Influence of Mechanical Yielding on Predictions of Saturation: The Saturation Line

It is now well accepted that the mechanical and the water retention behaviour of a soil under unsaturated conditions are coupled and, that such coupling, should be incorporated into a constitutive model for a realistic representation of soil’s response. In existing models, the influence of the mechanical behaviour on the water retention is often represented by a shift of the main wetting retention curve to higher values of matric suction (the difference between pore air and pore water pressures) when the specific volume decreases. This means that any variation of total volumetric strains of compression (whether these are elastic or elasto-plastic) will result in a shift of the main wetting and drying curves to the right, when these curves are represented in the water retention plane. This shift of the main water retention curves, however, should not only influence the unsaturated stress states as often described in the literature, it should also have some impact on the saturated stress states and, more specifically, on the predictions of de-saturation (air-entry point) and saturation (air-exclusion point). From a modelling point of view, it is advantageous to represent this influence through the plastic component of volumetric strain of compression only because, in this way, a consistent representation of the mechanical behaviour for both unsaturated and saturated states can be naturally achieved. This and other advantages resulting from this singular approach are demonstrated in the paper in the context of the Glasgow Coupled Model (GCM)

The Bases of Association Rules of High Confidence

We develop a new approach for distributed computing of the association rules of high confidence in a binary table. It is derived from the D-basis algorithm in K. Adaricheva and J.B. Nation (TCS 2017), which is performed on multiple sub-tables of a table given by removing several rows at a time. The set of rules is then aggregated using the same approach as the D-basis is retrieved from a larger set of implications. This allows to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute using the relevance parameter introduced in K. Adaricheva et al. (Proceedings of ICFCA-2015). This paper focuses on the technical implementation of the new algorithm. Some testing results are performed on transaction data and medical data.Comment: Presented at DTMN, Sydney, Australia, July 28, 201

Extending Conceptual Schemas with Business Process Information

The specification of business processes is becoming a more and more critical aspect for organizations. Such processes are specified as workflow models expressing the logical precedence among the different business activities (i.e., the units of work). Typically, workflow models are managed through specific subsystems, called workflow management systems, to ensure a consistent behavior of the applications with respect to the organization business process. However, for small organizations and/or simple business processes, the complexity and capabilities of these dedicated workflow engines may be overwhelming. In this paper, we therefore, advocate for a different and lightweight approach, consisting in the integration of the business process specification within the system conceptual schema. We show how a workflow-extended conceptual schema can be automatically obtained, which serves both to enforce the organization business process and to manage all its relevant domain data in a unified way. This extended model can be directly processed with current CASE tools, for instance, to generate an implementation of the system (including its business process) in any technological platform

Endogenous opioid analgesia in peripheral tissues and the clinical implications for pain control

Opioid receptors are widely expressed in the central and peripheral nervous system as well as in numerous nonneuronal tissues. Both animal models and human clinical data support the involvement of peripheral opioid receptors in analgesia, particularly in inflammation where both opioid receptor expression and efficacy are increased. Immune cells have been shown to contain numerous opioid peptides such as β-endorphin (END), met-enkephalin (ENK), and dynorphin-A (DYN), although the predominant opioid peptide involved in immune-cell mediated antinociception is thought to be END. These opioid-containing immune cells migrate to inflamed tissues during a complex process of recruitment by chemokines, adhesion, and extravasation. In these tissues, opioid peptide is released from the immune cells upon stimulation with corticotrophin-releasing factor (CRF), noradrenaline, and interleukin 1β (IL-1β), and the immune cells return to the local lymph node depleted of peptide. Consistent with this model, systemic immunosuppression may lead to impaired endogenous analgesia as competent immune cells are essential to achieve release of endogenous opioid peptides within inflamed tissue. A further level of complexity is added by the observation that exogenous opioids may impair immune cell function, although there is some evidence to suggest that endogenous opioid peptides do not share this immunosuppressive effect. Improving our understanding of endogenous opioid mechanisms will provide valuable insight towards the development of novel treatments for pain with improved side effect profiles

Adapting integrity checking techniques for concurrent operation executions

One challenge for achieving executable models is preserving the integrity of the data. That is, given a structural model describing the constraints that the data should satisfy, and a behavioral model describing the operations that might change the data, the integrity checking problem consists in ensuring that, after executing the modeled operations, none of the specified constraints is violated. A multitude of techniques have been presented so far to solve the integrity checking problem. However, to the best of our knowledge, all of them assume that operations are not executed concurrently. As we are going to see, concurrent operation executions might lead to violations not detected by these techniques. In this paper, we present a technique for detecting and serializing those operations that can cause a constraint violation when executed concurrently , so that, previous incremental techniques, exploiting our approach, can be safely applied in systems with concurrent operation executions guaranteeing the integrity of the data.Peer ReviewedPostprint (author's final draft