SUMMARY: BondGraphs.jl is a Julia implementation of bond graphs. Bond graphs provide a modelling framework that describes energy flow through a physical system and by construction enforce thermodynamic constraints. The framework is widely used in engineering and has recently been shown to be a powerful approach for modelling biology. Models are mutable, hierarchical, multiscale, and multiphysics, and BondGraphs.jl is compatible with the Julia modelling ecosystem. AVAILABILITY AND IMPLEMENTATION: BondGraphs.jl is freely available under the MIT license. Source code and documentation can be found at https://github.com/jedforrest/BondGraphs.jl.

Modern storage engines and key-value stores have come to rely on the log-structured merge-tree (LSM-tree) as their core data structure. LSM-tree operates by gradually merge-sorting data across levels of exponentially increasing capacities in storage. A crucial design dimension of LSM-tree is its compaction granularity. Some designs perform Full Merge , whereby entire levels get compacted at once. Others perform Partial Merge , whereby smaller groups of files with overlapping key ranges are compacted independently. This paper shows that both strategies exhibit serious flaws. With Full Merge, space-amplification is exorbitant. The reason is that while compacting the LSM-tree's largest level, there must be at least twice as much storage space as data to store both the original and new files until the compaction is finished. On the other hand, Partial Merge exhibits excessive write-amplification. The reason is twofold. (1) The files getting compacted typically do not have perfectly overlapping key ranges, and so some non-overlapping data is superfluously rewritten in each compaction. (2) Files with different lifetimes become interspersed within the SSD leading to high SSD garbage-collection overheads. As the data size grows, these problems grow in magnitude. We introduce Spooky, a novel compaction granulation method to address these problems. Spooky partitions data at the largest level into equally sized files, and it partitions data at smaller levels based on the file boundaries at the largest level. This allows merging one group of perfectly overlapping files at a time to limit space-amplification and compaction overheads. At the same time, Spooky writes larger though fewer files simultaneously so that files with different lifetimes do not become as interspersed within the SSD. This cheapens garbage-collection. We show empirically that Spooky achieves >2x lower space-amplification than Full Merge and >2x lower write-amplification than Partial Merge at the same time.

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.

Handoff latency is a severe bottleneck impacting the service continuity for voice and multimedia applications in WLAN. IEEE 802.11k neighbor report defines the neighbor APs which are potential transition candidates for the roaming target. But the selection method for the roaming target AP is left undefined. Several schemes have been proposed for fast handoff with neighbor APpsilas information. However, these schemes result in huge redundant transition messages overheads in the WLAN and require high computing power for the AAA (Authentication, Authorization, and Access control) server. In this paper, we propose an adaptive neighbor caching (ANC) method to achieve higher handoff prediction accuracy for selecting proper candidate APs in the Neighbor Report. An adaptive predictability index is introduced for selecting those potential roaming APs, which can mitigate the scanning latency and the pre-authentication key distribution message overhead in the WLAN as well as computing loading for the AAA server. Simulation results present up to 83.5% of transition messages are reduced in comparison to the Proactive Neighbor Caching (PNC), 56.4% of candidate AP selection accuracy is improved and 37.5% of transition messages are reduced in comparison to the Selective Neighbor Caching (SNC).

With the dynamic and mobile nature of ad hoc wireless networks, links may fail due to topological changes by mobile nodes. As the degree of mobility increases, the wireless network would suffer more link errors. Ad hoc routing protocols that use broadcast to discover routes may become inefficient due to frequent failures of intermediate connections in an end-to-end communication. When an intermediate link breaks, it is beneficial to discover a new route locally without resorting to an end-to-end route discovery. Based on the concept of localizing the route request query, we propose an efficient approach to repair error links quickly. The approach can apply to the ad hoc on-demand distance vector (AODV) routing protocol. As an enhancement to AODV, the proposed approach leads to two routing protocols, called AODV-LRQ and AODV-LRT, which are aimed to efficiently repair the link errors. To evaluate the effects of the route repair, we define a factor, called bonus gain, as the ratio between the throughput increment to the routing overhead increment. Simulation results show that the proposed methods can get high bonus gain, that is, it can maintain the throughput as well as reduce the routing overheads.