Search Results (7,140)

Stepper-motor-driven Selective Compliance Assembly Robot Arms are susceptible to motion control challenges under short-stroke and high-frequency start–stop conditions, including high sensitivity to pulse timing, difficulty in multi-joint coordination, and insufficient trajectory smoothness. To address these issues, this paper proposes an optimized motion control method for smooth execution of obstacle-avoidance trajectories, integrating path smoothing, asymmetric S-curve velocity planning, and pulse-frequency-based multi-axis synchronization. First, piecewise cubic Hermite interpolation, Gaussian smoothing, and end-effector-based equidistant resampling are applied to post-process Rapidly-exploring Random Tree-generated paths, thereby eliminating polyline turning points and improving uniformity of waypoint distribution. Second, an asymmetric S-curve velocity planning method with nonzero boundary velocity constraints is developed, and multi-axis synchronization is achieved based on the maximum segment duration principle. Finally, instantaneous reference velocities are converted into per-axis pulse frequency commands via proportional mapping, enabling real-time stepper motor drive control. Experimental results show that the proposed method reduces the obstacle-avoidance path length by 8.52% and significantly decreases the dispersion of trajectory step sizes. In single-segment dynamic simulations, the proposed method reduces the peak dynamic output force by 62%. In real robot experiments, the average motion time across three obstacle-avoidance tasks is reduced by approximately 55.21%, while end-effector trajectory continuity and inter-joint coordination are improved, suggesting the effectiveness and preliminary engineering feasibility of the proposed method under the tested conditions. Full article

Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, but many RAG systems represent knowledge either as flat text chunks or as automatically constructed indexing graphs. This assumption is incomplete for curated thematic corpora, including religious scriptures, legal codes, clinical guidelines, educational taxonomies, policy documents, and library classification systems, where domain experts have already organized knowledge into thematic paths and citeable canonical units. This paper investigates how RAG can exploit such expert-authored structures while pruning evidence to a compact and query-specific set. We conduct a critical survey supported by a bibliometric analysis of 2815 Scopus-indexed RAG-related records exported on 26 May 2026, of which 2809 records were retained after duplicate removal. The bibliometric results indicate rapid growth in RAG research but limited explicit consolidation around curated thematic paths, canonical evidence units, or thematic path-guided evidence pruning. We therefore propose ThemePath-RAG, a retrieval framework that retrieves curated thematic paths as high-recall semantic routes, expands candidate canonical evidence, and applies query-aware scoring and global pruning before generation. To assess operational feasibility, we implement ThemePath-RAG for Qur’anic question answering and compare it with a Vector RAG baseline on 150 paired questions using RAGAS context relevance with gpt-4o-mini as the LLM evaluator. Both methods return approximately three final ayat per question. Vector RAG achieves higher mean context relevance than ThemePath-RAG ($0.920$ versus $0.798$; $p<0.001$). Thus, the proof of concept establishes the feasibility of thematic-path-guided retrieval and identifies evidence-selection challenges, rather than demonstrating superiority over conventional vector retrieval. The paper clarifies the framework’s relationship to GraphRAG, LightRAG, HippoRAG, PathRAG, ontology-based RAG, and AI-augmented bibliometric systems, and outlines a language-matched, multi-baseline evaluation agenda for future cross-domain validation. Full article

Background/Objectives It is not established whether melanoma and lung cancer are part of the tumor spectrum of the Lynch syndromes (LS). Our first hypothesis was that, if melanoma is associated with LS, most prospectively observed cases would be carriers of pathogenic MSH2 variants (path_MSH2), as for other LS non-endodermal tumors. Our second hypothesis, which was derived from findings of the first study, was that the pattern of differences in the incidence of lung cancer in path_MMR carriers would mirror that for melanoma. Methods We used the Prospective Lynch Syndrome Database (PLSD) data and methods. Results Firstly, consistent with hypothesis, ten of 3154 path_MSH2 carriers followed for 26,309 years developed melanoma, compared with 6 of 5288 path_MLH1/MSH6/PMS2 carriers followed for 45,081 years (p = 0.04). Secondly, although the incidence of melanoma in carriers of path_MSH2 was 0.00041—similar to the populations in which the carriers resided—the average incidence rates for path_MLH1/MSH6/PMS2 carriers was 0.00012, lower than the corresponding population rates. Thirdly, the average lung cancer incidence rates in PLSD were 0.00112 in path_MSH2 carriers and 0.00032 in path_MLH1/MSH6/PMS2 carriers (p = 0.02), both lower than the population rates. Conclusions Melanomas and lung cancers—both of which are immunogenic through mechanisms independent of mismatch repair deficiency—may become even more immunogenic when they also exhibit microsatellite instability. This may lead to an increased likelihood that both tumors are eliminated by the immune system and, thus, show variable and lower incidence. These insights may help inform strategies for cancer prevention or treatment that are aimed at simultaneously interrupting multiple carcinogenic pathways. Full article

Against the backdrop of the “storing grain in land and technology” strategy and pressing food security challenges, understanding biased technological progress in grain production is crucial. This study constructs a three-factor Constant Elasticity of Substitution (CES) production function—incorporating agricultural machinery, chemical fertilizer, and labor—and employs a normalized supply-side system with Nonlinear Seemingly Unrelated Regression (NLSUR) to analyze biased technological progress across 26 Chinese grain-producing provinces from 2004 to 2023. Total factor productivity (TFP) is also decomposed to assess this progress’s contribution. The results indicate that first, technological progress generally evolves along a “machinery–fertilizer–labor” path. Recently, the primary driver shifted from machinery to fertilizers, aligning with fertilizer reduction and green development policies. Second, TFP growth exhibits phased characteristics, declining during the post-agricultural tax reform period (2005–2013) and rising amid supply-side structural reforms and the “storing grain in technology” strategy (2014–2023). Third, marketization, demand, and digitalization promote fertilizer-oriented progress; specialization drives machinery-oriented progress; and road infrastructure facilitates labor-oriented progress. These results offer empirical evidence for policy evaluation and guidance for optimizing factor allocation, advancing the green transition, and safeguarding food security. Full article

Detecting small targets in Unmanned Aerial Vehicle (UAV) imagery is challenging due to low pixel coverage, complex backgrounds, and information loss during downsampling. Existing detectors lack explicit mechanisms for enhancing weak target signals. We propose GHF-DETR, a Transformer-based detector featuring three collaboratively designed modules. First, a Heterogeneous Multi-Path Convolutional Network (HMC) backbone uses partial convolution and gated linear units to reduce computational redundancy while maintaining discrimination of small-object features. Second, a Dynamic Multi-Scale Focusing (DMSF) module integrates learned offset alignment with multi-kernel depthwise convolutions for cross-scale feature fusion. Third, a High-Frequency Selective Preservation (HSP) downsampling module combines space-to-depth convolution with 2D Discrete Wavelet Transform (DWT) to compensate for information loss in both spatial and frequency domains. On VisDrone2019, GHF-DETR achieves 33.1% mAP@0.5 and 18.6% mAP@0.5:0.95 with 15.4 GFLOPs and 7.59 M parameters, improving over the DFINE-n baseline by 5.4% and 3.1%, respectively, with AP_S reaching 10.1%. Generalization is validated on NWPU VHR-10. These results demonstrate that GHF-DETR achieves a favorable accuracy–efficiency balance for efficient UAV small-object detection. Full article

Predicting present-day pore pressure and 3D in situ stress in ultra-deep fold-thrust belts is severely hindered by the inadequacies of traditional 1D vertical compaction models, which fail to account for massive lateral tectonic compression and continuous elastoplastic yielding. To overcome this, a 3D poro-elastoplastic analytical framework is developed based on the Modified Cam-Clay model to decode the irreversible “geomechanical memory” of deeply buried argillaceous rocks. Applied to the highly compressed Kelasu Thrust Belt, this method links volumetric strain with mean and deviatoric stresses in stress-invariant space to reconstruct the maximum paleo-pore pressure and 3D paleo-stress tensor during the Coulomb Failure Period (CFP). The quantitative decoupling reveals an extreme state of geopressure prior to macroscopic faulting (pore pressure ratio α = 0.85–0.89). Crucially, the mean stress surge is identified as the dominant driver, generating ~91% of the excess overpressure. Consequently, horizontal tectonic compression accounts for 80–90% of the total overpressure anomaly, fundamentally overturning the classical assumption that vertical undercompaction (10–20%) is the primary mechanism. Furthermore, it is demonstrated that during subsequent tectonic uplift, the heavily compacted, salt-capped mudstones follow an undrained unloading path; the reduction in lithostatic burden is almost entirely offset by fluid depressurization, maintaining a constant effective stress state. This physically decoupled framework provides a rigorous basis for optimizing pre-drill safe mud-weight windows, designing hydraulic fracturing in highly deviatoric stress regimes, and assessing caprock integrity for deep geo-energy storage. Full article

With the rapid development of networked cyber-physical systems, the coordinated control of heterogeneous multi-agent systems has attracted increasing attention in applications such as autonomous vehicles, robotic arms, and distributed sensor networks. This paper investigates the leader-following cluster consensus problem for heterogeneous multi-agent systems over weighted cooperative–competitive networks with matched disturbances generated by linear exosystems. Unlike purely cooperative or binary signed networks, the considered network allows interaction weights to take arbitrary positive or negative values, thereby describing both the type and intensity of cooperative or competitive interactions. To handle heterogeneous agent dynamics and matched disturbances, a disturbance-observer-based distributed control protocol is developed for both first-order and second-order followers. Based on path-product-based coordinate transformations and Lyapunov stability analysis, sufficient conditions are derived to guarantee topology-dependent scaled leader-following cluster consensus under interactively balanced and interactively sub-balanced topologies. For interactively unbalanced topologies, a structurally selected pinning control strategy is introduced to compensate for sign conflicts caused by unbalanced directed cycles and ensure global asymptotic convergence. Numerical simulations verify the effectiveness of the proposed protocol under heterogeneous dynamics, weighted cooperative–competitive interactions, and matched disturbances. Full article

The bearingless induction motor (BIM) relies on accurate displacement feedback to maintain stable magnetic suspension, but sensor faults, degradation, and noise can distort feedback and induce transients during branch switching. This paper proposes a confidence-fusion-based fault-tolerant displacement measurement method for the BIM suspension feedback chain. A four-channel asymmetric redundant sensor configuration is developed, and channel state evaluation functions are constructed from sampling-difference terms and geometric-consistency residuals. A decreasing Sigmoid mapping with first-order smoothing generates continuous confidence coefficients to represent channel health. Combined with discrete fault flags of the primary channels, four reconstruction branches, AB, BC, AC, and CD, are adaptively weighted to obtain the reconstructed displacement, which is connected to the original suspension controller through a smooth feedback access mechanism. A MATLAB/Simulink closed-loop suspension model is used to evaluate the method under fault-free operation, an abrupt fault of primary channel A, simultaneous and sequential faults of primary channels A and B, abrupt and gradual degradation, constant bias, intermittent signal dropouts, and noise disturbance of primary channel B. Results show that the method identifies abnormal primary channels, redistributes reconstruction weights according to sensor conditions, and maintains a fallback path through the CD branch under dual-primary-channel failure. Under channel-B degradation, the confidence coefficient tracks the deterioration and supports the subsequent AB-to-AC branch transfer, whereas under noise disturbance, the fault flag remains inactive and unnecessary branch switching is avoided. The method improves feedback continuity without changing the main suspension controller. Full article

Laser marking of two-dimensional composite contours requires hatching paths that balance path length, computation time, coverage continuity, and marking accuracy. Existing industrial and academic systems commonly provide zigzag and contour-parallel hatching strategies, but the choice between them is often made empirically, especially for nested or irregular contours. This study is positioned as a geometry-aware toolpath-selection and parameter-optimization framework. Three benchmark contour types, namely a single-layer regular contour, a nested multi-layer contour, and an irregular contour, are evaluated using zigzag parallel hatching and contour-parallel hatching. The MATLAB results show that for the irregular benchmark contour, zigzag hatching reduces the toolpath-generation time by 73.91–89.62% compared with contour-parallel hatching when the hatch spacing varies from 0.01 to 0.10 mm. Five repeated numerical runs further show that the maximum relative timing deviation is below 0.003%, indicating stable computation in the software environment. To avoid confusing algorithmic computation time with real machine execution time, this paper explicitly separates toolpath-generation time from physical marking time and outlines a repeatable physical validation protocol including execution time, dimensional error, mark contrast, and line width uniformity. The results provide practical guidance for selecting hatching strategies according to contour complexity and accuracy requirements. Full article

This paper combines the application of AI technology in the field of architectural design to construct a “three-stage model” (imitation, exploration, and creation) centered on cultivating students’ creative thinking and innovative ability, with the goals of AI literacy cultivation, digital twin practice, and interdisciplinary collaboration. By integrating the theoretical model with the latest practical cases, the effectiveness of the new generation of AI-driven innovative teaching modes is verified. Taking library architectural design and old building renovation teaching as examples, the teaching process and evaluation system with real-time feedback, intelligent assessment, and full-process traceability are designed to achieve the dual improvement of teaching efficiency and students’ practical innovation ability. The research shows that the characteristics of artificial intelligence, including multimodal generation, immersive interaction, and full-cycle simulation, are reconstructing the core logic of architectural design education, promoting the in-depth transformation of the teaching mode from “imitation” to “creation”, building a talent cultivation system adapted to the future development of the construction industry, and providing a feasible reference path for the innovation of education modes. Full article

The cascading damage of expressways induced by extreme heavy rainfall presents a persistent threat to transportation safety and regional sustainable development. To investigate the chain-like evolution characteristics of expressway damage caused by heavy rainfall and to identify precise strategies for mitigating disaster risks by breaking the chain. Firstly, directed causal event pairs were extracted, and clustering generalization was performed on disaster events.; the asymmetric Jaccard index was used to calculate edge weights, thereby establishing a directed causal knowledge graph of disaster chain evolution; Secondly, based on systematic risk assessment and chain-breaking priority indicators, we achieved the precise identification and quantification of critical vulnerable links; finally, we selected three typical damage cases—the ‘5·1’ case on the Meida Expressway in Guangdong, the ‘7·19’ case on the Danning Expressway in Shaanxi, and the ‘8·3’ case on the Yakang Expressway in Sichuan—for case validation, and proposed chain-breaking strategies. The research findings indicate that: (1) under specific hazard-forming environment, secondary disasters can supplant the primary causative factors to become the dominant driving nodes in chain evolution; (2) edge vulnerability and source-path diversity loss indicators respectively point to two distinct categories of high-risk edges; the comprehensive chain-breaking index compensates for the assessment blind spots of single indicators through two-dimensional weighting; (3) core vulnerabilities in disaster chains vary significantly across different regions: the Meida Expressway, the Danning Expressway, and the Yakang Expressway correspond to terminal response, pavement control node, and dual vulnerabilities at the source and structural levels, respectively, necessitating tailored chain-breaking strategies adapted to local conditions. These research findings offer a quantitative tool for infrastructure risk governance, contributing to the safety and sustainability of expressway transportation. Full article

Unmanned aerial vehicles (UAVs) have been widely used in surveying and mapping, inspection, emergency rescue, and environmental monitoring. However, effective path planning remains a key challenge in complex three-dimensional terrain, where UAVs must simultaneously cope with terrain undulations, no-fly zones, safety-clearance requirements, and trajectory-smoothness constraints. In addition, conventional intelligent optimization algorithms often suffer from search instability and premature convergence. To address these challenges, this study proposes a reinforcement-learning-driven multi-strategy spherical-vector grey wolf optimizer, termed TLQ-SGWO, where TLQ denotes the combined use of Tent–Logistic hybrid initialization and Q-learning search-strategy scheduling. In the proposed method, candidate trajectories are encoded using spherical-vector increments; Tent–Logistic hybrid initialization is introduced to enhance population diversity; and Q-learning is incorporated to adaptively select search strategies, thereby dynamically balancing exploration and exploitation. A comprehensive cost function integrating path length, threat avoidance, terrain clearance, and trajectory smoothness is further constructed to improve the feasibility and safety of the planned trajectories. Experiments are conducted on the CEC2017 benchmark functions and artificially generated complex mountainous terrain scenarios. On the CEC2017 benchmark suite, TLQ-SGWO achieves the best average rankings in both mean error and standard deviation among the seven compared algorithms, indicating a stronger balance between optimization accuracy and robustness. In artificial mountainous scenarios, TLQ-SGWO obtains the lowest mean path cost in three of the four scenarios and remains statistically comparable to the strongest hybrid baseline in the remaining scenario, while maintaining stable feasible 3D trajectories under increasing no-fly-zone complexity. Full article

With the development of the socio-economic landscape and the increasing demand for urban power supply, user expectations for power supply reliability have risen significantly. To address this challenge, dual-petal distribution networks, characterized by multiple tie-line structures and inter-regional load transfer paths, have significantly enhanced fault recovery capability and are gradually replacing traditional radial configurations as a key form of modern distribution systems. However, their multi-regional coupling characteristics introduce complex issues such as dynamic changes in load transfer paths and islanded operation, resulting in significant limitations in the accuracy and adaptability of existing reliability assessment methods. To this end, this paper proposes an analytical method for calculating reliability indices of dual-petal distribution networks, considering the characteristics of load transfer zones. First, typical operation modes of dual-petal distribution networks are extracted, and a time-sequential component reliability analysis model is established. Second, a load transfer zone matrix is constructed based on the impact of distribution network faults on load nodes across different regions. Third, based on the fault ride-through capability of distributed generation (DG), a load restoration strategy considering load transfer zone characteristics is formulated, and the DG Island Recovery Matrix (DGIRM) is derived. Finally, by performing algebraic operations among various matrices and reliability parameter vectors, an explicit analytical calculation of reliability indices for dual-petal distribution networks with different DG configurations is achieved. The effectiveness of the proposed method is validated using a typical dual-petal network. The results demonstrate that the proposed method offers high computational efficiency and accuracy, effectively quantifying the impact of DG on the power supply reliability of dual-petal distribution networks, and providing theoretical and methodological support for the reliability assessment and planning of complex distribution systems. Full article

China’s road transport, especially private vehicles, has experienced continuous growth in energy consumption and carbon emissions in recent years. Electrification-driven net-zero pathways and their impacts on the power sector have drawn broad concern. Current research insufficiently explores vehicle-to-grid (V2G) advantages and fails to update data and assumptions aligned with the latest policies. This study establishes a provincial bottom-up model to calculate the energy demand and carbon emissions of private vehicles and evaluates decarbonization paths and their impacts on the power sector across different scenarios. Private vehicle ownership will rise first and then fall, hitting around 453 million by 2060. Near-term improvements in energy efficiency combined with the long-term diffusion of new energy vehicles can drive private transport toward net-zero emissions after 2050. Vehicle electrification raises electricity consumption remarkably, whereas V2G effectively mitigates carbon shift and offsets over half of cumulative power generation emissions. Marked regional disparities prevail in vehicle usage and emissions, with eastern China presenting higher values compared with western regions. Decarbonization of road transport is more than just addressing carbon shifting, and V2G facilitates cross-sector coordinated emission reduction. Future research is needed to explore the technical, economic and institutional potential for deepening decarbonization. Full article

Over the past decade, drone-based aerial inspection of overhead power lines has proven superior to traditional ground-based methods. However, in flatland areas, it remains costlier, as total expenses include not only flights but also extensive mission planning. Operators must select takeoff zones and conduct flights in compliance with weather conditions and numerous regulations. Automating mission planning can reduce operator workload, lower the risk of rule violations, and boost inspection efficiency. This paper introduces a framework for automating power line inspection route planning. It selects takeoff areas and generates drone routes for specified line segments, which meet all regulatory requirements. The framework incorporates a novel method for automatic pole-type identification using satellite imagery. The approach combines a YOLO detector, trained on synthetic data, with an expert system, resulting in a 36.9% improvement in performance (on the tested dataset) compared to prior solutions. The final solution was implemented as an open-source QGIS plugin. The experimental results demonstrate that the automated path-planning approach successfully generates inspection routes for line segments exceeding 50 km (135 poles) and increases the number of inspected poles by 58.7%, enabling the capture of power line insulators, which can then be automatically segmented and analyzed using machine learning algorithms. Full article