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.

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 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 demand for higher-quality education has become a reality in today’s global landscape, driven by rapid societal change and technological innovation. UNESCO’s Sustainable Development Goals place improving education at the forefront, emphasizing the need to raise standards, reduce inequalities, and foster equity in access and delivery. Achieving these goals requires a comprehensive, cross-sectoral strategy that applies specialized tools to address complex challenges within educational contexts. A central element in meeting these objectives is enhancing the Student eXperience (SX), an area increasingly recognized in higher education. The SX encompasses all physical, cognitive, and emotional interactions that students—both current and prospective—have with Higher Education Institutions (HEIs). Similar to the Customer eXperience (CX) area, evaluating the SX entails considering not only academic and infrastructural provisions but also psycho-emotional support, social integration, and availability of resources. Addressing the SX must account for diverse cultural backgrounds, as these can substantially influence student interactions and expectations. Incorporating models like Hofstede’s cultural dimensions into assessment methods allows HEIs to gain deeper insight into how varying cultural factors shape student perceptions and behaviors. In this context, we propose a property checklist as part of a structured evaluation methodology for SX. Designed as an initial diagnostic instrument, the checklist guides evaluators through a systematic review of key properties, criteria, and components influencing the student journey. By illuminating specific areas for improvement, this tool opens opportunities for targeted interventions that cater to the unique needs of diverse student populations. Ultimately, such a multifaceted approach fosters higher-quality education, mitigates inequalities, and advances equity and diversity across educational services.

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.

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.

Personal Comfort Systems (PCS) are equipments that heat and/or cool occupants without affecting surrounding environments, ranging from commonly used devices to innovative technologies, and that tend to be controlled by people. These systems aim to address energy consumption and occupant satisfaction issues related to centralised air-conditioning. Although there are systematic studies on these systems, there is a lack of documentation regarding mediation characteristics between people and the built environment. This article presents a systematic review of PCS using a search of academic literature and patents, classifying PCS based on thermal categories and device typologies while introducing post-phenomenological mediation categories. The results show that most PCS fall into the thermal categories of ‘Heating’ and ‘Cooling and ventilation’. The review also presents a view of the PCS territory based on mediation attributes and technological complexity. Finally, the PCS’ characteristics are discussed based on the post-phenomenological concepts of Embodiment, Hermeneutic, and Background providing insights for future research opportunities and PCS development.

Background: Academic stress can negatively influence academic performance, particularly in health science programs with high demands. Objective: To analyze the association between perceived academic stress and academic performance in first-year kinesiology students, considering gender differences and stress dimensions (stressors, reactions, and coping strategies). Methods: This cross-sectional study included 82 students assessed using the SISCO-IIA inventory. Academic performance was measured via final course grades. Descriptive analyses, chi-square tests, t-tests, and binary logistic regression models were applied. Results: Students with high levels of academic stressors were significantly more likely to underperform (OR = 4.62, 95% CI [1.24, 17.22], p = 0.023). Female students reported higher emotional and behavioral responses to stress but were less likely to underperform (OR = 0.30, 95% CI [0.11, 0.79], p = 0.014). Age was not significantly associated with performance (p = 0.433). Conclusion: Academic stress negatively influences academic outcomes among first-year students. Gender differences in stress response patterns may affect vulnerability and coping effectiveness. These findings highlight the need for early screening, targeted interventions, and support systems to mitigate academic stress and promote student success.

This study explores the passengers’ accessibility dilemma in Valparaíso, Chile, through field observations and laboratory experiments. The aim is to investigate the accessibility in metro stations based on the users’ emotional experience. Perceptions were reported through the emotions of passengers according to a circumplex psychological model and an accessibility ranking. Passengers reported their emotions (e.g., stress, sadness, relaxation, and happiness) during different trip moments. Results indicate that rearranging train seats parallel to movement creates a more spacious aisle, enhancing mobility and evoking positive emotions such as happiness. However, an unexpected rise in sadness suggests that social dynamics may influence emotional responses, warranting further investigation. Overcrowding increases stress and sadness, emphasizing the need for capacity management to improve passengers’ emotional experiences. Field observations reveal that early journey stages, such as walking to the station or waiting on the platform, are associated with unpleasant experiences due to poor infrastructure and accessibility barriers. In contrast, train rides foster more positive emotions, credited to better accessibility onboard. Passenger dissatisfaction arises from issues such as elevator malfunctions, inconsistent train schedules, and inadequate station accessibility. This study could help to understand passenger behavior when the accessibility conditions of metro stations and their surroundings are changed. Further studies will expand the concept of emotions by considering social and psychological factors and explore different types of stations and their surroundings considering a larger sample size in laboratory experiments and field studies.

Acidophiles comprise a group of microorganisms adapted to live in acidic environments. Despite acidophiles are usually associated with an autotrophic metabolism, more than 80 microorganisms capable of utilizing organic matter have been isolated from natural and man-made environments. The ability to reduce soluble and insoluble iron compounds has been described for many of these species and may be harnessed to develop new or improved mining processes when oxidative bioleaching is ineffective. Similarly, as these microorganisms grow in highly acidic media and the chances of contamination are reduced by the low pH, they may be employed to implement robust fermentation processes. By conducting an extensive literature review, this work presents an updated view of basic aspects and technological applications in biomining, bioremediation, fermentation processes aimed at biopolymers production, microbial electrochemical systems, and the potential use of extremozymes.

Background: Active parents may have more active children, as parents act as role models for their children’s physical activity (PA). Parents can facilitate access to PA, provide information, validate physical-sport behaviours, and offer emotional support. Also, the influence of parents’ PA on children’s PA in and out of the school context has not been well-established. Objective: This study aimed to examine the associations between parental PA and the PA levels of their children in both school and out-of-school settings, considering parental sex. Methods: A cross-sectional study was conducted in 2021 involving 1,030 parent-child dyads. Sociodemographic data and self-reported active behaviour measures were administered to both groups simultaneously. Multiple logistic regressions were performed to determine the associations. Results: A positive trend between parental and children’s PA was observed only out of school (Trend OR = 1.55, 95% CI = 1.25–1.95; p < 0.001). Maternal moderate-to-vigorous physical activity (MVPA) of 151–300 min per week was associated with MVPA in children on school days with physical education classes (OR = 3.48; 95% CI, 1.04–12.1). A trend was found in the maternal group for MVPA in children out of school (Trend OR = 1.57 [95% CI = 1.24; 2.01]). Conclusion: Positive associations between MVPA in children and more than 150 min of MVPA per week from parents were found. Also, a trend between maternal PA and their children’s out-of-school MVPA was identified, supporting future initiatives focused on family-based PA interventions.

Featured Application: Importantly, measurements obtained through VOT-NIRS can vary significantly post-exercise, which should be considered by researchers before application. This study evaluated the influence of acute fatigue on heart rate variability (HRV) and muscle oxygen saturation (SmO2) at rest, as well as the reliability of SmO2 data measured using near-infrared spectroscopy (NIRS) during a vascular occlusion test (VOT). Twelve physically active subjects participated. Measurements included perceived muscle soreness using the visual analog scale (VAS pain), HRV parameters, variables of resting SmO2 (desaturation and resaturation), and reoxygenation kinetics (mean response time, MRT) through a VOT-NIRS located in the vastus lateralis (VL). Measurements were taken at three points: 24 h before, before exhaustive exercise, and 30 min after exhaustive exercise. The results indicated that acute fatigue increased resting muscle oxygen consumption in desaturation (+22 SmO2) and resaturation (+18 SmO2), improved MRT (−15 s), and elevated sympathetic nervous system (SNS) activity, as observed in the R-R interval (−262 ms) and SNS index (+0.5). HRV significantly influenced desaturation (r2 = 0.69), resaturation (r2 = 0.60), and MRT (r2 = 0.54). Reliability was established with an ICC of 0.49 and 0.63 for desaturation and resaturation, respectively. Real changes in desaturation and resaturation should be considered ≥ 7% SmO2 at rest and ≥ 11% SmO2 to avoid daily fatigue interference. In conclusion, acute fatigue increases resting SmO2 consumption and is associated with higher SNS activity and increased VAS pain.

Portable near-infrared spectroscopy (NIRS) provides a noninvasive measure of local muscle metabolic demand through muscle oxygen saturation (SmO2). This study aimed to evaluate acute SmO2 responses in exercising and non-exercising muscles during an isometric test conducted before and after exercise. Twelve physically active men (25 ± 6 years; 1.75 ± 0.07 m) performed three maximal voluntary contractions (MVC) and a 45-s isometric contraction at 70% MVC (IC70%) before and after completing five sets of 30 concentric knee extensions at 180°·s−1, with 60 s rest between sets, on an isokinetic dynamometer. SmO2 was recorded in the vastus lateralis (exercised and non-exercised leg) and triceps brachii. Significant differences in SmO2 were observed between exercised and non-exercised legs both pre- (p = 0.048) and post-exercise (p < 0.001), whereas no significant changes were found in the triceps brachii or non-exercising vastus lateralis. Acute responses to isometric exercise differed between SmO2 and torque measures. While the torque of the IC70% did not change following the exercise protocol, SmO2 showed a higher deoxygenation response during exercise. The isometric test did not indicate any redistribution of muscle oxygenation from non-exercising to exercising muscles. These results provide novel insights into the dissociation between mechanical and microvascular responses during isometric exercise and support the potential of this test as a practical tool for monitoring training loads.

Coastal floodsCoastal floodshave recordedLaguna Verde a high recurrence and destructive impact in the central area of ChileChile in recent years. In areas of irregular occupation, the impact on residential infrastructure is visibly greater. However, it is observed that in the context of irregularity there are faster adaptationAdaptation responses to floods than in consolidated urban areas. These adaptationsAdaptation are mostly intuitive and lack professional judgment. To establish which type of building adapts best to flood conditions in the context of irregular occupation within the central area of ChileChile, the case of Laguna VerdeLaguna Verde, Valparaíso, was studied. Here it was detected that adaptationAdaptation occurs from transformations of the houses generally from the cube, which is the most governable figure culturally for the residents. Tests were developed with scale models to identify a structural form that allows supporting a progressive house. Subsequently, a prototype of a residential module was built, taking care to make a simple, replicable, affordable system that can be self-built. The experience allowed guiding an initial approach toward the designDesign of progressive houses adapted for areas of irregular occupation in the process of settlement.

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ( (Formula presented.) ). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the (Formula presented.) -optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments.