Abstract: To support Model Based Design of Cyber-Physical Systems (CPSs) many simulation based approaches to System Level Formal Verification (SLFV) have been devised. Basically, these are Bounded Model Checking approaches (since simulation horizon is of course bounded) relying on simulators to compute the system dynamics and thereby verify the given system properties. The main obstacle to simulation based SLFV is the large number of simulation scenarios to be considered and thus the huge amount of simulation time needed to complete the verification task. To save on computation time, simulation based SLFV approaches exploit the capability of simulators to save and restore simulation states. Essentially, such a time saving is obtained by optimising the simulation script defining the simulation activity needed to carry out the verification task. Although such approaches aim to (bounded) formal verification, as a matter of fact, the proof of correctness of the methods to optimise simulation scripts basically relies on an intuitive semantics for simulation scripting languages. This hampers the possibility of formally showing that the optimisations introduced to speed up the simulation activity do not actually omit checking of relevant behaviours for the system under verification. The aim of this paper is to fill the above gap by presenting an operational semantics for simulation scripting languages and by proving soundness and completeness properties for it. This, in turn, enables formal proofs of equivalence between unoptimised and optimised simulation scripts.

Abstract: Many software as well digital hardware automatic
synthesis methods define the set of
implementations meeting the given system
specifications with a boolean relation K. In
such a context a fundamental step in the software
(hardware) synthesis process is finding effective
solutions to the functional equation defined by
K. This entails finding a (set of) boolean
function(s) F (typically represented using
OBDDs, Ordered Binary Decision Diagrams)
such that: 1) for all x for which K is
satisfiable, K(x, F(x)) = 1 holds; 2) the
implementation of F is efficient with respect
to given implementation parameters such as code
size or execution time. While this problem has
been widely studied in digital hardware synthesis,
little has been done in a software synthesis
context. Unfortunately, the approaches developed
for hardware synthesis cannot be directly used in
a software context. This motivates investigation
of effective methods to solve the above problem
when F has to be implemented with software. In
this paper, we present an algorithm that, from an
OBDD representation for K, generates a C code
implementation for F that has the same size as
the OBDD for F and a worst case execution time
linear in nr, being n = |x| the number of
input arguments for functions in F and r the
number of functions in F. Moreover, a formal
proof of the proposed algorithm correctness is
also shown. Finally, we present experimental
results showing effectiveness of the proposed
algorithm.

Abstract: SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g., enable in-silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models.Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems.In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2 -compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate, and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialised SBML simulators.SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica

Abstract: Stress is a risk factor for impaired general, mental and reproductive health. The role of physiological and supraphysiological estradiol concentrations in stress perception and stress processing is less well understood. We therefore, conducted a prospective observational study to investigate the association between estradiol, stress perception and stress-related cognitive performance within serial measurements either during the natural menstrual cycle or during fertility treatment, where estradiol levels are strongly above the physiological level of a natural cycle and consequently, represent a good model to study dose-dependent effects of estradiol. Data from 44 women receiving in vitro fertilization at the Department of Reproductive Endocrinology in Zurich, Switzerland was compared to data from 88 women with measurements during their natural menstrual cycle. The german version of the Perceived Stress Questionnaire (PSQ) and the Cognitive Bias Test (CBT), in which cognitive performance is tested under time stress were used to evaluate subjective and functional aspects of stress. Estradiol levels were investigated at four different time points during the menstrual cycle and at two different time points during a fertility treatment. Cycle phase were associated with PSQ worry and cognitive bias in normally cycling women, but different phases of fertility treatment were not associated with subjectively perceived stress and stress-related cognitive bias. PSQ lack of joy and PSQ demands related to CBT in women receiving fertility treatment but not in women with a normal menstrual cycle. Only strong changes of the estradiol level during fertility treatment were weakly associated with CBT, but not with subjectively experienced stress. Our research emphasises the multidimensional character of stress and the necessity to adjust stress research to the complex nature of stress perception and processing. Infertility is associated with an increased psychological burden in patients. However, not all phases of the process to overcome infertility do significantly increase patient stress levels. Also, research on the psychological burden of infertility should consider that stress may vary during the different phases of fertility treatment.