Abstract
Anomaly detection has been an active research area for decades, with high application potential. Recent work has explored deep learning approaches to the detection of abnormal behaviour and abandoned objects in outdoor video surveillance scenarios. The extension of this recent work to in-vehicle monitoring using solely visual data represents a relevant research opportunity that has been overlooked in the accessible literature. With the increasing importance of public and shared transportation for urban mobility, it becomes imperative to provide autonomous intelligent systems capable of detecting abnormal behaviour that threatens passenger safety. To investigate the applicability of current works to this scenario, a recapitulation of relevant state-of-the-art techniques and resources is presented, including available datasets for their training and benchmarking. The lack of public datasets dedicated to in-vehicle monitoring is addressed alongside other issues not considered in previous works, such as moving backgrounds and frequent illumination changes. Despite its relevance, similar surveys and reviews have disregarded this scenario and its specificities. This work initiates an important discussion on application-oriented issues, proposing solutions to be followed in future works, particularly synthetic data augmentation to achieve representative instances with the low amount of available sequences.

Abstract
The interrogation of optic fiber sensors usually relies in complex and costly equipment with low portability due to their size such as Optical Spectrum Analyzers (OSA) or high-resolution spectrometers. Because of this, micro spectrometer devices, such as Micro-Electromechanical Systems (MEMS) with Fabry-Pérot tunable filters, are emerging as simpler and compact alternatives capable of being used to acquire spectral information in a wide wavelength band. In this work it is described the development of an interrogation system capable of infrared spectroscopy using a MEMS Fabry-Pérot Interferometer (MEMS-FPI) with a spectral response in the 1350nm to 1650nm range. Its performance is tested with the interrogation of long period fiber gratings both as a refractive index sensor and as a temperature sensor. Deconvolution techniques such as Wiener filtering are used to reduce the impact of the tunable filter's impulse response in the measured signal. Results are comparable to those obtained using a typical OSA which shows the system's potential as a cheaper and more transportable alternative. © Published under licence by IOP Publishing Ltd.

Abstract
This paper proposes a real time Walrasian based market design for local electricity trading, considering the roles of the different players, the settlement procedures, and the necessary balance responsibilities with the wholesale market under collective self-consumption rules. A Walrasian mechanism based on consecutive auctions for very short delivery periods is proposed, where the auctioneer defines a price for each of these delivery periods to which peers react by generating and consuming accordingly and informing if they trade with the auctioneer or with their retailer or aggregator. This market has no energy purchase contracts, and energy is billed based on each peer's generation or consumption for each delivery period with the price defined by the auctioneer. © 2022 IEEE.

Abstract

Abstract
Electric mobility with all of its advantages has gained momentum during the last decade with increasing utilization by many sectors of the society. However, professional fleets' operators (e.g. taxis) are still conservative in switching to this new mobility paradigm in many parts of the world. In this paper, we empirically evaluate whether electric vehicles together with conventional charging stations could replace current internal combustion engine vehicles for taxi mobility and study the implications for the taxi business. To perform this study, we resort to a detailed mobility dataset of a taxi fleet collected in a mid-sized European city. The results provide a first indication that such transition towards electric mobility is feasible for the vast majority of the vehicles of the fleet and that simple AC chargers at taxi stands could suffice to provide the necessary range autonomy.

Abstract
This research establishes the relationship between the digital channels that organizations use to communicate with their audience and the stages of the consumer buying decision process in online stores. Researchers have not treated this relationship in much detail and little-known empirical research has focused on exploring relationships between the two subjects. Establishing this relationship is of crucial importance for organizations and consumers, as it ensures organizations use the digital channels that consumers want. A literature review of digital channels and consumer behavior models was performed, which allowed us to define which are the digital channels and to identify the different models of consumer behavior appropriate for the digital age. A quantitative methodology was used, supported on a questionnaire that allowed us to find out which digital channels are the most appropriate for each stage of the buying decision process. The results show that consumers use more than one digital channel at each stage of the buying decision process and for each stage, a set of digital channels is identifiable that is most preferred. In light of the above, those who are responsible for defining the digital marketing strategy know what types of content they should produce for each digital channel, allowing them to guarantee efficiency in the use of resources while ensuring that consumers get what they want.