Search Results (160)

Innovation is a strategic driver of sustainable competitive advantage and long-term economic growth. This study proposes an empirical framework to support the sustained development of national innovation capacity by examining key enabling factors. Drawing on an extensive review of the literature, the research investigates the interrelationships among governmental support (GS), innovation agents (IA), university–industry R&D collaborations (UIRD), and innovation cluster development (ICD), and their influence on two critical innovation outcomes, knowledge creation (KC) and knowledge diffusion (KD). Using panel data from G7 countries spanning 2008 to 2018, sourced from international organizations such as the World Bank, the World Intellectual Property Organization, and the World Economic Forum, the study applies regression analysis to test the proposed conceptual model. Results highlight the foundational role of GS in providing a balanced framework to foster collaborative networks among IA and enhancing the effectiveness of UIRD. Furthermore, IA emerges as a pivotal actor in advancing innovation efforts, while the development of innovation clusters is shown to selectively enhance specific innovation outcomes. These findings offer theoretical and practical contributions for policymakers, researchers, and stakeholders aiming to design supportive ecosystems that strengthen sustainable national innovation capacity. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

The electrocatalytic CO₂ reduction reaction (CO₂RR) offers an energy-saving and environmentally friendly approach to producing hydrocarbon fuels. The use of a gas diffusion electrode (GDE) flow cell has generally improved the rate of CO₂RR, while the gas diffusion layer (GDL) remains a significant challenge. In this study, we successfully engineered a novel metal–organic framework (MOF) heterojunction through the controlled coating of zeolitic imidazolate framework (ZIF-L) on ultrathin nickel—metal–organic framework (Ni-MOF) nanosheets. This innovative architecture simultaneously integrates GDL functionality and exposes abundant solid–liquid–gas triple-phase boundaries. The resulting Ni-MOF@ZIF-L heterostructure demonstrates exceptional performance, achieving a formate Faradaic efficiency of 92.4% while suppressing the hydrogen evolution reaction (HER) to 6.7%. Through computational modeling of the optimized heterojunction configuration, we further elucidated its competitive adsorption behavior and electronic modulation effects. The experimental and theoretical results demonstrate an improvement in electrochemical CO₂ reduction activity with suppressed hydrogen evolution for the heterojunction because of its hydrophobic interface, good electron transfer capability, and high CO₂ adsorption at the catalyst interface. This work provides a new insight into the rational design of porous crystalline materials in electrocatalytic CO₂RR. Full article

Facilitating industrial restructuring and modernization plays a pivotal role in realizing China’s dual-carbon objectives (carbon peaking and carbon neutrality) and advancing sustainable socioeconomic progress. Leveraging panel data from 30 provincial-level administrative units (2005–2022) and adopting the Spatial Durbin Model, this research investigates how industrial structure upgrading influences carbon emission intensity within the framework of a unified national market, while elucidating its operational mechanisms. The key findings include the following: (1) Provincial carbon emission intensity demonstrates pronounced “high-high” and “low-low” spatial agglomeration during the study period. Industrial restructuring exhibits marked carbon abatement effects, accompanied by discernible cross-regional spillover benefits. (2) Industrial structure upgrading can reduce carbon emission levels by promoting the technology diffusion effect, while the competitive demonstration effect of digitalization has not yet manifested. (3) The establishment of an integrated national market enhances the capacity of industrial upgrading to suppress carbon emission intensity. (4) The emission-reducing impacts of industrial restructuring manifest heterogeneous patterns across regions and temporal phases: In Eastern China, industrial upgrading paradoxically elevates emission intensity. Central-western regions experience significant emission reductions. Temporally, the relationship follows an inverted U-shaped trajectory. These insights underscore the necessity for policymakers to refine industrial modernization strategies, expedite nationwide market integration mechanisms, and cultivate region-specific green transition roadmaps. Full article

This study explores the effects of Tricholoma matsutake-derived insoluble dietary fiber (TMIDF) on the pasting behavior, structural properties, and in vitro digestibility of rice flour. The incorporation of 5% TMIDF significantly increased the peak viscosity (from 2573.21 to 2814.52 mPa·s) by competitively adsorbing water and forming a dense transient network, while simultaneously reducing the final viscosity (from 1998.27 to 1886.18 mPa·s) by inhibiting amylose recrystallization. Multi-scale structural analyses revealed that TMIDF enhanced V-type crystallinity and limited enzyme access via a porous fibrous matrix. Fourier-transform infrared spectroscopy and low-field nuclear magnetic resonance analyses confirmed that hydrogen bonding and water redistribution were key interaction mechanisms. TMIDF significantly lowered in vitro starch digestibility and increased resistant starch content by 16% (from 14.36% to 30.94%) through synergistic effects, including physical encapsulation of starch granules, formation of enzyme-resistant amylose-lipid complexes, and α-amylase inhibition (31.08%). These results demonstrate that TMIDF possesses a unique multi-tiered modulation mechanism, involving structural optimization, enzyme suppression, and diffusion control, which collectively surpasses the functional performance of conventional plant-derived insoluble dietary fibers. This research establishes a theoretical basis for applying fungal insoluble dietary fibers to develop low glycemic index functional foods, highlighting their dual role in improving processing performance and nutritional quality. Full article

Deep neural video compression codecs have shown great promise in recent years. However, there are still considerable challenges for ultra-low bitrate video coding. Inspired by recent diffusion models for image and video compression attempts, we attempt to leverage diffusion models for ultra-low bitrate portrait video compression. In this paper, we propose a predictive portrait video compression method that leverages the temporal prediction capabilities of diffusion models. Specifically, we develop a temporal diffusion predictor based on a conditional latent diffusion model, with the predicted results serving as decoded frames. We symmetrically integrate a temporal diffusion predictor at the encoding and decoding side, respectively. When the perceptual quality of the predicted results in encoding end falls below a predefined threshold, a new frame sequence is employed for prediction. While the predictor at the decoding side directly generates predicted frames as reconstruction based on the evaluation results. This symmetry ensures that the prediction frames generated at the decoding end are consistent with those at the encoding end. We also design an adaptive coding strategy that incorporates frame quality assessment and adaptive keyframe control. To ensure consistent quality of subsequent predicted frames and achieve high perceptual reconstruction, this strategy dynamically evaluates the visual quality of the predicted results during encoding, retains the predicted frames that meet the quality threshold, and adaptively adjusts the length of the keyframe sequence based on motion complexity. The experimental results demonstrate that, compared with the traditional video codecs and other popular methods, the proposed scheme provides superior compression performance at ultra-low bitrates while maintaining competitiveness in visual effects, achieving more than 24% bitrate savings compared with VVC in terms of perceptual distortion. Full article

CO₂-enhanced coalbed methane recovery (CO₂-ECBM) represents a promising pathway within carbon capture, utilization, and storage (CCUS) technologies, offering dual benefits of methane production and long-term CO₂ sequestration. This review provides a comprehensive analysis of CO₂-ECBM from a full-chain perspective (Mechanism, Practices, and Outlook), covering fundamental mechanisms and key engineering practices. It highlights the complex multi-physics processes involved, including competitive adsorption–desorption, diffusion and seepage, thermal effects, stress responses, and geochemical interactions. Recent progress in laboratory experiments, capacity assessments, site evaluations, monitoring techniques, and numerical simulations are systematically reviewed. Field studies indicate that CO₂-ECBM performance is strongly influenced by reservoir pressure, temperature, injection rate, and coal seam properties. Structural conditions and multi-field coupling further affect storage efficiency and long-term security. This work also addresses major technical challenges such as real-time monitoring limitations, environmental risks, injection-induced seismicity, and economic constraints. Future research directions emphasize the need to deepen understanding of coupling mechanisms, improve monitoring frameworks, and advance integrated engineering optimization. By synthesizing recent advances and identifying research priorities, this review aims to provide theoretical support and practical guidance for the scalable deployment of CO₂-ECBM, contributing to global energy transition and carbon neutrality goals. Full article

Magnesium metal has a high theoretical volume capacity and abundant reserves. Magnesium ion battery is theoretically secure and eco-friendly. In recent years, magnesium ion battery has attracted wide attention and is expected to become a competitive energy storage candidate in the next generation. However, due to the large polarization effect and slow migration kinetics of magnesium ions, magnesium ions are hard to insert/desert in cathode materials, resulting in a poor cycle and rate performance. CoHCF, a typical Prussian blue analog, has an open frame structure and double REDOX sites, and it is regarded as a candidate for rechargeable ion battery. Herein, a Ni-doping method was utilized to improve the performance of CoHCF. Compared with the original CoHCF, the maximum specific discharge capacity of the Ni-doped CoHCF at 50 mA/g charging and discharging current increased from 70 mAh/g to 89 mAh/g, and the cyclic performance and rate performance improved. These improvements result from the fact that the electrode reaction process of Ni-doped CoHCF changes from diffusion-driven to reaction-driven. The Ni-doped CoHCF is more stable, and the lattice changes during Mg²⁺ (de-)intercalation are smaller. This study can provide a reference for the development of Prussian blue analogs as cathode materials for magnesium ion batteries. Full article

Activated carbons (ACs) are employed in the nuclear industry to mitigate the emission of potential radioactive iodine species. Their retention performances towards iodine are mainly dependent on the relative humidity due to the competitive effect induced by adsorbed water molecules. Thus, this work will focus on the prediction of AC behavior toward the capture of water vapor to better assess the poisoning effect on radiotoxic iodine removal. For the first time, H₂O breakthrough curves (BTCs) on nuclear grade ACs are predicted through a specific methodology based on the combination of transport phenomena with adsorption kinetics and equilibrium. Three ACs, similar to those deployed in the nuclear context, are considered within the present study. Our model is based on the Linear Driving Force Model (LDF), governed by an intraparticle diffusion mechanism, notably surface and Knudsen diffusions. In addition, the type V isotherms obtained for H₂O and the investigated carbon supports were described through the Klotz equation, taking into account the formation and progressive growth of H₂O clusters within the internal porosity. This methodology allowed us to successfully simulate the H₂O adsorption by a non-impregnated AC, where only physisorption phenomena are involved. In addition, promising results were highlighted when extrapolating to the two other impregnated ACs (AC 5KI and AC Nuclear). Full article

Issues encompassing hazardous waste management face challenges, particularly those involving the manufacture of electronic devices such as PCBs that are in high demand with continual growth. Therefore, upcycling to create new products viable for highly valued markets emphasizes alternative solutions towards the circular economy. This research highlights the advantages of copper sulfate recovery from the cupric chloride etching waste solution from PCB manufacturing, combined with the synthesis of copper nanoparticles for antibacterial application. First, aluminium cementation, sulfuric acid leaching, and crystallization were incorporated in the recovery step to ensure a high purity of 99.95% and a recovery of 94.76%. Aluminium cementation selectively offered copper-containing precipitates suitable for leaching to gain high-purity recovered products. In the second step, copper nanoparticles were synthesized using 0.01–0.20 M copper sulfate precursors via sonochemical reduction. In total, 1–5 mL of hydrazine and 5–30 mL of 0.01 M ethylene glycol were added into a 50 mL precursor as reducing and capping agents, respectively. Hydrazine addition under high pH played a key role in controlling the shape, size, and purity of the copper nanoparticles, required for their antibacterial properties. The optimum condition gave spherical or polygonal copper nanoparticles of 54.54 nm at 99.95% purity and >92% recovery. The antibacterial test of the synthesized copper nanoparticles using E. coli via agar well diffusion exhibited a zone of inhibition (ZOI) of 50 mm at 127 mg/mL, similar to the antibiotic-controlled condition, proving their antibacterial potential. Along with process effectiveness, a feasibility study of the inventing process confirmed the environmental and economic impacts of minimizing energy consumption and processing time, which are competitive with respect to the existing recycling technologies. Full article

Market competition and industrial environment have a significant impact on firms’ innovation behavior. Hence, this study aims to uncover the connection between industry concentration and digital process innovation in Chinese rail transit firms. Grounded in innovation diffusion theory, we explore the effects of industry concentration on digital process innovation and analyze the contingent factors of firm size and environmental support on the above effects. Through empirical analyses of data from Chinese rail transit firms, this study reveals that industry concentration inhibits digital process innovation. Firm size strengthens the negative impacts of industry concentration, while environmental support weakens the main effect. Our findings offer a complementary framework for industry organization activities and practical implications for digital process innovation. Full article

This study utilizes Computational Fluid Dynamics (CFD) to optimize the aerodynamic performance of a Formula 1 (F1) car diffuser, investigating the effects of vane placements, end-flap positions, and other structural modifications. Diffusers are critical in managing airflow, enhancing downforce, and reducing drag, directly influencing vehicle stability and speed. Despite ongoing advancements, the interaction between diffuser designs and turbulent flow dynamics requires further exploration. A Three-Dimensional k-Omega-SST RANS-based CFD methodology was developed to evaluate the aerodynamic performance of various diffuser configurations using Star CCM+. The findings reveal that adding lateral vane parallel to the divergence section improved high-intensity fluid flow distribution within the main channel, achieving 13.49% increment in downforce and 5.58% reduction in drag compared to the baseline simulation. However, incorporating an airfoil cross-section flap parallel to the divergence end significantly enhances the car’s performance, leading to a substantial improvement in downforce while relatively small increase in drag force. This underscores the critical importance of precise flap positioning for optimizing aerodynamic efficiency. Additionally, the influence of adding flaps underneath the divergence section was also analyzed to manipulate boundary layer separation to achieve improved performance by producing additional downforce. This research emphasizes the critical role of vortex management in preventing flow detachment and improving diffuser efficiency. The findings offer valuable insights for potential FIA F1 2023 undertray regulation changes, with implications for faster lap times and heightened competitiveness in motorsports. Full article

In this paper, we propose an approach for constructing quasiparticle-like asymptotic solutions within the weak diffusion approximation for the generalized population Fisher–Kolmogorov–Petrovskii–Piskunov (Fisher–KPP) equation, which incorporates nonlocal quadratic competitive losses and a fractal time derivative of non-integer order ($\alpha$, where $0<\alpha \le 1$). This approach is based on the semiclassical approximation and the principles of the Maslov method. The fractal time derivative is introduced in the framework of $F^{\alpha }$ calculus. The Fisher–KPP equation is decomposed into a system of nonlinear equations that describe the dynamics of interacting quasiparticles within classes of trajectory-concentrated functions. A key element in constructing approximate quasiparticle solutions is the interplay between the dynamical system of quasiparticle moments and an auxiliary linear system of equations, which is coupled with the nonlinear system. General constructions are illustrated through examples that examine the effect of the fractal parameter ($\alpha$) on quasiparticle behavior. Full article

Hyperspectral Imagery (HSI) plays a crucial role in military and civilian target detection. However, HSI target detection remains highly challenging due to the interference caused by complex and diverse real-world scenarios. This paper proposes a Spectral Information Divergence-driven Diffusion Network model (SID-DN) for hyperspectral target detection, which significantly enhances detection robustness in complex scenes by decoupling background distribution modeling from target detection. The proposed method focuses on learning the background distribution in hyperspectral data and achieves target detection by accurately reconstructing background samples to identify differences between background and target samples. This method introduces an adaptive coarse detection module, which optimizes the coarse detection process in generative hyperspectral target detection, effectively reducing the background-target misclassification. Additionally, a SID-based Diffusion model is designed to optimize the loss of Diffusion, effectively reducing the interference of suspected target samples during the background learning process. Experiments on three real-world datasets demonstrate that the method is highly competitive, with detection results significantly outperforming current state-of-the-art methods. Full article

This study investigates a model of competition and cooperation between two enterprises with reaction, diffusion, and delays. The stability and Hopf bifurcation for variants with two, one, and no delays are considered by examining a system of delay ODE equations analytically and numerically, applying the Galerkin method. A condition is obtained that helps characterize the existence of Hopf bifurcation points. Full maps of stability analysis are discussed in detail. With bifurcation diagrams, three different cases of delay are shown to determine the stable and unstable regions. It is found that when ${\tau }_i>0$, there are two different stability regions, and that without a delay (${\tau }_i=0$), there is only one stable region. Furthermore, the effects of delays and diffusion parameters on all other free rates in the system are considered; these can significantly affect the stability areas, with important economic consequences for the development of enterprises. Moreover, the relationship between the diffusion and delay parameters is discussed in more detail: it is found that the value of the time delay at the Hopf point increases exponentially with the diffusion coefficient. An increase in the diffusion coefficient can also lead to an increase in the Hopf-point values of the intrinsic growth rates. Finally, bifurcation diagrams are used to identify specific instances of limit cycles, and 2-D phase portraits for both systems are presented to validate all theoretical results discussed in this work. Full article