949 resultados para honey orange
Resumo:
数据分配是研究数据如何分布到多个物理节点的NP-Complete问题.给出数据分配算法的数学模型,提出基于时序片段评价的数据分配算法——DATE.该算法利用数据在短时域访问量分布不均的特点,将多目标优化问题转化为单一目标求解,采用蜜蜂算法(collective Honey bee behavior)调整参数并反馈算法结果,以实现系统负载均衡.随机实验结果表明,DATE相比于同类Random,roundrobin,Bubba算法在系统总时段均衡ET、系统时段内均衡值ES、系统最大波峰值EM 3个指标中表现更优.
Resumo:
一、蜜蜂嗅觉学习记忆应用基础研究特殊气味的探测在刑侦工作中意义重大,常用的警犬探测和仪器分析都有其局限之处。蜜蜂嗅觉灵敏,且学习记忆能力突出,具有为刑侦工作所用的潜力。基于此,我们希望通过训练蜜蜂将其对糖水奖励的伸喙反应与指定气味建立条件反射的原理,配合适当的训练方法,达到利用蜜蜂探测危险气味的目的。在实验中,我们首先比较了不同喂养方式的蜜蜂在气味学习中的差别。由于低浓度气味无法直接使蜜蜂建立条件化,我们采用了逐渐降低气味浓度的方法,成功训练蜜蜂对低浓度(3.6×10-7) 醋酸气味建立了条件反射。结果如下: 1)自然放养与人工孵化两种不同喂养方式的蜜蜂,各两组,分别学习醋酸CS+/薄荷CS-,或柠檬CS+/薄荷CS-的气味配对。以“获得(CS+),巩固(CS-/CS+ CS+/CS- CS-/CS+),检测,干净空气假阳性检测”的顺序操作。结果显示自然放养蜜蜂对醋酸气味没有偏好(第一次给醋酸气味伸喙率:6%),学习醋酸气味能力较低(24小时后检测正确率:66%, n=25),相对应,该类蜜蜂对柠檬气味显示出明显偏好(第一次给柠檬气味伸喙率:41%,P< 0.01),而学习效果(检测正确率:50%,n=20)与醋酸组相近(P>0.05)。人工孵化的蜜蜂对醋酸气味学习能力较自然放养蜜蜂大大提高(检测正确率:96%, n=32, P<0.01),同时对柠檬的学习结果(检测正确率:80%, n=32)也明显提高(0.01
Resumo:
Based on the dimer-monomer equilibrium movement of the fluorescent dye Pyronin Y (PY), a rapid, simple, highly sensitive, label-free method for protein detection was developed by microchip electrophoresis with LIF detection. PY formed a nonfluorescent dimer induced by the premicellar aggregation of an anionic surfactant, SDS, however, the fluorescence intensity of the system increased dramatically when proteins such as BSA, bovine hemoglobin, cytochrome c, and trypsin were added to the solution due to the transition of dimer to fluorescent monomer. Furthermore, 1-ethyl-3-methylimidazolium tetrafluoroborate (EMImBF(4)) instead of PBS was applied as running buffers in microchip electrophoresis.
Resumo:
ESI-MS was used to optimize the Ephedra sinica refinement. The ratio of honey to drug is 20/100, and the ratio of water to honey is 1/2. The toast temperature is 80 ℃, and the toast time is 2 h. The change of Ephedra sinica after processing was given.