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This paper deals with the problem of selecting a set of clients that will receive an offer of one or more products during a promotional campaign. Such campaigns are essential marketing tools to improve the economic profit of an enterprise, either by acquiring new customers or generating additional revenue from existing customers. In this research, a well-known mathematical model for the problem is used and extended with the cannibalism constraint which avoids some products being offered simultaneously to simulate competing products cannibalizing each other’s market. To solve this problem, a hybrid heuristic is proposed, which uses a genetic algorithm (GA) as long-term memory for a tabu search (TS). The main idea is not to use GA exclusively as an optimization procedure but also as a diversification strategy. In particular, GA elite solutions replace the TS’s current solutions exploring in this way new areas in the search space. GA also receives the best TS solutions to maintain its population with high-quality solutions. Extensive computational experiments are performed on a set of existing benchmark test problems integrated with the restriction of cannibalism. A new set of instances with a high degree of difficulty is generated and are available to the research community through GitHub. The proposed method is compared with state-of-the-art methods demonstrating better overall performance (sometimes more than 10 percentage points) and statistical significance.

In this paper, we propose a microstrip patch antenna (MPA) operating in the TM50-like mode, designed for gain enhanced and sidelobe levels (SLLs) reduction. Our methodology involves altering the resonator’s surface current distribution by introducing three groups of transverse slots at points of null electric field. Additionally, two symmetrically positioned stubs are utilized to the antenna’s gain enhanced. We verify that proper combinations of such artifacts on the proposed radiators significantly reduce the SLL while maintaining the high-gain characteristics of the TM50-like mode. A prototype was fabricated and characterized for operation around 7.6 GHz, in the C band. The results demonstrate that a realized gain of 15.0 dBi can be achieved, with the SLL reduced to approximately 15 dB, representing an excellent option for high-gain applications requiring a low profile and compact size.

This work introduces a Ka-band coaxial horn antenna that incorporates a specialized dielectric supporting structure and a transition to a 2.4 mm connector. The inner and outer radii of the coaxial aperture were sized using an approximated model for an open-ended coaxial waveguide. The theory of small reflections was then used to account for the reflection coefficient resulting from an additional cascading cylindrical-conical section. A refined numerical model, representing more accurately a prototype, featured a transition region to standardized connectors and a dielectric structure that offers mechanical support for the inner conductor and impedance matching. Ansys HFSS full-wave electromagnetic finite-element method solver was used to compute the parameters of the antenna, and a genetic algorithm optimizer was employed to improve the performance of the complete coaxial horn. A prototype was fabricated using metal additive manufacturing for the inner and outer horn conductors, while the dielectric support was created using 3D polymer printing. Experimental measurements demonstrate that the prototyped antenna has an impedance bandwidth of above 79.36% (19–44 GHz), a peak realized gain of 11.53 dBi, and a maximum efficiency of 89.83%. Additionally, a sensitivity analysis was conducted to evaluate the potential impact of additive manufacturing imperfections and assembly errors on the antenna’s performance.

Hydroelectricity has been widely deployed in many countries for decades, and remains the main source of electricity in Canada, Norway, and Brazil. Despite the recent diversification, hydroelectricity accounts for approximately 65% of the electricity generated in Brazil. It also represents the largest non-polluting source in the country, and is important for complying with the global goals of reducing carbon emissions. Meeting energy and environmental sustainability requirements impose challenges on the hydroelectric sector in terms of resilience to climate change observed around the planet. These changes affect electricity generation by altering seasonality and increasing the variability of streamflow and evaporation losses in reservoirs. Therefore, in this study, among a set of 27 climate indices, we identified the most relevant for improving the performance of the models applied to monthly seasonal streamflow series forecasting. A database provided by the NOAA Physical Sciences Laboratory was used as exogenous variables for three machine learning models (support vector regression, extreme learning machine, and kernel ridge regression) and one linear model (seasonal autoregressive integrated moving average with exogenous factors, SARIMAX). Random forest with recursive feature elimination was used as the feature-selection technique. The results obtained allowed for the identification of the most relevant set of indices for the analyzed plants, thereby improving streamflow predictions.

All over the world, there is a lot of patient health data in different locations such as hospitals, clinics, insurance companies, and other organizations. In this sense, global identification of the patient has emerged as an everyday healthcare challenge. Governments and institutions have to prioritize satisfactory, quick, and integrated decision-making in a wide, dispersed, and global environment because of unexpected challenges like pandemics or threats. In the current scientific literature, some of the existing challenges include support for a standard global unique identification that considers privacy issues, the combination of multiple technological biometry implementations, and personal documents. Thus, we propose a decentralized software model based on blockchain and smart contracts that includes privacy, global unique person identification supporting multiple combinations of documents, and biometric data using the Global Standards 1 - GS1 healthcare industry standard. Furthermore, we defined a methodology to evaluate a hypothetical use case of this model where an integrated and standard global health data sharing personal identification is crucial. For this, we implemented the proposed model in a global-wide continent location through cloud machines, fog computing, and blockchain considering the unique patient data identification and evaluate a use case scenario based on the top 5 most globally visited tourist destinations (France, Spain, the United States of America, China, and Italy), with an approach based on this model. The results show that using a model for a global id for healthcare can help reduce costs, time, and efforts, especially in the context of health threats, where agility and financial support must be prioritized.

A middleware software can be seem as an abstraction layer between hardware and user applications, that facilitates the development and deployment of services in various scenarios, such as those found in Home Energy Management Systems (HEMS). There are several middleware proposals for HEMS, with most of them taking the cloud computing approach. This approach is unconcerned about computing resources but raises a dependency on external connections. This paper presents a middleware for energy management systems, based on the concept of edge computing for smart homes. The paper presents a reference model for the proposed architecture, considering specific requirements for this type of application. The proposed architecture employs the concept of microservices for data access and system configuration. The proposed middleware is designed to work with embedded systems under computational constraints, such as processing capability and storage, to reduce costs and allow its application closer to the user. The middleware is open and customizable to meet the developer’s needs. The proposed solution was implemented and tested in a university laboratory, as well as at the Eldorado Research Institute to confirm the effectiveness of the middleware. The proposal stands out from others found in the literature as it can be implemented using low cost hardware. In addition to using microservices concepts, the proposed middleware is a valuable option for applications that need an edge computing approach. A performance analysis was carried out, using low cost hardware with limited resources. The results show that the proposal can handle a significant number of devices, offering low latency and low error rate, and consuming few processing resources and memory.

Nowadays, there are many fragmented records of patient’s health data in different locations like hospitals, clinics, and organizations all around the world. With the arrival of the COVID-19 pandemic, several governments and institutions struggled to have satisfactory, fast, and accurate decision-making in a wide, dispersed, and global environment. In the current literature, we found that the most common related challenges include delay (network latency), software scalability, health data privacy, and global patient identification. We propose to design, implement and evaluate a healthcare software architecture focused on a global vaccination strategy, considering healthcare privacy issues, latency mitigation, support of scalability, and the use of a global identification. We have designed and implemented a prototype of a healthcare software called Fog-Care, evaluating performance metrics like latency, throughput and send rate of a hypothetical scenario where a global integrated vaccination campaign is adopted in wide dispensed locations (Brazil, USA, and United Kingdom), with an approach based on blockchain, unique identity, and fog computing technologies. The evaluation results demonstrate that the minimum latency spends less than 1 second to run, and the average of this metric grows in a linear progression, showing that a decentralized infrastructure integrating blockchain, global unique identification, and fog computing are feasible to make a scalable solution for a global vaccination campaign within other hospitals, clinics, and research institutions around the world and its data-sharing issues of privacy, and identification.

The Internet of Things (IoT) adoption grows significantly and is successful in many different domains. Nevertheless, the ever-growing demand for more connected devices pushes the requirement for scalable IoT architectures capable of maintaining the security and privacy of collected data. The latter is a particularly critical aspect when considering sensitive data, e.g., medical records. One solution to address this challenge is to modify the centralized back-end model to one based on a Blockchain, changing the way IoT data is stored and shared by providing a decentralized peer-to-peer network. This technology enables naming and tracking for connected devices, and in the case of this article, features a high availability of Personal Health Records, yet protecting patients’ privacy through the use of cryptography. Furthermore, the addition of Fog computing mechanisms helps to achieve real-time data processing, supports precision medicine, and avoids single points of failure. As a result, devices have a local and more resilient ecosystem for operation. In this context, this work proposes an architecture model named FogChain, which combines the technologies Blockchain, Fog computing, and the IoT for the healthcare domain. Our main contribution is the FogChain model itself, and its concept of overcoming IoT constraints by employing a differential approach, adding an intermediary Fog layer near to the edge to improve their capabilities and resources. Experiments demonstrate that FogChain can achieve a 62.6% faster response time when compared to Cloud-like Blockchain infrastructures. The results obtained from the evaluation endorses the capacity of our model in achieving its goals while retaining application performance.

Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).