Search Results (495,633)

In the era of digital economy, manufacturing industry transcends geographical space to build virtual networks. Revealing the complex network characteristics and driver paths of virtual agglomeration is of great significance for accelerating the digitalization of manufacturing. First, this paper explains the formation mechanism and proposes the model of virtual agglomeration; moreover, the paper identifies complex network characteristics. Finally, this paper constructs a driving path framework based on the “Technology–Organization–Environment” theory, and uses fuzzy set qualitative comparative analysis to identify paths. The results show that the technological platform foundation plays a core role in enhancing the level of virtual agglomeration. Differentiated combinations of organizational and environmental conditions also have a positive impact. This study provides theoretical support and practical reference for cities to accelerate virtual agglomeration according to local conditions. Full article

Wheat, soybean, and peanut are recognized as major food allergens, with their prevalence rising globally, necessitating rapid and reliable detection methods. A new detection approach was developed in this research, which integrates Ladder-shape Melting Temperature Isothermal Amplification (LMTIA) with Proofreading Enzyme-Mediated Probe Cleavage (Proofman) technology to enable the concurrent identification of wheat, soybean, and peanut allergens. Compared with the loop-mediated isothermal amplification (LAMP) method under the experimental conditions set in this study, this approach can reduce the false-positive results associated with LAMP, and it does not rely on sophisticated instrumentation required by technologies like mass spectrometry. The GAG56D (wheat), Ara h 2.01 (peanut), and Lectin (soybean) genes were selected as target genes for the three allergens. Specific primers and probes were designed according to these target genes, and the reaction system was optimized. A systematic evaluation of the triplex Proofman-LMTIA method was then conducted regarding its specificity, sensitivity, limit of detection, and repeatability. Finally, the method’s practical applicability was validated using commercial products. The optimized system achieved simultaneous detection within 40 min at 61 °C, showing no cross-reactivity with common foods. The method demonstrated good sensitivity, with a sensitivity of 5 pg/μL for genomic DNA and a detection limit of 5% (w/w) in a powder matrix, along with excellent repeatability. In practical sample testing, the results were fully consistent with product label declarations, accurately identifying single and multiple allergen contaminations. The Proofman-LMTIA detection method, with its rapid, simple, sensitive, and specific characteristics, demonstrates significant potential for applications in food safety supervision. Full article

To enhance the thermal decomposition properties of glycidyl azide polymer energetic thermoplastic elastomer (GAP-ETPE), the effects of nano-CuO supported on different carbon carriers (GO and CNT) were systematically investigated in this study. The structural characteristics and catalytic performances were comprehensively analyzed using XRD, Raman, XPS, UPS, BET, SEM, and TEM, coupled with thermal analysis techniques including TG-DSC and TG-MS. The results indicate that the catalytic performance follows the descending order of CuO/CNT > CuO/GO > CuO. Notably, CuO/CNT exhibits the optimal catalytic activity, advancing the exothermic peak temperature of the azide groups by approximately 33 °C and resulting in a more concentrated heat release process. The superior synergistic catalytic effect of CuO/CNT is attributed to the following: the three-dimensional network constructed by CNT effectively overcomes the agglomeration of CuO nanoparticles and the restacking defects typical of GO nanosheets, thereby significantly reducing the gas–solid mass transfer resistance. Simultaneously, the highly graphitized sp² conjugated skeleton of CNT provides an exceptional electron transport capability, facilitating rapid electron migration. These findings demonstrate that the structure of carbon supports profoundly influences the synergistic catalytic effect of CuO, offering valuable insights into the design of highly efficient catalysts for energetic binders. Full article

Accurate cloud detection remains a significant challenge due to the spectral ambiguity between clouds and bright or heterogeneous surfaces (e.g., snow, desert). While multi-angle and polarization data offer rich information, the discriminative power of joint spectral analysis for resolving these ambiguities has been underexploited. In this work, we demonstrate that physically motivated spectral band ratios and differences can robustly enhance cloud signatures. Motivated by this insight, we propose a novel deep learning framework, the Multi-angle Polarization Feature Pyramid Structure (MP-FPS), that explicitly leverages joint spectral features as discriminative priors. Our architecture employs a dual-branch network to disentangle and adaptively fuse spectral and multi-angle polarization modalities. Within this framework, a hierarchical, multi-scale cross-channel multi-angle fusion module dynamically captures spatial–spectral–angular dependencies, enriching the structural representation of clouds. Furthermore, a channel-space dual-path attention mechanism refines sub-pixel responses, significantly improving detection accuracy in challenging regions such as cloud edges and thin cirrus. Evaluated on the global POLDER-3 dataset, MP-FPS achieves a mean Intersection over Union (mIoU) of 0.8662 across diverse surface types, surpassing the official baseline by 12.4%. This study establishes joint spectral analysis as a critical enabler for high-precision cloud masking, and demonstrates its synergistic value when integrated with multi-angle polarimetric information in a unified deep architecture. Full article

Understanding the mechanism of fracture evolution in underground stacked coal–rock composite structures is crucial for the accurate prediction and prevention of mine disasters. In this study, the fracture evolution characteristics of a coal–rock–coal (CRC) composite structure under uniaxial compression were monitored and studied using three-dimensional digital image correlation and an RA-AF method based on acoustic emission (AE) parameters. The fracture mechanisms of the CRC composites were revealed based on experimental results and theoretical analyses. The results indicate that the compressive strength and elastic modulus of CRC composites increase with the thickness of the rock layer and the strength of the coal and rock. Owing to the differences in the thickness and strength characteristics of coal and rock in CRC composites, three fracture modes were identified. The fracture of the CRC composite structure is determined by the stress redistribution and energy release, which are dominated by the mechanical and size effects of coal and rock. Full article

The development of all-concrete liquefied natural gas (LNG) storage tanks is hindered by the susceptibility of conventional concrete to ultra-low temperature (ULT) cycling down to −70 °C. While redispersible polymer powder (RPP) and polypropylene (PP) fibers individually enhance performance, their combined effect in various mineral admixture systems remains unclear. This study investigates the synergy and selective compatibility in hybrid-modified concrete containing fly ash (FA), silica fume (SF), or slag (SG). Comprehensive assessments after 50 ULT cycles reveal that the efficacy of hybrid modification is intrinsically governed by the mineral admixture. Among all systems, the silica fume-based hybrid system (EPSF) exhibits the highest residual compressive strength (57.5 MPa), the lowest strength loss (6.7%), and the most balanced durability. Microstructural analysis reveals that this synergy arises from a dense matrix, continuous polymer network, and effective fiber bridging—achieved only when the mineral admixture enables uniform RPP distribution. In contrast, the FA system exhibits a strength–durability trade-off, with RPP localized at interfaces, while the SG system shows a polymer-activated hydration mechanism. Microstructural and nano-mechanical analyses confirm that RPP acts as a pore filler in cement, an interfacial modifier in FA, a cohesive network former in SF, and a hydration activator in SG. This work establishes that superior ULT resilience requires not merely additive modifications but a matrix-enabled synergy, providing a scientific basis for the rational design of cryogenic concrete. Full article

With the continuous advancement of marine development, underwater operational tasks are becoming increasingly diverse and complex. Addressing the limitations of traditional methods and intelligent planning—which focus solely on acquiring task skills while separating grasp planning from force planning—this paper proposes a modeling approach integrating impedance control with deep reinforcement learning. Using a five-finger humanoid underwater dexterous hand as the grasping execution platform, this method achieves collaborative decision-making between grasp planning and force control for underwater dexterous hands. First, a modular underwater dexterous grasping scenario is established. Its kinematic model and inverse solution are analyzed, and the grasping problem is modeled as a Markov decision process. Second, based on the dexterous fingertip impedance control model for simulation, a grasping strategy learning method grounded in deep reinforcement learning is constructed to address the complex control challenges posed by the high degrees of freedom of the dexterous manipulator. Finally, the Proximal Policy Optimization (PPO) algorithm is employed for grasping strategy learning. An underwater dexterous grasping parallel training and testing environment is established using the Isaac Lab simulation platform to rapidly validate the learning method. Simulation results demonstrate the proposed method’s excellent dexterous compliant control performance and strong robustness to underwater variable environments: the PPO-based impedance control scheme reduces contact force variance by 56% compared to pure position control. The average maximum contact force is suppressed to 3.26 N, representing a 60.4% reduction compared to pure position control. This study achieves the organic integration of underwater hydrodynamic compensation, adaptive impedance control, and grasping strategy learning, providing a novel and effective solution for compliant grasping control of underwater dexterous manipulators. Full article

National modern agricultural industrial parks are the core carriers for promoting agricultural modernization. Clarifying their spatial differentiation patterns is of great significance for revealing the efficiency of resource allocation and promoting coordinated regional development. Based on the data from 338 national modern agricultural industrial parks in China, this study uses methods such as the nearest neighbor index, Voronoi spatial statistics, and spatial autocorrelation to identify their spatial distribution characteristics, and adopts the XGBoost–SHAP model to explore the nonlinear effects of driving factors. The research found the following: (1) The parks exhibit a distinct “sparse west–concentrated middle–dense east” agglomeration pattern aligned with China’s Hu Huanyong Line agro–economic divide. (2) At the municipal level, four high-density cores emerged in central-eastern regions with “dual hot spots–gradient diffusion” characteristics. (3) Farmers’ professional cooperatives and transportation accessibility are the most consistent fundamental driving elements, reflecting the transition of the development momentum of contemporary agriculture from “resource dependency” to “circulation dependence.” Heterogeneity analysis shows elevation, cooperatives and rural income differentially drive agglomeration across regions, with elevation constituting a universal constraint. (4) While regional development and mechanization show adaptive synergy, excessive urbanization generates a distinct “non–agriculturalization” crowding–out effect on agricultural development. Full article

Urban green space (UGS) is widely recognized as a core component of sustainable urban development in megacities. The study of the synergistic relationship between high-intensity urban development and daily accessibility of UGS, however, remains insufficient. This paper therefore critically assesses the inherent correlation between building intensity and the UGS daily accessibility in a typical megacity context. The analysis is twofold: What is the inherent correlation between building intensity and the daily accessibility of UGS in megacities? And, if such a correlation exists, how can daily accessibility be improved by integrating building intensity into the UGS planning process? Using a case study in Beijing, methods of multi-source data integration, GIS spatial analysis, and statistical correlation models are used to address the issues. Results indicate that building intensity exhibits a statistically positive spatial association with the Daily Accessibility Index (DAI). Mere expansion of the total UGS area does not necessarily lead to improved daily accessibility for residents. The findings include a clarified dual-effect mechanism of high-intensity development on UGS services, as well as evidence-based planning strategies for sustainable UGS layout in dense megacities. Full article

The new-generation global navigation satellite system (GNSS) demands enhanced satellite autonomy, where high-precision orbit prediction plays a pivotal role. Traditional dynamic models depend heavily on long-term on-orbit observations, making hybrid deep-learning-based orbit prediction models an efficient alternative. Although existing studies have validated that temporal networks can effectively capture orbit error variations, improving prediction accuracy under short input sequences remains a critical challenge. To address this issue, this paper proposes an improved short-sequence-adaptive Bidirectional Long Short-Term Memory (BiLSTM) network to enhance orbit prediction performance of BeiDou Medium Earth Orbit satellites. Specifically, we design a scale-aware hybrid convolution module and an attention-driven feature fusion module to generate feature representations with high information density, which outperform the standalone BiLSTM under short input sequences. Experiments on the BeiDou system (BDS) C19 satellite demonstrate that our method reduces the mean residual rates from 54.03%, 41.18%, 80.10% to 4.36%, 6.12%, 5.39% in the X, Y, and Z axes, respectively, surpassing BiLSTM alone by over 85% across all metrics. Notably, the proposed method exhibits robust generalization capabilities across similar satellites with similar orbital configurations and dynamic environments. Full article

This study examined the complex relationships among task complexity, task-specific emotions, and learners’ L2 writing performance. Sixty-one Chinese EFL learners were divided into two groups and completed two writing tasks in a counterbalanced order. They reported their perceptions about task complexity and their emotional experience during the task after completing each task. The results revealed that (1) task complexity exerted a significant negative effect on learners’ writing performance in terms of content, but no significant effects on language use, organization and communicative achievement, or overall writing performance; (2) task complexity did not significantly affect learners’ task enjoyment, task anxiety or task boredom; (3) task-specific emotions did not predict learners’ overall writing performance in the simple task, while task anxiety exerted a significant negative effect on learners’ performance in the complex task. These results are discussed with reference to the Cognition Hypothesis and the Control-Value Theory, particularly the intensified role of affect under increased cognitive demands and the negative impact of task anxiety on writing performance, offering pedagogical implications for L2 writing. Full article

The traditional normalized least-mean-square (NLMS) algorithm faces an inherent trade-off between convergence rate and steady-state error, and its adaptability is limited in non-stationary environments. This paper proposes a neural network-assisted variable step-size NLMS algorithm (NN-VSS-NLMS). An analytically motivated reference step size is first derived under a zero-mean statistically symmetric signal assumption to characterize the desired step-size trend. Based on this reference, an eight-dimensional feature vector composed of input signal power, error energy, and related statistical descriptors is constructed to describe the instantaneous signal state, and a two-layer fully connected neural network (NN) is introduced as an auxiliary tool to provide data-driven correction to the reference step size. In addition, dynamic modulation, step-size constraints, and smoothing operations are incorporated to regulate the predicted step size and enhance its controllability under time-varying conditions. Through simulations with stationary and non-stationary inputs as well as time-invariant and time-varying systems, the proposed algorithm achieves up to a fourfold improvement in convergence rate and more than 8 dB reduction in steady-state error compared with the classical NLMS algorithm, while maintaining improved tracking ability. Full article

As the electrification of transportation systems accelerates, incentivizing electric vehicle (EV) participation in vehicle-to-grid (V2G) operations is becoming increasingly crucial. This paper introduces a dynamic EV charging and discharging strategy that incorporates integrated energy station (IES) congestion and electricity purchase and sale scenarios. The proposed strategy seeks to facilitate orderly EV charging and discharging within a real-time simulation framework that integrates the transportation network (TN), IES, and the external grid (EG). First, we develop a real-time collaborative simulation framework that combines microscopic traffic flow (MTL) and IES–grid energy interaction models to account for mutual feedback among these components. Second, we propose an EV IES selection strategy aimed at maximizing discharge revenue, which takes into account various factors, including driving distance, time costs, battery degradation, discharge benefits, and government subsidies. Finally, we design a dynamic discharge pricing model based on real-time vehicle arrival patterns at the IES and the status of electricity purchases and sales. Simulation results show that the EV IES selection strategy, optimized for discharge revenue, reduces average user waiting time by 5.36%, decreases network time loss by 3.86%, and increases EV discharge revenue by 6.79%. Furthermore, the introduction of dynamic pricing leads to additional reductions in waiting time and network time loss by 3.46% and 4.80%, respectively. The proposed mechanism and pricing strategy effectively mitigate traffic congestion, enhance user discharge revenue, and provide flexible scheduling options for IES operations. Full article

Background: Skeletal-related events (SREs), including pathological fractures, spinal cord compression, radiotherapy to bone, and bone surgery, substantially worsen quality of life in breast cancer with bone metastases. Denosumab, a monoclonal antibody targeting RANKL, mechanistically differs from bisphosphonates and is not renally cleared, offering potential clinical advantages. In practice, an increasing number of patients transition from bisphosphonates to denosumab. However, the comparative effectiveness of sequential therapy versus initial denosumab remains unclear. Methods: We retrospectively analyzed 165 patients with breast cancer and radiologically confirmed bone metastases treated between 1 January 2019 and 30 April 2024 at a tertiary center in China. Patients were categorized into an initial denosumab group (n = 67) or a sequential bisphosphonate-to-denosumab group (n = 98). The primary endpoint was time to first on-treatment SRE; the 12-month first on-treatment SRE rate was also reported as a descriptive summary measure. Secondary endpoints included cumulative SRE incidence and safety. Kaplan–Meier and log-rank tests compared SRE-free survival; Cox regression explored prognostic factors. Results: The median age at bone-metastasis diagnosis was 54.7 years. Median time from diagnosis to bone-targeted agents (BTAs) initiation was 0.9 months in both groups; median follow-up was longer in the sequential group (22.5 vs. 11.3 months). At diagnosis, 46 of 165 patients (27.9%) presented with synchronous SREs, more frequent in the initial denosumab group (37.3% vs. 21.4%; p = 0.040). During follow-up, 31 patients (18.8%) developed SREs: 25 of 98 (25.5%) in the sequential group versus 6 of 67 (9.0%) in the initial denosumab group (p = 0.008). After BTA initiation, on-treatment SREs occurred in 28 of 165 patients (17.0%): 25 of 98 (25.5%) in the sequential group versus 3 of 67 (4.7%) in the initial denosumab group (p < 0.001). The 12-month first on-treatment SRE rate was 15.7% (95% CI 8.1–22.7) for sequential therapy and 5.9% (0–12.3) for initial denosumab. In Cox analysis, second-line systemic therapy increased SRE risk (HR = 2.651, p = 0.021). Safety outcomes were generally manageable and consistent with known class effects, with no clear exposure-adjusted safety advantage of one strategy over another. Conclusions: Initial denosumab was associated with fewer and delayed SREs compared with sequential bisphosphonate-to-denosumab therapy, supporting early denosumab initiation as a potentially preferable BTA strategy. Prospective studies are warranted to confirm these findings. Full article