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To study the command and control system in an island air defence system under the background of systemic conflict, this paper proposes a new modelling method based on the opinion dynamics leader-follower model to simulate the target threat assessment process in the command and control activities. Through example simulation, it is obtained that in a centralised command and control system, a command post (CP)-led model can provide the most accurate threat assessment results, but it consumes a longer time. While in a distributed command and control system, a model without a leader can provide a target threat value accurately and quickly. The consistency of simulation results and actual situation indicates that, at present, under a high degree of battlefield information sharing, it is necessary to down-pass the authority of the CP to improve operational efficiency. In summary, the proposed model can comprehensively reflect the process that the island air defence system assesses the threat of an attacking target, and it gives a new idea for investigating the command and control procedure and system of the island air defence system.

To reduce the fluid resistance on the surface of flow-through components and improve energy utilization efficiency, a biomimetic fitting structure model is constructed based on the ridge-like features of beluga skin. The SST k-ω model is employed to numerically simulate the drag reduction characteristics of three biomimetic structures (fitting structure, V-shaped structure, and arc structure) included in the design. The variations of the fitting structure's viscous resistance and pressure drop resistance with different widths and depths are compared. The drag reduction mechanism of the fitting structure surface is studied based on the pressure stress, velocity field, and shear stress. The results demonstrate that the fitting structure exhibits the best drag reduction performance. The fitting structure with a width of 30 mm and a depth of 0.7 mm achieves an optimal drag reduction effect of 4.18%. The fitting structure exhibits a large low shear stress region, which increases the thickness of the bottom boundary layer, thereby reducing surface velocity and viscous resistance. [ABSTRACT FROM AUTHOR]

As intelligent manufacturing expands globally, efficient resource allocation strategies are critical for optimizing production efficiency, costs, and quality. Although multi-objective optimization algorithms can handle conflicting objectives, traditional approaches struggle with complex manufacturing systems. This research proposes an optimization model integrating an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Fishbone layout for intelligent manufacturing resource allocation. The Fishbone-based model provides efficient decision support, while the enhanced NSGA-II improves solution efficiency and quality. Flexsim simulation demonstrates the practical value of the proposed method in optimizing resource allocation. This work extends the application of multi-objective optimization in intelligent manufacturing and offers a novel tool for resource allocation optimization in the manufacturing industry. [ABSTRACT FROM AUTHOR]

With economic globalization, ships tend to be larger and faster, and the volume of maritime traffic is increasing. Ships sailing in waters with dense traffic flow are easy to fall into complicated multi-ship encounter situations and have a high risk of collision. Thus, it is crucial to conduct risk analysis in such situations. In this paper, a modified collision analysis method for detecting dangerous multi-ship encounters in ports and waterways is proposed. The velocity obstacle algorithm is utilized to detect encounters. The model of the elliptic ship domain was integrated into the algorithm as the criteria. The Boolean operation was also used in the multi-ship encounter. A case study is conducted to illustrate the efficacy of the improved model, and a comparison between the existing method and the formal model is also performed. The results indicate that with the integration of the ship domain, the proposed method can effectively detect the encounters of multiple ships which are dangerous to collide. [ABSTRACT FROM AUTHOR]

A method using artificial neural networks and inverse kinematics for machine tool error correction is presented. A generalized error model is derived, by using rigid body kinematics, to describe the error motion between the cutting tool and workpiece at discrete temperature conditions. Neural network models are then built to track the time-varying machine tool errors at various thermal conditions. The output of the neural network models can be used to periodically modify, using inverse kinematics technique, the error model's coefficients as the cutting processes proceeded. Thus, the time-varying positioning errors at other points within the designated workspace can be estimated. Experimental results show that the time-varying machine tool errors can be estimated and corrected with desired accuracy. The estimated errors resulted from the proposed methodology could be used to adjust the depth of cut on the finish pass, or correct the probing data for process-intermittent inspection to improve the accuracy of workpieces.

A method for enhancing the effectiveness and robustness of process-intermittent inspection is presented. The method is developed first using mathematical models and measurements closely related to the real parts, thus reducing the uncertainty in both the error estimation and compensation for multi-axis machines. A predictive search algorithm is then developed to identify the minimum number of appropriate measuring points (with respect to the designated tolerance) for arbitrarily shaped manufacturing parts. The identified measuring points are robust in that they least sensitive to uncertainties in measurement and modeling. The search algorithm uses computer simulation with information obtained from previous measurements. Consequently, the measurement effort involved in error modeling has been greatly reduced. The error model derived from the method can be used to correct the process-intermittent probing data for a more accurate assessment of workpiece dimensions. Although the method is designed for general application in multi-axis machines (i.e., machine tools, robots, and coordinate measuring machines), this paper focuses on the specific application of a machining center. Experimental results demonstrate the effectiveness of the error modeling method for accuracy improvement of the machining center for process-intermittent inspection.

The development and derivation of an adaptive error correction method using an interactive inspection system and feature-based analysis technique for machine performance improvement were presented in Part 1 of this paper. Here, in Part 2, the experimental verification of the derived feature-based residual error models and analysis technique is described in detail. The experimental procedure and results of both the process-intermittent and the post-process gauging are presented. A polynomial regression method is used to derive the parametric functions that represent the deviation of imperfect features from nominal. The output of these polynomial functions is compared with process-intermittent inspection data to determine the machine errors and then to identify the residual systematic errors. Feature-based residual error models are used to correlate the residual errors to the systematic residual error components. Inverse kinematics technique and multiple regression methods are used to identify and characterize the contribution of each error component as a function of the machine's temperature profile and nominal position. The residual error components are then combined with the error components identified through the pre-process characterization process to refine the geometric-thermal error model. After the error model is adaptively fine tuned, it can be used more effectively to compensate the machine tool error for more precise manufacturing.

An adaptive error correction method using a feature-based analysis technique for improving the accuracy of CNC machine tools is presented. The method described in this paper emphasizes the integration of the process-intermittent gauging and analysis techniques with information from pre-process characterization and post-process inspection. The proposed method utilizes the information from pre-process characterization, process-intermittent gauging, and post-process inspection to improve machine performance automatically. Algorithms are derived for analyzing the post-process and process-intermittent inspection data to decouple process-related errors from machine errors and for identifying the residual systematic errors. A feature-based analysis technique is developed to relate the dimensional and form errors of manufactured features to the systematic machine tool errors. Inverse kinematics and statistical methods are used to identify and characterize the contribution of each residual error component on imperfect features. Also a recursive tuning algorithm is developed for fine tuning the geometric-thermal model.

Glycogen plays essential roles in glucose metabolism. Imaging glycogen in the liver, the major glycogen reservoir in the body, may shed new light on many metabolic disorders. 13C magnetic resonance spectroscopy (MRS) has become the mainstream method for monitoring glycogen in the body. However, the equipment of special hardware to standard clinical magnetic resonance imaging (MRI) scanners limits its clinical applications. Herein, we utilized endogenous glycogen as a T2-based relaxation contrast agent for imaging glycogen metabolism in the liver in vivo. The in vitro results demonstrated that the transverse relaxation rate of glycogen strongly correlates with the concentration, pH, and field strength. Based on the Swift-Connick theory, we characterized the exchange property of glycogen and measured the exchange rate of glycogen as 31,847 Hz at 37 °C. Besides, the viscosity and echo spacing showed no apparent effect on the transverse relaxation rate. This unique feature enables visualization of glycogen signaling in vivo through T2-weighted MRI. Two hours-post intraperitoneal injection of glucagon, a clinical drug to promote glycogenolysis and gluconeogenesis, the signal intensity of the mice's liver increased by 1.8 times from the T2-weighted imaging experiment due to the decomposition of glycogen. This study provides a convenient imaging strategy to non-invasively investigate glycogen metabolism in the liver, which may find clinical applications in metabolic diseases.

C36H32Co2N8O4, monoclinic, P21/c (no. 14), a = 12.2971(12) Å, b = 13.1008(13) Å, c = 11.0864(11) Å, β = 99.174(2)°, V = 1763.2(3) Å3, Z = 2, Rgt(F) = 0.0277, wRref(F2) = 0.0818, T = 296 K.