Theoretical analysis and numerical verification of the consistency of viscous smoothed-particle-hydrodynamics formulations in simulating free-surface flows

The theoretical formulation of the smoothed particle hydrodynamics (SPH) method deserves great care because of some inconsistencies occurring when considering free-surface inviscid flows. Actually, in SPH formulations one usually assumes that (i) surface integral terms on the boundary of the interpolation kernel support are neglected, (ii) free-surface conditions are implicitly verified. These assumptions are studied in detail in the present work for free-surface Newtonian viscous flow. The consistency of classical viscous weakly compressible SPH formulations is investigated. In particular, the principle of virtual work is used to study the verification of the free-surface boundary conditions in a weak sense. The latter can be related to the global energy dissipation induced by the viscous term formulations and their consistency. Numerical verification of this theoretical analysis is provided on three free-surface test cases including a standing wave, with the three viscous term formulations investigated.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Harmony: Towards automated self-adaptive consistency in cloud storage

In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra.

Data consistency management in an open smart home management platform

In this paper, the authors introduce a novel mechanism for data management in a middleware for smart home control, where a relational database and semantic ontology storage are used at the same time in a Data Warehouse. An annotation system has been designed for instructing the storage format and location, registering new ontology concepts and most importantly, guaranteeing the Data Consistency between the two storage methods. For easing the data persistence process, the Data Access Object (DAO) pattern is applied and optimized to enhance the Data Consistency assurance. Finally, this novel mechanism provides an easy manner for the development of applications and their integration with BATMP. Finally, an application named "Parameter Monitoring Service" is given as an example for assessing the feasibility of the system.

Spatial analysis of geometric design consistency and road sight distance

Because of the high number of crashes occurring on highways, it is necessary to intensify the search for new tools that help in understanding their causes. This research explores the use of a geographic information system (GIS) for an integrated analysis, taking into account two accident-related factors: design consistency (DC) (based on vehicle speed) and available sight distance (ASD) (based on visibility). Both factors require specific GIS software add-ins, which are explained. Digital terrain models (DTMs), vehicle paths, road centerlines, a speed prediction model, and crash data are integrated in the GIS. The usefulness of this approach has been assessed through a study of more than 500 crashes. From a regularly spaced grid, the terrain (bare ground) has been modeled through a triangulated irregular network (TIN). The length of the roads analyzed is greater than 100 km. Results have shown that DC and ASD could be related to crashes in approximately 4% of cases. In order to illustrate the potential of GIS, two crashes are fully analyzed: a car rollover after running off road on the right side and a rear-end collision of two moving vehicles. Although this procedure uses two software add-ins that are available only for ArcGIS, the study gives a practical demonstration of the suitability of GIS for conducting integrated studies of road safety.

Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.

Rapamycin-modulated transcription defines the subset of nutrient-sensitive signaling pathways directly controlled by the Tor proteins

The immunosuppressant rapamycin inhibits Tor1p and Tor2p (target of rapamycin proteins), ultimately resulting in cellular responses characteristic of nutrient deprivation through a mechanism involving translational arrest. We measured the immediate transcriptional response of yeast grown in rich media and treated with rapamycin to investigate the direct effects of Tor proteins on nutrient-sensitive signaling pathways. The results suggest that Tor proteins directly modulate the glucose activation and nitrogen discrimination pathways and the pathways that respond to the diauxic shift (including glycolysis and the citric acid cycle). Tor proteins do not directly modulate the general amino acid control, nitrogen starvation, or sporulation (in diploid cells) pathways. Poor nitrogen quality activates the nitrogen discrimination pathway, which is controlled by the complex of the transcriptional repressor Ure2p and activator Gln3p. Inhibiting Tor proteins with rapamycin increases the electrophoretic mobility of Ure2p. The work presented here illustrates the coordinated use of genome-based and biochemical approaches to delineate a cellular pathway modulated by the protein target of a small molecule.

A subset of skin tumor modifier loci determines survival time of tumor-bearing mice

Studies of mouse models of human cancer have established the existence of multiple tumor modifiers that influence parameters of cancer susceptibility such as tumor multiplicity, tumor size, or the probability of malignant progression. We have carried out an analysis of skin tumor susceptibility in interspecific Mus musculus/Mus spretus hybrid mice and have identified another seven loci showing either significant (six loci) or suggestive (one locus) linkage to tumor susceptibility or resistance. A specific search was carried out for skin tumor modifier loci associated with time of survival after development of a malignant tumor. A combination of resistance alleles at three markers [D6Mit15 (Skts12), D7Mit12 (Skts2), and D17Mit7 (Skts10)], all of which are close to or the same as loci associated with carcinoma incidence and/or papilloma multiplicity, is significantly associated with increased survival of mice with carcinomas, whereas the reverse combination of susceptibility alleles is significantly linked to early mortality caused by rapid carcinoma growth (χ2 = 25.22; P = 5.1 × 10−8). These data indicate that host genetic factors may be used to predict carcinoma growth rate and/or survival of individual backcross mice exposed to the same carcinogenic stimulus and suggest that mouse models may provide an approach to the identification of genetic modifiers of cancer survival in humans.

A cGMP-signaling pathway in a subset of olfactory sensory neurons

It is well established that signal transduction in sensory neurons of the rat olfactory epithelium involves a cAMP-signaling pathway. However, a small number of olfactory neurons specifically express cGMP-signaling components, namely a guanylyl cyclase (GC-D) and a cGMP-stimulated phosphodiesterase (PDE2). Here, we show that this subset of olfactory neurons expressing GC-D and PDE2 does also express the subunit of a cGMP-selective cyclic nucleotide-gated (CNG) channel that has been previously identified in cone photoreceptors. Further, components of the prototypical cAMP-signaling pathway could not be detected in this subpopulation of cells. These results imply that these neurons use an alternative signaling pathway, with cGMP as the intracellular messenger, and that, in these cells, the receptor current is initiated by the opening of cGMP-gated channels.

neuralized is essential for a subset of Notch pathway-dependent cell fate decisions during Drosophila eye development

neuralized (neur) is a neurogenic mutant of Drosophila in which many signaling events mediated by the Notch (N) receptor are disrupted. Here, we analyze the role of neur during eye development. Neur is required in a cell-autonomous fashion to restrict R8 and other photoreceptor fates and is involved in lateral inhibition of interommatidial bristles but is not required for induction of the cone cell fate. The latter contrasts with the absolute requirement for Suppressor of Hairless and the Enhancer of split-Complex for cone cell induction. Using gain-of-function experiments, we further demonstrate that ectopic wild-type and truncated Neur proteins can interfere with multiple N-controlled aspects of eye development, including both neur-dependent and neur-independent processes.

On the forecast accuracy and consistency of exchange rate expectations: The Spanish PwC Survey.

We examine the predictive ability and consistency properties of exchange rate expectations for the dollar/euro using a survey conducted in Spain by PwC among a panel of experts and entrepreneurs. Our results suggest that the PwC panel have some forecasting ability for time horizons from 3 to 9 months, although only for the 3-month ahead expectations we obtain marginal evidence of unbiasedness and efficiency in the forecasts. As for the consistency properties of the exchange rate expectations formation process, we find that survey participants form stabilising expectations in the short-run and destabilising expectations in the long- run and that the expectation formation process is closer to fundamentalists than chartists.

Pitfalls in the Evaluation of the Thermodynamic Consistency of Experimental VLE Data Sets

The thermodynamic consistency of almost 90 VLE data series, including isothermal and isobaric conditions for systems of both total and partial miscibility in the liquid phase, has been examined by means of the area and point-to-point tests. In addition, the Gibbs energy of mixing function calculated from these experimental data has been inspected, with some rather surprising results: certain data sets exhibiting high dispersion or leading to Gibbs energy of mixing curves inconsistent with the total or partial miscibility of the liquid phase, surprisingly, pass the tests. Several possible inconsistencies in the tests themselves or in their application are discussed. Related to this is a very interesting and ambitious initiative that arose within the NIST organization: the development of an algorithm to assess the quality of experimental VLE data. The present paper questions the applicability of two of the five tests that are combined in the algorithm. It further shows that the deviation of the experimental VLE data from the correlation obtained by a given model, the basis of some point-to-point tests, should not be used to evaluate the quality of these data.

Improving some thermodynamic consistency tests for VLE data evaluation

Thermodynamics Conference 2013 (Statistical Mechanics and Thermodynamics Group of the Royal Society of Chemistry), The University of Manchester, 3-6 September 2013.

On the Evaluation of a DSL for Architectural Consistency Checking

Software architecture erodes over time and needs to be constantly monitored to be kept consistent with its original intended design. Consistency is rarely monitored using automated techniques. The cost associated to such an activity is typically not considered proportional to its benefits. To improve this situation, we propose Dicto, a uniform DSL for specifying architectural invariants. This language is designed to reduce the cost of consistency checking by offering a framework in which existing validation tools can be matched to newly-defined language constructs. In this paper we discuss how such a DSL can be qualitatively and qualitatively evaluated in practice.

Screening for Language Disorders in Stroke: German Validation of the Language Screening Test (LAST)

BACKGROUND Screening of aphasia in acute stroke is crucial for directing patients to early language therapy. The Language Screening Test (LAST), originally developed in French, is a validated language screening test that allows detection of a language deficit within a few minutes. The aim of the present study was to develop and validate two parallel German versions of the LAST. METHODS The LAST includes subtests for naming, repetition, automatic speech, and comprehension. For the translation into German, task constructs and psycholinguistic criteria for item selection were identical to the French LAST. A cohort of 101 stroke patients were tested, all of whom were native German speakers. Validation of the LAST was based on (1) analysis of equivalence of the German versions, which was established by administering both versions successively in a subset of patients, (2) internal validity by means of internal consistency analysis, and (3) external validity by comparison with the short version of the Token Test in another subset of patients. RESULTS The two German versions were equivalent as demonstrated by a high intraclass correlation coefficient of 0.91. Furthermore, an acceptable internal structure of the LAST was found (Cronbach's α = 0.74). A highly significant correlation (r = 0.74, p < 0.0001) between the LAST and the short version of the Token Test indicated good external validity of the scale. CONCLUSION The German version of the LAST, available in two parallel versions, is a new and valid language screening test in stroke.