Cold chain logistics distribution, a vital activity supporting global urbanization, faces complex challenges in balancing economic, environmental, and social objectives. This study investigates the green vehicle routing problem with a mixed fleet, simultaneously optimizing total cost, carbon emissions, and customer satisfaction. To solve this NP-hard problem, a novel multi-strategy NSGA-III algorithm is proposed, which integrates an adaptive pheromone update mechanism, elite route guidance, and genetic operators to significantly enhance search efficiency and solution diversity in complex solution spaces. Computational experiments on benchmark instances and a real-world case demonstrate the algorithm’s superior performance over mainstream multi-objective optimizers like NSGA-III and NSGA-II in metrics such as hypervolume. Sensitivity analysis further elucidates the impact of key operational parameters on system performance and provides a quantitative decision-making basis for greening urban cold chain fleets. This research offers an effective computational tool for complex sustainable logistics problems, with a modeling framework extensible to other industrial systems facing similar multi-objective trade-offs.

This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties.

Accurate detection of pressure-relief boreholes is crucial for evaluating drilling quality and monitoring safety in coal mine roadways. Nevertheless, the highly challenging underground environment—characterized by insufficient lighting, severe dust and water mist disturbances, and frequent occlusions—poses substantial difficulties for current object detection approaches, particularly in identifying small-scale and low-visibility targets. To effectively tackle these issues, a lightweight and robust detection framework, referred to as YOLO-DFBL, is developed using the YOLOv11n architecture. The proposed approach incorporates a DualConv-based lightweight convolution module to optimize the efficiency of feature extraction, a Frequency Spectrum Dynamic Aggregation (FSDA) module for noise-robust enhancement, and a Biformer (Bi-level Routing Transformer)-based routing attention mechanism for improved long-range dependency modeling. In addition, a Lightweight Shared Convolution Head (LSCH) is incorporated to effectively decrease the overall model complexity. Experimental results on a real coal mine roadway dataset demonstrate that YOLO-DFBL achieves an mAP@50:95 of 78.9%, with a compact model size of 1.94 M parameters, a computational complexity of 4.7 GFLOPs, and an inference speed of 157.3 FPS, demonstrating superior accuracy–efficiency trade-offs compared with representative lightweight YOLO variants and classical detectors. Field experiments under challenging low-illumination and occlusion environments confirm the robustness of the proposed approach in real mining scenarios. The developed method enables reliable visual perception for underground drilling equipment and facilitates safer and more intelligent operations in coal mine engineering.

In recognising both the emerging industrial applications of multi-link robotic manipulators and the inherent challenges of modelling and controlling their highly complex nonlinear dynamics, this work proposes a completely model-free terminal sliding mode control (MFTSMC) design approach to reduce the sensitivity and complexity often associated with model-based routines. Consequently, the proposed design achieves strong robustness, simplicity, and good operation tuning by eliminating the need for system modelling and enabling direct operator–machine interaction. Simulink simulations on a 3-link case subjected to different disturbance conditions (free, low-frequency, high-frequency, and mixed) show rapid dynamic convergence, good tracking precision, and strong disturbance rejection. The system reaches the sliding surface within 0.07 s, maintains steady-state errors around 10−2, and achieves a smooth torque response with low energy costs. The benchmark results confirm the finite-time convergence and demonstrate that the proposed framework is practical and scalable for multi-DOF systems and has potential for underactuated manipulators. It should be noted that a generalised dynamic model for a planar n-link manipulator is presented in the study for (1) the ground truth of the manipulator in simulation (not for the MFTSMC design), (2) the model-based controller designs in comparison to the MFTSMC, and (3) understanding the dynamic characteristics.

Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters.

The tool-point frequency response function (FRF) of a spindle–tool system plays a crucial role in predicting machining stability. Among the factors influencing the FRF, the interface characteristics between the spindle and the tool holder are particularly significant, especially when different holder designs are used. This study focused on identifying these interface characteristics for two common tool holder types—BT and BBT—to improve FRF prediction accuracy. The receptance coupling substructure analysis (RCSA) method was employed in conjunction with finite element modeling (FEM) to characterize the spindle–tool holder interfaces without needing extensive experimental tapping tests. Finite element models were developed to generate receptance components for various tool holder–tool assemblies, enabling efficient and accurate coupling within the RCSA framework. The identified interface parameters were applied to predict the tool-point FRFs of the cutter clamped in a BT tool holder with different overhang lengths. The predicted and measured tool compliances differed by 3–4.6%, demonstrating high agreement and reliability. The proposed methodology provides a powerful tool for predictive modeling of dynamic behavior in spindle–tool systems under varying tooling conditions, enhancing process planning and evaluation of the cutting stability in high-precision machining.

Considering the variability of driving conditions in mining areas, existing control strategies are difficult to meet the comprehensive performance requirements of mining dump trucks in the longitudinal, lateral, and vertical directions. Longitudinal, lateral, and vertical (LLV) coordinated control of active hydro-pneumatic suspension system based on model predictive control (MPC) is constructed in this paper. The vehicle dynamic response under random road surface input based on wheelbase characteristics is established, and the rationality of the active hydro-pneumatic suspension LLV coordinated control strategy based on MPC is analyzed. Handling stability is taken as the overall control objective for active hydro-pneumatic suspension on C-class road surfaces. The dynamic tire loads of the six wheels of the mining dump truck are reduced by 25.8%, 29.1%, 30.6%, 27.6%, 29.9%, and 28.1%, respectively, in the unloaded state, while the longitudinal, lateral, and vertical body accelerations have not deteriorated. Under the E-class road surface, the overall control objective of the mining dump truck is comfort, and the longitudinal, lateral, and vertical accelerations in the unloaded state have been optimized by 34.6%, 31.4%, and 34.1%, respectively.

To investigate the performance of skewed roller thrust bearings (SRTBs) in the no-back brake of horizontal stabilizer trim actuators (HSTAs), this study conducts systematic theoretical modelling, experimental validation, and numerical simulation focusing on torque and contact characteristic optimization. First, a theoretical model for resistance torque of the SRTB was established based on the kinematics and load behaviours, followed by a systematic investigation into the effects of roller centre position and skew angle on the bearing’s resistance torque. An experimental platform was built, and tests were carried out on the bearings to verify the results of the theoretical analysis. Subsequently, a tangent arc profile was applied to the rollers to mitigate stress concentration at their ends, and the influences of crown drop and straight segment length on roller contact stress were explored by finite element method. Finally, considering the actual operating conditions of no-back brake components, the effect of roller centre position on brake deformation and roller contact stress was studied. The results show that the resistance torque increases with both roller skew angle and centre position, but is insensitive to rotational speed. Roller contact stress first decreases rapidly and then increases gradually with crown drop, indicating the existence of an optimal crown drop value. This optimal value first decreases and then increases with increasing straight segment length, with the optimal parameters determined as 9 μm (crown drop) and 4 mm (straight segment length). In practical applications, asymmetric loading on the two sides of the ratchet disc causes uneven roller contact distribution and stress concentration. Adjusting the roller centre position to balance the deformation of the ratchet disc and rod shoulder can effectively reduce contact stress, with the optimal position being approximately 48 mm (slightly offset from the load centre of 49 mm). This study provides valuable insights for the optimal design of SRTBs and no-back brakes.

Biomimetic pectoral fin propulsion offers a low-noise, highly maneuverable alternative to conventional propellers for next-generation underwater robotic systems. This study develops a manta ray-inspired dual-servo pectoral fin module with a CPG-based controller and employs it as a single-fin test article in a recirculating water tunnel to quantify its hydrodynamic performance. Controlled experiments demonstrate that the fin generates stable thrust over a range of flapping amplitudes, with mean thrust increasing markedly as the amplitude rises, while also revealing an optimal frequency band in which thrust and thrust work are maximized and beyond which efficiency saturates. To interpret these trends, a quasi-steady CFD analysis using the k–ω SST turbulence model is conducted for a series of static angles of attack representative of the instantaneous effective angles experienced during flapping. The simulations show a transition from attached flow with favorable lift-to-drag ratios at moderate angles of attack to massive separation, deep stall, and high drag at extreme angles, corresponding to high-amplitude fin motion. By linking the experimentally observed thrust saturation to the onset of deep stall in the numerical flow fields, this work establishes a unified experimental–numerical framework that clarifies the hydrodynamic limits of pectoral fin propulsion and provides guidance for the design and operation of low-noise, highly maneuverable biomimetic underwater robots.

Recent advances in Large Vision–Language Models (LVLMs) have demonstrated strong cross-modal reasoning capabilities, offering new opportunities for decision-making in autonomous driving. However, existing end-to-end approaches still suffer from limited semantic consistency, weak task controllability, and insufficient interpretability. To address these challenges, we propose SemAlign-E2E (Semantic-Aligned End-to-End), a semantic-aligned multimodal LVLM framework that unifies visual, LiDAR, and task-oriented textual inputs through cross-modal attention. This design enables end-to-end reasoning from scene understanding to high-level driving command generation. Beyond producing structured control instructions, the framework also provides natural-language explanations to enhance interpretability. We conduct extensive evaluations on the nuScenes dataset and CARLA simulation platform. Experimental results show that SemAlign-E2E achieves substantial improvements in driving stability, safety, multi-task generalization, and semantic comprehension, consistently outperforming state-of-the-art baselines. Notably, the framework exhibits superior behavioral consistency and risk-aware decision-making in complex traffic scenarios. These findings highlight the potential of LVLM-driven semantic reasoning for autonomous driving and provide a scalable pathway toward future semantic-enhanced end-to-end driving systems.