Search Results (478)

eSports and recreational video gaming are expanding in Peru, yet evidence on gamers’ dietary habits and correlates is scarce. We aimed to identify factors associated with eating habits among Peruvian video gamers and professional eSports players. Quantitative and cross-sectional study (Peru, 2023). A culturally adapted version of the German Sport University Cologne questionnaire (28 items; Cronbach’s α = 0.86) was administered online using non-probability snowball sampling. The primary outcome was eating-habit classification (adequate vs. inadequate) based on the instrument’s scoring. Associations with hypothesized correlates (e.g., gaming-related influences, peer interaction, advertising) were assessed with χ² or Fisher’s exact test (α = 0.05). We analyzed 288 respondents (median age 21 years). Overall, 77.8% exhibited inadequate eating habits. Daily water intake was reported by 72%, whereas daily fruit and vegetable consumption was 21% and 32%, respectively. Peer interaction within the gaming environment (p = 0.037) and the perceived influence of video games (p = 0.031) were significantly associated with poorer eating habits. Sitting time, number of meals per day, daily water intake volume, and weekly gaming hours showed no significant association (all p > 0.05). Most Peruvian gamers report suboptimal diets. Social dynamics in the gamer community and gaming-related influences are linked to poorer eating habits, suggesting that nutrition strategies should be embedded in gamer ecosystems (teams, communities, platforms). Longitudinal and interventional studies are warranted to test targeted behavior-change approaches. Full article

The significant side effects associated with platinum-based anticancer agents have driven the continuous pursuit of novel, non-platinum-based metal compounds. Ruthenium-based organometallic compounds have emerged as promising alternatives, owing to their distinctive and adaptable biochemical properties. The research efforts are focused on the development of ruthenacarborane-based anticancer drugs. The combination of ruthenium(II) complexes, recognized for their inherent anticancer potential, with carboranes, boron-rich clusters possessing unique chemical and physical characteristics, and NSAIDs, known to inhibit COX, an enzyme overexpressed in tumors, offers a novel approach for cancer therapy. Consequently, combining these three moieties into a single molecule represents a compelling strategy to develop drugs with a dual mode of action. Herein, we report the synthesis of a series of ruthenacarborane-(η⁶-p-cymene)–NSAID conjugates (4a, 4b, 5b, and 6b) by linking NSAIDs (flurbiprofen, fenoprofen, and ibuprofen) to ruthenacarborane complexes using methylene and ethylene spacers, while maintaining the integrity of the sensitive ester groups present in the system. The synthesized conjugates were thoroughly characterized using multinuclear (¹H, ¹¹B, and ¹³C) NMR spectroscopy. Notably, the conjugates demonstrated low COX inhibition and no cytotoxic potential against different cancer cell lines, probably due to oxidative deactivation confirmed by cyclic voltammetry (CV). This indicates that the conjugation of this type of ruthenacarborane with NSAIDs does not result in novel anticancer drugs. Full article

Background: The current polio epidemiology in Pakistan poses a unique challenge for global eradication, with polio transmission dynamics influenced by regional variations in immunity and disparities in immunization coverage. This study assesses the immunity level for all three poliovirus types among children aged 6–11 months in polio high-risk regions of Pakistan. Methods: Four consecutive rounds of cross-sectional serological surveys were conducted in polio high-risk areas of Pakistan between November 2016 and October 2023. Twelve high-risk areas were covered in the first three rounds of the survey, while 44 high-risk areas were covered in the fourth round. 25 clusters from each geographical stratum were selected utilizing probability proportional to size. Results: Across the four rounds of the survey, 32,907 children aged 6–11 months from 2084 clusters and 32,371 households were covered. Comparative analysis across the survey rounds showed that seroprevalence of poliovirus type 1 was high in provinces (>95%), albeit consistently lower in Balochistan (going down to 89.7% in Round 4). Type 2 seroprevalence was significantly lower and more heterogeneous, from 34.6% in Sindh to 83.4% in Punjab, with sharp declines by round 4, particularly in Balochistan (40.4%). Type 3 seroprevalence was overall high (>94% in Punjab, Sindh, and KPK) but dropped in the last round, while Balochistan exhibited continually lower immunity (81.1%). Conclusions: The findings reflect the variations in population immunity to poliovirus in the country, with notable fluctuations over the years. The gaps in type 2 immunity over time and consistently lowest in Balochistan highlight the need for continued monitoring of immunity levels and adaptable vaccination strategies. Full article

At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even within the same semantic class, there are problems such as poor optimization performance, slow convergence speed, and low stability. Therefore, to address the challenges of instance segmentation, an improved image segmentation model is proposed, and a novel BAS algorithm called the Crossover and Mutation Beetle Antennae Search (CMBAS) algorithm is designed to optimize it. The core of our approach treats instance segmentation as a sophisticated clustering problem, where each cluster center corresponds to a unique object instance. Firstly, an improved intra-class distance based on fuzzy membership weighting is designed to enhance the compactness of individual instances. Secondly, to quantify the genetic potential of individuals through their fitness performance, CMBAS uses an adaptive crossover rate mechanism based on fitness ranking and establishes a ranking-driven crossover probability allocation model. Thirdly, to guide individuals to evolve towards excellence, CMBAS uses a strategy for individual mutation of longicorn beetle antennae based on DE/current-to-best/1. Furthermore, the symmetry-aware adaptive crossover and mutation operations enhance the balance between exploration and exploitation, leading to more robust and consistent instance-level segmentation results. Experimental results on five typical benchmark functions demonstrate that CMBAS achieves superior accuracy and stability compared to the BAGWO, BAS, GWO, PSO, GA, Jaya, and FA algorithms. In image segmentation applications, CMBAS exhibits exceptional instance segmentation performance, including an enhanced ability to distinguish between adjacent or overlapping objects of the same class, resulting in smoother and more continuous instance boundaries, clearer segmented targets, and excellent convergence performance. Full article

Cold-climate urban neighborhoods face mounting energy and thermal risks from extreme weather and power outages, creating trade-offs between different resilience capacities and objectives. This study develops a scalable, data-driven decision-making tool to support early-stage prioritization of resilience strategies at both the building component and neighborhood levels. A database of 48 active and passive strategies was systematically linked to 14 resilience objectives, reflecting energy- and thermally oriented capacities. Each strategy–objective pair was qualitatively assessed through a literature review and translated into probability distributions. Monte Carlo simulations (10,000 iterations) were performed to generate possible outcomes and several scores were calculated. Comparative scenario analysis—spanning holistic, short-term, long-term, energy-oriented, and thermally oriented perspectives—highlighted distinct adoption patterns. Active energy strategies, such as ESS, decentralized RES, microgrids, and CHP, consistently achieved the highest adoption (A) scores across levels and scenarios. Several passive measures, including green roofs, natural ventilation with passive heat recovery, and responsive glazing, also demonstrated strong multi-objective performance and outage resilience. A case study application integrated stakeholder-specific objective weightings, revealing convergent strategies suitable for immediate adoption and divergent ones requiring negotiation. This tool provides an adaptable probabilistic foundation for evaluating resilience strategies under uncertainty. Full article

Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article

To alleviate the communication burden of wide-area protection and enhance the fault tolerance of multi-source criteria, this paper introduces an improved wide-area backup protection method based on multi-source information fusion. Initially, the variation characteristics of bus sequence voltages after a fault are utilized to screen suspected fault lines, thereby reducing communication traffic. Subsequently, four basic probability assignment functions are constructed using the polarity of zero-sequence current charge, the polarity of phase-difference current charge, and the starting signals of Zone II/III distance protection from the local and adjacent lines. The confidence of each probability function is evaluated using normalized information entropy, while consistency is analyzed via Gaussian similarity, enabling dynamic allocation of fusion weights. Additionally, a conflict adaptation factor is designed to adjust the fusion strategy dynamically, improving fault tolerance in high-conflict scenarios and mitigating the impact of abnormal single criteria on decision results. Finally, the fused fault probability is used to identify the fault line. Simulation results based on the IEEE 39-bus model demonstrate that the proposed algorithm can accurately identify fault lines under different fault types and locations and remains robust under conditions such as information loss and protection maloperation or failure. Full article

Hyperedge prediction is crucial for uncovering higher-order relationships in complex systems but faces core challenges, including unmodeled node influence heterogeneity, overlooked hyperedge order effects, and data sparsity. This paper proposes Order propagation Fusion Self-supervised learning for Hyperedge prediction (OFSH) to address these issues. OFSH introduces a hyperedge order propagation mechanism that dynamically learns node importance weights and groups neighbor hyperedges by order, applying max–min pooling to amplify feature distinctions. To mitigate data sparsity, OFSH incorporates a key node-guided augmentation strategy with adaptive masking, preserving core high-order semantics. It identifies topological hub nodes based on their comprehensive influence and employs adaptive masking probabilities to generate augmented views preserving core high-order semantics. Finally, a triadic contrastive loss is employed to maximize cross-view consistency and capture invariant semantic information under perturbations. Extensive experiments on five public real-world hypergraph datasets demonstrate significant improvements over state-of-the-art methods in AUROC and AP. Full article

To overcome the obstacles of maintaining covert transmissions in wireless networks employing collaborative wardens, we develop a reinforcement learning framework that jointly optimizes cooperative jamming strategies and relay selection mechanisms. The study focuses on a multi-relay-assisted two-hop network, where potential relays dynamically act as information relays or cooperative jammers to enhance covertness. A reinforcement learning-based relay selection scheme (RLRS) is employed to dynamically select optimal relays for signal forwarding and jamming; the framework simultaneously maximizes covert throughput and guarantees warden detection failure probability, subject to rigorous power budgets. Numerical simulations reveal that the developed reinforcement learning approach outperforms conventional random relay selection (RRS) across multiple performance metrics, achieving (i) higher peak covert transmission rates, (ii) lower outage probabilities, and (iii) superior adaptability to dynamic network parameters including relay density, power allocation variations, and additive white Gaussian noise (AWGN) fluctuations. These findings validate the effectiveness of reinforcement learning in optimizing relay and jammer selection for secure covert communications under colluding warden scenarios. Full article

Edge intelligence plays an increasingly vital role in ensuring the reliability of distributed microservice-based applications, which are widely used in domains such as e-commerce, industrial IoT, and cloud-edge collaborative platforms. However, anomaly detection in these systems encounters a critical challenge: labeled anomaly data are scarce. This scarcity leads to severe class asymmetry and compromised detection performance, particularly under the resource constraints of edge environments. Recent approaches based on Graph Neural Networks (GNNs)—often integrated with DeepSVDD and regularization techniques—have shown potential, but they rarely address this asymmetry in an adaptive, scenario-specific way. This work proposes Heterogeneous Graph Adaptive Augmentation (HGAA), a framework tailored for edge intelligence scenarios. HGAA dynamically optimizes graph data augmentation by leveraging feedback from online anomaly detection. To enhance detection accuracy while adhering to resource constraints, the framework incorporates a selective bias toward underrepresented anomaly types. It uses knowledge distillation to model dataset-dependent distributions and adaptively adjusts augmentation probabilities, thus avoiding excessive computational overhead in edge environments. Additionally, a dynamic adjustment mechanism evaluates augmentation success rates in real time, refining the selection processes to maintain model robustness. Experiments were conducted on two real-world datasets (TraceLog and FlowGraph) under simulated edge scenarios. Results show that HGAA consistently outperforms competitive baseline methods. Specifically, compared with the best non-adaptive augmentation strategies, HGAA achieves an average improvement of 4.5% in AUC and 4.6% in AP. Even larger gains are observed in challenging cases: for example, when using the HGT model on the TraceLog dataset, AUC improves by 14.6% and AP by 18.1%. Beyond accuracy, HGAA also significantly enhances efficiency: compared with filter-based methods, training time is reduced by up to 71% on TraceLog and 8.6% on FlowGraph, confirming its suitability for resource-constrained edge environments. These results highlight the potential of adaptive, edge-aware augmentation techniques in improving microservice anomaly detection within heterogeneous, resource-limited environments. Full article

Knowledge Distillation (KD) is a machine learning technique in which a compact student model learns to replicate the performance of a larger teacher model by mimicking its output predictions. Multi-Teacher Knowledge Distillation extends this paradigm by aggregating knowledge from multiple teacher models to improve generalization and robustness. However, effectively integrating outputs from diverse teachers, especially in the presence of noise or conflicting predictions, remains a key challenge. In this work, we propose a Multi-Round Parallel Multi-Teacher Distillation (MPMTD) that systematically explores and combines multiple aggregation techniques. Specifically, we investigate aggregation at different levels, including loss-based and probability-distribution-based fusion. Our framework applies different strategies across distillation rounds, enabling adaptive and synergistic knowledge transfer. Through extensive experimentation, we analyze the strengths and weaknesses of individual aggregation methods and demonstrate that strategic sequencing across rounds significantly outperforms static approaches. Notably, we introduce the Byzantine-Resilient Probability Distribution aggregation method applied for the first time in a KD context, which achieves state-of-the-art performance, with an accuracy of 99.29% and an F1-score of 99.27%. We further identify optimal configurations in terms of the number of distillation rounds and the ordering of aggregation strategies, balancing accuracy with computational efficiency. Our contributions include (i) the introduction of advanced aggregation strategies into the KD setting, (ii) a systematic evaluation of their performance, and (iii) practical recommendations for real-world deployment. These findings have significant implications for distributed learning, edge computing, and IoT environments, where efficient and resilient model compression is essential. Full article

Climate change is intensifying hydrological extremes, posing growing threats to the safety and operational reliability of embankment dams worldwide, particularly those in regions susceptible to heavy rainfall and flooding. This study evaluates the overtopping risk for Batu Dam, a critical flood mitigation and water supply structure near Kuala Lumpur, Malaysia, under future climate scenarios, with the aim of informing risk-informed dam safety strategies. Using historical hydrological data (1975–2020) and downscaled climate projections from the CMIP5 database under three Representative Concentration Pathways (RCP4.5, RCP6.0, RCP8.5), we conducted flood routing simulations and probabilistic risk assessments employing the iPRESAS software. Our results demonstrate that the annual probability of overtopping increases substantially under higher-emission scenarios, reaching up to 0.08% by the late century under RCP8.5, driven by increased frequency and intensity of extreme rainfall events. These projections highlight significant spillway capacity limitations and underscore the heightened risk of downstream consequences, including economic losses exceeding RM 200 million and potential loss of life surpassing 2900 individuals in worst-case scenarios. The findings confirm the urgent need for both structural adaptations, such as spillway expansion and crest elevation, and non-structural measures, including enhanced real-time monitoring and early warning systems. This integrated approach offers a robust and replicable framework for strengthening dam safety under evolving climate conditions. Full article

In maritime target search missions, UAV swarm task scheduling faces several challenges. These include uncertainties in target states, the high-dimensional multimodal characteristic of the solution space, and dynamic constraints on swarm collaboration. In terms of target position estimation, existing methods ignore the spatiotemporal correlation of target movement. At the level of optimization algorithms, existing algorithms struggle to balance global exploration and local exploitation, and they tend to fall into local optima. To address the above shortcomings, this paper constructs a technical system of “state perception-strategy optimization-collaborative execution”. First, a Serial Memory Iterative Method (GMMIM) integrated with the Gaussian–Markov model is proposed. This method recursively corrects the probability distribution of target positions using historical state data, thereby providing accurate situational support for decision-making. As a result, task scheduling efficiency is improved by 5.36%. Second, the sliding window technique is introduced to improve the Grey Wolf Optimizer (GWO). Based on the convergence of the population’s optimal fitness, the decay rate of the convergence factor is dynamically and adaptively adjusted. This balances the capabilities of global exploration and local exploitation to ensure swarm scheduling efficiency. Simulations demonstrate that the optimization performance of the proposed FSW-GWO algorithm is 16.95% higher than that of the IPSO method. Finally, a dynamic task weight update mechanism is designed. By combining resource load and task timeliness requirements, this mechanism achieves complementary adaptation between swarm resources and tasks. Full article

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on $L_2$, $L_1$, and $L_p$ regularizations have been widely adopted. Based on this foundation and combining successful experiences of previous work, this paper introduces $L_N$ regularization, a novel nonlinear sparse regularization combining the $L_p$ and $L_{\mathrm{\infty }}$ norms via nonlinear coupling. Statistical probability analysis demonstrates that $L_N$ regularization achieves stronger sparsity than traditional regularizations like $L_2$, $L_1$, and $L_p$ regularizations. Furthermore, building upon the $L_N$ regularization, we propose a novel nonlinear sparse model for blind image deblurring. To optimize the proposed $L_N$ regularization, we introduce an Adaptive Generalized Soft-Thresholding (AGST) algorithm and further develop an efficient optimization strategy by integrating AGST with the Half-Quadratic Splitting (HQS) strategy. Extensive experiments conducted on synthetic datasets and real-world images demonstrate that the proposed nonlinear sparse model achieves superior deblurring performance while maintaining completive computational efficiency. Full article

The transition toward low-carbon energy systems requires reliable tools for assessing renewable-based hydrogen production under real-world climatic and economic conditions. This study presents a novel probabilistic framework integrating the following three complementary elements: (1) a Photovoltaic Geographical Information System (PVGIS) for high-resolution, location-specific solar energy data; (2) Metalog probability distributions for advanced modeling of variability and uncertainty in photovoltaic (PV) energy generation; and (3) Levelized Cost of Hydrogen (LCOH) calculations to evaluate the economic viability of hydrogen production systems. The methodology is applied to three diverse European locations—Lublin (Poland), Budapest (Hungary), and Malaga (Spain)—to demonstrate regional differences in hydrogen production potential. The results indicate annual PV energy yields of 108.3 MWh, 124.6 MWh, and 170.95 MWh, respectively, which translate into LCOH values of EUR 9.67/kg (Poland), EUR 8.40/kg (Hungary), and EUR 6.13/kg (Spain). The probabilistic analysis reveals seasonal production risks and quantifies the probability of achieving specific monthly energy thresholds, providing critical insights for designing systems with continuous hydrogen output. This combined use of a PVGIS, Metalog, and LCOH calculations offers a unique decision-support tool for investors, policymakers, and SMEs planning green hydrogen projects. The proposed methodology is scalable and adaptable to other renewable energy systems, enabling informed investment decisions and improved regional energy transition strategies. Full article