Search Results (16,248)

Forest management interventions in fire-prone western U.S. forests aim to restore structural heterogeneity, yet tracking treatment efficacy at landscape scales remains a persistent challenge. Traditional monitoring tools often lack the spatial resolution or temporal frequency needed to assess fine-scale structural outcomes. While deep learning approaches for mapping canopy structure from high-resolution satellite imagery have advanced rapidly, their application to operational monitoring of restoration outcomes with independent validation remains limited. This study demonstrates and validates a scalable monitoring workflow that integrates high-resolution PlanetScope multispectral imagery (~4.77 m) with a residual U-Net convolutional neural network (CNN) to quantify canopy structure dynamics in support of forest restoration programs. Trained using 3 m canopy cover data from the California Forest Observatory (CFO) as a reference, the model accurately segmented forest canopy from openings across a large, independent test area of ~1761 km², with an overall accuracy of 92.2%, and an F1-score of 95.1%. Independent validation against airborne LiDAR across 140 km² of heterogeneous terrain confirmed operational performance (overall accuracy 85.9%, F1-score 0.77 for canopy gaps). We applied this framework to quantify structural changes within the North Yuba Collaborative Forest Landscape Restoration Program from 2020 to 2024, providing managers with actionable metrics to evaluate treatment effectiveness against historical reference conditions. The treatments created 564 acres of new openings, significantly increasing structural heterogeneity, with 56% of new open area located within 12 m of residual canopy. While treatment outcomes aligned with the goal of fragmenting dense canopy, the resulting large openings (>5 acres) slightly exceeded historical reference conditions for the area. This validated workflow translates high-resolution satellite imagery into timely, actionable metrics of forest structure, enabling managers to rapidly evaluate treatment impacts and refine restoration strategies in fire-prone ecosystems. Full article

This work presents a hierarchical deep reinforcement learning (DRL) framework based on Soft Actor–Critic (SAC) for the autonomy of rock-breaking machinery in surface mining comminution processes. The proposed approach explicitly integrates mobile navigation and hydraulic manipulation as coupled subprocesses within a unified decision-making architecture, designed to operate under the unstructured and highly uncertain conditions characteristic of open-pit mining operations. The system employs a hysteresis-based switching mechanism between specialized SAC subagents, incorporating automatic entropy tuning to balance exploration and exploitation, twin critics to mitigate value overestimation, and curriculum learning to manage the progressive complexity of the task. Two coupled subsystems are considered, namely: (i) a tracked mobile machine with a differential drive, whose continuous control enables safe navigation, and (ii) a hydraulic manipulator equipped with an impact hammer, responsible for the fragmentation and dismantling of rock piles through continuous joint torque actuation. Environmental perception is modeled using processed perceptual variables obtained from point clouds generated by an overhead depth camera, complemented with state variables of the machinery. System performance is evaluated in unstructured and uncertain simulated environments using process-oriented metrics, including operational safety, task effectiveness, control smoothness, and energy consumption. The results show that the proposed framework yields robust, stable policies that achieve superior overall process performance compared to equivalent hierarchical configurations and ablation variants, thereby supporting its potential applicability to DRL-based mining automation systems. Full article

The integration of AI tools like ChatGPT into educational assessments, particularly in the context of Multivariable Calculus, represents a transformative approach to personalized and scalable learning. This study examines the Exams as a Service (EaaS)-Flipped Chatbot Test (FCT) framework, implemented through the AIQuest platform, to explore how chatbots can support assessment processes while addressing risks related to automation and academic integrity. The methodology combines static and dynamic assessment modes within a cloud-based environment that generates, evaluates, and provides feedback on student responses. Quantitative survey data and qualitative written reflections were analyzed using a mixed-methods approach, incorporating Grounded Theory to identify emerging cognitive patterns. The results reveal differences in students’ engagement, performance, and reasoning patterns between AI-assisted and non-AI assessment conditions, highlighting the role of structured AI-generated feedback in supporting reflective and metacognitive processes. Quantitative results indicate higher and more homogeneous performance under the reverse evaluation, while survey responses show generally positive perceptions of feedback usefulness and task appropriateness. This study contributes integrated quantitative and qualitative evidence on the design of AI-assisted evaluation frameworks as formative and diagnostic tools, offering guidance for educators to implement AI-based evaluation systems. Full article

We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise in scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today’s AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: these countries account for a large share of global GDP and energy use and are therefore expected to be most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion. Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth. Full article

The fire performance of existing reinforced concrete (RC) bridge decks strengthened by external prestressing systems is investigated, with particular attention to the vulnerability of externally applied tendons under realistic fire scenarios. Fire exposure represents a critical condition for such retrofitted structures, as the structural response is strongly influenced by load level, prestressing effectiveness, and thermal degradation of the strengthening system. A comprehensive assessment framework is proposed, combining thermal and mechanical analyses applied to representative highway overpass bridges. The thermal input adopted for the analyses is first validated through computational fluid dynamics (CFD) simulations, aimed at evaluating temperature development in typical RC beam–girder grillage decks subjected to fire from below. The CFD study considers variations in clearance height and span length and confirms that, in the case of hydrocarbon tanker accidents with fuel spilled on the roadway, conventional fire curves commonly adopted in the literature provide a reliable and conservative representation of both the temperature levels reached and their rate of increase within structural elements, thus supporting their use for rapid and simplified assessments. The validated thermal input is then employed in an analytical fire safety procedure applied to several realistic bridge case-studies. A parametric investigation is carried out by varying deck geometry, span length, reinforcement layout, and the presence of external prestressing retrofit, allowing the evaluation of the reduction in bending capacity and the time-dependent degradation of mechanical properties under fire exposure. The results highlight the critical role of external prestressing in fire scenarios, showing that significant loss of prestressing effectiveness may occur within the first minutes of fire, potentially leading to critical conditions even at service load levels. Finally, a multi-hazard assessment is performed by combining fire effects with pre-existing aging-related deterioration, such as reinforcement corrosion and long-term prestressing losses, demonstrating a marked increase in failure risk and, in the most severe cases, the possibility of premature collapse under dead loads. Full article

Frontline workers across health, emergency, and social care sectors are repeatedly exposed to distressing events and chronic stressors as part of their occupational roles. Unlike single-event trauma, these cumulative exposures accrue over time, generating persistent psychological and physiological strain. Traditional diagnostic frameworks, particularly post-traumatic stress disorder (PTSD), were not designed to capture the layered and ongoing nature of this occupational trauma. This commentary introduces the concept of Persistent Traumatic Stress Exposure (PTSE), a framework that reframes trauma among frontline workers as an exposure arising from organisational and systemic conditions rather than solely an individual disorder. It aims to reorient understanding, responsibility, and intervention from a purely clinical lens toward systems, cultures, and organisational duties of care. PTSE is presented as an integrative paradigm informed by contemporary theory and evidence on trauma, moral injury, organisational stress, and trauma-informed systems. The framework synthesises findings from health, emergency, and social care settings, illustrating how repeated exposure, ethical conflict, and institutional pressures contribute to cumulative psychological harm. PTSE highlights that psychological injury may build across shifts, careers, and lifetimes, requiring preventive, real-time, and sustained responses. The framework emphasises that effective support is dependent on both organisational readiness, the structural conditions that enable trauma-informed work, and organisational preparedness, the practical capability to enact safe, predictable, and stigma-free responses to trauma exposure. PTSE challenges prevailing stigma by framing trauma as a predictable occupational hazard rather than a personal weakness. It aligns with modern occupational health perspectives by advocating for systems that strengthen psychological safety, leadership capability and access to support. By adopting PTSE, organisations can shift from reactive treatment models toward proactive cultural and structural protection, honouring the lived realities of frontline workers and promoting long-term wellbeing and resilience. Full article

Hydrogen has emerged as a cornerstone of global decarbonisation strategies, offering a flexible pathway to reduce dependence on fossil fuels and accelerate progress towards net-zero targets. However, the development of a globally integrated hydrogen economy remains uneven, reflecting disparities in renewable energy potential, infrastructure readiness, investment capacity, and policy commitment. To better understand these differences and the barriers they create, this study undertakes a comprehensive comparative assessment of the global hydrogen supply chain encompassing resources, production, storage, transport, and end-use applications. Further, a structured analytical framework comprising ten principles and twenty-nine sub-factors was developed to evaluate national hydrogen policies, technological readiness, and enabling conditions across twenty-six countries. The results show that the United States, China, Japan, South Korea, and Germany lead global progress, while many countries remain at an early stage of engagement. These findings further inform persistent regional asymmetries and emphasise the need for stronger international coordination. Drawing on these findings, the paper advances targeted policy and research recommendations to lower production costs, expand storage and transport capacity, and harmonise regulatory frameworks, thereby defining a coherent pathway towards a secure, cost-competitive, and equitable global hydrogen economy by 2050. Full article

Wind-assisted propulsion using Flettner rotors has gained attention as the shipping sector faces stricter decarbonization regulations. This study compares conventional Fixed Speed Control with Adaptive Speed Control for a 100 m coastal vessel. The proposed Adaptive Speed Control selectively activates the rotor based on relative wind conditions and adjusts rotor speed according to the surge-direction projection of Magnus force. A simulation framework based on the MMG maneuvering model evaluates path-following performance, fuel consumption, and annual performance indicators. Results show that Adaptive Speed Control achieves 18.84% reduction in fuel consumption, corresponding to annual savings of 212.02 tons of fuel, USD 190,823 in OPEX, and 679.76 tons of CO₂ emissions. Selective rotor operation reduces the Fatigue Damage Index by approximately 89%, resulting in 84.48% reduction in annual maintenance costs. Unwanted lateral forces and yaw disturbances are mitigated, improving path-following and maneuvering stability. These findings demonstrate that situationally aware Adaptive Speed Control improves energy efficiency and operational characteristics of Flettner-rotor-assisted propulsion systems while maintaining maneuvering performance, providing practical guidance for wind-assisted ship operation under realistic coastal conditions. Full article

Driving safety under low-visibility or distracted conditions remains a critical challenge for intelligent transportation systems. This paper presents Edge-VisionGuard, a lightweight framework that integrates signal processing and edge artificial intelligence to enhance real-time driver monitoring and hazard detection. The system fuses multi-modal sensor data—including visual, inertial, and illumination cues—to jointly estimate driver attention and environmental visibility. A hybrid temporal–spatial feature extractor (TS-FE) is introduced, combining convolutional and B-spline reconstruction filters to improve robustness against illumination changes and sensor noise. To enable deployment on resource-constrained automotive hardware, a structured pruning and quantization pipeline is proposed. Experiments on synthetic VR-based driving scenes demonstrate that the full-precision model achieves 89.6% driver-state accuracy (F1 = 0.893) and 100% visibility accuracy, with an average inference latency of 16.5 ms. After 60% parameter reduction and short fine-tuning, the pruned model preserves 87.1% accuracy (F1 = 0.866) and <3 ms latency overhead. These results confirm that Edge-VisionGuard maintains near-baseline performance under strict computational constraints, advancing the integration of computer vision and Edge AI for next-generation safe and reliable driving assistance systems. Full article

Accurate brightness temperature (TB) measurements and microwave emissivity retrieval in passive microwave sensing conventionally rely on absolute radiometric calibration, which often requires additional hardware and complex procedures. Under well-defined geometric and environmental conditions, this study proposes a mirror-reflection-based method for measuring the microwave emissivity of flat scenes using ground-based radiometers without conventional absolute calibration. The method employs a simplified four-step observation sequence, in which the radiometer measures the pure flat scene, the flat scene with mirror reflection, the reference wall, and the cold sky. A geometric model is developed to determine the effective incidence-angle range, and an analytical framework is developed to evaluate retrieval accuracy. Numerical simulations are conducted to examine the effects of scene material, reference-wall property, operating frequency, polarization, and radiometric sensitivity. Outdoor experiments are further performed to assess feasibility under practical measurement conditions. The results show that, within moderate incidence-angle ranges and under stable radiometric conditions, the retrieved emissivities of flat scenes agree well with theoretical predictions. These findings indicate that the proposed mirror-reflection-based approach provides a feasible supplementary or alternative solution for emissivity estimation of flat targets in ground-based measurements when absolute calibration is unavailable or impractical, rather than a replacement for conventional calibration techniques. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

Pedestrian safety in school zones is shaped by traffic conditions and street design characteristics, whose combined effects involve uncertainty and gradual transitions rather than sharp thresholds. This study presents an integrated assessment framework based on the analytic hierarchy process (AHP) and fuzzy logic to evaluate pedestrian traffic stress level (PTSL) at the street-segment scale in school environments. AHP is used to derive input-variable weights from expert judgments, while a Mamdani-type fuzzy inference system models the relationships between traffic and geometric variables and pedestrian stress. The model incorporates vehicle density, pedestrian density, lane width, sidewalk width, buffer zone, and estimated traffic flow speed as input variables, represented using triangular membership functions. Genetic Algorithm (GA) optimization is applied to calibrate membership-function parameters, improving numerical consistency without altering the linguistic structure of the model. A comprehensive rule base is implemented in MATLAB (R2024b) to generate a continuous traffic stress score ranging from 0 to 10. The framework is applied to street segments surrounding major schools in the study area, enabling comparison of spatial variations in pedestrian stress. The results demonstrate how combinations of traffic intensity and street geometry influence stress levels, supporting data-driven pedestrian safety interventions for sustainable school environments and low-stress urban mobility. Full article

Smartphones are ubiquitous and continuously carried high-performance devices equipped with speech recognition capabilities that enable the analysis of surrounding conversations. When leveraged for public purposes, networks of smartphones can function as a large-scale sensing infrastructure capable of detecting and reporting early signs of serious crimes or terrorist activities. This paper proposes the concept of “Smartphone as Societal Safety Guard” as an approach to substantially enhancing public safety through relatively low additional cost and the combination of existing technologies (first pillar). At the same time, this concept entails serious risks of privacy infringement, as exemplified by the potential introduction of always-on eavesdropping through operating system updates. The originality of this study lies in redefining smartphones not merely as personal tools but as public safety infrastructure within democratic societies, and in systematizing the conditions for their social acceptability from the perspective of institutional design. This research presents a reference architecture and a regulatory framework, and organizes six key challenges that must be addressed to reconcile public safety with privacy protection: external attacks, mitigation of privacy information, false positives, expansion of the scope of application, discriminatory use, and misuse by authorized insiders. In particular, misuse by authorized insiders is positioned as the core challenge. As a necessary condition for acceptance in democratic societies (second pillar), this paper proposes a privacy-protective infrastructure centered on the Verifiable Record of AI Output (VRAIO). By combining on-device two-stage urgency classification with the review and recording of AI outputs by independent third-party entities, the proposed framework aims to provide a mechanism that can ensure, as a design requirement, that information unrelated to emergencies is not released outside the device. In summary, this paper presents a design framework for reconciling enhanced public safety with the protection of privacy. Full article

Accurate prediction of marine visibility is critical for ensuring safe and efficient maritime operations, particularly in dynamic and data-sparse ocean environments. Although visibility reduction is a natural and unavoidable atmospheric phenomenon, improved short-term prediction can substantially enhance navigational safety and operational planning. While deep learning methods have demonstrated strong performance in land-based visibility prediction, their effectiveness in marine environments remains constrained by the lack of fixed observation stations, rapidly changing meteorological conditions, and pronounced spatiotemporal variability. This paper introduces SeADL, a self-adaptive deep learning framework for real-time marine visibility forecasting using multi-source time-series data from onboard sensors and drone-borne atmospheric measurements. SeADL incorporates a continuous online learning mechanism that updates model parameters in real time, enabling robust adaptation to both short-term weather fluctuations and long-term environmental trends. Case studies, including a realistic storm simulation, demonstrate that SeADL achieves high prediction accuracy and maintains robust performance under diverse and extreme conditions. These results highlight the potential of combining self-adaptive deep learning with real-time sensor streams to enhance marine situational awareness and improve operational safety in dynamic ocean environments. Full article

Industrial aluminum-block heating processes exhibit nonlinear dynamics, substantial time delays, and stringent requirements for fault detection and diagnosis, especially in semiconductor manufacturing and other high-precision electronic processes, where slight temperature deviations can accelerate device degradation or even cause catastrophic failures. To address these challenges, this study presents a digital twin-based intelligent heating platform for aluminum blocks with a dual-artificial-intelligence framework (dual-AI) for control and diagnosis, which is applicable to multi-port aluminum-block heating systems. The system enables real-time observation and simulation of high-temperature operational conditions via virtual-real interaction. The platform precisely regulates a nonlinear temperature control system with a prolonged time delay by integrating a conventional proportional–integral–derivative (PID) controller with a Levenberg–Marquardt-optimized backpropagation (LM-optimized BP) neural network. Simultaneously, a relay is employed to sever the connection to the heater, thereby simulating an open-circuit fault. Throughout this procedure, sensor data are gathered simultaneously, facilitating the creation of a spatiotemporal time-series dataset under both normal and fault conditions. A one-dimensional convolutional neural network (1D-CNN) is trained to attain high-accuracy fault detection and localization. PID+LM-BP achieves a response time of about 200 s in simulation. In the 100 °C to 105 °C step experiment, it reaches a settling time of 6 min with a 3 °C overshoot. Fault detection uses a 0.38 °C threshold defined based on the absolute minute-to-minute change of the 1-min mean temperature. Full article