Abstract
This work performs an in-depth study of the impact of distance functions on K-Nearest Neighbours imputation of heterogeneous datasets. Missing data is generated at several percentages, on a large benchmark of 150 datasets (50 continuous, 50 categorical and 50 heterogeneous datasets) and data imputation is performed using different distance functions (HEOM, HEOM-R, HVDM, HVDM-R, HVDM-S, MDE and SIMDIST) and k values (1, 3, 5 and 7). The impact of distance functions on kNN imputation is then evaluated in terms of classification performance, through the analysis of a classifier learned from the imputed data, and in terms of imputation quality, where the quality of the reconstruction of the original values is assessed. By analysing the properties of heterogeneous distance functions over continuous and categorical datasets individually, we then study their behaviour over heterogeneous data. We discuss whether datasets with different natures may benefit from different distance functions and to what extent the component of a distance function that deals with missing values influences such choice. Our experiments show that missing data has a significant impact on distance computation and the obtained results provide guidelines on how to choose appropriate distance functions depending on data characteristics (continuous, categorical or heterogeneous datasets) and the objective of the study (classification or imputation tasks).

Abstract
Problem definition: Online retailers are on a consistent drive to increase on-time delivery and reduce customer lead time. However, in reality, an increasing share of consumers places orders early. Academic/practical relevance: Such advance demand information can be deployed strategically to reduce costs and improve the customer service experience. This requires inventory and allocation policies that make optimal use of this information and that induce consumers to place their orders early. An increasing number of online retailers not only offer customers a choice of lead time but also, actively back-order missing items from a consumer basket. Methodology: We develop new allocation policies that commit to a customer order upon arrival of the order rather than at the moment the order is due. We provide analytical results for the performance of these allocation policies and evaluate their behavior with real data from a large food retailer. Results: Our policy leads to a higher fill rate at the expense of a slight increase in average delay. The analysis based on real-life data suggests a sizeable impact that should impact current best practices in online retail. Managerial implications: With the changing landscape in online retail, customers increasingly place baskets of orders that they would like to receive at a planned and confirmed moment in time. Especially in grocery, this has grown fast. This fundamentally changes the strategic management of inventory. We demonstrate that online retailers should commit early to customer orders to enhance the customer service experience and eventually, to also create opportunities for reducing the cost of operations. Superscript/Subscript Available

Abstract
A major effort is put into the production of green energy as a countermeasure to climatic changes and sustainability. Thus, the energy industry is currently betting on offshore wind energy, using wind turbines with fixed and floating platforms. This technology can benefit greatly from interventive autonomous underwater vehicles (AUVs) to assist in the maintenance and control of underwater structures. A wireless charger system can extend the time the AUV remains underwater, by allowing it to charge its batteries through a docking station. The present work details the development process of a housing component for a wireless charging system to be implemented in an AUV, addressed as wireless charger housing (WCH), from the concept stage to the final physical verification and operation stage. The wireless charger system prepared in this research aims to improve the longevity of the vehicle mission, without having to return to the surface, by enabling battery charging at a docking station. This product was designed following a design for excellence (DfX) and modular design philosophy, implementing visual scorecards to measure the success of certain design aspects. For an adequate choice of materials, the Ashby method was implemented. The structural performance of the prototypes was validated via a linear static finite element analysis (FEA). These prototypes were further physically verified in a hyperbaric chamber. Results showed that the application of FEA, together with well-defined design goals, enable the WCH optimisation while ensuring up to 75% power efficiency. This methodology produced a system capable of transmitting energy for underwater robotic applications.

Abstract
Nowadays, social networks are one of the biggest ways of sharing real time information. These networks, have several groups focused on sharing information about road incidents and other traffic events. The work here presented aims the creation of an AI model capable of identifying publications related to traffic events in a specific road, based on publications shared on social networks. A predictive model was obtained by training a deep learning model for the detection of publications related with road incidents with an average accuracy of 95%. The model deployed as a service is already fully functional and is operating in 24/7 while awaits a final integration with the road management system of a company where it will be used to support the Control Center team in the decision making. © 2022 Elsevier B.V.. All rights reserved.

Abstract

Abstract
Population aging is a global reality, which can be characterized by morphological, psychological and functional changes. With the limitations resulting from the aging state, changes can occur in the performance of activities of daily living, such as decreased muscle strength, reduced bone mass, loss of flexibility and decreased capacity of the sensory system. Physiotherapy actively assists in the health promotion and prevention process, reducing the effects of the aging and improving the life quality. This paper has the objective of develop a movement monitoring device to assist in the rehabilitation of elderly people. The accelerometers or motion sensors, store data for long periods of time, providing information about the movement activities of the subjects over a desired period. These sensors are a useful tool in the assessment of physical activity. Through the data of accelerometer, it is possible to evaluate all type of physical activities. This is a pilot study composed by three steps: development, validation and application of the instrument. For the application and validation of this study, it was used a protocol of exercises, in order to capture the variations of the X, Y and Z axes of the accelerometers in each exercise. In the study, it was possible to identify the movements during the performance of the proposed protocol. Through he accelerometers position and the varying of his axes, it was possible to classify the exercises according to the number of repetitions and the time spent performing the exercise. In addition, it was possible to monitor remotely in real time by a physiotherapist. It is concluded that this device can assist in the management of elderly patients, assisting the health professional during the rehabilitation process.