Search Results (28)

The accurate automatic classification of underwater acoustic signals from passive SoNaR is vital for naval operational readiness, enabling timely vessel identification and real-time maritime surveillance. This study evaluated seven supervised machine learning algorithms for ship identification using passive SoNaR recordings collected by the Brazilian Navy. The dataset encompassed 12 distinct ship classes and was processed in two ways—full-resolution and downsampled inputs—to assess the impacts of preprocessing on the model accuracy and computational efficiency. The classifiers included standard Support Vector Machines, K-Nearest Neighbors, Random Forests, Neural Networks and two less conventional approaches in this context: Linear Discriminant Analysis (LDA) and the XGBoost ensemble method. Experimental results indicate that data decimation significantly affects classification accuracy. LDA and XGBoost delivered the strongest performance overall, with XGBoost offering particularly robust accuracy and computational efficiency suitable for real-time naval applications. These findings highlight the promise of advanced machine learning techniques for complex multiclass ship classification tasks, enhancing acoustic signal intelligence for military maritime surveillance and contributing to improved naval situational awareness. Full article

Understanding the relationship between geological and geomorphological processes is essential for reconstructing landscape evolution. This study examines how geology and geomorphology shape landscape development in central Brazil, focusing on the Natividade Group area. Sentinel-2 and SRTM data were integrated with geospatial analyses to produce two key maps: (i) a pedo-geomorphological map, classifying landforms and soil–landscape relationships, and (ii) a predictive geological–geomorphological map, based on a machine learning-based prediction of geomorphic units, which employed a Random Forest classifier trained with 15 environmental predictors from remote sensing datasets. The predictive model classified the landscape into six classes, revealing the ongoing interactions between geology, geomorphology, and surface processes. The pedo-geomorphological map identified nine pedoforms, grouped into three slope classes, each reflecting distinct lithology–relief–soil relationships. Resistant lithologies, such as quartzite-rich metasedimentary rocks, are associated with shallow, poorly developed soils, particularly in the Natividade Group. In contrast, phyllite, schist, and Paleoproterozoic basement rocks from the Almas and Aurumina Terranes support deeper, more weathered soils. These findings highlight soil formation as a critical indicator of landscape evolution in tropical climates. Although the model captured geological and geomorphological patterns, its moderate accuracy suggests that incorporating geophysical data could enhance the results. The landscape bears the imprint of several tectonic events, including the Rhyacian amalgamation (~2.2 Ga), Statherian taphrogenesis (~1.6 Ga), Neoproterozoic orogeny (~600 Ma), and the development of the Sanfranciscana Basin (~100 Ma). The results confirm that the interplay between geology and geomorphology significantly influences landscape evolution, though other factors, such as climate and vegetation, also play crucial roles in landscape development. Overall, the integration of remote sensing, geospatial analysis, and machine learning offers a robust framework for interpreting landscape evolution. These insights are valuable for applications in land-use planning, environmental management, and geohazard assessment in geologically complex regions. Full article

Temporomandibular joint (TMJ) ankylosis compromises essential functions such as chewing, phonation, and breathing. Surgical treatment aims to restore mandibular mobility and prevent the recurrence of joint fusion. This article describes a technical variation based on Puricelli biconvex arthroplasty, using surgical cement, screw fixation, and 3D-printed cutting guides based on virtual planning, allowing for greater precision in joint reconstruction. In this work, we present the step-by-step process used in the customization of cutting guides, virtual planning, and the production of the interposition material with PMMA associated with fixation with titanium screws as a hybrid method for the treatment of recurrent TMJ ankylosis. This reported technique is demonstrated to be reproducible, low-cost, and effective. Full article

Membrane distillation (MD) is an evolving thermal separation technique most frequently aimed at water desalination, compatible with low-grade heat sources such as waste heat from thermal engines, solar collectors, and high-concentration photovoltaic panels. This study presents a comprehensive theoretical–experimental evaluation of three commercial membranes of different materials (PE, PVDF, and PTFE), tested for two distinct MD modules—a Direct Contact Membrane Distillation (DCMD) module and an Air Gap Membrane Distillation (AGMD) module—analyzing the impact of key operational parameters on the performance of the individual membranes in each configuration. The results showed that increasing the feed saline concentration from 7 g/L to 70 g/L led to distillate flux reductions of 12.2% in the DCMD module and 42.9% in the AGMD one, averaged over the whole set of experiments. The increase in feed temperature from 65 °C to 85 °C resulted in distillate fluxes up to 2.36 times higher in the DCMD module and 2.70 times higher in the AGMD one. The PE-made membrane demonstrated the highest distillate fluxes, while the PVDF and PTFE membranes exhibited superior performance under high-salinity conditions in the AGMD module. Membranes with high contact angles, such as PTFE with 143.4°, performed better under high salinity conditions. Variations in operational parameters, such as flow rate and temperature, markedly affect the temperature and concentration polarization effects. The analyses underscored the necessity of a careful selection of membrane type for each distillation configuration by the specific characteristics of the process and its operational conditions. In addition to experimental findings, the proposed heat and mass transfer-reduced model showed good agreement with experimental data, with deviations within ±15%, effectively capturing the influence of operational parameters. Theoretical predictions showed good agreement with experimental data, confirming the model’s validity, which can be applied to optimization methodologies to improve the membrane distillation process. Full article

The majority of the global population now resides in cities, and this trend continues to grow. In this context, the Internet of Things (IoT) is crucial in transforming existing urban areas into Smart Cities. However, IoT architectures mainly focus on machine-to-machine interactions, leaving human involvement aside. The Internet of Everything (IoE) includes human-to-human and human–machine collaboration, but the specifics of these interactions are still under-explored. As urban populations grow and IoT integrates into city infrastructure, efficient, collaborative architectures become crucial. In this work, we use the Rapid Review methodology to analyze collaboration in four prevalent computing architectures in the IoE paradigm, namely Edge Computing, Cloud Computing, Blockchain/Web Services, and Fog Computing. To analyze the collaboration, we use the 3C collaboration model, comprising communication, cooperation, and coordination. Our findings highlight the importance of Edge and Cloud Computing for enhancing collaborative coordination, focusing on efficiency and network optimization. Edge Computing supports real-time, low-latency processing at data sources, while Cloud Computing offers scalable resources for diverse workloads, optimizing coordination and productivity. Effective resource allocation and network configuration in these architectures are essential for cohesive IoT ecosystems. Therefore, this work offers a comparative analysis of four computing architectures, clarifying their capabilities and limitations. Smart Cities are a major beneficiary of these insights. This knowledge can help researchers and practitioners choose the best architecture for IoT and IoE environments. Additionally, by applying the 3C collaboration model, the article provides a framework for improving collaboration in IoT and IoE systems. Full article

Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts’ tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain. Full article

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal’s ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than $-10$ dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems. Full article

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector. Full article

Hybrid composites are expanding applications in cutting-edge technology industries, which need materials capable of meeting combined properties in order to guarantee high performance and cost-effectiveness. This original article aimed for the first time to investigate the hybrid laminated composite thermal behavior, made of two types of fibers: synthetic Twaron^® fabric and natural curaua non-woven mat, reinforcing epoxy matrix. The composite processing was based on the ballistic helmets methodology from the North American Personal Armor System for Ground Troops, currently used by the Brazilian Army, aiming at reduced costs, total weight, and environmental impact associated with the material without compromising ballistic performance. Thermal properties of plain epoxy, aramid fabric, and curaua mat were evaluated, as well as the other five configurations of hybrid laminated composites. These properties were compared using thermogravimetric analysis (TGA) with its derivative (DTG), differential thermal analysis (DTA), and thermomechanical analysis (TMA). The results showed that the plain epoxy begins thermal degradation at 208 °C while the curaua mat at 231 °C and the aramid fabric at 477 °C. The hybrid laminated composites curves showed two or three inflections in terms of mass loss. The only sample that underwent thermal expansion was the five-aramid and three-curaua layers composite. In the third analyzed temperature interval, related to the glass transition temperature of the composites, there was, in general, an increasing thermal stability behavior. Full article

This article discusses the Brazilian maritime authority’s efforts to monitor and control vessels in specific maritime areas using data from the naval traffic control system. Anomalies in vessel locations can signal security threats or illegal activities, such as drug trafficking and illegal fishing. A reliable Maritime Domain Awareness (MDA) is necessary to reduce such occurrences. This study proposes a data-driven framework, CV-MDA, which uses computer vision to enhance MDA. The approach integrates vessel records and camera images to create an annotated dataset for a Convolutional Neural Network (CNN) model. This solution supports detecting, classifying, and identifying small vessels without trackers or that have deliberately shut down their tracking systems in order to engage in illegal activities. Improving MDA could enhance maritime security, including identifying warships invading territorial waters and preventing illegal activities. Full article

The fast growth of the urban population increases the demand for energy, water, and transportation, amongst other needs. This study explores the current state and future scenarios of Smart Cities and the environmental, economic, and social challenges that must be overcome. We used the Rapid Review method to understand the challenges of implementing Smart Cities in different urban contexts and the potential impact of research on Smart City planning in future Smart Cities. The study offers insights into the potential for Smart City growth while identifying obstacles that must be addressed to ensure sustainability. Results serve as a foundation for planning and decision-making, highlighting aspects such as the adoption of alternative energies, reduction in car use, preservation of ecosystems, waste reduction, citizen participation, infrastructure, and citizen data privacy, among others. These aspects are essential to overcome obstacles and promoting Smart Cities’ development. Full article

Water temperature controls the physiology, growth rate, distribution, and behavior of most plankton populations in the sea and thus affects the energy transfer in marine ecosystems. The present study focuses on the influence of seasonal changes in sea surface temperature on phytoplankton and the size distribution of copepods in the Arraial do Cabo Upwelling System (Brazil), where a wind-driven coastal upwelling can lead to multiple distinct bottom-up cascade effects on the food web. To address the potential effect of the seasonal changes, environmental data were obtained and the abundance of plankton determined from monthly samples collected in triplicate from 2010 to 2014. The samples were analyzed on a Benchtop FlowCAM (FC), and copepods (<1000 µm) were classified according to their Ellipses Equivalent Major Axis using image analysis software ImageJ (IJ). For IJ analysis, a batch-processing macro was built to open all FC raw images and then crop each copepod individually into a single picture. Using these images, prosome and urosome lengths were manually measured with the straight-line tool in IJ. With the combinations of measurements obtained in the IJ adjusted as FC measurements, we established a new, faster, and more effective way to measure copepods. With the copepod size classification, we found that there is a cycle in copepod size combined with the upwelling cycle that is related to temperature rather than to phytoplankton growth. Copepod abundance as a whole peaked during the autumn, winter, and spring seasons. The method performed here proved that FC is an effective tool for classifying copepod sizes and detecting seasonal variation. Full article

Regarding high-level and complex decision-making scenarios, the study presents an extensive approach to the Simple Aggregation of Preferences Expressed by Ordinal Vectors-Multi Decision Making method (SAPEVO-M). In this context, the modeling proposal, named SAPEVO-Hybrid and Hierarchical (SAPEVO-H²), the objective of this study, based on the concepts of multi-criteria analysis, provides the evaluation of alternatives under the light of multiple criteria and perceptions, enabling the integration of the objectives of a problem, which are transcribed into attributes and structured in a hierarchical model, analyzing qualitative and quantitative data through ordinal and cardinal entries, respectively. As a case study, a decision analysis concerning the defense strategies against anti-Remotely Piloted Aircraft Systems (RPAS) strategies for the Brazilian Navy is carried out. Using the technique of the causal maps approach based on Strategic Options Development and Analysis (SODA) methodology, the problematic situation is structured for numerical implementation, demonstrating the performance of objectives and elements of a hierarchical structure. As a result, rankings concerning objectives and anti-RPAS technologies, based on the treatment of subjective information, are presented. In the end, the main contribution of the study and its limitations are discussed, along with the conclusions and some proposals for future studies. Full article

We present a method to acquire 3D position measurements for decentralized target tracking with an asynchronous camera network. Cameras with known poses have fields of view with overlapping projections on the ground and 3D volumes above a reference ground plane. The purpose is to track targets in 3D space without constraining motion to a reference ground plane. Cameras exchange line-of-sight vectors and respective time tags asynchronously. From stereoscopy, we obtain the fused 3D measurement at the local frame capture instant. We use local decentralized Kalman information filtering and particle filtering for target state estimation to test our approach with only local estimation. Monte Carlo simulation includes communication losses due to frame processing delays. We measure performance with the average root mean square error of 3D position estimates projected on the image planes of the cameras. We then compare only local estimation to exchanging additional asynchronous communications using the Batch Asynchronous Filter and the Sequential Asynchronous Particle Filter for further fusion of information pairs’ estimates and fused 3D position measurements, respectively. Similar performance occurs in spite of the additional communication load relative to our local estimation approach, which exchanges just line-of-sight vectors. Full article

Personnel selection is increasingly proving to be an essential factor for the success of organizations. These issues almost universally involve multiple conflicting objectives, uncertainties, costs, and benefits in decision-making. In this context, personnel assessment problems, which include several candidates as alternatives, along with several complex evaluation criteria, can be solved by applying Multicriteria Decision Making (MCDM) methods. Uncertainty and subjectivity characterize the choice of personnel for missions or promotions at the military level. In this paper, we evaluated 30 Brazilian Navy officers in the light of four criteria and 34 subcriteria. To support the decision-making process regarding the promotion of officers, we applied the ELECTRE-Mor MCDM method. We categorized the alternatives into three classes in the modeling proposed in this work, namely: Class A (Promotion by deserving), Class B (Promotion by seniority), and Class C (Military not promoted). As a result, the method presented 20% of the officers evaluated with performance corresponding to class A, 53% of the alternatives to class B, and 26.7% with performances attributed to class C. In addition, we presented a sensitivity analysis procedure through variation of the cut-off level λ, allowing decision-making on more flexible or rigorous scenarios at the discretion of the Naval High Administration. This work brings a valuable contribution to academia and society since it represents the application of an MCDM method in state of the art to contribute to solving a real problem. Full article