Search Results (469)

Previous studies on the relationship between climate policy uncertainty (CPU) and green technology innovation (GTI) generally belong to one of two opposing schools of thought: real options theory and growth options theory. This study proposes that, according to prospect theory, a nonlinear relationship exists between these variables. Using panel data from Chinese A-share listed companies spanning 2010–2022, we empirically test this hypothesis. Results indicate an inverted U-shaped relationship between CPU and GTI, with moderate levels of CPU facilitating optimal GTI. As CPU intensity strengthens, there exists an appropriate threshold at which GTI reaches its peak. More importantly, moderation analysis reveals that firms’ internal capabilities—including risk-bearing capability, cash flow, operational capability, and marketing capability—significantly outperform external government support in moderating the CPU-GTI relationship. These findings provide valuable implications for firms’ climate risk management and climate policymakers’ policy formulation in advancing sustainable development goals. 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

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article

Robust speed estimation in induction motors (IM) is essential for control systems (especially in sensorless drive applications) and condition monitoring. Traditional model-based techniques for inverter-fed IM provide a high accuracy but rely heavily on precise motor parameter identification, requiring multiple sensors to monitor the necessary variables. In contrast, model-independent methods that use rotor slot harmonics (RSH) in the stator current spectrum offer a better adaptability to various motor types and conditions. However, many of these techniques are dependent on full-band processing, which reduces noise immunity and increases computational cost. This paper introduces a novel subband signal processing approach for rotor speed estimation focused on RSH tracking under both steady and non-steady states. By limiting spectral analysis to relevant content, the method significantly reduces computational demand. The technique employs an advanced time-frequency analysis for high-resolution frequency identification, even in noisy settings. Simulations and experiments show that the proposed approach outperforms conventional RSH-based estimators, offering a robust and cost-effective solution for integrated speed monitoring in practical applications. Full article

Under the “dual carbon” strategy, clarifying the relationship between economic growth and carbon emissions and revealing the differences in green transition pathways among different urban tiers within the metropolitan area is of great significance for promoting regional low-carbon development. Based on panel data of prefecture-level cities in 27 national metropolitan areas in China from 2000 to 2020, this paper employs a two-way fixed effects model and a mediation effect model to test the Environmental Kuznets Curve (EKC) hypothesis and to evaluate the mediating role of industrial structure advancement. The results show that, at the national level, carbon emissions and economic growth exhibit a significant inverted U-shaped relationship, but the EKC becomes invalid in non-core cities after dividing the sample into core and non-core cities. Industrial structure advancement significantly curbs carbon emissions in core cities, while its effect is insignificant in non-core cities, indicating insufficient structural transformation capacity. The findings suggest that core cities have initially formed a “structure-embedded” emission reduction pathway, whereas non-core cities face a dual challenge of growth and emission reduction. In terms of policy, excessive reliance on the “automatic decoupling of growth” should be avoided, and a differentiated governance system centred on structural transformation capacity should be established, with particular attention to enhancing the green transition capacity of non-core cities so as to promote regionally equitable and coordinated low-carbon development. Full article

Currently, there is a lack of methods for detecting the mechanism of gas explosion propagation within flameproof enclosures and the dynamic behavior of flameproof enclosures under explosion impact. Therefore, this paper studies a method for detecting the vibration characteristics of coal mine explosion-proof equipment under internal gas explosions using laser Doppler. First, a model of gas explosion propagation and explosion transmission response in flameproof enclosures is established to reveal the mechanism of gas explosion transmission inside coal mine flameproof enclosures. Second, a laser Doppler measurement method for coal mine flameproof enclosures is proposed, along with a step-by-step progressive vibration characteristic analysis method. This begins with a single-frequency dimension analysis using the Fourier transform (FFT), extends to time–frequency joint analysis using the short-time Fourier transform (STFT) to incorporate a time scale, and then advances to a three-dimensional linkage of scale, time, and frequency using the wavelet transform (DWT) to solve the limitation of the fixed window length of the STFT, thereby achieving a dynamic characterization of the detonation response characteristics. Finally, a non-symmetric Gaussian impact load inversion model is constructed to validate the overall scheme. The experimental results show that the FFT analysis identified a 2000 Hz main frequency, along with the global frequency components of the flameproof enclosure vibration signal, the STFT analysis revealed the dynamic evolution of the 2000 Hz main frequency and global frequency over time, and the wavelet transform achieved higher accuracy positioning of the frequency amplitude in the time domain, with better time resolution. Finally, the experimental platform showed an error of less than 5% compared with the actual measured impact load, and the error between the inverted impact load and the actual load was less than 15%. The experimental platform is feasible, and the inversion model has good accuracy. The laser Doppler measurement method has significant advantages over traditional coal mine flameproof equipment measurement and analysis methods and can provide further failure analysis and prevention, design optimization, and safety performance evaluation of flameproof enclosures in the future. Full article

Ultrafast lasers operating at 740 nm and 820 nm have attracted widespread attention as two-photon light sources for the detection of biological metabolism. Here, we report on a solid-like quartz-encapsulated femtosecond laser with a repetition rate of 80 MHz, delivering 740 nm and 820 nm femtosecond laser pulses. This home-built laser system was realized by employing an erbium-doped 1560 nm fiber laser as the fundamental laser source. A quartz-encapsulated nonlinear frequency conversion stage, consisting of a second-harmonic generation (SHG) stage and self-phase modulation (SPM)-based nonlinear spectral broadening stage, was utilized to deliver 30 mW, 53.7 fs, 740 nm laser pulses and the 15 mW, 60.8 fs, 820 nm laser pulses. Further imaging capabilities of both wavelengths were validated using a custom-built inverted two-photon microscope. Clear imaging results were obtained from mouse kidney sections and pollen samples by collecting the corresponding fluorescence signals. The achieved results demonstrate the great potential of this laser source for advanced two-photon microscopy in metabolic detection. Full article

With the advancement of global climate governance, public attention—an emerging form of social capital—has played an increasingly important role in the carbon emission effects of green technological innovation. Based on panel data from 267 prefecture-level cities in China from 2012 to 2022, this study employed a two-way fixed-effects model to identify the nonlinear relationship between green innovation and carbon emissions, incorporated interaction terms to examine the moderating effect of public attention, and applied a spatial Durbin model to analyze the spatial spillover effects of green innovation. The results reveal an inverted U-shaped relationship between green innovation and carbon emissions, with the inflection point corresponding to 8.58 authorized green patents per 10,000 people—a threshold that most cities have yet to reach. Public attention significantly altered the shape of the carbon effect curve by making it steeper; in cities with a higher share of secondary industry, it delayed the inflection point, whereas in cities dominated by the tertiary industry, the turning point appeared earlier. In addition, green innovation had significant spatial spillover effects, and its impact on carbon emissions in neighboring cities displayed a U-shaped pattern. This paper proposes an analytical framework of “socially empowered innovation” to reveal the nonlinear moderating mechanism through which public attention influences the carbon effects of green innovation. The findings offer important policy implications: efforts should focus on long-term innovation, promote regional coordination, guide rational public participation, and avoid short-sighted and unsustainable mitigation practices. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

Power electronics has significantly contributed to advances in developing single-stage integrated converter topologies, enabling DC/AC conversion with voltage step-up capability in a compact and efficient structure. This work proposes a novel Integrated Zeta–Ćuk Inverter (IZCI), derived from combining the Zeta and Ćuk DC/DC converter structures. In addition, the proposed topology achieves high efficiency and full utilization of the input voltage. A potential application for the IZCI topology involves DC microgrids, in which the proposed topology can supply AC local loads, achieving high power quality, such as a low total harmonic distortion (THD). The IZCI operates in discontinuous conduction mode (DCM), exhibiting three distinct operating stages for each switching period. The DCM operation guarantees a linear relationship between output and duty cycle, simplifying the control strategy and requiring fewer sensors, thereby reducing the cost and processing requirements. The feasibility and performance of the IZCI topology are evaluated and validated through experimental results in a standalone application. The results demonstrate high energy conversion efficiency and reliability, providing an AC output voltage with low harmonic distortion. Full article

Urban sustainability has become the most important urban development issue globally. Facing the problem of spatial structure optimization during urbanization, how to effectively use public data access to promote urban polycentric development has become a new area of concern for urban planners and policy makers. To quantify how government open-data platforms shape polycentric urban spatial structure across Chinese cities, this study takes the launch of government data platforms as a quasi-natural experiment, constructs the multi-period differences-in-differences model, uses data of 271 Chinese prefectural-level cities from 2010 to 2021, and examines the impact and mechanism of public data access on urban spatial structure. We find that public data access promotes urban polycentric development, especially in large cities, those in urban agglomerations, and resource-abundant cities. The effect follows an inverted ‘N’ trend, which reflects the evolving role of PDA across different urban development stages, highlighting the need for adaptive policies to optimize its benefits. Mechanisms include information process radicalization and industrial structure upgrading, moderated positively by government intervention and regional competition. These insights can inform policies for optimizing urban spatial patterns and advancing sustainable urban development. Full article

China’s healthcare expenditure tripled during 2010–2019, prompting the nationwide implementation of centralized volume-based procurement (CVBP). While effective in reducing drug prices, CVBP introduces sustainability challenges including supply chain vulnerabilities and welfare trade-offs. This study develops a pharmaceutical sector-embedded computable general equilibrium (CGE) model to quantify CVBP’s multidimensional sustainability impacts. Using China’s 2020 Social Accounting Matrix (SAM) with simulated 10–50% price reductions, key findings reveal that (1) >40% price reductions trigger sectoral output reversal; (2) GDP exhibits an inverted U-shape; (3) household income declines despite corporate/government gains; and (4) industrial contraction impairs innovation capacity and employment stability. Our analysis identifies potential sustainability risks, emphasizing the need for rigorous empirical validation prior to implementing aggressive price reduction policies, and underscores the importance of integrating supply chain considerations into procurement policy design. This approach maximizes resource allocation efficiency while advancing socioeconomic resilience in healthcare systems. Full article

The coordinated development of tourism and industrialization is essential for achieving sustainable and inclusive growth in the tourism sector, as well as for ensuring long-term regional economic sustainability. This study is motivated by the observation that land is a key factor influencing the coordination between tourism and industrialization, yet the specific role of land use remains underexplored. Therefore, the objective of this paper is to investigate the nonlinear relationship and underlying mechanisms through which tourism development impacts industrialization, with a particular focus on land transfers. To achieve this, the study employs an empirical approach using multi-source data—including data on China’s A-level scenic areas and land transfers—combined with an econometric method. The results indicate a U-shaped relationship between both the quantity and quality of tourism resources and the growth of industrial enterprises, as well as an inverted U-shaped relationship between the concentration of tourism resources and industrial development. The research finds that tourism development influences industrialization through two primary land-related mechanisms: the factor competition effect and the resource conservation effect. This study also investigates the potential for synergistic development between the tourism and industrial sectors, providing valuable insights for the sustainable economic advancement of land-based tourism and industrialization. Full article

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article