A significant percentage of scholarly materials, including theoretical references and didactic resources, presents numerical sets in a way that creates doubts and inconsistencies in interpretations. This is particularly true regarding the inclusion of one set within another and the relevance of specific numbers to particular sets. This issue prompts us to develop alternative methods that facilitate the visualization and interpretation of the interrelationships between numerical sets and their respective elements. Therefore, this essay aims to propose more fluid ways of presenting numerical sets, likening them to reservoirs of liquids that flow to form other reservoirs, conceptualized as infinite numerical reservoirs.
Electric vehicles (EVs) are crucial for mitigating greenhouse gas emissions in urban transportation. However, their integration requires efficient charging infrastructure and allocation strategies. In this paper, five heuristic algorithms were developed to allocate EVs to urban charging stations. This allocation process incorporates critical constraints, such as user preferences and charger type compatibility, while respecting station capacities governed by power output rules. The proposed methods include four initial allocation heuristics, ranging from capacity-centric and nearest-neighbor approaches to random assignments, complemented by a local search algorithm for solution refinement. To evaluate these heuristics, an optimization model minimizing station establishment and vehicle travel costs was adapted from the literature. Computational experiments were performed on both synthetic instances and real-world case studies. The results indicate that the developed heuristics, especially when enhanced by local search, deliver high-quality, near-optimal solutions within highly competitive computational times. Consequently, this study offers a scalable decision-support tool for urban planners, demonstrating how the joint optimization of infrastructure costs and user preferences can foster sustainable urban mobility and accelerate EV adoption. Ultimately, these findings offer actionable insights for scaling heterogeneous EV infrastructure, fostering urban sustainability and mitigating transport-related carbon emissions.
PurposeGrounded in information processing theory, this study aims to investigate the impact of digital technologies and memory on humanitarian supply chains' readiness to respond to natural disasters. It also examines whether the use of digital technologies influences memory development within these supply chains.Design/methodology/approachWe collected data from 255 key professionals involved in humanitarian operations and analyzed the proposed model using partial least squares structural equation modeling.FindingsThe results show that emerging digital technologies (e.g. artificial intelligence, machine learning, the Internet of Things, blockchain, 3D printing, virtual reality and drones) act as essential information-processing mechanisms that do not directly affect readiness. Instead, they facilitate the systematic generation, storage and retrieval of complex disaster data, transforming it into a humanitarian supply chain memory. This memory serves as a full mediator, ensuring that processed information becomes a durable repository of actionable knowledge that significantly enhances predisaster readiness.Originality/valueThis study contributes to both the literature and practice by empirically demonstrating that humanitarian supply chain memory is a necessary mechanism through which digital technologies improve readiness, and by highlighting the importance of prior knowledge, beyond experience alone, for enhancing humanitarian supply chains' ability to respond to natural disasters. For policymakers and public governance, the results emphasize the strategic importance of integrating digital technologies and memory into disaster management policies. Policies that preserve and mobilize disaster memory, supported by digital technologies, can enhance readiness, foster collective learning and build more adaptive and connected responses to natural disasters.
Usually, the photovoltaic (PV) systems control architecture is composed by cascaded linear controllers, which can present difficulties in the control design process. In this context, this work presents a real-time implementation and analysis of a grid-connected PV generation system composed of a PV array, a DC-DC and an AC-DC converter connected to the three-phase electrical grid. Finite Control Set Model Predictive Control (FCS-MPC) strategy without weighting factors are applied for both converters controllers, aiming to regulate the system voltage levels and manage the active and reactive powers supplied to the electrical grid. As the proposed control strategy does not use traditional linear controllers or even weighting factors, the proposed control is free from any kind of control design process. The controllers’ strategies validation is carried out through real-time simulations using Typhoon HIL platform, enabling high fidelity in representing the system dynamic behavior. The Software-in-the-Loop (SIL) results demonstrate the effectiveness of the proposed control strategies, ensuring stable and proper system operation under different operating conditions, presenting a maximum current Total Harmonic Distortion (THD) of 4.1%, a maximum DC-link voltage deviation of 1% and a maximum efficiency of 92%.
Os traumatismos dentários constituem uma condição que demanda identificação e orientação inicial adequadas, especialmente em situações nas quais o atendimento odontológico imediato pode não estar disponível. Este estudo desenvolveu um sistema inteligente baseado em visão computacional para apoio à identificação inicial de traumatismos dentários, integrando o modelo YOLO11n a uma arquitetura automatizada de comunicação e orientação ao usuário. Na etapa de visão computacional, foram utilizadas 1.142 imagens, distribuídas em quatro classes: avulsão, fratura pulpar, fratura de esmalte e fratura dentinária. O conjunto de dados foi dividido em 70% para treinamento, 20% para validação e 10% para teste do modelo YOLO11n. O desempenho obtido apresentou Precision de 66,58%, Recall de 70,52%, mAP@0.50 de 69,20%, mAP@0.50:0.95 de 36,35% e F1-Score de 0,67. Os resultados demonstram desempenho experimental moderado do modelo na identificação das classes de traumatismos dentários avaliadas. A arquitetura desenvolvida possibilitou a integração da visão computacional com o fluxo automatizado de comunicação e orientação ao usuário. A avaliação foi realizada em ambiente experimental, não envolvendo validação clínica prospectiva, comparação direta com cirurgiões-dentistas ou aplicação em ambiente assistencial real. Dessa forma, o sistema deve ser compreendido como uma ferramenta de apoio à identificação inicial e à orientação, não substituindo a avaliação realizada por um cirurgião-dentista.
