Abstract
Electronic fare payment systems have gained much popularity around the world. These systems adopt a convenient and almost instantaneous payment process for public transport while also gathering data regarding onboard transactions in public transport. Much information about public transport passengers can be extracted, such as travel patterns, activities performed, and travel behavior. Despite the continuous growth of studies regarding these systems, there is still a lack of research to understand occasional passengers’ movement, such as tourists. This work presents the state of the art in these areas and presents a proposal to explore AFC data to understand the mobility profiles of tourists. This manuscript represents an advance in the literature and opens doors to the definition of policies to promote less visited places and mobility services adapted to tourists’ needs, resulting in a positive impact on the city’s economy and the overall enjoyment of the city for tourists. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Sustainable mobility has become one of the most pressing issues in modern society. The need to raise awareness of climate change, combined with the overcrowding of metropolitan and urban areas, has produced a situation that requires an urgent solution. Some earlier approaches dealt primarily with transport-related issues, while some conceptual models attempted to increase the appeal of public transport by linking the services provided by public transport operators to a variety of city services. A practical and empirical answer, on the other hand, has not yet been given. This research addrebes these issues by taking a holistic approach and presenting a personalized recommendation system based on users' everyday activities as well as their mobility profiles. The crossing of both sources of information allows for a more user-centric experience, ensuring that the offers presented are adapted to the tastes of customers. The potential of such a system is proven using data from Porto, Portugal. Two types of data sources were used to obtain more accurate results: data from the automated fare collection system of the Porto Metropolitan Area, Portugal, and data from city services taken from Google Places. The fundamental idea behind tackling this problem is to encourage people to use public transport by providing them with incentives such as discounts, promotions and service offers to encourage them to use cleaner and more efficient modes of transport. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
Automatic Root Cause Analysis solutions aid analysts in finding problems' root causes by using automatic data analysis. When trying to locate the root cause of a problem in a manufacturing process, an issue-denominated overlap can occur. Overlap can impede automated diagnosis using algorithms, as the data make it impossible to discern the influence of each machine on the quality of products. This paper proposes a new measure of overlap based on an information theory concept called Positive Mutual Information. This new measure allows for a more detailed analysis. A new approach is developed for automatically finding the root causes of problems when overlap occurs. A visualization that depicts overlapped locations is also proposed to ease practitioners' analysis. The proposed solution is validated in simulated and real case-study data. Compared to previous solutions, the proposed approach improves the capacity to pinpoint a problem's root causes.

Abstract
PurposeThe paper aims to define a dashboard of indicators to assess the quality performance of higher education institutions (HEI). The instrument is termed SMART-QUAL.Design/methodology/approachTwo sources were used in order to explore potential indicators. In the first step, information disclosed in official websites or institutional documentation of 36 selected HEIs was analyzed. This first step also included in depth structured high managers' interviews. A total of 223 indicators emerged. In a second step, recent specialized literature was revised searching for indicators, capturing additional 302 indicators.FindingsEach one of the 525 total indicators was classified according to some attributes and distributed into 94 intermediate groups. These groups feed a debugging, prioritization and selection process, which ended up in the SMART-QUAL instrument: a set of 56 key performance indicators, which are grouped in 15 standards, and, in turn, classified into the 3 HEI missions. A basic model and an extended model are also proposed.Originality/valueThe paper provides a useful measure of quality performance of HEIs, showing a holistic view to monitor HEI quality from three fundamental missions. This instrument might assist HEI managers for both assessing and benchmarking purposes. The paper ends with recommendations for university managers and public administration authorities.

Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

Abstract
Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.