Adaptive leadership and dynamic adaptive approaches to decision-making are becoming increasingly important for organizations operating in complex and uncertain environments. This study aims to examine how the utilization of a dynamic adaptive approach influences adaptive leadership within strategic processes. Adaptive leadership enables organizations to navigate unpredictable challenges by fostering resilience, learning and change, while the applied dynamic adaptive approach supports strategic decision-making under deep uncertainty. A qualitative multiple-case study design was used, involving four Dutch healthcare organizations - two elder care organizations and two disability care organizations. Each organization participated in a strategic decision-making workshop in which a dynamic adaptive approach was applied. Data were collected through workshop observations, interviews and supporting documentation. The findings show that the applied dynamic adaptive approach strengthens a supportive environment and contributes to sensemaking necessary, helping participants engage with long-term uncertainties and strategic complexity. This, in turn, encouraged behaviors and mindsets associated with adaptive leadership. However, the study also reveals limitations: a single workshop is not sufficient to fully design an intervention for an organization and develop adaptive leadership capabilities. Iterative, ongoing engagement is necessary to build strategic resilience and adaptive capacity. This research complements recent calls for more empirical research on adaptive leadership and foresight approaches. The study reveals how the dynamic adaptive approach in strategic decision-making processes empowers adaptive leadership. This is especially important in today's complex organizations facing deep uncertainties in a rapidly changing environment.

With the rapid development of high-resolution satellite remote sensing technology, wind turbine detection based on remote sensing imagery has emerged as a crucial research area in renewable energy. However, accurate identification of wind turbines remains challenging due to complex geographical backgrounds and their typical appearance as small objects in images, where limited features and background interference hinder detection performance. To address these issues, this paper proposes CGA-YOLO, a specialized network for detecting small targets in high-resolution remote sensing images, and constructs the SDWT dataset, containing Gaofen-2 imagery covering various terrains in Shandong Province, China. The network incorporates three key enhancements: dynamic convolution improves multi-scale feature representation for precise localization; the Convolutional Block Attention Module (CBAM) enhances feature convergence through channel and spatial attention mechanisms; and GhostBottleneck maintains high-resolution details while strengthening feature channels for small targets. Experimental results demonstrate that CGA-YOLO achieves an F1-score of 0.93 and an mAP50 of 0.938 on the SDWT dataset, and obtains an mAP50 of 0.9033 on both RSOD and VEDAI public datasets. CGA-YOLO establishes its superior accuracy over multiple mainstream detection models under identical experimental conditions, confirming its potential as a reliable technical solution for accurate wind turbine identification in complex environments.

Electromagnetic interference (EMI) in underground coal mines, generated by large-scale machinery and electronic devices, severely disrupts the operation of sensitive electronics, leading to performance degradation, equipment failure, and significant safety hazards. To address these challenges, we propose an ultra-wideband, ultra-thin, and optically transparent metamaterial absorber (MA) that combines patterned indium tin oxide (ITO) films with a water-filled resin shell. This design, independent of the incident polarization maintains high absorption efficiency over a wide range of incident angles. The absorber achieves over 90% microwave absorption efficiency across an ultra-wide frequency ranging from 0.52 to 40 GHz, corresponding to a remarkable relative bandwidth of 194.9%, with a thickness of only 1/50 of the maximum operating wavelength. Experimental evaluations conducted in a simulated coal mine tunnel environment demonstrate its exceptional EMI shielding performance: the MA effectively stabilized digital tube displays that previously flickered under strong interference and restored normal operation to analog multimeters exhibiting erratic behavior. These results confirm the capability of our absorber to enhance the operational reliability and measurement accuracy of sensitive electronic equipment in high-EMI conditions while preserving optical transparency for real-time visual monitoring. The proposed MA offers a promising solution for robust electromagnetic protection in complex and harsh electromagnetic environments.

Aim: Natural ventilation in buildings can enhance indoor health and well-being while also reducing the energy consumption required for mechanical cooling and heating. However, due to the complexity of many building floor plans, achieving effective natural ventilation can be difficult. To investigate how natural ventilation in buildings can be improved, a study was conducted to identify a prototype window design feature that can generate differential air pressure levels sufficient to create improved natural air flow for indoor spaces having one exterior exposure only. The purpose was to identify basic aerodynamic principles that can be applied to more complex architectural environments later. Methods: A wind-tunnel experiment using a scale building model compared airflow performance across cross-ventilation, corner-ventilation, and single-sided configurations, including a prototype window-panel design. Air velocities were recorded at multiple orientations and wind speeds, and airflow patterns were visualized using smoke tracers. Results: Maximum indoor air velocities for the cross-ventilation and corner-ventilation configurations were observed at orientation angles between 600 and 900. However, maximum air velocities for a standard single-sided window configuration were observed at 500. Adding external architectural panels to the prototype design, the maximum airflow rate occurred at an orientation angle of 00. Increasing the wind-tunnel air velocity incrementally from 20 m/min to 80 m/min resulted in linear changes, indicating the absence of confounding turbulence factors influencing the measurement protocol. Conclusion: The prototype window-panel system substantially improved airflow under single-sided ventilation conditions and, in some orientations, approached cross-ventilation performance. These findings suggest potential applications for improving ventilation in buildings with limited exterior exposure, though validation in full-scale environments is needed. Recommendation: The design shows promise for retrofit and new-built applications in single-exposure rooms. Further research should evaluate full-scale performance, thermal comfort outcomes, and long-term energy effects.

Modeling lipid nanoparticle transport in extracellular matrix: Effects of particle size and rigidity.

This study includes a comparative analysis of four graphene-based electrochemical sensors used for the detection of melatonin, an endogenous hormone involved in circadian rhythm regulation and associated with various neurological pathologies. The sensors were based on screen-printed electrodes (SPE) modified with graphene (G), graphene modified with gold nanoparticles (AuNPs/G), graphene oxide (GO), and reduced graphene oxide (rGO). Melatonin was extracted from commercially available pharmaceutical products, purified, and characterized using UV-Vis spectroscopy, FTIR spectrometry, and HPLC. The performance of the electrodes was evaluated via cyclic voltammetry, using potassium ferrocyanide and standard melatonin solutions to determine the kinetic characteristics, while square-wave voltammetry was employed to determine the detection and quantification limits. G/SPE showed the best performance, with a detection limit of 0.3424 μM, followed by AuNPs/G/SPE with an LOD of 1.2768 μM. GO/SPE had the poorest performance (LOD 23.1056 μM), and rGO/SPE had an LOD of 5.8503 μM. Testing of sensors on pharmaceuticals showed accurate quantification of melatonin in a complex environment. The results highlight the potential of G/SPE and AuNPs/G/SPE sensors for use in the rapid and accurate detection of melatonin in pharmaceutical and biomedical applications.

As the operation mode of construction machinery is being transformed from traditional manual operation to unmanned autonomous operation, a trajectory planning scheme for obstacle avoidance is proposed in this paper to meet the requirements of unmanned autonomous operation of excavators in complex environments and confined spaces. First, a simulation model is established to represent the working mechanism of the excavator and its surrounding obstacle environment. Next, considering the spatial constraints and environmental obstacles present during excavation and swing-loading operations, an optimal trajectory planning strategy is developed for autonomous excavator operation under restricted conditions. The proposed approach enhances the conventional RRT* algorithm through the incorporation of an environmental parameter-based heuristic search and an adaptive goal-biased strategy featuring dynamic step size adjustment. These improvements collectively augment path search efficiency and trajectory quality. For trajectory optimization, a quintic Non-Uniform Rational B-Spline (NURBS) curve is employed to plan the motion path of the bucket tip. The method concurrently optimizes operation duration and motion smoothness, producing a multi-objective optimal trajectory. Results show the enhanced RRT* reduces path length, iteration count, and computation time by 3.65%, 64.15%, and 67.9%, while improving trajectory smoothness by 33.4%. The approach reliably generates collision-free, smooth, and energy-efficient trajectories, ensuring high efficiency and mechanical reliability for autonomous excavator operations.

Covert communication assisted by unmanned aerial vehicles (UAVs) can achieve a low detection probability in complex environments through auxiliary strategies, including dynamic trajectory planning and power management, etc. This paper proposes a dual-UAV scheme, where one UAV transmits covert information while the other one generates stochastic jamming to disrupt the eavesdropper and reduce the probability of detection. We propose a dual-mode jamming scheme which can efficiently enhance the average covert rate (ACR). A joint optimization of the dual UAVs’ flight speeds, accelerations, transmit power, and trajectories is conducted to achieve the maximum ACR. Given the high complexity and non-convexity, we develop a dedicated algorithm to solve it. To be specific, the optimization is decomposed into three sub-problems, and we transform them into tractable convex forms using successive convex approximation (SCA). Numerical results verify the efficacy of dual-mode jamming in boosting ACR and confirm the effectiveness of this algorithm in enhancing CC performance.

In the field of precision agriculture, accurately detecting rice panicles is crucial for monitoring rice growth and managing rice production. To address the challenges posed by complex field backgrounds, including variety differences, variations across growth stages, background interference, and occlusion due to dense distribution, this study develops an improved YOLO11-based rice panicle detection model, termed DRPU-YOLO11. The model incorporates a task-oriented CSP-PGMA module in the backbone to enhance multi-scale feature extraction and provide richer representations for downstream detection. In the neck network, DySample and CGDown are adopted to strengthen global contextual feature aggregation and suppress background interference for small targets. Furthermore, fine-grained P2 level information is integrated with higher-level features through a cross-scale fusion module (CSP-ONMK) to improve detection robustness in dense and occluded scenes. In addition, the PowerTAL strategy adapts quality-aware label assignment to emphasize high-quality predictions during training. The experimental results based on a self-constructed dataset demonstrate that DRPU-YOLO11 significantly outperforms baseline models in rice panicle detection under complex field environments, achieving an accuracy of 82.5%. Compared with the baseline model YOLO11 and RT-DETR, the mAP50 increases by 2.4% and 5.0%, respectively. These results indicate that the proposed task-driven design provides a practical and high-precision solution for rice panicle detection, with potential applications in rice growth monitoring and yield estimation.

Purpose The purpose of this paper is to propose a hierarchical deep reinforcement learning (H-DRL) framework for real-time tactical decision-making in team sports. The framework addresses challenges such as continuous action spaces, partial observability and adversarial environments by leveraging multi-agent collaboration, adaptive strategy optimization and explainable artificial intelligence (AI). It aims to enhance tactical accuracy, decision speed and resource efficiency while uncovering novel tactical patterns that human experts may overlook. Design/methodology/approach The study combines graph neural networks (GNNs) for spatial-temporal player interactions and transformer-based attention for strategic pattern recognition. It integrates opponent modeling via inverse reinforcement learning (IRL) and self-play. The hierarchical architecture decomposes decisions into strategic, tactical and technical levels. Experiments were conducted on professional basketball, soccer and rugby datasets, with validation by expert coaches. The framework was tested in real-world deployments, including youth academies and professional teams, to evaluate performance and tactical innovations. Findings The H-DRL framework achieved a 34.7% higher tactical accuracy, 28.3% faster decision-making and 41.2% lower resource usage compared to state-of-the-art methods. It identified 17 new tactical patterns, such as dynamic role-switching, which improved scoring efficiency by 23.6%. Real-world deployments demonstrated significant performance gains, including a 42.3% improvement in tactical decision-making for youth teams. The system's explainable AI module bridged algorithmic insights with coach expertise, fostering trust and adoption. Research limitations/implications The study is limited by its reliance on proprietary tracking data and the computational demands of real-time deployment. Future research could explore cross-sport generalization and integration of physiological/psychological factors. The framework's scalability to larger team sizes and more complex environments remains a challenge. These limitations highlight opportunities for advancements in model compression and hardware optimization. Practical implications The framework provides actionable insights for coaches and players, enhancing in-game decision-making and training efficiency. It enables teams to adopt data-driven tactics, such as elastic pressing in soccer or optimized phase play in rugby. The system's real-time capabilities (30.9 ms latency) make it suitable for live match analysis. Professional teams reported improved tactical understanding (91.7% of coaches) and scoring efficiency (23.6% increase). The technology is applicable beyond sports, including autonomous systems and emergency response. Social implications The study promotes the ethical use of AI in sports, emphasizing augmentation over replacement of human expertise. It fosters collaboration between coaches and AI, enhancing tactical literacy and innovation. The framework's transparency builds trust, addressing concerns about black-box AI. By uncovering counterintuitive strategies, it challenges traditional coaching paradigms and encourages continuous learning. The technology's broader applications (e.g. military, robotics) underscore its societal impact. Originality/value This paper presents a hierarchical DRL framework for real-time tactical decision-making in team sports, integrating GNNs, transformers and IRL. Its dual-stream architecture and adaptive computation are novel contributions. The system's ability to discover and explain tactical innovations (e.g. dynamic role-switching) sets it apart from prior work. The rigorous validation across multiple sports and real-world deployments demonstrates its practical value. The study advances multi-agent AI, offering scalable solutions for complex, dynamic environments.

Introduction With the increasing complexity of underwater operations, remotely operated vehicles systems face the dual challenges of multi-source interference and component failures in unknown environments. Methods To achieve high-precision control of remotely operated vehicles arms under fault conditions, this paper proposes an online fault compensation control method based on a decoupling algorithm. This method separates the end-effector position and attitude control of the master and slave arms through a pose decoupling algorithm, constructs an observer-based fault diagnosis mechanism, and combines H∞ robust control and online adaptive strategies to achieve dynamic compensation for combined sensor and thruster faults. Results The results show that in dual-arm cooperative operation, the spatial trajectory tracking deviation of the robotic arm can be controlled within 4.3 mm, with a maximum deviation of 2.643 mm in the X-axis direction and a planning deviation of 3.075 mm in the Y-axis direction. Compared with backstepping fault-tolerant control and power sliding mode control, the method used in this study has a maximum deviation of only 0.01° in yaw angle control, a position control error reduced to 1.2 mm, and a maximum trajectory tracking error of 2.1 mm, which is significantly better than the comparative methods. Furthermore, the system can rapidly approach the desired posture within 50 seconds and maintains stable operation under various fault scenarios. Discussion This demonstrates that the proposed method can effectively improve the operational accuracy and fault “tolerance of remotely operated vehicles in complex environments, providing a new technology for solving the control problems of robot systems under fault conditions.”

The development of electronic devices for communication systems, radar warning, and satellite detection requires lightweight materials that exhibit exceptional electromagnetic interference (EMI) shielding while maintaining mechanical robustness. However, designing three-dimensional (3D) structured pristine graphene (PG) that achieves both high EMI shielding and substantial load-bearing capacity remains a significant challenge. In this work, an innovative method of 3D skeleton preconstruction-infiltration filling is proposed. This approach demonstrates that molten AZ91D is infiltrated into the 3D structured PG@pyrocarbon (PG@PyC), and its 3D structure can be maintained in the AZ91D matrix via a liquid-solid infiltration extrusion method. By utilizing this strategy, PG@PyC reinforced AZ91D matrix (PG@PyC/AZ91D) composites display remarkable comprehensive performance, realizing an EMI shielding effectiveness of 76.70 dB (at 3 mm thickness), ultimate compressive strength of 276 MPa, and ultimate tensile strength of 231 MPa. The developed composites are promising lightweight materials for the integration of structural and functional applications in complex environments.

Renewable energy projects suffer from deep uncertainties associated with volatile market conditions, unstable policy regimes and changing technological landscapes. Traditional valuation tools like Net Present Value (NPV) are increasingly being accepted as insufficient to capture the managerial flexibility needed to deal with this complex environment. As a result, a powerful alternative investment framework, Real Options Analysis (ROA), has been proposed, in which the possibility of strategic adaptability under uncertainty is valued explicitly for renewable energy investment. This paper reports a systematic review between 2000-2025 of research works on ROA application in the renewable energy sector. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, 288 peer-reviewed studies were identified from twelve major academic databases (Scopus, IEEE Xplore, and Wiley Online Library). Each study was reviewed in terms of key dimensions: renewable technology type, real option category, modelling technique, dominant sources of uncertainty and geographical focus. The results show the dominance of the decision to defer (timing option) as the most important strategic flexibility for all technologies, emphasising the key problem of optimal investment timing. Methodologically, the field has transitioned from basic analytical models to complex simulation-based models, with binomial lattices and Monte Carlo models dominating the scene, followed by a significant move to hybrid, fuzzy, and AI-enhanced models after 2015. The analysis also reveals clear regional patterns in the types of uncertainties modelled with European studies focusing on market and policy risks, Asian studies on resource availability and work in the Americas taking into account technical risks. However, a serious underrepresentation in Africa, especially in Nigeria, is also revealed, which constitutes a major gap in the research. This review concludes that while the methodological foundations of ROA are well established, its practical application remains limited, particularly outside developed countries. Expanding the use of ROA could better support the global energy transition, but achieving this requires addressing barriers such as computational complexity, limited modeling expertise, and regulatory reliance on deterministic valuation methods. Greater integration of these flexible decision-making tools into policy design and project appraisal, especially in high-risk and underrepresented regions, is therefore necessary.

Multi-component collaborative design yields robust hydrogel sensors with superior environmental adaptability for machine learning-assisted gesture recognition.

Abstract In this paper, we propose a fast sampling-based planner with submodular information gain measure for rapid continuous exploration of large and complex 3D environments using unmanned aerial vehicles (UAVs). Existing sampling-based methods suffer from three critical limitations: (i) inherent overestimation of information gain; (ii) computational inefficiency in branch expansion; (iii) frequent deceleration or even stopping in the boundary area. To address these limitations, we first introduce a depth image based submodularity checking method that computes strictly submodular information gains and eliminates the multi-counting of unknown voxels, thereby overcoming the inherent overestimation of information gain. Second, instead of the conventional local-window paradigm, we apply an innovative branch pruning strategy that removes branches with low potential utility to significantly improve computational efficiency during branch expansion. Third, we employ a heuristic approach to rank and expand candidate nodes, and further allow branches to extend into unknown regions covered by the field of view of predecessor viewpoints along the planned trajectory. This effectively avoids frequent deceleration or stopping near boundary areas, ensuring continuous and rapid exploration. Extensive simulation and real-world experiments demonstrate that our planner achieves 37.2% higher exploration efficiency than state-of-the-art methods, while sustaining real-time capability on both laptop and self-built quadrotor UAV.

Oil and Gas corporation operates within a highly complex energy value chain, exposing the company to strategic, operational, financial, and market risks. These dynamics underscore the need for a risk management function that not only protects value but also supports value creation in line with ISO 31000:2018 principles. This study examines the transformation of the risk management function from a traditional Second Line into a One Point Five Line model, introduced to address governance gaps and comply with UU No. 40 Tahun 2007. Using a qualitative case study approach, data were collected through in-depth interviews with key informants and document analysis focusing on the application of the Four Eyes Principle in investment, hedging, and credit processes. The results reveal that the transformation significantly enhances the integration of risk considerations into strategic decision-making. The involvement of the risk management function in investment, hedging, and credit committees strengthens oversight, improves alignment with corporate risk appetite, and increases accountability across organizational lines. The model also fosters more transparent documentation and higher-quality risk analysis, contributing to a more disciplined and data-driven decision-making process. Overall, the One Point Five Line model proves effective in improving internal controls while ensuring a balanced approach between value protection and value creation. These findings offer practical insights for state-owned enterprises and energy companies seeking to strengthen risk governance in increasingly complex business environments.

Abstract Electrochemical sensing has emerged as a powerful analytical approach for the ultra trace determination and speciation of radionuclides across environmental systems and nuclear fuel cycle process streams. This review synthesizes recent advances in electrode engineering, surface modification strategies, and redox mechanistic understanding that have collectively transformed electroanalytical radiochemistry. Fundamental techniques such as adsorptive stripping voltammetry and ion selective potentiometry are discussed in the context of their ability to deliver picomolar level detection while providing direct electronic signatures of oxidation states, a capability not readily achievable with spectrometric methods. Progress in replacing mercury based systems with environmentally benign solid state platforms, including bismuth film, boron doped diamond, screen printed electrodes, and nanomaterial modified carbon architectures, has significantly enhanced sensitivity, stability, and field deployability. Equally important are advances in selective interfaces such as ion imprinted polymers, molecularly imprinted polymers, metal organic frameworks, and Prussian Blue analogues, which mitigate matrix interferences in seawater, groundwater, biological samples, and high level liquid waste. This review comprehensively examines the electrochemical behaviors of uranium, plutonium, neptunium, americium, technetium, strontium, and cesium, highlighting the interplay between electrode materials, complexation chemistry, radiation induced perturbations, and kinetic limitations. In addition, operational considerations including memory effects, waste minimization, radiation damage, and comparative performance with inductively coupled plasma mass spectrometry are critically evaluated. Collectively, these developments demonstrate that modern electrochemical sensors offer a versatile, low cost, and highly informative complement to traditional radiometric and mass spectrometric techniques, particularly for applications requiring rapid on site screening and oxidation state resolved monitoring in complex nuclear environments.

Multi-UAV Standoff Tracking in Unknown Complex Environments Using a Modulated Adaptive Guiding Vector Field

Introduction Pine wilt disease (PWD) is a highly destructive infectious disease that severely damages pine forests worldwide. Because symptoms emerge first in the tree crown, detection from unmanned aerial vehicles (UAVs) is efficient. However, most methods perform only binary classification and lack pixel-level staging, which leads to missed initial symptoms and confusion with other species. Methods We propose MSCF-LUNet, a lightweight three-stage semantic segmentation model based on multi-scale context fusion. The model uses an improved multi-scale patch embedding guided by attention with relative position encoding (AWRP) to adapt the sampling field of view and to fuse local details with global context. Under contextual attention, the network learns fine-grained features and location cues. Results In complex environments, MSCF-LUNet achieves 89.56% precision, 92.13% recall, 88.92% intersection over union (IoU), and 96.54% pixel accuracy (PA), balancing performance and computational cost. Discussion The model effectively segments PWD-infected regions and determines disease stages from remote-sensing imagery.

Understanding spatial navigation and memory formation is critical to exploring how humans learn and adapt in complex environments. To investigate these processes, we conducted an experiment using the Minecraft Memory and Navigation Task, collecting detailed three-dimensional (3D) path data in a virtual open-world setting. Statistically, we developed a novel methodology to convert complex high-dimensional 3D movement data into functional representations, enabling standardized comparisons and analyses across participants and environments. We applied techniques such as functional clustering and regression to identify navigation patterns and their relationships with cognitive map development and memory retention. Our analysis uncovered two significant insights: first, participants who adopted moderately exploratory behaviors during training demonstrated superior retention of object locations; second, inefficient navigation strategies were strongly linked to poorer spatial memory and navigation performance. These findings highlight the effectiveness of our methodology in advancing the study of navigation behaviors and cognitive processes in dynamic 3D environments.