Abstract

Abstract
This paper presents a thorough analysis on the temperature dependence of the thermo-optic coefficient, dn/dT, of four bulk annealed pure-silica glass samples (type I-natural quartz: Infrasil 301; type II-quartz crystal powder: Heraeus Homosil; type III-synthetic vitreous silica: Corning 7980 and Suprasil 3001) from room temperature down to 0 K. The three/four term temperature dependent Sellmeier equations and respective coefficients were considered, which results from fitting to the raw data obtained by Leviton et al. The thermo-optic coefficient was extrapolated down to zero Kelvin. We have obtained dn/dT values ranging from 8.16 x 10(-6) up to 8.53 x 10(-6) for the four samples at 293 K and for a wavelength of 1.55 & mu;m. For the Corning 7980 SiO2 glass, the thermo-optic coefficient decreases monotonically, from 8.74 x 10(-6) down to 8.16 x 10(-6), from the visible range up to the third telecommunication window, being almost constant above 1.3 & mu;m. The Ghosh's model was revisited, and it was concluded that the thermal expansion coefficient only accounts for about 2% of the thermo-optic coefficient, and we have obtained an expression for the temperature behavior of the silica excitonic bandgap. Wemple's model was also analyzed where we have also considered the material dispersion in order to determine the coefficients and respective temperature dependences. The limitations of this model were also discussed.

Abstract

Abstract
A sensor based on a balloon-like interferometer and a spring-shaped structure for micro curvature measurement is proposed and experimentally demonstrated. The sensor is composed by singlemode fiber inserted into a capillary tube. The experimental results show micro-curvature sensitivities of -35.04 pm/mu m, -28.07 pm/mu m e -18.7 pm/mu m in the range from 0 to 200 mu m for three resonants dips lambda(1), lambda(2) and lambda(3), respectively. In addition, the sensor has advantages of easy fabrication, low cost, and satisfactory sensitivity, which shows good results of sensing of micro curvature in some applications.

Abstract

Abstract
With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce or even on a volunteer basis. However, there is a lack of systematic and empirical examination of the interplay among the processes and activities combining crowd-machine hybrid interaction. To uncover the enduring aspects characterizing the human-centered AI design space when involving ensembles of crowds and algorithms and their symbiotic relations and requirements, a Computer-Supported Cooperative Work (CSCW) lens strongly rooted in the taxonomic tradition of conceptual scheme development is taken with the aim of aggregating and characterizing some of the main component entities in the burgeoning domain of hybrid crowd-AI centered systems. The goal of this article is thus to propose a theoretically grounded and empirically validated analytical framework for the study of crowd-machine interaction and its environment. Based on a scoping review and several cross-sectional analyses of research studies comprising hybrid forms of human interaction with AI systems and applications at a crowd scale, the available literature was distilled and incorporated into a unifying framework comprised of taxonomic units distributed across integration dimensions that range from the original time and space axes in which every collaborative activity take place to the main attributes that constitute a hybrid intelligence architecture. The upshot is that when turning to the challenges that are inherent in tasks requiring massive participation, novel properties can be obtained for a set of potential scenarios that go beyond the single experience of a human interacting with the technology to comprise a vast set of massive machine-crowd interactions.