Honeybee dances communicate distances measured by optic flow

In honeybees, employed foragers recruit unemployed hive mates to food sources by dances from which a human observer can read the distance and direction of the food source. When foragers collect food in a short, narrow tunnel, they dance as if the food source were much farther away. Dancers gauge distance by retinal image flow on the way to their destination. Their visually driven odometer misreads distance because the close tunnel walls increase optic flow. We examined how hive mates interpret these dances. Here we show that recruited bees search outside in the direction of the tunnel at exaggerated distances and not inside the tunnel where the foragers come from. Thus, dances must convey information about the direction of the food source and the total amount of image motion en route to the food source, but they do not convey information about absolute distances. We also found that perceived distances on various outdoor routes from the same hive could be considerably different. Navigational errors are avoided as recruits and dancers tend to fly in the same direction. Reported racial differences in honeybee dances could have arisen merely from differences in the environments in which these bees flew.     

Visual place learning in Drosophila melanogaster

The ability of insects to learn and navigate to specific locations in the environment has fascinated naturalists for decades. The impressive navigational abilities of ants, bees, wasps and other insects demonstrate that insects are capable of visual place learning, but little is known about the underlying neural circuits that mediate these behaviours. Drosophila melanogaster (common fruit fly) is a powerful model organism for dissecting the neural circuitry underlying complex behaviours, from sensory perception to learning and memory. Drosophila can identify and remember visual features such as size, colour and contour orientation. However, the extent to which they use vision to recall specific locations remains unclear. Here we describe a visual place learning platform and demonstrate that Drosophila are capable of forming and retaining visual place memories to guide selective navigation. By targeted genetic silencing of small subsets of cells in the Drosophila brain, we show that neurons in the ellipsoid body, but not in the mushroom bodies, are necessary for visual place learning. Together, these studies reveal distinct neuroanatomical substrates for spatial versus non-spatial learning, and establish Drosophila as a powerful model for the study of spatial memories.     

The concepts of 'sameness' and 'difference' in an insect

Insects process and learn information flexibly to adapt to their environment. The honeybee Apis mellifera constitutes a traditional model for studying learning and memory at behavioural, cellular and molecular levels. Earlier studies focused on elementary associative and non-associative forms of learning determined by either olfactory conditioning of the proboscis extension reflex or the learning of visual stimuli in an operant context. However, research has indicated that bees are capable of cognitive performances that were thought to occur only in some vertebrate species. For example, honeybees can interpolate visual information, exhibit associative recall, categorize visual information and learn contextual information. Here we show that honeybees can form 'sameness' and 'difference' concepts. They learn to solve 'delayed matching-to-sample' tasks, in which they are required to respond to a matching stimulus, and 'delayed non-matching-to-sample' tasks, in which they are required to respond to a different stimulus; they can also transfer the learned rules to new stimuli of the same or a different sensory modality. Thus, not only can bees learn specific objects and their physical parameters, but they can also master abstract inter-relationships, such as sameness and difference.     

Carrying large fuel loads during sustained bird flight is cheaper than expected

Birds on migration alternate between consuming fuel stores during flights and accumulating fuel stores during stopovers. The optimal timing and length of flights and stopovers for successful migration depend heavily on the extra metabolic power input (fuel use) required to carry the fuel stores during flight. The effect of large fuel loads on metabolic power input has never been empirically determined. We measured the total metabolic power input of a long-distance migrant, the red knot (Calidris canutus), flying for 6 to 10 h in a wind tunnel, using the doubly labelled water technique. Here we show that total metabolic power input increased with fuel load, but proportionally less than the predicted mechanical power output from the flight muscles. The most likely explanation is that the efficiency with which metabolic power input is converted into mechanical output by the flight muscles increases with fuel load. This will influence current models of bird flight and bird migration. It may also help to explain why some shorebirds, despite the high metabolic power input required to fly, routinely make nonstop flights of 4,000 km longer.     

Energy saving in flight formation

Many species of large bird fly together in formation, perhaps because flight power demands and energy expenditure can be reduced when the birds fly at an optimal spacing, or because orientation is improved by communication within groups. We have measured heart rates as an estimate of energy expenditure in imprinted great white pelicans (Pelecanus onocrotalus) trained to fly in 'V' formation, and show that these birds save a significant amount of energy by flying in formation. This advantage is probably a principal reason for the evolution of flight formation in large birds that migrate in groups.     

Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer

The study of animal foraging behaviour is of practical ecological importance, and exemplifies the wider scientific problem of optimizing search strategies. Lévy flights are random walks, the step lengths of which come from probability distributions with heavy power-law tails, such that clusters of short steps are connected by rare long steps. Lévy flights display fractal properties, have no typical scale, and occur in physical and chemical systems. An attempt to demonstrate their existence in a natural biological system presented evidence that wandering albatrosses perform Lévy flights when searching for prey on the ocean surface. This well known finding was followed by similar inferences about the search strategies of deer and bumblebees. These pioneering studies have triggered much theoretical work in physics (for example, refs 11, 12), as well as empirical ecological analyses regarding reindeer, microzooplankton, grey seals, spider monkeys and fishing boats. Here we analyse a new, high-resolution data set of wandering albatross flights, and find no evidence for Lévy flight behaviour. Instead we find that flight times are gamma distributed, with an exponential decay for the longest flights. We re-analyse the original albatross data using additional information, and conclude that the extremely long flights, essential for demonstrating Lévy flight behaviour, were spurious. Furthermore, we propose a widely applicable method to test for power-law distributions using likelihood and Akaike weights. We apply this to the four original deer and bumblebee data sets, finding that none exhibits evidence of Lévy flights, and that the original graphical approach is insufficient. Such a graphical approach has been adopted to conclude Lévy flight movement for other organisms, and to propose Lévy flight analysis as a potential real-time ecosystem monitoring tool. Our results question the strength of the empirical evidence for biological Lévy flights.     

Olfaction: scent-triggered navigation in honeybees

The honeybee, Apis mellifera, navigates rapidly and accurately to food sources that are often kilometres away. They achieve this by learning visual cues, such as the location and colour of nectar-bearing flowers, and chemical cues, such as the scent and the taste of the nectar. Here we train bees to visit differently scented sugar feeders placed at specific outdoor locations and find that they can be induced to visit the same locations simply by having the corresponding scent blown into the hive, even when the destinations no longer have the food or carry the scent. A familiar nectar scent can trigger specific memories of a route and therefore expedite navigation to the food source.     

考虑风向概率特征的航班时刻优化方法

在航班时刻表进行实际运行过程中,风向的变化对航班到达终端区共用航路点的时间造成影响,进而造成容量过载或容量浪费。因此,根据风向的统计概率对航班时刻进行调整,目的是制定在一定程度上能减少共用航路点的容量过载或容量浪费的航班时刻表。根据风向对起飞航班跑道分配的影响提出了基准风向的概念,并基于航季中各月份在过去5年间的机场基准风向概率预测了下一年各月的机场基准风向概率。以误差平方和与轮廓系数作为聚类指标,将下一年中机场基准风向概率相似的月份聚为一类。在聚类结果的基础上建立考虑风向不确定性的航班时刻优化模型,并将-约束法与改进粒子群算法结合提出-约束-PSO组合算法实现多目标模型的求解,以北京终端区的离场航班为研究对象进行验证。研究结果表明:相比初始航班时刻表,共用航路点小时流量的最大值减少了12%,在不同基准风向时的共用航路点流量方差分别降低49%和56%;相比线性加权求和的方法,该方法可以实现共用航路点的溢出航班总量减少70%。研究结果表明在风向不确定的条件下,该模型可以在一定程度上使共用航路点的流量更均衡,减少出现共用航路点容量过载或容量浪费的现象。     

基于不确定因素扰动的机场大面积航班恢复规划研究

航班恢复过程往往会受到不确定因素的干扰,这些不确定因素可能还会进而引发一系列不确定因素的产生,造成更加严重的延误情况。为了提高航班恢复计划的鲁棒性,对不确定因素扰动下的航班恢复问题进行研究。首先,利用中国某机场的地面服务数据,研究了不确定因素对航班恢复过程中地面服务保障时间的影响。然后,建立以最小化延误成本和保证航班公平性为优化目标的航班恢复调度模型;同时考虑进场航班对离场运行的影响;并在模型中加入不确定因素对航班的干扰。最后,运用模拟退火算法对中国某机场的航班数据进行仿真,与先到先服务方法进行对比。结果表明:对于解决机场大面积航班恢复问题具有可行性。     

基于滚动时域的遗传-免疫算法优化航班着陆调度

航班调度问题一直是空中交通管制(ATC)中的一个复杂而具有重要意义的任务,而航班着陆问题(ALS)是其中的核心问题.航班着陆调度是NP-hard问题,具有规模大、约束条件多的特点.因此,为了有效合理地解决航班着陆问题,本文提出了基于滚动时域的遗传-免疫算法(RHC HGIA)的航班着陆调度算法.RHC HGIA主要从两个方面解决航班着陆问题,一方面根据设定的滚动时域长度与大小选择需要进行优化的待降落航班;另一方面对选择的待降落航班使用遗传-免疫算法进行优化并确定其实际着陆时间.经过优化后的航班组成新的航班降落序列,从该序列中选择实际着陆时间在给定时域范围内的航班进行着陆.重新设置滚动时域长度,选择待降落航班进行优化,直到所有待着陆航班都已着陆为止.本文仿真实验以某机场一天内的20架待着陆航班数据为基础,并在机场管制仿真系统中进行模拟仿真.仿真实验表明,与传统航班着陆调度算法(FCFS)相比,经过RHC_HGIA算法优化后的待着陆航班的额外成本有明显的降低.     

基于数字孪生的航班保障预警系统

为了提高航班和保障车辆的安全性,本文提出了一种基于数字孪生的新型航班保障预警系统架构。数字孪生系统中包括实际机场、赛博空间的仿真模型以及实时数据。系统可以根据当前作业状况,预测保障活动未来会出现的风险情况。文章中详细介绍了航班保障的规则,仿真模型的设计开发,如何实现保障车辆和航班数据的实时采集,以及潜在碰撞风险的预测。为了验证预警逻辑和算法的有效性,在Anylogic平台进行系统开发和仿真实验,实验结果表明本文提出的架构可以对车辆和航班进行提前预警,保障运营安全。     

电力拍卖市场的智能代理仿真模型

用自适应自治的智能代理模型表示市场中的发电厂商构造了电力拍卖市场的仿真模型。该模型中发电厂商Agent采用Roth—Erev强化学习算法，具有对市场结果自适应能力，在重复的拍卖市场可以发现唯一的Nash均衡。同时，该模型还可以揭示市场中发电厂商在不同的市场规则下的行为特性。基于IEEE—RTS系统的数据研究表明，该模型提供了一种合适的电力拍卖市场分析工具。     

柔性翼微型飞行器的稳定特性

为提高微型飞行器(M AV)的抗风扰动飞行稳定性,需要遵循仿生学原理,效仿自然界中的鸟类和昆虫,探讨抗风飞行的新方法。提出了柔性仿生机翼的概念,阐述了其设计思想与工作原理,并导出了大气扰动下柔性机翼气动力的解析表达。为验证柔性仿生机翼的实际性能,设计和制作了柔性仿生机翼M AV原理样机,并在风中进行了飞行试验。试验结果表明:采用柔性机翼能够显著改善有风条件下M AV的飞行品质,突风过载可降低40%左右,大大减弱了大气扰动对飞行的影响,有效提高了M AV的飞行稳定性。     

基于微卫星标记的西方蜜蜂抗螨蜂种群体遗传多态性分析

为了分析西方蜜蜂抗螨蜂种的群体遗传多态性情况,采用18对微卫星(SSR)引物对抗螨蜂种子一代和子二代,并与同场饲养不抗螨的本地意蜂和浙大蜜浆高产蜂种进行比对,共计22个群体进行遗传多样性检测.结果表明:遗传一致度在0.2时,抗螨蜂种子代发生一定的分化,其他两个品系基本一致.抗螨蜂种子代之间的亲缘关系较近(遗传距离在0.043 9以内),与本地意蜂和浙大蜜浆高产蜂种各自相互分开.从而对抗螨蜂种的选育和保护具有重要的指导意义.     

新时期大学生价值导向路径研究

时代的新变化给当代大学生的心理和思想都带来了新的变化,大学生的价值取向也呈现出许多新的特点。高校的教育工作者们应对这些新特点进行充分的认识和了解,从而对他们进行有针对性的价值导向。     

基于惯性动作捕捉的多螺旋桨飞行器姿态研究

目前多螺旋桨飞行器在飞行过程中存在着飞行姿态控制复杂、稳定性差、抗毁性差等问题。针对于此,提出了基于惯性动作捕捉技术的多螺旋桨飞行器的运动姿态研究方案。该方案利用惯性动作捕捉设备,记录飞行器的各个螺旋桨的位置姿态数据,并根据所获取的数据对飞行器进行运动学分析,进而对其飞行姿态规律进行研究。该方案的研究为飞行器的多螺旋桨转速控制提供了理论依据,从而提高多螺旋桨飞行器的稳定性和可靠性。     

Context generalization in Drosophila visual learning requires the mushroom bodies.

The world is permanently changing. Laboratory experiments on learning and memory normally minimize this feature of reality, keeping all conditions except the conditioned and unconditioned stimuli as constant as possible. In the real world, however, animals need to extract from the universe of sensory signals the actual predictors of salient events by separating them from non-predictive stimuli (context). In principle, this can be achieved if only those sensory inputs that resemble the reinforcer in their temporal structure are taken as predictors. Here we study visual learning in the fly Drosophila melanogaster, using a flight simulator, and show that memory retrieval is, indeed, partially context-independent. Moreover, we show that the mushroom bodies, which are required for olfactory but not visual or tactile learning, effectively support context generalization. In visual learning in Drosophila, it appears that a facilitating effect of context cues for memory retrieval is the default state, whereas making recall context-independent requires additional processing.