Abstract
Energy consumption has been increasing in the last years and thus, energy efficiency is one of the most important topics actually. Besides, the consumption and energy generation forecast help in efficiency optimization. This paper presents the development of a system for forecasting surplus power generation to be used by residential loads connected to smart plugs. In this way, it is intended to collaborate with the use of surplus energy production in electrical devices in a residence instead of sending to batteries or to the grid. This work presents the theoretical basis of the project and the architecture of the developed system. A Machine Learning method applied to photovoltaic generation data in a residence was used to predict surplus energy. © 2023 IEEE.

Abstract
The importance of systems that can extract structured information from textual data becomes increasingly pronounced given the ever-increasing volume of text produced on a daily basis. Having a system that can effectively extract such information in an interoperable manner would be an asset for several domains, be it finance, health, or legal. Recent developments in natural language processing led to the production of powerful language models that can, to some degree, mimic human intelligence. Such effectiveness raises a pertinent question: Can these models be leveraged for the extraction of structured information? In this work, we address this question by evaluating the capabilities of two state-of-the-art language models - GPT-3 and GPT-3.5, commonly known as ChatGPT - in the extraction of narrative entities, namely events, participants, and temporal expressions. This study is conducted on the Text2Story Lusa dataset, a collection of 119 Portuguese news articles whose annotation framework includes a set of entity structures along with several tags and attribute values. We first select the best prompt template through an ablation study over prompt components that provide varying degrees of information on a subset of documents of the dataset. Subsequently, we use the best templates to evaluate the effectiveness of the models on the remaining documents. The results obtained indicate that GPT models are competitive with out-of-the-box baseline systems, presenting an all-in-one alternative for practitioners with limited resources. By studying the strengths and limitations of these models in the context of information extraction, we offer insights that can guide future improvements and avenues to explore in this field.

Abstract

Abstract
Managing multiple knowledge transfers between headquarters and subsidiaries, among subsidiaries, and also within each of these units is crucial for multinational corporations' (MNCs) survival. Therefore, this article aims to uncover the main factors influencing internal knowledge transfers in MNCs-including intra-unit knowledge transfers and transfers between units, namely, conventional, horizontal, and reverse knowledge transfers. To achieve this goal, a systematic literature review (SLR) was conducted to synthesize the content of 85 articles. From a set of 1439 papers, only 85 related to knowledge transfer and knowledge sharing were considered. Based on an inductive thematic approach, eight different research categories and 97 topics were identified. Four different internal knowledge transfers (intra knowledge transfer (IKT), horizontal knowledge transfer (HKT), conventional knowledge transfer (CKT), and reverse knowledge transfer (RKT)) are compared across eight thematic categories and 97 topics. According to the results obtained, the depth of the topics analyzed varies, as does the variety of categories, with RKT being more deeply analyzed than IKT. There is a clear dominance of vertical knowledge transfer (CKT + RHT) over HKT. The exercise of power (e.g., size, knowledge base) still dominates CKT and RKT in most of the studies analyzed, which are traditionally affected by the characteristics of MNCs, HQs and subsidiaries. The debate on HKT is affected by the classical perspectives of power-based relations (e.g., expatriates, size, knowledge base) among subsidiaries. Although important, intra-unit knowledge transfer is greatly influenced by characteristics.

Abstract

Abstract
Biomedical engineering has been targeted as a potential research candidate for machine learning applications, with the purpose of detecting or diagnosing pathologies. However, acquiring relevant, high-quality, and heterogeneous medical datasets is challenging due to privacy and security issues and the effort required to annotate the data. Generative models have recently gained a growing interest in the computer vision field due to their ability to increase dataset size by generating new high-quality samples from the initial set, which can be used as data augmentation of a training dataset. This study aimed to synthesize artificial lung images from corresponding positional and semantic annotations using two generative adversarial networks and databases of real computed tomography scans: the Pix2Pix approach that generates lung images from the lung segmentation maps; and the conditional generative adversarial network (cCGAN) approach that was implemented with additional semantic labels in the generation process. To evaluate the quality of the generated images, two quantitative measures were used: the domain-specific Frechet Inception Distance and Structural Similarity Index. Additionally, an expert assessment was performed to measure the capability to distinguish between real and generated images. The assessment performed shows the high quality of synthesized images, which was confirmed by the expert evaluation. This work represents an innovative application of GAN approaches for medical application taking into consideration the pathological findings in the CT images and the clinical evaluation to assess the realism of these features in the generated images.