Search Results (219)

Pile foundations are critical load-bearing components in bridge structures, particularly in soft, high-moisture soils susceptible to external disturbances. This study investigated the impact of large-scale soil excavation on the stability of adjacent pile foundations through comprehensive field monitoring of a newly constructed bridge during both the bridge construction and channel excavation phases. The close proximity of the excavation site to the pile caps facilitated a detailed assessment of soil–structure interaction. The results indicate that the pile axial force peaked at the pile head and decreased progressively with depth, consistent with the load transfer mechanism of friction piles. Notably, a distinct variation in axial force was observed at the bedrock interface, attributed to reduced relative displacement between the pile and the surrounding soil. Furthermore, channel water filling raised the local groundwater table, which increased the buoyancy and reduced negative skin friction, thereby decreasing the pile axial force. The study also highlighted the sensitivity of pile deformation in soft soil to unbalanced earth pressure. Asymmetric excavation and surface surcharge loading were identified as critical factors compromising pile stability and overall structural safety. These findings provide valuable insights for construction practices and offer effective strategies to mitigate adverse excavation effects, ensuring long-term structural stability. Full article

We present a theoretical and numerical framework for computing asymmetric two-dimensional droplet shapes on surfaces with a sharp wetting boundary separating regions of distinct contact angles. Through Lagrange multiplier analysis of the constrained Gibbs free energy functional, we derive a simplified spreading condition that relates the contact line position ratio to the ratio of spreading functions encoding unbalanced Young stress at each contact line, reducing to an explicit algebraic relation that eliminates iterative computation. Gravitational effects substantially modify droplet height and curvature distribution across Bond number regimes, yet the contact line position ratio remains invariant, confirming that horizontal partitioning depends exclusively on interfacial energy ratios rather than body forces. Hydrophilic surfaces exhibit intuitive spreading toward regions with better wettability, producing flattened asymmetric profiles, while hydrophobic surfaces display counterintuitive behavior where droplets preferentially occupy regions with poorer wettability, maintaining tall compact geometries. Mixed hydrophilic–hydrophobic boundaries violate equilibrium conditions and drive spontaneous droplet migration. We develop an efficient two-stage computational strategy decoupling shape computation from equilibrium position determination, reducing computational cost by orders of magnitude. These findings provide quantitative design criteria for controlled droplet positioning on patterned substrates, with implications for microfluidic devices and droplet-based technologies. Full article

This study investigates the dynamic relationship between risk-taking, capital adequacy, and operational efficiency in the MENA banking sector, with a particular emphasis on the role of ESG performance in shaping sustainable financial behavior. Using an unbalanced panel of 167 commercial banks from 2015 to 2024, we develop a three-equation framework and estimate it using the two-step System-GMM method to address endogeneity, simultaneity, and dynamic effects. The empirical results reveal significant interdependencies among risk, capital, and efficiency, confirming the existence of a sustainable risk–capital–efficiency nexus. The results reveal that bank risk is strongly persistent; however, ESG performance significantly mitigates credit risk, particularly through its social and governance dimensions, which enhance transparency, borrower discipline, and stakeholder trust. Efficiency also acts as a stabilizing force by reducing overall risk. Capital adequacy is positively influenced by ESG performance and efficiency, indicating that sustainable and well-managed banks maintain stronger capital buffers and more resilient balance sheets. Furthermore, bank efficiency improves with profitability, capitalization, favorable macroeconomic conditions, and socially oriented ESG engagement. These findings demonstrate that ESG adoption is a strategic driver of financial soundness, simultaneously lowering risk, reinforcing capital strength, and enhancing operational performance. The paper offers important implications for regulators and bank managers, highlighting the need to embed ESG metrics into supervisory frameworks, risk-management systems, and long-term strategic planning. Full article

Institutional quality and globalization are crucial in influencing both the prevalence and quality of sustainable entrepreneurial ecosystems within an economy. This study examines the relationship between Opportunity-Driven Entrepreneurship (ODE); entrepreneurial quality, as measured by the Motivational Index (MI), and institutional quality, assessed through economic freedom and governance, in high- and middle-income countries. It also examines how globalization impacts both ODE and MI in these country groups. Using data from the Global Entrepreneurship Monitor (GEM) and combined indices of economic freedom, governance, and globalization, the study analyzes an unbalanced panel dataset comprising 64 countries from 2004 to 2018. Estimation is performed using the Robust Least Squares (RLS) method. The findings show that economic freedom has a positive and significant effect on both ODE and MI across high- and middle-income countries. In contrast, governance has a significant impact on ODE and MI only in high-income countries. Globalization exerts a negative influence on ODE across both income groups, with the adverse effect being more pronounced in middle-income countries. Conversely, its effect on MI is positive in middle-income countries but shows no significant influence in high-income economies. The study offers valuable insights for economists, policymakers, and scholars interested in the forces that shape ODE. Full article

The rapid development of rural tourism has become a significant force in promoting rural revitalization. However, the unbalanced distribution of value increment generated by the land tourism-oriented transformation has led to various conflicts and has affected the sustainable development of rural tourism. This study selected Zhongyuan Township, Jing’an County, Jiangxi Province, China, as the research site. Data were collected through semi-structured interviews and questionnaire surveys from government entities, village collectives, and investors, yielding 24 interview transcripts and 665 valid questionnaires. By integrating the Shapley value method, grounded theory, structural equation modeling, and the analytic hierarchy process, this study was conducted according to the frame-work of “tracing influencing factors, deconstructing influence mechanisms, and optimizing the distribution model”. The data were analyzed using grounded theory and structural equation modeling. The results indicated that, in addition to the factors influencing land value increment, policy drivers and risk factors also exerted a direct and significant impact on value increment distribution. Based on these findings, the traditional Shapley value model was optimized to produce a more equitable and efficient distribution framework. The optimized value increment distribution model overcomes the limitations of the traditional Shapley value method in addressing complex multi-stakeholder interests, not only making the distribution of land value increment in rural tourism contexts more equitable and efficient but also providing a scientific decision-making tool for balancing economic development, social equity, and ecological protection—laying a solid foundation for the sustainable development of rural tourism destinations. This study provides a scientific decision-making tool for balancing stakeholder interests in rural tourism development and contributes to the theoretical refinement of benefit distribution mechanisms in sustainable tourism. Full article

The safe and stable operation of ultra-high-voltage (UHV) transmission lines is fundamental to ensuring efficient and large-capacity power delivery. Critical fittings, as essential load-bearing components connecting towers, conductors, and insulator strings, are highly susceptible to damage under complex ice–wind conditions, thereby posing significant threats to grid security. To address the prevalent issues of jumper spacer breakage and conductor abrasion observed in field maintenance, a systematic finite element analysis model incorporating bundled conductors, jumper structures, and associated fittings was established. This model enabled comprehensive investigation of the effects of non-uniform ice accretion, wind loading, and ice-shedding impacts on the bearing characteristics of critical fittings. Through high-throughput computational simulations, a large-scale dataset capturing the bearing characteristics of jumper spacers was constructed. Based on this dataset, a damage risk assessment model under complex ice–wind conditions was developed using a multi-layer feedforward deep neural network (MLF-DNN). The results indicated that wind loading had a relatively minor influence on jumper spacers, whereas ice accretion and ice-shedding impacts were the dominant factors leading to damage. In particular, non-uniform ice-shedding readily induced unbalanced forces among sub-conductors, significantly increasing stress levels in jumper spacers and resulting in substantial risk. The proposed risk assessment model demonstrated high predictive accuracy and strong generalization capability, providing effective support for rapid evaluation and early warning of damage to fittings in UHV transmission lines under complex ice–wind environments. Full article

This paper presents the design, optimization, and validation of a dual three-phase yokeless and segmented armature (YASA) axial flux permanent magnet (AFPM) machine for aerospace actuators. The proposed 12-slot, 10-pole topology employs segmented soft magnetic composite (SMC) stator teeth integrated into an additively manufactured aluminium holder, combining modularity, weight reduction, and improved thermal conduction. A multi-objective optimization process based on 3D finite element analysis (FEA) was applied to balance torque capability and losses. The manufacturable design achieved a peak torque of 28.3 Nm at 1400 rpm and a peak output power of 3.5 kW with an efficiency of 81.6%, while limiting short-circuit currents to 14 Arms. Transient structural simulations revealed that three-phase short circuits induce unbalanced axial forces, exciting rotor wobbling—a phenomenon not previously reported for YASA machines. A prototype was fabricated and tested, with static torque measurements deviating by 8.6% from FEA predictions. By contrast, line-to-line back-EMF and generator-mode power output exhibited larger discrepancies (up to 20%), attributed to the frequency-dependent permeability and localized eddy currents of the SMC stator material introduced during EDM machining. These results demonstrate both the feasibility and the limitations of YASA AFPM machines for aerospace applications. Full article

This study proposes a dynamic balancing method without trial weights for power turbine rotors and investigates how the axial location chosen for unbalance identification affects the balancing performance. A finite element model of the power turbine rotor system was established to compute transient vibration responses and principal modes. Both continuous and isolated unbalances are employed to identify unbalanced excitation forces, enabling the determination of unbalance parameters. Furthermore, variations in identification accuracy across four designated axial positions on the rotor were analyzed. Simulations and experiments conducted on boss 2 and boss 3 confirmed the method’s efficacy: the maximum vibration amplitudes were reduced by 70.48% and 45.81% for boss 2, and by 64.48% and 61.00% for boss 3, respectively. These results verify the effectiveness of the proposed method. The unbalance parameters identified from simulations exhibited errors within ±6°, ±0.12 g, and ±0.15 × 10⁻⁴ m, while experimental errors remained within ±5°, ±0.11 g, and ±0.10 × 10⁻⁴ m, demonstrating high accuracy and reliability. Notably, this method improves balancing efficiency by requiring only a single startup and facilitates vibration data acquisition in confined spaces. Full article

Bearing health monitoring is essential for ensuring the reliability and operational safety of induction machines, as bearing faults remain among the most frequent failure modes in rotating electrical equipment. This work contributes to condition monitoring by enhancing the robustness of health indicators and developing a supply-frequency-independent health indicator (HI) for bearing fault diagnosis using Motor Current Signature Analysis (MCSA). The objective is to design an HI capable of reliably representing the bearing degradation state under varying operating conditions, particularly when the supply frequency changes. To achieve this, the study briefly examines the key physical mechanisms governing the detectability of bearing-related spectral signatures—including rotational frequency, unbalanced magnetic pull, eddy currents, skin effect, and hydrodynamic forces. The theoretical analysis establishes the overall trend expected under varying supply frequencies and clarifies how these phenomena collectively influence the spectral characteristics of the fault components and the frequency-dependent evolution of their amplitudes. These insights are experimentally validated using induction machines fitted with bearings of two fault severities. Leveraging this physical understanding, a modified regression-based compensation model is introduced to reduce the frequency-dependent variation in the HI. The resulting compensating factor effectively stabilizes the frequency response, producing a more consistent and monotonic degradation trend across the tested conditions. The proposed method is computationally lightweight, does not require run-to-failure data or detailed physical modeling, and is suitable for real-time implementation. By integrating physical insight with data-driven modeling, this work presents a practical and frequency-independent HI framework that can be readily deployed within digital-twin-based condition monitoring architectures for induction machines. Full article

In-wheel motor (IWM)-driven electric vehicles (EVs) are susceptible to road excitation, which can induce eccentricity between the stator and rotor of the IWM. This eccentricity leads to unbalanced electromagnetic forces (UEFs) and electromechanical coupling (EMC) effects, severely degrading vehicle dynamic performance. To address this issue, this study first established an EMC system model encompassing UEF, IWM drive, and vehicle dynamics. Based on this model, four typical operating conditions—constant speed, acceleration, deceleration, and steering—were designed to thoroughly analyze the influence of EMC effects on vehicle dynamic response characteristics. The analysis results were validated through real-vehicle experiments. The results indicate that the EMC effects caused by motor eccentricity primarily affect the vehicle’s vertical dynamics performance (especially during acceleration and deceleration), leading to increased vertical body acceleration and reduced ride comfort, while having a relatively minor impact on longitudinal and lateral dynamics performance. Additionally, these effects significantly increase the relative eccentricity of the motor under various operating conditions, further degrading motor performance. To mitigate these negative effects, this paper designs an active suspension controller based on distributed model predictive control (DMPC). Simulation and experimental validation demonstrate that the proposed controller effectively improves ride comfort and body posture stability while significantly suppressing the growth of the motor’s relative eccentricity, thereby enhancing motor operational performance. Full article

This study investigates the electromagnetic and NVH characteristics of an outer-rotor surface-mounted permanent magnet synchronous generator (SPMSG) for wave energy applications, focusing on the effect of rotor eccentricity. To reflect potential fault due to manufacturing or assembly defects, a 0.5 mm rotor eccentricity was introduced in finite element method (FEM) simulations. The torque ripple waveform was analyzed using fast Fourier transform (FFT) to identify dominant harmonic components that generate unbalanced electromagnetic forces and induce structural vibration. These harmonic components were further examined under variable marine operating conditions to evaluate their impact on acoustic radiation and vibration responses. Based on the simulation and analysis results, a design-stage methodology is proposed for predicting vibration and noise by targeting critical harmonic excitations, providing practical insights for marine generator design and improving long-term operational reliability in wave energy systems. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

As the core driving force of the new generation of industrial revolution, artificial intelligence technology has brought new opportunities for empowering enterprise innovation and advancing sustainability. Focusing on Chinese A-share listed enterprises and based on the unbalanced panel data from 2011 to 2023, this study systematically examines the relationship mechanism between artificial intelligence (AI) application and enterprise breakthrough innovation, and further explores the mediating effect of knowledge recombination and the moderating role of market competition. The empirical results show that AI application has a significant promoting effect on the enterprise breakthrough innovation. Knowledge recombination creation and knowledge recombination reuse play mediating roles in the relationship between the AI application and enterprise breakthrough innovation, forming the key transmission path for empowering breakthrough innovation with AI. In addition, market competition positively moderates the relationship between knowledge recombination and enterprise breakthrough innovation and strengthens the driving effect of knowledge recombination on innovation output, thus fostering more sustainable competitive advantages. Full article

This study proposes a novel computational method, employing the integral dynamics of multibody systems to simulate the transverse vibrations of the rotor in a cantilever-type centrifugal pump. This method was applied to the kinematic assembly of the rotor and its supports, with the latter modeled as springs possessing stiffness and damping properties equivalent to those of real bearings supporting the shaft in an actual design. To investigate transverse vibrations within the system, three key observation points were defined—at the locations of the left and right bearings, as well as at the rotor’s center of mass—to allow for a thorough dynamic analysis. Additionally, the influence of motor rotational speed and the impeller’s eccentricity on the transverse vibrations of the supports and the shaft was examined. The results have revealed that transverse vibrations significantly affect the system’s dynamics at lower rotational speeds, leading to the classification of the shaft as flexible. As the rotational speed increases, the system exhibits enhanced dynamic stability. Furthermore, it was found that for impellers with a diameter less than 300 mm, the unbalanced forces are negligible and can be disregarded in pump design. To reduce vibration levels, an elastic damping ring was selected and incorporated into the system. This novel method provides an effective tool for analyzing the transverse vibrations of centrifugal pump rotors and for optimizing vibration mitigation strategies. Full article