Search Results (5,637)

Live streaming commerce is increasingly relying on high-intensity persuasive tactics, yet such tactics may activate consumers’ persuasion knowledge and trigger defensive processing. This research examines whether AI streamers mitigate this defense more effectively than human streamers. Drawing on the Persuasion Knowledge Model, two experiments reveal that, under conditions of high persuasive intensity, consumers perceive lower persuasive intent from AI streamers than from human streamers, which, in turn, reduces consumer suspicion and increases purchase intention. Moreover, this serial mediating effect is stronger for independent accounts than for brand official accounts. These findings provide evidence for a PKM-based mechanism of AI-mediated persuasion and suggest that platforms should consider using AI streamers in high-pressure promotional contexts. Full article

All rotating electrical machines are susceptible to vibrations arising from electromagnetic (EM) forces, electrical faults, mechanical defects, imbalance, and structural resonance. In Doubly Fed Induction Generators (DFIGs), such electromechanical vibrations are especially important because they can degrade reliability, increase noise, and lead to severe damage if resonance-prone operating conditions are not identified in time. Although fault diagnosis in DFIGs has been widely investigated using current, voltage, and flux signatures, comparatively fewer studies have examined fault-specific vibration behaviour under stator and rotor interturn faults (ITTFs), particularly through a coupled EM structural framework. In addition, prior vibration-based studies have not examined the influence of end winding ITTFs, its location, severity, and modal interaction investigating resonance risk. This paper considers vibration characteristics of a variable-speed 2.8 MW DFIG used in a grid-connected Type-3 wind turbine unit (WTU) at no-load operating condition. The DFIG is modelled in ANSYS Academic Research v 2022 R2 Maxwell for EM behaviour assessment for ITTFs in both stator and rotor windings along with modal analysis (MA) in ANSYS Workbench to examine the undamped stator and rotor modes over a range of frequencies. This coupled approach enables identification of vibration signatures associated with different ITTF types. The results show the magnetic flux density near faulty end-winding region increases with fault severity and ranges from 4.19 T to 4.39 T in proximity to faulty windings. A dominant modal frequency band of 60–65 Hz is identified, where stator and rotor modes coincide, creating probable resonance conditions. A severe vibration response is observed for single-phase stator ITTF, showing an amplitude of 2116 mm/s at 480 Hz for a larger number of shorted turns, indicating that asymmetric faults can produce stronger EM excitation than multi-phase faults. The main contribution of this paper is demonstration of a fault-specific, MA and vibration-based Condition monitoring system (CMS) implementation workflow for a DFIG. Unlike prior vibration-based studies that primarily focus on general machine vibration, mechanical faults, bearings, etc., this paper links stator and rotor ITTF induced EM excitation to modal characteristics, resonance behaviour, and measurable vibration signatures, establishing vibration analysis (VA) as a practical complementary technique for CMS of ITTFs in DFIGs. Full article

Demand for greener and safer chemistries has driven the innovation of bioinspired and enzyme-mimicking catalysts for selective and efficient oxidation and hydrogenation under mild conditions. Natural catalysts, including peroxidases, oxidases, hydrogenases, oxygenases and dehydrogenases, boast remarkable activity, specificity, stability, selectivity, low energy requirements and atom economy. Disadvantages of enzymes, such as poor thermal stability, a narrow operational range, low recovery yield and the expense of purification, are motivating the discovery and design of enzyme substitutes. Several artificial platforms have appeared recently: nanozymes, artificial metalloenzymes, biomimetic metal Complexes, MOFs, atomic catalysts, bioinorganic hybrid systems, among others. These systems aim to replicate key structural and mechanistic features of enzymes while providing greater operational stability, recyclability, and scalability. Recent work has demonstrated the benefit of enzyme mimics in increasing eco-sustainability in reactions such as alcohol oxidation, selective alkane oxidation, waste degradation, catalytic photooxygen activation and biomass waste conversion. Similarly, biomimetic hydrogenation catalysts have shown outstanding activity in asymmetrically hydrogenating chemicals, reducing CO₂ into chemicals, hydrogenation by hydrogen transfer and creating hydrogen through water. Through control of active sites, second coordination sites, defects and electrons/protons in the system, significant gains have been seen in reaction selectivity and frequency of turning over substrate into product. Nanozymes, biohybrid catalysis and artificial catalysts guided by deep learning are further broadening the applications of biomimetic catalysis in oxidation and hydrogenation. The article review aims to provide a summary of the most current progress with bioinspired and enzyme-mimicking catalysts, focusing on catalytic mechanisms, how to design such catalysts, how green chemistry benefits from their development and where further application is likely in the coming years. Full article

Surge reliability is a crucial aspect of silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) reliability. This study investigates the degradation behavior and mechanisms of switching characteristics in 1.2 kV planar-gate SiC MOSFETs under repetitive surge current. A surge current test platform is established to conduct surge tests on the device, while monitoring the evolution of its switching characteristics. The results indicate that after 4000 surge current cycles, the device’s turn-on delay time (t_d(on)), rise time (t_r), and turn-on loss (E_ON) show no significant changes. In contrast, the turn-off delay time (t_d(off)), fall time (t_f), and turn-off loss (E_OFF) increase by 9%, 7.5%, and 8.3%, respectively. Switching characteristics variations are closely linked to the reduction in threshold voltage (V_TH) and the increase in gate-source capacitance (C_GS) and gate-drain capacitance (C_GD). The degradation of these parameters stems from the accumulation of positive trapped charge in the gate oxide layer above the channel and junction field-effect transistor (JFET) region. The increase in charges results from the combined effects of negative gate bias and cyclic high temperature induced by repetitive surge current. This study provides a theoretical basis for the comprehensive understanding of the impact of surge current on SiC MOSFET performance. Full article

This article examines two reports dated 4 October 1897 and 6 January 1898 written by the Ottoman military attaché in Madrid, Reşid bin Galib, Staff Senior Captain (Kolağası), to analyze how the late Ottoman Empire interpreted the Spanish military structure and the insurrection in Cuba. Situated within the broader development of nineteenth-century military intelligence practices, the study employs textual and contextual analysis, focusing on institutional language, strategic categorization, and threat perception. The report dated 4 October 1897 provides a detailed account of the military-administrative organization in Cuba, including command hierarchy, troop distribution, logistical infrastructure, and internal security mechanisms, while the report dated 6 January 1898 evaluates the historical trajectory of the rebellion and offers a comparative assessment of combat- and disease-related casualties, highlighting the importance of logistical and administrative capacity in warfare. Taken together, these documents show that Ottoman military intelligence systematically monitored a colonial crisis beyond Europe and interpreted it through an institutional military framework. The reports also reflect late Ottoman concerns regarding external intervention, security, and imperial stability. By examining a non-European colonial conflict, the article demonstrates how military knowledge was transferred, reframed, and integrated across imperial contexts, thereby contributing to the historiography of Ottoman military attachés and highlighting their role in shaping the Empire’s global strategic awareness at the turn of the twentieth century. Full article

Multimodal large language models can read architectural images and design instructions but they still struggle to turn architectural rules into editable, executable models in professional modeling environments. To address this limitation, this paper presents MLLMto3D, an MCP-driven closed-loop framework that connects multimodal reasoning with Rhino-based modeling, feedback, and revision. The framework consists of five phases: visual parsing, JSON-based intent serialization, code synthesis, MCP-driven Rhino execution and feedback, and verification with bounded repair. Its core mechanism is JSON-based intent serialization, which converts image-derived architectural information into machine-readable modeling parameters under a predefined JSON schema. The schema separates geometric and compositional constraints, including height, bay rhythm, facade zones, and alignment rules, from design variables such as materials, openings, and ornament. Building on this mechanism, Skills modules externalize facade typology knowledge and safe Rhino scripting patterns, providing callable professional constraints for code synthesis to reduce design-intent deviation and API hallucination. The framework is evaluated through an experimental design case study on a site in Shanghai’s Hengfu Historic District, where the generation of new façades is informed by a nearby heritage architectural reference. The results show that MLLMto3D can generate a parametrically adjustable Rhino model while preserving the main compositional constraints, thereby advancing AI-assisted architectural 3D generation toward a controllable, verifiable, and iterative modeling process. Full article

Firms are increasingly expected to pursue multiple sustainability priorities, but the implementation consequences of expanding sustainability agendas remain insufficiently understood. This study investigates whether and how sustainability overload reduces sustainability execution consistency. Drawing on the attention-based view and the corporate sustainability tensions perspective, the study proposes that a broad and simultaneous sustainability agenda exceeds managers’ attentional and coordination capacity, thereby weakening implementation coherence across departments. Specifically, the study hypothesizes that sustainability overload increases managerial attention strain, which in turn increases interunit priority divergence, ultimately reducing sustainability execution consistency. To test this sequential mechanism, a randomized experimental vignette study was conducted with 300 middle- and senior-level managers working in Türkiye-based firms operating in sustainability-exposed sectors. Participants were assigned to either a focused sustainability strategy condition or an overloaded sustainability strategy condition. The results support all proposed hypotheses. The overloaded condition increased managerial attention strain and interunit priority divergence, while reducing perceived sustainability execution consistency. PROCESS Model 6 analysis confirmed the sequential mediation mechanism. The findings suggest that sustainability implementation depends not only on the breadth of sustainability goals, but also on whether these goals are organized through a manageable priority architecture. The study contributes to sustainability strategy implementation research by highlighting managerial attention and cross-functional divergence as mechanisms linking sustainability overload to execution inconsistency. Full article

Source apportionment and the elucidation of driving mechanisms are essential for targeted soil pollution management. This study investigated surface soils across six towns in southern Shimen County, northwestern Hunan Province, where 662 samples were collected to determine the concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn. Multivariate statistics and the APCS-MLR receptor model were integrated to quantify pollution sources, while three machine learning models (RF, XGBoost, and LightGBM) were applied to identify key drivers of the spatial enrichment of Cd. Results showed that Cd was significantly enriched, with a mean concentration of 0.43 mg/kg (3.41 times the provincial background value). The mean concentrations of As, Cr, Cu, Ni, Pb and Zn were 11.97 mg/kg, 81.01 mg/kg, 24.15 mg/kg, 49.25 mg/kg, 29.56 mg/kg and 76.77 mg/kg, respectively, and these PTEs remained at normal background levels. Significant inter-element correlations indicated common sources. Three primary sources were quantified—natural parent material (43.83%), mining activities (30.99%), and mixed sources of coal mining and agricultural inputs (7.84%), with 17.34% attributed to unidentified mixed sources. Natural sources dominated the geogenic enrichment of Cd, Cu, Ni, Pb, and Zn; mining activities governed the accumulation of As, Cr, Cu, and Pb; a mixed source of coal mining and agricultural practices contributed substantially to Cd enrichment. Machine learning identified PM10, topography, strata, and soil type as dominant drivers, with their total feature importance reaching 70.05%. Among these factors, natural factors and anthropogenic factors accounted for 44.23% and 55.77% of the total feature importance, in turn revealing coupled natural–anthropogenic controls. This study establishes an integrated framework linking source apportionment and driver identification, providing scientific insights for potentially toxic elements (PTEs) control in analogous mining–agricultural regions. Full article

Bamboo construction faces a persistent “high technical potential–low market adoption” paradox: despite advances in material mechanics and structural engineering, industry adoption remains limited, and the organisational mechanisms of knowledge transformation behind this gap remain underexamined. Shifting the analysis from technology to organisation, this study develops an integrated framework grounded in the knowledge-based view, Knowledge Reuse Theory, and circular economy (CE) principles. Within the Input–Tools and Techniques–Output (ITTO) framework, this study examines how influence factors, knowledge requirements, and knowledge classification are converted, via the knowledge management process (KMP), into structured knowledge assets (KA), and reconceptualises CE awareness as an organisational cognitive orientation that applies CE asset-management logic to knowledge resources. Survey data from 372 bamboo construction practitioners in mainland China were analysed using structural equation modelling (SEM). The three inputs were significantly associated with the KMP (β 0.200 to 0.299, p < 0.01); the KMP, in turn, was positively associated with KA (β = 0.183, p < 0.01); and CE awareness positively moderated the pathway (interaction β = 0.148, p < 0.05). The study offers a domain-respecified CE awareness construct with an empirical mechanism, providing a tractable organisational cognition perspective on the mechanisms that may support adoption readiness in bamboo construction. Full article

Freshwater aquaculture in China has expanded rapidly in recent decades, raising growing concerns about its environmental sustainability. However, the relationship between industrial agglomeration and environmental efficiency in freshwater aquaculture remains insufficiently understood. Using panel data from 18 major freshwater aquaculture provinces in China from 2009 to 2023, this study investigates the nonlinear effects of industrial agglomeration on environmental efficiency. Environmental efficiency is evaluated using a Global Super-SBM model incorporating undesirable outputs, while industrial agglomeration is measured by the location quotient index. A two-way fixed-effects model is employed for empirical estimation. The results reveal a significant inverted U-shaped relationship between industrial agglomeration and environmental efficiency, with a turning point at an agglomeration level of 2.519. Moderate agglomeration improves environmental efficiency through economies of scale and technology diffusion, whereas excessive agglomeration generates crowding effects that reduce efficiency. Further mechanism analysis shows that technology diffusion, proxied by the number of trained fishermen, plays a significant mediating role in this relationship. This study provides new empirical evidence on the nonlinear environmental effects of industrial agglomeration in freshwater aquaculture and offers policy implications for optimizing industrial spatial layout and developing differentiated environmental regulations to support the green and sustainable development of the sector. Full article

Ants, plants, and hemipterans in tritrophic mutualisms represent closer approximations to real ecosystems compared to twofold mutualisms, playing a critical role in ecosystem functioning. Although habitat degradation is a useful framework for investigating the stability of mutualisms, few studies have focused on such mutualistic interactions in degraded grassland. In this study, we conducted both a field and a greenhouse experiment to assess the effect of grassland degradation on the organization of ant–plant networks and ant-hemipteran-plant tritrophic interactions in the light and severely degraded grassland of Songnen Plain, China. In general, we found that severe degradation of grassland changed the spatial distribution pattern of ant–plant networks from uniform to aggregation and increased the species diversity within these networks and facilitated the Lasius flavus-aphid/mealybugs-Artemisia scoparia tritrophic mutualisms. L. flavus improves individual plant performance by increasing plant height, reducing soil moisture content, and facilitating seed transportation of A. scoparia. These advantages enhance plant fitness and population spread of A. scoparia, consequently boosting its dominance within degraded grassland habitats. In turn, the well-developed root of A. scoparia attracted more L. flavus and aphid/mealybugs by providing living space and food. Our findings enhance the understanding of tritrophic mutualisms and their mechanisms in the context of grassland degradation, thus providing valuable information for the conservation, management, and restoration of degraded grassland. Full article

Tourism destinations increasingly rely on digitalization and innovation to strengthen their competitive position and stimulate tourists’ post-visit behaviors. However, empirical evidence explaining how tourists’ perceptions of digitalization and innovation are translated into sharing intentions, particularly from an integrated competitiveness perspective, remains limited. Addressing this gap and grounded in Service-Dominant Logic, this study investigates the relationships between digitalization perceptions, innovation perceptions, destination competitiveness, and tourists’ sharing intentions. Using a quantitative approach, data were collected from Generation Z domestic tourists who had visited digitally enabled destinations within the past 12 months. The proposed research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that perceptions of digitalization and innovation significantly enhance destination competitiveness, which in turn positively influences tourists’ sharing intentions. Moreover, destination competitiveness is confirmed as a partial mediator in the relationships between digitalization perceptions and sharing intentions, as well as between innovation perceptions and sharing intentions. The novelty of this study lies in conceptualizing destination competitiveness as a behavioral transmission mechanism that converts perceived digital and innovative destination attributes into tourists’ voluntary sharing behavior, rather than treating digitalization and innovation as direct antecedents alone. The theoretical contribution of this study lies in reconceptualizing destination competitiveness as a dynamic value co-creation mechanism that mediates the transformation of digitalization and innovation into tourists’ sharing intentions within a Service-Dominant Logic framework. Full article

High-entropy MAX phases (TiVNbTa)₂AlC have attracted increasing attention due to their potential advantages in structural stability, damage tolerance, and mechanical reliability under complex service environments. This work studied the crystal and electrical structures with the elastic properties, the synthesis reactions and further wear resistance of HE-MAX (TiVNbTa)₂AlC theoretically and experimentally. The charge transfer between both M-C atoms and M-Al atoms turned more intense, which correspondingly strengthened the M-C and M-Al bonds, respectively. Because of the dope on M-sites, (TiVNbTa)₂AlC exhibited larger fracture toughness K_IC and a lower brittle index M, which suggested lower brittleness, better crack extension resistance, and higher damage tolerance than Ti₂AlC. In this work, high-entropy (TiVNbTa)₂AlC MAX phase ceramics were successfully synthesized by a powder metallurgy route combined with pressureless sintering and spark plasma sintering (SPS). The effects of raw material composition and sintering temperature on phase evolution, microstructure formation, mechanical properties, and tribological behavior were systematically investigated. The results show that a highly pure (TiVNbTa)₂AlC phase with a phase fraction of 96.8% could be obtained at a molar ratio of M:Al:C = 2:1.2:0.8 and a sintering temperature of 1550 °C. Phase evolution analysis indicated that the reaction process followed the sequence of intermetallic compound (IMC) formation → carbide formation → MAX phase formation. Severe lattice distortion induced by the multi-principal-element solid solution significantly enhanced the hardness of the material, which was markedly higher than that of conventional ternary MAX phases. Owing to its higher hardness and more homogeneous solid-solution structure, HE-MAX (TiVNbTa)₂AlC could inhibit the formation of surface microcracks and reduce the driving force for crack propagation to some extent. Therefore, the lower wear rate not only reflected improved tribological performance but also demonstrated the beneficial role of high-entropy design in enhancing resistance to surface damage. Full article

We compare classical and modern regression models for next-day cryptocurrency forecasting on 14 USD-denominated coins across three liquidity tiers from 2018 through 2025, and we use the resulting panel to formally test three pre-specified hypotheses. The features are a strictly past-only 28-element set; the evaluation uses expanding-window walk-forward cross-validation with nested hyperparameter tuning, stationary block-bootstrap 95% confidence intervals, and pairwise Diebold–Mariano tests. Methodologically, we derive a bias-variance bound that turns the ‘no model beats the mean’ observation from a null finding into a predicted outcome under weak-form market efficiency. Empirically, (H1) the threshold–effect interaction is not supported (slope −1.7 × 10⁻⁴, 95% CI [−4.8 × 10⁻⁴, +1.4 × 10⁻⁴], p = 0.25). (H2) Statistical loss minimisation is decoupled from risk-adjusted economic outcome: the cluster-bootstrapped 95% CI for the Spearman rank correlation between the within-ticker MAE rank and within-ticker post-cost Sharpe rank is [−0.39, +0.10] overall, lies *strictly below zero* on the mid-cap (CI [−0.71, −0.04]) and long-tail (CI [−0.26, −0.09]) tiers, and decisively rejects perfect alignment (ρ = +1) on every tier. None of the seven (ticker, model) pairs with annualised Sharpe ≥ 0.5 has a hit rate significantly different from 0.5; high-Sharpe outcomes reflect return skew, not directional skill—formally predicted by a closed-form Sharpe–MSE decoupling proposition we derive in Section 3.6 under non-zero return skewness. (H3) Lo–MacKinlay variance ratio tests show top-tier coins are indistinguishable from a random walk (|z| ≤ 1.5 at q ∈ {2, 5, 10}), while mid- and long-tail tiers reject the random-walk null at q = 2 (z = −2.36, z = −2.60). The findings extend across two robustness layers. An AR(1)-GARCH(1,1) baseline produces R² ≈ −0.005 on every tier and is indistinguishable from Lasso, supporting the bias-variance bound; Giacomini–White conditional predictive ability tests reject equal predictive ability between Lasso and tree-based models on every coin in every tier, complicating naive DM interpretations; and a forward-walking 2026-Q1 holdout—83 daily observations per coin entirely outside the training window—confirms that H1 is even more decisively null on unseen data and that the H3 efficiency conclusion holds. Together, these results give a formally tested EMH-style picture for daily crypto: no model meaningfully forecasts log-returns; statistical accuracy and trading P&L are decoupled by an analytically derived mechanism; and weak-form efficiency is approximately satisfied in most liquid coins and in the convergence across the cross-section. Full article

Various immunostimulants have been employed to enhance the efficacy of aquaculture. However, the mechanisms by which Eucommia ulmoides extracts affect the pathogen resistance of large yellow croakers remain to be elucidated. In this study, a multi-omics investigation was conducted to investigate the potential for enhancing pathogen resistance and to elucidate the underlying mechanisms of Eucommia ulmoides extracts on large yellow croaker juveniles. It revealed that the survival rate of the large yellow croaker juveniles was significantly enhanced (approximately doubled) by oral extracts of Eucommia ulmoides in the context of pathogen invasion. Examining the transcriptome of the liver revealed that Eucommia ulmoides extracts have the capacity to activate the class I MHC pathway for antigen processing, including genes for HSP70, HSP90, CALR, TAPBP, MHCI, and TCR. This, in turn, facilitates the phagocytosis of pathogens by the immune system. Furthermore, the alpha-diversity of the large yellow croaker juveniles’ gut microbiota was increased by the Eucommia ulmoides extracts, maintaining the stability of the gut microbiota and reducing the ratio of pathogens (Burkholderia cenocepacia and Streptococcus iniae) following a pathogen challenge. The application of correlation analyses indicated that a reduction in pathogens was closely associated with variations in whole gut microbiota. Full article