A recommender system for process discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft

Derivative based global sensitivity measures

The method of derivative based global sensitivity measures (DGSM) has recently become popular among practitioners. It has a strong link with the Morris screening method and Sobol' sensitivity indices and has several advantages over them. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is generally much lower than that for estimation of Sobol' sensitivity indices. This paper presents a survey of recent advances in DGSM concerning lower and upper bounds on the values of Sobol' total sensitivity indices $S\_{i}^{tot}$. Using these bounds it is possible in most cases to get a good practical estimation of the values of $S\_{i}^{tot}$. Several examples are used to illustrate an application of DGSM

Usefulness of Low-Dose Statin Plus Ezetimibe and/or Nutraceuticals in Patients With Coronary Artery Disease Intolerant to High-Dose Statin Treatment.

High-dose statin (HDS) therapy is recommended to reduce low-density lipoprotein cholesterol (LDL-C); however, some patients are unable to tolerate the associated side effects. Nutraceuticals have shown efficacy in lowering LDL-C. The aim of this study was to evaluate whether the combination of low-dose statin (LDS) plus ezetimibe (EZE) or LDS plus nutraceutical (Armolipid Plus [ALP] containing red yeast rice, policosanol, and berberine) can lead to a higher proportion of high-risk patients achieving target LDL-C. A secondary objective was to assess the efficacy of triple combination LDS + EZE + ALP in resistant patients (LDL-C >70 mg/dl). A randomized, prospective, parallel-group, single-blind study was conducted in patients with coronary artery disease (n = 100) who had undergone percutaneous coronary intervention in the preceding 12 months, were HDS-intolerant, and were not at LDL-C target (<70 mg/dl) with LDS alone. Patients received either LDS + EZE or LDS + ALP. Of the 100 patients, 33 patients (66%) treated with LDS + EZE and 31 patients (62%) treated with LDS + ALP achieved target LDL-C after 3 months, which was maintained at 6 months. Patients who did not achieve the therapeutic goal received a triple combination of LDS + EZE + ALP for a further 3 months. At 6 months, 28 of 36 patients (78%) achieved LDL-C target. Overall, 92% of patients enrolled in this study were at target LDL-C at 6 months. No patients in any group experienced major side effects. In conclusion, in HDS-intolerant coronary artery disease patients, the combination of LDS plus EZE and/or ALP represents a valuable therapeutic option allowing most patients to reach target LDL-C within 3 to 6 months

Prenatal stress induces a depressive-like phenotype in adolescent rats: The key role of TGF-β1 pathway

Stressful experiences early in life, especially in the prenatal period, can increase the risk to develop depression during adolescence. However, there may be important qualitative and quantitative differences in outcome of prenatal stress (PNS), where some individuals exposed to PNS are vulnerable and develop a depressive-like phenotype, while others appear to be resilient. PNS exposure, a well-established rat model of early life stress, is known to increase vulnerability to depression and a recent study demonstrated a strong interaction between transforming growth factor-β1 (TGF-β1) gene and PNS in the pathogenesis of depression. Moreover, it is well-known that the exposure to early life stress experiences induces brain oxidative damage by increasing nitric oxide levels and decreasing antioxidant factors. In the present work, we examined the role of TGF-β1 pathway in an animal model of adolescent depression induced by PNS obtained by exposing pregnant females to a stressful condition during the last week of gestation. We performed behavioral tests to identify vulnerable or resilient subjects in the obtained litters (postnatal day, PND &gt; 35) and we carried out molecular analyses on hippocampus, a brain area with a key role in the pathogenesis of depression. We found that female, but not male, PNS adolescent rats exhibited a depressive-like behavior in forced swim test (FST), whereas both male and female PNS rats showed a deficit of recognition memory as assessed by novel object recognition test (NOR). Interestingly, we found an increased expression of type 2 TGF-β1 receptor (TGFβ-R2) in the hippocampus of both male and female resilient PNS rats, with higher plasma TGF-β1 levels in male, but not in female, PNS rats. Furthermore, PNS induced the activation of oxidative stress pathways by increasing inducible nitric oxide synthase (iNOS), NADPH oxidase 1 (NOX1) and NOX2 levels in the hippocampus of both male and female PNS adolescent rats. Our data suggest that high levels of TGF-β1 and its receptor TGFβ-R2 can significantly increase the resiliency of adolescent rats to PNS, suggesting that TGF-β1 pathway might represent a novel pharmacological target to prevent adolescent depression in rats

Derivative based global sensitivity measures

International audienceThe method of derivative based global sensitivity measures (DGSM) has recently become popular among practitioners. It has a strong link with the Morris screening method and Sobol' sensitivity indices and has several advantages over them. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is generally much lower than that for estimation of Sobol' sensitivity indices. This paper presents a survey of recent advances in DGSM concerning lower and upper bounds on the values of Sobol' total sensitivity indices $S_{i}^{tot}$. Using these bounds it is possible in most cases to get a good practical estimation of the values of $S_{i}^{tot}$. Several examples are used to illustrate an application of DGSM

The spatial aspects of fairness

As well as their family background, an individual's chances in life are determined by the opportunities available to them in their geographical context. This chapter therefore deals with the spatial aspects of fairness. It focuses, firstly, on socio-economic factors which are not randomly distributed in space (i.e. they have a geographical pattern). Secondly, it focuses, not on first nature geographical differences which cannot be changed (such as the presence of mountains), but on second nature geographical factors (such as access to basic services or hospitals) which can be altered and which are important in overcoming a region's natural disadvantages. It then links the two

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Developmental consequences of perinatal cannabis exposure: behavioral and neuroendocrine effects in adult rodents

Cannabis is the most commonly used illicit drug among pregnant women. Since the endocannabinoid system plays a crucial role in brain development, maternal exposure to cannabis derivatives might result in long-lasting neurobehavioral abnormalities in the exposed offspring. It is difficult to detect these effects, and their underlying neurobiological mechanisms, in clinical cohorts, because of their intrinsic methodological and interpretative issues. The present paper reviews relevant rodent studies examining the long-term behavioral consequences of exposure to cannabinoid compounds during pregnancy and/or lactation. Maternal exposure to even low doses of cannabinoid compounds results in atypical locomotor activity, cognitive impairments, altered emotional behavior, and enhanced sensitivity to drugs of abuse in the adult rodent offspring. Some of the observed behavioral abnormalities might be related to alterations in stress hormone levels induced by maternal cannabis exposure. There is increasing evidence from animal studies showing that cannabinoid drugs are neuroteratogens which induce enduring neurobehavioral abnormalities in the exposed offspring. Several preclinical findings reviewed in this paper are in line with clinical studies reporting hyperactivity, cognitive impairments and altered emotionality in humans exposed in utero to cannabis. Conversely, genetic, environmental and social factors could also influence the neurobiological effects of early cannabis exposure in humans

Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modellingPeer reviewe

Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality

We present a composite design methodology for the simulation and optimization of the solar cell performance. Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost); (f) Local, Global and “Glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based Identifiability Analysis is used to study the relationship between parameters. The results obtained show a noteworthy improvement with respect to the quantum efficiency of the reference cell demonstrating that the methodology presented is suitable for effective optimization of solar cell devices