Abstract
We propose a new approach for the lastmile delivery problem where, besides serving as collecting points of orders for customers, parcel lockers are also used as transshipment nodes in a 2-echelon delivery system. Moreover, we consider that a customer (occasional courier) visiting a locker may accept a compensation to make a delivery to another customer on their regular traveling path. The proposed shared use of the locker facilities - by customers that prefer to self-pick up their orders, and also as a transfer deposit for customers that prefer home delivery - will contribute to better usage of an already available storage capacity. Furthermore, the use of occasional couriers (OCs) brings an extra layer of flexibility to the delivery process and may positively contribute to achieving some environmental goals: although non-consolidation of deliveries may, at first sight, seem negative, by only considering OCs that would go to the locker independently of making or not a delivery on their way home, and their selection being constrained by a maximum detour, the carbon footprint can be potentially reduced when compared to that of dedicated vehicles. We present a mixed-integer linear programming formulation for the problem that integrates three delivery options - depot to locker, depot to locker followed by final delivery by a professional fleet, and depot to locker followed by final delivery by an OC. Furthermore, to assess the impact of OCs' no show on the delivery process, we extend the formulation to re-schedule the delivery of previous undelivered parcels, and analyze the impact of different no-show rates. Thorough computational experiments show that the use of OCs has a positive impact both on the delivery cost and on the total distance traveled by the dedicated fleets. Experiments also show that the negative impact of no-shows may be reduced by using lockers with higher capacities.

Abstract
Despite the wide variety of literature on the impact of the COVID-19 pandemic in the service industry, there is still a lack of an integrated systematized view of these multiple impacts. This study contributes to service research by identifying a variety of academic and managerial perspectives about the influence of COVID-19. We pay attention to the service industry, but with an especial focus on the tourism and hospitality industries, which have been more severely affected. This paper presents two multi-approach studies blending a systematic literature review (SLR) and a focus group methodology. Hence, it integrates and synthesizes the main results of the two studies considered to assist researchers and practitioners. It offers a complete overview of the state of the art and identifies three key service trends that have been accelerated by COVID-19: (1) the increasingly digital and autonomous customer; (2) the growing potential of data-driven services versus privacy concerns, and (3) the evolution from firm-centric to customer-centric and networked business models. Finally, this study provides relevant theoretical implications where we suggest relevant theories, constructs, and methodologies for future research to advance the current knowledge, and useful guidelines for business managers to better understand how to respond to market changes.

Abstract
The advantages associated with mobile ticketing solutions are undeniable; however, most of these solutions are designed for the local population without taking into account the specific needs of tourists. Therefore, this study fills an important research gap in the literature by assessing the adoption drivers of mobile ticketing services by tourists and pointing out possible directions to the design of such services. The proposed model includes constructs of the technology acceptance model (TAM), diffusion of innovations (DOI) theory, and others widely disseminated in the literature on mobile payments, such as mobility. The model was empirically tested through an online survey, and Structural Equation Modeling (SEM) was applied to analyze the data. The results show that the intention of tourists to use mobile ticketing services is positively affected by the perceived usefulness and mobility. The survey findings also describe additional services that respondents value in a mobile ticket service for tourists, both in normal and in pandemic contexts, useful to shape future mobile ticketing solutions for tourists.

Abstract
Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic's spatial and temporal correlations. Besides, they usually require data from adjacent roads to achieve accurate predictions. Hence, this article presents a one-dimensional (1D) convolution neural network (CNN) and long short-term memory (LSTM)-based traffic state prediction model, which was evaluated using the Zenodo and PeMS datasets. The model used three stacked layers of 1D CNN, and LSTM with a logarithmic hyperbolic cosine loss function. The 1D CNN layers extract the features from the data, and the goodness of the LSTM is used to remember the past events to leverage them for the learnt features for traffic state prediction. A comparative performance analysis of the proposed model against support vector regression, standard LSTM, gated recurrent units (GRUs), and CNN and GRU-based models under the same conditions is also presented. The results demonstrate very encouraging performance of the proposed model, improving the mean absolute error, root mean squared error, mean percentage absolute error, and coefficient of determination scores by a mean of 16.97%, 52.1%, 54.15%, and 7.87%, respectively, relative to the baselines under comparison.

Abstract
To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.

Abstract
Despite being one of the most cost-effective methods, email marketing remains challenging due to the low rate of opened emails and the high percentage of unsubscribed campaigns. Since the sender and the subject line are the only information that the recipient sees at first when receiving an email, the decision to open an email critically depends on these two factors, which should stand out and catch the recipient's attention. Therefore, the motivation behind this study is to support email campaign editors in choosing a subject line based on its potential quality. We propose and compare several models to measure the quality of a subject line, considering its potential to promote the email opening. The subject lines' structure and content are explored together with different machine learning techniques (Random Forest, Decision Trees, Neural Networks, Naive Bayes, Support Vector Machines, and Gradient Boosting). To validate the proposed model, a data set of 140,000 emails' subject lines was used. The results revealed that the models proposed are very promising to support the definition of the email marketing subject lines and show that the combination of data regarding the structure, the content of the subject lines, and senders characteristics leads to more accurate classifications of the potential of the subject line.