Search Results (29,341)

In the context of China’s “dual carbon” strategy, building materials enterprises (BMEs) are in a critical period of digital and green transformation. Their diverse ownership structure and complex industrial types make them important objects of research. To address gaps in the existing literature, particularly regarding executive cognitive structure segmentation, ecological scenario (ES) influence mechanisms, and enterprise heterogeneity, this study uses Chinese BMEs as samples and incorporates industry characteristics, such as strong policy-driven conditions, a complete industrial chain, and diverse ownership types, to explore the relationship between executive cognition, ability reconstruction, and digital green innovation (DGI) performance (DGIP). Executive cognition is conceptualized through two dimensions: environmental protection cognition and digital intelligence cognition (DIC). A comprehensive test is conducted using fuzzy set qualitative comparative analysis (fsQCA). The results show that (1) both executive cognition and capability reconstruction (CR) significantly promote DGIP, and executive cognition has a positive effect on CR; (2) competency reconfiguration plays a mediating role in the influence of executives’ cognition on innovation performance, with the ES having a positive moderating effect on the relationship between the two types of cognitive role competency reconfiguration; (3) the influence of executive cognition varies depending on the nature of the enterprise and the industry; and (4) three types of performance improvement paths emerge: environmental-cognition-driven, cognitive ability connection, and ES-guided paths. The research’s contributions include (1) dividing executive cognition into two dimensions to enrich its conceptualization; (2) introducing the ES to reveal the dynamic mechanisms of cognition–ability–performance; and (3) conducting a heterogeneity analysis based on the nature of enterprises to deepen insights into paths of differentiated influence. This study provides a theoretical basis and practical inspiration for BMEs to enhance their DGIP. Full article

Extensive research on path planning and automated navigation has been carried out for weeding robots in fields such as corn, soybean, wheat, and sugar beet, but until now, no literature reports relative studies in turfs that are not cultivated using row-crop methods. This paper proposes a practical solution that comprises path planning and path tracking to minimize the weeding robot’s travel distance in turfs for the first time. An inter-sub-region scheduling algorithm is developed using the Traveling Salesman Problem (TSP) model, followed by a boundary-shifting-based coverage path planning algorithm to achieve full coverage within each weed subregion. For path tracking, a Real-Time Kinematic Global Positioning System (RTK-GPS) fusion positioning method is developed and combined with a dynamic pure pursuit algorithm featuring a variable preview distance to enable precise path following. After path planning based on real-world site data, the weeding robot traverses all weed subregions via the shortest possible path. Field experiments showed that the robot traveled along the shortest path at speeds of 0.6, 0.8, and 1.0 m/s; the root mean square errors of autonomous navigation deviation were 0.35, 0.81, and 1.41 cm, respectively. The proposed autonomous navigation solution significantly reduces the robot’s travel distance while maintaining acceptable tracking accuracy. Full article

This study was motivated by the persistent difficulty of accurately identifying morphologically similar macrofungi species, which remains a significant challenge in fungal taxonomy and biodiversity monitoring. This study presents a deep learning framework for the automated classification of seven morphologically similar coprinoid macrofungi species. A curated dataset of 1692 high-resolution images was used to evaluate ten state-of-the-art convolutional neural networks (CNNs) and three novel fusion models. The Dual Path Network (DPN) achieved the highest performance as a single model with 89.35% accuracy, a 0.8764 Matthews Correlation Coefficient (MCC), and a 0.9886 Area Under the Curve (AUC). The feature-level fusion of Xception and DPN yielded competitive results, reaching 88.89% accuracy and 0.8803 MCC, demonstrating the synergistic potential of combining architectures. In contrast, lighter models like LCNet and MixNet showed lower performance, achieving only 72.05% accuracy. Explainable AI (XAI) techniques, including Grad-CAM and Integrated Gradients, confirmed that high-performing models focused accurately on discriminative morphological structures such as caps and gills. The results underscore the efficacy of deep learning, particularly deeper architectures and strategic fusion models, in overcoming the challenges of fine-grained visual classification in mycology. This work provides a robust, interpretable computational tool for automated fungal identification, with significant implications for biodiversity research and taxonomic studies. Full article

Background/Objectives: Innovation capacity has become a strategic pillar for strengthening healthcare systems in the European Union, yet its effects vary considerably across countries with different levels of institutional development and technological readiness. This study examines how national innovation capacity, measured through the Global Innovation Index, influences health expenditure, healthy life expectancy, and childhood obesity across the EU-27. Methods: Using an unbalanced panel dataset for 2011–2024, we applied panel quantile regression to capture heterogeneous effects across the conditional distribution of health outcomes. Four dependent variables were analyzed: government expenditure on health, provider-level healthcare spending, healthy life expectancy at birth, and childhood obesity prevalence. GDP growth and population were included as controls. Diagnostic tests confirmed cross-sectional dependence and heteroskedasticity, supporting the choice of distributionally robust estimators. Results: Higher innovation capacity was positively and significantly associated with government health expenditure and provider-level spending across all quantiles (p < 0.001), with the strongest effects in lower-performing systems. For healthy life expectancy, innovation exhibited declining coefficients across quantiles, indicating diminishing marginal returns in more advanced systems. No stable association was observed for childhood obesity, which remained largely unaffected by national innovation capacity. Conclusions: Innovation contributes to structural improvements in health financing and population health, particularly in countries with lower baseline performance. In high-performing systems, its role shifts toward incremental efficiency gains. The absence of effects on childhood obesity highlights the dominance of socio-behavioral determinants. Findings are associative and call for future causal and sector-specific research. Full article

More than half of the world’s highly regulated rivers are currently experiencing an unsustainable balance between ecological protection and economic development. The value realization of river eco-products is considered a key pathway to addressing this challenge; however, its effectiveness remains to be empirically verified. Therefore, the objective of this study is to develop an integrated framework for evaluating the sustainability of river ecological protection and economic development through eco-product value realization. The framework integrates the classification of river eco-products, the estimation of their potential and realized values, and the analysis of value realization pathways. Taking the Baoji section of the Weihe River (BSWHR) as a case study, the framework is applied with hydrological, hydraulic, and socio-economic datasets to empirically evaluate the coordination between ecological protection and economic development. The main results showed that: (1) River eco-products are divided into three types: public, operational, and physical operational eco-products; (2) The potential ecological value of all river eco-products in the BSWHR is estimated at 549 million CNY; (3) The realized value of all river eco-products is 288.75 million CNY under current realization paths, corresponding to a sustainability index of 0.63, indicating that the BSWHR is less sustainable and represents an asset liability river; and (4) Enhancing the protection level of river ecological flow (e-flow) and establishing a multi-stakeholder compensation mechanism can improve the sustainability of ecological protection and economic development in highly regulated rivers. The proposed framework provides a practical basis for assessing river sustainability and guiding the effective allocation of ecological protection funds. Full article

Whether an unmanned aerial vehicle (UAV) can complete its mission successfully is determined by trajectory planning. Reasonable and efficient UAV trajectory planning in 3D environments is a complex global optimization problem, in which numerous constraints need to be considered carefully, including mountainous terrain, obstacles, no-fly zones, safety altitude, smoothness, flight distance, and so on. Generally speaking, symmetry characteristics from the starting point to the endpoint can be concluded from the potential spatial multiple trajectories. Aiming at the deficiencies of the Sparrow Search Algorithm (SSA) in 3D symmetric trajectory planning such as population diversity and local optimization, the sine–cosine function and the Lévy flight strategy are combined, and the Improved Sparrow Search Algorithm (ISSA) is proposed, which can find a better solution in a shorter time by dynamically adjusting the search step size and increasing the occasional large step jumps so as to increase the symmetry balance of the global search and the local development. In order to verify the effectiveness of the improved algorithm, ISSA is simulated and compared with the Sparrow Search Algorithm (SSA), Particle Swarm Algorithm (PSO), Gray Wolf Algorithm (GWO) and Whale Optimization Algorithm (WOA) in the same environment. The results show that the ISSA algorithm outperforms the comparison algorithms in key indexes such as convergence speed, path cost, obstacle avoidance safety, and path smoothness, and can meet the requirement of obtaining a higher-quality flight path in a shorter number of iterations. Full article

To address the efficiency and safety challenges arising from the mixed operation of autonomous and human-driven container trucks during the automation transformation of traditional container terminals, this study designed a simulation-based optimization framework for mixed vehicle scheduling. A spatio-temporal graph dynamic scheduling model was constructed, incorporating node capacity, arc capacity, and path constraints, to establish a multi-objective optimization model aimed at minimizing the maximum completion time of internal trucks and the average waiting time of external trucks. An improved NSGA-II algorithm was employed to generate task assignment solutions, which were evaluated using discrete-event simulation, integrating a dynamic programming-based yard block selection strategy for external trucks and a congestion-aware path planning algorithm. Experimental results demonstrate that the dynamic priority strategy effectively adapts to different traffic flow scenarios: under low external truck flow, the autonomous internal truck priority strategy reduces task completion time by 18% to 25%, while under high flow, the external truck priority strategy significantly decreases the average waiting time. The optimal configuration ratio between internal and external trucks was identified as approximately 1:2. This research provides a theoretical basis and decision support for enhancing terminal operational efficiency and automation transformation. Full article

Vicarious Posttraumatic Growth (VPTG) is a critical yet underexplored phenomenon among trauma-focused helping professionals. While secondary trauma (ST), compassion fatigue, and burnout are widely recognized negative aspects of working with trauma survivors, less is known about the potential benefits of this work and its contributions to well-being. This qualitative study explored peer-support specialists’ perceptions of growth arising from indirect exposure to trauma and examined how these experiences relate to well-being. Using interpretative phenomenological analysis, researchers conducted semi-structured interviews with 13 participants, independently coded transcripts, and developed themes through consensus. Findings suggest VPTG may follow a similar path to Posttraumatic Growth (PTG), with participants reporting challenges to core beliefs, emotional distress, and transformative cognitive-emotional shifts that facilitated growth across domains that appear to mirror the five PTG domains. Outcomes of working with trauma survivors extended beyond the PTG domains to include compassion satisfaction, hope, expanded coping skills, and improved mental health. Taken together, these findings illustrate the participants’ subjective experiences of both challenge and transformation through their work with trauma survivors, offering preliminary insight into how indirect trauma exposure may contribute to VPTG and well-being. Full article

The development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling experimental validation while reducing costs and risks. This work presents a 1:5 scale modular vehicle platform, derived from a commercial Radio-Controlled (RC) vehicle and adapted as experimental testbed for control strategy validation and vehicle dynamics studies. The vehicle features an electric powertrain, operated through a Speedgoat Baseline Real-Time Target Machine (SBRTM). The hardware architecture includes a high-performance Inertial Measurement Unit (IMU) with embedded Global Navigation Satellite System (GNSS). An Extended Kalman Filter (EKF) is implemented to enhance positioning accuracy by fusing inertial and GNSS data, providing reliable estimates of the vehicle position, velocity, and orientation. Two path-following algorithms, i.e., Stanley Controller (SC) and the Linear Quadratic Regulator (LQR), are designed and integrated. Outdoor experimental tests enable the evaluation of tracking accuracy and robustness. The results demonstrate that the proposed scaled testbed constitutes a reliable and flexible platform for benchmarking autonomous vehicle controllers and enabling experimental testing. Full article

Elevated stations are essential auxiliary structures within the high-speed rail (HSR) network. The newly constructed integrated elevated station for bridge building possesses a distinctive construction and intricate force transmission pathways, complicating the assessment of the dynamic coupling of train vibrations. Consequently, it is essential to examine the dynamic reaction of trains at such stations. This study utilises numerical simulation and field measurement techniques to examine the dynamic features of the newly constructed integrated elevated station for bridge building. Initially, vibration tests were performed on existing integrated elevated stations for bridge construction to assess their dynamic properties. The collected data were utilised to validate the modelling approach and parameter selection for the numerical model of existing stations, yielding a numerical solution method appropriate for bridge-station integrated stations. Secondly, utilising this technology, a numerical model of the newly integrated elevated station for bridge construction was developed to examine its dynamic features. Moreover, the impact of spatial configuration, train velocity, and operational organisation on the dynamic characteristics was analysed in greater depth. The vibration response level in the waiting hall was assessed. Research results indicate that structural joints alter the transmission path of train vibration energy, thereby significantly affecting the vibration characteristics of the station. The vibration response under double-track operation is notably greater than that under single-track operation. When two trains pass simultaneously at a speed of 200 km/h or higher, or a single train passes at 350 km/h, the maximum Z-vibration level of the waiting hall floor exceeds 75 dB, which goes beyond the specification limit. Full article

In controlled environment agriculture (CEA), CO₂ enrichment can promote photosynthesis while simultaneously reducing evapotranspiration, but the optimal settings vary depending on crop type, growth stage, and microclimate. This study presents a near-field remote sensing framework that fuses RGB image features with environmental variables to predict the CO₂ uptake/respiration dynamics of five leafy vegetables grown in a hydroponic culture system and evaluate their impact on resource efficiency under CO₂ control. A hybrid deep model incorporating You Only Look Once version 11 (YOLOv11) and a Residual Network with 50 layers (ResNet50) extracts growth-related visual cues and integrates them with tabular features (CO₂, temperature, and light conditions) to predict chamber CO₂ dynamics. Performance was evaluated by Mean Absolute Error (MAE)/Mean Squared Error (MSE) on withheld data, and the system-level impacts on water use (ET), pumping energy, and relative yield were analyzed using a conventional greenhouse model. The model exhibited high accuracy (MAE = 0.95; MSE = 1.62). Scenario analysis results showed that increasing ambient CO₂ concentration from 400 to 1200 ppm reduced modeled water demand by approximately 11%, increased modeled yield by approximately 9%, and resulted in a corresponding reduction in pumping energy per unit area. Unlike conventional single-crop, table-based approaches, this study demonstrates multi-crop generalization and image-environment fusion for CO₂ dynamic prediction, establishing proximity sensing as a viable decision-making layer for CEA. While yield/ET results were simulated rather than measured in long-term trials, and leaf area normalization was not available, the proposed framework provides a viable path for data-driven CO₂ control in indoor farms by linking image-based monitoring with operational optimization. Full article

The energy transition is a common challenge faced by all nations. Cities, as the primary hubs of energy consumption, serve as the “front line” in this energy transition. Existing literature acknowledges the importance of governments in energy transitions, but has left little room for systematic, semi-quantitative discussion. Using Qingdao, China, as a case, this study integrates social network analysis with text analysis to explore the multiple roles, influence, and temporal evolution of local governments in urban energy transition, aiming to illuminate the governance logic embedded in the Chinese context. Multi-dimensional measurements of Qingdao’s energy-transition cooperation network from 2010 to 2020 reveal that local governments simultaneously act as leaders, drivers, implementers, duty-bearers, path shapers, conveners and catalysts. These roles display clear stage-specific dynamics, yet the strengthening of governmental leadership over time emerges as the most consistent pattern, offering empirical support for the strong state-led characteristic of China’s energy-governance model. This study concludes with stage-based policy recommendations regarding the positioning and actions of local governments across the transition process. Overall, this study provides a new analytical framework for understanding the mechanisms of urban energy transition and contributes empirical evidence for interpreting the logic of energy governance in China. Full article

The Feynman path integral plays a central role in quantum mechanics, linking classical action to propagators and relating quantum electrodynamics (QED) to Feynman diagrams. However, the path-integral formulations used in non-relativistic quantum mechanics and in QED are neither unified nor directly connected. This suggests the existence of a missing path integral that bridges relativistic action and the Dirac equation at the single-particle level. In this work, we analyze the consistency and completeness of existing path-integral theories and identify a spinor path integral that fills this gap. Starting from a relativistic action written in spinor form, we construct a spacetime path integral whose kernel reproduces the Dirac Hamiltonian. The resulting formulation provides a direct link between the relativistic classical action and the Dirac equation, and it naturally extends the scalar relativistic path integral developed in our earlier work. Beyond establishing this structural connection, the spinor path integral offers a new way to interpret the origin of classical mechanics for the Dirac equation and suggests a spacetime mechanism for spin and quantum nonlocal correlations. These features indicate that the spinor path integral can serve as a unifying framework for existing path-integral approaches and as a starting point for further investigations into the spacetime structure of quantum mechanics. Full article

Driven by innovations in digital technologies, the digital economy is reshaping societal production and consumption patterns, exerting systematic effects on the energy system through the digital transformation of both the supply and demand sides of energy. Based on empirical analysis using provincial panel data from China between 2011 and 2022, this study demonstrates that the development of the digital economy significantly suppresses the scale of energy consumption while simultaneously improving energy utilization efficiency. After applying the instrumental variable method (with the interaction term of fixed-line telephones and information technology service revenue as the IV) and conducting multiple robustness checks (including lagged explanatory variables, variable substitution, sample trimming, and additional control variables), the core conclusion remains statistically significant. Mechanism tests reveal that the collaborative effects of green technological innovation, the upgrading of industrial structure, and digital inclusive finance form the key transmission path. Finally, heterogeneity analysis shows that the impact of the digital economy on energy consumption and energy efficiency is particularly pronounced in western regions, demonstrating significant regional heterogeneity. Full article

With the accelerated development of the global digital economy, data spaces have become a crucial infrastructure for cross-domain data circulation and value creation. However, cross-organizational and cross-regional data sharing still faces several challenges, including insufficient trust, fragmented governance, and inconsistent standards. Against this backdrop, blockchain technology, with its decentralized, traceable, and tamper-resistant characteristics, offers new avenues for building collaborative trust mechanisms within trustworthy data spaces. This paper systematically reviews the current research on trustworthy data spaces, the blockchain, zero-knowledge proofs, and federated learning. It proposes a technology-governance-standardization (TGS) framework for cross-border governance. To verify the framework, we proposed a collaborative trust mechanism combining “on-chain light attest, off-chain deep store, and cross-layer verifiable bridge” (LPHS–XV), which achieves data availability without visibility and compliance auditability. A prototype was then validated in the cross-border medical data space at the Macao-Hengqin Station, providing a scalable experience for global data governance. Full article