Search Results (5,875)

The solidification process is crucial for preparing high-performance superconductors and is strongly dependent on the characteristics of the starting powder, including particle size, morphology, and phase purity. This review concisely examines the study on four key superconductors: REBCO, Bi-2212, FeSeTe, and MgB₂. In REBCO, additives such as CeO₂, Pt, or BaO₂ powder can refine the RE-211 phase. In Bi-2212, Pb doping stabilizes the high-T_c phase. For FeSeTe, doping with F or Co modifies phase separation and introduces Δκ pinning. Meanwhile, in MgB₂, the incorporation of SiC nanoparticles powder generates effective pinning centers. Concurrently, processing conditions exert a decisive influence on the final microstructure, as demonstrated by the TSMG/TSIG route in REBCO, partial melting parameters for Bi-2212, specific cooling protocols and thermal treatments for FeSeTe, and optimized sintering and post-annealing processes for MgB₂. Future research directions should prioritize fundamental understanding of phase separation mechanisms during powder processing, development of multi-component doping strategies for powder modification, and advancement of scalable powder processing routes for practical conductor architectures. Full article

China’s steel decarbonization is a key sustainability challenge because cleaner production routes must be evaluated not only by their mitigation potential, but also by their implications for industrial continuity, cost affordability, resource security, and transition manageability. This study develops a national-scale soft-linked sustainability assessment framework that translates policy-conditioned macro signals into a multi-period, multi-objective optimization model of steelmaking-route transition from 2025 to 2050. Three policy environments are examined: carbon-control pressure, electricity-cost support for electrified routes, and their combined application. The model evaluates route portfolios by cumulative system cost, emissions, and transition adjustment intensity, linking mitigation with affordability and implementation feasibility. Results show that policy environments do not shift pathways uniformly; instead, they reshape the feasible trade-off frontier and alter which route combinations emerge as plausible compromise solutions. Across scenarios, scrap-based electric arc furnace steelmaking (Scrap-EAF) becomes the central medium-term route, while blast furnace–basic oxygen furnace steelmaking (BF-BOF) contracts but remains residual. Hydrogen-based direct reduced iron–electric arc furnace steelmaking (H₂-DRI-EAF) expands under favorable conditions, but does not become dominant by 2050 under the baseline national-scale parameterization. Overall, this study contributes to sustainability-oriented industrial transition analysis by showing how policy-conditioned environments reshape route feasibility, transition sequencing, affordability–mitigation trade-offs, and the practical manageability of China’s steel-sector decarbonization. Full article

Because high-precision DEMs are costly to obtain, while low-precision DEMs often fail to meet accuracy requirements for watershed flood simulation, this study proposes a multi-source DEM fusion method based on the Random Forest algorithm. This method combines K-Means slope clustering and Optuna hyperparameter optimization to realize adaptive weight allocation across eight slope zones. After multi-source DEM fusion, the fused DEM is applied to the flood simulation model of the Wei River Basin to simulate the catastrophic flood event in July 2021. The results show that the Mean Absolute Error (MAE) of the fused DEM ranges from 0.9855 to 1.7218, the Root Mean Square Error (RMSE) ranges from 1.0902 to 2.3953, and the Mean Error (ME) is close to 0 with no significant systematic bias. Compared with single-source DEM, the fused DEM reduces MAE by 21.32–85.32% and RMSE by 7.63–82.03%. In flood simulation, the peak discharge error based on the fused DEM is controlled within 0.013–0.059, and the coefficient of determination (R²) is not less than 0.9808. The simulated errors of inundation area and flood detention volume in flood detention areas are significantly lower than those using a single-source DEM. The proposed multi-source DEM fusion method can effectively improve terrain accuracy and the reliability of flood routing simulation, providing technical support for flood control scheduling in the Wei River Basin and watershed hydrological and flood simulation in data-scarce regions. Full article

Dominant flow channels formed during the late stages of waterflooding can severely reduce sweep efficiency and intensify ineffective interwell circulation. Conventional identification approaches, including tracer testing, well testing, and numerical simulation, often suffer from high operational cost, long execution time, or limited adaptability to heterogeneous interwell connectivity. Although ant colony optimization (ACO) is suitable for path-search problems in reservoir networks, its performance depends strongly on hyperparameter settings, and sample-by-sample parameter tuning introduces substantial online computational overhead. This study proposes a structure-informed GIN–ACO framework for adaptive dominant flow channel identification in connection-element reservoir graphs. A physics-constrained benchmark model is first established using Darcy’s law and the connection element method to provide reference flow paths. A geometry-based surrogate model is then developed to approximate flow splitting coefficients efficiently while preserving the main physical trends. Based on graph topology and geometric descriptors, a graph isomorphism network is trained to predict task-specific ACO parameters, replacing iterative online search with direct parameter inference. Experiments on 1000 synthetic reservoir graphs show that the proposed method achieves a 100% success rate with an average online computation time of 143.5 ms, outperforming fixed-parameter ACO, PSO-ACO, and BO-ACO. On 20 semi-realistic SPE10 reservoir models, GIN–ACO achieves a success rate of 92 ± 1% with an average runtime of 160.3 ± 5 ms. Ablation studies further confirm that graph-structure learning, combined topology–geometry features, and GIN-based parameter prediction are essential for robust performance. The proposed framework provides a promising and computationally efficient route for structure-aware dominant channel identification in connection-element reservoir models. Full article

As a novel urban leisure activity, Citywalk is reshaping the spatial organization of urban tourism resources and spatial experience patterns. This phenomenon provides a crucial entry point for understanding new tourist–destination relationships from the perspective of spatial behavior. This paper takes Harbin, an Internet-Famous City (IFC), as a case study and integrates multi-source data, including pedestrian trajectories, social media texts, and urban infrastructure. A cross-modal analytical framework for Citywalk networks is constructed to examine the structural evolution of Citywalk networks and the relationship between digital-space and physical-space in the context of IFCs. The results indicate that: (1) During its rise as an IFC, Harbin’s Citywalk network transformed from a single-core agglomeration structure to a multi-nodal radial structure, exhibiting a pattern of core reinforcement and outward expansion. (2) Online visibility was associated with the emergence of new nodes and network expansion, but a structural misalignment was observed between digital-space association and physical-space linkage. (3) Emotional differentiation among newly visible nodes further reflected the uneven development of the Citywalk network, while concentrated digital attention was accompanied by persistent structural imbalance. This study highlights the digital–physical misalignment in urban tourism networks, suggests the important role of social media in shaping tourists’ route imagination and emotional evaluation, and provides references for the spatial optimization and sustainable management of urban tourism resources in the new development stage. Full article

International and regional decarbonisation policies are accelerating the deployment of hybrid electric propulsion systems (HEPSs) in short-sea and coastal trades, yet most existing design studies focus on ferries or tugs, rely on stylised duty cycles, and treat battery degradation only superficially. This paper proposes an integrated, data-driven framework for the design and dynamic performance optimisation of a diesel–battery HEPS for a coastal general cargo vessel operating on short-sea routes. A multi-year automatic identification system (AIS) and logbook data are processed to derive route-specific, time-resolved operating profiles, which drive a DC-based hybrid propulsion model comprising diesel generator sets, propulsion motors and a lithium-ion battery energy storage system (ESS). A degradation-aware ESS model is embedded in a life-cycle cost (LCC) formulation that explicitly accounts for battery replacement timing and residual value. The hybrid design problem is cast as a bi-level optimisation: an upper level determines engine rating and ESS capacity to minimise LCC, while fuel savings and emissions are evaluated as key parallel performance indicators, while a lower level uses dynamic programming to compute optimal power split trajectories under state-of-charge, C-rate and power constraints. A surrogate-assisted global search with Kriging and Expected Improvement is employed to manage the computational burden of repeated lower-level optimisations. Case-study results for representative coastal routes show that the optimised hybrid configurations achieve fuel savings of 16–21%, CO₂ reductions of 17–20%, and LCC reductions of 8–14% relative to a conventional mechanical baseline, outperforming a rule-based hybrid design. Sensitivity analyses with varying fuel prices and ESS costs confirm the robustness of the proposed framework and highlight the importance of explicitly coupling degradation-aware ESS. Full article

The fusion of heterocycles onto an anthraquinone scaffold represents a promising strategy to optimize anticancer activity. This study has the aim to synthesize and characterize novel anthra[1,2-b]furan compounds based on the natural product aloe-emodin. Six novel anthra[1,2-b]furans bearing phenyl, n-hexane, and methoxy carbonyl substituents were synthesized starting from aloe-emodin. The synthetic route employed involved acetyl protection of aloe-emodin, electrophilic aromatic halogenation, subsequent Castro–Stephens coupling, spontaneous intramolecular cyclization, and deprotection of hydroxyl groups. These newly synthesized compounds were evaluated for their cytotoxic activity against various cancer cell lines, including lung adenocarcinoma (A5492), colorectal carcinoma (HCT116), hepatocellular carcinoma (HepG2), ovarian cancer (Skov3), and breast cancer (MCF-7), using the CCK8 assay. The anthra[1,2-b]furan derivative 10c, which contains a methoxy carbonyl group, demonstrated excellent potency against lung (A549) and breast (MCF-7) cancer cell lines, with IC₅₀ values of 0.49 and 2.91 µM, respectively. This preliminary cytotoxic finding shows compound 10c as a promising hit for further investigations towards a promising lead compound. Full article

High-resolution images of non-cooperative spacecraft are essential for on-board autonomous operations. Hardware bandwidth limits and continuously changing observation distances mean that a practical super-resolution (SR) system must handle arbitrary, non-integer magnification factors without retraining, a setting known as arbitrary-scale SR (ASSR). Recent 2D Gaussian splatting (2DGS) methods represent image content with explicit anisotropic Gaussian primitives and render at any continuous coordinate, offering substantially faster inference than implicit neural representation (INR) approaches. Yet spacecraft imagery presents a structural mismatch for uniform 2DGS regression: the target occupies a small, densely structured region within a vast, featureless deep-space background, so a network that minimizes average reconstruction loss inevitably over-invests capacity in the irrelevant background and smears the fine edges of antennas and solar panels. We propose S³R-GS, a saliency-guided framework that embeds semantic spatial priors into the 2DGS pipeline at three levels: an encoder-level module that suppresses background noise before it reaches the splatting stage; a discrete Gaussian routing mechanism that assigns each spatial location to a semantically appropriate kernel group and reformulates Gaussian modeling as semantic prototype selection; and a saliency-weighted training strategy that concentrates the optimization gradient on the spacecraft target. Experiments on the SPEED and SPEED+ benchmarks show that S³R-GS achieves strong PSNR performance, competitive SSIM, and improved perceptual quality across scale factors from $\times 2$ to $\times 12$; additional ablation, extreme-lighting, and efficiency analyses further support the robustness and practicality of the proposed design. Full article

The automotive industry in Mexico faces significant logistical challenges in optimizing distribution routes, particularly in border regions, where traffic variability directly affects operational performance. This study proposes a multiperiod route optimization approach for a Tier 1 automotive supplier by applying the Floyd–Warshall algorithm to a cross-border transportation network. Distance matrices are constructed for multiple time windows to capture traffic-related variations in route efficiency. The algorithm is applied independently to each scenario, enabling the identification of time-dependent optimal routes and the development of alternative routing strategies. The results show that optimal routes vary across different periods of the day, leading to measurable improvements in routing efficiency and enhanced decision-making flexibility. The proposed approach supports more realistic logistics planning in congested urban environments and improves operational performance in cross-border automotive supply chains. Full article

Forests in Poland play a key recreational role, and the growing interest in sylvaturism requires optimized management. Despite the growing body of research on forest recreation, existing studies rarely address the role of small-scale infrastructure in shaping user preferences and its integration into spatial planning frameworks, which constitutes a research gap in this study. This study aimed to identify user preferences for small infrastructure and to develop an application-oriented, socially sensitive model for forest trail design that supports sustainable management. The research was conducted in 2021–2024 using the CAWI method on a group of 402 adult Poles. Data analysis included descriptive statistics, Pearson’s chi-square tests to assess demographic differences, and correspondence analysis to identify user preference profiles. The results not only confirmed a clear hierarchy of needs but also demonstrated that differences between user groups relate primarily to the intensity rather than the structure of preferences. A clear hierarchy of needs was confirmed, with route map boards (86.32%), educational boards (72.64%), and benches (71.14%) dominating. Based on the results, a modular design model was developed (modules: basic, comfort, accessibility, and activity), which constitutes a conceptual advancement over existing planning approaches by introducing a flexible, user-oriented framework that links social preferences with spatial decision-making. By integrating empirical social data into the planning process, the proposed framework extends current knowledge on recreation planning and provides a structured basis for adaptive forest trail design. This tool could help managers efficiently channel tourist traffic, protect ecosystems, and promote public health. Full article

OLIVIA (OperationaL Intentions adVIser for Aviation) was developed in the HAIKU project. It is a flight deck tool providing support to mission-level decisions in complex situations by assessing and prioritizing route options according to operational intentions. It uses Artificial Intelligence to translate (1) operational intentions from pilots to route generation and optimization inputs and (2) route proposal KPIs into operational intention assessments. This paper reports on the final development of OLIVIA, the results from the human-in-the-loop experiments, and insights and recommendations regarding the development of similar assistants for the flight deck. Full article

The increasing resolution of global ocean circulation models has made data output an important constraint on runtime efficiency and operational timeliness. The current dedicated-process asynchronous I/O scheme in the Mass Conservation Ocean Model (MaCOM) sends output data from compute processes to a group of reserved I/O processes. Although this design separates part of the writing work from the main time-stepping loop, it still introduces centralized data aggregation, additional I/O process management, and high memory pressure on the I/O side at large process counts. This paper presents MaCOM–PAIO, a PAIO-enabled asynchronous I/O optimization for MaCOM. Built on the existing PAIO/PAIOM asynchronous I/O stack, MaCOM–PAIO implements a thread-based asynchronous output path, adapts the PnetCDF execution path used by MaCOM to route selected collective writes to PAIO, and uses PAIOM asynchronous zones to submit history and restart output operations as background tasks. The implementation keeps the numerical solver unchanged and preserves the PnetCDF-style calling path at the application level, while replacing the dedicated I/O process path with I/O–thread-based asynchronous execution on the allocated HPC nodes. Experiments were conducted on a 1/12${}^{\circ }$ global MaCOM configuration. Strong-scaling tests show that, at 1646 compute processes, MaCOM–PAIO reduces the total runtime from 1167.45 s to 276.53 s and lowers the compute-side I/O blocking ratio from 67.2% to 4.9% under the tested configuration. In an independent bandwidth test at 1080 compute processes, the measured write bandwidth increases from approximately 0.10 GiB/s to 0.90 GiB/s for output volumes of about 82 GiB. The maximum memory footprint of the I/O entities is also reduced from approximately 18.2 GiB in the legacy dedicated-I/O scheme to approximately 1.9 GiB in MaCOM–PAIO. These results demonstrate that PAIO-based integration is a practical approach for improving MaCOM I/O performance under the evaluated hardware/software environment and workload. Full article

Real-world time series forecasting is often challenging due to non-stationarity and distribution shifts, where the optimal forecasting model varies across different temporal regimes and horizons. In this work, we introduce a method called Adaptive Future-Guided Ensemble Learning (AFG-EL), a two-stage framework that performs drift-aware, sample-level routing over a heterogeneous model zoo. AFG-EL learns dynamic fusion weights from meta-features of the historical window and incorporates a future-guided training signal from a relative-future teacher or scorer, emphasizing learning on regime transitions and drift-sensitive segments. Crucially, the inference process remains strictly causal, requiring only historical data and extracted meta-features. We further use sparse routing with an entropy-based fallback mechanism to enhance stability when routing confidence is low. Our experiments on several commonly used forecasting datasets demonstrate that AFG-EL consistently outperforms strong single-model baselines, uniform averaging, and adaptive fusion baselines. Full article

Asymmetry is a prevalent phenomenon in low-power and lossy networks (LLNs) due to resource constraints and unstable links. The routing protocol for the low power and lossy network (RPL), standardized by the Internet Engineering Task Force (IETF), is specifically designed for LLNs with characteristics of resource constraints, lossy links, and complex communication environments. However, its performance is fundamentally limited by node capabilities and unstable links, a contradiction exacerbated by the stringent QoS demands of emerging applications like IIoT or precision agriculture. Consequently, new RPL routing technologies based on the digital twin-enabled computing power network, called RPL-DTCP, were designed to improve network QoS and support practical applications. First, a low-power and lossy network architecture based on twin-enabled computing network was proposed, considering LLN requirements and computing twin services. Second, in response to the requirements of the digital twin, computing power network and LLNs for low synchronization latency, high data accuracy, efficient computing resource utilization, and energy conservation, several routing metrics were designed, including the data processing model, model deployment rate, end-to-end delay, node remaining energy, and ETX. Then an initial matrix and a comprehensive objective function were formulated to comprehensively evaluate these metrics. Third, to solve the multi-objective optimization problem, an enhanced whale optimization algorithm (E-WOA) was developed. E-WOA improved upon the standard version by using improved Tent chaotic mapping for population initialization, nonlinear adaptive convergence factor, and Cauchy variation mutation operator for solution perturbation, thereby enhancing its global search capability and convergence speed, enabling it to effectively identify the optimal routing path. Simulations confirmed that RPL-DTCP outperforms benchmark algorithms, achieving significant reductions in end-to-end delay, higher packet delivery ratios, extended network lifetime, etc. These findings demonstrate that RPL-DTCP effectively addresses the resource-performance contradiction in LLNs, providing a reliable and efficient routing framework for emerging compute-intensive IoT applications. Full article

Salient object detection in optical remote sensing images remains challenging due to complex backgrounds, blurred boundaries, small objects, unstable foreground–background contrast, and dense object distributions. Existing convolution-based methods are effective at modeling local structures, but they are limited in capturing long-range dependencies, whereas Transformer-based approaches usually incur substantial computational cost when handling high-resolution remote sensing imagery. To address these issues, this paper proposes EGMamba-Net, an edge-guided global–local collaborative network for salient object detection in optical remote sensing images. Specifically, a hybrid global–local backbone is first constructed to preserve shallow texture, edge, and geometric details while introducing Mamba-based global modeling in deeper stages for efficient long-range dependency representation. An Edge Prior Enhancement Module (EPEM) is then designed to explicitly extract boundary priors from shallow features and refine feature representations through edge-guided modulation. To alleviate the representation conflict between global semantics and local details, a Global–Local Interaction Module (GLIM) is further developed, where convolutional local modeling and Mamba-based global modeling interact through cross-gating for complementary feature learning. Moreover, a Region-Adaptive Routing Decoder (RARD) is introduced to dynamically assign different refinement paths according to regional saliency response, boundary intensity, and contextual complexity, thereby improving the recovery of small, low-contrast, and densely distributed objects. In addition, a Difficulty-Aware Joint Loss (DAJL) is designed to enhance optimization on boundary regions and hard samples, improving robustness under challenging conditions. Extensiveexperiments on ORSSD, EORSSD, and ORSI-4199 datasets demonstrate the superiority of the proposed method. In particular, on the more challenging EORSSD dataset, EGMamba-Net achieves 0.9389 S-measure, 0.8972 max F-measure, and 0.0066 MAE. Compared with the representative remote-sensing method DAF-Net, it improves S-measure and max F-measure by 0.0223 and 0.0358, respectively, indicating stronger capability in background suppression, structural preservation, and boundary recovery. Full article