Search Results (1,107)

Continuum robots offer inherent compliance and dexterity for operation in confined and unstructured environments; however, achieving hybrid multi-segment functionality typically requires application-specific redesign and tightly coupled architectures. To address this limitation, this study proposes a reconfigurable hybrid continuum robot architecture based around a multi-lumen central integration backbone that supports multiple actuation modalities and robot configurations. The proposed design combines external tendon-driven disk modules for proximal actuation with a pneumatically actuated distal tip, while internal lumens allow routing of pneumatic lines and the insertion of optional stiffening elements without structural interference. The reconfigurability of the architecture is demonstrated through two configurations: Concept-1, a two-segment hybrid system, and Concept-2, a miniaturized three-segment configuration achieved by reducing the disk diameter and extending tendon actuation to the backbone. Experimental evaluations are conducted to characterize segment-wise actuation, coupled deformation behavior, and workspace capabilities, hysteresis response, tip contact force, and phantom-based target reachability. Results show that the integration of tendon-driven and pneumatic actuation significantly expands and reorients the reachable workspace. Additional functional tests showed repeatable loading–unloading behaviour of the tendon-driven segment, a maximum pneumatic tip contact force of approximately 0.45 N, and successful access to five representative targets within a stomach-like phantom using Concept-2. A kinematic model based on a constant-curvature formulation is validated against experimental data, yielding root-mean-square errors (RMSE) of 5.44 mm and 6.12 mm for Concept-1 and Concept-2, respectively. These results demonstrate consistent model accuracy across different configurations and scales. Overall, the proposed architecture enables modular, scalable, and reconfigurable hybrid continuum robots, providing a flexible framework for applications ranging from large-scale manipulation to gastroscopy-inspired minimally invasive procedures. Full article

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

Utilizing underwater vehicles for hydropower infrastructure inspection is increasingly vital. However, these GNSS-denied and confined environments pose significant navigation challenges: Inertial Navigation Systems (INSs) suffer cumulative drift, Doppler Velocity Logs (DVLs) face acoustic blind zones near walls, and visual navigation frequently fails in highly turbid waters. To address these issues, this paper proposes a tightly coupled multi-source (INS/acoustic/optical/vision) navigation algorithm leveraging prior wall geometry constraints. Developed within an Error-State Kalman Filter (ESKF) framework, the model seamlessly accommodates sensor spatiotemporal heterogeneity. To overcome optical failures, a structural surface constraint model is innovatively constructed using single-beam sonar ranging. The core contribution involves transforming sonar ranging data into 6-DOF spatial pose constraints based on the dam’s planar characteristics, effectively bounding the localization drift perpendicular to the surface. Field experiments at the hydropower station dam demonstrate that under extreme conditions with total visual failure, the proposed algorithm effectively constrains critical motion degrees of freedom. By maintaining the wall-tracking error within 0.08 m (Root Mean Square Error, RMSE)—which effectively represents the relative localization error given the known absolute position of the structural wall—this method significantly enhances the operational robustness and precision of close-wall inspections in extreme underwater environments. Full article

Split-type robots are increasingly deployed in unstructured confined environments such as underground coal mines, where autonomous navigation and cooperative tracking control remain critical challenges. This paper presents a visual target-assisted tracking control scheme for a split-type drilling robot, adopting an active leader–passive follower architecture. The leader robot performs autonomous mobility and obstacle avoidance using 3D LiDAR-based offline path generation and online optimal search. The follower robot uses AprilTag visual fiducial markers to estimate the six-degree-of-freedom relative pose via the Perspective-N-Point algorithm, and it tracks the leader using a two-dimensional fuzzy PID controller that adaptively tunes PID parameters. Extensive experiments are conducted in simulation, simulated tunnels, a large-scale robot platform, and a real drilling robot prototype. Results demonstrate that the leader achieves an average navigation error below 0.175 m, while the follower maintains an average relative tracking error within 0.06 m. The proposed method enables stable, comparable accuracy with smoother, less oscillatory response, and high-precision cooperative navigation for heavy-duty split-type robots, offering a practical solution for intelligent drilling operations in underground confined spaces. Full article

The chronic digitalisation deficit within the construction sector induces design anomalies and human errors, leading to a severe erosion of investment profitability. This study aims to implement the automation of resource generation and validation processes, acting as a systemic safety barrier to stabilise analytical workflows. The proposed methodology relies on a Multi-Agent System (MAS) architecture embedded within the n8n environment and powered by Gemini-class language models. The framework integrates a deterministic PostgreSQL database within a Retrieval-Augmented Generation (RAG) architecture, enabling the precise, real-time processing of Construction Law regulations. Applying Chain-of-Thought reasoning alongside structured prompt templates helped eliminate model logic drift, ensuring comprehensive result reproducibility. The deployment of this platform induced a 96% acceleration in the pre-construction phase, reducing the formulation time of Work Breakdown Structure (WBS)/Critical Path Method (CPM) structures from a baseline of 480 min to an average of 20 min. The empirical data demonstrates a radical compression of operational costs (OPEX) concurrent with the marginalisation of the Human Error Probability (HEP) index to a residual level of < 1%. Ultimately, the solution drastically minimised the iterative overhead, confining the design cycle to a single execution while maintaining high level of compliance with the 7R (7 Rights) Logistics Directive. Full article

Rockburst is one of the major geological hazards in the construction of deep-buried and high-geotemperature tunnels. Using triaxial compression tests and acoustic emission (AE) techniques, this paper conducts a preliminary exploratory investigation on the deformation and failure characteristics, mechanical parameters, acoustic emission responses and energy evolution laws of typical rockburst-prone rocks under confining pressures of 10–30 MPa and temperatures of 100–250 °C. The results show that within the research scope, sandstone exhibits brittle characteristics including compaction, linear elasticity, crack initiation and propagation, stable crack propagation stage, accelerated crack propagation stage, and stress drop stage. Within a certain range, peak strength and damage strength increase with the rise in confining pressure and temperature. The elastic modulus increases with rising confining pressure. The damage point may be the critical point of energy conversion and acoustic emission activity. After damage, the work done by external forces is mainly converted into dissipated energy. With the intensification of surrounding rock damage, the ratio of elastic strain energy to total energy gradually decreases, while the ratio of dissipated energy to total energy gradually increases. Acoustic emission activity increases significantly at the damage point and reaches its peak at the peak strength. The cumulative acoustic emission ring count and cumulative energy increase slowly before the peak and grow rapidly after the peak. Under thermo-mechanical action, new cracks in sandstone preferentially initiate along grain boundaries, and the inconsistent deformation between grains will promote the formation of transgranular cracks. The connection, convergence and final penetration of cracks lead to sample failure. The elevation of temperature and confining pressure can enhance the bearing capacity of sandstone, indicating that a high-temperature and high-stress environment may be conducive to the occurrence of rockbursts. The research results provide scientific support for an in-depth understanding of the mechanical behavior and instability risk of rockburst in deep-buried and high-geotemperature tunnels, and can provide a theoretical basis for rockburst prevention and control of high-geotemperature tunnels of the CZ Railway. Full article

Neoclassical tearing modes (NTMs) are magnetohydrodynamic instabilities that generate magnetic islands in tokamak plasmas, degrading confinement and potentially limiting high-performance operation. Their stabilization typically requires precise alignment and appropriate injection of electron cyclotron (EC) power beams, making real-time control a challenging task. In this work, we present a proof-of-principle study aimed at investigating the potential role of neural networks in the control of plasma instabilities. The objective is not the design of a controller for a specific machine, but rather to study how a learning-based agent can autonomously discover effective stabilization strategies through reinforcement learning. A synthetic environment based on a tokamak scenario is used as a test bed for this investigation; the specific scenario plays no essential role in the methodological conclusions. The controller is trained using reinforcement learning techniques and operates solely on a representation of the magnetic island width, without relying on equilibrium reconstruction or explicit knowledge of the deposition location relative to the island. Two control tasks are considered: pure angular alignment and combined angular alignment with power control. The strategies that autonomously emerge are consistent with hand-designed approaches reported in the literature, while the framework remains flexible for incorporating additional objectives such as power minimization. This exploratory study establishes a framework for assessing the potential advantages of data-driven approaches in magnetic island control and provides a basis for future investigations aimed at improving alignment and suppression strategies in fusion plasmas. Full article

Topologically chiral molecular knots and links represent a unique class of stereochemical architectures in which handedness is encoded by the global crossing pattern of an entangled framework rather than by a local stereogenic element. Their configurational robustness and shape-persistent chiral environments make them promising platforms for molecular recognition, catalysis, chiroptical response, and spin-selective transport. This review summarizes recent progress in the stereoselective synthesis of topologically chiral knots and links, with emphasis on chirality transfer from point, axial and helical elements into persistent topological handedness. Major synthetic strategies are organized into helicity-driven approaches, template-free dynamic systems, coordination-driven self-assembly, and chiral self-sorting. The applications of knots in host–guest confinement, asymmetric catalysis, chiral recognition, and spin-selective transport are also discussed. Full article

The drilling and blasting method is the mainstream approach for excavating deep-buried tunnels. Nevertheless, a complex static–dynamic coupling environment is formed by the directional high in situ stress and the widely distributed weakly intercalated layers in rock masses, which frequently result in uncontrolled propagation of blasting-induced cracks. In this paper, deep-buried tunnels with weakly intercalated layers are selected as the research subject, and a numerical model for simulating blasting-induced crack evolution is developed using the material point method. After the model’s reliability is verified through single-hole blasting tests, the effects of in situ stress and weakly intercalated layers on the evolution of blasting-induced cracks are investigated using a typical case. The results demonstrate that geostress direction significantly guides and restrains crack propagation, with cracks extending preferentially along the maximum principal stress but being limited in the perpendicular direction. Compared with the zero-confining-pressure condition, the maximum crack length is reduced by more than 80% when an equal biaxial confining pressure of 20 MPa is applied. Weak interlayers attenuate the transmission of blasting energy, and crack propagation at their ends is significantly amplified when the principal in situ stress aligns with the interlayer orientation, leading to over-excavation. In addition, a targeted optimization strategy for blasting parameters was proposed that reduced the particle vibration velocity at the arch shoulder by 49%. Full article

Agriculture is currently transitioning toward higher intelligence and facility-based production, where harvesting robots play a crucial role in enhancing efficiency and ensuring standardized output. Addressing the challenges of inaccurate picking pose estimation and limited reachability in greenhouse environments, this paper proposes a reachable grasping pose estimation method based on Particle Swarm Optimization (PSO). First, initial poses are calculated via instance segmentation and keypoint extraction. Subsequently, a fitness function is constructed based on inverse kinematics, and the PSO algorithm is employed to iteratively search for optimal reachable poses. To further tackle planning difficulties in confined spaces, a two-stage path planning method based on cost maps is introduced. A series of performance metrics were designed to validate the proposed pose estimation and path planning methods through simulation experiments. In real-world field tests, the system achieved a harvesting success rate of 85%, significantly outperforming existing methods. The results demonstrate that the proposed approach substantially enhances the operational feasibility and success rate of tomato harvesting robots. Full article

Growing public concern over the living environment, food safety and the healthcare industry has spurred rapid advances in advanced sensing technology for environmental monitoring, food-safety screening, and biomedical surveillance. Consequently, developing a novel sensing strategy which is efficient, inexpensive, and easy to operate has become a major research focus in recent years. MnO₂ nanostructures combine advantages of high specific surface area, quantum confinement, surface effects originating from their nanostructure, pronounced redox activity, broad optical absorption, distinctive electrochemical behavior, multiple accessible oxidation states, low cost and environmental benignity contributed by MnO₂, which make them a critical material candidate for developing advanced sensing technology. This paper provides a comprehensive overview of MnO₂ nanostructure-based novel sensors over the past five years. The contents of this review are listed as follows: (1) synthetic strategy and sensing advantages of MnO₂ quantum dots, 1D MnO₂, 2D MnO₂ and hierarchical MnO₂; (2) recent research advances in detection methodology and corresponding principles based on MnO₂ nanostructures; and (3) the applicational progress of MnO₂ nanostructure-based novel sensing technology in the field of food safety and biosensing. Finally, the foregoing discussion is integrated, and the current shortcomings and future development trends of novel sensors based on MnO₂ nanostructures are critically assessed. Full article

The electrochemical carbon dioxide reduction reaction (CO₂RR) offers a promising route to mitigate excessive CO₂ emissions while enabling the production of value-added chemicals. However, achieving high catalytic selectivity and activity toward specific products remains a critical challenge. Here, we engineer a confined interfacial environment formed between adjacent copper nanoparticles and systematically investigate its impact on CO₂RR performance toward CO production. Our theoretical calculations reveal that the confined space effectively stabilizes the *COOH intermediate, a key species governing the CO₂-to-CO conversion pathway. In contrast, this geometric confinement exerts a negligible influence on the adsorption energetics of *H, which is associated with the competing hydrogen evolution reaction (HER). As a consequence, the catalyst exhibits a markedly reduced onset potential for CO₂RR, accompanied by enhanced selectivity and catalytic activity toward CO formation. These findings highlight the critical role of nanoscale confinement in modulating reaction energetics and provide a viable strategy for the rational design of highly efficient and selective catalysts for CO₂RR. Full article

Photocycloaddition reactions provide an efficient strategy for converting alkenes into structurally complex and high-value molecules that are often difficult to access under conventional thermal conditions. Herein, two readily accessible triarylamine-based imine molecular cages possessing distinct cavity environments were investigated as supramolecular photocatalysts for reactions of pyridinium-masked enol (PME) substrates with unactivated alkenes. Spectroscopic studies are consistent with the formation of electron donor–acceptor (EDA) interactions between the electron-rich cage frameworks and electron-deficient PME substrates. Upon blue-light irradiation (450 nm), these charge-transfer assemblies undergo photoinduced activation, likely involving single-electron transfer, N–O bond cleavage, and subsequent radical generation. The resulting radical intermediates participate in formal [4 + 2] cycloaddition reactions to afford tetralone derivatives under metal-free conditions. Comparative studies revealed that the two cages produce distinct product distributions and selectivities, suggesting that subtle variations in cage architecture and confined supramolecular environments influence the fate of reactive radical intermediates and the balance between productive cyclization and competing side pathways. While the detailed mechanistic origin of these effects remains unresolved, this work demonstrates the potential of covalent organic cages as structurally tunable platforms for modulating EDA-mediated photochemical reactivity and radical selectivity. Full article

Sign language understanding for automated vehicles sits at the intersection of accessibility, intelligent transportation, and safety-critical human–machine interaction. The existing sign-language recognition systems are largely confined to controlled environments, limiting their utility in mobility scenarios characterized by lighting variation, motion blur, and partial occlusion. This paper proposes STCM-HVNet, a safety-aware hybrid multimodal architecture integrating four components: a spatial visual encoder, a MediaPipe-based pose encoder, a bidirectional LSTM temporal encoder, and a context-aware fusion and safety decision module. The architecture is formulated as a multi-task system that jointly predicts sign category, interaction intent, and urgency level, and incorporates confidence-aware rejection and fail-safe action mapping. Experiments are conducted on two Arabic sign-language resources. On the RGBArS image benchmark (31 classes, 7856 images), the proposed pipeline achieves a Top-1 accuracy of 45.38%, Top-3 accuracy of 75.15%, and Macro-F1 of 0.4479, outperforming LinearECOC, kNN-5, and Bagged Trees baselines. On the Arabic sign-language video benchmark (12 classes, 479 clips), the BiLSTM temporal encoder achieves a Top-1 accuracy of 93.15% and Macro-F1 of 0.9383, outperforming frame-aggregation (87.67%) and CNN-LSTM (89.04%) baselines. Ablation results confirm complementary contributions from the visual and pose branches. A safety-threshold analysis and a Monte Carlo dropout comparison demonstrate that the proposed safety decision/gating layer provides a controllable trade-off between prediction coverage and reliability. Full article

Background/Objectives: Access to neurosurgical care remains limited in many trauma systems worldwide, particularly in low- and middle-income countries (LMICs). The Brain Injury Guidelines (BIG) were developed to guide the management of traumatic brain injury (TBI) and optimize resource utilization; however, their applicability in resource-limited environments without on-site neurosurgical coverage remains unclear. The aim of this study was to evaluate the performance and applicability of the BIG in a trauma center without neurosurgical support. Methods: We performed a retrospective analysis of adult patients with TBI admitted to a trauma center without neurosurgical support in São Paulo, Brazil, between 2013 and 2017. Patients were classified according to the BIG criteria (BIG 1–3) based on clinical and radiological findings. Primary outcomes were clinical and radiological deterioration and mortality; secondary outcomes included neurosurgical transfer, repeat computed tomography (CT) utilization, and length of stay. Results: A total of 178 patients were included: 12 (6.7%) BIG 1, 53 (29.8%) BIG 2, and 113 (63.5%) BIG 3. No patient classified as BIG 1 or BIG 2 experienced clinical or radiological deterioration, required neurosurgical intervention, or died; adverse outcomes were confined to the BIG 3 cohort, with a mortality rate of 11.5%. The combined BIG 1–2 group showed a sensitivity and negative predictive value (NPV) of 100% for identifying patients without deterioration or need for neurosurgical intervention. Despite the absence of adverse events in the BIG 1–2 group, 76.4% of patients underwent transfer for neurosurgical evaluation, and repeated CT imaging was frequently performed. Conclusions: In this single-center retrospective cohort, the BIG demonstrated excellent discriminatory ability for identifying low-risk TBI patients in a setting without neurosurgical coverage. BIG 1 and BIG 2 categories reliably ruled out the need for neurosurgical intervention, supporting selective non-transfer strategies to optimize resource utilization. Full article