Cross-disciplinary collaboration versus coexistence in LIS serials: analysis of authorship affiliations in four European countries

Scientometrics - The interdisciplinary nature of library and information science (LIS) research has been highlighted for some time now. The term “interdisciplinary” is used primarily in...     

Scene segmentation using neuromorphic oscillatory networks

Using the neuromorphic approach, we propose an analog very large-scale integration (VLSI) implementation of an oscillatory segmentation algorithm based on local excitatory couplings and global inhibition. The original model has been simplified and adapted for its efficient VLSI implementation while preserving its segmentation properties. To demonstrate the feasibility of the approach, a 16/spl times/16-pixel testchip has been manufactured. Extensive experimental results demonstrate that it can properly segment binary images. Power consumption, segmentation time per cell, and system complexity are very low compared to other hardware and software implementation schemes. We also show two main differences between the original algorithm and the analog approach. First, the network is noise tolerant without the need of additional elements and second, delays between oscillators due to the combination of mismatch and output capacitances have to be accounted for network performance.     

Improvement of the deflocculating power of polyacrylates in ceramic slips by small additions of quaternary ammonium salts

In this work, we examine the effect of small additions of cationic quaternary ammonium salts (QAS) of different molecular weight on the rheology of an industrial ceramic suspension deflocculated with sodium polyacrylate and sodium metasilicate. The observed shear thinning behaviors obey the typical power law of fluid rheology. In order to characterize the rheological behavior of these slurries, three new parameters are introduced: a low shear rate consistency index and two transient viscosities, distant from the equilibrium, after increasing and decreasing the shear rates. These parameters vary with polyacrylate molecular weight and on additions of small quantities of QAS, which we found to be useful for decreasing the slurry viscosity.     

Adenosine Signaling in Mast Cells and Allergic Diseases

Adenosine is a nucleoside involved in the pathogenesis of allergic diseases. Its effects are mediated through its binding to G protein-coupled receptors: A1, A2a, A2b and A3. The receptors differ in the type of G protein they recruit, in the effect on adenylyl cyclase (AC) activity and the downstream signaling pathway triggered. Adenosine can produce both an enhancement and an inhibition of mast cell degranulation, indicating that adenosine effects on these receptors is controversial and remains to be clarified. Depending on the study model, A1, A2b, and A3 receptors have shown anti- or pro-inflammatory activity. However, most studies reported an anti-inflammatory activity of A2a receptor. The precise knowledge of the adenosine mechanism of action may allow to develop more efficient therapies for allergic diseases by using selective agonist and antagonist against specific receptor subtypes.     

Speeding up the spatial adiabatic passage of matter waves in optical microtraps by optimal control

We numerically investigate the performance of atomic transport in optical microtraps via the so called spatial adiabatic passage technique. Our analysis is carried out by means of optimal control methods, which enable us to determine suitable transport control pulses. We investigate the ultimate limits of the optimal control in speeding up the transport process in a triple well configuration for both a single atomic wave packet and a Bose-Einstein condensate within a regime of experimental parameters achievable with current optical technology.     

Self-adaptive utility-based web session management

In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers.In the present study we describe the architecture of AUGURES, a system that learns to predict Web user’s intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.