning was proposed. The results showed that the Stacking–LSSVM–Elman–DBN (S-L-E-D) model with pressure–flow collaborative input achieved the best localization performance, with an accuracy of 0.932, Root Mean Square Error (RMSE) of 0.053, Mean Absolute Percentage Error (MAPE) of 0.082, Theil Inequality Coefficient (TIC) of 0.056, and a distance error below 1 m. Compared with a single-parameter input, the collaborative pressure–flow input improved the localization accuracy by more than 10%, while the RMSE and MAPE decreased by 39.0% and 37.4%, respectively. Under monitoring point fault conditions, the localization accuracies of monitoring points 1, 4, and 5 were 0.884, 0.891, and 0.881, respectively, while the dual-fault condition of monitoring points 1 and 4 still maintained an accuracy of 0.861. The study provides a feasible multi-source collaborative learning framework for leakage localization in gas extraction pipelines and offers a methodological reference for improving leakage monitoring and early warning. Full article

This paper focuses on the development of a mobile robot-based security surveillance and target-tracking application that combines image-processing algorithms with the Navigation Stack in the robot operating system (ROS). The proposed approach integrates a 3D camera with the MobileNet-SSD object detection model to estimate the target’s three-dimensional spatial coordinates in real time. These coordinates are continuously transmitted to the ROS Navigation Stack as dynamic goal points, enabling the robot to perform path planning and target-following while maintaining a predefined safety distance and avoiding obstacles. The proposed solution has been validated on a real differentially driven wheeled mobile robot. Experimental results demonstrate smooth and stable robot motion, accurate maintenance of the desired following distance, and reliable static obstacle avoidance while continuously tracking the target. These outcomes confirm the effectiveness and robustness of the integrated system for vision-based navigation tasks in indoor environments. Full article

Herein, we report a method to additively manufacture carbon fiber-reinforced siliconized silicon carbide composites. The process involves the pyrolysis of a 3D-printed carbon fiber-reinforced poly-ether-ether-ketone (PEEK) composite to produce a porous carbon fiber-reinforced carbon matrix composite preform, which is subsequently infiltrated with molten silicon to obtain a carbon fiber-reinforced siliconized silicon carbide composite. A key aspect of the method is limiting polymer melt flow during pyrolysis of PEEK, which is achieved by thermally annealing the 3D-printed carbon fiber-reinforced PEEK preform in air at a temperature below PEEK’s melting temperature. Rheological and differential scanning calorimetry (DSC) measurements demonstrate that the thermal annealing treatment altered the melting behavior of PEEK, while NMR and FTIR measurements provided a mechanistic explanation for the structural changes responsible for the behavior. It was also found that dimensional changes during pyrolysis were anisotropic with greater shrinkage in the stacking direction of the material. Full article

To address the challenges posed by complex Cretaceous(K) deep structural deformation and the poorly understood decoupling mechanism between deep and shallow structural layers in the foreland thrust belt of the Kuqa depression, Tarim Basin, this study integrates high-precision 3D seismic interpretation with balanced cross-section restoration techniques to systematically elucidate the controlling role of rheological heterogeneity within the Shushanhe Formation (K₁s) mudstone on the stress–lithology–structure coupling mechanism. Our findings demonstrate that variations in thickness and rheological properties of the Shushanhe Formation mudstone govern the structural segmentation along the Zhongqiu–Dongqiu transect. In the Dongqiu area, an exceptionally thick and highly ductile mudstone layer induces principal stress deflection and horizontal shearing, effectively absorbing vertical strain transmitted from deep-seated tectonic wedges. This results in pronounced decoupling between deep and shallow strata, giving rise to broad, gentle anticlines and ramp-flat imbricate structures at depth. Conversely, in the Zhongqiu area, the mudstone thins significantly and becomes more brittle, increasing the friction coefficient and impeding vertical stress transmission. Consequently, near-vertical stacking occurs in the proximal compressional segment, leading to the development of high-angle thrust faults and strike-slip-modified fault-bend folds. This study clarifies the genetic mechanism of non-coaxial structures controlled by the mudstone detachment layer and confirms that the plastic flow of this layer not only enhances lateral sealing capacity but also acts as an effective rheological barrier, thereby preserving the deep overpressured hydrocarbon reservoirs in the Yageliemu Formation (K₁y). These insights provide a robust theoretical foundation for shifting exploration strategies from shallow structural traps to deep, subtle lithologic–structural composite plays, offering critical guidance for sweet spot prediction in ultra-deep settings. Full article

Laser beam welding (LBW) of aluminium busbar-to-terminal connections for electric-vehicle battery packs requires precise in-process monitoring. Membrane-free optical microphones provide a high-bandwidth (DC–MHz) acoustic channel that captures keyhole, melt-pool, and plume dynamics. This study proposes Acoustic Signal Intelligence (ASI), a deep learning framework for LBW state classification from a single optical microphone, evaluated on an open dataset (183 AA1050 welds, $f_s$ = 2.5 MHz) under a five-class taxonomy: lack of fusion, lack of connection, sound, marginal, and piercing. The contributions are: (i) a compact 1-D CNN encoder on a mel-scale STFT spectrogram, reaching the highest macro-F1 (0.72 mean across three-fold replicate-out cross-validation) and 100% piercing recall in every fold—a multi-representation fusion variant adding a wavelet-packet decomposition and a 24-feature library targeting the 8, 63 and 110 kHz keyhole-resonance peaks was evaluated as an ablation arm and did not survive cross-validation, so the proposed model is mel-only; (ii) a systematic benchmark against six classical-ML and four deep learning baselines in which Transformer-hybrid ablations and ACGAN-style augmentation underperform compared to the compact CNN on the 122-sample training set, with the Transformer underperformance confirmed by a 30-configuration grid search over learning rate, weight decay, and dropout (best tuned macro-F1 = 0.441 vs. CNN 0.724); and (iii) a Grad-CAM analysis that recovers the keyhole-resonance bands without prior knowledge. A single optical microphone is thus a viable real-time alternative to multi-sensor stacks for battery-pack laser welding. Full article

City Information Modelling (CIM) and Urban Digital Twins (UDT) are pivotal for advancing smart urban planning and city management, yet empirical evidence on their real-world implementation is scarce. Following a sequential mixed-methods design, this study addresses this gap through a global investigation analyzing 33 projects across diverse geographic contexts. Findings reveal that these technologies are predominantly applied in 3D visualization (60.6%) and urban planning (48.5%), with significant underutilization in climate adaptation (9.1%) and AI-driven robotics (3.0%). A pronounced physical–social data divide exists, with infrastructure data prioritized over human-centric inputs. Technology stacks converge on GIS, IoT, and BIM. However, an interoperability paradox persists, as internal integration outpaces cross-organizational connectivity. Governance is predominantly public-sector-led, but multi-actor ecosystems are also involved. The study concludes with actionable recommendations to rebalance implementation portfolios, integrate socio-economic data, and advance both technical and institutional interoperability, thereby harnessing CIM and UDT for transformative urban planning and city management. Full article

Depression screening from social media has increasingly benefited from transformer-based architectures; however, integrating symptom-level analysis with clinically grounded diagnostic screening remains challenging. This study proposes a unified two-phase framework for social media-based depression detection aligned with DSM-5 criteria. In Phase 1, transformer-based learning strategies—Single, Voting, Stacking, Bagging, and Boosting—are employed to perform symptom-level multi-class classification of depressive symptoms. In Phase 2, the predicted symptoms are aggregated over a 14-day observation window to enable DSM-5-oriented binary depression screening. To ensure a robust and consistent evaluation, eight preprocessing configurations (D1–D8) are incorporated into the framework. Experimental results demonstrate that Bagging achieves the highest performance in symptom-level classification (F1 = 0.9394), while Voting and Boosting yield superior performance in DSM-5-oriented screening (F1-Yes = 0.7273). The findings reveal that different learning mechanisms play distinct roles across diagnostic levels, with variance-reduction strategies enhancing symptom differentiation and consensus-based approaches improving recall in clinical screening. This study provides a structured and clinically aligned framework for social media-based depression detection, offering practical insights for developing robust and scalable mental health screening systems. Full article

Polymer-based composites with magnetic properties are promising materials that are able to combine the usual polymer features (low density, high electrical resistance, enhanced flexibility, and processability, etc.) with magnetic properties typically associated with ferro- or ferrimagnetic metals, alloys or metal oxide. The combination of recycled NdFeB powders with additive manufacturing techniques based on material extrusion enables the production of magnetic composites. The novelty of this approach lies in the use of 3D printing supported by an external magnetic field, which is used to align the particles during the printing process and thus improve the final magnetic properties. This approach represents a sustainable strategy for the recovery of electronic waste, converting it into high-value-added magnetic materials intended for additive manufacturing applications. Micrometric particles made of a Neodymium–Iron–Boron (NdFeB) alloy are compounded with a flexible thermoplastic matrix made of polybutylene adipate-co-terephthalate (PBAT). The NdFeB alloy is recovered from permanent magnets of obsolete hard drives and is demagnetized, ground to powder under an inert atmosphere, and finally sieved to a particle size below 50 µm. The obtained powder is mixed with the polymer using a twin-screw extruder. The composite material containing the NdFeB particles is then processed to obtain a calibrated filament, used for the fused deposition modeling (FDM) three-dimensional (3D) printing of magnetic composites. To improve the composite’s ferromagnetic behavior, the particles were aligned along the stacking direction of the layers during the 3D FDM process by printing directly onto a permanent magnet placed on the build plate. Composites containing up to 50% by weight of recycled NdFeB powder were successfully processed using FDM technology, exhibiting increased stiffness, with the storage modulus rising from 123 to 178 MPa at 20 °C, while magnetic field-assisted printing increased the remanence from 11 to 28 emu/g and improved the reduced remanence from 0.21 to 0.49, corresponding to an estimated fourfold improvement in the magnetic energy product. Full article

Radar quantitative precipitation estimation (QPE) is a crucial product for nowcasting and disaster warning. However, its accuracy is constrained by factors such as radar band, attenuation effects, and variations in the phase and microphysical properties of precipitation particles. Based on X-band phased-array radar data from Zhongshan City, Guangdong Province, this study compares and evaluates the QPE correction performance of three deep learning models: stacking ensemble learning, gated recurrent unit (GRU), and three-dimensional convolutional neural network (3D CNN). The aim is to explore the applicability of different model types under complex precipitation conditions. Data from August 2023 to August 2024 were used to construct the samples, with records from May 2024 held out as an independent test set and excluded from model training and hyperparameter tuning. Model performance was assessed under different radar combinations (three-radar, dual-radar, and single-radar configurations), temporal scales (minute and hourly), and precipitation intensities. The results show that: (1) at the minute scale, all three models improved the original QPE, reducing average relative error (RE) by approximately 24.6–29.5%, mean absolute error (MAE) by 23.2–27.7%, and root-mean-square error (RMSE) by 19.7–22.8%, while increasing correlation coefficient (CC) by approximately 20.4–20.9%. Specifically, GRU achieved the largest reduction in RE, stacking showed slight advantages in controlling MAE and RMSE, and 3D CNN and GRU showed similar improvements in CC. (2) At the hourly scale, the correction effect varied with precipitation intensity. In the light-to-moderate rainfall range ($0.1\le R<8.0\mathrm{mm}{\mathrm{h}}^{-1}$, where R denotes hourly rainfall), 3D CNN generally showed better error-control performance, whereas the advantage of GRU was less consistent among radar combinations. In the heavy-rainfall range ($R\ge 16.0\mathrm{mm}{\mathrm{h}}^{-1}$), stacking and GRU provided complementary value in some radar configurations, although model performance remained configuration dependent. (3) Case analysis shows that stacking can improve the original QPE at some extreme-precipitation stations, but correction performance in the extreme high-value range remains unstable, and GRU and 3D CNN are more prone to underestimation. Oriented toward operational applications, this study systematically evaluates the applicability and limitations of three model types under different scenarios while considering computational-resource constraints and timeliness requirements, thereby providing a reference for model selection and operational application in radar QPE correction. Full article