Abstract
A monolithic lab-on-a-chip fabricated by femtosecond laser micromachining capable of label-free biosensing is reported. The device is entirely made of fused silica, and consists of a microdisk resonator integrated inside a microfluidic channel. Whispering gallery modes are excited by the evanescent field of a circular suspended waveguide, also incorporated within the channel. Thermal annealing is performed to decrease the surface roughness of the microstructures to a nanometric scale, thereby reducing intrinsic losses and maximizing the Q-factor. Further, thermally-induced morphing is used to position, with submicrometric precision, the suspended waveguide tangent to the microresonator to enhance the spatial overlap between the evanescent field of both optical modes. With this fabrication method and geometry, the alignment between the waveguide and the resonator is robust and guaranteed at all instances. A maximum sensitivity of 121.5 nm/RIU was obtained at a refractive index of 1.363, whereas near the refractive index range of water-based solutions the sensitivity is 40 nm/RIU. A high Q-factor of 10(5) is kept throughout the entire measurement range.

Abstract
In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

Abstract

Abstract
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Abstract
We compare two interval time series (ITS) by testing whether their underlying distributions are significantly different or not. To perform hypothesis testing, we make use of the discrete wavelet transform (DWT) which decomposes a time series into a set of coefficients over a number of frequency bands or scales. We obtain the DWT of the radius and centre of each of the two ITS at different scales, and perform randomisation tests. In order to use a randomisation test, the observations must be uncorrelated; this condition is more or less satisfied since at each scale, the DWT coefficients are approximately uncorrelated with each other. Our proposed test statistic is the ratio of the determinants of the covariance matrix of radius and centre DWTs of the two ITS, at each scale. This test statistic ensures that the variability between the upper and lower bounds of each ITS is encompassed. Simulation studies conducted to evaluate the performance of the test show reasonably good estimates of size and power under most conditions, and applications to real interval time series reveal the practical usefulness of this test.

Abstract
Keeping students engaged while learning programming is becoming more and more imperative. Of the several proposed techniques, gamification is presumably the most widely studied and has already proven as an effective means to engage students. However, there is a complete lack of public and customizable solutions to gamified programming education that can be reused with personalized rules and learning material. FGPE Gamification Service (FGPE GS) is an open-source GraphQL service that transforms a package containing the gamification layer – adhering to a dedicated open-source language, GEdIL – into a game. The game provides students with a gamified experience leveraging on the automatically-assessable activities referenced by the challenges. This paper presents FGPE GS, its architecture, data model, and validation.