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This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography–mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies. [ABSTRACT FROM AUTHOR]

The integrated navigation system ensures maritime autonomous surface ships (MASSs) to safely, efficiently, and autonomously complete various operations in different complex navigation environments. Investigating robust algorithms for integrated navigation is crucial for enhancing the fault tolerance of the system and ensuring the stable and continuous output of the ship's motion state. However, existing research primarily focused on optimising particular filtering algorithms or examining the foundations of information allocation within a predetermined integrated navigation structure. As such, strategies for enhancing the robustness of the MASS integrated navigation system and the design of subsystems for federated filters in the event of navigation sensor failures have not been sufficiently investigated for complex maritime navigation scenarios. Consequently, this research introduces an observability sharing factor accounting for both system characteristics and state estimation performance in integrated navigation systems, employing nonlinear sampling filtering. Subsequently, a robust integrated navigation framework with distributed federal filter is developed. Within this framework, an adaptive federated filtering integrated navigation algorithm is proposed based on the observability sharing factor to allocate information in federated filtering. Finally, both the theoretical correctness and effectiveness of the algorithm were verified through simulations and real-ship experiments to assist with the development of accurate and fault-tolerant maritime navigation systems. [ABSTRACT FROM AUTHOR]

Soft robots have been investigated for various applications due to their inherently superior deformability and flexibility compared to rigid-link robots. However, these robots struggle to perform tasks that require on-demand stiffness, that is, exerting sufficient forces within allowable deflection. In addition, the soft and compliant materials also introduce large deformation and non-negligible nonlinearity, which makes the stiffness analysis and modelling of soft robots fundamentally challenging. This paper proposes a modelling framework to investigate the underlying stiffness and the equivalent compliance properties of soft robots under different configurations. Firstly, a modelling and analysis methodology is described based on Lie theory. Here, we derive two sets (the piecewise constant curvature and Cosserat rod model) of compliance models. Furthermore, the methodology can accommodate the nonlinear responses (e.g., bending angles) resulting from elongation of robots. Using this proposed methodology, the general Cartesian stiffness or compliance matrix can be derived and used for configuration-dependent stiffness analysis. The proposed framework is then instantiated and implemented on fluidic-driven soft continuum robots. The efficacy and modelling accuracy of the proposed methodology are validated using both simulations and experiments. [ABSTRACT FROM AUTHOR]

In marine applications, the scaled wind turbine model (SWTM) based aerodynamic testing apparatus is regarded as one of the most effective approaches to predict the aerodynamic performance of offshore floating wind turbines (OFWTs). To fully disclose the aerodynamics of OFWTs, such kind of apparatus should meet two design requirements: (1) accurate representation of the aerodynamic characteristics of reference wind turbines and (2) reasonable reproduction of the environmental factors from real-world wind-wave-coupled field. Baring with these considerations, the authors propose a cost-effective SWTM-based aerodynamic testing apparatus, which consists of an innovative SWTM, a wind-wave simulator (WWS) and a data acquisition and analyses system. The proposed SWTM is featured by a double-deck tower, which satisfies the requirements of similarity in geometry, stiffness and mass, and a set of redesigned blades. The proposed WWS is capable of simulating both the time-varying inflow wind and the wave-induced platform motions through a wind field generator and a motion simulator. Based on the developed apparatus, a series of aerodynamic tests are conducted to validate the effectiveness of the apparatus design. In addition, the coupling effects of inflow wind and platform motions on the aerodynamics of OFWT are further revealed through experimental tests. [ABSTRACT FROM AUTHOR]

When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel's structure, are to be considered. [ABSTRACT FROM AUTHOR]

Determining the mechanism that effects embolism repair in the xylem vessels of plants is of great significance in predicting plant distribution and the screening of drought-resistant plants. However, the mechanism of perforation plates of xylem vessels in the acceleration of embolism repair is still not clear by using conventional methods of anatomy and visualization technology. Microfluidic devices have shown their ability to simulate physiological environments and conduct quantitative experiments. This work proposes a biomimetic microfluidic device to study the mechanism of perforation plates of xylem vessels in the acceleration of embolism repair. The results proffered that the perforation plates increase the rate of embolism removal by increasing the pressure differential through the vessel, and the rate of embolism removal is related to the structural parameters of the perforation plate. A combination of the perforation size, the vessel diameter and the perforation plate angle can be optimised to generate higher pressure differentials, which can accelerate the process of embolism repair. This work provides a new method for studying the mechanism of microstructures of natural plants. Furthermore, the mechanism that perforation plates accelerate embolism repair was applied to an electrochemical flow sensor for online determination of heavy metal ions. Test results of this application indicate that the mechanism can be applied in the engineering field to solve the problems of reduced sensitivity of devices, inaccuracy of analysis results and poor reaction performance caused by bubbles that are generated or introduced easily in microdevices, which paves the way for applying the theory to engineering. [ABSTRACT FROM AUTHOR]

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations. [ABSTRACT FROM AUTHOR]