This study to examine associations between cardiorespiratory fitness (CRF), 24-hour movement components, and cardiometabolic risk factors in children from southern Brazil, emphasizing the critical variables in these relationships. The sample included 186 schoolchildren (6–11 years, 8.57±1.56). Waist circumference (WC) and CRF were assessed using PROESP-Br protocols, while physical activity (moderate to vigorous–MVPA, and light–LPA) was measured via accelerometers. Sleep and screen time were reported by parents, and fasting blood samples provided data on triglycerides, HDL cholesterol, glucose, insulin, and HOMA-IR. Network analysis highlight WC, systolic blood pressure, and MVPA as central variables with significant connectivity. MVPA emerged as central among 24-hour movement behaviors, with CRF playing an intermediary role. Results underscore WC and MVPA’s relationship on cardiometabolic health, supporting interventions targeting MVPA to prevent early cardiometabolic risks in children.

Metallic nanoparticles with virucidal properties dispersed in a polymeric matrix have gained prominence in the scientific community as a rapid and effective alternative that employs the additive manufacturing (AM) or 3D printing method. This study aims to produce filaments for 3D printing using polymer nanocomposites based on polylactic acid (PLA) and copper nanoparticles (CuNPs) in different proportions. The virucidal activity of various proportions of nanoparticles in PLA was investigated. The composites were produced following a mixture design (DOE) with concentrations ranging from 1% to 2% copper nanoparticles, which were blended with PLA using a single-screw extruder. The samples were characterized by thermogravimetry (TG), differential scanning calorimetry (DSC), tensile strength testing, and fracture analysis using scanning electron microscopy (SEM). A thermal analysis of the composites indicated that the CuNPs contributed to an increase in the degradation temperature and crystallization of the PLA. Sample S7 (1.25% of CuNPs) exhibited a 4% increase in the degradation temperature compared to pure PLA. The best tensile strength results were observed in sample S7 (1.25% of CuNPs), 30% more than sample S3 (1.33% of CuNPs) due to good material cohesion, as evidenced by microscopy analyses. Regarding virucidal analyses, most composites demonstrated virus inhibition activity.

Determining a fair trading price is challenging, and this complexity is further heightened in power systems as the price must consider network constraints, fluctuating and endogenous electricity prices, and generation cost. This paper proposes a novel Nash-in-Stackelberg model designed to coordinate a collection of independently operated microgrids. Within this framework, the microgrids engage in a bargaining game to collectively decide a fair trading price as well as other operational decisions. We use the Nash bargaining solution (NBS) to model this interaction. Microgrids’ trading decisions impact the locational marginal prices of the main power grid. Hence, a Stackelberg game is used to determine these prices. The Stackelberg model considers microgrids as leaders (upper-level problem) and the independent system operator as follower (lower-level problem). The NBS guarantees a fair allocation of the generated profit based on individual characteristics of the microgrids. In specific, in scenarios of higher generation, we achieve a cost reduction of $884 and $904 per microgrid compared with a grand coalition where only one microgrid receives the total cost reduction ($1788). Also we noticed that increasing the amount of solar-generated electricity reduces the nodal price in 1.53% and consequently the trading price in 1.27%.

This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great potential to navigate complex search spaces for optimization tasks. On the other hand, chaotic maps are nonlinear dynamical systems that generate pseudo-random sequences, allowing for better solution diversification and avoiding local optima. By hybridizing WSO and chaotic maps through adaptive binarization rules, the complementary strengths of both approaches are leveraged to obtain high-quality solutions. We have solved the Set Covering Problem (SCP), a well-known NP-hard combinatorial optimization challenge with real-world applications in several domains, and experimental results indicate that LOG and TENT chaotic maps are better after statistical testing. This hybrid approach could have practical applications in telecommunication network optimization, transportation route planning, and resource-constrained allocation.

In medium- and low-voltage three-phase distribution networks, the load imbalance among the phases may compromise the network voltage symmetry. Inverter-interfaced distributed energy resources (DERs) can contribute to compensating for such imbalances by sharing the required negative sequence current while providing active power synchronized with the positive sequence voltage. However, positive and negative sequences are conventionally defined in a steady state and are not directly observed from the instantaneous voltage and current measurements at the DER unit’s point of connection. In this article, an estimation algorithm for sequence separation based on the Kalman filter is proposed. Furthermore, the proposed filter uses a complex vector representation of the asymmetric three-phase signals in synchronous coordinates to allow for the implementation of the Kalman filter in its stationary form, resulting in a simple dynamic filter able to estimate positive and negative sequences even during transient operation. The proposed stationary complex Kalman filter performs better than state-of-the-art techniques like DSOGI and very similarly to other Kalman filter implementations found in the literature but at a fraction of its computational cost (23.5%).

In this study, a comprehensive and holistic bibliometric analysis is conducted at the intersection of quality of life and the technical efficiency of local governments. A corpus of 96 scientific articles, published between 2010 and the second quarter of 2024, was used as the basis for the analysis. The methods employed rely primarily on bibliometric tools, incorporating network analysis, thematic grouping and mapping, and topic analysis. The analysis aims to offer an integrated overview of scholarly activity in this interdisciplinary field, identify central themes and collaboration patterns, and highlight key research areas and opportunities according to gaps identified in the literature. The rigorous analysis identified seven information clusters, illustrating the network of collaborations among topics related to quality of life and efficiency. Additionally, four research groups were identified through topic analysis, revealing the main thematic areas that have received scholarly attention. Results show a growing scholarly focus on eco-efficiency, decentralized governance, and multidimensional quality of life metrics. The study also identifies key gaps in the literature, including the lack of subjective well-being indicators and limited policy implementation analysis. This analysis emphasizes the importance of evaluating the efficiency and effectiveness of local governments, particularly in the context of urban well-being. This study highlights the collaborative efforts undertaken by researchers to address and comprehend the complexity of these intertwined topics. It also reflects the necessity of multidisciplinary approaches to achieve a deeper understanding of both phenomena. The findings provide a framework for future research, grounded in the methods and analytical approach employed in the present study.

Construction remains one of the most hazardous industries, with high accident rates driven by insufficient planning, coordination, and safety training. While extended reality (XR) technologies, encompassing virtual, augmented, and mixed reality, have shown promise in improving safety outcomes, existing applications are typically isolated, lacking integration across the project lifecycle and alignment with digital methodologies such as those found in Construction 4.0. This study proposes a comprehensive workflow and framework for the integration of XR technologies into construction safety management, grounded in Building Information Modelling, Lean Construction, and Prevention through Design. This methodology structures the use of XR to support safety planning, training, inspection, and control, with a focus on lifecycle integration and proactive risk mitigation. Implementation examples are presented to illustrate the framework’s applicability and scalability. These demonstrate how XR can support immersive walkthroughs, synchronisation with BIM data, and simulation of human–machine interactions. This study contributes a structured, replicable approach that addresses the current fragmentation of XR safety applications, offering both a theoretical basis and practical guidance for adopting XR in construction safety workflows.

This paper provides a comprehensive review of Partial Power Converter (PPC) topologies and control methods for fast electric vehicle (EV) charging applications. Partial Power Converters are gaining traction to enhance converter efficiency, reduce power losses, and minimize component sizes by processing only a portion of the total power. This review covers key PPC topologies, including different partial power converters, and highlights their advantages and limitations in the context of EV charging. Various control methods that optimize the performance of these converters are also discussed. The paper presents a comparative analysis between partial power and full power converters. Finally, this review synthesizes the main findings and proposes guidelines for selecting appropriate PPC architectures for future fast EV charging stations.

Main conclusion: This review discusses the molecular modifications of grapevines by arbuscular mycorrhizal fungi, increasing anthocyanins and other phenolic molecules, potentially improving wine quality and plant stress tolerance. Abstract: Grapevines are naturally associated with arbuscular mycorrhizal fungi (AMF). These fungi, as obligate symbionts, are capable of influencing molecular, biochemical, and metabolic pathways, leading to alterations in the concentrations of various molecules within the host plant. Recent studies have addressed the transcriptomic and metabolic modifications triggered by AMF in grapevines. These AMF-induced alterations are involved in cell transport, sugar metabolism, plant defense mechanisms, and increased tolerance to both biotic and abiotic stressors. Notably, the shikimate pathway exhibits heightened activity following AMF inoculation in grapevines, resulting in the accumulation of anthocyanins, flavonols, phenolic acids, and stilbenes. Phenolic compounds are the main metabolites influencing grape and wine quality attributes, such as color, flavor, and potential health benefits. This review aims to provide an updated overview of current research on the transcriptomic and metabolic aspects of AMF–grapevine interactions, focusing on their impact on plant performance and quality traits. Graphic abstract: (Figure presented.)

The presence of natural areas can significantly influence the provision of multiple ecosystem services to agricultural landscapes. Although its role within agricultural landscapes as habitat for wild pollinators is increasingly recognized, it remains unclear whether these pollinators spill over into orchards to supplement pollination services—a critical knowledge gap, particularly in the threatened Mediterranean ecosystem of South America. This study addresses the role of neighboring natural areas in providing pollination services to a sentinel plant, Cucurbita pepo, which is highly dependent on animal pollination, in an agricultural landscape of Mediterranean-type ecosystems in central Chile. We hypothesized that pollination services (measured as fruit set) and wild pollinator richness would increase with the proportion of natural areas in the surrounding landscape. The study was conducted in conventional apple orchards of the same variety. Orchards were grouped into three categories based on the proportion of natural habitat within a 1 km buffer. We found that fruit set increased with the proportion of surrounding natural areas, showing a two-fold increase in those orchards surrounded by > 71% of natural areas compared to those with < 35%. These findings suggest that natural areas within agricultural landscapes enhance pollination services. In conclusion, conserving natural habitats within agricultural landscapes can serve as an effective ecological intensification practice to enhance pollination services and improve orchard productivity.

In Sergio Mansilla Torres’s first book, Noche de agua (1986), a poetic vision is inaugurated whose vital framework is rooted in the waters surrounding the islands of Chiloé. The intersection of historical time and mythological discourse building a language that integrates the concept of ancestors and tradition. The notion of space is symbolized by a houseboat that navigates diverse waters, which can be interpreted as representing different periods of life, including childhood and youth, as well as historical time, archetypal beliefs and rituals. This house is notable for its ability to transcend the confines of temporality, navigating the maritorium in a manner that symbolizes a renewal of the poetic home it acknowledges as its heritage.

Information disorder has significant negative impacts on contemporary societies. This study presents a hybrid methodology that combines machine learning and natural language processing to analyze corpora of disinformation texts in Spanish. The approach not only adapts linguistic features originally developed for English to another major but less researched language, but also incorporates 251 features organized into six categories, surpassing previous methods in both the number and organization of features. Applied to the CLNews dataset of Spanish rumors, the analysis identified 17 features with statistically significant differences between false and real rumors. Linguistic analysis reveals that false rumors are characterized by more emotional language, greater sentence fragmentation, frequent use of auxiliary verbs, and lower information density, which creates an appearance of detail. Additionally, using BERT, a large language model (LLM), five topics were identified among false rumors, each exhibiting different strategies in terms of fragmentation, grammatical complexity, and information density. Given the above, linguistic features were employed to develop machine learning classifiers, with a linear SVM achieving 86% accuracy. This methodology offers a replicable framework for future research on disinformation and text analysis in Spanish, enhancing the interpretability of results. The methodology shows that classical machine learning models trained on carefully chosen linguistic features can deliver competitive results, surpassing BETO (57%) and RoBERTa-BNE (64%) in accuracy on the CLNews dataset. Moreover, these models demonstrate strong performance when the same features are applied to a different dataset and continue to perform well when the feature selection is adjusted to fit the new context.

The widespread adoption of Internet of Things (IoT) devices has significantly increased the exposure of cloud-based architectures to cybersecurity risks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection methods often fail to efficiently identify and mitigate these threats in dynamic IoT/Cloud environments. This study proposes a machine-learning-based framework to enhance DDoS attack detection and mitigation, employing Random Forest, XGBoost, and Long Short-Term Memory (LSTM) models. Two well-established datasets, CIC-DDoS2019 and N-BaIoT, were used to train and evaluate the models, with feature selection techniques applied to optimize performance. A comparative analysis was conducted using key performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that Random Forest outperforms other models, achieving a precision of 99.96% and an F1-score of 95.84%. Additionally, a web-based dashboard was developed to visualize detection outcomes, facilitating real-time monitoring. This research highlights the importance of efficient data preprocessing and feature selection for improving detection capabilities in IoT/Cloud infrastructures. Furthermore, the potential integration of metaheuristic optimization for hyperparameter tuning and feature selection is identified as a promising direction for future work. The findings contribute to the development of more resilient and adaptive cybersecurity solutions for IoT/Cloud-based environments.

The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional integer programming solutions may become inefficient in real-life instances. Given this complexity, various metaheuristics have been successfully applied to solve the set-covering problem and related issues. This study introduces, implements, and analyzes a novel metaheuristic inspired by the well-established Growth Optimizer algorithm. Drawing insights from human behavioral patterns, this approach has shown promise in optimizing complex problems in continuous domains, where experimental results demonstrate the effectiveness and competitiveness of the metaheuristic compared to other strategies. The Growth Optimizer algorithm is modified and adapted to the realm of binary optimization for solving the set-covering problem, resulting in the creation of the Binary Growth Optimizer algorithm. Upon the implementation and analysis of its outcomes, the findings illustrate its capability to achieve competitive and efficient solutions in terms of resolution time and result quality.

The final filtration stage in wine bottling is critical for ensuring microbiological quality but is often affected by high variability and costs due to filter fouling. Traditional filterability indices lack physical interpretability and fail to account for key operational factors such as batch size and filtration area. This study introduces the Operational Filtration Index (OFI), derived from a hyperbolic model that describes the filtered volume over time under constant pressure, assuming standard membrane blocking. The Operational Filtration Index (OFI) quantifies the volume filtered when the flow rate drops to 25% of its initial value, which corresponds to a 75% obstruction of the effective filter surface under the assumption of direct proportionality between flow rate and active filtration area. Based on 46 controlled experiments with volume recorded every second, the model was fitted using nonlinear optimization. Integer optimization identified five measurement points that achieved a maximum error below 5%. External validation with 14 independent curves confirmed the model's consistency (R2 = 0.999) and low error levels. The OFI outperformed classical indicators in both discriminative power and stability. When adjusted by filtration area and combined with slack calculation, it supports operational decision-making by improving batch allocation and filtration line management. By providing a physically meaningful, replicable framework, the area-adjusted OFI helps close the gap between theoretical model development and practical implementation. This integrated approach bridges the gap between theoretical modeling and industrial practice, offering a reliable and physically interpretable index to guide filtration strategies in wine bottling operations.

Groundwater is an important resource that can help in climate change adaptation. However, the pollution of these aquifers with nitrate is a widespread problem of growing concern. Biological denitrification using inorganic electron donors shows significant advantages in treating nitrate-polluted groundwater where organic matter presence is negligible. However, mass transfer limitations and secondary contamination seem to be the major hinderance to spread the use of these technologies. This could be solved by the use of bioelectrochemical systems (BES), which emerge as an attractive technology to solve these problems due to the reported low energy demand and high denitrification rates. However, technical and operational issues must be considered to replicate these results at full-scale. This review summarizes the biological basis of autotrophic denitrification and the key aspects of its application in bioelectrochemical systems. In addition, an estimation of the capital costs required for the implementation of a BES considering different population sizes and initial nitrate concentration in the groundwater is made.

Technologies providing flexibility options to power systems, such as Power-To-X (PtX) technologies, have become more important with the increasing deployment of distributed energy resources, particularly microgrids. However, uncertainty in renewable resources creates ambiguity regarding the necessary PtX capacity to install. This paper proposes a two-stage stochastic Stackelberg approach for multi-energy microgrids, focusing on long-term investment decisions in PtX technologies and hourly operational strategies. The Stackelberg problem considers microgrids as leaders (upper level) and the independent system operator as a follower (lower level). In the first stage, investment levels for various PtX technologies are determined as one-time decisions. The second stage focuses on hourly operational decisions, including the integration of microgrids with the independent system operator with marginal endogenous prices. The results provide insights into how uncertainty in renewable generation and electric battery levels affect investment levels. Larger hydrogen and thermal storage volumes lead to more flexible and self-sufficient microgrid systems. In scenarios with higher flexibility, microgrids can: (1) satisfy up to 10% of the independent system operator demand using renewable electricity and (2) regulate supply variability by storing excess generation during peak periods and releasing it during low generation periods.

Intellectual precocity in children poses unique challenges and opportunities for educational systems. This systematic review aims to comprehensively analyze intellectual precocity in children until 6 years old, including its definition, manifestations, and various educational programs for intellectually precocious learners. Following PRISMA guidelines, a comprehensive search of electronic databases was conducted. The study included 26 articles published between 2013 and 2023 that provided a conceptualization of precocity or giftedness, and/or focused on characteristics of precocity, and/or investigated educational programs for intellectually precocious children. The authors’ conceptualizations of precocity varied, with some providing clear definitions based on a developmental view of precocity, while others merely mentioned the concept. Early indicators of superior traits have been observed in areas such as reading, math, problem-solving, and even in fields that have been traditionally disregarded, such as visual arts. Educational provisions varied widely, including approaches based on enrichment and project-based learning; however, interventions based on socioemotional elements are also highlighted. The findings emphasize the importance of early identification and targeted educational strategies to support the unique needs of intellectually precocious individuals. Future research should focus on longitudinal studies and the development of evidence-based interventions.

The construction industry, a cornerstone of global economic growth, continues to struggle with entrenched inefficiencies, including low productivity, cost overruns, and fragmented project delivery. Addressing these persistent challenges requires more than incremental improvements, it demands a strategic unification of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies. This systematic review synthesizes evidence from 64 academic studies to identify the most influential tools, techniques, and methodologies across these domains, revealing both their individual strengths and untapped synergies. The analysis highlights widely adopted Lean practices such as the Last Planner System (LPS) and Just-In-Time (JIT); BIM capabilities across 3D, 4D, 5D, 6D, and 7D dimensions; and a spectrum of digital innovations including Digital Twins, AR/VR/MR, AI, IoT, robotics, and blockchain. Crucially, the review demonstrates that despite rapid advancements, integration remains sporadic and unstructured, representing a critical research and industry gap. By moving beyond descriptive mapping, this study establishes an essential foundation for the development of robust, adaptable integration frameworks capable of bridging theory and practice. Such frameworks are urgently needed to optimize efficiency, enhance sustainability, and enable innovation in large-scale and complex construction projects, positioning this work as both a scholarly contribution and a practical roadmap for future research and implementation.

The rapid growth of complex textual data in domains such as medicine, law, and science has heightened the relevance of Long Document Summarization (LDS). Effective summarization not only requires advanced techniques but also robust evaluation metrics capable of capturing summary quality, coherence, and factual accuracy. We analyze 113 peer-reviewed studies from last two years, selected through comprehensive searches in SCOPUS, Web of Science, and PubMed, following PRISMA 2020 guidelines. We focus on LDS methods and the metrics used to evaluate them. Results indicate a rising adoption of hybrid models combining extractive and abstractive strategies, frequently powered by deep learning and optimization. Concurrently, evaluation practices have shifted from traditional overlap-based metrics (e.g., ROUGE) toward semantic measures such as BERTScore and MoverScore. However, these metrics still face challenges related to interpretability, domain adaptation, and computational cost. We advocate for the development of holistic, explainable, and reference-free evaluation frameworks aligned with human judgment to enhance the reliability and applicability of LDS systems across domains.