Declarative process modeling in BPMN

Traditional business process modeling notations, including the standard Business Process Model and Notation (BPMN), rely on an imperative paradigm wherein the process model captures all allowed activity flows. In other words, every flow that is not specified is implicitly disallowed. In the past decade, several researchers have exposed the limitations of this paradigm in the context of business processes with high variability. As an alternative, declarative process modeling notations have been proposed (e.g., Declare). These notations allow modelers to capture constraints on the allowed activity flows, meaning that all flows are allowed provided that they do not violate the specified constraints. Recently, it has been recognized that the boundary between imperative and declarative process modeling is not crisp. Instead, mixtures of declarative and imperative process modeling styles are sometimes preferable, leading to proposals for hybrid process modeling notations. These developments raise the question of whether completely new notations are needed to support hybrid process modeling. This paper answers this question negatively. The paper presents a conservative extension of BPMN for declarative process modeling, namely BPMN-D, and shows that Declare models can be transformed into readable BPMN-D models. © Springer International Publishing Switzerland 2015

Giant capacitance of a plane capacitor with a two-dimensional electron gas in a magnetic field

If a clean two-dimensional electron gas (2DEG) with small concentration $n$ comprises one (or both) electrodes of a plane capacitor, the resulting capacitance $C$ can be larger than the "geometric capacitance" $C_g$ determined by the physical separation $d$ between electrodes. A recent paper [1] argued that when the effective Bohr radius $a_B$ of the 2DEG satisfies $a_B << d$, one can achieve $C >> C_g$ at low concentration $nd^2 << 1$. Here we show that even for devices with $a_B > d$, including graphene, for which $a_B$ is effectively infinite, one also arrives at $C >> C_g$ at low electron concentration if there is a strong perpendicular magnetic field.Comment: 6 pages, 5 figures; updated discussion about bilayer systems; added discussion of fractional quantum Hall state

Adhesively-bonded GFRP-glass sandwich components for structurally efficient glazing applications

Composite sandwich structures made of thick glass face sheets adhesively-bonded to glass fibre-reinforced polymer (GFRP) core profiles have the potential to outperform existing non-composite glazing configurations but their feasibility has yet to be investigated and there are no analytical models that describe their structural response. This paper presents the new analytical models for predicting deflections and strains in adhesively-bonded GFRP-glass sandwich beams. The new analytical models successfully account for: the shear deformations of the core and adhesive layers; the local bending of the constituent parts about their centroidal axes; and the global bending of the sandwich component as a whole. The deflections and strains predicted by analytical models are validated by finite element simulations and compared with the results of destructive tests performed on adhesively-bonded GFRP-glass beams in a four-point bending configuration. The analytical models were also evaluated for alternative GFRP-glass configurations tested by others. The GFRP-glass beams specially assembled in this study confirm the physical feasibility of constructing these proposed components.The authors would like to thank the Engineering and Physical Sciences Research Council – United Kingdom for the financial support of the project

Voice assistants in hospital triage operations

This paper analyzes the creation and usage of a voice assistant for the triage of emergency room patients. This human-centred intelligent system strongly relies on Mycroft, an extensible open source voice assistant. The patients are able to declare their symptoms to the agent, which recognizes the urgency and acts accordingly. The software can even provide useful medical informations to the users

Knowledge-rich optimisation of prefabricated façades to support conceptual design

One of the principal challenges in façade design is to support the architectural intent by devising technically viable (i.e. standard-compliant and manufacturable) solutions from as early as possible in the design stage. This is increasingly relevant as prefabricated façades increase in complexity and bespokedness in response to current societal, financial and environmental challenges. In this paper a process that addresses this challenge is presented. The process consists of two sub-processes 1) to build product-oriented knowledge bases and digital tools for supporting design on a project-by-project basis and 2) to help designers identify a set of optimal solutions that consider the façade-specific trade-off between architectural intent and performance requirements, while meeting the largest number of production-related constraints. This process was applied to a case study and the results were compared with those obtained from a recently-developed façade. It was found that, although the proposed process produces optimal solutions that are approximated, designers can benefit from more control over the product’s manufacturability, performance and architectural intent in less time.Laing O'Rourke pl

Familial hypercholesterolemia: The Italian Atherosclerosis Society Network (LIPIGEN)

Primary dyslipidemias are a heterogeneous group of disorders characterized by abnormal levels of circulating lipoproteins. Among them, familial hypercholesterolemia is the most common lipid disorder that predisposes for premature cardiovascular disease. We set up an Italian nationwide network aimed at facilitating the clinical and genetic diagnosis of genetic dyslipidemias named LIPIGEN (LIpid TransPort Disorders Italian GEnetic Network)

Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review

Recent years have seen the emergence of object-centric process mining techniques. Born as a response to the limitations of traditional process mining in analyzing event data from prevalent information systems like CRM and ERP, these techniques aim to tackle the deficiency, convergence, and divergence issues seen in traditional event logs. Despite the promise, the adoption in real-world process mining analyses remains limited. This paper embarks on a comprehensive literature review of object-centric process mining, providing insights into the current status of the discipline and its historical trajectory

Data-aware conformance checking with SMT

Conformance checking is a key process mining task to confront the normative behavior imposed by a process model with the actual behavior recorded in a log. While this problem has been extensively studied for pure control-flow processes, data-aware conformance checking has received comparatively little attention. In this paper, we tackle the conformance checking problem for the challenging scenario of processes that combine data and control-flow dimensions. Concretely, we adopt the formalism of data Petri nets (DPNs) and show how solid, well-established automated reasoning techniques from the area of Satisfiability Modulo Theories (SMT) can be effectively harnessed to compute conformance metrics and optimal data-aware alignments. To this end, we introduce the CoCoMoT (Computing Conformance Modulo Theories) framework, with a fourfold contribution. First, we show how SMT allows to leverage SAT-based encodings for the pure control-flow setting to the data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering, to speed up the computation of conformance checking outputs. Third, we show how our approach extends seamlessly to the more comprehensive conformance checking artifacts of multi- and anti-alignments. Fourth, we describe a proof-of-concept implementation based on state-of-the-art SMT solvers, and report on experiments. Finally, we discuss how CoCoMoT directly lends itself to further process mining tasks like log analysis by clustering and model repair, and the use of SMT facilitates the support of even richer multi-perspective models, where, for example, more expressive DPN guards languages are considered or generic datatypes (other than integers or reals) are employed

FluoroSpot assay to analyze SARS-CoV-2-specific T cell responses

Monitoring antigen-specific T cell frequency and function is essential to assess the host immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we present a FluoroSpot assay for concurrently detecting ex vivo antiviral cytokine production by SARS-CoV-2-specific T cells following peptide stimulation. We then detail intracellular cytokine staining by flow cytometry to further validate the FluoroSpot assay results and define the specific T cell subpopulations. For complete details on the use and execution of this protocol, please refer to Tiezzi et al. (2023).

Evaluation of polygenic determinants of non-alcoholic fatty liver disease (NAFLD) by a candidate genes resequencing strategy

NAFLD is a polygenic condition but the individual and cumulative contribution of identified genes remains to be established. To get additional insight into the genetic architecture of NAFLD, GWAS-identified GCKR, PPP1R3B, NCAN, LYPLAL1 and TM6SF2 genes were resequenced by next generation sequencing in a cohort of 218 NAFLD subjects and 227 controls, where PNPLA3 rs738409 and MBOAT7 rs641738 genotypes were also obtained. A total of 168 sequence variants were detected and 47 were annotated as functional. When all functional variants within each gene were considered, only those in TM6SF2 accumulate in NAFLD subjects compared to controls (P = 0.04). Among individual variants, rs1260326 in GCKR and rs641738 in MBOAT7 (recessive), rs58542926 in TM6SF2 and rs738409 in PNPLA3 (dominant) emerged as associated to NAFLD, with PNPLA3 rs738409 being the strongest predictor (OR 3.12, 95% CI, 1.8-5.5, P 0.28 was associated with a 3-fold increased risk of NAFLD. Interestingly, rs61756425 in PPP1R3B and rs641738 in MBOAT7 genes were predictors of NAFLD severity. Overall, TM6SF2, GCKR, PNPLA3 and MBOAT7 were confirmed to be associated with NAFLD and a score based on these genes was highly predictive of this condition. In addition, PPP1R3B and MBOAT7 might influence NAFLD severity