A component-based model and language for wireless sensor network applications

Wireless sensor networks are often used by experts in many different fields to gather data pertinent to their work. Although their expertise may not include software engineering, these users are expected to produce low-level software for a concurrent, real-time and resource-constrained computing environment. In this paper, we introduce a component-based model for wireless sensor network applications and a language, Insense, for supporting the model. An application is modelled as a composition of interacting components and the application model is preserved in the Insense implementation where active components communicate via typed channels. The primary design criteria for Insense include: to abstract over low-level concerns for ease of programming; to permit worst-case space and time usage of programs to be determinable; to support the fractal composition of components whilst eliminating implicit dependencies between them; and, to facilitate the construction of low footprint programs suitable for resource-constrained devices. This paper presents an overview of the component model and Insense, and demonstrates how they meet the above criteria.Preprin

Towards verifying correctness of wireless sensor network applications using Insense and Spin

The design and implementation of wireless sensor network applications often require domain experts, who may lack expertise in software engineering, to produce resource-constrained, concurrent, real-time software without the support of high-level software engineering facilities. The Insense language aims to address this mismatch by allowing the complexities of synchronisation, memory management and event-driven programming to be borne by the language implementation rather than by the programmer. The main contribution of this paper is all initial step towards verifying the correctness of WSN applications with a focus on concurrency. We model part of the synchronisation mechanism of the Insense language implementation using Promela constructs and verify its correctness using SPIN. We demonstrate how a previously published version of the mechanism is shown to be incorrect by SPIN, and give complete verification results for the revised mechanism.Preprin

Using subgoal chaining to address the local minimum problem

A common problem in the area of non-linear function optimisation is that of not being able to guarantee finding the global optimum of the function in a feasible time especially when local optima exist. This problem applies to various areas of heuristic search. One of these areas concerns standard training techniques for feedforward neural networks. The element of heuristic search consists of attempting to find a neural weight state corresponding to the lowest training error. This problem may be termed the local minimum problem. The local minimum problem is addressed for feedforward neural networks. This is done by first establishing the conditions under which local minimum interference for the training process is to be expected. A target based approach to subgoal chaining in supervised learning is then investigated. This is a method to improve travel for neural networks by directing it more precisely through local subgoals than may be achieved through a more distant goal. It is shown however that linear subgoal chains are not sufficient to overcome the local minimum problem. Two novel training techniques are presented which use non-linear subgoal chains and are examined for their capability to address the local minimum problem. It is found that attempting to target a neural network to do something it cannot may lead to suboptimal training. It is also found that targeting a network to do something it is capable of generally leads to successful training. A novel system is presented which is designed to create optimal realisable targets for unrealisable goals. This allows neural networks to subsequently achieve the optimal weight state through a sufficiently powerful training method such as subgoal chaining. The results are shown to be consistent with the theoretical expectations

Distinct Quantum States Can Be Compatible with a Single State of Reality

Perhaps the quantum state represents information about reality, and not reality directly. Wave function collapse is then possibly no more mysterious than a Bayesian update of a probability distribution given new data. We consider models for quantum systems with measurement outcomes determined by an underlying physical state of the system but where several quantum states are consistent with a single underlying state---i.e., probability distributions for distinct quantum states overlap. Significantly, we demonstrate by example that additional assumptions are always necessary to rule out such a model.Comment: 5 pages, 2 figure

The role of wind in controlling the connectivity of blue mussels (Mytilus edulis L.) populations

BACKGROUND: Larval connectivity between distinct benthic populations is essential for their persistence. Although connectivity is difficult to measure in situ, it can be predicted via models that simulate biophysical interactions between larval behaviour and ocean currents. The blue mussel (Mytilus Edulis L.) is widespread throughout the northern hemisphere and extensively commercialised worldwide. In the Irish Sea, this industry represents ~ 50% of Welsh shellfisheries, where cultivation is mainly based on wild spat. However, the main sources and amount of spat varied interannually (1100 tonnes harvest in 2014 against zero in 2018). The aim of this study is to characterise the structure and dynamics of the blue mussel metapopulation within the northern part of the Irish Sea. METHODS: We develop a Lagrangian particle tracking model, driven by a high-resolution (from 30 to 5000 m) validated unstructured coastal hydrodynamic model of the Irish Sea, to simulate spatial and temporal variability of larval dispersal and connectivity between distinct mussel populations and potential settlement areas. RESULTS: Our results showed that: (1) larvae positioned near the surface were strongly influenced by wind-driven currents suggesting that connectivity networks had the potential to span hundreds of kilometres; (2) in contrast, larvae positioned deeper in the water column were driven by tidal currents, producing intricate spatial patterns of connectivity between mussel beds over tens of kilometres that were consistent over time. CONCLUSIONS: Dispersal of mussel larvae in the tidally energetic Irish Sea during the April–May spawning season is potentially driven by wind-driven surface currents, as confirmed by fisherman observations of inter-annual variability in wild spat collection. These results have important implications for metapopulation dynamics within the context of climate change and sustainable shellfisheries management (i.e. gain and loss of populations and harvest areas according to wind conditions). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-022-00301-0

Longer-term mortality following SARS-CoV-2 infection in people with severe mental illness: retrospective case-matched study.

Persisting symptoms and dysfunction after SARS-CoV-2 infection have frequently been observed. However, information on the aftermath of COVID-19 is inadequate. We followed up people with severe mental illness (SMI) infected with SARS-CoV-2, and evaluated their longer-term mortality, using data from Cambridgeshire and Peterborough NHS Foundation Trust, UK. We examined the time course and duration of mortality risk from the point of diagnosis. After SARS-CoV-2 infection, people with SMI had a substantially higher risk of death (hazard ratio (HR) = 5.16, 95% confidence interval (CI) 1.56-17.03; P = 0.007) during the first 28 days and during the following 28-60 days (HR = 2.96, 95% CI 1.21-7.26; P = 0.018) than those without infection, but after 60 days the additional risk of death was no longer significant (HR = 2.33, 95% CI 0.83-6.53; P = 0.107)

Fabrication and characterization of high-contrast mid-infrared GeTe₄ channel waveguides

We report the fabrication and characterization of high index contrast (Δn ~ 0.9) GeTe4 channel waveguides on ZnSe substrate for evanescent-field based biosensing applications in the mid-infrared spectral region. GeTe4 films were deposited by RF sputtering and characterized for their structure, composition, transparency and dispersion. The lift-off technique was used to pattern the waveguide channels. Waveguiding between 2.5-3.7 µm and 6.4-7.5 µm was demonstrated and mode intensity profile and estimated propagation losses are given for the 3.5 µm wavelength