Abstract
Organizations find themselves at a pivotal crossroads in an era propelled by the sweeping tide of digital transformation, where the wake of the COVID-19 pandemic has reshaped the global landscape. Within these novel contexts, the imperative to cultivate Learning Organizations (LOs) has emerged as a beacon of adaptability and progress. Creating Learning Organizations Through Digital Transformation weaves the fabric of LOs within the digital tapestry, where minds perpetually expand, and learning begets learning. This journey hinges on the synergy of knowledge and digital prowess, as LOs harness data and digital content with finesse. From immersive learning to artificial intelligence, these technological frontiers reshape learning, spurring change. Unveiling the core concepts, implementations, and global impacts of LOs, this book is a compass for academics, researchers, and practitioners. It deciphers people capacities, digital contents, learning technologies, and evaluation, nurturing the symbiotic relationship between learning and transformation. Creating Learning Organizations Through Digital Transformation is the scholarly guidepost in a swiftly evolving landscape. It beckons to those attuned to academia and those shaping real-world organizations, resonating with the pursuit of knowledge in an era of unceasing change.

Abstract

Abstract
The rising adoption rates and integration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) into the energy grid introduces complex challenges, including the need to balance supply and demand and smooth peak consumptions. Addressing these challenges requires innovative solutions such as Demand Response (DR), Renewable Energy Communities (RECs), and more specifically for EVs, Vehicle-to-Grid (V2G). However, existing V2G approaches often fall short in real-world applicability, adaptability, and user engagement. To bridge this gap, this paper proposes EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management algorithm leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. EnergAIze enables user-centric multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. Additionally, it architects' data protection and ownership through decentralized deployment, where each prosumer can situate an energy management node directly at their own dwelling. The local node not only manages local EVs and other energy assets but also fosters REC wide optimization. EnergAIze is evaluated through a case study using the CityLearn framework. The results show reduction in peak loads, ramping, carbon emissions, and electricity costs at the REC level while optimizing for individual prosumers objectives.

Abstract
Keratitis is an inflammatory corneal condition responsible for 10% of visual impairment in low- and middleincome countries (LMICs), with bacteria, fungi, or amoeba as the most common infection etiologies. While an accurate and timely diagnosis is crucial for the selected treatment and the patients' sight outcomes, due to the high cost and limited availability of laboratory diagnostics in LMICs, diagnosis is often made by clinical observation alone, despite its lower accuracy. In this study, we investigate and compare different deep learning approaches to diagnose the source of infection: 1) three separate binary models for infection type predictions; 2) a multitask model with a shared backbone and three parallel classification layers (Multitask V1); and, 3) a multitask model with a shared backbone and a multi-head classification layer (Multitask V2). We used a private Brazilian cornea dataset to conduct the empirical evaluation. We achieved the best results with Multitask V2, with an area under the receiver operating characteristic curve (AUROC) confidence intervals of 0.7413-0.7740 (bacteria), 0.83950.8725 (fungi), and 0.9448-0.9616 (amoeba). A statistical analysis of the impact of patient features on models' performance revealed that sex significantly affects amoeba infection prediction, and age seems to affect fungi and bacteria predictions.

Abstract
Effective navigation in mobile robotics relies on precise environmental mapping, including the detection of complex objects as geometric primitives. This work introduces a deep learning model that determines the pose, type, and dimensions of 2D primitives using a mobile robot equipped with a noisy LiDAR sensor. Simulated experiments conducted in Webots involved randomly placed primitives, with the robot capturing point clouds which were used to progressively build a map of the environment. Two mapping techniques were considered, a deterministic and probabilistic (Bayesian) mapping, and different levels of noise for the LiDAR were compared. The maps were used as input to a YOLOv5 network that detected the position and type of the primitives. A cropped image of each primitive was then fed to a Convolutional Neural Network (CNN) that determined the dimensions and orientation of a given primitive. Results show that the primitive classification achieved an accuracy of 95% in low noise, dropping to 85% under higher noise conditions, while the prediction of the shapes' dimensions had error rates from 5% to 12%, as the noise increased. The probabilistic mapping approach improved accuracy by 10-15% compared to deterministic methods, showcasing robustness to noise levels up to 0.1. Therefore, these findings highlight the effectiveness of probabilistic mapping in enhancing detection accuracy for mobile robot perception in noisy environments.

Abstract
This paper focuses on the importance of Business Intelligence (BI) tools in the business context and the urgent need for more effective implementation of time series forecasting models in these resources. It shows the utility and applicability of Sage Enterprise Intelligence (SEI), an integrated BI tool in Enterprise Resource Planning (ERP) Sage, by illustrating how it enhances data analysis and decision-making processes. Additionally, a study will show the application of time series forecasting models: Seasonal AutoRegressive Integrated Moving Average (SARIMA) and additive Holt-Winters to the sales value of a fuel sector company. The research was conducted through a case study in which sales data were collected from 2016 to 2023. The results indicate that neither of the two models exceeded the sales figures reflecting the company's market position. In this case study, both models performed well, with the residuals verifying the assumptions. However, the additive Holt-Winters model had lower errors, which is why it was selected for the final step: forecasting 12 months.