Specifying timing requirements in domain specific languages for modeling

Complex Real-Time Embedded Systems (RTESs) can be developed using model-based engineering. The problem is choosing a modeling language that has capabilities to model the most important characteristic of RTESs: timing. This paper shows an analysis of the most popular modeling languages and their capabilities to model timing constraints in RTESs. It includes UML, SysML, AADL, MARTE and EAST-ADL. A brief comparison between MARTE and EAST-ADL, based on the case study from the automotive industry, is also included

Model-based engineering in real-time embedded systems: specifying timing constraints

This paper presents the results from a research project on development of Real-Time Embedded Systems (RTESs) using a Model-Based Engineering (MBE) approach. A review of the state-of-the-art modelling languageswas done in order to assess their capabilities to model time. A chosen case study,a Brake-By-Wire (BBW) system, was taken from the automotive industry.The case study focuses on the use of EAST-ADL to model the RTES and TADL to specify timing constraints. A different approach using MARTE to model the BBW system was developed within our project. This approach is used to compare MARTE (and OCL) with EAST-ADL (and TADL). The results show that MARTE can be used to model an RTES from the automotive industry but lacks some important semantic expressions for the timing constraints which are present in TADL

Population-level differences in disease transmission: A Bayesian analysis of multiple smallpox epidemics

Estimates of a disease\u27s basic reproductive rate R0 play a central role in understanding outbreaks and planning intervention strategies. In many calculations of R0, a simplifying assumption is that different host populations have effectively identical transmission rates. This assumption can lead to an underestimate of the overall uncertainty associated with R0, which, due to the non-linearity of epidemic processes, may result in a mis-estimate of epidemic intensity and miscalculated expenditures associated with public-health interventions. In this paper, we utilize a Bayesian method for quantifying the overall uncertainty arising from differences in population-specific basic reproductive rates. Using this method, we fit spatial and non-spatial susceptible-exposed-infected-recovered (SEIR) models to a series of 13 smallpox outbreaks. Five outbreaks occurred in populations that had been previously exposed to smallpox, while the remaining eight occurred in Native-American populations that were naïve to the disease at the time. The Native-American outbreaks were close in a spatial and temporal sense. Using Bayesian Information Criterion (BIC), we show that the best model includes population-specific R0 values. These differences in R0 values may, in part, be due to differences in genetic background, social structure, or food and water availability. As a result of these inter-population differences, the overall uncertainty associated with the population average value of smallpox R0 is larger, a finding that can have important consequences for controlling epidemics. In general, Bayesian hierarchical models are able to properly account for the uncertainty associated with multiple epidemics, provide a clearer understanding of variability in epidemic dynamics, and yield a better assessment of the range of potential risks and consequences that decision makers face. © 2013 Elsevier B.V

A Note on Species Richness and the Variance of Epidemic Severity

The commonly observed negative correlation between the number of species in an ecological community and disease risk, typically referred to as "the dilution effect", has received a substantial amount of attention over the past decade. Attempts to test this relationship experimentally have revealed that, in addition to the mean disease risk decreasing with species number, so too does the variance of disease risk. This is referred to as the "variance reduction effect", and has received relatively little attention in the disease-diversity literature. Here, we set out to clarify and quantify some of these relationships in an idealized model of a randomly assembled multi-species community undergoing an epidemic. We specifically investigate the variance of the community disease reproductive ratio, a multi-species extension of the basic reproductive ratio R_0, for a family of random-parameter meta-community SIR models, and show how the variance of community $R_0$ varies depending on whether transmission is density or frequency-dependent. We finally outline areas of further research on how changes in variance affect transmission dynamics in other systems

Uncertainty in predictions of disease spread and public health responses to bioterrorism and emerging diseases

Concerns over bioterrorism and emerging diseases have led to the widespread use of epidemic models for evaluating public health strategies. Partly because epidemic models often capture the dynamics of prior epidemics remarkably well, little attention has been paid to how uncertainty in parameter estimates might affect model predictions. To understand such effects, we used Bayesian statistics to rigorously estimate the uncertainty in the parameters of an epidemic model, focusing on smallpox bioterrorism. We then used a vaccination model to translate the uncertainty in the model parameters into uncertainty in which of two vaccination strategies would provide a better response to bioterrorism, mass vaccination, or vaccination of social contacts, so-called trace vaccination. Our results show that the uncertainty in the model parameters is remarkably high and that this uncertainty has important implications for vaccination strategies. For example, under one plausible scenario, the most likely outcome is that mass vaccination would save ≈100,000 more lives than trace vaccination. Because of the high uncertainty in the parameters, however, there is also a substantial probability that mass vaccination would save 200,000 or more lives than trace vaccination. In addition to providing the best response to the most likely outcome, mass vaccination thus has the advantage of preventing outcomes that are only slightly less likely but that are substantially more horrific. Rigorous estimates of uncertainty thus can reveal hidden advantages of public health strategies, suggesting that formal uncertainty estimation should play a key role in planning for epidemics. © 2006 by The National Academy of Sciences of the USA

Anthriscus sylvestris—Noxious Weed or Sustainable Source of Bioactive Lignans?

Anthriscus sylvestris (L.) Hoffm. (Apiaceae), commonly known as wild chervil, has gained scientific interest owing to its diverse phytochemical profile and potential therapeutic applications. The plant, despite being categorized as a noxious weed, is traditionally used in treating various conditions like headaches, dressing wounds, and as a tonic, antitussive, antipyretic, analgesic, and diuretic. Its pharmacological importance stems from containing diverse bioactive lignans, especially aryltetralins and dibenzylbutyrolactones. One of the main compounds of A. sylvestris, deoxypodophyllotoxin, among its wide-ranging effects, including antitumor, antiproliferative, antiplatelet aggregation, antiviral, anti-inflammatory, and insecticidal properties, serves as a pivotal precursor to epipodophyllotoxin, crucial in the semisynthesis of cytostatic agents like etoposide and teniposide. The main starting compound for these anticancer medicines was podophyllotoxin, intensively isolated from Sinopodophyllum hexandrum, now listed as an endangered species due to overexploitation. Since new species are being investigated as potential sources, A. sylvestris emerges as a highly promising candidate owing to its abundant lignan content. This review summarizes the current knowledge on A. sylvestris, investigating its biological and morphological characteristics, and pharmacological properties. Emphasizing the biological activities and structure–activity relationship, this review underscores its therapeutic potential, thus encouraging further exploration and utilization of this valuable plant resource

Formal change impact analyses for emulated control software

Processor emulators are a software tool for allowing legacy computer programs to be executed on a modern processor. In the past emulators have been used in trivial applications such as maintenance of video games. Now, however, processor emulation is being applied to safety-critical control systems, including military avionics. These applications demand utmost guarantees of correctness, but no verification techniques exist for proving that an emulated system preserves the original system’s functional and timing properties. Here we show how this can be done by combining concepts previously used for reasoning about real-time program compilation, coupled with an understanding of the new and old software architectures. In particular, we show how both the old and new systems can be given a common semantics, thus allowing their behaviours to be compared directly