Abstract
The synchronization of Public Transportation (PT) systems usually considers a simplified network to optimize the flows of passengers at the principal axes of the network. This work aims to identify the most relevant transfer-connections in a PT network. This goal is pursued with the development of a methodology to identify relevant transfer-connections from entry-only Automatic Fare Collection (AFC) data. The methodology has three main steps: the implementation of the Trip-Chaining-Method (TCM) to estimate the alighting stops of each AFC record, the identification of transfers, and finally, the selection of relevant transfer-connections. The adequacy of the methodology was demonstrated with its implementation to the case study of Porto. This methodology can also be applied to PT systems using entry-exit AFC data, and in that case, the TCM would not be required.

Abstract
Transformer buildings are at the heart of the effective operation of distribution systems, and heating problems of transformers under severe operational conditions are among the main factors affecting the lifetime, efficiency, technical losses, etc., of such important power system assets. It is crucial that the inside temperature of transformer buildings is higher than the outside temperature due to the operation of the transformer and the effect of ambient conditions. This issue may cause several problems such as additional transformer aging, losses, and moisture. The main purpose of this study is to decrease the inside temperature of transformer buildings; in other words, to prevent the inside temperature from being higher than the outside temperature. To realize this, it is recommended to apply a combined heat reduction technique by covering the outer surface with a reflective surface and use a low-emitting material on the inner surface. The relevant results of the practical evidence in this manner are presented in detail at a distribution system in Turkey with different climate and loading conditions in the summertime.

Abstract
Training firefighters using Virtual Reality (VR) technology brings several benefits over traditional training methods including the reduction of costs and risks. The ability of causing the same level of stress as a real situation so that firefighters can learn how to deal with stress was investigated. An experiment aiming to study the influence that additional stimuli (heat, weight, smell and using personal protective equipment-PPE) have on user's stress level while performing a Virtual Environment (VE) designed to train firefighters was developed. Participants' stress and Heart Rate Variability (HRV) were obtained from electrocardiograms recorded during the experiment. The results suggest that wearing the PPE has the largest impact on user's stress level. The results also showed that HRV was able to evidence differences between two phases of the experiment, which suggests that it can be used to monitor users' quantified reaction to VEs.

Abstract
Purpose - Perfume is arguably one of the most challenging experience products offered online. This article explores how the associations between the human senses of olfaction and audition can help consumers recognize the characteristics of the scent in the absence of the real scent. Method - Given the scarcity of contributions regarding online shopping behavior for perfume, an exploratory qualitative approach was implemented. Twenty-seven in-depth interviews were conducted among individuals from three countries with prior experience of purchasing perfume online. Results -The study highlights the associations made by the interviewees between the types of sounds/voice and types of scents and the complementary role of these cues in online shopping of perfume. Practical implications - Both e-sellers and e-buyers can benefit from the findings of this research. In fact, it is likely that e-shoppers decide to either consider or ignore an unknown perfume for purchase based on the scent signals perceived from sounds, which can provide guidance for e-sellers as well.

Abstract
The high asymmetry of international termination rates, where calls are charged with higher values, are fertile ground for the appearance of frauds in Telecom Companies. In this paper, we present three different and complementary solutions for a real problem called Interconnect Bypass Fraud. This problem is one of the most common in the telecommunication domain and can be detected by the occurrence of abnormal behaviours from specific numbers. Our goal is to detect as soon as possible numbers with abnormal behaviours, e.g. bursts of calls, repetitions and mirror behaviours. Based on this assumption, we propose: (i) the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm; (ii) the proposal of a single pass hierarchical heavy hitter algorithm that also contains a forgetting technique; and (iii) the application of the HyperLogLog sketches for each phone number. We used the heavy hitters to detect abnormal behaviours, e.g. burst of calls, repetition and mirror. The hierarchical heavy hitters algorithm is used to detect the numbers that make calls for a huge set of destinations and destination numbers that receives a huge set of calls to provoke a denial of service. Additionally, to detect the cardinality of destination numbers of each origin number we use the HyperLogLog algorithm. The results shows that these three approaches combined complements the techniques used by the telecom company and make the fraud task more difficult.

Abstract
Users tend to search over the Internet to get the most updated news when an event occurs. Search engines should then be capable of effectively retrieving relevant documents for event-related queries. As the previous studies have shown, different retrieval models are needed for different types of events. Therefore, the first step for improving effectiveness is identifying the event-related queries and determining their types. In this paper, we propose a novel model based on deep neural networks to classify event-related queries into four categories: periodic, aperiodic, one-time-only, and non-event. The proposed model combines recurrent neural networks (by feeding two LSTM layers with query frequencies) and visual recognition models (by transforming time-series data from a 1D signal to a 2D image - later passed to a CNN model) for effective query type estimation. Worth noting is that our method uses only the time-series data of query frequencies, without the need to resort to any external sources such as contextual data, which makes it language and domain-independent with regards to the query issued. For evaluation, we build upon the previous datasets on event-related queries to create a new dataset that fits the purpose of our experiments. The obtained results show that our proposed model can achieve an F1-score of 0.87.