外军地面无人装备发展现状与趋势研究

随着通信、计算机、人工智能和传感器等技术的飞速发展与广泛应用，地面无人装备日趋活跃在作战、搜救和消防等多种军民用任务场景中。对于军事领域而言，地面无人装备是陆军和其他地面作战部队迈向无人化、智能化战场的核心装备，将在未来陆战场各类作战任务中发挥不可替代的作用。通过对外军各类典型地面无人装备发展现状进行深入分析，总结出该装备领域的发展特点与趋势，对地面无人装备发展有一定的借鉴意义。     

生物矿化法制备氧化镓颗粒及其性能表征

以蚕丝蛋白为模板,在相对温和的条件下通过生物矿化的手段形成具有特殊形貌的α-GaOOH颗粒,并通过在不同温度下煅烧α-GaOOH得到α-Ga2O3和β-Ga2O3.采用扫描电镜(SEM)、透射电镜(TEM)、X射线衍射仪(XRD)和荧光分光光度计(PL)等手段研究了丝素蛋白多肽和矿化时间对颗粒的影响,对其生物矿化机理进行了初步探讨.结果表明,所制备的β-Ga2O3具有优良的发光特性,丝素蛋白多肽模板以无定形的结构与产物结合在一起,并且经过高温烧结后仍以碳膜的形式包覆在材料的表面.这种碳膜结构对于提高材料的生物学性能起着重要的作用.     

主存高效的公交网络路径规划索引

在公交时间表下给定起始和目标站点,路径规划查询返回一组到达时间早和换乘次数少的帕雷托最优路径.现有的索引方法需要大量运行时内存.本文提出主存空间高效的索引方法(a-)PAINT.(a-)PAINT对每个站点v预计算一组标签,使得对于从站点s到站点d的查询可以通过匹配s和d相关的标签高效地生成查询结果的一条路径.PAINT对任意查询返回最优路径.a-PAINT只需要很小的预处理开销,但可能返回多一趟换乘的次优路径.用真实的公交时间表与模拟查询测试,PAINT具有合理的预处理开销.a-PAINT需要更少量的预处理开销,在大规模公交网络下准确率达90％.     

中药桂枝总黄酮的提取工艺研究

优化桂枝总黄酮的提取工艺，建立桂枝的提取和含量测定方法。通过单因素试验，考察提取方法、提取溶剂浓度、提取体积、提取时间对药材总黄酮含量的影响，采用正交试验，优化提取工艺条件，筛选出桂枝总黄酮的最佳提取工艺。总黄酮最佳提取工艺为75%乙醇、料液比1:40、回流提取时间60 min。该方法能有效测定桂枝药材总黄酮的含量，为该药材的质量标准研究提供一定的科学依据。本方法重复性较好、方法稳定、可行。     

Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps

Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANN_FCM-GRN. In MANN_FCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANN_FCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANN_FCM-GRN and show that MANN_FCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANN_FCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.