Search Results (6,901)

With the deepening construction of ecological civilization, the realization of ecological product value, referring to the value derived from ecosystems’ material goods, regulation, support, and cultural services, has become a strategic key point for national sustainable development. Data factors, distinguished from digital technologies as the actual resources used in production, exchange, and consumption, are becoming increasingly important as a new catalyst for empowering the realization of ecological product value. Drawing on panel data spanning 2011 to 2023 across China’s 31 provinces, this research employs the entropy weight method to construct evaluation indices for both the development of data factors and the realization of ecological product value, deriving weights from the data’s intrinsic variability. The effect of data factors on the realization of ecological product value is examined using a two-way fixed effects framework. Our outcomes are presented below. First, data factors can significantly promote the realization of ecological product value, and this conclusion is supported by a series of robustness checks and endogeneity treatments. Second, the mechanism analysis reveals that data factors empower the realization of ecological product value through new quality productive forces, energy consumption intensity, and innovation and entrepreneurship. Third, results from the threshold model suggest that the promoting effect of data factors on the realization of ecological product value is subject to a threshold constraint, characterized by diminishing marginal returns beyond this point. Fourth, regarding regional disparities, the results indicate that data factors primarily drive ecological product value realization in the central region, as it is at a critical stage of digital transformation, with a secondary effect in the east, while their influence in the western region remains insignificant. These findings provide important guidance for integrating data factors and ecological resources to achieve sustainable development. Full article

This review explores the hypothesis that schizophrenic symptoms may be understood not as isolated deficits, but as interconnected manifestations of a structural reorganization of consciousness. The premises of this work are grounded in a comparative matrix that suggests an underlying “consanguinity” between the philosopher’s voluntary epoché—the suspension of the natural attitude performed to study the inner workings of consciousness—and the involuntary “unworlding” passively experienced in schizophrenia. By exploring this shared ontological ground, the text suggests how specific phenomenological shifts, such as the collapse of the “vital drive,” may manifest as clinical markers; this process may eventually lead to an involuntary “transcendental reduction” where the mind’s internal machinery becomes an object of forced awareness. Building on these premises, the review tentatively outlines several key achievements. It addresses the substrate-subjectivity gap by linking biological sensory-binding failures to the onset of involuntary hyper-reflexivity. Regarding structural loss and gain of function, it suggests that the psychotic transition involves a simultaneous erosion of common-sense coherence and an intensified receptivity to unfiltered perceptual fragments, which may trigger a search for metaphysical meanings. In terms of a therapeutic synthesis, it proposes exploring the conversion of “artless decentering” into a manageable, strategic distance through mindfulness and person-centered position-taking. Finally, it discusses a potential nosographic evolution, advocating for future diagnostic classifications that prioritize the experiencing self and qualitative insights to support a more translational and empathetic approach to psychiatry. Full article

One-dimensional mass transfer resistance-in-series framework was developed theoretically and validated experimentally using a flat-plate polytetrafluoroethylene/polypropylene (PTFE/PP) membrane module to predict CO₂ absorption fluxes and concentration distributions. The decline in CO₂ absorption efficiency along the membrane module is primarily attributed to increased concentration polarization resistance and a reduced driving force concentration gradient. To alleviate these limitations, carbon fiber promoters were strategically embedded to suppress concentration polarization, reduce the mass transfer resistances, and enhance turbulence intensity. In the present study, device performance was further improved by implementing properly ascending or descending hydraulic equivalent widths along the absorbent feed channel. Under the descending configuration, an absorption flux enhancement of up to 44.94% was achieved relative to an empty-channel module (i.e., without S-ribbed carbon fiber inserts). Theoretical formulations were established to predict absorption fluxes under varying monoethanolamine (MEA) volumetric flow rates, CO₂/N₂ mixture flow rates, and inlet CO₂ feed concentrations. The model predictions showed good agreement with experimental results obtained using MEA solutions under both ascending and descending hydraulic width operations, demonstrating effective mitigation of polarization effects and enhanced absorption flux along the absorbent feed channel. An economic assessment of the S-ribbed carbon fiber module was conducted by evaluating the trade-off between absorption flux enhancement and incremental power consumption. The results indicate that the proposed design provides a practical and economically viable approach for improving the performance of membrane-based CO₂ capture technologies. In addition, an enhanced Sherwood number correlation, expressed in a simplified form, was developed and employed to estimate the mass transfer coefficients of CO₂ membrane absorption modules incorporating S-ribbed carbon fiber promoters. Full article

To address the issues of sealing leakage and airflow-induced vibration in high-speed turbomachinery, a conical straight-tooth annular clearance sealed hybrid aerostatic/aerodynamic bearing is investigated. A three-dimensional CFD model is established to study the effects of radial clearance height, inlet pressure, rotor speed, and eccentricity on pressure distribution, velocity distribution, and leakage rate. The results show that leakage exhibits a strong positive nonlinear correlation with clearance height and inlet pressure, following a power-law or polynomial relationship, while rotor speed and eccentricity exert negligible effects (less than 5%). The underlying mechanisms are identified as the kinetic energy diversion caused by circumferential shear and the mutual cancelation of throttling and backflow effects. Increasing the gap height enhances leakage by expanding the hydraulic diameter and strengthening vortex disturbance; increasing inlet pressure promotes leakage by elevating the driving force and intensifying local flow separation. Full article

Urban agglomerations serve as crucial spatial carriers for advancing people-centered new urbanization. However, the integrated analysis of urban expansion dynamics, landscape pattern responses, and their driving mechanisms, particularly in ecologically sensitive, late-developing urban agglomerations, remains insufficiently understood. Taking the Guanzhong Plain Urban Agglomeration (GPUA) as the study area, this paper utilizes the Urban Expansion Rate Index (UERI), Urban Expansion Intensity Index (UEII), Landscape Expansion Index (LEI), and Landscape Pattern Metrics (LPMs) to examine urban land expansion and landscape pattern changes, and employs GeoDetector to analyze the driving forces behind these changes. The findings indicate that from 1990 to 2020, the urban land area of the GPUA expanded continuously, with UERI and UEII showing an “increase-then-decrease” trend. Significant disparities exist among cities in the urban expansion areas, with the coexistence of “edge” and “infilling” modes profoundly influencing landscape responses. The driving forces of urban expansion have undergone a stage-specific transition from socioeconomic dominance to ecological policies and natural constraints, with policy–institutional control, socioeconomic development drivers, natural endowment constraints, and improved locational conditions collectively shaping the GPUA’s “spatial landscape” system. The findings of this study provide a scientific basis for territorial spatial governance and sustainable development in ecologically fragile urban agglomerations. Full article

Resolving the dichotomy between wide detection ranges and low mechanical hysteresis remains a critical challenge in flexible electronics, largely governed by the intrinsic viscoelastic creep of polymeric dielectrics. Drawing inspiration from the distinctive load-bearing mechanisms of traditional Chinese Sparrow Brace architecture, we report a mechanically optimized tilted micro-architecture designed to enhance structural resilience. Unlike conventional soft elastomeric pillars that easily succumb to mechanical failure, this BOPS-based tilted geometry provides excellent load-bearing capacity, effectively preventing premature failure. Finite element analysis (FEA) confirms that this tilted geometry forces a fundamental shift from conventional bulk compression to structural bending. Because this bending-dominated architecture drives rapid elastic recovery, it significantly mitigates the severe effects of the polymer’s viscoelastic creep under the tested loading conditions, achieving reliable signal reversibility with low hysteresis. We fabricated this specific architecture via programmable femtosecond laser direct writing (FsLDW) on biaxially oriented polystyrene (BOPS) films, harnessing the material’s entropy-driven self-growth kinetics. By merging this localized growth mechanism with the architectural design, we effectively bypassed the complexities of traditional molding, achieving mask-free, in situ growth of large-scale, highly uniform dielectric micro-arrays. The resulting sensor delivers a remarkably broad working range (up to ~2.28 MPa) coupled with a negligible recovery error (~1.3%), an agile dynamic response (~70/80 ms), and consistent operational durability. Ultimately, this work combines architecture-inspired structural design with advanced femtosecond laser surface microengineering, providing a conceptually novel and scalable pathway for next-generation flexible sensing. Full article

Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article

Efficient uranium recovery from productive leaching solutions requires accurate prediction of hydrodynamic and mass-transfer processes in ion-exchange sorption columns. In this study, a coupled multidimensional hydrodynamic and mass-transfer model is developed to investigate uranium sorption in a packed ion-exchange column equipped with a conical flow distributor. Fluid flow in the porous resin bed is described using the Forchheimer filtration law combined with the mass conservation equation, while transport of dissolved uranium species is modeled using a convective–dispersion equation coupled with a linear driving force kinetic model. The numerical solution is obtained using the fictitious domain method, which enables accurate representation of complex column geometries. The results reveal pronounced radial flow non-uniformity, incomplete flow equalization, and the formation of a ring-shaped sorption zone, indicating uneven utilization of the sorbent bed. It is shown that under practical operating conditions, mass-transfer dynamics are governed primarily by hydrodynamics rather than intrinsic sorption kinetics. The proposed model provides a practical tool for analysis and optimization of industrial uranium recovery columns. Full article

To address the issue of insufficient output voltage of the self-powered unit of intelligent bearings under low-amplitude working conditions, a piezoelectric–friction composite energy harvester driven by rotating magnetic force is proposed based on the multi-physical field coupling and synergy of magnetoelectric, piezoelectric and triboelectric effects, which effectively enhances the voltage output in low-amplitude vibration environments. The intelligent bearing adopts an extended structure, consisting of an outer ring sleeve, an inner ring extension ring, magnetic poles and a composite energy harvester. The outer ring sleeve is nested on the outer ring of the bearing and fixes the composite energy harvester, while the inner ring extension ring is fixed on the inner ring of the bearing and installs the magnetic poles. The composite energy harvester adopts a magnetic double-mass block single-crystal piezoelectric simply supported beam structure and integrates a contact-separation type triboelectric nanogenerator in the vibration direction, achieving the collaborative power supply of the piezoelectric and triboelectric units. A mechanical-electrical coupling dynamic model of the composite energy harvester is developed. Using COMSOL software, the effects of various structural dimensions and magnetic pole configurations on the output voltage are analyzed. Experimental validation confirms the model’s effectiveness. The results demonstrate that the energy harvester operates effectively under varying bearing rotational speeds. The rotational speed of the magnetic poles has little influence on the output voltage amplitude but primarily affects its frequency. Under the condition that the rotational speed is within 600 r/min, the piezoelectric module stably outputs a peak voltage of approximately 16.6 V, and the triboelectric unit stably outputs a peak voltage of approximately 4.4 V, which can effectively meet the self-driving requirements of intelligent bearings. Full article

Deep eutectic solvents (DESs) have emerged as powerful media for enhancing the solubility of poorly water-soluble active pharmaceutical ingredients (APIs). However, their rational design remains challenging due to the complex interplay of intermolecular interactions and non-ideal thermodynamic behavior. This study develops a comprehensive, data-driven taxonomy of solute–solvent systems by integrating COSMO-RS-derived descriptors with principal component analysis (PCA) and unsupervised clustering. This approach establishes a constrained, evidence-based decision framework, which is more appropriate for complex physicochemical systems like DESs than traditional empirical rules. The analysis successfully reduces the multidimensional descriptor space to five physically interpretable axes: solvation driving force, API thermodynamic stability, solvent interaction profile, hydrogen-bond network strength, and hydration effects. Two primary solubilization mechanisms are identified: interaction-driven solvation, characterized by high API–DES affinity, and destabilization-driven solvation. Furthermore, comparison of dry and water-containing systems reveals that water acts as a thermodynamic structuring agent, fundamentally reducing system dimensionality and promoting the emergence of more distinct solvation regimes. Validated through the projection of benzocaine and lidocaine, this framework enables a transition from trial-and-error screening to mechanism-guided formulation design, providing a robust roadmap for navigating the complex solubility landscape of pharmaceutical DESs. Full article

This study aims to enhance the precision of humanoid robots in imitating complex human “walking–grasping” coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control—guided by zero moment point (ZMP) and roll/pitch data—and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments. Full article

The construction industry, a global economic pillar and carbon emission giant, faces a critical gap between digital transformation and risk management, which ultimately undermines the sector’s capacity for risk management. This study combines social technical systems theory with the technology–organization–environment framework, using panel data from Chinese listed construction firms to explore how digital transformation affects project risk management. Key findings reveal that digital transformation significantly boosts risk management through two distinct pathways. While environmental governance capacity and green innovation efficiency both serve as significant mediators, the study identifies a notable disparity in the driving forces: digital transformation exerts a stronger impact on green innovation efficiency (17.8%) compared to environmental governance (4.4%). However, the resulting mediating effects of these two paths are found to be remarkably similar (0.0060 vs. 0.0068). Furthermore, labor investment efficiency is identified as a critical boundary condition, with a threshold effect (−0.385) below which the benefits of digital transformation weaken. These findings provide empirical evidence from Chinese context regarding the “technology-institution” co-evolution mechanism in construction. While centered on China, the study offers valuable insights for global stakeholders on how to harness digitalization to mitigate project risks and enhance sustainability. Full article

Relief is an important driver in the spatial differentiation of river runoff and its regime both in the mountains and on the plains, which is most evident in arid and semi-arid regions of the Earth’s land. Based on 22 small and medium-sized rivers of the zones of forest–steppe and steppe in the temperate climate zone of the eastern part of the East European (Russian) Plain, within the Middle Volga region and the eastern part of the Don River basin, the role of this factor in the spatiotemporal changes in various key runoff parameters (annual average runoff (Q), annual maximum runoff (Q_max), and annual minimum runoff for both cold (Q_min-CP) and warm (Q_min-WP) seasons) between two baseline climatic periods of the Anthropocene (1961–1990 and 1991–2020) is considered using the average elevation of river basin (H) as its quantitative indicator and statistical procedures of regression and correlation analysis. It is found that in the interperiod trends of the Anthropocene, the H factor was the leading (and statistically significant) cause of spatial variability in the changes in Q_max in the forest–steppe zone, through its inverse relationship with H (with a 70% contribution of influence), Q_min-CP in the forest–steppe and steppe zones (with a 55–75% contribution of influence), and Q_min-WP in the steppe zone (with a 64% contribution of influence), through their direct relationships with H. It is also shown that H acted as an important factor (with a 47% contribution of influence) of statistically significant strengthening of the spatiotemporal coherence of Q and Q_max values between the studied periods, but only in the river basins of the Middle Volga region: during the period of the most active climate warming (1991–2020), the region’s upland rivers turned out to be more coherent in these two runoffs than the lowland ones. An ambiguous influence of H on the mutual correlation of all the examined runoff parameters over the baseline periods was revealed. The identified patterns are a consequence of the reaction of the complex altitudinal zoning of the plain’s landscapes mainly to climate changes, especially during the cold season (frequent thaws, decreasing soil freezing depth, etc.). The achieved results are intended to contribute to the understanding of the role of so-called “passive” driving forces in contemporary regional changes in river runoff. Full article

Hardware-in-the-Loop (HiL) driving simulators are increasingly adopted in vehicle development to improve efficiency, flexibility, and repeatability across the product life cycle. Their implementation, however, remains challenging, as the integration of real vehicle components into simulation environments significantly increases system complexity and requires the coherent interaction of real hardware, actuation subsystems, and numerical models representing non-physical components. This paper addresses these challenges through the development of a remotely controlled HiL test rig for the braking system, focusing on its integration with a driver’s station in a driving simulator. The role of braking systems within HiL simulators is first discussed, highlighting their relevance for early development, debugging, and calibration activities. An exemplary development pipeline is then presented, introducing a modular and scalable software architecture implemented in MATLAB/Simulink to manage remote brake actuation and force feedback. The performance of the proposed actuation system is experimentally evaluated and discussed, including its integration with a commercial force-feedback device. The results demonstrate the feasibility and effectiveness of the proposed framework, showing concrete benefits in respect of development efficiency and industrial applicability. Full article

Brake-by-wire (BBW) systems face challenges such as structural complexity, high energy consumption, and control inaccuracies induced by nonlinear factors. This study develops a novel self-locking dual-side synchronously clamping electronic wedge brake (EWB) system as an advanced BBW architecture. This novel design consists of a single screw with opposite-handed threads to drive the wedge mechanism bidirectionally, leveraging the self-energizing effect and the self-interlocking effect to significantly reduce energy consumption while achieving hydraulic-free synchronous braking. Additionally, the inherent precise displacement control of the screw transmission offers a simplified solution for air gap management. A multi-domain coupled model integrating mechanical dynamics and control algorithms is developed based on the proposed architecture, with finite element analysis (FEA) validating the mechanical strength and thermal degradation resistance of key components under extreme conditions. A hybrid control algorithm combining model predictive current control (MPCC) and a PI controller is developed. Compared with the active disturbance rejection control (ADRC), the proposed method achieves a 55% improvement in dynamic response and a $69.1$% reduction in steady-state error. The vehicle braking performance is validated through a CarSim–Simulink co-simulation, while the rapid dynamic response and precise clamping force control of the key actuator are verified via bench testing, demonstrating the effectiveness of the proposed EWB system architecture and its control strategy, thereby laying a solid theoretical foundation for its future industrial implementation. Full article