Search Results (572)

The top management team (TMT) plays a key role in shaping corporate strategy. However, how and when TMT faultlines influence corporate digital transformation remains unclear. Drawing on the categorization–elaboration model, this study examines the nonlinear relationship between TMT faultlines and corporate digital transformation and the moderating role of board governance. Using panel data from Chinese A-share-listed manufacturing firms, the results suggest an inverted U-shaped relationship between TMT faultlines and corporate digital transformation. Board monitoring and board networking are associated with a steeper inverted U-shaped curve, whereas board advising is associated with a flatter curve. Heterogeneity analysis shows that the inverted U-shaped relationship is mainly observed in non-state-owned enterprises, younger firms, and firms with high ownership concentration. These findings extend research on faultlines, strategic leadership, and corporate governance and offer practical implications for configuring TMTs and designing board governance mechanisms to support corporate digital transformation. Full article

Tourism, despite being popularly regarded as a leisure activity, is shaped by a complex set of influencing factors and itself generates complex consequences—economic, societal, and environmental. The interaction between economic growth, tourism, and the environment is a crucial policy challenge for transitional economies striving to achieve sustainable development. This paper analyzes the impact of income, international tourism receipts, and energy use on CO₂ emissions as a proxy for environmental deterioration for five Western Balkan economies over the period of 2007–2022. The empirical investigation is conducted following the Autoregressive Distributed Lag (ARDL)—Pooled Mean Group (PMG) approach, favoring a long-run relationship among all the variables with a moderate convergence towards the long-run equilibrium. Tourism development and energy use are significantly and positively associated with carbon emissions in the region, with energy use emerging as the most robust long-run driver of emissions. The results do not support the conventional Environmental Kuznets Curve (EKC). Instead, a U-shaped income–emissions relationship is found, with income levels in all the five economies lying above the corresponding thresholds implied by the common long-run relationship by the end of the sample period. These results call for urgent policy action towards sustainable tourism development and energy transition in the Western Balkan countries. Full article

Stabilizing food prices is essential for ensuring food security in an aging society. Using panel data from 30 Chinese provinces spanning 2005 to 2022, this study employs a nonlinear panel model and a Spatial Durbin Model (SDM) to analyze the impact of rural population aging on food prices and its spatial spillover effects. This study derives the following findings based on empirical research: (1) Rural population aging exhibits a significant inverted U-shaped relationship with food prices, with an inflection point at approximately 19.03%. Before reaching this point, rural population aging helps facilitate food prices. When the inflection point is passed, rural population aging adversely impacts food prices. This effect is significant in western regions but not in eastern and central regions. (2) Farmland transfer and agricultural technological progress significantly influence this relationship, causing the curve to reverse into a U-shaped pattern, which implies a gradual future increase in food prices. (3) Local rural population aging has a significant U-shaped spillover effect on food prices in neighboring provinces. These findings indicate that China’s rural population aging presents a complex dynamic for food price fluctuations. To address the current changes in the population and capital structure and ensure food security, the government will need to formulate forward-looking policies, further improve socialized agricultural services, and systematically optimize food production models. Full article

Bounded continuous data arise in many areas of applied probability and statistics, particularly when the underlying variable is restricted to a finite interval and the density is expected to vanish at the endpoints. This paper studies a parsimonious fixed-shape submodel of the Kummer–beta-G family, referred to as the asymmetric quadratic-exponential bounded generator model, abbreviated as AQEB-G. The model is obtained by fixing the two beta shape parameters at $a=b=2$, which yields the unit quadratic-exponential kernel $w_{\beta }\left(u\right)=u(1-u)exp(-\beta u)$, $0<u<1$, where $\beta \in \mathbb{R}$ is a dimensionless tilting parameter. Composing its normalized distribution function with an absolutely continuous baseline cdf G produces the corresponding fixed-shape Kummer–beta-G specialization on the support inherited from G. The bounded AQEB distribution on $(0,\alpha )$ is obtained by using the uniform baseline $G(x;\alpha )=x/\alpha$ and the endpoint-scale parametrization $\beta =\lambda \alpha$. We derive the cumulative distribution, density, survival, hazard and reversed hazard functions. We also show that, after standardization, the bounded AQEB model is exactly the natural exponential tilt of a beta$(2,2)$ distribution, and, hence, its version on $(0,\alpha )$ is a scaled exponentially tilted beta$(2,2)$ model. Several mathematical properties are obtained, including ordinary and incomplete moments, generating functions, entropy measures, Lorenz and Bonferroni curves, shape properties, stochastic representations and ordering results. Likelihood-based inference is also discussed with attention to the support-dependent endpoint parameter. The finite-sample behavior of the estimators is examined through a Monte Carlo simulation study, and two empirical applications illustrate the practical use of the AQEB model. In the examples considered, the AQEB model performs competitively relative to the evaluated alternatives according to the reported likelihood-based criteria, goodness-of-fit statistics, and residual diagnostics. Full article

Images used in intelligent graphite ore sorting can reveal grade-related visual and spatial information. Selective encryption reduces the cost of full-image protection and preserves nonsensitive background regions, but the number, location, and shape of protected regions may become public side channels. This paper proposes TASR-AE (Tile-wise Authenticated Selective ROI Encryption), a tile-wise authenticated selective ROI (region of interest) encryption method for graphite ore images. Low-, mid-, and high-grade ore instances are merged into a grade-agnostic ore ROI, dilated for boundary tolerance, quantized onto a fixed grid, and encrypted tile by tile with HKDF (HMAC-based Extract-and-Expand Key Derivation Function)-separated ChaCha20-Poly1305 (an authenticated encryption algorithm). An authenticated sidecar binds tile coordinates, anonymized object identity, algorithm metadata, and the public background. We evaluate correctness, overhead, automatic ROI localization, public-geometry grade-membership inference, decoy-budget trade-offs, ciphertext-position leakage, and adaptive real-ROI recovery. On an audited 121-image graphite test split, TASR-AE with ground-truth masks achieves exact recovery and background authentication while protecting 10.81% of pixels without decoys. Grade-agnostic ROI selection removes deterministic high-grade existence leakage, but full public geometry still yields a High-membership ROC-AUC (area under the receiver operating characteristic curve) of 0.7539. A frozen 576-tile decoy budget lowers the validation ROC-AUC to 0.5088, but the test ROC-AUC rises to 0.6491 while protecting 80.86% of the image. Ciphertext texture does not distinguish real and decoy tiles (pooled ROC-AUC 0.5064), yet a position-and-occupancy adaptive attack recovers real ROI membership with ROC-AUC 0.9868 and mean tile IoU (intersection over union) 0.7051. The results show that TASR-AE provides authenticated content protection with public background visibility, but fixed-budget decoys do not provide strong ROI-location hiding. Full article

Artificial intelligence technologies, as exemplified by industrial robots, have become a key factor reshaping the spatial distribution of the labor force in China’s urban and rural areas. Building on the classic Lewis paradigm, this study uncovers the underlying logic and evolutionary patterns of rural–urban labor migration in China under the dual influences of industrial robotics applications and Chinese institutional constraints. Subsequently, in this study, we conducted an empirical analysis using interprovincial data from China between 2006 and 2024 and found that the impact of industrial robot applications on rural-to-urban labor migration follows an inverted U-shaped pattern. Mechanistic analysis indicated that this was primarily attributable to the labor price effect triggered by changes in the urban–rural income gap and the dynamic evolution of the urban sector’s demand for migrant workers. Further, we found that the urban–rural dual-system structure exerted a significant moderating effect on the aforementioned inverted U-shaped relationship. Specifically, regions with relatively solidified dual-system structures showed a delay in the inflection point of the inverted U-shaped curve. This indicated that, in response to the impact of industrial robots, the dual-system structure objectively provides additional transition adaptation time for migrant workers. The conclusions from this study are of great significance for sustainable urbanization in China in the digital intelligence era. Full article

Agricultural operations such as pepper harvesting, fruit counting, and field phenotyping rely on accurate visual recognition and instance segmentation algorithms. However, pepper fruits in complex field environments often exhibit slender and curved shapes, partial occlusion, ambiguous boundaries, and adhesion between adjacent instances. Existing object detection and instance segmentation methods therefore struggle to obtain complete fruit masks, which adversely affects subsequent fruit counting, contour measurement, and picking-point localization. To improve the instance segmentation accuracy of occluded peppers in complex field scenes, this study proposes OccPepSeg-YOLO, an improved model based on YOLO11n-seg. First, a P2FreqFusion module is introduced to fuse shallow, high-resolution detail features with deep semantic features, thereby enhancing the representation of fruit edges and tip regions. Second, an ASC module is designed to model the directional and scale-related morphological characteristics of pepper fruits, while a BoundaryGate module strengthens responses at occlusion interfaces and boundaries between adjacent instances. Finally, an OccPepSegment multi-scale prototype segmentation head is constructed, and a BDoU loss function is introduced to improve the boundary consistency of instance masks. Experiments on a self-constructed field-pepper instance segmentation dataset showed that OccPepSeg-YOLO achieved M-P, M-R, M-mAP50, and M-mAP50–95 values of 93.87%, 92.09%, 97.17%, and 82.31%, respectively, representing improvements of 5.59, 3.18, 3.83, and 9.52 percentage points over YOLO11n-seg. Further comparisons with representative YOLO-based instance segmentation models, including YOLOv8n-seg, YOLOv9c-seg, YOLO12n-seg, and YOLOv26n-seg, demonstrated that OccPepSeg-YOLO achieved the best overall segmentation performance. In particular, its M-mAP50–95 exceeded the best competing result obtained by YOLOv9c-seg by 8.35 percentage points. Under a unified repeated-inference protocol on an RTX 3090 GPU using FP32 precision, a batch size of 1, and 640 × 640 inputs, OccPepSeg-YOLO achieved a mean inference latency of 15.801 ± 1.238 ms, a P95 latency of 17.323 ms, and a throughput of 63.29 FPS. These results demonstrate that the proposed model can produce more complete pepper instance masks under leaf occlusion, fruit overlap, and complex background conditions, providing technical support for field-pepper recognition, fruit counting, and visual perception by agricultural robots. Full article

Foliation-induced anisotropy in phyllite leads to complex deformation and failure responses, making excavation stability analysis and support design particularly challenging for geo-energy and geo-resource applications. To elucidate the influence of foliation angle (β) on mechanical behavior and fracture mechanisms, uniaxial compression tests were conducted and Particle Flow Code numerical simulations were performed on phyllite specimens with seven different foliation angles. The anisotropic mechanical behavior of phyllite under uniaxial compression was systematically analyzed across varying foliation angles. Furthermore, the interaction mechanism between matrix and foliation-induced cracks was investigated based on relevant theoretical frameworks. The foliation angle β has a significant effect on the stress–strain curve of phyllite. The elastic modulus generally increases with increasing β, whereas the peak strength displays an overall U-shaped distribution. As β increases, the macroscopic failure mode shifts from matrix-dominated fracturing to foliation plane-controlled slip/opening, and then trends back toward matrix-dominated fracturing at high β. In the simulations, microcrack type, initiation sequence, and the internal mechanism of instability vary markedly across β. Combined with the energy release rate criterion of fracture mechanics and the compression bar stability theory, the mutual induction mechanism between matrix cracks and foliation cracks is further clarified. The penetration or deflection behavior of matrix cracks near weak planes is jointly controlled by the crack propagation direction and foliation angle. When the foliation plane is parallel to the loading direction, the buckling instability of thin rock flakes resulting from tensile cracking along weak planes constitutes the primary factor contributing to the reduction in the strength of specimens where the foliation plane is normal to the loading direction. This finding clarifies the intrinsic cause of the prediction deviation of the conventional Jaeger weak plane theory and can provide a more accurate theoretical reference for the stability evaluation of layered rock mass engineering. Full article

This study investigates the influence of diaphragm stiffness on the seismic vulnerability of low-rise masonry buildings using nonlinear static pushover analysis. Two limiting structural idealisations are considered: a rigid-diaphragm model and a conservative model with no effective in-plane diaphragm action. The models are applied to buildings with quasi-rectangular and U-shaped floor plans, and their responses are compared using capacity curves, vulnerability functions, and EMS-98 damage grades. The results show that rigid diaphragms improve the redistribution of seismic forces among load-bearing walls, increase global lateral capacity, reduce displacement demand, and produce a more uniform structural response. In contrast, the absence of effective diaphragm action leads to non-uniform force transfer, increased differential displacements, amplified torsional effects, and substantial reductions in lateral capacity, particularly in the U-shaped building. These effects become more pronounced under higher seismic demand. The results demonstrate that both diaphragm action and floor-plan regularity should be considered explicitly in the seismic assessment of low-rise masonry buildings. Full article

Weekday commuting and weekend recreation are two mobility domains through which urban spatial structure affects residents’ daily experience and urban functioning. Although commuting and recreational mobility are theoretically related, whether their spatial relationship creates scope for coordinated improvement in both domains remains insufficiently understood. A preceding study in Chongqing identified a U-shaped relationship between weekday commuting costs and weekend recreational mobility costs at the individual level, but its proposed spatial-structural interpretation remained untested. This study examines whether the same relationship emerges across residential locations and whether the resulting mobility-cost structure corresponds to the spatial configuration of employment and recreational opportunities. Using location-based services data from the central urban area of Chongqing, individual mobility records were aggregated to residential grids and analysed through a three-stage framework examining the grid-level U-shaped relationship, multivariate dependence between the opportunity and cost structures, and their curve-based correspondence. The U-shaped relationship was reproduced at the grid level. The opportunity and cost structures exhibited multivariate dependence, and the curve-based analysis showed a positive correspondence between residential grids’ positions relative to the fitted opportunity and cost reference points. These findings support the interpretation that the U-shaped relationship partly reflects how residential locations are positioned relative to multiple employment centres and spatially distributed recreational destinations. The findings indicate that commuting and recreational mobility should be considered jointly in jobs–housing–recreation planning and suggest potential scope for coordinated improvement across both domains. Full article

Tourism expansion can stimulate economic activity that can potentially increase energy consumption and environmental pressures. The tourism-induced Environmental Kuznets Curve (T-EKC) literature has focused mainly on CO₂ emissions and has greatly conceptualized economic growth on aggregate indicators, such as GDP. In this way, model specifications might not adequately capture the environmental effects directly associated with tourism activity. Addressing this gap, the present study examines the nonlinear relationship between tourism development and industrial-combustion CO₂ and power-industry methane emissions across Eurozone countries during 1996–2019. Unlike conventional T-EKC studies, internal travel and tourism consumption is employed as a sector-specific measure. Furthermore, primary energy consumption, an energy efficiency measure, and renewables are included in the model specifications to capture structural energy-related dynamics. The empirical analysis discloses heterogeneous relationships across pollutants. An inverted U-shaped relationship is found between tourism consumption and industrial-combustion CO₂ emissions. Methane emissions exhibit a U-shaped relationship. Causality analysis indicates a feedback relationship between tourism consumption and industrial-combustion CO₂ emissions. A unidirectional relationship running from methane emissions to tourism consumption is also justified. The study contributes to the literature by demonstrating that tourism-environment relationships depend on both the pollutant examined and the measurement of tourism-related economic activity. The findings highlight the need for differentiated environmental strategies that combine decoupling-oriented measures for industrial-combustion CO₂ emissions with targeted energy-sector and methane-mitigation policies to support more sustainable tourism growth. Full article

Newly Industrialized Countries (NICs) represent the most dynamic economic clusters globally and drive the marginal growth of global carbon emissions, rendering their carbon mitigation performance pivotal to delivering the climate targets pledged under the Paris Agreement. Grounded in the Environmental Kuznets Curve (EKC) hypothesis, this study applies the cross-sectional augmented autoregressive distributed lag (CS-ARDL) framework to the panel data from NICs for the 1990–2022 period and incorporates dynamic autoregressive distributed lag simulation methods to investigate the dynamic relationship between energy transition and CO₂ emissions across these economies. The CS-ARDL estimation results suggest that the conventional inverted U-shaped Environmental Kuznets Curve (EKC) hypothesis is invalid under the conditions of cross-sectional dependence and slope heterogeneity for NICs. Per capita GDP growth is found to exert a significant negative impact on CO₂ emissions in these economies. In both the long and short run, rising per capita non-renewable energy consumption substantially increases CO₂ emissions, whereas a higher share of renewable energy in total final energy consumption yields a significant CO₂ mitigation effect. Estimations conducted via the Augmented Mean Group (AMG), Common Correlation Effect Mean Group (CCEMG), and Dynamic Common Correlation Effect Mean Group (DCCEMG) estimators yield consistent findings with the CS-ARDL model. Dynamic ARDL simulation results reveal that positive shocks to per capita non-renewable energy consumption and renewable energy share in total final energy consumption induce heterogeneous dynamic response trajectories of CO₂ emissions across countries. Nevertheless, rising per capita non-renewable energy consumption triggers an overall sustained growth in CO₂ emissions, while a higher proportion of renewable energy achieves long-term mitigation of CO₂ emissions. Full article

Determining the optimal timing for support installation is of critical importance to the stability of surrounding rock in inclined mine roadways. Existing expressions for the longitudinal deformation profile (LDP) commonly overlook the coupled effects of spatial parameters and geomechanical parameters in inclined roadways. In this study, the LDP expression for a circular inclined roadway under three-dimensional in situ stress conditions is firstly derived analytically. Subsequently, the coupled influence of spatial and geomechanical parameters on the LDP is investigated using FLAC3D numerical simulations. Finally, building upon the work of Unlu and Gercek, a modified LDP formula tailored to circular inclined roadways is proposed. The results indicate the following: (1) The parameters exerting a significant influence on the LDP shape can be ranked in descending order of importance as cohesion, internal friction angle, β, and α. Parameter α affects the LDP curve within the range of X/R = −6 to 6. As α increases from 5° to 35°, the displacement release coefficient at the tunnel face (u₀*) drops sharply from 15.830% to 5.107%. In contrast, β, cohesion, and internal friction angle influence the LDP within the range of X/R = −6 to 9. Specifically, as β varies from 0° to 90°, u₀* decreases from 15.830% to 8.920%; as cohesion increases from 2 MPa to 3.5 MPa, u₀* declines from 18.80% to 14.38%; and as the internal friction angle rises from 20° to 35°, u₀* drops from 18.40% to 12.08%. (2) Poisson’s ratio, the axial lateral pressure coefficient, the horizontal lateral pressure coefficient, and the elastic modulus exhibit relatively minor effects. Poisson’s ratio primarily influences the displacement release coefficient in the vicinity of the excavation face, within the range of X/R = −1 to 3. As Poisson’s ratio increases from 0.2 to 0.35, u₀* decreases from 15.83% to 13.54%. Although its effect at the tunnel face is modest, it should not be disregarded. (3) Validation against existing LDP formulas and field data demonstrates that the proposed modified formula more effectively characterizes the spatiotemporal evolution of displacement release in the surrounding rock of circular inclined roadways, thereby providing a more reliable reference for determining the optimal support installation timing and optimizing the support design of inclined shafts. Full article

Enterprise green innovation driven by the market is a requirement in the new era to implement the new development concept and fulfill the “dual carbon” goals, integrate them into the new development pattern of “dual circulation”, and boost sustainable economic development. Using the moderated effect regression model and selecting data on manufacturing companies listed on the Shenzhen Stock Exchange from 2014 to 2023, this paper empirically examines the impact of the most important dimension of market orientation—customer orientation—on enterprise green innovation and its internal mechanism. Research findings: Firstly, customer orientation affects corporate green innovation via demand identification, knowledge acquisition, and optimal resource allocation. Reactive customer orientation boosts incremental green innovation through economies of scale, while a proactive one drives radical green innovation via the substitution effect. The alternating effect of the two makes the impact of customer orientation on green innovation exhibit an inverted U-shaped-curve relationship, first rising and then falling. Secondly, corporate operational capacity influences the realization of customer orientation’s effect on green innovation through the following four pathways: learning capability, financial capability, managerial capability, and decision-making capability. Enterprises with a strong operational capacity exhibit a flatter inverted U-shaped curve with a right-shifted inflection point, whereas those with a weaker operational capacity demonstrate a steeper curve with a left-shifted inflection point. Thirdly, heterogeneity analysis by enterprise type reveals that customer orientation does not exert a statistically significant direct impact on green innovation in state-owned enterprises. Conversely, an inverted U-shaped relationship exists between customer orientation and green innovation in private enterprises, with operational capacity serving as a moderating effect. Finally, regional heterogeneity analysis indicates that the moderating effect of operational capacity results in a rightward shift of the inflection point in the inverted U-shaped curve for enterprises in eastern regions, while causing a leftward shift for those in central and western regions. Therefore, when enterprises choose green innovation strategies, they should clearly identify the customer-oriented type, pay attention to the trend in demands, optimize supporting measures, enhance operational capabilities, and build an innovative enterprise. Full article

Traditional crack monitoring methods, relying on discrete point sensors, cannot capture the full spatiotemporal evolution of cracks. To address this limitation, this paper presents a distributed online monitoring approach using ultra-weak Fiber Bragg Grating (UWFBG) array sensing technology. A 16 m full-scale π-beam was instrumented with a grating array strain sensing system and tested under progressive mid-span loading until failure. The array successfully detected crack initiation at 848.7 kN (0.9P1) and tracked the transformation from L-shaped to U-shaped cracks, yielding a final crack count of 90 with a maximum width of 1.21 mm and length of 246.5 cm at 1791.7 kN. The strain–load curves exhibited a clear linear-to-nonlinear transition and continuous slope increase, closely matching manual observations. Quantitative correlation analysis further established a strong linear relationship between UWFBG peak strains and manually measured crack widths, with the fitting equation $\mathsf{\epsilon }=7918·\mathrm{w}-110$ and a coefficient of determination R² = 0.971, providing a specimen-specific basis for strain-based crack severity estimation that requires in-situ calibration before field application. The UWFBG array maintained stable signal acquisition throughout the entire loading process, offering superior data continuity and measurement range compared to resistive strain gauges, which suffered progressive data loss after cracking. The results demonstrate that the proposed method can provide real-time, full-field strain mapping and quantitative crack evolution monitoring, offering a powerful tool for bridge health assessment. Full article