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Purpose Lack of biomechanical force model of soft tissue hinders the development of virtual surgical simulation in maxillofacial surgery. In this study, a physical model of facial soft tissue based on real biomechanical parameters was constructed, and a haptics-enabled virtual surgical system was developed to simulate incision-making process on facial soft tissue and to help maxillofacial surgery training. Methods CT data of a 25-year-old female patient were imported into Mimics software to reconstruct 3D models of maxillofacial soft and skeletal tissues. 3dMD stereo-photo of the patient was fused on facial surface to include texture information. Insertion and cutting parameters of facial soft tissue measured on fresh cadavers were integrated, and a maxillofacial biomechanical force model was established. Rapid deformation and force feedback were realized through localized deformation algorithm and axis aligned bounding box (AABB)-based collision detection. The virtual model was validated quantitatively and qualitatively. Results A patient-specific physical model composed of skeletal and facial soft tissue was constructed and embedded in the virtual surgical system. Insertion and cutting in different regions of facial soft tissue were simulated using omega 6, and real-time feedback force was recorded. The feedback force was consistent with acquired force data of experiments conducted on tissue specimen. Real-time graphic and haptic feedback were realized. The mean score of the system performance was 3.71 given by surgeons in evaluation questionnaires. Conclusion The maxillofacial physical model enabled operators to simulate insertion and cutting on facial soft tissue with realization of realistic deformation and haptic feedback. The combination of localized deformation algorithm and AABB-based collision detection improved computational efficiency. The proposed virtual surgical system demonstrated excellent performance in simulation and training of incision-making process.

In view of the shortcomings of water supply network optimization design based on the traditional genetic algorithm in water supply safety and economy, an improved crossover operator adaptive algorithm and penalty function are proposed to improve the traditional genetic algorithm, which can effectively solve the problem of local optimal solution caused by too early convergence of the traditional genetic algorithm in pipe network optimization design. Taking a typical annular water supply network as an example, the calculation results show that the economy of the design scheme of the improved genetic algorithm is better than the traditional genetic algorithm, which fully shows that the improved genetic algorithm is practical and effective for the optimal design of water supply network.

Health condition monitoring of lithium-ion batteries plays a crucial role in guaranteeing the reliability and safety of energy storage system. However, it is difficult to directly measure state of health of cell, which is sensitive to the complex application scenarios and related to battery internal physicochemical characters. This paper proposes a remaining capacity prediction technique for lithium-ion batteries based on partial charging curve and health feature fusion. Here, health features are extracted from partial charging profile and the fused health feature is obtained via canonical correlation analysis, which is utilized to improve feature correlation and reduce data dimension. A battery aging model is established by Gaussian process regression and validation is conducted on six battery data sets from National Aeronautics and Space Administration (NASA) and University of Oxford. The results show that the proposed method can achieve reliable and accurate battery capacity estimation.