Search Results (1,188)

JP-10 (exo-tetrahydrodicyclopentadiene) is a high-energy-density hydrocarbon broadly used in advanced aerospace propulsion as a regenerative cooling fluid; in this study, we aimed to clarify how fuel pressure affects its thermal degradation (oxidative and pyrolytic) in near-isothermal flowing reactor. Experiments were performed under oxidative conditions (wall temperature 623.15 K, p = 0.708–6.816 MPa) and pyrolytic conditions (wall temperature 793.15 K, p = 2.706–7.165 MPa); carbon deposits were quantified by LECO analysis, oxidation activity was assessed by temperature-programmed oxidation (TPO), and morphology was performed by FESEM and EDS. Results show that oxidative coking is minimal (5.37–14.95 μg·cm²) and largely insensitive to pressure in the liquid phase (1.882–6.816 MPa), whereas at 0.708 MPa (gas/phase-change conditions), deposition increases, implicating phase and local heat-transfer effects. Under oxidative conditions, deposits are predominantly amorphous carbon with a disordered structure, formed at relatively low temperatures, with only a few fiber-like metal sulfides identified by EDS. In contrast, under pyrolysis conditions, the deposits are predominantly carbon nanotubes, exhibiting well-defined tubular morphology formed at elevated temperatures via metal-catalyzed growth. The pyrolysis coking yield is substantially higher (66.88–221.89 μg·cm⁻²) and increases with pressure. The findings imply that the pressure influences the coking of JP-10 via phase state under oxidative conditions and residence time under pyrolytic conditions, while basic morphologies of coke deposits remain similar; operationally, maintaining the working pressure higher than the saturated vapor pressure can mitigate oxidation coking associated with phase transitions, and minimizing residence time can mitigate pyrolytic coking. Full article

Coke is an essential raw material in the blast furnace (BF) ironmaking process. Its moisture content significantly impacts BF ironmaking production. This study employs a coupled Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) approach to simulate the drying process of wet coke within a coke silo (CS) dryer. Initially, the model was validated by comparing numerical results with experimental data from the literature. Subsequently, it investigated the gas flow dynamics, heat and mass transfer characteristics, and differences in drying behaviour across distinct dryer zones. Finally, the effects of inlet gas velocity and inlet gas temperature on the drying process were systematically quantified. Simulation results reveal that the bottom of the CS dryer exhibits a low-velocity laminar state, while the middle and upper regions display intense gas flow motion. Consequently, the bottom region exhibits insufficient particle drying in comparison to other zones, with the average particle moisture content decreasing by less than 20%. Under the continuous heat exchange between the hot gas and the particles, the moisture content of the particles decreases rapidly. Based on the drying rate behaviour, the drying process exhibits the following three different stages: the pre-heating period, the constant-rate period, and the falling-rate period. Compared to zones 1 and 3, zone 2 exhibits higher temperatures due to its high heat transfer efficiency, which significantly promotes a reduction in particle moisture content. An increase in inlet gas velocity enhances the particle drying rate and heat flux, accelerates moisture reduction, and raises the temperature. The impact of inlet gas velocity is most pronounced after the constant-rate period, with particle drying uniformity decreasing as the inlet gas velocity increases, consequently leading to a decline in drying quality. Increasing inlet gas temperature significantly increases particle temperature and heat flux throughout the drying period and accelerates the high-rate drying stage. These findings provide fundamental insights for further understanding and studying the coke drying process. Full article

Floating two-body wave energy converters (WECs) exhibit advantages, including insensitivity to water depth and tidal range, along with adaptability to multi-level sea states. However, WECs suffer from drawbacks, including unstable power generation and low wave energy capture efficiency. To enhance the hydrodynamic performance and energy capture efficiency, a dish-shaped buoy and perforated damping plate configuration was designed based on conventional two-body WECs. First, four two-body WECs were developed according to these configurations. Second, a numerical model based on potential flow theory and the boundary element method (BEM) was established, with its accuracy validated through sea trials. Finally, the frequency domain response, motion response, mooring tension and power absorption effect of the WECs under wave excitation of grades 3, 4 and 5 were analyzed. The results demonstrate that both the dish-shaped buoy and perforated damping plate significantly improve the device stability and energy capture potential. Regarding the motion response, both configurations reduced the peak response amplitudes in heave and roll, enhancing the device stability. For mooring tension, both configurations reduced the mooring line tension. For power absorption, the perforated damping plate effectively increased the energy capture efficiency, while the dish-shaped buoy also demonstrated superior performance under higher-energy wave conditions. Overall, this study provides a theoretical foundation and design guidance for floating two-body WECs. Full article

Facial recognition systems are increasingly used for authentication across domains such as finance, e-commerce, and public services, but their growing adoption raises significant concerns about spoofing attacks enabled by printed photos, replayed videos, or AI-generated deepfakes. To address this gap, we introduce a multi-layered Face Recognition-as-a-Service (FRaaS) platform that integrates passive liveness detection with active challenge–response mechanisms, thereby defending against both low-effort and sophisticated presentation attacks. The platform is designed as a scalable cloud-based solution, complemented by an open-source SDK for seamless third-party integration, and guided by ethical AI principles of fairness, transparency, and privacy. A comprehensive evaluation validates the system’s logic and implementation: (i) Frontend audits using Lighthouse consistently scored above 96% in performance, accessibility, and best practices; (ii) SDK testing achieved over 91% code coverage with reliable OAuth flow and error resilience; (iii) Passive liveness layer employed the DeepPixBiS model, which achieves an Average Classification Error Rate (ACER) of 0.4 on the OULU–NPU benchmark, outperforming prior state-of-the-art methods; and (iv) Load simulations confirmed high throughput (276 req/s), low latency (95th percentile at 1.51 ms), and zero error rates. Together, these results demonstrate that the proposed platform is robust, scalable, and trustworthy for security-critical applications. Full article

Information geometry provides a data-informed geometric lens for understanding data or physical systems, treating data or physical states as points on statistical manifolds endowed with information metrics, such as the Fisher information. Building on this foundation, we develop a robust mathematical framework for analyzing data residing on Riemannian manifolds, integrating geometric insights into information-theoretic principles to reveal how information is structured by curvature and nonlinear manifold geometry. Central to our approach are tools that respect intrinsic geometry: gradient flow lines, exponential and logarithmic maps, and kernel-based principal component analysis. These ingredients enable faithful, low-dimensional representations and insightful visualization of complex data, capturing both local and global relationships that are critical for interpreting physical phenomena, ranging from microscopic to cosmological scales. This framework may elucidate how information manifests in physical systems and how informational principles may constrain or shape dynamical laws. Ultimately, this could lead to groundbreaking discoveries and significant advancements that reshape our understanding of reality itself. Full article

This study examines the challenges and opportunities of managing electronic waste (e-waste) in the Galapagos Islands, a globally significant yet vulnerable subnational insular jurisdiction (SNIJ). Drawing on theories of Circular Economy (CE) and Political Industrial Ecology (PIE), the research investigates the status of e-waste in the archipelago, the barriers to implementing CE practices, and the institutional dynamics shaping material flows. Using a mixed-methods approach—including archival analysis, participant observation, and semi-structured interviews with key informants from government, private, and nonprofit sectors—the findings presented here demonstrate that e-waste management is hindered by limited capital, infrastructure, public awareness, and fragmented governance. While some high-capital institutions can export e-waste to mainland Ecuador, most residents and low-capital entities lack viable disposal options, leading to accumulation and improper disposal. The PIE analysis yielded findings that highlight how institutional power and financial capacity dictate the sustainability of e-waste pathways, with CE loops remaining largely incomplete. Despite national policy support for CE, implementation in Galapagos remains aspirational without targeted financial and logistical support. This case contributes to broader discussions on waste governance in island settings and underscores the need for integrated, equity-focused strategies to address e-waste in small island developing states (SIDS) and SNIJs globally. Full article

Optimal Power Flow (OPF) problems are essential in power system planning, but their nonlinear and large-scale nature makes them difficult to solve with traditional optimization methods. Metaheuristic algorithms have become increasingly popular for solving OPF problems due to their ability to handle complex search spaces and multiple objectives. The Jellyfish Search Optimizer (JSO) is a metaheuristic algorithm that performs well for solving various optimization problems. However, it suffers from low exploration and an imbalance between exploration and exploitation. Therefore, this study introduces an improved JSO called Conscious Neighborhood-based JSO (CNJSO) to address these shortcomings. The proposed CNJSO suggests a new movement strategy named Best archive and Non-neighborhood-based Global Search (BNGS) to enhance the exploration ability. In addition, CNJSO adapts the concept of conscious neighborhood and the Wandering Around Search (WAS) strategy. The proposed CNJSO facilitates exploration of the search space and strikes a suitable balance between exploration and exploitation. The performance of CNJSO was evaluated on CEC 2018 benchmark functions, and the results were compared with those of ten state-of-the-art metaheuristic algorithms. In addition, the results were statistically validated using the Wilcoxon rank-sum and Friedman tests. Additionally, the effectiveness of CNJSO was assessed through the resolution of OPF problems. The experimental and statistical results confirm that the proposed CNJSO algorithm is competitive and superior to the compared algorithms. Full article

Efficient prediction of aerodynamic loads on wind turbine blades under yawed inflow remains challenging due to the complexity of three-dimensional unsteady flow phenomena. In this work, a modified blade element momentum (BEM) method, incorporating multiple correction models, is systematically compared with high-fidelity computational fluid dynamics (CFD) simulations for the NREL Phase VI wind turbine across a range of inflow velocities (7–15 m/s) and yaw angles ($0^{°}$–${60}^{°}$). A normalized absolute error metric, referenced to experimental measurements, is employed to quantify prediction discrepancies at different yaw conditions, wind speeds, and spanwise blade locations. Results indicate that the corrected BEM method maintains good agreement with measurements under non-yawed attached flow, with errors within 2%, but its accuracy declines substantially in separated and yawed flow regimes, where errors can exceed 20% at high yaw angles (e.g., ${60}^{°}$) and low tip-speed ratios. CFD flow-field visualizations, including vorticity and Q-criterion iso-surfaces, reveal that yawed inflow strengthens vortex interactions on the leeward side and generates Coriolis-driven spanwise vortex structures, promoting stall progression from tip to root. These unsteady phenomena induce load fluctuations that are not captured by steady-state BEM formulations. Based on these insights, future studies could incorporate vortex structure and spanwise flow features extracted from CFD into unsteady correction models for BEM, enhancing prediction robustness under complex operating conditions. Full article

This review covers the current state, challenges, and future directions of catalytic combustion technologies for mitigating fugitive methane emissions from the fossil fuel industry. Methane, a potent greenhouse gas, is released from diverse sources, including natural gas production, oil operations, coal mining, and natural gas engines. The paper details the primary emission sources, and addresses the technical difficulties associated with dilute and variable methane streams such as ventilation air methane (VAM) from underground coal mines and low-concentration leaks from oil and gas infrastructure. Catalytic combustion is a useful abatement solution due to its ability to destruct methane in lean and challenging conditions at lower temperatures than conventional combustion, thereby minimizing secondary pollutant formation such as NO_X. The review surveys the key catalyst classes, including precious metals, transition metal oxides, hexa-aluminates, and perovskites, and underscores the crucial role of reactor internals, comparing packed beds, monoliths, and open-cell foams in terms of activity, mass transfer, and pressure drop. The paper discusses advanced reactor designs, including flow-reversal and other recuperative systems, modelling approaches, and the promise of advanced manufacturing for next-generation catalytic devices. The review highlights the research needs for catalyst durability, reactor integration, and real-world deployment to enable reliable methane abatement. Full article

The Hard X-ray Modulation Telescope (HXMT), China’s first X-ray astronomy satellite, has significantly contributed to the study of fast variability in black hole X-ray binaries through its broad energy coverage (1–250 keV), high timing resolution, and sensitivity to hard X-rays. This review presents a comprehensive overview of timing analysis techniques applied to black hole X-ray binaries using Insight-HXMT data. We introduce the application and comparative strengths of several time-frequency analysis methods, including traditional Fourier analysis, wavelet transform, bicoherence analysis, and Hilbert-Huang transform. These methods offer complementary insights into the non-stationary and nonlinear variability patterns observed in black hole X-ray binaries, particularly during spectral state transitions and quasi-periodic oscillations. We discuss how each technique has been employed in recent Insight-HXMT studies to characterize timing features such as low-frequency QPOs, phase lags, and power spectrum evolution across different energy bands. Moreover, we present novel phenomena revealed by Insight-HXMT observations, including the detection of high-energy QPOs, spectral parameter modulation with QPO phase, and a new classification scheme for QPO types. The integration of multiple analysis methods enables a more nuanced understanding of the accretion dynamics and the geometry of the inner accretion flow, shedding light on fundamental physical processes in relativistic environments. Full article

Optimizing warehouse operations is a strategic priority for ensuring the timely and efficient flow of materials in industrial environments. In contexts with limited digital infrastructure, organizations often face persistent challenges such as inefficient picking, poor material traceability, and suboptimal space utilization, ultimately leading to productivity losses and operational delays. This paper introduces a systematic, lean-driven framework for warehouse optimization, structured around a sequential methodology involving Define, Improve, and Control. The approach begins with a comprehensive diagnostic phase to evaluate the current state and identify performance gaps. It then guides the development and implementation of targeted interventions aimed at eliminating waste, standardizing operations, and aligning resources with value-added activities. Finally, the framework supports long-term sustainability through continuous monitoring, process standardization, and performance control. The methodology is validated through its application in a parts warehouse within the glass transformation industry, highlighting its adaptability, practical relevance, and capacity to generate meaningful improvements, even in low-digitalization environments. The framework offers a scalable solution for organizations seeking to enhance warehouse performance through structured lean practices. Full article

To address the difficulty in eliminating low-frequency vibrations in the hydraulic pipelines of large marine vessels, this study first investigates the vibration characteristics of hydraulic pipelines. The research is conducted based on the stress states of pipelines under external excitations—specifically axial (X-direction), radial (Y-direction), and combined radial–axial (X + Y) excitations and integrates theoretical derivation, simulation, and experimental validation. Firstly, a multidimensional directional vibration equation for the pipeline was derived based on its stress distribution, yielding a more accurate vibration model for marine pipelines. Subsequently, simulations were performed to analyze the effects of fluid velocity, pipeline layout, and support distribution on the pipeline’s vibration characteristics. Finally, experiments were designed to verify the simulation results and examine the impact of external interference on pipeline vibration. The experimental results indicate the following: the influence of flow velocity variations on pipeline modes is generally negligible; increasing the number of pipeline circuits effectively reduces its natural frequencies; increasing the number of supports not only lowers the overall vibration intensity of the pipeline but also achieves peak shaving, thereby effectively reducing the maximum vibration amplitude; and the impact of external environmental interference on the pipeline’s vibration characteristics is complex, as it not only enhances vibration intensity but also weakens vibrations in specific directions. This study lays a theoretical foundation for subsequent vibration reduction efforts for marine hydraulic pipelines. Full article

Multi Small-Object Tracking (MSOT) is crucial for drone inspection and intelligent monitoring, yet traditional Multiple-object Tracking (MOT) methods perform poorly in such scenarios. The reasons include the following: small targets have low resolution and sparse features, leading to high missed detection rates; frequent occlusion and motion blur in dense scenes cause trajectory interruption and identity switches. To address these issues, an MSOT method combining dual motion modeling and dynamic Region of Interest (ROI) detection is proposed. The dual motion framework integrates Kalman filtering and optical flow through dynamic weighting to optimize target state estimation. The Kalman filter-guided dynamic ROI mechanism, combined with multi-feature fusion, enables trajectory recovery when targets are lost. Experiments on the VisDrone-MOT and UAVDT datasets show that this method outperforms mainstream algorithms in core metrics such as MOTA and HOTA, with better trajectory continuity and identity consistency while maintaining good real-time performance. Full article

Conventional hydraulic fracturing aims to minimize the loss of fracturing fluid, with the fluid serving solely to generate fractures, whereas the fracturing-flooding process involves the injection of an agent that facilitates fracture generation without flowing back. This injection agent not only acts as a fracturing treatment but also engages in a displacement process. Currently, there exists a notable gap in systematic research concerning the mechanisms of production enhancement via fracturing-flooding. The characterization of the flow pattern and production behavior associated with fracturing-flooding remains unclear. By integrating physical laboratory experiments with numerical simulations, this study finds that an increase of the displacement pressure gradient can increase the matrix permeability by 10–20%. The residual oil distribution also transfers from a contiguous state to an oil film. It results in a smaller pore–throat size, so oil can be mobilized. Stepwise reduced rate injection leverages the advantages of short-wide fractures while enhancing lateral fracture complexity and oil production rates. Furthermore, we aim to quantitatively characterize the fractures resulting from fracturing-flooding at the microscale and to establish a physical simulation methodology for low-permeability sandstone. Full article

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time. Full article