Search Results (7,572)

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and stakeholder participation. Although smart governance frameworks increasingly recognise participation as a normative principle, limited empirical attention has been paid to the participation motivators that drive engagement among different urban stakeholder groups. This study addresses this gap by analysing the key motivators influencing stakeholder participation in urban development within a smart city context. Building on established behavioural and participation theories, the article develops an Urban Participation Motivator Model comprising four core motivators: social pressure, emotional trigger, rational motivation, and reward for participation. The model is empirically tested using quantitative survey data from 620 respondents representing four stakeholder groups in Riga, Latvia: municipal residents, municipal employees, municipal politicians, and real estate developers. Data are analysed using descriptive statistics and non-parametric methods, including the Kruskal–Wallis test. The results reveal statistically significant differences in the perceived importance of participation motivators across stakeholder groups. Emotional triggers and social pressure emerge as the most influential motivators overall, while rational motivation is particularly salient for professional stakeholders. Reward for participation plays a weaker but differentiated role, being most relevant for municipal employees. These findings highlight the need for differentiated motivator-sensitive urban communication and participation strategies to enhance inclusiveness, democratic legitimacy, and long-term engagement in smart city development. Full article

In the context of the coordinated development of high-proportion renewable energy integration and alternating current/direct current (AC/DC) hybrid transmission, the sending-end power grid faces challenges such as decreased system strength, contracted stability boundaries, and difficulties in covering high-risk operating conditions. This paper proposes a new renewable energy scale impact analysis method that integrates “typical scenario construction-scale ladder comparison–prediction-driven time series injection” in response to the operational constraints of AC/DC transmission. In terms of method implementation, firstly, a two-layer typical scenario system is constructed under unified transmission constraints and fixed grid boundaries: A regular benchmark scenario covers the main operating range, and a set of high-risk scenarios near the boundaries is obtained through multi-objective intelligent search, which is then refined through clustering to form a computable stress-test scenario library. Here, the boundary scenarios are generated by a multi-objective search that simultaneously drives multiple key section load rates towards their limits, subject to AC power-flow feasibility and operational constraints, and the resulting Pareto candidates are reduced into a compact stress-test library by clustering. Secondly, a ladder scenario with increasing renewable energy scale is constructed, and cross-scale comparisons are carried out within the same scenario system to extract the scale effect and critical laws of key safety indicators. Finally, data resampling and Gated Recurrent Unit multi-step prediction are introduced to generate wind power output time series, enabling the temporal mapping of prediction results to scenario injection quantities, and constructing a closed-loop input interface of “prediction–scenario–grid indicators”. The results demonstrate that the proposed hierarchical framework, under unified AC/DC export constraints, can effectively construct a compact stress-test scenario library with enhanced boundary-risk coverage and can reveal how transient voltage security evolves across renewable expansion scales. By coupling boundary-oriented scenario construction, cross-scale comparable assessment, and forecasting-driven time series injection, the framework improves engineering interpretability and practical applicability compared with conventional scenario sampling/reduction workflows. For the forecasting module, the Gated Recurrent Unit (GRU) model achieves MAPE = 8.58% and RMSE = 104.32 kW on the test set, outperforming Linear Regression (LR)/Random Forest (RF)/Support Vector Regression (SVR) in multi-step ahead prediction. Full article

As a transformative force, the digital economy serves as a critical engine for driving high-quality economic development and fostering New Quality Productive Forces (NQPF)—characterized by high technology, high efficiency, and high quality. Viewing the establishment of China’s National Digital Economy Innovation and Development Pilot Zones as a quasi-natural experiment in economic system management, this study employs a Double Machine Learning (DML) framework to evaluate its systemic impact on A-share listed companies from 2015 to 2023. Unlike traditional linear models, the DML approach flexibly controls for high-dimensional confounding variables and functional form misspecification, thereby ensuring highly rigorous causal inference. The empirical results demonstrate that these pilot zones create an optimized “digital environment” that significantly enhances corporate NQPF, a conclusion that remains highly robust across a comprehensive battery of robustness and endogeneity tests. Mechanism analysis reveals three systemic transmission pathways through which the policy operates: optimizing factor allocation, deepening digital technology empowerment, and promoting green innovation and sustainability. Furthermore, heterogeneity analyses indicate that the policy’s efficacy varies significantly across corporate profiles, manifesting most prominently in non-state-owned enterprises, high-tech firms, and those located in eastern regions. These findings provide robust micro-level evidence for policymakers aiming to optimize digital economic systems and accelerate the systemic formation of advanced productive forces. Full article

Against the backdrop of the United Nations Sustainable Development Goals (SDGs), innovation-driven development serves as the core engine of long-term sustainable economic development, while resource misallocation has emerged as a critical bottleneck constraining sustainable innovation and coordinated regional development. Grounded in the neoclassical theory of factor allocation, this paper incorporates capital misallocation, labor misallocation and the digital economy into a unified analytical framework. Using China’s provincial panel data spanning 2001 to 2024, we systematically investigate the impact effects, underlying transmission mechanisms and regional heterogeneity of resource misallocation and the digital economy on scientific and technological innovation through benchmark regression, robustness tests and heterogeneity analysis. The results show that resource misallocation exerts a significant and robust inhibitory effect on technological innovation, with the inhibitory effect of capital misallocation being more pronounced than that of labor misallocation. The digital economy has a significant positive driving effect on technological innovation, and it can also indirectly boost technological innovation by alleviating resource misallocation, with its mitigating effect on resource misallocation presenting obvious structural differences and a stronger correction effect on capital misallocation than on labor misallocation. Economic growth and technological innovation form a mutually reinforcing “growth-innovation” virtuous cycle. In addition, the innovation effects of both resource misallocation and the digital economy exhibit significant regional heterogeneity, where the digital economy’s innovation-driven effect and misallocation-mitigating effect are notably stronger in eastern China than in the central and western regions. The theoretical contribution of this paper lies in constructing a transmission mechanism framework of “digital economy to resource misallocation to technological innovation”, which enriches the connotations of factor allocation and innovation theories. Its practical value is to provide policymakers with differentiated development paths for the digital economy and optimization strategies for factor allocation, thus facilitating the effective implementation of the innovation-driven development strategy. Full article

To address the lack of suitable glass systems for silicon nitride (Si₃N₄) surface metallization, which requires high wettability and thermal stability, and robust bonding between the copper layer and the ceramic substrate, a novel Bi₂O₃-TeO₂-B₂O₃-CuO glass system was developed. This study systematically investigated the influence of Bi₂O₃ concentration, glass properties, optimized paste composition, and brazing mechanism using phase analysis, microstructural characterization, particle size statistics, thermal analysis, and tensile testing. An optimal glass composition containing 20 mol% Bi₂O₃ was identified, exhibiting high thermal stability (ΔT = 224 °C) and a coefficient of thermal expansion of 9.63 × 10⁻⁶ °C⁻¹. At a brazing temperature of 750 °C, the glass demonstrated excellent wettability with a contact angle of 27°. A conductive paste comprising 94 wt% Cu and 6 wt% glass yielded a thick film with a minimum resistivity of 6.25 μΩ·cm and a maximum tensile strength of 25.2 MPa. Mechanism analysis revealed that the superior wettability drives the liquid glass phase to form a thin intermediate layer that significantly reinforces adhesion. These findings contribute to the research and development of subsequent novel glass systems with superior performance. Full article

Under the strategic constraints of global carbon emission targets, how supply chain management can effectively drive enterprises’ low-carbon transformation has become an important issue. Based on China’s Supply Chain Innovation and Application Pilot Program (SCIAPP), this paper approaches it as a quasi-natural experiment to empirically investigate how supply chain management affects enterprises’ low-carbon technological innovation (LCTI). This paper uses the data from publicly listed companies in China. and the difference-in-differences approach to empirically test the policy effect of SCIAPP and determine its influencing path. The study finds that first, SCIAPP significantly enhances enterprises’ LCTI level by approximately 14.2%. Second, SCIAPP mainly achieves this through three mechanisms, including strengthening enterprises’ green management, promoting digital transformation, and improving operational efficiency. Third, the impact effect is stronger in enterprises with more robust environmental management systems, fewer financing constraints and higher capital intensity. Additionally, the LCTI driven by SCIAPP can further positively impact the supply chain resilience. This study innovatively incorporates pilot policies, supply chain management, and LCTI for analysis, providing theoretical evidence and empirical support for the government to optimize supply chain governance and achieve climate goals. Full article

In urban driving cycles, battery electric vehicles are subject to frequent start–stop operations, which lead to substantial braking energy losses. Although fuzzy control (FC) strategies are commonly employed for regenerative braking, their performance is often constrained by subjectively defined membership functions and rules. To address this limitation, this paper proposes an improved FC strategy that is optimized using the particle swarm optimization (PSO) algorithm. Focusing on a front-wheel-drive BEV, a three-input single-output fuzzy controller is developed in accordance with ECE regulations, where braking intensity, battery state of charge (SOC), and vehicle speed serve as inputs, and the motor braking force ratio serves as the output. A co-simulation platform based on AVL-Cruise 2019 and Matlab/Simulink 2017a is established to evaluate the strategy under the New European Driving Cycle (NEDC) and the Worldwide Light Vehicles Test Cycle (WLTC). Additionally, hardware-in-the-loop (HIL) tests are conducted to validate the practical feasibility and accuracy of the optimized strategy. The results demonstrate that the PSO-optimized FC strategy achieves a performance in real-world controllers that is comparable to that observed in a simulation, confirming its real-time applicability. Specifically, under the NEDC, the optimized strategy reduces battery SOC from 0.90 to 0.8795, representing improvements of 0.2515% and 0.4670% over the unoptimized FC strategy and the ideal distribution strategy, respectively. The regenerative braking efficiency is enhanced by 2.45% and 10.48%. Under the WLTC, the final SOC with the optimized strategy is 0.8488, reflecting gains of 0.5202% and 0.8380% over the two reference strategies, while regenerative braking efficiency improves by 2.32% and 8.95%. These findings indicate that the proposed strategy offers a safe and effective solution for improving the regenerative braking performance in electric vehicles. Full article

This paper presents the results from in-lab chassis dynamometer testing of two battery electric vehicles of the same make and model: a 2022 model year vehicle with a heat pump and a 2020 model year vehicle with a resistive positive temperature coefficient (PTC)-type heater. The vehicles were tested over a series of standard drive cycles at −10 °C, −7 °C, 0 °C, and 25 °C to determine the impacts of the different heating systems on vehicle energy consumption and driving range in cold temperatures. The results indicate that in most (but not all) heating situations the heat pump heated its vehicle’s cabin more efficiently than the PTC heater did, especially at 0 °C. At the lowest temperature, −10 °C, the heat pump used more energy than the PTC heater on cold-start but was more efficient than the PTC heater once the cabin was warmed up. Over standard drive cycles and using SAE J1634 calculation methods to obtain a single range value for each cycle type, the improvement in the percentage of driving range retained by the heat pump-equipped vehicle over the PTC heater-equipped vehicle varied between 1% and 15% depending on ambient conditions and drive cycle, with the average advantage in percentage range retained being 7% over the UDDS cycle, 7% over the HWFET cycle, and 4% over the US06 cycle for all cold temperatures combined. Full article

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

Thrombocytopenia is a frequent complication of patients presenting emergently across the world for a wide array of etiologies. From patients who develop thrombocytopenia due to invasive neoplastic disease affecting the bone marrow to patients who develop immune complications secondary to the formation of auto-antibody responses that drive patients’ platelet counts lower or even cause infection, these patients stress the clearest need for prompt tests to discern the more likely thrombocytopenic-inducing cause. It is in this setting that looking at other platelet variables easily obtainable from modern hematology analyzers has gained traction. One of the elements found in extended platelet profiles are immature platelets (youngest and newly released platelets), also known as reticulated platelets, which are readily measurable from a complete blood count. One of the advantages of obtaining these counts is that they represent the immediate response of the bone marrow to the thrombocytopenia and, depending on etiology inducing the thrombocytopenia, they also provide information on the marrow’s response to therapeutic approaches. It is in this context that this review will present information of how these relatively novel platelet parameters can be used in clinical practice and how they can be a rapid gauge of the body’s response to disease processes leading to platelet losses. Thrombocytopenias resulting from infection (sepsis, viremia), autoantibody formation (immune thrombocytopenia and immune-mediated thrombotic thrombocytopenic purpura), immune dysregulation (systemic lupus erythematosus), and iatrogenic (drug-induced) will be discussed and used to explain how these young platelet measurements can provide valuable clinical information. Full article

Real-time performance and adaptability are critical factors influencing the safety and stability of autonomous vehicle trajectory tracking. Therefore, enhancing these aspects is essential for improving driving safety. This paper proposes a trajectory tracking control method for autonomous vehicles based on an adaptive model parameter correction controller (MPACC). First, by integrating the variable universe fuzzy control (VUFC) principle with a model predictive controller (MPC), a variable universe fuzzy model predictive controller (VUFMPC) is designed. This controller enables adaptive adjustment of MPC weighting coefficients, thereby effectively improving the real-time capability and adaptability of the MPC. Second, an adaptive square root cubature Kalman filter (ASRCKF) tire lateral force estimator with adaptive scaling factors is introduced to obtain real-time tire cornering stiffness values as MPC parameters, achieving adaptive correction of the MPC parameters and forming an adaptive model predictive controller (AMPC). Furthermore, an MPACC is designed by integrating VUFMPC and AMPC. This controller allows for real-time adaptive correction of control parameters according to the vehicle’s driving state. Finally, hardware in loop (HIL) tests are conducted for comparative analysis. The results demonstrate that the proposed MPACC exhibits excellent real-time performance and adaptability, while effectively balancing trajectory tracking accuracy and driving stability of autonomous vehicles. Full article

Conservation easements (CEs) represent a complex policy instrument designed to mediate the feedback loops within coupled human and natural systems in protected areas. However, their efficacy is often constrained by a lack of systemic understanding of the localized drivers of community support. Building upon the successful implementation of Forest Land Easements (FLEs) within China’s Qianjiangyuan National Park Pilot, this study investigates the potential to expand this policy model to other land types. This study investigates the multilevel factors influencing residents’ willingness to adopt three types of CEs, including forest land (FLE), agricultural land (ALE) and homestead land (HLE) easements in China’s Qianjiangyuan National Park Pilot, the country’s primary CE reform site. We conceptualize a hierarchical support model wherein community participation (CP) and human well-being (HW) interact with support for park management (SM), forming a subsystem that drives decisions within the broader land-use. Utilizing structural equation modelling (SEM) and stepwise regression analysis on survey data from 336 households, we tested this model. The results reveal that SM acts as a critical direct mediator and positive driver of CE acceptance, while CP and HW exert significant indirect effects through SM, demonstrating a key feedback pathway. Regression analyses further elucidate that support for different CE types is driven by distinct configurations of factors, highlighting the heterogeneous nature of subsystems. Notably, livelihood benefits and prior participation experiences emerged as consistent, cross-cutting systemic leverages. It demonstrates that leveraging the implementation experience and community support gained from existing forest land easements is crucial. This study concludes that effective CE design must move beyond one-size-fits-all approaches. It necessitates differentiated, adaptive policies that are coherently aligned with local livelihood subsystems and strategically strengthen participatory feedback mechanisms initiated by successful FLEs. Our findings provide an evidence-based framework for designing resilient, socially sustainable conservation policies in complex protected area systems, grounded in proven practice. Full article

The Camino de Santiago is one of the most important cultural routes in the world and a privileged laboratory for analysing the challenges of sustainability in long-distance heritage destinations. The aim of this research is to understand the underlying dynamics of the Way, as well as its degree of sustainability. To achieve this, we examine the recent evolution of tourist demand for the Way from a territorial and sustainability perspective, integrating official statistical data with digital interest indicators from Google Trends (2004–2025). The methodology combines quantitative analyses of trends, seasonality, spatial diversification and internationalisation of demand, applying robust techniques such as the Theil–Sen slope and the Mann–Kendall test. The results show structural growth and high resilience of the Jacobean tourism system, even after the disruption caused by COVID-19, together with a growing internationalisation of flows. However, this tourism success is accompanied by strong spatial and temporal imbalances, with a marked concentration on the French Way and in the summer months, which increases environmental and social pressure on the most travelled territories. The analysis of digital interest also reveals a progressive decline in the importance of Holy Years as a driving force for attraction, especially in international markets. Full article

Chronic crop diseases caused by uncultured, obligate, or host-dependent pathogens challenge traditional pathogen-centric paradigms that often interpret symptoms as direct outcomes of pathogen toxins, effectors, or tissue colonization. Here, we advance a host-centric immune framework that reframes disease as an emergent consequence of dysregulated host immune network activity, including prolonged activation, signaling miscoordination, and systemic physiological disruption. Using citrus huanglongbing (HLB) as a primary exemplar and canola clubroot as a parallel system, we synthesize evidence that persistent immune stimulation can drive self-damaging outputs, including sustained reactive oxygen species accumulation, chronic vascular and transport dysfunction, hormone imbalance, and growth–defense trade-offs. While many observations derive from transcriptomic, physiological, and genetic studies conducted under controlled experimental conditions, the available evidence collectively suggests that persistent immune activation may contribute substantially to disease-associated decline in these systems. We argue that pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) operate as an integrated immune network whose feedback structure can become destabilized under chronic infection, generating immune states that are simultaneously harmful and often ineffective at pathogen clearance. We further discuss how panomic profiling, spatially resolved analyses, and network inference can diagnose host immune states at tissue and cell-type resolution, and how genome editing enables causal tests and rational immune tuning strategies that optimize defense amplitude, timing, and localization rather than indiscriminately amplifying resistance. By centering the host immune system as both a source of protection and pathology, this framework provides a conceptual and practical roadmap for understanding and engineering resilience in HLB, clubroot, and other chronic crop diseases in which pathogen biology remains experimentally opaque. Full article

Background: The progression of idiopathic pulmonary fibrosis (IPF) involves distal airway remodeling and bronchiolization; however, the mechanisms driving these changes, particularly the contributions of epithelial stem cells, are not fully understood. K13⁺ hillock cells, normally quiescent in proximal airways, were examined for their potential contribution to IPF pathogenesis. Methods: Spatial immunofluorescence was used to profile K13 expression along the airway axes in IPF and control lungs. Multiplex staining complemented by ex vivo culture assays was used to test expression stability. Single-cell RNA-sequencing (scRNA-seq) data were re-analyzed to identify cell subclusters and pathway enrichments. Meanwhile, cell–cell communication was inferred by using CellChat. Results: K13 was ectopically upregulated in IPF honeycomb cysts, triggering a proximal-like pseudostratified phenotype. This shift was marked by surges in K13⁺ regionally overlapping expression patterns (K5⁺, ~9%; CC10⁺, ~53%; ACE-TUB⁺, ~44%; MUC5AC⁺, ~23%) and a decline in SOX2 expression (~95% to ~64%), with ~70% of residual SOX2^low cells exhibiting elevated K13. Accompanying the expansion of K13⁺ subclusters (basal: 1.8% to 41.5%; club: 10.7% to 31.5%), it was observed that the profibrotic markers (K17, S100A2, LGALS7, IGFBP6) and ontologies related to RNA processing, stress response, and senescence were also enriched. These subclusters also amplified pro-fibrotic signaling (e.g., TGF-β, SEMA3, and GALECTIN-9) associated with epithelial subtypes and HAS1^high fibroblasts. Conclusions: Here, we demonstrate that K13⁺ cell activation is a pivotal event, driving the dysregulated proximalization of distal airways in IPF through fate reprogramming and epithelial-mesenchymal crosstalk. Thus, elucidating these K13-mediated fate dynamics provides a critical framework for understanding IPF pathogenesis. Full article