Cerebral microbleeds and cognitive decline in a hemodialysis patient: Case report and review of literature

Cerebral microbleeds (CMBs) are small hemosiderin deposits indicative of prior cerebral microscopic hemorrhage and previously thought to be clinically silent. Recent population‐based cross‐sectional studies and prospective longitudinal cohort studies have revealed association between CMB and cognitive dysfunction. In the general population, CMBs are associated with age, hypertension, and cerebral amyloid angiopathy. In the chronic kidney disease (CKD) population, diminished estimated glomerular filtration rate has been found to be an independent risk factor for CMB, raising the possibility that a uremic milieu may predispose to microbleeds. In the end‐stage renal disease (ESRD) population on hemodialysis, the incidence of microbleeds is significantly higher compared with a control group without history of CKD or stroke. We present an ESRD patient on chronic hemodialysis with a history of gradual cognitive decline and progressive CMBs. Through this case and literature review, we illustrate the need to develop detection and prediction models to treat this frequent development in ESRD patients.     

Linear friction welding of Ti6Al4V: experiments and modelling

Linear friction welding of the Ti6Al4V alloy is studied. A new definition of the energy input rate is proposed, based on an integration over time of the in-plane force and velocity; a strong correlation with the upset rate is then found. The effective friction coefficient is estimated to be 0·5±0·1 for varying frequencies and amplitudes, with only a weak dependence on the processing conditions displayed. A model is proposed that accounts for both the conditioning and equilibrium stages of the process, which is shown to be in good agreement with the experimental data. The model is used to study the mechanism by which the flash is formed. A criterion is proposed by which the rippled nature of its morphology can be predicted.     

Word length and lexical activation: Longer is better.

Many models of spoken word recognition posit the existence of lexical and sublexical representations, with excitatory and inhibitory mechanisms used to affect the activation levels of such representations. Bottom-up evidence provides excitatory input, and inhibition from phonetically similar representations leads to lexical competition. In such a system, long words should produce stronger lexical activation than short words, for 2 reasons: Long words provide more bottom-up evidence than short words, and short words are subject to greater inhibition due to the existence of more similar words. Four experiments provide evidence for this view. In addition, reaction-time-based partitioning of the data shows that long words generate greater activation that is available both earlier and for a longer time than is the case for short words. As a result, lexical influences on phoneme identification are extremely robust for long words but are quite fragile and condition-dependent for short words. Models of word recognition must consider words of all lengths to capture the true dynamics of lexical activation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On the origin of strain hardening in glassy polymers

The influence of network density on the strain hardening behaviour of amorphous polymers is studied. The network density of polystyrene is altered by blending with poly(2,6-dimethyl-1,4-phenylene-oxide) and by cross-linking during polymerisation. The network density is derived from the rubber-plateau modulus determined by dynamic mechanical thermal analysis. Subsequently uniaxial compression tests are performed to obtain the intrinsic deformation behaviour and, in particular, the strain hardening modulus. At room temperature, the strain hardening modulus proves to be proportional to the network density, irrespective of the nature of the network, i.e. physical entanglements or chemical cross-links. With increasing temperature, the strain hardening modulus is observed to decrease. This decrease appears to be related to the influence of thermal mobility of the chains, determined by the distance to the glass-transition temperature (T−T_g).     

Prediction of brittle-to-ductile transitions in polystyrene

In this study it is attempted to predict brittle-to-ductile transitions (BDTs) in polystyrene blends, induced either by an increase in temperature or by a decrease in inter-particle distance. A representative, two-dimensional volume element (RVE) of a polystyrene matrix with 20% circular voids, is deformed in tension. During deformation a hydrostatic-stress based craze-nucleation criterion [1] is evaluated. The simulations demonstrate that crazes initiate at low temperatures while a transition from crazing to shear yielding (BDT) is found around 75 °C. The numerical results correlate well with tensile tests on similar heterogeneous polystyrene. The presence of an absolute length, as experimentally found, is more difficult to explain. Near a free surface a T_g-depression is measured for polystyrene and also the resistance to indentation in polystyrene is lower than expected from bulk properties. Both observations are rationalised by an enhanced segmental mobility of chains near a free surface. As a consequence of these findings, an absolute length-scale could be incorporated in the numerical simulations. For simplicity, the length-scale is modelled by taking a temperature gradient over a thin layer near the internal free surfaces of the RVE. Deformation of the RVE with different absolute length-scales shows that indeed also the experimentally found brittle-to-ductile transition can be predicted if the ligament thickness between the inclusions (‘voids’) in polystyrene is below a critical value of ca. 15 nm.     

Sigma-delta modulators operating at a limit cycle

A new type of sigma-delta modulator that operates in a special mode named limit-cycle mode (LCM) is proposed. In this mode, most of the SDM building blocks operate at a frequency that is an integer fraction of the applied sampling frequency. That brings several very attractive advantages: a reduction of the required power consumption per converted bandwidth, an immunity to excessive loop delays and to digital-analog converter waveform asymmetry and a higher tolerance to clock imperfections. The LCMs are studied via a graphical application of the describing function theory. A second-order continuous time SDM with 5 MHz conversion bandwidth, 1 GHz sampling frequency and 125 MHz limit-cycle frequency is used as a test case for the evaluation of the performance of the proposed type of modulators. High level and transistor simulations are presented and compared with the traditional SDM designs.     

Training and optimization of an artificial neural network controlling a hybrid power filter

A hybrid power compensator (HPC) consisting of a static VAr compensator and a dynamic compensator needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimizing its total cost. An artificial neural network (ANN) is used to control the HPC amidst a very dynamic power system environment. The performance of a reference ANN is evaluated while controlling an HPC connected to a typical nonlinear industrial load. The training and performance of the ANN is then optimized in terms of training set size, training set packing and ANN topology and the performance compared to the reference ANN. This paper highlights the importance of optimising the mentioned ANN parameters to achieve optimum ANN training and modeling accuracy. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.