Search Results (686)

To improve the sampling efficiency of Markov Chain Monte Carlo in complex parameter spaces, this paper proposes an adaptive sampling method that integrates a swarm intelligence mechanism called the BeeSwarm-MH algorithm. The method combines global exploration by scout bees with local exploitation by worker bees. It employs multi-stage perturbation intensities and adaptive step-size tuning to enable efficient posterior sampling. Focusing on Bayesian inference for parameter estimation in the soliton solutions of the two-dimensional complex Ginzburg–Landau equation, we design a dedicated inference framework to systematically compare the performance of BeeSwarm-MH with the classical Metropolis–Hastings algorithm. Experimental results demonstrate that BeeSwarm-MH achieves comparable estimation accuracy while significantly reducing the required number of iterations and total computation time for convergence. Moreover, it exhibits superior global search capabilities and adaptive features, offering a practical approach for efficient Bayesian inference in complex physical models. Full article

Functional foods like flax (Linum usitatissimum L.) are rich sources of specialized metabolites that contribute to their nutritional and health-promoting properties. Understanding the biosynthesis of these compounds is essential for improving their quality and potential applications. However, dissecting complex metabolic networks in plants remains challenging due to the dynamic nature and interconnectedness of biosynthetic pathways. In this study, we present a synergistic approach combining stable isotopic labeling (SIL), Candidate Substrate–Product Pair (CSPP) networks, and a time-course study with high temporal resolution to reveal the biosynthetic fluxes shaping phenylpropanoid metabolism in young flax seedlings. By feeding the seedlings with ¹³C₃-p-coumaric acid and isolating isotopically labeled metabolization products prior to the construction of CSPP networks, the biochemical validity of the connections in the network was supported by SIL, independent of spectral similarity or abundance correlation. This method, in combination with multistage mass spectrometry (MSⁿ), allowed confident structural proposals of lignans, neolignans, and hydroxycinnamic acid conjugates, including the presence of newly identified chicoric acid and related tartaric acid esters in flax. High-resolution time-course analyses revealed successive waves of metabolite formation, providing insights into distinct biosynthetic fluxes toward lignans and early lignification intermediates. No evidence was found here for the involvement of chlorogenic or caftaric acid intermediates in chicoric acid biosynthesis in flax, as has been described in other species. Instead, our findings suggest that in flax seedlings, chicoric acid is synthesized through successive hydroxylation steps of p-coumaroyl tartaric acid esters. This work demonstrates the power of combining SIL and CSPP strategies to uncover novel metabolic routes and highlights the nutritional potential of flax sprouts rich in chicoric acid. Full article

With the rapid proliferation of real-time digital communication, particularly in multimedia applications, securing transmitted image data has become a vital concern. While chaotic systems have shown strong potential for cryptographic use, most existing approaches rely on low-dimensional, integer-order architectures, limiting their complexity and resistance to attacks. Advances in fractional calculus and memristive technologies offer new avenues for enhancing security through more complex and tunable dynamics. However, the practical deployment of high-dimensional fractional-order memristive chaotic systems in hardware remains underexplored. This study addresses this gap by presenting a secure image transmission system implemented on a field-programmable gate array (FPGA) using a universal high-dimensional memristive chaotic topology with arbitrary-order dynamics. The design leverages four- and five-dimensional hyperchaotic oscillators, analyzed through bifurcation diagrams and Lyapunov exponents. To enable efficient hardware realization, the chaotic dynamics are approximated using the explicit fractional-order Runge–Kutta (EFORK) method with the Caputo fractional derivative, implemented in VHDL. Deployed on the Xilinx Artix-7 AC701 platform, synchronized master–slave chaotic generators drive a multi-stage stream cipher. This encryption process supports both RGB and grayscale images. Evaluation shows strong cryptographic properties: correlation of $-6.1081\times {10}^{-5}$, entropy of 7.9991, NPCR of 99.9776%, UACI of 33.4154%, and a key space of $2^{1344}$, confirming high security and robustness. Full article

Superhydrophobic and superoleophilic nanofibrous or microfibrous membranes are regarded as ideal oil/water separation materials owing to their controllable porosity, superior separation efficiency, and ease of operation. However, developing efficient, scalable, and environmentally friendly strategies for fabricating such membranes remains a significant challenge. In this study, isotactic polypropylene (iPP) fibrous membranes with morphologies ranging from ellipsoidal stacking to microfiber stacking were successfully fabricated via a multistage stretching extrusion and leaching process using in situ microfibrillar composites (MFCs). The results establish a significant relationship between microfiber morphology and membrane oil adsorption performance. Compared with membranes formed from high-aspect-ratio microfibers, those comprising short microfibers feature larger pores and a more open structure, which enhances their oil adsorption capacity. Among the fabricated membranes, the iPP membrane with an ellipsoidal stacking morphology exhibits optimal performance, achieving a porosity of 65% and demonstrating both hydrophobicity and superoleophilicity, with a silicone oil adsorption capacity of up to 312.5%. Furthermore, this membrane shows excellent reusability and stability over ten adsorption–desorption cycles using chloroform. This study presents a novel approach leveraging in situ microfibrillar composites to prepare high-performance oil/water separation membranes in this study, underscoring their considerable promise for practical use. Full article

This paper presents a novel, multi-stage modelling approach for integrating Generative AI (GenAI) tools into design-based STEM education, promoting sustainability and 21st-century problem-solving skills. The proposed methodology includes (i) a conceptual model that defines structural aspects of the domain at a high abstraction level; (ii) a contextual model for defining the internal context; (iii) a GenAI-based model for solving the STEM task, which consists of a generic model for integrating GenAI tools into STEM-driven education and a process model, presenting learning/design processes using those tools. A case study involving the design of an autonomous folkrace robot illustrates the implementation of the approach. Based on Likert-scale evaluations, quantitative results demonstrate a significant impact of GenAI tools in enhancing critical thinking, conceptual understanding, creativity, and engineering practices, particularly during the prototyping and testing phases. This paper concludes that the structured integration of GenAI tools supports personalized, inquiry-based, and sustainable STEM education, while also raising new challenges in prompt engineering and ethical use. This approach provides educators with a systematic pathway for leveraging AI to develop STEM-based skills essential for future sustainable development. Full article

Multistage centrifugal pumps, owing to their high head characteristics, are commonly applied in domains like subsea resource exploitation and groundwater extraction. However, the wear of flow passage components caused by solid particles in the fluid severely threatens equipment lifespan and system safety. To investigate the influence of solid-liquid two-phase flow on pump performance and wear, this study conducted numerical simulations of the solid-liquid two-phase flow within multistage centrifugal pumps based on the Euler–Lagrange approach and the Tabakoff wear model. The simulation results showed good agreement with experimental data. Under the design operating condition, compared to the clear water condition, the efficiency under the solid-liquid two-phase flow condition decreased by 1.64%, and the head coefficient decreased by 0.13. As the flow rate increases, particle momentum increases, the particle Stokes number increases, inertial forces are enhanced, and the coupling effect with the fluid weakens, leading to an increased impact intensity on flow passage components. This results in a gradual increase in the wear area of the impeller front shroud, back shroud, pressure side, and the peripheral casing. Under the same flow rate condition, when particles enter the pump chamber of a subsequent stage from a preceding stage, the fluid, after being rectified by the return guide vane, exhibits a more uniform flow pattern and reduced turbulence intensity. The particle Stokes number in the subsequent stage is smaller than that in the preceding stage, weakening inertial effects and enhancing the coupling effect with the fluid. This leads to a reduced impact intensity on flow passage components, resulting in a smaller wear area of these components in the subsequent stage compared to the preceding stage. This research offers critical theoretical foundations and practical guidelines for developing wear-resistant multistage centrifugal pumps in solid-liquid two-phase flow applications, with direct implications for extending service life and optimizing hydraulic performance. Full article

The integration of vision and language processing into a cohesive system has already shown promise with the application of large language models (LLMs) in medical image analysis. Their capabilities encompass the generation of medical reports, disease classification, visual question answering, and segmentation, providing yet another approach to interpreting multimodal data. This survey aims to compile all known applications of LLMs in the medical image analysis field, spotlighting their promises alongside critical challenges and future avenues. We introduce the concept of X-stage tuning which serves as a framework for LLMs fine-tuning across multiple stages: zero stage, one stage, and multi-stage, wherein each stage corresponds to task complexity and available data. The survey describes issues like sparsity of data, hallucination in outputs, privacy issues, and the requirement for dynamic knowledge updating. Alongside these, we cover prospective features including integration of LLMs with decision support systems, multimodal learning, and federated learning for privacy-preserving model training. The goal of this work is to provide structured guidance to the targeted audience, demystifying the prospects of LLMs in medical image analysis. Full article

The growing density of space objects in low Earth orbit (LEO), driven by the deployment of large satellite constellations, has elevated the risk of orbital collisions and the need for high-precision conjunction analysis. Traditional methods based on Two-Line Element (TLE) data suffer from limited accuracy and insufficient uncertainty modeling. This study proposes a hybrid collision assessment framework that combines Monte Carlo simulation, spline-based refinement of the time of closest approach (TCA), and a multi-stage deterministic refinement process. The methodology begins with probabilistic sampling of TLE uncertainties, followed by a coarse search for TCA using the SGP4 propagator. A cubic spline interpolation then enhances temporal resolution, and a hierarchical multi-stage refinement computes the final TCA and minimum distance with sub-second and sub-kilometer accuracy. The framework was validated using real-world TLE data from over 2600 debris objects and active satellites. Results demonstrated a reduction in average TCA error to 0.081 s and distance estimation error to 0.688 km. The approach is computationally efficient, with average processing times below one minute per conjunction event using standard hardware. Its compatibility with operational space situational awareness (SSA) systems and scalability for high-volume screening make it suitable for integration into real-time space traffic management workflows. Full article

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

Due to the limited scientific exploration of Argania spinosa (L.) skeel husk, this study presents the first investigation of the metabolite profile of methanol and acetone extracts analyzed by liquid chromatography coupled with electrospray ionization and high-resolution multistage mass spectrometry (LC-ESI/HRMSMS). A total of 43 compounds, including hydroxycinnamic acid and flavonoid derivatives, saponins, and triterpenic acids, were identified, some of which have not been previously reported in this species. The total phenols (TPC) and flavonoids (TFC) content were spectrophotometrically determined. A multi-target approach was applied to investigate the antioxidant potential using 1,1-Diphenyl-2-picrylhydrazyl (DPPH), 2,2-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS), β-carotene bleaching, and Ferric Reducing Ability Power (FRAP) tests. Carbohydrate hydrolyzing enzymes and lipase inhibitory activities were also assessed. The acetone extract exhibited the highest TPC and TFC values, resulting in being the most active in β-carotene bleaching test with IC₅₀ values of 26.68 and 13.82 µg/mL, after 30 and 60 min of incubation, respectively. Moreover, it was the most active against both α-glucosidase and α-amylase enzymes with IC₅₀ values of 12.37 and 18.93 µg/mL, respectively. These results pointed out that this by-product is a rich source of bioactive phytochemicals potentially useful for prevention of type 2 diabetes and obesity. Full article

This paper proposes relaxed Simultaneous Signal and Noise Matching (SSNM) conditions to address limitations in selecting source degeneration inductors for multistage LNA design, achieved by introducing controlled mismatches at the external ports. Additionally, a novel frequency-bounded mismatch envelope is introduced to guide load termination selection based on desired IM-OM (input mismatch-output mismatch) characteristics across the operating band. Building on these concepts, a systematic, easy-to-follow strategy is presented for implementing wideband multistage low-noise amplifiers (LNAs), significantly reducing reliance on blind CAD-based optimisation. This approach is validated through a three-stage MMIC LNA prototype, fabricated using a 0.15 μm GaAs process and operating from 28 to 34 GHz. The measured results closely match the simulation, demonstrating a stable gain of 23 ± 1 dB and a noise figure of 2–2.5 dB, confirming the practical effectiveness of the proposed design approach for wideband amplifiers. Full article

Recent advancements in Earth observation technologies have accelerated remote sensing (RS) data acquisition, yet cross-domain semantic segmentation remains challenged by domain shifts. Traditional unsupervised domain adaptation (UDA) methods often rely on computationally intensive and unstable generative adversarial networks (GANs). This study introduces elevation-aware domain adaptation (EADA), a multi-task framework that integrates elevation estimation (via digital surface models) with semantic segmentation to address distribution discrepancies. EADA employs a shared encoder and task-specific decoders, enhanced by a spatial attention-based feature fusion module. Experiments on Potsdam and Vaihingen datasets under cross-domain settings (e.g., Potsdam IRRG → Vaihingen IRRG) show that EADA achieves state-of-the-art performance, with a mean IoU of 54.62% and an F1-score of 65.47%, outperforming single-stage baselines. Elevation awareness significantly improves the segmentation of height-sensitive classes, such as buildings, while maintaining computational efficiency. Compared to multi-stage approaches, EADA’s end-to-end design reduces training complexity without sacrificing accuracy. These results demonstrate that incorporating elevation data effectively mitigates domain shifts in RS imagery. However, lower accuracy for elevation-insensitive classes suggests the need for further refinement to enhance overall generalizability. Full article

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application. Full article

As the global energy system accelerates its transition toward a low-carbon economy, renewable energy sources (RESs), such as wind and photovoltaic power, are rapidly replacing traditional fossil fuels. These RESs are becoming a critical element of deeply decarbonized power systems (DDPSs). However, the inherent non-stationarity, multi-scale volatility, and uncontrollability of RES output significantly increase the risk of source–load imbalance, posing serious challenges to the reliability and economic efficiency of power systems. Scenario generation technology has emerged as a critical tool to quantify uncertainty and support dispatch optimization. Nevertheless, conventional scenario generation methods often fail to produce highly credible wind and solar output scenarios. To address this gap, this paper proposes a novel renewable energy scenario generation method based on a multi-resolution diffusion model. To accurately capture fluctuation characteristics across multiple time scales, we introduce a diffusion model in conjunction with a multi-scale time series decomposition approach, forming a multi-stage diffusion modeling framework capable of representing both long-term trends and short-term fluctuations in RES output. A cascaded conditional diffusion modeling framework is designed, leveraging historical trend information as a conditioning input to enhance the physical consistency of generated scenarios. Furthermore, a forecast-guided fusion strategy is proposed to jointly model long-term and short-term dynamics, thereby improving the generalization capability of long-term scenario generation. Simulation results demonstrate that MDDPM achieves a Wasserstein Distance (WD) of 0.0156 in the wind power scenario, outperforming DDPM (WD = 0.0185) and MC (WD = 0.0305). Additionally, MDDPM improves the Global Coverage Rate (GCR) by 15% compared to MC and other baselines. Full article

This study presents an innovative solution to improve the mechanical performance of traditional cemented tailings backfill (CTB) by incorporating 3D-printed polymer lattice (3DPPL) reinforcements. We systematically investigated three distinct 3DPPL configurations (four-column FC, six-column SC, and cross-shaped CO) through comprehensive experimental methods including Brazilian splitting tests, digital image correlation (DIC), and scanning electron microscopy (SEM). The results show that the 3DPPL reinforcement significantly enhances the CTB’s tensile properties, with the CO structure demonstrating the most substantial improvement—increasing the tensile strength by 85.6% (to 0.386 MPa) at a cement-to-tailings ratio of 1:8. The 3DPPL-modified CTB exhibited superior ductility and progressive failure characteristics, as evidenced by multi-stage load-deflection behavior and a significantly higher strain capacity (41.698–51.765%) compared to unreinforced specimens (2.504–4.841%). The reinforcement mechanism involved synergistic effects of macroscopic truss behavior and microscopic interfacial bonding, which effectively redistributed the stress and dissipated energy. This multi-scale approach successfully transforms CTB’s failure mode from brittle to progressive while optimizing both strength and toughness, providing a promising advancement for mine backfill material design. Full article