Adaptive Spiral Flying Sparrow Search Algorithm

The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.

A population of descending neurons that regulate the flight motor of Drosophila

Like many insect species, Drosophila melanogaster are capable of maintaining a stable flight trajectory for periods lasting up to several hours(1, 2). Because aerodynamic torque is roughly proportional to the fifth power of wing length(3), even small asymmetries in wing size require the maintenance of subtle bilateral differences in flapping motion to maintain a stable path. Flies can even fly straight after losing half of a wing, a feat they accomplish via very large, sustained kinematic changes to the both damaged and intact wings(4). Thus, the neural network responsible for stable flight must be capable of sustaining fine-scaled control over wing motion across a large dynamic range. In this paper, we describe an unusual type of descending neurons (DNg02) that project directly from visual output regions of the brain to the dorsal flight neuropil of the ventral nerve cord. Unlike most descending neurons, which exist as single bilateral pairs with unique morphology, there is a population of at least 15 DNg02 cell pairs with nearly identical shape. By optogenetically activating different numbers of DNg02 cells, we demonstrate that these neurons regulate wingbeat amplitude over a wide dynamic range via a population code. Using 2-photon functional imaging, we show that DNg02 cells are responsive to visual motion during flight in a manner that would make them well suited to continuously regulate bilateral changes in wing kinematics. Collectively, we have identified a critical set of DNs that provide the sensitivity and dynamic range required for flight control.

Jóvenes mayas o de origen maya hacia la universidad: desigualdades, agencia y movilidad social

In recent years, the indigenous population in Mexico increased their access to higher education. From an analytical perspective on social inequalities, this article approaches the educational trajectories of Maya or Mayan origin youths who have accessed at the university in Yucatan. It shows how a restricted access to the structure of opportunities based on social origin and a precarious school environment translates into the perpetuation of educational disadvantageous conditions up to the university level. However, it is also identified that in these environments, the interaction with social agents is a key factor in favoring a change of direction towards more favorable educational contexts. It also identifies a privileged sector of young people from families who have experienced processes of social mobility, that makes their educational trajectories a more stable path with less obstacles.

Improving BGP Convergence And Reachability Through Stable Path Aggregation (SPAGG)

BGP is the standard inter-domain routing protocol of the internet. It has proven to be scalable enough to accommodate the exceptional growth of the Internet. However, because of the sheer size of the Internet and the complexity of its topology, the behaviour of BGP can be unpredictable sometimes. Researchers have been proposing various changes and enhancements in the past 10 to 15 years to improve the security, stability and convergence of BGP. Some of the solutions have been adopted, but BGP is still suffering from possible deficiencies when it comes to convergence time and stability at specific situations and scenarios. In this thesis, we focus on providing a reasonable solution for the problem of BGP instability but without causing long convergence, which leads eventually into minimizing BGP churn and path exploration. We, first, analyse the current BGP standard protocol and previous proposed solutions. Then, we study current problems associated with a recently proposed improvement, suggest a new algorithm that avoids path selection problem at the aggregator and the path shortening problem. We also describe its implementation in OPNET. Finally, we show the results from our simulation and compare them to the results of previous work suggested. Our results show a great improvement of the convergence of BGP while preserving reachability and optimality all the time.

Improving BGP Convergence And Reachability Through Stable Path Aggregation (SPAGG)

BGP is the standard inter-domain routing protocol of the internet. It has proven to be scalable enough to accommodate the exceptional growth of the Internet. However, because of the sheer size of the Internet and the complexity of its topology, the behaviour of BGP can be unpredictable sometimes. Researchers have been proposing various changes and enhancements in the past 10 to 15 years to improve the security, stability and convergence of BGP. Some of the solutions have been adopted, but BGP is still suffering from possible deficiencies when it comes to convergence time and stability at specific situations and scenarios. In this thesis, we focus on providing a reasonable solution for the problem of BGP instability but without causing long convergence, which leads eventually into minimizing BGP churn and path exploration. We, first, analyse the current BGP standard protocol and previous proposed solutions. Then, we study current problems associated with a recently proposed improvement, suggest a new algorithm that avoids path selection problem at the aggregator and the path shortening problem. We also describe its implementation in OPNET. Finally, we show the results from our simulation and compare them to the results of previous work suggested. Our results show a great improvement of the convergence of BGP while preserving reachability and optimality all the time.

Incentivizing Stable Path Selection in Future Internet Architectures

By delegating path control to end-hosts, future Internet architectures offer flexibility for path selection. However, a concern arises that the distributed routing decisions by endhosts, in particular load-adaptive routing, can lead to oscillations if path selection is performed without coordination or accurate load information. Prior research has addressed this problem by devising local path-selection policies that lead to global stability. However, little is known about the viability of these policies in the Internet context, where selfish end-hosts can deviate from a prescribed policy if such a deviation is beneficial from their individual perspective. In order to achieve network stability in future Internet architectures, it is essential that end-hosts have an incentive to adopt a stability-oriented path-selection policy. In this work, we perform the first incentive analysis of the stability-inducing path-selection policies proposed in the literature. Building on a game-theoretic model of end-host path selection, we show that these policies are in fact incompatible with the self-interest of end-hosts, as these strategies make it worthwhile to pursue an oscillatory path-selection strategy. Therefore, stability in networks with selfish endhosts must be enforced by incentive-compatible mechanisms. We present two such mechanisms and formally prove their incentive compatibility.

Stable routing and energy-conserved data transmission over wireless sensor networks

Stable routing and energy conservation over a wireless sensor network (WSN) is a major issue in Internet of Things applications. The network lifetime can be increased when studying this issue with interest. Data transmission is a dominant factor in IoT networks for communication overhead and energy consumption. A proposed efficient node stable routing ($$ENSR$$ ENSR ) protocol is introduced to guarantee the stability of transmission data between the source and destination nodes, in a dynamic WSN conditions. $$ENSR$$ ENSR minimizes energy consumption and selects more stable nodes for packets forwarding. Stability becomes the most important factor that qualifies the node's centrality. A node’s stability is characterized by residual energy, link quality, and number of hops needed to reach the destination from the node. To calculate node's stability, an enhanced centrality concept, known as stable betweenness centrality ($$SBC$$ SBC ) is introduced. In $$ENSR$$ ENSR , at first, some nodes will be selected as the stable forwarding nodes, usually with maximum $$SBC$$ SBC between their neighbors within a limited communication radio range of a particular region. Furthermore, each stable forwarding node then broadcasts its identity, including $$SBC$$ SBC , to the source node separately. The source node can compute a stable path to forward packets to the corresponding stable forwarding node, based on a proper designed stable path routing metric ($$SPRM$$ SPRM ). Then, the stable forwarding node will behave as a new source node and start another stable path routing process until the packets are forwarded and reached to the destination node. In addition, the change of stable nodes over time balances and conserves node energy consumption, thereby mitigating “hot spots”. The proposed routing protocol is validated through simulation. The numerical results show that the proposed protocol outperforms the existing algorithms, global and local reliability-based routing ($$GLRR$$ GLRR ) and reliable energy-aware routing protocol $$(RER)$$ ( R E R ) , in terms of network efficiency and reliability.

Agglomeration economies, congestion diseconomies, and fertility dynamics in a two-region economy

Using a small, open, two-region economy model populated by two-period-lived overlapping generations, we analyze long-term agglomeration economy and congestion diseconomy effects of young worker concentration on migration and the overall fertility rate. When the migration-stability condition is satisfied, the distribution of young workers between regions is obtainable in each period for a predetermined population size. Results show that migration stability does not guarantee dynamic stability of the economy. The stationary population size stability depends on the model parameters and the initial population size. On a stable trajectory converging to the stationary equilibrium, the overall fertility rate might change non-monotonically with the population size of the economy because of interregional migration. In each period, interregional migration mitigates regional population changes caused by fertility differences on the stable path. Results show that the inter-regional migration-stability condition does not guarantee stability of the population dynamics of the economy.