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
The semantic foundations for logic programming are usually separated into two different approaches. The operational semantics, which uses SLD-resolution, the proof method that computes answers in logic programming, and the declarative semantics, which sees logic programs as formulas and its semantics as models. Here, we define a new operational semantics called TSLD-resolution, which stands for Typed SLD-resolution, where we include a value wrong, that corresponds to the detection of a type error at run-time. For this we define a new typed unification algorithm. Finally we prove the correctness of TSLD-resolution with respect to a typed declarative semantics.

Abstract
This study critically investigates and evaluates the childhood and adolescent year strategies, and efforts that parent-owners of family businesses incorporate to encourage and prepare children for a successful future succession. The sample consisted of six family businesses in the North East of Scotland: two successfully introduced a second-generation, two a third generation and one a fourth generation, with one still in the founder stage. The findings reveal that the succession planning process was an instantaneous event into generational bridging, where no formal planning process was commenced. Parent-owners influenced and facilitated knowledge transfer and education, leaving control to the child successors with career options. The research has also shown the difficulties in how the child successors of the future may find succession challenging and demanding with contextually complex issues. © 2022: Jahangir Wasim, Fernando Almeida.

Abstract
Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

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
Reinforcement Learning techniques allow learning complex behaviors to deal with a variety of situations in a matter of hours. This complexity is even more prominent in multi-agent continuous 3D environments. This paper compares how the actions taken by two agents independently trained via a self-play approach differ from the ones taken when they are controlled by the same policy. It also explores the emergence of competitive or collaborative behaviors in a natural game setting. By implementing a 3D simulated version of the Dance Dance Revolution, the acquisition of more specific abilities like equilibrium, balance, and dexterity was tested. The approach achieved very good results learning a predefined sequence of buttons (7 arrows correctly pressed in 20M timesteps), revealing a similar learning behavior to human beings (improving with training and performing better in this kind of sequence than in random ones). The self-play approach also produced some interesting effects by developing cooperative behaviors in theoretically competitive scenarios.