科创中国

随着人类社会的发展,人类活动在一定程度上影响了水文气象要素的变化,导致了干旱地区的扩大与干旱化程度的加重,这一趋势已成为全球关注的问题.在近年我国干旱灾害频繁发生的背景下,分析了河北省农业旱灾特点和发展趋势,研究了其严重危害,根据河北省农业实际提出了应对策略,为进一步应对干旱发生、抗旱减灾、确保农业生产、确保粮食安全具有重要的指导作用.

目的 探讨精神分裂症患者与正常人应对方式的不同,以及精神分裂症患者应对方式的特点.方法 以山东省18岁及以上人群精神障碍流行病学调查数据库中筛查出的151例精神分裂症患者作为研究组,按同性别、同年龄组(相差±3岁)、同村1:1配对选择151名无任何精神科疾病诊断者作为对照组,全部患者用SCID-I-P进行诊断.研究工具有一般资料调查问卷、简易应对方式问卷.结果 (1)精神分裂症患者的积极应对因子评分明显低于正常人的评分(P<0.01),消极应对评分显著高于正常人的评分(P<0.05).(2)所有受试者不同职业之间积极应时因子和消极应对因子评分的差异有统计学意义(P<0.05).(3)瓦解型精神分裂症患者的消极应对评分最高,而偏执型患者的消极应对评分最低,差异有统计学意义(P<0.01).结论 精神分裂症患者的应对方式与正常人有明显不同.

高质量创业师资短缺已经成为阻碍我国高校创业教育发展的主要瓶颈.国外高校在创业学学科体系建设、系统引入兼职教师、多渠道完善教师成长平台等方面积累了丰富的经验.我国创业教育师资队伍建设必须适应高校创业教育发展的需要,建立全员参与、与创业课程体系相适应的教师队伍,完善创业教育师资培训机制与评价机制.

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.