Search Results (4,071)

This study investigates how energy consumption and foreign direct investment (FDI) influenced economic growth in BRICS+ countries from 1990 to 2021, using a two-step System GMM estimator to address endogeneity and dynamic effects. While the results show that both energy and FDI positively affected growth, disaggregated analysis revealed that renewable energy promoted growth, whereas non-renewables hindered it. Similarly, FDI directed toward gross fixed capital formation (FDI_GFCF) consistently boosted growth, unlike aggregate FDI. Financial development moderated these effects, amplifying the benefits of energy use but dampening FDI’s growth impact in more developed financial systems. The effects of energy and FDI remained stable before and after the Paris Agreement, supporting the robustness of the findings. These results underscore the importance of tailored energy and FDI strategies, financial sector reforms, and supportive policy environments to advance sustainable growth in BRICS+ economies. Full article

The stochastic model in Precise Point Positioning (PPP) defines the statistical properties of observations and the dynamic behavior of parameters. An inaccurate stochastic model can degrade positioning accuracy, ambiguity resolution, and other aspects of performance. However, due to the influence of multiple factors, the stochastic model in PPP cannot be precisely predetermined, necessitating the development of an Adaptive Stochastic Model (ASM) based on Variance Component Estimation (VCE). While the benefits of ASMs for PPP float solutions are well documented, their contributions to other performance aspects remain insufficiently explored. This paper presents a comprehensive assessment of an ASM’s impact on PPP. First, the implementation of an ASM using VCE is described in detail. Then, experimental results demonstrate that the ASM effectively captures observational conditions through the estimated variance component factors. It enhances both PPP float and fixed solutions when the predefined stochastic model is inadequate, improves cycle-slip detection by tightening the stochastic model (reducing the missed detection rate from 19% to 8%), and accelerates both direct reconvergence and re-initialization after data gaps, with reconvergence times improved by 18% and 55%, respectively. Full article

AI-integrated robotics in Industry 5.0 demands advanced manufacturing systems capable of autonomously interpreting complex geometries and dynamically adjusting machining strategies in real time—particularly when dealing with aerospace components featuring large-curvature surfaces. Large-curvature aerospace components present significant challenges for precision drilling due to surface-normal deviations caused by curvature, roughness, and thin-wall deformation. This study presents a robotic drilling system that integrates adaptive PCA-based surface normal estimation with in-process pre-drilling correction and post-drilling verification. This system integrates a 660 nm wavelength linear laser projector and a 1.3-megapixel industrial camera arranged at a fixed 30° angle, which project and capture structured-light fringes. Based on triangulation, high-resolution point clouds are reconstructed for precise surface analysis. By adaptively selecting localized point-cloud regions during machining, the proposed algorithm converts raw measurements into precise normal vectors, thereby achieving an accurate solution of the normal direction of the surface of large curvature parts. Experimental validation on a 400 mm-diameter cylinder shows that using point clouds within a 100 mm radius yields deviations within an acceptable range of theoretical normals, demonstrating both high precision and reliability. Moreover, experiments on cylindrical aerospace-grade specimens demonstrate normal direction accuracy ≤ 0.2° and hole position error ≤ 0.25 mm, maintained across varying curvature radii and roughness levels. The research will make up for the shortcomings of existing manual drilling methods, improve the accuracy of hole-making positions, and meet the high fatigue service needs of aerospace and other industries. This system is significant in promoting the development of industrial automation and improving the productivity of enterprises by improving drilling precision and repeatability, enabling reliable assembly of high-curvature aerospace structures within stringent tolerance requirements. Full article

In complicated scenarios, due to the low precision of float solutions and poor reliability of fixed solutions, it is challenging to achieve a balance between accuracy and reliability of the integer least squares (ILS) estimation. To address this dilemma, the best integer equivariant (BIE) estimation, which makes a weighted sum of all possible candidates, has recently been attached great importance. The BIE solution approaches the float solution at a low ILS success rate, maintaining positioning reliability. As the success rate increases, it converges to the fixed solution, facilitating high-precision positioning. Furthermore, the posterior variance of BIE estimation provides the capability of reliability evaluation. However, in environments with a limited number or a deficient configuration of available satellites, there is a sharp decline in the strength of the GNSS precise positioning model. In this case, the exactness of weight allocation for integer candidates in BIE estimation will be severely compromised by unmodeled errors. When the ambiguity is incorrectly fixed, the wrongly determined optimal candidate is probably assigned an excessively high weight. Therefore, the BIE solution in a weak GNSS model always exhibits a significant positioning error consistent with the fixed solution. Moreover, the posterior variance of BIE estimation approximately resembles that of a fixed solution, losing error warning ability. Consequently, the BIE estimation may exhibit lower reliability compared to the ILS estimation employing a validation test with a loose acceptance threshold. To improve the positioning performance in weak GNSS models, a BIE ambiguity resolution (AR) method based on Tikhonov regularization is proposed in this paper. The method introduces Tikhonov regularization into the least squares (LS) estimation and the ILS ambiguity search, mitigating the serious impact of unmodeled errors on the BIE estimation under weak observation conditions. Meanwhile, the regularization factors are appropriately selected by utilizing an optimized approach established on the L-curve method. Simulation experiments and field tests have demonstrated that the method can significantly enhance the positioning accuracy and reliability in weak GNSS models. Compared to the traditional BIE estimation, the proposed method achieved accuracy improvements of 73.6% and 69.3% in the field tests with 10 km and 18 km baselines, respectively. Full article

In this study, a procedure based on Phase-locked Proper Orthogonal Decomposition (PPOD) was applied to Large Eddy Simulations (LESs) of two low-pressure turbine blades operating with unsteady inflow. This decomposition allows the inspection of the effect of blade loading on loss generation mechanisms, focusing especially on their variation throughout the incoming wake period. After sorting snapshots based on their phase within the wake cycle using temporal POD coefficients associated with wake migration, POD was reapplied to each sub-ensemble of snapshots at a given phase, providing an optimal representation of the dynamics at fixed wake locations. This highlighted the effects of the migration, bowing, tilting, and reorientation of the incoming wake filaments, as well as the breakup of streaky structures in the blade boundary layer and the formation of Von Karman vortices at the blade trailing edge. PPOD offered us the opportunity to observe how all these processes are modulated and change throughout the wake period. The comparison between the two analyzed blades showed that overall loss generation follows similar temporal patterns during the wake-passing cycle, increasing with the propagation of the upstream wake and reaching its maximum value when the wake is in the peak suction position. According to the specific blade loading distribution, the production of TKE was observed in different regions of the computational domain. The described procedure may contribute to the development of advanced design processes based on physically informed strategies. Full article

As urbanization progresses rapidly, cities face growing challenges of land resource scarcity and the pressure on green ecological spaces. This not only affects the sustainable development of cities but also presents a major challenge to the resilience of urban ecosystems (UER). As an emerging land use model, land financialization (LF), which involves the circulation and financing of land as a financial asset, has become an important means to promote UER. Therefore, this paper examines 221 prefecture-level cities across mainland China to explore the impact of land financialization on urban ecological resilience and aims to reveal the specific pathways through which land financialization improves urban ecological resilience through mechanisms like resource reallocation, industrial structure rationalization, green innovation, green signals, and environmental regulation. This paper employs a two-way fixed effects model, robustness tests, and endogeneity tests, supplemented by mechanism and heterogeneity analysis, to explore the impact of LF on UER. The findings show that LF plays a significant role in improving UER. Mechanism analysis reveals that LF significantly boosts UER by optimizing the distribution of land and financial resources, as well as enhancing the rationalization of the industrial structure. Additionally, enterprise green technology innovation, green value, and the intensity of environmental regulation play a positive moderating role in this process. In addition, the heterogeneity analysis reveals the inclusive characteristics of LF on urban ecological transformation. In cities with higher levels of land price distortion, as well as in old industrial cities, small cities, and peripheral cities with poorer resource characteristics and administrative resources, LF has a more significant impact on promoting the improvement of UER. Based on the findings, this paper proposes policy recommendations to promote the improvement of urban green ecology and support the innovation of land financialization. These insights contribute to the theoretical discourse on greenization and provide essential, practical guidance for optimizing the allocation of land and financial resources, as well as establishing a framework for green and high-quality development. Full article

This study investigates how new ventures on global e-commerce platforms use textual marketing signals to attract foreign consumers, a critical challenge characterized by information asymmetry. Integrating Signaling Theory and Optimal Distinctiveness Theory (ODT), we examine how two key creator-controlled textual signals—International Orientation Expression (IOE) intensity (a signal of legitimacy) and Project Genre Atypicality (GA) (a signal of differentiation)—non-linearly and interactively influence foreign customer engagement. Analyzing a large-scale dataset of 17,084 Kickstarter projects using computer-aided text analysis and fixed-effects regression models, we yield several key insights. First, we find a robust inverted U-shaped relationship between IOE intensity and foreign backer engagement, suggesting that while moderate international emphasis enhances legitimacy, excessive claims can undermine credibility. Second, GA exhibits a positive linear relationship with foreign engagement, indicating that novelty-seeking foreign consumers consistently value textual differentiation. Third, and most critically, we uncover a significant negative interaction, termed the “cost of dual extremes”, where simultaneously signaling extreme international ambition and extreme product novelty deters foreign consumers, likely due to perceived strategic incoherence and heightened execution risk. Finally, we confirm that attracting a diverse foreign audience is a strong predictor of overall project funding success. This research extends ODT by identifying a novel interactive boundary condition for distinctiveness in digital markets and advances signaling theory by demonstrating the complex, non-linear effectiveness of textual signals, offering actionable insights for optimizing communication strategy in global e-commerce. Full article

Against the dual backdrop of iterative AI advancement and deepening green development imperatives, AI-driven industrial intelligence (INT) has emerged as a pivotal force in driving sustainable economic growth. While the existing literature has explored the correlation between INT and green total factor productivity (GTFP), significant gaps remain in the design of multidimensional variables, analysis of environmental regulation (ER), and capture of dynamic effects. From the perspective of ER, this study utilizes provincial panel data from China (2012–2023) to construct an 11-indicator evaluation system for INT development and employs the EBM super-efficiency model to measure GTFP. Furthermore, a two-way fixed effects model combined with a moderated mediation model is established to systematically elucidate the intrinsic linkage mechanism between INT and GTFP. The key findings are as follows: First, INT has a significant positive impact on GTFP. Second, green innovation and spatio-economic synergy are crucial pathways through which INT empowers GTFP. Third, ER exhibits a substitutive effect within both the direct and indirect impacts of INT on GTFP, where intensified ER significantly attenuates INT’s positive impacts. Fourth, the enhancement effect of INT on GTFP remains statistically significant with a one-year lag, and the substitution effect of ER persists. This study provides an in-depth analysis of the mechanisms of INT-driven green economic transformation, offering valuable insights for governments to implement differentiated environmental governance strategies tailored to local conditions. Full article

The circular economy (CE) aims to reduce environmental degradation by ensuring the continuous use of materials and energy resources, aligning with the decarbonization agenda. However, despite the rising acceptance of CE concepts, the economic and managerial aspects remain underexplored in policy and practice. Therefore, this study seeks to bridge the knowledge–practice gap by studying how technology-driven innovation, renewable energy, and energy efficiency interact with CE principles to advance sustainable environmental connections in a detailed manner. The economic analysis of this study was conducted using two base and moderation models, utilizing global data from 78 developing and developed countries, and applying Fixed Effect, Random Effect, and Generalized Method of Moments estimates. The samples were selected based on data availability from internationally recognized databases from 2010 to 2021. The key findings suggest that technology-driven innovation and renewable energy reduce carbon emissions, whereas gross domestic product (GDP) growth and energy efficiency show no standalone positive effects. Notably, moderation effects reveal that the integration of technology with GDP promotes sustainability outcomes, but energy efficiency and renewable energy interact negatively with emissions, a contradictory result warranting further policy investigation. CE-driven innovation promotes decarbonization by striking a balance between environmental preservation, economic expansion, and technology uptake. This study emphasizes region-specific techniques and offers policy insights for combining the CE with natural capital and green GDP. It increases the knowledge of how circular business models powered by technology support sustainable growth and the shift to a circular economy. Full article

Urbanization affects bird communities by reducing habitat and fragmenting ecosystems. Urban parks can help counteract these effects. However, anthropogenic noise can further alter bird composition. We examined the distribution and abundance of bird species in nine urban parks in Mexico City. We used a ten minute fixed-radius (25 m) point-counting technique to count birds along their annual cycle, with ten minutes allocated for bird counts. The quality of green areas was analyzed in terms of vegetation (Normalized Difference Vegetation Index), park size, and mean noise level dB(A) (based on MIN and MAX values), and species were grouped into trophic guilds. A total of 108 bird species were recorded, 5 of which are under special protection; we grouped all species into 14 trophic guilds, showing different responses to environmental gradients. Redundancy analysis (RDA) explained 89.98% of the variance, with noise and park size being the most influential variables. Granivores and omnivores were more tolerant to noise, while insectivores and frugivores preferred quieter areas with more vegetation. A positive association was observed between the presence of winter resident species and the park size. On the other hand, mean noise level dB(A) was negatively related to permanent resident species, winter resident species, and those with protected status. Conservation efforts should focus on maintaining ample green spaces and reducing noise pollution, as recorded high mean noise levels (>53 dB(A)) exceed the recommended thresholds for avifauna conservation. Full article

The increased importance of sustainability imperatives has required a profound reconsideration of the interaction between materials, manufacturing, and design fields. Biomimetic smart materials such as shape-memory polymers, hydrogels, and electro-active composites represent an opportunity to combine adaptability, responsiveness, and ecological intelligence in systems and products. This work reviews the confluence of such materials with leading-edge manufacturing technologies, notably additive and 4D printing, and how their combining opens the door to the realization of time-responsive, low-waste, and user-adaptive design solutions. Through computational modeling and mathematical simulations, the adaptive performance of these materials can be predicted and optimized, supporting functional integration with high precision. On the basis of case studies in regenerative medicine, architecture, wearables, and sustainable product design, this work formulates the possibility of biomimetic strategies in shifting design paradigms away from static towards dynamic, from fixed products to evolvable systems. Major material categories of stimuli-responsive materials are systematically reviewed, existing 4D printing workflows are outlined, and the way temporal design principles are revolutionizing production, interaction, and lifecycle management is discussed. Quantitative advances such as actuation efficiencies exceeding 85%, printing resolution improvements of up to 50 μm, and lifecycle material savings of over 30% are presented where available, to underscore measurable impact. Challenges such as material scalability, process integration, and design education shortages are critically debated. Ethical and cultural implications such as material autonomy, transparency, and cross-cultural design paradigms are also addressed. By identifying existing limitations and proposing a future-proof framework, this work positions itself within the ongoing discussion on regenerative, interdisciplinary design. Ultimately, it contributes to the advancement of sustainable innovation by equipping researchers and practitioners with a set of adaptable tools grounded in biomimicry, computational intelligence, and temporal design thinking. Full article

3D object detection is a crucial technology in fields such as autonomous driving and robotics. As a direct representation of the 3D world, point cloud data plays a vital role in feature extraction and geometric representation. However, in real-world applications, point cloud data often suffers from occlusion, resulting in incomplete observations and degraded detection performance. Existing methods, such as PG-RCNN, generate semantic surface points within each Region of Interest (RoI) using a single grid size. However, a fixed grid scale cannot adequately capture multi-scale features. A grid that is too small may miss fine structures—especially problematic when dealing with small or sparse objects—while a grid that is too large may introduce excessive background noise, reducing the precision of feature representation. To address this issue, we propose an enhanced PG-RCNN architecture with a Multi-Scale Grid Attention Module as the core contribution. This module improves the expressiveness of point features by aggregating multi-scale information and dynamically weighting features from different grid resolutions. Using a simple linear transformation, we generate attention weights to guide the model to focus on regions that contribute more to object recognition, while effectively filtering out redundant noise. We evaluate our method on the KITTI 3D object detection validation set. Experimental results show that, compared to the original PG-RCNN, our approach improves performance on the Cyclist category by 2.66% and 2.54% in the Moderate and Hard settings, respectively. Additionally, our approach shows more stable performance on small object detection tasks, with an average improvement of 2.57%, validating the positive impact of the Multi-Scale Grid Attention Module on fine-grained geometric modeling, and highlighting the efficiency and generalizability of our model. Full article

Unmanned aerial vehicles (UAVs) are increasingly favored for emergency search and rescue operations due to their high adaptability to harsh environments and low operational costs. However, the dynamic nature of search path endpoints, influenced by target movement, limits the applicability of shortest path models between fixed points in moving target search problems. Consequently, the moving target search problem using UAVs in complex environments presents considerable challenges, constituting an NP-hard problem. The Grey Wolf Optimizer (GWO) is known for addressing such problems. However, it suffers from limitations, including premature convergence and instability. To resolve these constraints, a Grey Wolf Optimizer with variable population and strategy (GWO-VPS) is developed in this work. GWO-VPS implements a variable encoding scheme for UAV movement patterns, combining motion-based encoding with path-based encoding. The algorithm iteratively alternates between global optimization and local smoothing phases. The global optimization phase incorporates: (1) a Q-learning-based strategy selection; (2) position updates with obstacle avoidance and energy consumption reduction; and (3) adaptive exploration factor. The local smoothing phase employs four path smoothing operators and Q-learning-based strategy selection. Experimental results demonstrate that GWO-VPS outperforms both enhanced GWO variants and standard algorithms, confirming the algorithm’s effectiveness in UAV-based moving target search simulations. Full article

Background: The diagnostic work-up of small peripheral pulmonary nodules (PPNs) is becoming increasingly important, especially in light of the upcoming lung cancer screening programs and recommendations in practice. The systematic clinical introduction of the ION robotic-assisted navigational bronchoscopy (RNB) system represents a significant innovation in Germany, whereas clinical experience in the United States has already yielded promising results. The objective of this study is to present the outcomes of the first 50 patients examined with the ION system at our institutions. Materials and Methods: This is a retrospective, single-center analysis. We included the first 50 consecutive patients who underwent diagnostic evaluation of pulmonary nodules using the ION-RNB system, either in the Department of Thoracic Surgery or the Department of Pulmonology. Results: A total of 50 patients were evaluated, including 24 from the Department of Thoracic Surgery and 26 from the Department of Pulmonology. The pulmonary nodules were found in the peripheral third of the lung in 74% of cases, in the middle third in 18% of cases, and in the central third in 8% of cases. The mean lesion size was 1.64 cm (±0.91 cm). In all, 84% of the nodules were solid, 4% were subsolid, and 12% presented as ground-glass opacities (GGOs). Cone beam computed tomography (CBCT) was used to confirm tool-in-lesion position in 68% of cases compared to C-arm fluoroscopy in 32%. Additionally, radial endobronchial ultrasound (rEBUS) was applied in 30% of procedures. The overall diagnostic yield, independent of imaging modality or histological processing method, was 78%. When CBCT and formalin-fixed paraffin-embedded (FFPE) histological analysis were utilized, the diagnostic yield exceeded 90%. Conclusions: Initial clinical experience with the ION-RNB system in Germany shows encouraging results. The high diagnostic accuracy underlines the system’s potential for evaluating peripheral pulmonary lesions precisely. The use of advanced imaging techniques, particularly CBCT, and the choice of histopathological processing methods are critical variables in optimizing patient-centered diagnostic pathways. Further prospective studies are warranted to assess the long-term clinical utility of robotic-assisted bronchoscopy in diverse clinical settings. Full article

This study investigates the effect of the educational level of top management team (TMT) family members on firm innovation among publicly listed family firms in China. Using a panel of 14,338 firm-year observations from 2015 to 2023, this study employs fixed effects regressions to show that the educational background of family members positively influences firm innovation, measured by the proportion of R&D personnel and capitalized R&D expenditures. Moreover, this positive effect is more pronounced under greater industry competition, higher transparency, and smaller firms. The mediation analysis identifies potential channels of asset tangibility, ownership concentration, and management fees through which family education influences firm innovation. Sectoral heterogeneity reveals a more pronounced effect within the manufacturing and service sectors, while no statistically significant relationship emerges in the agriculture sector. Concerns over endogeneity are mitigated using lagged family education, two-stage least squares regressions, and panel vector autoregressions. The baseline result remains robust when firm innovation is alternatively measured by the number of patents. These findings contribute to the literature on innovation in family firms and offer implications for investors, corporate decision-makers, and policymakers in emerging markets. Full article