Search Results (620)

With the development of digital technology and artificial intelligence (AI), numerous studies have focused on the applications and impacts of digital technology in the public sector. However, few studies have explored how frontline public service employees, the core subject of public organizations, can improve their proactive service performance. Based on the model of proactive motivation, this paper investigates the influence of digital leadership on employees’ proactive service performance from a micro perspective, as well as the internal mechanisms and boundary conditions underlying this process. Through an analysis of three-wave questionnaire survey data from 234 employees, this study finds that digital leadership has a positive impact on public employees’ proactive service performance through the serial mediation effects of AI service awareness and AI crafting. Furthermore, as an important boundary condition, employees’ public service motivation strengthens the serial indirect effect of digital leadership on proactive service performance. This paper not only extends the literature on digital leadership by adopting a micro-level perspective within the context of public sector digital transformation but also identifies the individual and contextual antecedents of proactive service performance by examining the interactive effect of public service motivation and leadership. Furthermore, this paper offers valuable implications for the practice of digital transformation in public organizations. Full article

Thermal overload in internal combustion engines may progressively destabilize lubricant-film integrity and promote severe tribological deterioration within highly stressed contact interfaces. This study investigates the thermo-tribological degradation sequence of a high-mileage gasoline engine subjected to prolonged idle operation under impaired cooling conditions, ultimately resulting in engine seizure. The investigated engine had accumulated 356,724 km, while the lubricant had remained in service for approximately 26,724 km prior to the experiment. The post-failure investigation combined teardown inspection, geometrical camshaft assessment, reverse gravimetric reconstruction, hydraulic tappet surface profiling, XRF surface characterization, laboratory oil analysis, and SEM/EDS evaluation of wear debris. The results demonstrated strongly localized degradation concentrated primarily within the cam–tappet interfaces. Severe non-uniform camshaft wear was accompanied by pronounced hydraulic tappet surface damage and evidence of unstable boundary-lubrication conditions. Laboratory oil analysis revealed elevated wear-metal concentrations, depletion of the alkaline reserve, increased oxidation indicators, and a final Class D oil condition assessment. SEM/EDS characterization identified Fe-bearing wear debris associated with sustained material removal and debris recirculation during the final degradation stage. The combined evidence supports a coupled thermo-tribological degradation mechanism involving lubricant deterioration, boundary-lubrication instability, adhesive wear acceleration, oxidative surface degradation, and debris-assisted surface damage preceding final engine seizure. The present case study provides experimentally documented evidence of lubrication collapse under real-engine thermal runaway conditions and highlights the critical role of lubricant condition in maintaining tribological stability under severe thermal loading. Full article

The sustainable development of forest ecotourism relies on the effective stimulation of tourists’ environmentally responsible behavior, and the intervention of participatory art and aesthetics provides a new driving force for this process. Taking Xiqiaoshan National Forest Park (Nanhai Land Art Festival) as a case study, we propose an extended stimulus–organism–response (S-O-R) theoretical framework to reveal the psychological perception and transmission mechanism of participatory art and aesthetic experience in empowering the sustainable development of ecotourism. We used a hybrid approach combining PLS-SEM and artificial neural networks (ANNs) to analyze survey data from 596 Chinese tourists. The study found that sensory impressions driven by art and aesthetics significantly and positively influence tourists’ natural connections, perceived value, and ecotourism attitudes. These three constructs function as significant parallel mediators between sensory impressions and environmentally responsible behavior, while chain mediation effects are statistically significant but of small magnitude. The new environmental paradigm (NEP), conceptualized as an individual trait boundary condition, exhibits a significant negative moderating effect on the relationship between sensory impressions and connectedness to nature. ANN sensitivity analysis further complements the findings by demonstrating the prominent nonlinear predictive role of ecotourism attitudes in behavioral transformation. This study extends the application boundaries of the S-O-R theory to art-integrated ecotourism research, clarifies the internalization process of tourist experiences from sensory perception to behavioral enactment, and provides empirical evidence for forest tourism managers to optimize experience design and implement differentiated guidance strategies. Full article

The Easterlin paradox and recent distributional reassessments suggest that average effects obscure how subjective disadvantage is generated and reproduced over time. We propose the Social Comparison System (SCS), a framework that represents subjective well-being (SWB) as an internal state and relative income rank as an external conditioning variable within a feedback structure, with three structural properties: threshold activation, state-dependent gain, and rank-conditioned attractor displacement. The properties are recovered through a sample-isolated three-stage framework integrating tree-based machine learning, forest-based heterogeneity estimation, panel-data estimation, and hierarchical Bayesian Markov modeling on a balanced four-wave panel of the China Family Panel Studies (CFPS; 8099 individuals; 32,396 person-wave observations). Stage 1 locates a discrete predictive discontinuity in relative income rank between rank 2 and rank 3 (SHAP jump = 0.383, permutation p < 0.001). Stage 2 carries this boundary into a disjoint validation panel and recovers a negative rank-by-prior-SWB interaction (β = −0.036) and a 2.30-fold larger conditional effect in low- than in high-prior-SWB strata. Stage 3 recovers a 22.6-percentage-point gap in the rank-conditioned occupancy of the lowest within-wave SWB quartile between low- and high-rank subsystems, which under a first-order Markov approximation corresponds to a long-run stationary gap, robust to alternative state-space discretizations. Throughout this paper, relative income rank is treated as a conditioning variable, and the rank-conditioned patterns are interpreted as associational; the long-run quantities are reported under a first-order dynamical approximation rather than as identified causal or fully validated long-run effects. Persistent subjective disadvantage is therefore characterized by unequal dynamics of activation, amplification, and escape, rather than by unequal resources alone. This reframing provides a methodological template for identifying rank-conditioned feedback structures in social-systems data. Full article

Lattice-based heat sinks have attracted increasing attention as volumetric thermal management architectures for forced-convection cooling of high-power electronic systems. In contrast to conventional plate-fin, pin-fin, and straight-channel configurations, lattice geometries promote three-dimensional flow–solid interaction through interconnected ligament networks that modify boundary-layer development, wake formation, and internal heat-spreading pathways. This review synthesizes recent experimental and numerical studies to examine the thermo-fluid mechanisms governing lattice performance, with emphasis on the coupled influence of porosity, ligament dimensions, topology, orientation, and channel confinement on heat-transfer enhancement and hydraulic resistance. The analysis indicates that while lattice structures can increase average Nusselt number and improve temperature uniformity, these gains are intrinsically linked to pressure-drop penalties associated with flow tortuosity and form drag, resulting in regime-dependent thermal-hydraulic behavior. Apparent discrepancies reported across the literature are frequently attributable to differences in geometric definition, Reynolds-number normalization, and boundary-condition specification rather than to inconsistencies in physical mechanisms. By consolidating geometric scaling, performance metrics, manufacturing considerations, and system-level constraints, this review clarifies the conditions under which lattice heat sinks may provide net benefit relative to conventional cooling technologies and identifies key research directions required to support application-relevant design and evaluation. Full article

The permeability of coal seams plays a critical role in the efficiency of coalbed methane extraction and gas disaster prevention. Traditional permeability models often overlook the anisotropic and dynamic evolution characteristics of coal under varying stress and gas adsorption conditions. This paper proposes a novel permeability evolution model that integrates the effects of effective stress variation and gas sorption-induced deformation on coal permeability. Starting from the concept of face porosity and utilizing a representative voxel approach, the model incorporates the anisotropy of mechanical parameters and adsorption expansion strain to derive the evolution of permeability in three dimensions. The model is validated against experimental permeability data from two distinct coal samples (Sulcis and Sydney), demonstrating its ability to accurately capture permeability changes under different boundary conditions. Furthermore, the concept of “internal expansion strain coefficient” is introduced to quantify the impact of adsorption-induced matrix deformation on permeability. The model provides a theoretical foundation for predicting gas flow behavior in coal seams under complex in-situ conditions and offers significant insights into the optimization of gas extraction strategies. Full article

Large-format photovoltaic modules are increasingly adopted to improve power output and reduce system cost, but their larger exposed area may also increase wind-induced structural demand and reduce structural safety under strong wind loading. This study investigated whether large-size photovoltaic modules and their support system could remain within an acceptable safety range under representative wind loading conditions in boundary free one-directional solar arrays in Ontario. Finite element models were developed in SAP2000 to assess the effects of module size, wind speed, and tilt angle on internal force, displacement, stress, and safety factor under static wind loading. For the array comparison, literature-derived pressure coefficients were used to represent the difference between the isolated single-row case and the front row of the 8-row array. The results showed that the large-size module consistently developed higher bending moments and larger displacements than the normal-size module under the same loading condition, indicating a clear size effect. The isolated single-row case produced a larger immediate structural response than the front row of the 8-row array under the selected loading input. Under a fixed 0° tilt angle and increasing wind speed, the glass panel remained the governing safety component. Under the fixed 27 m/s wind condition and increasing tilt angle, the governing component shifted to the purlin in the large-size module, especially under high-tilt cases. These findings provide a design-oriented basis for assessing the structural safety of large-size photovoltaic systems under wind loading. Full article

This study examines how organizational pro-environmental values are associated with employee sustainable behavior through differentiated motivational pathways and under what leadership conditions these associations are strengthened. Drawing on self-determination theory, we propose that autonomous and controlled motivation serve as distinct mediating mechanisms, while green transformational leadership facilitates the internalization of organizational pro-environmental values into autonomous motivation. A two-study design was adopted. Study 1, based on individual-level survey data from 158 employees in tourism enterprises, showed that employee-rated organizational pro-environmental values were positively associated with both high-level and general-level sustainable behavior through different pathways: autonomous motivation mediated the relationship with high-level sustainable behavior, whereas controlled motivation mediated the relationship with general-level sustainable behavior. Study 2, a scenario-based experiment with 234 participants with tourism-related work experience, showed that green transformational leadership strengthened the effect of organizational pro-environmental values on autonomous motivation and amplified the corresponding indirect effect on high-level sustainable behavior intention. Supplementary analyses further indicated that this moderating role did not equivalently extend to the controlled-motivation pathway toward general-level intention. These findings advance research on employee sustainable behavior by distinguishing behavioral levels, motivational mechanisms, and leadership-based boundary conditions. Practically, the study suggests that organizations should embed pro-environmental values, align motivational strategies with behavioral goals, and develop green transformational leadership. Full article

Named entity recognition (NER) in marine meteorological disaster texts is essential for automated information extraction and disaster management. However, disaster-chain descriptions often contain nested entities that are difficult for conventional flat NER models to represent. This paper proposes PRSpan, a position-role-aware span classification model for nested NER. PRSpan incorporates Rotary Position Embedding (RoPE)-enhanced attention for relative position-aware boundary modeling and uses Conditional Layer Normalization (CLN) to generate role-specific Head, Mid, and Tail token features. A Positional Role Pooling strategy further aggregates these features into span representations to preserve boundary cues and internal semantic coherence. To support evaluation, we construct MMD-NER, a domain-specific dataset containing 1899 sentences, 17,017 entities in 11 categories, and 2978 nested entity pairs through a four-step LLM-assisted pipeline. Experimental results show that PRSpan achieves Micro-F1 and Macro-F1 scores of 94.58% and 93.47%, outperforming the strongest baseline by 3.61 and 3.93 percentage points, respectively. Additional analyses verify the effectiveness of RoPE-enhanced attention, role-specific feature generation, and Positional Role Pooling. Cross-domain transfer and LLM prompting comparisons further demonstrate the practical value of PRSpan for nested entity extraction in low-resource Earth science domains. Full article

Open innovation has become a central framework for explaining how firms access, integrate, and exploit knowledge beyond organizational boundaries. However, the conditions shaping its adoption by small- and medium-sized enterprises remain insufficiently understood, particularly in Latin American contexts. This study examines the determinants of open innovation adoption in Colombian SMEs and develops an analytical model that integrates six explanatory dimensions: external partnership and cooperation, government support, rules and regulatory factors, market and customer factors, organizational and human resource factors, and technological factors. Empirically, the study combines an exploratory qualitative phase, based on semi-structured interviews with SME managers in Bogotá, D.C., with a quantitative phase using survey data from 319 SMEs operating in ISIC 6201 and 6202. The hypotheses were tested using partial least squares structural equation modeling. The results show that technological factors have the strongest direct association with open innovation adoption, followed by government support and external partnership and cooperation. Market and customer factors, as well as organizational and human resource factors, also exert positive and significant effects, whereas rules and regulatory factors do not show a significant direct effect. Additional analyses indicate that organizational and human resource factors partially mediate the relationship between technological factors and open innovation adoption, while a complementary moderation test does not support an interaction-based effect. These findings suggest that open innovation adoption in SMEs is technologically enabled, partially translated through organizational and human resource capabilities, and shaped by a configuration of relational, institutional, market-based, and internal conditions rather than by any single determinant. Full article

Pipes conveying fluids are important fluid–structure interaction systems encountered in aerospace, energy, marine, and industrial applications. Their dynamic behavior is strongly influenced by the interaction between structural motion and internal or external flow, leading to complex phenomena such as divergence, flutter, and flow-induced vibration. This review presents a comprehensive assessment of the dynamics and stability of pipes conveying fluids by integrating classical theories with recent developments in modeling, computation, materials, and control. The review covers mathematical formulations based on Euler–Bernoulli, Rayleigh, Timoshenko, and shell theories, together with analytical and numerical solution methods used for stability and vibration analysis. The effects of geometry, boundary conditions, flow configuration, damping, and material properties on dynamic response and instability thresholds are discussed. Special attention is given to composite, viscoelastic, functionally graded, and smart materials, as well as micro- and nanoscale pipe systems. Recent advances in vibration suppression, reduced-order modeling, machine learning, and physics-informed computational approaches are also reviewed. Finally, the paper identifies current challenges and future research directions, including multiphysics coupling, experimental validation, digital twins, and AI-assisted predictive modeling for fluid-conveying pipe systems. Full article

Environmental Management Accounting (EMA) is increasingly recognized as a vital internal tool for improving corporate environmental performance. This paper examines the hypothesis of the existence and degree of the impact of EMA on environmental performance (EP) in the Bangladesh ready-made garment (RMG) industry, the mediating factor is resource efficiency performance (REP), and the moderating boundary condition is good governance (GG). Based on the resource-based theory, dynamic capability theory, and institutional theory, the moderated mediation model is examined using partial least squares structural equation modeling (PLS-SEM) and survey data collected from 331 managers at medium- and large-scale RMG manufacturers. The findings confirm that EMA has a significant positive effect on EP, either directly or indirectly through REP, with REP accounting for about 47 percent of the overall effect. Good governance has a significant, albeit weakening, moderating effect on the EMA-REP pathway: in high-governance contexts, external regulatory pressures seem to partially replace internal EMA systems, thereby promoting resource efficiency. The results add to the literature on environmental accounting by explaining a process-based, governance-mechanism-contingent mechanism through which EMA affects environmental performance and by offering practical advice to managers and policymakers in the context of developing-economy manufacturing. Full article

Background: Accurate segmentation of white blood cells (WBCs) in peripheral blood smear images is a fundamental step in computational hematology, enabling downstream tasks such as classification, morphological assessment, and quantitative analysis. However, reliable segmentation remains challenging due to staining variability, complex cellular morphology, overlapping structures, and limited availability of high-quality annotations. Aim and Methods: The aim of this study is to develop a robust and fully automated segmentation framework for white blood cells (WBCs) in microscopic blood smear images, providing a reliable foundation for subsequent computational analysis. We propose TransNet–SAM2, a hybrid deep learning architecture that integrates hierarchical transformer-based feature extraction with a foundation-model-based decoder for prompt-free segmentation. Specifically, a Swin Transformer backbone is employed to capture multi-scale contextual representations, which are subsequently aligned and fused through a feature adaptation module. The fused features are directly injected into the SAM2 mask decoder, replacing conventional prompt-based conditioning and enabling fully automatic segmentation. In addition, a weakly supervised self-training strategy is incorporated to utilize partially annotated data, improving model generalization while reducing annotation requirements. The proposed framework is evaluated using a clinically curated dataset from Dicle University, the publicly available Raabin-WBC dataset, and an additional external leukemic blast validation dataset (ALL-IDB) to assess robustness under both routine and atypical hematological conditions. Results: TransNet-SAM2 achieved a Dice coefficient of 0.95 ± 0.01 and IoU of 0.90 on internal testing, significantly outperforming U-Net (0.89), Mask R-CNN (0.90), and SAM2 (0.92) (p < 0.05). In cross-dataset evaluation (Dicle training, Raabin-WBC testing), the framework maintained strong performance (Dice: 0.91, IoU: 0.84), demonstrating robustness to domain shifts. Ablation studies confirmed each component’s contribution, with the full model improving Dice by 6% over a CNN baseline. Qualitative analysis showed accurate boundary delineation even with cell overlap and background clutter. Conclusions: These findings indicate that the proposed framework provides a promising and scalable framework for WBC segmentation. While the current study focuses on segmentation, future work will investigate integration with classification and radiomics pipelines, as well as validation on more diverse clinical datasets, including bone marrow and leukemia samples. Full article

This study conducted a numerical simulation of laminar flow within a cylindrical pipe using a semi-implicit method. The full Navier–Stokes equations in cylindrical coordinates were solved, with modifications to the SIMPLE algorithm to handle pressure-linked equations. We evaluated three key thermophysical parameters—dynamic viscosity, specific heat capacity, and thermal conductivity—under both constant and variable conditions in the entrance region. Due to the process’s two-dimensional, time-dependent nature, third-kind boundary conditions were used to accurately model the effects of ambient temperature, external wind, and the pipe’s geometric and physical features. From the numerical results, we analyzed the velocity field, pressure distribution, surface friction coefficient, and temperature distribution at various pipe cross-sections. These findings are of practical and scientific importance: they offer insights into the hydrodynamics and thermal behavior of the internal flow and enhance understanding of fluid flow and heat transfer, improving predictive models. This advancement supports better design and operational control in pipeline systems. Full article

Reinforced concrete (RC) slabs, as the core load-bearing components in construction engineering, are prone to internal damage induced by impact loads, and accurate positioning of impact locations is a key task in structural health monitoring. The proposed method was developed for typical RC slabs such as building floors, bridge decks, and road slabs. Traditional acoustic emission (AE) positioning methods suffer from low positioning accuracy and a tendency to fall into local optimum when applied to RC slabs, which is attributed to the material’s heterogeneity, the complex propagation characteristics of stress waves and ambient noise interference. In this study, a GA-PSO hybrid algorithm is proposed, which integrates the global search capability of the Genetic Algorithm (GA) with the superior local convergence performance of the Particle Swarm Optimization (PSO) algorithm. The premature convergence issue of the traditional PSO algorithm is alleviated by adopting strategies including tournament selection, α hybrid crossover, boundary-constrained mutation, and linearly decreasing inertia weight. Based on the Time Difference of Arrival (TDOA) principle, the root mean square error between the theoretical and measured time differences is taken as the fitness function, and a boundary penalty mechanism is incorporated to ensure the physical validity of positioning results. AE data were acquired through drop weight impact tests to verify the performance of the proposed algorithm. Compared with traditional TDOA grid search, pure GA, and pure PSO methods under the same conditions, the proposed GA-PSO algorithm achieves an average localization error of only 54.95 mm, which is 61.0% lower than that of pure GA, while reducing the error standard deviation from approximately 114 mm to 24.87 mm. The average positioning error for all impact sources on the RC slab is within 100 mm, with the error in the central area as low as 42.97 mm. These results demonstrate that the GA-PSO algorithm significantly outperforms existing methods in terms of accuracy, stability, and maximum error control, verifying its high potential for impact source localization in complex heterogeneous materials. Full article