Big data and sentiment analysis to highlight decision behaviours: a case study for student population

ABSTRACT

Starting from the assumption that the factors orienting University choice are heterogeneous and multidimensional, the study explores student’s motivations in higher education. To this aim, a big data analysis has been performed through ‘TalkWalker’, a tool based on the algorithms developed in the context of Social Data Intelligence, which allows understanding the sentiment of a group of people regarding a specific theme. The data have been extracted by drawing on published posts from anywhere in the world over a 12-month period from many online sources. According to the findings, the main variable capable of influencing the choice of University is training offer, followed by physical structure, work opportunities, prestige, affordability, communication, organisation, environmental sustainability. The study establishes an innovative research agenda for further studies by proposing the elaboration of a systems and process-based view for higher education. However, it presents the limitation of the superficial investigation, determined by the analysis of a large amount of data. Therefore, for future research, it might be appropriate to apply a different technique to realise a comparison and to check whether the size of the considered sample and the depth of the analysis technique can affect the results and the consequent considerations.     

A genetic approach for adaptive multiagent control in heterarchical manufacturing systems

In this paper, we apply genetic algorithms to adapt the decision strategies of autonomous controllers in a part-driven heterarchical manufacturing system. The control agents use pre-assigned decision rules only for a limited amount of time, and obey a rule replacement policy propagating the most successful rules to the subsequent populations of concurrently operating agents. The twofold objective of this approach is to automatically optimize the performance of the control system during the steady-state unperturbed conditions of the manufacturing floor, and to improve the reactions of the agents to unforeseen disturbances (e.g., failures, shortages of materials) by adapting their decision strategies. Results on a detailed discrete event model of a multiagent heterarchical manufacturing system confirm the effectiveness of the approach.     

Thiele’s continued fractions in digital implementation of noninteger differintegrators

A rational approximation is the preliminary step of all the indirect methods for implementing digital fractional differintegrators s ^??, with ${\nu \in \mathbb{R}, 0<|\nu| <1 }$ , and where ${s \in \mathbb{C}}$ . This paper employs the convergents of two Thiele??s continued fractions as rational approximations of s ^??. In a second step, it uses known s-to-z transformation rules to obtain a rational, stable, and minimum-phase z-transfer function, with zeros interlacing poles. The paper concludes with a comparative analysis of the quality of the proposed approximations in dependence of the used s-to-z transformations and of the sampling period.     

High-resolution geoelectrical tomographies in the study of Giarrossa landslide (southern Italy)

Ohne Zusammenfassung     

Synthesis of excitation controllers for synchronous generators setting specified stability margins in the under-excited operating region

A design procedure of field controllers for synchronous machines is presented. A prescribed enlargement of the stable operating domain in the P-Q plane and an aperiodic type of instability for the system under steady-state instability conditions are assumed as performance specifications. The effectiveness of the proposed design procedure is validated by digital simulations on a single machine infinite bus-bar system.     

Bridge life-cycle performance and cost: analysis,prediction, optimisation and decision-making

The development of a generalised framework for assessing bridge life-cycle performance and cost, with emphasis on analysis, prediction, optimisation and decision-making under uncertainty, is briefly addressed. The central issue underlying the importance of the life-cycle approach to bridge engineering is the need for a rational basis for making informed decisions regarding design, construction, inspection, monitoring, maintenance, repair, rehabilitation, replacement and management of bridges under uncertainty which is carried out by using multi-objective optimisation procedures that balance conflicting criteria such as performance and cost. A number of significant developments are summarised, including time-variant reliability, risk, resilience, and sustainability of bridges, bridge transportation networks and interdependent infrastructure systems. Furthermore, the effects of climate change on the probabilistic life-cycle performance assessment of highway bridges are addressed. Moreover, integration of SHM and updating in bridge management and probabilistic life-cycle optimisation considering multi-attribute utility and risk attitudes are presented.     

Numerical simulation of atmospheric pollutant dispersion in an urban street canyon: Comparison between RANS and LES

Prediction accuracy of pollutant dispersion within an urban street canyon of width to height ratio W/H=1 is examined using two steady-state Reynolds-averaged Navier–Stokes (RANS) turbulence closure models, the standard k–ε and Reynolds Stress Model (RSM), and Large Eddy Simulation (LES) coupled with the advection–diffusion method for species transport. The numerical results, which include the statistical properties of pollutant dispersion, e.g. mean concentration distributions, time-evolution and three-dimensional spreads of the pollutant, are then compared to wind-tunnel (WT) measurements. The accuracy and computational cost of both numerical approaches are evaluated. The time-evolution of the pollutant concentration (for LES only) and the mean (time-averaged) values are presented. It is observed that amongst the two RANS models, RSM performed better than standard k–ε except at the centerline of the canyon walls. However, LES, although computationally more expensive, did better than RANS in predicting the concentration distribution because it was able to capture the unsteady and intermittent fluctuations of the flow field, and hence resolve the transient mixing process within the street canyon.     

Synchrotron‐based microprobe investigation of impurities in raw quartz‐bearing and carbon‐bearing feedstock materials for photovoltaic applications

Using synchrotron‐based analytical microprobe techniques, we determine micrometer‐scale elemental composition, spatial distribution, and oxidation state of impurities in raw feedstock materials used in the photovoltaic industry. Investigated Si‐bearing compounds are pegmatitic quartz, hydrothermal quartz, and quartzite. Micrometer‐scale clusters containing Fe, Ti, and/or Ca are frequently observed at structural defects in oxidized states and in bulk concentrations equivalent to inductively coupled plasma mass spectroscopy measurements. Investigated C‐bearing compounds are pine wood, pine charcoal, and eucalyptus charcoal. Clustered metals are observed only in the charcoal samples. Impurity clustering implies that industrial processing could be adapted to take advantage of this “natural gettering” phenomenon, expanding the usable range of raw feedstock materials to dirtier, cheaper, and more abundant ones, currently underexploited for solar‐grade silicon production. Copyright © 2011 John Wiley & Sons, Ltd.