哈尔滨广厦学院（HARBIN GuangSha College），简称“广厦学院”，位于黑龙江省哈尔滨市，是经国家教育部批准的民办全日制本科普通综合类高等学校。学校始建于2000年，校名为“黑龙江广厦学院”，2003年3月，经黑龙江省政府批准与哈尔滨商业大学合作办学，更名为“哈尔滨商业大学广厦学院”。同年10月，经教育部确认为本科独立学院。2012年3月经教育部批准转设为民办普通本科高等学校，更名为“哈尔滨广厦学院”。截至2018年5月，学校占地面积45.72万平方米，建筑面积21.76万平方米；学校拥有纸质图书65万余册，电子图书184万册，电子期刊54万余册。楼宇间万兆光纤连接，出口带宽达8Gbps，保障了教学科研需求。有专任教师358人，在校生7343人；设有六个二级学院和思想政治理论课教学部，18个本科专业。
Sea anemones detect external stimuli through the deformation of their soft tentacles, which exhibit multi-directional force sensitivity. Inspired by this mechanism, we designed a capacitive three-dimensional force flexible tactile sensor composed of a hollow hemisphere and a hollow cylinder. The device was fabricated using 3D printing combined with a Layer-By-Layer assembly process. For normal forces, the sensor achieved sensitivities of approximately 0.66 N-1 in the 0-1 N range and 0.15 N-1 in the 2-10 N range. For tangential forces, the four symmetrically distributed electrodes exhibited opposite monotonic capacitance variation trends. The sensor exhibited a force resolution of 0.02 N, a lower detection limit of 0.04 N, a hysteresis error as low as 3.5%, and a response/recovery time of up to 50 ms under a 0-10 N load. Moreover, the device demonstrated good stability under 1000 load-unload cycles and over a temperature range from 20 °C to 100 °C. Its utility was further validated through multi-scenario applications, including game controller manipulation, gripper-based object recognition, Morse code and Huffman coding transmission, as well as multi-joint human motion detection. These results demonstrate that the proposed bioinspired sensor offers a promising solution for flexible force sensing, human-machine interaction, and wearable health monitoring.
To achieve process-oriented and quantitative quality monitoring of the effectiveness of industry-education integration in universities, this study proposes a quality evaluation method based on the Back Propagation neural network (BPNN). The method conducts predictive modeling and performance verification for different types of university-enterprise cooperation projects in universities. Based on the Input-Process-Output (IPO) evaluation framework, this study constructs a multi-dimensional indicator system and develops an enhanced Back Propagation Neural Network (BPNN) model to improve its robustness and adaptability in heterogeneous educational data. The results show that the constructed BPNN achieves a mean squared error (MSE) of 0.0114 and a coefficient of determination (R²) of 0.947 on the validation set. Compared with the LR model, its R² is improved by 41.2%, ranking the best-performing model among all comparative models. Moreover, it maintains stable predictive performance with R² > 0.93 across eight professional subsets. The research indicates that the BPNN can effectively capture the complex relationships among multi-dimensional educational indicators and is suitable for quality evaluation tasks of industry-education integration in multiple scenarios of universities. This approach provides a robust, intelligent tool for enhancing the dynamic management and continuous improvement of industry-education integration initiatives in higher education.
