This research focuses on the environmental taxation applicable to the agri-food sector and aligns with the objectives of the Farm to Fork (F2F) Strategy of the European Green Deal context. Indeed, the methodology of the research develops a theoretical analysis through a literature review to assess environmental taxation and documental analysis of Portuguese tax codes. Furthermore, the Portuguese context of the empirical analysis assesses the contribution to the desired sustainability of the agri-food sector. The results show that the existing tax rules applicable to the agri-food sector are scant, with a few existing rules being misaligned with the objectives of the F2F strategy, because their potential impact on its objectives has been mostly negative or null. Most regulations predated the definition of the F2F strategy, seeking to respond to the need to minimise the price of basic food products, namely agri-food products, without considering concerns such as welfare and sustainability. It is worrisome, however, that a tax rule that does not conform to the F2F strategy has been approved after its definition, indicating that the Portuguese government does not envisage using taxation as an environmental tool in favour of the objectives outlined in the F2F strategy.

The present work aims to carry out a bibliometric analysis of financial planning in SMEs. The general objective of this article is to analyze the articles published in the last 10 years in order to compose the core of a bibliometric references on the subject in question. A bibliographic study was carried out through the systematic literature review (SLR) approach, having as a search the theme financial planning of SMEs in the web of science database. The authors found 784 articles published in the last 10 years dealing with the topic, but only 21 of these correlated financial planning or financial management applied to SMEs. 5198 articles were found in the database of the most diverse areas of knowledge; of these 272 articles refer to the financial area and only 05 articles in the area that addressed the topic of financial planning research applied to SMEs. With the accomplishment of this study, it will be necessary to expand the studies that can contribute to the effective management of resources and financial planning of SMEs.

This chapter introduces the Personalized Health Assistant, an integrated system that combines Internet of Things devices, machine learning, and natural language processing to monitor diabetes and promote proactive, personalized healthcare. For diabetic patients, the system leverages wearable sensors data. For non-diabetic users, physiological parameters are inferred through lifestyle and stress indicators. To support predictive health monitoring, the system incorporates advanced machine learning models: Long Short-Term Memory, Multiple Back-Propagation, and a Convolutional Neural Network. Another component of the system is a clinically aware chatbot, built upon the Falcon-7B large language model and semantically validated using Bio_ClinicalBERT. The chatbot pipeline integrates FAISS-based context retrieval, clinical term detection, and semantic similarity scoring. The validation results underscore the system's reliability for predictive analytics, intelligent interaction, and real-time clinical support, making it a viable solution for scalable and secure chronic disease management.