Search Results (9,261)

Background/Objectives: Periodontitis represents a significant global health burden, yet preventive health literacy remains critically low among emerging adults—a developmental stage where lifelong health behaviors crystallize. This study evaluated the effectiveness of the GUM (an acronym of Gum Understanding Mission) game, an interactive gamified digital tool incorporating AI-informed or manual feedback, for improving periodontitis literacy among tenth-semester Software Engineering students at the University of Guayaquil. Methods: In a controlled pre-test/post-test experiment, 50 participants were randomly assigned to either the GUM game intervention or a traditional lecture. Both groups completed identical knowledge assessments immediately before and after their respective 50-min instructional sessions. The GUM game featured adaptive questioning, immediate elaborated feedback, and comprehensive performance analytics, while the control group received instructor-led didactic instruction with a subsequent question-and-answer session. Results: The GUM group improved from a baseline of 21% to 94% correct responses, while the lecture group increased from 22% to 67% ($p<0.001$). Error reduction was 74% in the GUM group versus 45% in the control group. However, the study’s scope is currently limited to a single, digitally literate cohort, and knowledge retention over time was not assessed. Conclusions: These findings suggest that a self-directed, feedback-driven serious game can substantially outperform traditional methods in fostering periodontitis literacy within this population. Further research is needed across diverse populations with extended follow-up periods to assess knowledge retention and generalizability. Full article

The growing need for sustainable aviation propulsion has increased interest in hydrogen fuel cell systems as alternatives to combustion engines. This study presents the modeling, simulation, and optimization of a hybrid hydrogen–electric powertrain for the MIMIQ unmanned aerial vehicle (UAV). A 2 kW proton exchange membrane fuel cell is integrated with a 12S lithium-polymer battery via a DC–DC converter, enabling parallel power sharing and in-flight battery recharging. A MATLAB-based dynamic model was developed using mission power profiles derived from flight data and refined using momentum theory. The developed model was benchmarked through a comparative simulation of a combustion-based hybrid-electric powertrain variant of the same platform, demonstrating consistency in electrical and energetic behavior. Multi-objective optimization using NSGA-II was performed to maximize hover endurance and to minimize energy consumption while maximizing payload over a full mission. Results from this computational framework show that endurance is primarily constrained by hydrogen availability rather than battery capacity, with the fuel cell operating near its optimal efficiency region. The findings indicate that hydrogen–electric architectures offer improved endurance, reduced emissions and better scalability compared to combustion-based systems, supporting their suitability for long-endurance UAV applications. Full article

Background: Adventist spirituality has been identified as a relevant psychosocial resource for emotional well-being; however, evidence on its relationship with self-control and perceived stress in specific religious populations remains limited. Objective: The aim of this study was to examine the associations between Adventist spirituality, self-control, and perceived stress in a sample of adults belonging to the Seventh-day Adventist Church and residing in coastal regions of Peru. Methods: A cross-sectional study was conducted between December 2025 and January 2026 with 506 Seventh-day Adventist adults who completed an online questionnaire. Adventist spirituality was assessed using the Mission Commitment Questionnaire, which captures religious–spiritual commitment through three dimensions: personal devotion, participation, and witnessing. Self-control and perceived stress were measured using standardized scales. Data were analyzed using partial least squares structural equation modeling. Results: The constructs showed adequate internal consistency, with Cronbach’s alpha values ranging from 0.875 to 0.951 and composite reliability values ranging from 0.906 to 0.956. Adventist spirituality was positively associated with self-control (β = 0.479, p < 0.001) and negatively associated with perceived stress (β = −0.457, p < 0.001). Personal devotion showed the strongest contribution to the higher-order spirituality construct. The model explained 22.9% of the variance in self-control and 20.9% of the variance in perceived stress. Conclusions: Adventist spirituality, particularly personal devotion, was associated with higher self-control and lower perceived stress. Although the cross-sectional design does not allow causal inference, the findings support the relevance of Adventist spirituality as a psychosocial resource linked to emotional well-being in this religious population and justify future longitudinal studies. Full article

The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of $804.7\pm 1.7$ bytes and near-constant verification time of $0.662\pm 0.032$ s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling ($\alpha =0.256$, $R^2=0.608$), with statistically significant differences between circuit categories confirmed by one-way ANOVA ($F=355.0$, $p<{10}^{-79}$, ${\eta }^2=0.94$). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks. Full article

Uncrewed aerial systems (UASs) have diverse applications in natural environments, yet their deployment can inadvertently disturb wildlife. This PRISMA-guided systematic review synthesised 39 studies (2015–2025) encompassing birds, mammals, and marine taxa to identify UAS operational drivers of disturbance. We applied a Factors–Responses–Consequences (F–R–C) framework linking UAS operational characteristics, observed wildlife responses, and ecological consequences. Three patterns emerged: (i) Factors: Contextual and operational conditions such as flight altitude, noise, and approach direction interact with species-specific sensitivities to shape disturbance potential. (ii) Responses: Physiological measures (e.g., elevated heart rates) often reveal hidden stress not evident from behaviour alone. (iii) Consequences: Short-term effects may accumulate into long-term impacts on health, reproduction, and habitat use. These findings highlight the need for species- and context-specific flight envelopes rather than solely uniform altitude limits. By structuring existing evidence within the F–R–C framework, this synthesis provides a transferable foundation for UAS mission planning, drone development, operational decision-making, ethical practice, and environmental impact assessment in conservation and wildlife-management contexts. As all screening and data extraction were conducted by a single reviewer, the findings should be interpreted with appropriate caution pending independent validation. Full article

The increasing availability of microwave bistatic remote sensing data highlights the need for reliable and computationally efficient scattering models to support data interpretation, system design, and mission planning. This is particularly relevant in urban and maritime environments, where the electromagnetic (EM) interaction between buildings and ships with the surrounding environment significantly affects the observed bistatic signatures. This paper presents a fully analytical model for EM bistatic scattering from a canonical target, represented as a parallelepiped with smooth dielectric faces located over a lossy random rough surface. The formulation is developed within the framework of the Kirchhoff Approximation and accounts for both single- and multiple-bounce scattering mechanisms arising from the mutual interaction between the target and the underlying surface. Reflections from the target walls are modeled using the Geometrical Optics solution, while scattering from the rough surface is described through the zeroth-order Physical Optics approximation. The resulting closed-form expressions provide both coherent and incoherent components of the scattered field as explicit functions of system and scene parameters. The proposed closed-form model enables fast and reliable evaluation of bistatic scattering from parallelepiped-like structures, such as buildings and large ships interacting with surrounding rough surfaces. This capability is particularly beneficial for the design and optimization of bistatic remote sensing missions in urban and maritime contexts as well as the development and assessment of inversion methods and large-scale analyses. Validation against numerical simulations and experimental results available in the literature demonstrates the effectiveness of the proposed approach across different operating conditions. Full article

Hybrid-electric propulsion and alternative energy carriers are being considered to mitigate the climate impact of short-range regional aviation. Within this framework, the HERA (Hybrid Electric Regional Architecture) project investigates advanced propulsion architectures for a next-generation 72 passenger regional platform. This work presents a cradle-to-grave Life Cycle Assessment of two HERA reference configurations and compares them with a conventional 70 passenger turboprop representative of current service aircraft. The analysis focuses on lithium–sulphur batteries, proton exchange membrane fuel cells, liquid hydrogen storage tanks, and electric motors. The assessment is implemented through a parametric LCA tool supported by a detailed Life Cycle Inventory based on Ecoinvent v3.8 and evaluated using ReCiPe 2016 midpoint indicators. The system boundary includes raw material extraction, manufacturing and assembly, operation under defined mission profiles, maintenance with component replacement, and End-of-Life (EoL) treatment. Results show that the operational phase remains the main driver of climate change impacts, exceeding 95% of total CO₂ equivalent emissions across configurations. The battery-based hybrid reduces fuel consumption but increases manufacturing and maintenance burdens. The fuel cell configuration shows a more balanced life cycle profile, with platinum identified as a critical hotspot. Full article

Cross-domain formations composed of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs) are critical for maritime defense but face significant challenges in countering complex aerial threats and developing flexible, collaborative strategies. Addressing the limitations of traditional decision support systems in semantic understanding and dynamic adaptation, this paper proposes a novel Large Language Model (LLM)-driven decision support framework grounded in the Department of Defense Architecture Framework (DoDAF). By integrating Retrieval-Augmented Generation (RAG) with a domain-specific knowledge base, the framework enhances the LLM’s ability to align natural-language directives with standardized DoDAF view models, effectively mitigating hallucinations in tactical generation. The proposed framework coordinates a closed-loop process, using Petri net-based static logic verification to ensure structural consistency and Monte Carlo-based dynamic effectiveness evaluation to optimize the selection of kill chains. Experimental validations in a simulated UAV-USV maritime defense scenario demonstrate that the framework achieves 96.6% entity accuracy and 100% format compliance in model generation. In comparison, the generated cooperative kill chains significantly outperform non-cooperative methods by improving interception efficacy by approximately 26.08% under saturation attack conditions. This study develops an automated, interpretable workflow that transforms unstructured situational understanding into decision reporting, significantly enhancing the efficiency and reliability of cross-domain collaborative mission planning. Full article

The evolution toward 6G communications requires integrating Tiny Machine Learning (TinyML) principles to enable intelligent, energy-efficient, and adaptable signal processing at the network edge. However, current receiver architectures face a fundamental trade-off: classical model-driven designs, while naturally efficient due to their basis in communication theory, lack the flexibility to adapt to varying channel conditions. Meanwhile, fully data-driven deep-learning-based approaches break the stringent resource constraints of TinyML. This paper introduces the tiny neural receiver (TNR), a pioneering architecture that bridges these paradigms by integrating model-based signal processing with lightweight neural optimization to overcome this challenge. The TNR’s primary contribution is its unique hybrid design, which combines the efficiency and interpretability of traditional theory-based receivers with the ability to adapt to different contexts using trainable neural components. This integration occurs within resource budgets that align with TinyML specifications. Experimental results show that the TNR achieves a 5 dB SNR reduction at a target block error rate of ${10}^{-4}$. The reported 5 dB SNR gain is a direct result of our resource-aware design framework, which selectively applies lightweight neural optimization to only the most impactful receiver blocks (channel estimation and decoding) to maximize gain without exceeding TinyML complexity limits. This achievement is further supported by an end-to-end training protocol that uses 15,000 iterations of over-the-air data to fine-tune these parameters for the specific static 3.5 GHz propagation channel and OFDM configuration evaluated. Furthermore, the TNR’s modular design enables flexible deployment across a range of 6G scenarios, from mobile broadband to mission-critical IoT. This establishes the TNR as a promising framework for AI-native 6G receivers. Full article

Search and rescue (SAR) missions in collapsed or underground environments remain challenging due to GPS unavailability, which hinders localization and autonomous navigation. Systems that rely on single-sensor inputs or structured settings often degrade under smoke, dust, or dynamic clutter. This paper presents an autonomous ground robot for GPS-denied SAR that integrates low-cost thermal, visual, inertial, and acoustic cues within a unified, computation-efficient architecture. The stack combines Thermal–Visual Odometry (TV–VO) with Zero-Velocity Updates (ZUPT) for drift-resistant localization, RescueGraph for multimodal survivor detection, and a Proximal Policy Optimization (PPO) planner for adaptive navigation under uncertainty. Across simulated disaster scenarios and benchmark corridor runs, the system shows embedded-feasible runtime behavior and supports return to base without external beacons under the evaluated conditions. Quantitatively, TV–VO+ZUPT reduces drift in short internal evaluations, while RescueGraph attains an F1-score of 0.6923 and an area under the ROC curve (AUC) of 0.976 for survivor detection. At the system level, the integrated navigation stack achieves full mission completion in the reported SAR-style trials, while the separate A*/PPO comparison highlights a trade-off between completion rate, traversal time, and collisions. Overall, the results support the practical promise of a low-cost sensor-fusion and learning-assisted navigation framework for GPS-denied SAR robotics. Full article

In this paper, an integrated optimization is carried out to find the optimal hybrid rocket engine design for a modular multistage launch vehicle targeting a 500 km polar circular orbit. A single hybrid rocket engine unit is reused across the whole launch vehicle, with each stage constituted by a cluster of a specified number of units. Only the nozzle exit diameter of the units is allowed to change across each stage. This clustering approach is aimed at reducing the costs of the launch vehicle and at simplifying the optimization procedure. After a brief mission analysis based on Tsiolkovsky’s equation, a three-stage configuration is chosen for the launch vehicle, employing 16, 4, and 1 engine units for, respectively, the first, second, and third stage. A neural network-based surrogate model is employed to approximate the complex hybrid rocket internal ballistics, with the aim to reduce the computational cost of the optimization process. The surrogate model is trained to map a reduced number of design parameters to the performance and mass budget of a single engine unit using data from a 0-D hybrid rocket engine model. The accuracy of the trained network in predicting crucial features is then assessed. Finally, the trained network is integrated into a multidisciplinary optimization process. The aim is to identify the optimal rocket engine design and launch vehicle ascent trajectory that maximize the payload capacity to the target orbit. Full article

Task allocation for heterogeneous unmanned aerial vehicle (UAV) swarms requires complex spatiotemporal coordination. While traditional algorithms struggle to interpret abstract semantic intents, general large language models (LLMs) often suffer from physical hallucinations and superficial tactical reasoning. To address these limitations, we propose a generative task allocation paradigm augmented by a heterogeneous toolchain, shifting the approach from rigid numerical optimization toward tool-grounded semantic planning. To implement this and overcome domain data scarcity, we design a decoupled dual-model architecture. This architecture is optimized through an execution-manifold-anchored orthogonal evolution training method. By utilizing simulated self-play within a stable execution environment, this approach prevents gradient conflicts and autonomously generates abundant training data. Furthermore, to resolve the credit assignment problem in long-horizon scenarios, we develop a Recursive Causal Probe (RCP) algorithm. By tracing failures backward through the simulation, RCP synthesizes counterfactual preference data, effectively translating tactical mistakes into precise corrections for the planning model. Extensive simulations demonstrate that our method achieves an 82.34% mission success rate in complex scenarios, requiring significantly fewer interactive corrections than general LLMs, fully verifying its physical feasibility and practical robustness. Full article

The aviation industry is under increasing pressure to enhance sustainability by improving energy efficiency and reducing climate impact. A promising approach is to reduce aerodynamic drag using laminar flow technologies, particularly Natural Laminar Flow (NLF) and Hybrid Laminar Flow Control (HLFC). Previous research has primarily focused on aerodynamic performance, often considering only one technology at a time, using simplified HLFC system design models, and targeting long-range aircraft. This study adopts a more holistic approach by conducting a conceptual design of a short-medium range (SMR) aircraft equipped with both NLF and HLFC. The technologies are applied to the wing and empennage, with detailed HLFC system modelling integrated into the conceptual design process using established methods. A failure analysis is also performed to assess the performance impact of potential malfunctions. Results indicate that combining NLF and HLFC can reduce fuel consumption by 5.9% on the design mission compared to a fully turbulent reference aircraft. Moreover, selectively applying the technologies to specific components enhances fuel savings while reducing system complexity. These findings demonstrate the potential of laminar flow technologies to improve fuel efficiency in SMR aircraft and highlight the importance of integrated aerodynamic and systems-level evaluation. Full article

Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article