Search Results (3,036)

This article examines how ministry formation and ecclesial leadership in the Evangelical Lutheran Church of Finland (ELCF) are being reshaped by 21st-century dynamics such as secularization, pluralism, and post-Christendom cultural change. As increasing numbers of theology students enter formation with limited prior church experience, the gap between academic theological education and pastoral vocation is widening. The article treats formation and leadership as an integrated analytical lens, showing how ministerial identity, vocational resilience, and leadership capacity emerge from shared theological and pedagogical processes. Comparative insights from Sweden, the Church of England, and Roman Catholic formation models sharpen this analysis. The study proposes a theologically grounded, contextually realistic model that links formation, leadership, and innovation in a mutually reinforcing way. Innovation is conceptualized as targeted, incremental development—deepened theological reflection, collaborative learning, and adaptive practices—rather than large-scale structural reform. This approach aims to be realistic and to increase the capacity of future clergy to lead mission-oriented ministry in complex contemporary conditions. Full article

Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter wheat mapping in the Chaohu Basin, China, using a reconstructed 5-day Sentinel-2 NDVI time series and precipitation data from the Global Precipitation Measurement (GPM) mission. The model employs a dual-branch architecture to jointly learn vegetation and precipitation features and introduces a lag-aware dynamic gated fusion module to capture the delayed response of vegetation to precipitation and enhance multi-source feature fusion. The results show that the proposed method achieved reliable early-season winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional methods, including SVM, RF, DTW, and TCN, using the same reconstructed 5-day NDVI time series. Optimal performance uses a 7 × 7 patch size and 30-day precipitation window. Full article

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes. Full article

Atmospheric icing is one of the most critical meteorological hazards for unmanned aerial vehicles (UAV), whose operation under adverse conditions—high latitudes, elevated altitudes, long-endurance missions without pilot intervention—particularly exposes them to ice accumulation on aerodynamic surfaces and propellers. Unlike manned aviation, where this phenomenon has been extensively studied and regulated, a significant knowledge gap exists in the UAV domain that limits the development of effective protection systems adapted to energy constraints. This article provides an integrative review—conducted with a systematic search strategy following PRISMA reporting guidelines—of atmospheric ice formation mechanisms, their specific effects on UAV propellers, and the two most promising mitigation approaches: electrothermal modelling for the optimisation of electric heating systems and the development of functional surface materials including superhydrophobic coatings (SHC); composites with conductive nanofillers (graphene, carbon nanotubes); and piezoelectric actuators. The analysis demonstrates that hybrid systems combining passive and active strategies managed by intelligent control represent the most viable solution for extending UAV operational envelopes under known icing conditions, with a projected reduction in anti-icing system energy consumption of at least 40% relative to conventional continuous heating. This estimate is based on the most conservative published evidence: pulsed electrothermal de-icing achieves 40–60% savings versus continuous anti-icingSHC-assisted hybrid heating reduces IPS power by more than 80% on static aerofoils; and rotary-wing pulsed systems reduce mean consumption by 60–75% relative to continuous operation. Key research gaps are identified, and a prioritised future research agenda is proposed to support the development of certifiable anti-icing systems for rotary-wing UAV platforms. Full article

Research and development of Maritime Autonomous Surface Ships (MASS) has surged in recent years. However, questions remain regarding the safety of interactions between MASS and conventionally crewed ships in mixed-traffic environments, particularly during collision avoidance. Although the critical role of MASS technology is widely recognised, few studies have explored how these interactions may affect the behaviour of the conventional ship’s Officer of the Watch (OOW). The primary aim of this paper is to explore the challenges and complexities of mixed-traffic interaction from the perspective of the conventional OOW as an active participant in collision avoidance. A collision-avoidance framework is presented as an analytical lens linking situational awareness, motion prediction, mental models, conflict detection, conflict resolution, and manoeuvre execution. This framework is used to examine challenges related to communication, COLREG interpretation, uncertainty surrounding MASS capabilities and operations, knowledge and training, and trust. We argue that these factors may influence how OOWs interpret MASS behaviour and determine whether, when, and how to intervene. To address this research gap, key research questions are formulated, and a scenario-driven methodological approach using Full Mission Bridge Simulators (FMBS) is proposed to investigate changes in OOW behaviour and decision-making during mixed-traffic encounters. Full article

Owing to its inherent flight safety, low takeoff and landing requirements, and favorable economic efficiency, the autogyro UAV, especially its electric and hybrid-electric variants, has become a core platform for low-altitude aviation missions such as transportation, inspection, and surveillance in plateau and offshore regions. However, the low air density and low Reynolds number conditions encountered in plateau regions can induce aerodynamic issues such as premature laminar flow separation, dynamic stall, and increased induced drag, which directly reduce payload capacity and endurance of small electric autogyro UAVs. In offshore environments, strong winds, turbulence, and gust disturbances intensify rotor–wake interactions, cause abrupt variations in aerodynamic loads, and reduce control margins, which severely restricts the mission reliability and flight safety of low-altitude unmanned platforms. These environmental effects collectively degrade rotor performance, including reduced aerodynamic efficiency and insufficient lift generation, and further amplify the energy constraint of electric/hybrid-electric propulsion systems. In response to bottlenecks that restrict the practical application of autogyro UAVs in extreme environments, this paper systematically reviews research progress on lift vector control for autogyro UAV rotors operating under such conditions. First, the typical aerodynamic problems encountered by autogyro UAVs in plateau and offshore environments are summarized, and their underlying physical mechanisms are analyzed from both system-level and local-flow perspectives, with a focus on how environmental factors affect the autorotation stability of unmanned platforms. Subsequently, the development of passive lift vector control technologies is reviewed, with an emphasis on the aerodynamic benefits of passive pitch mechanisms, vortex generators, and blade-tip winglets, as well as their engineering feasibility for small autogyro UAV blades. Active lift vector control technologies are then examined, including air-jet flow control, synthetic jets, and trailing-edge flaps, with discussions of their potential to delay flow separation and stall, enhance rotor aerodynamic efficiency, and an assessment of their adaptability to the energy and structural constraints of unmanned platforms. Finally, a lift vector control strategy suitable for autorotating rotors of autogyro UAVs is proposed, based on careful consideration of energy consumption, structural constraints, and control effectiveness. It provides a reference for aerodynamic optimization and flight control research on electric and hybrid-electric autogyro UAVs operating in extremely low-altitude environments. Full article

Integrating Model-Based Systems Engineering (MBSE) with Digital Twins (DTs) offers a promising way to transform static system models into dynamic, runtime-connected representations that enable execution, monitoring, and validation. Much current research in this area remains focused on descriptive modeling, simulation-based workflows, or unidirectional data flow, leaving a gap in methods that link architectural models directly to physical system behavior in a closed-loop system. This study fills that gap by proposing and demonstrating a four-layer framework that supports traceable, bidirectional interaction between a system’s architecture and its physical implementation. The framework consists of four interconnected layers: System Definition, Model Translation and Integration, Execution and Monitoring, and Feedback and Synchronization. The System Definition Layer captures mission goals, functional responsibilities, subsystem decomposition, and mission parameters in Capella using the Arcadia methodology. The Model Translation and Integration Layer translates these properties into executable commands via Python4Capella, converting architecture-level parameters into actionable instructions. The Execution and Monitoring Layer executes these commands in MATLAB, while the Feedback and Synchronization Layer returns runtime data to the model, supporting validation, model awareness, and refinement. A UAV case study is used to validate the framework: the UAV architecture is decomposed into key logical subsystems, and mission behaviors such as takeoff, movement, turning, and landing were modeled parametrically using the Property Values Management Tool (PVMT). These properties were translated into MATLAB commands, executed on a physical UAV, and evaluated based on telemetry-based mission distance accuracy. This work demonstrates that an MBSE model can serve not only as a design artifact but also as an authoritative, execution-connected component of a digital twin workflow. The framework contributes to MBSE-driven digital twin research by providing a structured process for integrating architecture, translation, execution, monitoring, and synchronization in a traceable manner. Overall, the study provides a scalable foundation for future developments in hardware-in-the-loop testing and digital twin applications for cyber–physical systems, and further empirical evaluation is needed to assess the framework’s transferability across domains. Full article

Rapid cooperative detection of stationary targets by multiple unmanned aerial vehicles (UAVs) is important in time-critical missions such as search and rescue. However, the online coordination of probabilistic inference, distributed communication, and detection–motion decisions under local information remain challenging when targets exhibit spatial correlations that existing methods typically neglect. To address this challenge, we develop a distributed event-driven Bayesian search framework for stationary, spatially correlated targets at unknown locations. The framework couples three components. A pairwise spatial model and a distance-dependent Neyman–Pearson detector yield a Bayesian belief update whose unclipped product form is order-invariant to event-processing sequence. A distributed selective flooding algorithm propagates only positive detection events, achieving finite-time event-set consensus over connected graphs while avoiding full-map exchange. A decoupled detection–motion planner exhausts high-belief cells within each UAV’s field of view before selecting a waypoint that balances surrogate detection probability against travel cost, with responsibility regions dynamically renegotiated among neighbors when local high-value cells are depleted. In numerical experiments, the proposed method achieved zero uncoordinated repeat detection in all simulations and significantly reduced first-discovery coverage relative to static-partition and no-communication baselines, while adapted external baselines required 90-fold and 6-fold larger communication payloads and had nonzero repeat-detection rates. The framework thus occupies a specific tradeoff point of zero revisit, sparse communication, and early discovery gain in scenes where targets span multiple UAV search regions. Full article

Vineyard monitoring requires efficient methods for detecting plant stress and disease while limiting flight time, data volume, and labour effort. This paper presents a satellite-enabled two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, NDVI-based point-of-interest identification, adaptive flight path planning, low-altitude RGB inspection, and downstream vision-based disease analysis. Processing tasks are offloaded to a remote server accessed through an emulated Low Earth Orbit satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. A simulation framework was developed to evaluate mission behaviour under controlled and repeatable conditions, using both pseudo-random point generation and real multispectral vineyard images processed through the satellite emulation testbed. A flight with a real drone was also conducted to validate adaptive flight optimisation. Experimental results focus on the impact of path-adaptation strategies and communication bandwidth on mission efficiency. The results show that route optimisation can reduce mission time by up to $15\%$ when new low-altitude waypoints emerge, while bandwidth bottlenecks affect performance once image transmission can no longer keep pace with acquisition. The findings highlight the need to consider sensing, communication, and mission planning jointly in adaptive UAV-based crop monitoring. Full article

Abandoned, lost, or otherwise discarded fishing gear (ALDFG) is a persistent form of marine pollution requiring survey approaches capable of resolving individual items across large spatial extents. While uncrewed aerial vehicles (UAVs) can capture imagery at resolutions sufficient to resolve individual debris items, their use remains largely constrained to visual line-of-sight (VLOS) operations, limiting large-scale coastal monitoring. This case study develops and field-tests an operational framework for low-altitude beyond visual line-of-sight (BVLOS) UAV surveys, in which DEM-based communication viewshed modelling incorporating first Fresnel zone clearance is used to plan BVLOS missions. A lightweight fixed-wing UAV flown at 60 m AGL completed 20 missions across 210 km of remote northern Australian coastline. Communication viewshed modelling reliably guided mission planning with 90.5% of waypoints placed within predicted high-clearance zones maintaining moderate-to-strong command-and-control (C2) link quality in flight. Manual screening confirmed that the resulting imagery was of sufficient quality, with 291 derelict fishing nets detected. In a simulated VLOS operational scenario, 76.3% of these detections fell beyond VLOS range, and equivalent coverage would require an estimated 8.8-fold increase in mission count. These findings demonstrate that fixed-wing UAVs operating under low-altitude BVLOS conditions can support large-scale image acquisition in remote coastal areas, particularly when enabled by communication-aware mission planning. Full article

This article explores Christian mission and Islamic daʿwah fī sabīl Allāh as two distinct yet structurally comparable forms of theological engagement with the world. While both concepts have traditionally been studied in relation to religious transmission, doctrinal fidelity, and institutional expansion, contemporary scholarship increasingly recognizes their broader significance as practices of ethical responsibility, public witness, and interreligious encounter. The study proposes a comparative theological framework that goes beyond descriptive comparisons to examine the underlying structures that shape mission and daʿwah. Both traditions are approached as embodied practices through which religious communities cultivate specific forms of moral agency, religious subjectivity and interaction with others. From this perspective, mission and daʿwah are not merely understood as communicative activities, but as expressions of lived socio-ethical and theological commitments. The article posits that both traditions are organized around three interconnected dimensions: divine agency, human mediation, and epistemic orientation. These dimensions provide a common analytical framework while preserving the theological distinctiveness of Christianity and Islam. By developing this comparative axis, the study contributes to comparative theology in three ways: it offers a new conceptual framework for analysis; it highlights the dynamic and self-reflective nature of both traditions; and it demonstrates how mission and daʿwah can serve as platforms for interreligious understanding, fostering meaningful dialogue and deepening theological self-awareness through engagement with religious diversity. Full article

We propose a high-quality and computationally efficient trajectory replanning method for Unmanned Aerial Vehicle (UAV) swarms, termed RHCR-Opt, which is designed to continuously and efficiently generate multiple collision-free trajectories in dense obstacle environments. RHCR-Opt consists of three layers. The first two layers are the rolling-horizon collision resolution (RHCR) algorithm based on the Conflict-Based Search (CBS), while the third layer focuses on trajectory generation and optimization using Minimum Control (MINCO) trajectories. Within the two-layer RHCR framework, the improved Lifelong Planning A* (LPA*) algorithm incorporating spatiotemporal constraints is proposed and employed as the low-level solver of CBS to satisfy the frequent search requirements for feasible paths under varying spatiotemporal constraints, thereby significantly improving computational efficiency. Furthermore, the rolling-horizon collision resolution concept is adopted in the high-level CBS framework, where only the discovery of collision-free paths within a finite time window is considered. This substantially reduces the computational burden associated with trajectory generation and optimization beyond the time window. At the third layer, a MINCO-based trajectory generation scheme is designed, and a swarm trajectory joint optimization framework with a finite time window is proposed to generate dynamically feasible and collision-free trajectories. In addition, for swarm missions requiring simultaneous arrival, a two-stage temporal coordination optimization method is developed. Extensive simulation experiments demonstrate that, compared with state-of-the-art (SOTA) algorithms on the proposed benchmark, RHCR-Opt achieves significant improvements in both trajectory quality and computational efficiency. In particular, when the swarm size becomes large, the computational efficiency is improved by at least 23.7%. Full article

The rapid adoption of small drones for applications such as surveillance, disaster response, and infrastructure inspection has increased demand for coordinated multi-drone operations, in which information exchange is essential. However, effective collaboration across different drones remains challenging due to differences in information exchange and communication protocols. This paper proposes a Networked Drone Information Exchange Model (NDIEM), an XML-based model that enables interoperability of the structural information. NDIEM defines five essential element categories: Identification, Telemetry, Command and Control, Sensor, and Mission. NDIEM can be used with protocol-specific adapters for exchanging information in multi-drone operations. The proposed model has been implemented and evaluated on three different drones (an ArduPilot-based Hexacopter, a Crazyflie 2.1, and a Tello EDU) using real sensor telemetry captured under controlled conditions, in which each platform’s onboard sensors were manipulated to generate representative telemetry variation. Experimental results demonstrate information interoperability with 0.013–0.019 ms processing overhead, 0.049–0.073 ms transformation latency per message, and XSD schema validation compliance, achieving 69.7% transformation completeness for telemetry data. These findings show that NDIEM can provide a practical and scalable foundation for software development for drone collaboration and interoperability. Full article

Current research in commercial aviation is exploring numerous propulsion and aircraft technologies to mitigate its environmental impact. While purely hydrogen-powered aircraft face manifold challenges, combining hydrogen and conventional fuel may facilitate the introduction of hydrogen-based flight. This study dives into the conceptual design of a parallel-hybrid dual-fuel regional aircraft retrofit based on the D328eco. The assessed retrofit approach aims to extend airframe service life and reduce emissions by incorporating a novel propulsion system. By integrating high-temperature fuel cells (FCs) to assist conventional turboshaft engines, the powertrain reduces fuel consumption. Utilizing the Bauhaus Luftfahrt Aircraft Design Environment, various aircraft-level sensitivities and hybridization strategies were assessed. The fuel/payload ratio was identified as a key metric, and enabling FC support during diversion climb while minimizing that ratio shifted the corresponding hybridization degree from 20.3% to 37.2%. Retaining the reference turboshaft-engine for reduced retrofit development costs, a hybridization degree of 20.2% was attainable while the minimum allowable payload was carried aboard. Subsequent off-design mission analysis revealed a decrease in transport efficiency for reduced mission ranges, underlining the importance of market-tailored aircraft designs. The main studies were complemented by a higher-level emission and climate impact assessment to set the basis for more generalized retrofit statements. Full article

Unmanned Aerial Vehicles (UAVs) are used in numerous practical applications in industry, science, and ecology; however, the large-scale use of UAVs is hampered by the limited onboard energy capacity. Increasing the energy efficiency of UAVs has thus become one of the most important tasks in UAV research. This review examines approaches to energy optimization of UAVs using algorithms and artificial intelligence (AI). It evaluates and compares algorithms and methods used to optimize energy efficiency of trajectory planning, adaptive speed control, battery management in mission planning, and navigation that accounts for environmental characteristics. It differs from those focusing on hardware solutions by highlighting optimization problems where energy usage is considered the key target for optimization and not a limiting constraint. The analyzed methods have been categorized into five groups, including classical optimization, metaheuristics, machine learning (ML), reinforcement learning (RL), and hybrid approaches. Key approaches such as RL, Model Predictive Control, evolutionary algorithms, and data-driven energy modeling have been outlined and compared with regard to energy-model accuracy, type of validation, scalability, and deployment readiness. Additionally, it emphasizes practical aspects such as the accuracy of energy modeling, real-time capabilities, scalability to multiple UAVs, and robustness to environmental uncertainty. Finally, this review provides directions for future research that will help develop sustainable, intelligent, and energy-efficient UAVs. Full article