Abstract
This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multi-session, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the top-performing academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

Abstract
The paper deals with a relevant scheduling problem associated with moulding injection machines. A footwear company, equipped with advanced automation machinery, faces true difficulties in planning the injection equipment production. It is crucial to respect delivery times without disruptions. There are many conditions associated with footwear and technological issues to consider, such as the weekly demand for different models and sizes, which is major to satisfy them on time. The moulds for each size of a model and distinct available machines, with varying quantities of positions for the moulds, are other concerned matters. Changeover times, which occur when changing moulds, are critical. Stocks are also considered. The time horizon attains tens of weeks. We developed an integer optimisation model with the objectives of minimising both changeovers and stocks. That initial model underwent a few simplifications, acceptable from a strategic and technological point of view, due to the impossibility of reaching admissible solutions. The new version can solve the real dimension problems optimally, those that matter. The paper describes one case, and the solution obtained. The new approach followed, and the solutions obtained, are essential for the company, given the planning difficulties; moreover, the method may also be relevant for any footwear industry facing similar combinatorial optimisation problems. © IEOM Society International.

Abstract
This study analyzed the potential of proximal optical sensing as an effective approach for early disease detection. A compact, modular sensing system, combining direct UV–Vis spectroscopy with optical fibers, supported by a principal component analysis (PCA), was applied to evaluate the modifications promoted by the bacteria Xanthomonas euvesicatoria in tomato leaves (cv. cherry). Plant infection was achieved by spraying a bacterial suspension (108 CFU mL-1) until run-off occurred, and a similar approach was followed for the control group, where only water was applied. A total of 270 spectral measurements were performed on leaves, on five different time instances, including pre- and post-inoculation measurements. PCA was then applied to the acquired data from both healthy and inoculated leaves, which allowed their distinction and differentiation, three days after inoculation, when unhealthy plants were still asymptomatic.

Abstract
In the last two years, the world has gone through an unprecedented change in the most diverse dimensions (social, economic, and even political), leading that society had to adapt very quickly to the contingencies imposed by COVID-19. All organizations (independent of their area of activity) had to adjust their processes to respond, efficiently and effectively, to these constraints. In this context, companies with concerns in internationalization (those that are already internationalized and those in an internationalization process) have had to resort to technologies to support the change in their modus operandi. The digital transformation (until now had an essential role in the transformation of organizations, but which was in a relatively slow implementation process) started to perform, in an accelerated way, the base of work for the heads of the organizations to be able to respond to these challenges. In this context, the transformation of the business model, supported by digital technology, has been documented as one of the strategies used to respond to disruptive environmental changes, particularly technologies that help companies identify new business practices. This study aims to find evidence of the importance of integrating and influencing technological innovations in the practice of international business strategy before and during COVID-19 pandemic. The results show the influence of the digitalization on the business strategies.

Abstract
Recently, research concerning the navigation of Autonomous Surface Vehicles (ASVs) has been increasing. However, a big scale implementation of these vessels is still held back by a plethora of challenges such as multi-object tracking. This article presents the development of a tracking model through transfer learning techniques, based on referenced object trackers for urban scenarios. The work consisted in training a neural network through deep learning techniques, including data association and comparison of three different optimisers, Adadelta, Adam and SGD, determining the best hyper-parameters to maximise the training efficiency. The developed model achieved decent performance at tracking large vessels in the ocean, being successful even in harsh lighting conditions and lack of image focus.

Abstract
Compared to rodents, sheep offer several attractive features as an experimental model for testing different medical and surgical interventions related to pathological gait caused by neurological diseases and injuries. To use sheep for development of novel treatment strategies in the field of neuroscience, it is key to establish the relevant kinematic features of locomotion in this species. To use sheep for development of novel treatment strategies in the field of neuroscience, it is crucial to understand fundamental baseline characteristics of locomotion in this species. Despite their relevance for medical research, little is known about the locomotion in the ovine model, and next to nothing about the three-dimensional (3D) kinematics of the hindlimb. This study is the first to perform and compare two-dimensional (2D) and 3D hindlimb kinematics of the sagittal motion during treadmill walking in the ovine model. Our results show that the most significant differences took place throughout the swing phase of the gait cycle were for the distal joints, ankle and metatarsophalangeal joint, whereas the hip and knee joints were much less affected. The results provide evidence of the inadequacy of a 2D approach to the computation of joint kinematics in clinically normal sheep during treadmill walking when the interest is centered on the hoof's joints. The findings from the present investigation are likely to be useful for an accurate, quantitative and objective assessment of functionally altered gait and its underlying neuronal mechanisms and biomechanical consequences.