A knowledge-based exploratory framework to study quality of Italian mobile telecommunication services

A novel two-step knowledge-based exploratory framework is proposed in this paper for studying quality of Italian mobile telecommunication services (MTSs). Particularly, the Delphi technique is initially considered to finalize an overall quality structure of MTSs features, indicators and drivers, herein described on the basis of a comprehensive review of the fundamental references for the field, and also to select the key elements with reference to the Italian context. At the second step, selected key elements are prioritized via the analytic hierarchical process method according to viewpoints of the fundamental stakeholders for the sector. Furthermore, possible uncertainty and ambiguity of involved experts at this step of the study are addressed via a linguistic comparison scale represented by fuzzy numbers. Results of the first step revealed that the quality structure of Italian MTSs includes 15 key indicators with reference to four key MTSs quality features and seven key drivers. On the other hand, results of the second step pointed out that tangible aspects represents the fundamental key MTSs quality feature, whereas network population coverage, price of data services, and internet network performance represent the crucial key indicators. In addition, technological resources and technological innovation as well as informational resources constitute the most important key quality drivers of Italian MTSs. Obtained results may be of interest for MTSs managers and decision makers as well researchers of the field offering important suggestions as to how to evaluate and improve quality of MTSs.     

Exploiting the Catalytic Diversity of Short‐Chain Dehydrogenases/Reductases: Versatile Enzymes from Plants with Extended Imine Substrate Scope

Numerous short‐chain dehydrogenases/reductases (SDRs) have found biocatalytic applications in C=O and C=C (enone) reduction. For NADPH‐dependent C=N reduction, imine reductases (IREDs) have primarily been investigated for extension of the substrate range. Here, we show that SDRs are also suitable for a broad range of imine reductions. The SDR noroxomaritidine reductase (NR) is involved in Amaryllidaceae alkaloid biosynthesis, serving as an enone reductase. We have characterized NR by using a set of typical imine substrates and established that the enzyme is active with all four tested imine compounds (up to 99 % conversion, up to 92 % ee). Remarkably, NR reduced two keto compounds as well, thus highlighting this enzyme family's versatility. Using NR as a template, we have identified an as yet unexplored SDR from the Amaryllidacea Zephyranthes treatiae with imine‐reducing activity (≤95 % ee). Our results encourage the future characterization of SDR family members as a means of discovering new imine‐reducing enzymes.     

Stress calculation for plastic helical gears under a real transverse contact ratio

Calculation methods of plastic helical gears with a real transverse contact ratio have never been studied in depth. The methods used in some references assume a uniform load distribution along the line of contact or don’t take into consideration the effect of premature mesh between one or more pair of teeth during engagement, which is not in good agreement with numerical results. This paper is focused on analysis of plastic helical gears. The nonuniform load distribution obtained from the proposed mathematical model is based on the calculation of the real contact ratio representing the real number of pairs of teeth in contact. This model also leads to the distribution of the tooth bending stress and the contact stress along the area of contact.     

PECVDTM Coatings in Industrial Applications

The adoption of GPI's Linear PECVD^TM process has accelerated over the last year. Several R&D and production reactor modules were installed at major companies and a turnkey in‐line system was completed. Through these installations, customers are confirming the superior deposition rate, uniformity and stability of Linear PECVD^TM compared to reactive sputtering. Today Linear PECVD^TM is being used to deposit oxide and nitride films on a variety of substrates. The films include SiO₂, TiO₂, Al2₂O₃, SiN, ZnO, and SnO and the applications include multi‐layer AR coatings, single layer oxides and nitrides in combination with sputtered films, thin‐film solar coatings, barrier films, anti‐smudge coatings and TCO's. This progress demonstrates that GPI's Linear PECVD^TM technology may soon displace reactive sputtering of oxides and nitrides for large area substrates in architectural glass, flat panel display and flexible web applications.     

Retraining attitudes and stereotypes to affect motivation and cognitive capacity under stereotype threat.

In a series of experiments, a retraining paradigm was used to test the effects of attitudes and stereotypes on individuals' motivation and cognitive capacity in stereotype-threatening contexts. Women trained to have a more positive math attitude exhibited increased math motivation (Study 1). This effect was not observed for men but was magnified among women when negative stereotypes were either primed subtly (Study 2) or indirectly reinforced (Study 3). Although attitudes had no effect on working memory capacity, women retrained to associate their gender with being good at math exhibited increased working memory capacity (Studies 3 and 4), which in turn mediated increased math performance (Study 4) in a stereotype-threatening context. Results suggest that although positive attitudes can motivate stigmatized individuals to engage with threatening domains, stereotypes need to be retrained to give them the cognitive capacity critical for success. Implications for interventions to reduce stereotype threat are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The constructive role of diversity in the global response of coupled neuron systems

We study the effect that the heterogeneity present among the elements of an ensemble of coupled excitable neurons has on the collective response of the system to an external signal. We consider two different interaction scenarios, one in which the neurons are diffusively coupled and another in which the neurons interact via pulse-like signals. We find that the type of interaction between the neurons has a crucial role in determining the response of the system to the external modulation. We develop a mean-field theory based on an order parameter expansion that quantitatively reproduces the numerical results in the case of diffusive coupling.     

Fitting semi‐empirical drying models using a tool based on wavelet neural networks: Modeling a maize drying process

Maize drying is an important process, especially for storage and conservation. For this study, the experimental stage was carried out using a forced convection dryer with air heated at different temperature conditions (306.05–441.85 K) and flow (0.13–0.256 m³/hr), totalizing 15 drying curves. Then the performances of the classic drying kinetics methodology and the approach proposed in this paper, in which the increase in moisture content of the product with time was represented combining exponential models and neural networks based on wavelets, were compared. Good performance was obtained in predictions using the proposed approach. One of the main differentials of the methodology adopted was the obtainment of a model that has a global predictive capacity, within the range of tested operating conditions, which can be used in predicting drying curves for different operating conditions.

Practical applications

The drying process is also one of the most widely used methods for preserving food, and has the advantage of reducing the costs of storage and transport because of the low volume and weight of the end product. During the last years, this topic has attracted a broad industrial interest, resulting in many research studies investigating the drying process. Usually, with regard to the classic approach for modeling of the drying process, the kinetics of drying curves obtained in different operating conditions is affected separately, that is, the parameters are estimated independently, resulting in different regression problems. With the classical approach, in general, it is not possible to obtain a comprehensive prediction model with regards to operating conditions. We have proposed an alternative modeling method. Aiming to obtain a modeling tool with an overall predictive ability, an approach for drying kinetics prediction that combines exponential models and neural networks was proposed. The proposed modeling method was able to predict drying curves for different operating conditions.