A solid demand to integrate energy consumption and co-generation emerged worldwide, motivated, on one hand, by the need to diversify and enhance energy supply, and, one the other hand, by the pressure to attend to the requirements of a heterogeneous class of users. The coupling between energy service provision and final users also includes balancing user needs, eliminating excesses, and optimizing energy supply while avoiding blackouts. Another motivation is the challenge of having sustainable sources and many adapted to the user ecosystem. Altogether, these motivations lead to more abstract design approaches to co-generation-distributed systems, such as those based on goal-oriented requirements used to model smart grids. This work considers the available design practices and its difficulties in proposing a new method capable of producing a flexible requirement model that could serve for design and maintenance purposes. We suggest coupling the approach based on goal-oriented requirements with model-based engineering to support such a model. The expected result is a sound and flexible requirements model, including a model for the interaction with the final user (now being considered a producer and consumer simultaneously). A case study is presented, wherein a small energy service system in an isolated community in the Amazon rain forest was designed.

The accurate classification of cancer cells in the peripheral blood is essential for the diagnosis of leukemia and has traditionally been carried out by analyzing laboratory images. In this context, the use of deep learning techniques facilitates decision-making and accelerates the early diagnosis of the disease, allowing preventive measures to be adopted for the patient. This study explores the application of the YOLOv8 deep learning architecture for the classification of cancer cells in blood smear images owing, to its ability to perform this task quickly and accurately. The models were trained on two datasets, ALL and C-NMC Leukemia, and evaluated using top-1 accuracy, top-5 accuracy and loss metrics. The proposed approach achieved a top-1 accuracy of 99.982% and top-5 accuracy of 100% on the validation and test subsets of the ALL dataset respectively, with a final loss of 0.02952. For the C-NMC Leukemia dataset, the model obtained 66.897% and 89.612% top-1 accuracy in the validation and test subsets, respectively, while maintaining 100% top-5 accuracy in both subsets, with a loss of 0.18289. These results demonstrate the effectiveness of YOLOv8 in classifying cancer cells, particularly in the ALL set. However, future strategies such as expanding the data set and fine-tuning the hyperparameters could contribute to better generalization of the model to different data distributions.

Industry 4.0 is revolutionizing industrial processes by promoting automation and digitalization across various sectors. However, it is some times necessary to replace legacy systems with those more suited to this new reality, which demands resources that, in the case of small and medium-sized enterprises, pose a challenge for this transition. This study investigates the application of the Asset Administration Shell (AAS) for monitoring and controlling industrial processes, using an Level, Flow, Pressure, and Temperature (NVPT) test bench as an example of a legacy system. The Reference Architectural Model for Industry 4.0 (RAMI 4.0) was adopted to structure the system architecture. Virtualization was carried out using three AASs: two passive ones, responsible for data acquisition, and one reactive one, in charge of temperature and level control. The AASs were modeled using the AASX Package Explorer and implemented in Python using the Opua asyncio library. Execution was performed on a Raspberry Pi to explore the benefits of edge computing. The solution enabled digitalization of the test bench, allowing remote monitoring and automated control of the system variables. The tests demonstrated the feasibility of the method, highlighting its applicability in integrating legacy systems into Industry 4.0, in an economical and efficient manner.

Microgrids emerge as a practical, sustainable, and reliable solution to meet the demand of users who, for various reasons, have restricted access to electricity. However, the flexibility of microgrid systems requires considerable creative engineering effort, especially in the design process. The requirements analysis has a leading role in combining adaptability to users and good performance, better characterizing, understanding, and specifying the application domain and the problem that the microgrids must solve, then embodying and identifying solutions. This work seeks to contribute to a method (Orellana Postigo, 2021) inserted in the MBSE (Model-Based Systems Engineering) approach to model microgrids using IEC 61850 as a reference architecture for the preliminary design. Requirements would be represented in Objective Oriented Requirements Engineering (GORE), using visual diagrams based on the KAOS (Keep All Objectives Satisfied) method, but formally verified using Petri Nets. A case study shows how a preliminary design with matching goals, sub-goals, and algorithms is modeled in Petri Nets and transferred to Field Programmable Gate Array (FPGA), closing the proposed design cycle.

This paper presents a method for specifying requirements, applied to the operation of a microgrid. The challenge is to model Smart Grid (SG) systems requirements using formal methods based on schematic representations that will allows analysis, verification and validation, improving the reliability and performance of the design cycle. Considering the inherent complexity of these systems - generally heterogeneous, open and distributed - formal modeling is a key issue for the design of automated electrical systems to fit user experience expectations. In this article we propose a systemic approach that combines consolidated reference architectures and modern goal-oriented requirements engineering methods in the early steps of microgrid design. The formalism proposed is intended to obtain formal requirements models associated with standards and reference models proposed and accepted by the scientific and practitioner community.

Empresas que atuam no segmento de eletroeletrônicos na fabricação e validação, contam com uma variedade de produtos que necessitam de um sistema eficaz para o gerenciamento de movimentação durante o processo de testes. Durante a etapa de validação os produtos são direcionados aos funcionários que irão realizar os testes de funcionamento, nesse processo de empréstimos e devolução ao inventário podem conter brechas para extravios, o que resultará em prejuízos à empresa. Desta forma é necessário a implementação de um sistema confiável que gerencie as movimentações dos produtos e garanta a acuracidade das informações do inventário da empresa. O objetivo deste projeto baseia-se no desenvolvimento de um sistema utilizando a tecnologia RFID para a identificação e controle dos produtos eletrônicos utilizados nas etapas de testes pelos colaboradores da empresa. A identificação por radiofrequência - RFID, será gerenciada pelo microcontrolador ESP8266, conforme as amostras de eletrônicos forem identificadas, as informações coletadas serão exibidas em tempo real em um painel de acesso remoto aos funcionários responsáveis. O envio desses dados coletados será conforme o protocolo de comunicação MQTT para a plataforma Tago.IO, onde serão tratados e armazenados.