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ABSTRACT Microplastic pollution and its environmental impacts are under investigation in marine and coastal environments. However, in Ceará state, Brazil, the impacts of microplastic pollution in coastal areas have not been evaluated. This study assesses the quantity, composition, and physical forms of microplastics in sediment at Praia do Futuro and determines the sand’s geotechnical characteristics. Three study zones were defined, with 21 samples collected. Normality tests were conducted for statistical analyses, comparing results using ANOVA and Kruskal-Wallis tests. No significant differences were found between locations with and without tourist activity. The concentration of microplastics exceeded levels observed in other Brazilian studies, but it was lower than that typically registered in international studies. Additionally, geotechnical characteristics of Praia do Futuro’s sand indicated grain size with diameters under 600 µm and void ratios aligned with literature expectations. The porosity varied along the coast, as is typical of natural sandy soils.

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ABSTRACT In recent decades, the world has experienced rapid economic growth, driven by the intensive development and implementation of innovative technologies in various areas of production and services. Companies seeking to remain competitive in the market are forced to adapt as quickly as possible to the changes brought about by the fourth industrial revolution. The logistics sector, which is an integral part of any country’s economy and plays one of the leading roles in the physical movement of goods and services, is no exception. Industry 4.0 solutions in the field of transport and logistics mean the implementation of reading, automation, and advanced technologies, such as artificial intelligence, the Internet of Things (IoT), robotization, and big analytics, in industrial processes to simplify the logistics process and manage information in real time, ensuring timely data availability and accessibility for process managers in the supply chain. Taking into account the importance of logistics, the article analyses the trends in the implementation of Industry 4.0 technologies in the Lithuanian logistics sector. The purpose of the study is to assess how the Lithuanian logistics sector adapts to these changes, what technologies are already being applied, and what challenges it faces. During the study, it was observed that the size of the company has an impact on the implementation of technologies; therefore, the results in this paper will be presented through the logistics company size prism. Methods applied: systematic analysis of scientific literature, statistical data analysis.

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ABSTRACT We present new mixed-integer quadratic conic formulations for two variants of the multiple-allocation p-hub location problems in a competitive environment. The problems consist in locating p hubs so that an entrant company can establish its hub-and-spoke network to provide transportation services for pairs of origin-destination that exchange flows in a competitive market. The objective is to maximize the entrant’s market share when compared to its competitors. Both problems assume that the paths used to route the flows have one or at most two hubs. However, whereas the first problem allows an origin-destination to be serviced by multiple routes, the second problem requires that a single path be used. Here, we show that instead of maximizing the entrant’s market share, it is computationally more interesting to minimize the market lost so that equivalent, but more suitable programs to conic solvers can be obtained. When solved by a commercial conic programming solver, our proposed formulations achieve average speedups of 89 times for the multi-path variant and 37 times for the single-path variant, as shown in our extensive computational experiments on solving well-known datasets. Therefore, this work not only reformulates the problem but also substantially outperforms all prior works, demonstrating the practical applicability and effectiveness of our approaches.

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ABSTRACT The art of prediction using sample data plays a vital role in statistical modeling, particularly when dealing with count data. Among the various distributions used for modeling count data, the Poisson distribution is the most common. However, its assumption of equi-dispersion makes it unsuitable for datasets exhibiting over-dispersion, where the variance exceeds the mean. Classical models often fail in such contexts. To address this issue, we propose a new over-dispersed model based on the mixed-Poisson frame-work, termed the Poisson-Akshaya model. We derive several structural properties of the model, including factorial moments and moments about the origin. Parameter estimation is performed using the Maximum Likelihood Estimation (MLE) method and the Least Squares. A simulation study is conducted to evaluate the performance of the MLEs. Furthermore, the efficacy of the proposed model is demonstrated through its application to two real-life datasets. Based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and other goodness-of-fit statistics, the Poisson-Akshaya model outperforms classical and other mixed-Poisson models. Additionally, we develop the zero-inflated version of the model and assess the significance of the zero-inflation parameter using various test criteria.

Resumo em Inglês:

ABSTRACT Wireless sensor networks are becoming increasingly important in many fields, such as management and security. They enable the collection and transmission of large volumes of data from a specific area to a data center for processing and analysis. One of the most challenging problems in sensor deployment is determining the optimal placement of each sensor to maximize the coverage area. It has been demonstrated that this problem is NP-hard. Due to its huge complexity, various metaheuristics have been proposed to solve the coverage problem in wireless sensor networks. In this paper, we propose an efficient hybrid genetic algorithm to maximize the coverage area in wireless sensor networks. The proposed approach combines a genetic algorithm with an enhanced simulated annealing algorithm. The efficiency of the proposed algorithm has been tested in 15 benchmark instances taken from the literature and compared with state-of-the-art algorithms.

Resumo em Inglês:

ABSTRACT This paper introduces MTES-GA-DFS, a multi-task ensemble framework for road safety prediction. The proposed approach jointly predicts three key outcomes-accident severity, time of day, and accident hotspot-providing a unified view of road safety dynamics. Multiple heterogeneous base models are trained in parallel and combined through a stacking strategy using a multi-task meta-model. The stacking process is optimized via a genetic algorithm to select effective model combinations. In addition, a dynamic feature selection method is proposed to identify task- and model-specific relevant features, reducing dimensionality while preserving predictive performance. The proposed framework facilitates proactive interventions, supports efficient resource allocation, and enables informed decision-making for accident prevention. The framework is evaluated on the US Accidents dataset (2016-2023). Experimental results show an F1-score improvement of 22.56% and a reduction in false alarms of 47.24% compared to baseline approaches, demonstrating the effectiveness of the proposed method for accurate and robust road safety prediction.

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ABSTRACT While emergency care units must be integrated into a healthcare network to increase their effectiveness, operational strategies must be adopted to improve efficiency. We developed a simulation model and evaluated patient queues and staffing levels to assess the efficiency of clinical and pediatric care at a public health unit. Our results suggest that the public health unit is efficient, with 80% to 97% utilization of bottleneck resources; however, there are resource imbalances. The scenarios indicate that the level of effectiveness of the UPA can be increased by 24% if future investment prioritizes hiring a general practitioner, a screening nurse, and a secretary in this sequence. Conversely, the reduction of any of these three functions has a significant negative impact on system efficiency. The approach developed in open source can be applied and adjusted to other UPAs of SUS, the Brazilian Unified Health System.

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ABSTRACT An autonomous agent's behavior may be modeled by specifying an objective function such as utility function it attempts to optimize by adjusting a variable representing its current status. A historical insight in economics has been that the agent adjusts its current status based on marginal utility rather than utility, which applies equally well to the loss function interpreted as the negative utility function. This leads to a method to specify the derivative of loss function by quantifying the level of the agent's motivation to improve the current status as compared to the level at the least relevant value of the variable. Integrating the derivative specified recovers the loss function, which in turn defines the corresponding maximum entropy distribution enabling probability prediction of actions the agent will take.

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ABSTRACT This paper addresses the challenge of covering three-dimensional solids with spheres of varying radii, allowing for partial overlaps, which is particularly relevant for applications such as gamma knife radiotherapy. In this work a novel approach is introduced, approximating the covering problem in the sense that it focuses on an objective function that maximizes the total volume of the used spheres, but it does not consider the volume of the overlapped regions. This approximation is further refined by discretizing the target volume and utilizing a finite set of potential sphere centers. Exact and hybrid methods are developed to solve this approximation. The exact method employs either a Branch-and-Bound or Branch-and-Cut algorithm, while the hybrid method integrates heuristic solutions and data mining techniques to identify promising sphere configurations, which are then refined using either Branch-and-Bound or Branch-and-Cut on a reduced search space. Data Envelopment Analysis (DEA) is used to determine optimal overlap parameters. Computational experiments demonstrate that the hybrid method outperforms the exact method in both coverage and computational efficiency, highlighting the efficacy of integrating heuristic, data mining, and exact techniques for solving complex optimization problems.

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ABSTRACT This paper studies semidefinite programming (SDP) relaxations for the Boolean Quadratic Programming problem with Generalized Upper Bound constraints (BQP-GUB), which arises in several application domains. We show that conventional semidefinite relaxation (SDR) does not yield tight bounds for this problem. We then analyze strengthened SDP relaxations and identify formulations that produce tight or near-tight bounds, particularly those based on reduced reformulation-linearization techniques (SDR+RRLT and SSDR+RRLT. Numerical experiments demonstrate that these approaches significantly reduce the relaxation gap compared to SDR, while highlighting the trade-off between bound quality and computational scalability.

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ABSTRACT Time series anomaly detection (TSAD) is crucial for identifying unusual patterns in sequential data across domains such as finance, healthcare, cybersecurity, and predictive maintenance. A key challenge is selecting the most suitable anomaly detection algorithm for a given time series, due to the wide variability in instance characteristics. This paper tackles the algorithm selection problem for univariate TSAD by applying meta-learning to predict the performance ranking of candidate methods. We propose a meta-learning framework that extracts statistical, structural, and temporal meta-features to model algorithm behavior. The study evaluates four TSAD algorithms-two model-based (DeepAnt and Temporal Convolutional Autoencoder) and two discord detection methods (Matrix Profile and Merlin). Experimental results show that meta-learning is effective for unsupervised TSAD algorithm selection. A Random Forest meta-model achieved strong results, with a Spearman correlation of 0.61, Kendall tau of 0.52, top-1 accuracy of 64%, and top-2 accuracy of 89%, demonstrating its reliability even when performance differences between algorithms are small.

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ABSTRACT In this study we focus on the approximate solution of the one-dimensional Cutting Stock Problem (CSP) by leveraging insights from studies on the Cutting Stock Problem with a Limited Number of Open Stacks (CS-LOSP). The CS-LOSP, a variant of the classical CSP, imposes a limit on the number of open stacks during production, reflecting constraints observed in many industrial environments. Em-pirical findings from CS-LOSP studies have shown that high-quality CSP solutions can be achieved with low values of C (the permitted number of open stacks). Inspired by these observations, we investigate a matheuristic pattern screening strategy that discards all cutting patterns generating more than C distinct item types to solve the CSP. The work is empirical in nature, supported by extensive computational tests designed to verify whether the promising results observed in CS-LOSP can be replicated in the classical one-dimensional CSP. Our experiments identify the conditions under which the matheuristic produces optimal or near-optimal solutions, particularly under medium to high demand levels, while its effectiveness tends to decrease in low-demand scenarios. Beyond demonstrating significant reductions in the size of the feasible pattern set to be considered when solving the CSP, our results underline the practical relevance of our approach. This is especially pertinent for sequential optimization systems in industrial applications.

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ABSTRACT This survey presents a comprehensive review on the application of classical and logic-based Benders decomposition (BD) approaches to Assembly Line Balancing Problems (ALBP). The core decisions in assembly lines involve assigning a partially ordered set of tasks to (work)stations to maximise productivity or minimise resource usage in flow-oriented systems. ALBPs may contain stochastic data, multiple workers per station, or depend on the production sequence. Hence, they require further scheduling decisions - various stochastic and sequence-dependent problems have successfully applied this approach. This paper contributes to the current body of knowledge by providing a literature review. It introduces each article’s particular aspects and ideas and brings forth a summary and discussions on existing gaps. We offer insights into the BD method’s efficiency for combinatorial problems such as ALBPs from a managerial perspective, suggest potential directions for future research, and highlight the increasing number of contributions using this technique.