Study of the morphology and mechanical properties of polypropylene composites with silica or rice‐husk

The mechanical, morphological behavior and water absorption characteristics of polypropylene (PP) and silica, or PP and rice‐husk, composites have been studied. The silica used in this study as filler was a commercial type produced from soluble glass or rice husks. The compatibilizing effect of PP grafted with monomethyl itaconate (PP‐g‐MMI) and/or with vinyltriethoxysilane (PP‐g‐VTES) as polar monomers on the mechanical properties and water absorption was also investigated. In general, a high loading of the studied fillers in the polymer matrix increases the stiffness and the water absorption capacity. This effect is more noticeable in the tensile modulus of the PP/silica composite with PP‐g‐VTES as compatibilizer. However, the increase of the rice‐husk charge as a natural filler in the PP matrix decreases the stiffness, and in the presence of PP‐g‐MMI as compatibilizer in PP/rice‐husk, the tensile modulus and water absorption of the composite were improved. The better adhesion and phase continuity in the PP/silica and PP/rice‐husk composites with different compatibilizers was confirmed by the morphological study. Copyright © 2004 Society of Chemical Industry     

Wernicke's encephalopathy and dialysis: report of two cases

Wernicke encephalopathy is considered a complication of dialytic therapy, but there are few reports of this complication. We report a 57 years old man and a 45 years old woman, with grade IV renal failure, who after acute peritoneodialysis and chronic hemodialysis respectively, had a confusional syndrome that responded to the administration of thiamine. CT scans in both patients discarded abnormal blood collections or new cerebrovascular episodes. The man bad two previous cerebrovascular episodes, a severe anemia that was corrected, angina and an episode of arrhythmia during the dialytic procedure previous to the confusional episode. The woman had an acute uremic syndrome and a concomitant urinary tract infection during the confusional episode. Wernicke encephalopathy must be suspected in patients in dialysis with confusional episodes.     

Influence of grafted polypropylene on the mechanical properties of mineral‐filled polypropylene composites

The purpose of this work was to study how mineral fillers would behave in a polypropylene (PP) matrix when PP modified with maleic anhydride (MA) and/or itaconic acid (IA) was used as a coupling agent in the preparation of mineral‐filled PP composites. The composites were characterized with tensile mechanical measurements and morphological analysis. The optimum amount of the coupling agent to be used to obtain composites with improved mechanical properties was established. The results indicated that these coupling agents enhanced the tensile strength of the composites significantly, and the extent of the coupling effect depended on the nature of the interface that formed. The incorporation of coupling agents enhanced the resistance to deformation of the composite. The behavior of IA‐modified PP as a coupling agent was similar to that of a commercial MA‐modified PP for the filled PP composites. Evidence of improved interfacial bonding was revealed by scanning electron microscopy studies, which examined the surfaces of fractured tensile test specimens; their microstructures confirmed the mechanical results with respect to the observed homogeneous or optimized dispersion of the mineral‐filler phase in these composites. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2343–2350, 2007     

Extended Reflexive Ontologies for the Generation of Clinical Recommendations

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.     

Proteomic analysis in cerebrospinal fluid of patients with atypical nonketotic hyperglycinemia and pulmonary hypertension - A pilot study

A variant phenotype of nonketotic hyperglycinemia has been described by our group associated with pulmonary hypertension. The aim of this study is to investigate the cerebrospinal fluid proteomes to get an insight into this neurodegenerative process producing leukoencephalopathy with white matter spongiform degeneration. DIGE and MALDI-TOF-TOF analyses were performed to carry out the proteomic study of four patients against three normal controls and one additional control of a classical nonketotic hyperglycinemia. The differential proteomic analysis showed a displacement of some series of spots toward the acidic side. The shifted proteins showed a high degree of carbonylation and increased methionine sulfoxidation was found in cystatin C and in vitamin-D-binding protein. These findings in addition to the increase of serum malondialdehyde concentration provide evidence of an oxidative stress in the patients under study, which is probably systemic rather than mainly confined to the CNS. The similarities of our findings with those found in other neurodegenerative diseases suggest that oxidative damage is commonly involved in these pathologies. DIGE technology improves the 2-D PAGE differential analysis and it is suitable in proteomic studies with a small number of cases.     

Cohesive surface model for fracture based on a two-scale formulation: computational implementation aspects

The paper describes the computational aspects and numerical implementation of a two-scale cohesive surface methodology developed for analyzing fracture in heterogeneous materials with complex micro-structures. This approach can be categorized as a semi-concurrent model using the representative volume element concept. A variational multi-scale formulation of the methodology has been previously presented by the authors. Subsequently, the formulation has been generalized and improved in two aspects: (i) cohesive surfaces have been introduced at both scales of analysis, they are modeled with a strong discontinuity kinematics (new equations describing the insertion of the macro-scale strains, into the micro-scale and the posterior homogenization procedure have been considered); (ii) the computational procedure and numerical implementation have been adapted for this formulation. The first point has been presented elsewhere, and it is summarized here. Instead, the main objective of this paper is to address a rather detailed presentation of the second point. Finite element techniques for modeling cohesive surfaces at both scales of analysis (FE\(^2\) approach) are described: (i) finite elements with embedded strong discontinuities are used for the macro-scale simulation, and (ii) continuum-type finite elements with high aspect ratios, mimicking cohesive surfaces, are adopted for simulating the failure mechanisms at the micro-scale. The methodology is validated through numerical simulation of a quasi-brittle concrete fracture problem. The proposed multi-scale model is capable of unveiling the mechanisms that lead from the material degradation phenomenon at the meso-structural level to the activation and propagation of cohesive surfaces at the structural scale.     

An industry experiment on the effects of test-driven development on external quality and productivity

Existing empirical studies on test-driven development (TDD) report different conclusions about its effects on quality and productivity. Very few of those studies are experiments conducted with software professionals in industry. We aim to analyse the effects of TDD on the external quality of the work done and the productivity of developers in an industrial setting. We conducted an experiment with 24 professionals from three different sites of a software organization. We chose a repeated-measures design, and asked subjects to implement TDD and incremental test last development (ITLD) in two simple tasks and a realistic application close to real-life complexity. To analyse our findings, we applied a repeated-measures general linear model procedure and a linear mixed effects procedure. We did not observe a statistical difference between the quality of the work done by subjects in both treatments. We observed that the subjects are more productive when they implement TDD on a simple task compared to ITLD, but the productivity drops significantly when applying TDD to a complex brownfield task. So, the task complexity significantly obscured the effect of TDD. Further evidence is necessary to conclude whether TDD is better or worse than ITLD in terms of external quality and productivity in an industrial setting. We found that experimental factors such as selection of tasks could dominate the findings in TDD studies.     

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.