Search Results (176)

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

The stability of subsea tunnels is governed by the strong coupling among stress redistribution, damage evolution, and seepage flow (Stress–Damage–Seepage, SDS). The dynamic interplay, especially under high water pressure, often leads to catastrophic failures, yet its mechanisms, particularly the role of support timing, remain insufficiently understood due to limitations in conventional numerical methods. This study aims to unravel the SDS coupling mechanisms during tunnel excavation under high hydraulic head, and to quantitatively investigate how support timing influences the stability of the surrounding rock within this coupled system. A coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) framework was employed. In this approach, excavation-induced damage, crack propagation, and fluid–particle interactions are explicitly resolved at the particle scale, whereas the macroscopic permeability evolution is captured through an imposed empirical exponential relationship. Simulations were conducted under both steady-state and transient seepage conditions with varying stress ratios and water heads. High-head transient seepage intensifies SDS coupling, dynamically redistributing seepage forces to damage zone edges and amplifying damage. Support timing critically mediates this interaction: premature support risks tensile failure at the tunnel periphery, while delayed support allows a vicious cycle of shear failure and increased inflow. Optimal “timely” support, applied after initial deformation, diverts high seepage forces inward, minimizing final damage. The spatiotemporal synchronization of transient seepage forces with damage evolution is pivotal for stability. Support timing acts as a key control variable. The CFD-DEM framework effectively elucidates these micro-mechanisms, providing a scientific basis for the dynamic design of support in high-pressure subsea tunnels. Full article

The increasing demand for higher energy density in lithium-ion batteries has driven significant interest in layered oxide cathode materials. However, their development is hindered by an inherent trade-off between structural stability and ion transport kinetics. This compromise often manifests as a conflict between achieving high capacity, long cycle life, and excellent rate performance. Consequently, mitigating structural degradation and minimizing interfacial side reactions have emerged as core research priorities. Based on this, this review summarizes the crystal chemistry and key challenges of three main types of layered oxide cathode materials, and critically evaluates two main modification strategies: bulk doping, which enhances performance by regulating the electronic structure and suppressing phase transitions; and surface coating, which builds a protective layer at the particle–electrolyte interface to suppress side reactions and metal dissolution. Looking ahead, in terms of modification, the focus should be on multi-scale co-doping to construct a stable bulk phase structure and multi-functional coating to optimize the interface. Integrating artificial intelligence with high-throughput computation will powerfully enable the pursuit of these advanced modification strategies. This integrated approach may resolve the fundamental contradiction between energy density and stability, thereby paving a new pathway for next-generation lithium-ion batteries. Full article

Despite its importance in modeling subdiffusion in fractal and heterogeneous media, a rigorous and computational scheme for solving the fractional diffusion equation on generalized comb structures over unbounded domains has remained elusive, mainly due to the nonlocal memory effect and slow spatial decay of solutions. To the best of our knowledge, we address this long-standing gap by presenting a fully integrated framework that simultaneously resolves both challenges. We derive the governing equation from constitutive relations and establish exact absorbing boundary conditions (ABCs) for the multi-skeleton comb model, a result absent in prior work. A transparent Dirichlet-to-Neumann (DtN) map, constructed via Laplace analysis, rigorously handles skeletal Dirac delta singularities and eliminates spurious reflections without empirical parameters. Furthermore, we propose a novel structure-preserving finite difference scheme that applies the sum-of-exponentials (SOE) approximation not only to the interior Caputo derivative but also to the convolution kernels arising from the ABCs. This yields a dramatic reduction in computational complexity, from quadratic $O\left(N_t^2\right)$ to quasi-linear $O(N_{t}log{N}_t)$, while preserving the physics of anomalous transport. We prove the well-posedness, unconditional stability, and convergence of the method. Numerical results confirm theoretical error estimates and show excellent agreement between simulated particle distributions, mean square displacement profiles, and exact asymptotics, validating both accuracy and robustness. The speedup (CPU time ratio Direct/Fast) is about $1.00\times$–$1.23\times$ for $N_t=5000$ in our tests. Our approach sets a new benchmark for simulating anomalous dynamics in fractal-inspired media. Full article

Zero-valent iron (Fe(0)) nanoparticles have a wide range of applications, including catalysis, energy storage, and even reported roles in human neurochemistry. This study demonstrated that [Fe₃(CO)₁₂] dissolves in N,N-Dimethylformamide (DMF) within a minute to resolve the dissolution problem of this complex. Dodecylamine (DDA) was used to produce DDA-coated Fe(0) at 383 K in 30 s with a microwave reactor. The powder X-ray diffraction (PXRD) of the Fe(0) profile indicated a pure-phase face-centred cubic (FCC) structure with Fm$\overline{3}$m space group. Varying the synthesis time from 30 s to 5 min did not significantly affect the unit cell parameters (3.5276 (±0.0001) and 3.5391 (±0.0001) Å). Microwave use yielded well-dispersed, pure Fe(0) nanoparticles, and the particle size, shape, elemental analysis, and surface oxidation of the Fe(0) nanoparticles were studied using scanning electron microscopy and dispersive X-ray spectroscopy (SEM/EDX). Annular Dark-Field Scanning Transmission Electron Microscopy (ADF-STEM) and Fourier-transform infrared (FT-IR) spectroscopy confirmed the surface coating of Fe(0) nanoparticles with DDA. Thermogravimetric analysis (TGA) was used to demonstrate the surface adsorption of DDA on Fe(0) nanoparticles. In addition, STEM showed that the average nanoparticle size under the stated synthesis conditions was 25.7 nm. This comparatively straightforward procedure offers advantages over existing practical approaches to the synthesis of Fe(0) nanoparticles, including safety, speed and reaction control. Full article

An improved Sinh Cosh optimizer (ISCHO) is proposed to resolve load frequency control (LFC) tasks. The original Sinh Cosh optimizer (SCHO) employs a fixed iteration-based switching function to balance exploration and exploitation, which lacks awareness of search dynamics and leads to inefficient optimization. Therefore, this paper proposes a “first grabbing then washing” strategy to dynamically balance exploration and development. The proposed ISCHO technique is tested on 13 benchmark functions and compared with Particle Swarm Optimization, Sine Cosine Algorithm, and Grey Wolf Optimizer, demonstrating superior optimization performance. Furthermore, a new controller based on the two-degree-of freedom PID controller (2DOF-PID), the two-degree-of freedom with double integral feedback PID controller (2DOF-PIDF-II), is proposed. A two-area multi-source interconnected power system, incorporating thermal, hydraulic, wind, and solar generation units with nonlinearities (GRC and GDB), uncertainties, and load fluctuations, is employed to validate the proposed approach. Quantitative results under step load perturbation demonstrate that the ISCHO-optimized 2DOF-PIDF-II controller significantly outperforms other methods. For area 1 frequency deviation, ISCHO reduces the maximum overshoot by 38.37%, 19.09%, and 21.48% compared to PSO, SCA, and SCHO. For tie-line power deviation, maximum overshoot is reduced by 53.00% compared to PSO. These results confirm that the proposed ISCHO-tuned 2DOF-PIDF-II controller substantially enhances system frequency stability under various operating conditions. Full article

This study analyzes hydrodynamics and mass transfer in a packed-bed reactor (PBR) by comparing two representations of bed geometry. The first is a pseudo-homogeneous approach using effective parameters, such as a radial porosity distribution. The second is a heterogeneous approach with resolved particles in the CAD domain. Both models simulate single-phase flow and mass transfer of urea and NH₃ for an enzymatic reaction across a wide Reynolds number range $\left(5\le {\mathrm{Re}}_p\le 750\right)$. The pseudo-homogeneous model incorporated a detailed porosity distribution, derived from the heterogeneous model’s solids layout, which aligned well with literature, including classical correlations for radial porosity in packed beds. Additionally, hydrodynamic predictions were benchmarked against established pressure-drop correlations for confined packed beds, supporting the physical consistency of the particle-resolved framework. This non-uniform porosity informed local variations in permeability and dispersion coefficients. Velocity, pressure, and concentration fields from both approaches were compared to quantify predictive quality. Results indicate that a well-configured pseudo-homogeneous model can closely match heterogeneous model predictions, achieving similar accuracy in many flow regimes, with accumulated average relative errors below 8%. However, its performance varies with flow conditions. The optimal pseudo-homogeneous model (showing the highest predictive consistency with the particle-resolved simulations) was then used for transient simulations. These dynamic results support the preliminary sizing and conceptual design of a device for nutrient recovery from human urine for agricultural use, demonstrating the utility of simplified models for complex reactor design while acknowledging that full experimental validation under real urine-matrix conditions remains beyond the scope of the present study. Full article

Shutdown dose rate (SDDR) assessments have been performed for the DEMO tokamak model, including the latest design and environmental configurations. The main objective of this study was to evaluate the shutdown radiation fields and establish dose rate limits to ensure safe personnel access to the Vacuum Vessel (VV) and nearby components. The simulations were based on the DEMO baseline model, further refined with the minor updates of the lower port, equatorial port limiter, and upper port assemblies. The computational approach employed the Monte Carlo particle transport code MCNP for neutron and photon transport calculations, coupled with the activation and decay code FISPACT-II to determine time-dependent decay gamma source terms. The mesh-coupled Rigorous Two-Step (R2Smesh) methodology developed in KIT was applied to achieve spatially resolved decay of photon source distributions and to compute corresponding SDDR 3D maps within the DEMO reactor configuration. The results provide a detailed characterization of the residual radiation environment inside the VV, offering insight into the accumulated activity, shielding performance of different materials, and potential access scenarios for maintenance operations in next-generation fusion devices. Full article

With the rapid development of renewable energy technologies, numerous distributed energy resources (DERs) have been integrated into power systems. How to fully exploit renewable energy while maintaining the stable operation of power systems remains an urgent challenge. Furthermore, the diversity of DERs’ ownership requires scheduling approaches that account for the distinct interests and characteristics of multiple stakeholders. To address these challenges, this study introduces a two-stage operational optimization framework for the virtual power plant (VPP), which is grounded in a Stackelberg game model. This strategy innovatively combines two conventional control methods: the day-ahead stage employs direct control for global pre-scheduling, leveraging its cost optimization capability; the intraday stage utilizes dynamic pricing to guide prosumers, tapping into DERs’ flexibility while accommodating their individual energy usage preferences. The Stackelberg game is resolved through a tiered solution methodology employing particle swarm optimization (PSO). To enhance solution efficiency, a Kriging surrogate model is introduced to replace the prosumers’ models, significantly reducing the computational burden of the PSO. Case studies demonstrate that the proposed strategy can balance operating costs and energy usage preferences, and the proposed solution approach can significantly enhance solution efficiency. Full article

Airborne microplastics (AMPs) undergo ultraviolet (UV)-driven physicochemical aging during atmospheric transport, influencing cloud processes, greenhouse-gas release, and potential respiratory health impacts. Quantifying this transformation is particularly challenging for particles smaller than 10 µm and for polymers such as polyethylene terephthalate (PET), whose intrinsic ester carbonyl band obscures newly formed acid carbonyls in conventional infrared analyses. Here, we develop a µFTIR attenuated total reflection (µFTIR-ATR) imaging method combined with a fourth-derivative oxidation index (carbonyl ratio at 1701/1716 cm⁻¹) that resolves these overlapping bands and enables sensitive, quantitative evaluation of PET surface oxidation. The approach automates detection, identification, and oxidation analysis of particles down to ~2 µm. Laboratory UV irradiation experiments show a systematic increase in this derivative-based oxidation index with exposure dose. Application to ambient PET collected from Mt. Fuji, Tokyo, Osaka (Japan), and Siem Reap (Cambodia) reveals clear regional differences corresponding to local UV-A environments: PET from Siem Reap exhibited the highest oxidation, whereas particles from the Japanese sites showed moderate but variable aging. These results demonstrate that derivative-based µFTIR-ATR imaging provides a practical and highly sensitive tool for quantifying photo-oxidative degradation in fine AMPs and highlight the value of chemical-aging metrics for interpreting atmospheric processing and transport pathways. Full article

Mission-oriented supply chains involve multi-phase tasks, strong resource interdependencies, and stringent reliability requirements, which make demand planning complex and uncertain. This study develops a structured demand modeling framework to support multi-phase mission-oriented supply chains under budget and reliability constraints by integrating digital twin technology with an adaptive inertia weight particle swarm optimization (AIW-PSO) algorithm. The supply support process is decomposed into four sequential phases—storage, transportation, preparation, and execution—and phase-specific demand models are constructed based on system reliability theory, explicitly incorporating redundancy, maintainability, and repairability. In this work, digital twin technology functions as a data acquisition and virtual experimentation layer that supports parameter calibration, state-aware scenario simulation, and event-triggered re-optimization rather than continuous real-time control. Physical-state updates are mapped to model parameters such as phase durations, failure rates, repair rates, and instantaneous availability, after which the integrated optimization model is re-solved using a warm-start strategy to generate updated demand plans. The resulting multi-phase demand optimization problem is solved using AIW-PSO to enhance global search performance and mitigate premature convergence. The proposed method is validated using a representative mission-oriented supply support scenario with operational and simulated data. Simulation results demonstrate that, under identical budget constraints, the proposed approach achieves higher mission completion capability than conventional PSO-based methods, providing effective and practical decision support for multi-phase mission-oriented supply chain planning. Full article

Magnetorheological (MR) materials, with the ability of vectorial response, offer exciting opportunities for next-generation wearables and soft robotic systems. Although some existing MR materials and fiber designs can produce directional responses, they typically rely on strategies—such as hard-magnetic loading or pre-magnetization—that constrain safety and large-scale manufacturability. This Communication highlights a paradigm-shifting advance reported by Pu et al., that a soft-magnetic fibrous architecture achieves genuine vector-stimuli-responsiveness under low, safe magnetic fields without pre-magnetization. We articulate the great breakthrough of this work through a hierarchical design framework, demonstrating how the synergistic innovation at the material (magnetic dipole aligned in low-density polyethylene), fiber (drawing-induced magnetic easy axis), yarn (twist-induced cooperative effects), and fabric (vertical or horizontal magnetic field response capability) levels collectively resolves the longstanding trade-offs between performance, manufacturability, and safety. As a result, this strategy demonstrates strong universality in terms of materials, although only the carbonyl iron particles were used. This approach not only enables programmable bending, stiffening, shear, and compression in textiles but also establishes a versatile platform for magneto-programmable systems. Furthermore, we delineate the critical challenges and future trajectories—from theoretical modeling and integration of complementary stimuli to the development of three-dimensional textile architectures—that this new platform opens for the fields of haptics, soft robotics, and adaptive wearables. Full article

Airborne micro- and nanoplastic particles (MNPs) are increasingly recognized as a potential occupational exposure hazard, yet substance-specific workplace data remain limited. This study quantified airborne MNP concentrations during polyester microfiber production using a correlative SEM–Raman approach that enabled chemical identification and size-resolved particle characterization. The aerosol mixture at the workplace was dominated by sub-micrometer particles, with PET—handled onsite—representing the main process-related MNP type, and black tire rubber (BTR) forming a substantial background contribution. Across both sampling periods, total MNP particle number concentrations ranged between 6.2 × 10⁵ and 1.2 × 10⁶ particles/m³, indicating consistently high particle counts. In contrast, estimated MNP-related mass concentrations were much lower, with PM₁₀ levels of 12–15 µg/m³ and PM_2.5 levels of 1.3–1.6 µg/m³, remaining well below applicable occupational exposure limits and near or below 8 h-equivalent WHO guideline values. Comparison with earlier workplace and indoor studies suggests that previously reported concentrations were likely underestimated due to sampling strategies with low efficiency for small particles. Moreover, real-time optical measurements substantially underestimated particle number and mass in this study, reflecting their limited suitability for aerosols dominated by small or dark particles. Overall, the data show that workplace MNP exposure at the investigated site is driven primarily by very small particles present in high numbers but low mass. The findings underscore the need for substance-specific, size-resolved analytical approaches to adequately assess airborne MNP exposure and to support future development of MNP-relevant occupational health guidelines. Full article

This article aims to describe a novel workflow designed for generating a new family of minimal surface-based lattice structures with improved performance in terms of material budget compared to the well-known cells like Gyroid and Schwartz. The implemented method is based on the iterative resolution of a dynamic model, where proper forces are applied to generate minimal surface lattices, considering the boundary conditions and the constraint configurations. The novelty of the approach is given by the ability to create a minimal surface without resolving the partial differential equation and without knowing the exact minimal surface generative function. The starting geometry used for the lattice generation is the hypercube, parametrized to create different lattice configurations. Creating five different starting geometries and two constraint configurations, ten different lattice cells were created. For the comparison, a representative parameter of the material budget has been introduced and used to define the two best cells. The material budget is crucial for particle accelerator components, sensors, and detectors. These cells have been compared with Gyroid and Schwartz of the same thickness and bounding box, highlighting improvements of a factor of 2.3 and 1.7, respectively, in terms of material budget. The same cells have also been 3D-printed and tested under compression, and the obtained force–displacement curves were compared with those from a finite element analysis, demonstrating good agreement in the elastic region. Full article

Traditional position-based fluid (PBF) methods often suffer from interpolation inaccuracies and limited computational efficiency due to their fixed smoothing length. To address these limitations, this paper proposes an adaptive smoothing length model and implements full-pipeline parallel acceleration on GPUs. By incorporating both local neighbor count and density variation, the model dynamically adjusts particle smoothing length. This adaptation effectively mitigates two issues: surface distortion due to insufficient interpolation in sparse regions, and performance degradation caused by computational redundancy in dense regions. To resolve neighbor search asymmetry introduced by dynamic smoothing lengths, we designed a symmetry handling technique based on maximum smoothing length and an efficient spatial hashing search algorithm. Experimental results across multiple scenarios (including dam break and droplet impact) demonstrate that our method maintains simulation stability comparable to the fixed smoothing length approach while improving computational efficiency and enhancing local particle distribution uniformity. The improved uniformity is evidenced by a significant reduction in the variance of neighbor particle counts. Visually, the method yields more natural results for dynamic details such as splashing and fragmentation, thereby ensuring the visual realism of the simulations. Full article