Introducing requirements traceability support in model-driven development of web applications

In this work, we present an approach that introduces requirements traceability capabilities in the context of model-driven development of Web applications. This aspect allows us to define model-to-model transformations that not only provide a software artifact of lower abstraction (as model-to-model transformations usually do) but also to provide feedback about how they are applied. This feedback helps us to validate whether transformations are correctly applied. In particular, we present a model-to-model transformation that allows us to obtain navigational models of the Web engineering method OOWS from a requirements model. This transformation is defined as a set of mappings between these two models that have been implemented by means of graph transformations. The use of graph transformations allows us to develop a tool-supported strategy for applying mappings automatically. In addition, mechanisms for tracing requirements are also included in the definition of graph transformations. These mechanisms allow us to link each conceptual element to the requirements from which it is derived. In particular, we focus on tracing requirements throughout the navigational model, which describe the navigational structure of a Web application. To take advantage of these traceability mechanisms, we have developed a tool that obtains traceability reports after applying transformations. These reports help us to study aspects such as whether requirements are all supported, the impact of changing a requirement, or how requirements are modelled.     

Solving differential matrix Riccati equations by a piecewise-linearized method based on diagonal Padé approximants

Differential Matrix Riccati Equations (DMREs) appear in several branches of science such as applied physics and engineering. For example, these equations play a fundamental role in control theory, optimal control, filtering and estimation, decoupling and order reduction, etc. In this paper a new method based on a theorem proved in this paper is described for solving DMREs by a piecewise-linearized approach. This method is applied for developing two block-oriented algorithms based on diagonal Padé approximants. MATLAB versions of the above algorithms are developed, comparing, under equal conditions, accuracy and computational costs with other piecewise-linearized algorithms implemented by the authors. Experimental results show the advantages of solving stiff or non-stiff DMREs by the implemented algorithms.     

The Poisson-exponential lifetime distribution

In this paper we proposed a new two-parameters lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk problem base. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived analytically in order to obtaining the asymptotic covariance matrix. The methodology is illustrated on a real data set.     

Equivalence of some common linear feature extraction techniques for appearance-based object recognition tasks

Recently, a number of empirical studies have compared the performance of PCA and ICA as feature extraction methods in appearance-based object recognition systems, with mixed and seemingly contradictory results. In this paper, we briefly describe the connection between the two methods and argue that whitened PCA may yield identical results to ICA in some cases. Furthermore, we describe the specific situations in which ICA might significantly improve on PCA     

GPGPU implementation of growing neural gas: Application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6×$6\times$ compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.     

Insights into the Pharmacokinetics and In Vitro Cell-Based Studies of the Imidazoline I2 Receptor Ligand B06

The impact of neurodegenerative diseases (ND) is becoming unbearable for humankind due to their vast prevalence and the lack of efficacious treatments. In this scenario, we focused on imidazoline I₂ receptors (I₂-IR) that are widely distributed in the brain and are altered in patients with brain disorders. We took the challenge of modulating I₂-IR by developing structurally new molecules, in particular, a family of bicyclic α-iminophosphonates, endowed with high affinity and selectivity to these receptors. Treatment of two murine models, one for age-related cognitive decline and the other for Alzheimer’s disease (AD), with representative compound B06 ameliorated their cognitive impairment and improved their behavioural condition. Furthermore, B06 revealed beneficial in vitro ADME-Tox properties. The pharmacokinetics (PK) and metabolic profile are reported to de-risk B06 for progressing in the preclinical development. To further characterize the pharmacological properties of B06, we assessed its neuroprotective properties and beneficial effect in an in vitro model of Parkinson’s disease (PD). B06 rescued the human dopaminergic cell line SH-SY5Y from death after treatment with 6-hydroxydopamine (6-OHDA) and showed a crucial anti-inflammatory effect in a cellular model of neuroinflammation. This research reveals B06 as a putative candidate for advancing in the difficult path of drug discovery and supports the modulation of I₂-IR as a fresh approach for the therapy of ND.     

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence,Severity, and Treatment Response

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.     

Removal of an Azo Dye from Wastewater through the Use of Two Technologies: Magnetic Cyclodextrin Polymers and Pulsed Light

Water pollution by dyes is a huge environmental problem; there is a necessity to produce new decolorization methods that are effective, cost-attractive, and acceptable in industrial use. Magnetic cyclodextrin polymers offer the advantage of easy separation from the dye solution. In this work, the β-CD-EPI-magnetic (β-cyclodextrin-epichlorohydrin) polymer was synthesized, characterized, and tested for removal of the azo dye Direct Red 83:1 from water, and the fraction of non-adsorbed dye was degraded by an advanced oxidation process. The polymer was characterized in terms of the particle size distribution and surface morphology (FE-SEM), elemental analysis (EA), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), infrared spectrophotometry (IR), and X-ray powder diffraction (XRD). The reported results hint that 0.5 g and pH 5.0 were the best conditions to carry out both kinetic and isotherm models. A 30 min contact time was needed to reach equilibrium with a qmax of 32.0 mg/g. The results indicated that the pseudo-second-order and intraparticle diffusion models were involved in the assembly of Direct Red 83:1 onto the magnetic adsorbent. Regarding the isotherms discussed, the Freundlich model correctly reproduced the experimental data so that adsorption was confirmed to take place onto heterogeneous surfaces. The calculation of the thermodynamic parameters further demonstrates the spontaneous character of the adsorption phenomena (ΔG° = −27,556.9 J/mol) and endothermic phenomena (ΔH° = 8757.1 J/mol) at 25 °C. Furthermore, a good reusability of the polymer was evidenced after six cycles of regeneration, with a negligible decline in the adsorption extent (10%) regarding its initial capacity. Finally, the residual dye in solution after treatment with magnetic adsorbents was degraded by using an advanced oxidation process (AOP) with pulsed light and hydrogen peroxide (343 mg/L); >90% of the dye was degraded after receiving a fluence of 118 J/cm²; the discoloration followed a pseudo first-order kinetics where the degradation rate was 0.0196 cm²/J. The newly synthesized β-CD-EPI-magnetic polymer exhibited good adsorption properties and separability from water which, when complemented with a pulsed light-AOP, may offer a good alternative to remove dyes such as Direct Red 83:1 from water. It allows for the reuse of both the polymer and the dye in the dyeing process.     

Design,dynamic modelling and experimental validation of a 2DOF flexible antenna sensor

A two-degree-of-freedom flexible antenna sensor platform was designed to physically simulate the ability of a robotic arm, which rapidly reorients and targets itself towards specific surfaces from different approachable angles. An accurate antenna model involves non-linear expressions that represent the system dynamics. Therefore, a comprehensive study along with experimental work has been carried out in order to achieve accurate system identification and validate the dynamic model. The model developed has proven useful in controlling the antenna tip, minimising the effects of the non-linear flexural dynamics and the Coulomb friction. The system was driven by servo motors. Algebraic controllers were developed for the antenna tip to track the reference trajectory. The platform system used encoders to measure the joint angles and a loadcell sensor to obtain the flexible link tip position. To validate the sensory information, the results obtained by the integrated sensors were compared to that of an external camera system.