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Objectives To compare the differences in sagittal spinopelvic parameters between patients receiving monosegmental or multisegmental lumbar fusion and to assess the impact of fusion length on sitting balance. Methods The current study recruited 41 patients who had undergone lumbar fusion, consisting of 18 in the monosegmental group and 23 in the multisegmental group. And the control group included 50 lumbar degenerative patients who had no previous spinal fusion surgery. Spinopelvic parameters of patients were assessed: sagittal vertical axis, pelvic tilt, sacral slope, pelvic incidence, thoracic kyphosis, lumbar lordosis, and proximal femur angles. Results We observed significant differences in sitting TK (P = 0.031), LL (P = 0.012), PT (P = 0.009) and SVA (P = 0.009) among the three groups. When transitioning from standing to sitting, the multisegmental group had the least change in SVA (P = 0.016), PT (P = 0.043), and LL (P = 0.009), with a compensatory increase in TK (P = 0.021). Moderate to strong correlations were found between the change in the LL and those in the SVA (r = −0.548, P = 0.001), PT (r = −0.600, P = 0.001), and SS (r = 0.623, P = 0.001). Conclusion Multisegmental lumbar fusion significantly limits the lumbar mobility and affects the ability to compensate postural changes. Reducing the fusion segments as much as possible is of particular value in preserving lumbar mobility and maintaining the compensatory mechanism of spinopelvis.

Divide-and-conquer-based (DC-based) evolutionary algorithms (EAs) have achieved notable success in dealing with large-scale optimization problems (LSOPs). However, the appealing performance of this type of algorithms generally requires a high-precision decomposition of the optimization problem, which is still a challenging task for existing decomposition methods. This study attempts to address the above issue from a different perspective and proposes an eigenspace divide-and-conquer (EDC) approach. Different from existing DC-based algorithms that perform decomposition and optimization in the original solution space, EDC first establishes an eigenspace by conducting singular value decomposition on a set of high-quality solutions selected from recent generations. Then it transforms the optimization problem into the eigenspace, and thus significantly weakens the dependencies among the corresponding eigenvariables. Accordingly, these eigenvariables can be efficiently grouped by a simple random decomposition strategy and each of the resulting subproblems can be addressed more easily by a traditional EA. To verify the efficiency of EDC, comprehensive experimental studies were conducted on two sets of benchmark functions. Experimental results indicate that EDC is robust to its parameters and has good scalability to the problem dimension. The comparison with several state-of-the-art algorithms further confirms that EDC is pretty competitive and performs better on complicated LSOPs.