Abstract
The quality of mammography images is essential for the diagnosis of breast cancer and image imputation has become a popular technique to overcome noise, artifacts, and missing data to aid in the diagnosis of diseases. In this paper, we assess the performance of six imputation methodologies for the reconstruction of missing pixels in different morphologies in mammography images. The images included in this study are collected from four public datasets (CBIS-DDSM, Mini-MIAS, INbreast, and CSAW) and the imputation results are evaluated through the mean absolute error (MAE) and structural similarity index measure (SSIM). This study goes beyond the traditional evaluation of imputation algorithms, analyzing imputation quality, morphology preservation and classification performance. The effects of imputation on the morphology of cancer lesions are of utmost importance since it lays the foundation for physicians to interpret and analyze the imputation results. The results show that DIP is the most promising methodology for higher missing pixel rates, morphology preservation, and classifying malignant and benign images.

Abstract
Although the literature on crisis communication is quite vast, business communication related to global crises (e.g., natural disasters) is largely unexplored. This chapter aims to fill this gap and shed light on brand communication strategies during a pandemic. A netnographic study was carried out with the purpose of identifying brand positioning and communication strategies during the COVID-19 pandemic outbreak and of understanding the engagement of brands' followers during that period. The study included four brands of large Brazilian companies and comprised the analysis of brands' feed on Instagram during the first five weeks of the outbreak in Brazil. Findings enable to identify two distinct profiles: unprepared brands and leading brands. The chapter provides valuable clues for both managers and researchers dealing with crisis communication. © 2023, IGI Global. All rights reserved.

Abstract
On rugged slopes overlooking the Douro River we find the Alto Douro Wine Region in Portugal, populated by plantations in schist lands of difficult access and mostly manual work. The combined features of this region are a source of motivation to explore remote sensing techniques associated with artificial intelligence. In this paper, a preliminary approach for terrace vineyards detection is presented. This is a key-enabling task towards the achievement of important goals such as multi-temporal crop evaluation and cultures characterization. The proposed methodology consists in the application of a deep learning model (U-net) to detect the terrace vineyards using satellite images dataset acquired with Google Earth Pro. The proposed methodology showed very promising detection capabilities.

Abstract
This paper aims at contributing to personalize anesthetic drug administration during surgery. This study devel-ops an online robust model to predict the maintenance dose of atracurium necessary for the resulting effect, i.e. neuromuscular blockade, to attain a target profile. The model is based on the patient's neuromuscular blockade (NMB) response to the initial bolus only, overcoming the need for information on the patient's weight, age, height and Lean Body Mass usually associated to pharmacokinetic and pharmacodynamic models. To achieve this, a statistical analysis of the response of the patient to the initial bolus is carried out and a set of variables is established as predictors of the maintenance dose. The prediction is accomplished using Classification and Regression Trees, CART, which is a supervised learning method. Simulated data from a stochastic model for the NMB induced by atracurium is used as training set. All the 5000 doses predicted by the model lead to NMB level between 5% and 10%, which supports the proposed predictive model since it is clinically required that the steady state NMB level lies between this two values. The methodology is applied both to simulated and to clinical data sets and is found appropriate for online dose prediction.

Abstract
Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter.

Abstract