Search Results (4,273)

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation. Full article

Improving the cultivated land use eco-efficiency (CLUE) is crucial to achieving sustainable land use and the green transformation of agriculture. This study is based on the data from 353 prefecture-level cities in China from 2013 to 2021. The slacks-based measurement (SBM)-undesirable model, the social network analysis (SNA), and the fuzzy set qualitative comparative analysis (fsQCA) are adopted to measure and analyze the spatial patterns, network characteristics, and multiple driving pathways of inefficiency in the cultivated land use eco-efficiency in prefecture-level administrative units. Results show the following: (1) From 2013 to 2021, CLUE in the study areas shows spatial heterogeneity, with most efficiency values at a moderate level and showing a fluctuating downward trend over time. (2) The nine major agricultural regions have formed a complex association network, with the overall network connectivity being weak but efficiency relatively high. The hierarchical structure is gradually flattening, and inter-regional cooperation is increasing. (3) There are significant differences in influence, control, and accessibility within individual networks, and the collaborative network is developing into a “multi-core-hierarchical” structure. (4) The formation of inefficiency involves multiple concurrent mechanisms. Four typical inefficiency paths were identified, with significant heterogeneity across different agricultural regions. In the future, differentiated land use and ecological protection policies should be implemented based on the spatial network characteristics and inefficiency driving pathways of each agricultural region to promote the coordinated improvement of CLUE. Full article

Motoneurons are under strong pressure to maintain stable motor output throughout an individual life, through homeostatic regulation of their electrical properties. Dysregulated spinal motoneuron excitability has long been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Recent work in SOD1^G93A mice suggests that the homeostatic response of motoneurons becomes dysregulated as cellular processes are disrupted by the disease, causing fluctuations in motoneuron electrical properties. Yet, few studies directly test whether ALS motoneurons respond differently than wild-type motoneurons to a common chronic perturbation. Here, we used in vivo electrophysiology to test whether motoneurons from pre-symptomatic SOD1^G93A mice modulate excitability differently than wild-type motoneurons in response to the same homeostatic perturbation: chronic inhibition exerted by the benzodiazepine diazepam. Using linear mixed-effects statistical models, we assessed whether diazepam treatment differentially modulated passive properties, firing behavior, spike properties, and/or synaptic inputs in SOD1^G93A versus wild-type motoneurons. We identified a significant genotype × treatment interaction effect selectively for properties related to passive membrane integration and spike initiation, including membrane time constant, peak input resistance, and recruitment current. In contrast, firing gain, spike waveform characteristics, and synaptic inputs were largely unaffected. These findings indicate that sustained inhibitory perturbation selectively triggered overactive intrinsic compensatory mechanisms in SOD1^G93A motoneurons rather than inducing widespread changes in firing or synaptic transmission. Together, our results provide direct evidence for over-active homeostatic control of motoneuron excitability and support a view of motoneuron dysfunction in ALS as a problem of altered feedback regulation rather than simply hyper- or hypo-excitability. Full article

A novel non-contact piezoelectric motor modulated by electromagnetic force is proposed in this work. The motor consists of a driving system and a transmission system. The transmission system includes a driving torque modulation mechanism and a keeping torque modulation mechanism. The calculation model of the magnetic forces of the motor is deduced, based on which the calculated equations of the magnetic driving torque, the magnetic keeping torque, the total torque, and the torque fluctuation applied to the rotor are presented. The transfer functions of the motor torque and its proportional-integral (PI) control are also given. Compensation control is used to remove the torque fluctuation. Via the derived equations, the effects of the system parameters on the system gain and time constant are investigated. Moreover, the step responses of the motor torque and the effects of the system parameters on them are analyzed, as are the step responses of the closed-loop control system with a PI controller. Furthermore, the torque fluctuation of the rotor is investigated, and its compensation signals are determined. Finally, the compensation control of the torque fluctuation is realized by adding feedback compensation signals. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

Aiming at the deterioration in Noise, Vibration and Harshness (NVH) performance caused by broadband torsional vibration in the integrated electric drive system (IEDS) of electric vehicles, most existing studies independently focus on electromagnetic excitation suppression or torsional vibration control of mechanical transmissions. Few researchers consider the coupling characteristics between the electromagnetic nonlinearity of motors and the nonlinearity of gear transmissions, making it difficult to realize the coordinated suppression of high- and low-frequency torsional vibration. In this paper, a seven-degree-of-freedom electromechanical coupling dynamic model is firstly established, which incorporates the electromagnetic torque ripple of the motor, the time-varying meshing stiffness of gears, meshing errors, and gear backlash nonlinearity. Through modal analysis and Campbell diagram solution, the natural characteristics and critical speed range of the system are clarified, and the generation mechanism of full-frequency band torsional vibration as well as the high–low frequency coupling characteristics are systematically revealed. On this basis, a coordinated active control strategy based on PD pole placement and harmonic current injection (PD-HCI) is proposed. The PD pole placement controller is adopted to suppress the low-frequency torsional vibration (0–20 Hz) of the transmission system, and the 5th/7th harmonic current injection is used to counteract the high-frequency torque ripple (above 200 Hz) of the motor, thereby achieving the coordinated suppression of broadband torsional vibration. The Matlab/Simulink R2023a simulation results show that the proposed control strategy reduces the torque fluctuation rate from 3.11% to 1.96%, the speed fluctuation rate from 0.10% to 0.03%, and the total harmonic distortion (THD) of stator current from 8.69% to 1.77% under steady-state operating conditions. Under transient operating conditions with sudden load changes, the stabilization time of fluctuations in speed and half-shaft torque is shortened by more than 80%, the impact amplitude is significantly reduced, and there is no loss in the vehicle’s dynamic response and speed tracking performance. Experimental results show that the coefficients of determination R2 of vehicle speed, motor speed, acceleration and torque are 0.9990, 0.9982, 0.9997 and 0.9997, respectively, which verifies the reliability of the established model. Full article

Drilling in deep hard formations poses significant challenges for conventional polycrystalline diamond compact (PDC) cutters, which often suffer from low rock-breaking efficiency and premature failure due to severe cutter-face wear, high thermal loads, and stick-slip vibrations. To overcome these limitations, this study proposes a chisel-shaped PDC cutter and systematically investigates its rock-breaking and thermal characteristics. A coupled temperature–displacement finite element model (FEM) of cutter–granite interaction and a single-cutter indentation model were developed based on elastoplastic mechanics and the Drucker–Prager failure criterion. The rock constitutive parameters used in both models were validated through uniaxial compression tests. Using these models, the influences of cutter shape, back rake angle, and depth of cut (DOC) were analyzed. Compared with a conventional cylindrical cutter, the chisel cutter reduces the cutting force by 13.4% and the axial penetration reaction force by 22%. The cutting force of the chisel cutter remains consistently lower across all tested depths. The optimal back rake angle is 20–25°, and the optimal DOC is 1.5 mm. Full-bit simulations further demonstrate that the chisel-cutter bit creates a more concentrated bottomhole stress field, increases the rate of penetration (ROP) by 19.7%, reduces average torque by 11.34%, and produces smoother torque fluctuations, indicating higher drilling stability. Thermal analysis reveals that the chisel cutter exhibits lower and more stable cutter-face temperatures. Both simulation and experimental results confirm that the chisel design reduces the friction contact area between cuttings and the cutter face, thereby lowering temperature accumulation. Field drilling data corroborate the reliability of the conclusions. These findings provide guidance for the design of PDC bits intended for deep hard formations. Full article

Achieving a favorable synergy of strength, ductility, and toughness is a critical challenge for expanding the engineering applications of titanium alloys. In this work, a medium-strength and high-toughness novel Ti-Al-Mo-V-Cr-Sn-Zr (named Ti62F) titanium alloy in the form of a Φ400 mm bar was adopted to systematically investigate the regulation behavior of double annealing on its microstructure and mechanical properties, and quantitative correlations between microstructural parameters and macroscopic properties were established. Increasing the cooling rate during the first annealing stage (air cooling, force air cooling and water quenching) significantly refined the secondary α (α_s) phase and reduced the volume fraction and size of the primary α (α_p) phase, leading to an increase in the ultimate tensile strength of the alloy from 1077 MPa to 1229 MPa. However, the impact-absorbed energy decreased from 51.5 J to 23.3 J. When the second annealing temperature was varied within the range of 625–675 °C, the ultimate tensile strength fluctuated slightly and the impact toughness increased moderately. Equiaxed α_p phase and relatively thick α_s can induce multiple crack deflections, prolong the crack propagation path and enhance energy absorption. Dislocations are mainly piled up at α/β phase boundaries, triggering void nucleation and growth, which dominate the ductility and toughness levels. Tensile twinning acts only as an auxiliary deformation mechanism and contributes limitedly to toughness. After heat treatment under the optimized schedule of 880 °C/2 h/AC + 650 °C/4 h/AC, the Ti62F alloy exhibits a superior strength–toughness balance compared with conventional medium-strength titanium alloys such as TA15, TC4, and TC4-DT. The findings can provide a heat treatment basis for microstructural regulation of large-size Ti62F bars and their engineering applications in aerospace structural components. Full article

In past decades, the macroeconomic stability of India has been tested repeatedly by major global disruptions, including oil price shocks, the 2008 global financial crisis and the COVID-19 pandemic. Analysing how macroeconomic variables respond to these shocks is essential for evaluating external vulnerability and policy resilience in emerging economies. Our study provides a comprehensive empirical investigation of the dynamic responses of wholesale price inflation, industrial output, oil prices and exchange rates in India by employing monthly data from January 1993 to December 2024. To examine long-run equilibrium relationships along with short-run adjustment dynamics, the present study employs co-integration analysis within a Vector Error Correction Model (VECM) framework. Further, we applied impulse response functions and forecast error variance decomposition to track volatility spillover mechanisms. Quantile regression and ARCH–GARCH models were further estimated to account for distributional heterogeneity and time-varying volatility. The findings of our study suggested stable long-run linkages among the selected variables, where oil price shocks emerged as a key external source of macroeconomic fluctuations. Short-run dynamics suggested that shocks in oil prices are transmitted primarily through inflation and exchange rate channels and then affect industrial output. Distributional estimates revealed the effects were stronger during stress periods, indicating tail risks that were not captured by the mean-based models. Lastly, volatility analysis confirmed persistent clustering, especially during phases of crisis. Overall, the findings suggest that India’s macroeconomic system remains externally sensitive, with adjustment mechanisms that operate gradually but come under strain during global disruptions. These results underscore the importance of energy risk management and crisis-responsive macroeconomic stabilisation policies. Full article

Eustachian tube dysfunction (ETD) and related pressure-mediated otologic disorders often present with fluctuating auditory, vestibular, and pressure-related symptoms that are difficult to explain using static structural or symptom-based diagnostic labels alone. This conceptual review proposes the Neuro–Vascular–Mechanical–Inflammatory–Autonomic (NVMIA) framework as a hypothesis-generating architecture for organizing such variability. Within this framework, middle ear pressure (MEP) is interpreted as a clinically measurable physiologic variable through which interacting neural, vascular, mechanical, inflammatory, and autonomic influences may become mechanically expressed and clinically observable. The framework does not present NVMIA-based patterns as validated diagnostic categories, clinical decision tools, or treatment algorithms. Rather, it proposes provisional regulatory patterns that may help generate testable hypotheses regarding pressure-regulatory instability, cross-axis coupling, symptom fluctuation, and physiologic reversibility. Mechanical impedance may function as an accessible reference plane for future empirical assessment, while neural, vascular, inflammatory, and autonomic domains are conceptualized as modulatory axes that may alter symptom expression and response variability. The review further outlines future validation needs, including dynamic MEP measurement, patient-reported outcome integration, longitudinal response assessment, and cautious computational modeling. By reframing ETD as a model of state-dependent regulatory instability, the NVMIA framework provides a conceptual basis for future studies in precision otology while emphasizing that prospective validation is required before clinical implementation. Full article

This study investigates the synergistic regulation mechanism of water–heat–salt transport in the black soil of cold regions in Northeast China by combining field monitoring with HYDRUS-2D simulations. Four tillage treatments were evaluated: control group (CK), no-tillage with flat straw mulching (NM), ridge tillage with flat straw mulching (RM), and straw return with rotary tillage (RR). Monitoring data indicated that all straw incorporation treatments significantly improved soil moisture retention capacity. Compared with CK, soil water content under RM increased by 63.93% correspondingly; soil salinity in CK was 5.75–13.68% higher than that in straw-amended treatments. In addition, RM exerted a more prominent regulatory effect on soil temperature fluctuations relative to CK. Simulation results reveal that straw incorporation effectively reduces surface runoff, thereby substantially weakening the driving force for upward salt migration. Structural equation modeling (SEM) quantified path coefficients, revealing that straw incorporation optimizes the soil microenvironment. This integrated approach provides a mechanistic basis for black soil conservation in seasonally frozen regions, identifying RM as the optimal management practice to balance water retention and salt inhibition. Full article

Plunger pumps serve as core power equipment in oilfield water injection systems, where their reliable operation directly affects crude oil recovery efficiency and production safety. Failures such as mechanical wear and seal leakage can cause injection pressure fluctuations, increased energy consumption, and even pipeline burst accidents. This study addresses the challenges in plunger pump fault diagnosis, including the difficulty in capturing multi-scale fault features, interference from redundant information in high-dimensional feature spaces, and high model computational complexity. We propose a lightweight fault diagnosis approach called Multi-scale Attention Neural Network (MSLAN), which combines multi-scale convolution and attention mechanisms. In this model, a Separable Multi-scale Fusion Module (SMSF) employs parallel multi-branch convolutional kernels to acquire fault signatures across multiple scales, while computational overhead is reduced through depthwise separable convolution and shared pointwise convolution. Additionally, a Multi-Branch Parallel Attention Module (MBPA) is introduced to finely model complex inter-channel dependencies through a four-branch parallel structure, enhancing the perception of key features and suppressing redundant information. Experimental results on a self-constructed plunger pump dataset, the Case Western Reserve University bearing dataset, and the Southeast University gearbox dataset demonstrate that MSLAN achieves F1-scores of 88.95%, 98.89%, and 99.90%, respectively. While maintaining high diagnostic accuracy, the model exhibits significantly lower parameter count and computational cost compared to baseline models, effectively balancing diagnostic precision and computational efficiency. Ablation studies and visualization analyses further validate the effectiveness of each module. This study establishes an accurate and efficient intelligent fault diagnosis solution for plunger pumps, which is also readily applicable to a broader range of rotating machinery. Full article

Perinatal mental illness affects up to 20% of new mothers worldwide, yet despite a growing research interest over the past decade, the etiology is still not fully understood, and clinical treatment guidelines remain inconsistent across countries and services. In this review, recent findings on neurobiological processes and evolutionary mechanisms, as they occur during the menstrual cycle, pregnancy, birth, postpartum and breastfeeding, are discussed. The intention is to raise awareness of physiological changes in pregnancy that might be relevant to the differential diagnosis and clinical treatment of perinatal psychiatric disorders such as depression, anxiety, PTSD after childbirth, bipolar relapse, postpartum psychosis, obsessive-compulsive symptoms, substance-use disorders, and suicidality. Areas addressed include the activities of the immune system, thyroid gland, cortisol, sleep and individual sensitivity to ovarian hormone fluctuations. Evolutionary biological mechanisms intended to sustain pregnancy and to ensure the survival of the newborn are assumed to have potent effects on the maternal brain. These non-pathological adaptations could provide grounds for a better understanding of risk factors and the etiology of perinatal mental illness. Full article

Investigation of the drill–rock interaction mechanism is fundamental to the design and optimization of drilling engineering. Existing theoretical studies have primarily focused on intact and homogeneous rock formations, whereas analytical studies of drilling in jointed rock masses remain limited. To address this issue, this study examines the drilling behavior of jointed rock masses and analyzes the forces acting on the drill bit during joint crossing. It is proposed that the non-uniform distribution of rock at the borehole bottom generates an additional longitudinal torque on the drill bit within the vertical plane. Based on whether this torque is considered, both static and dynamic mechanical models for drilling across a joint interface are established. Using parametric analyses, the evolution of weight on bit (WOB) and torque under the two modeling approaches is investigated, together with their sensitivity to differences in mechanical properties of rocks on either side of the joint. The results show that when the drill bit penetrates from hard rock into soft rock across a joint interface, both WOB and torque continuously decrease if longitudinal torque is neglected. In contrast, when longitudinal torque is considered, WOB and torque first increase and then decrease. The longitudinal torque increases both drilling parameters, with maximum increments in WOB and torque reaching 56.0% and 2.8%, respectively, indicating a more pronounced influence on WOB. As the mechanical-property differences between rocks on either side of the joint increase, the relative increments of WOB and torque, which characterize load fluctuation magnitude, initially increase and then gradually stabilize. The critical increments at stabilization are 0.73 for the internal friction angle and 7.17 for cohesion. These findings indicate that WOB and torque variations during joint crossing increase with increasing strength contrast across the joint interface, and that differences in internal friction angle exert a greater influence than cohesion. This study is primarily theoretical, and the proposed models are preliminarily validated through comparison with results in the literature. The developed analytical models reveal the drill–rock interaction mechanisms during drilling in jointed rock masses, clarify the influence of longitudinal torque on drilling parameters, introduce quantitative indices characterizing WOB and torque fluctuations, and establish their relationships with mechanical-property contrasts across joint interfaces. The findings provide a theoretical basis for interpreting drilling responses induced by geological discontinuities, evaluating jointed rock masses, and optimizing drilling parameter design. Full article

Small and medium-sized (SMS) lakes in cold–arid regions are highly sensitive to climate change and play critical roles in regional hydrological and ecological processes. However, their long-term dynamic evolution along aridity–humidity gradients remains insufficiently understood. This study aims to reveal the spatiotemporal variations in SMS lakes on the Inner Mongolia Plateau and clarify their climatic driving mechanisms. Based on Landsat imagery and meteorological data (1984–2021) on the Google Earth Engine (GEE) platform, this study quantified the spatiotemporal variations in SMS lakes and adopted an ecological–geographical zoning framework to characterize lake responses across aridity–humidity gradients. Results indicate that, from 1984 to 2021, the total area of SMS lakes showed an insignificant linear trend but a net increase of 117% (396.50–860.33 km²), while the lake number increased by 155%, with 59 new lakes. The dynamics followed four stages: expansion (1984–1993), fluctuation (1994–2002), low-level stability (2003–2011), and recovery (2012–2021). Notably, recovery levels remained below the pre-2003 peak, with 2003 identified as a critical turning point. Lake numbers responded to climatic stress earlier than area changes. Spatially, lake variations in arid regions were primarily controlled by energy-related factors (e.g., temperature and potential evapotranspiration), while lake changes in semi-humid regions were dominated by precipitation-regulated water availability. Semi-arid regions presented transitional characteristics constrained by both energy and water factors. Although extreme weather events did not dominate long-term lake evolution, they significantly exacerbated short-term lake fluctuations. Overall, the controlling mechanism of SMS lakes shifted from energy limitation to water regulation under ongoing climate warming, highlighting pronounced regional differences in climate–lake interactions. Full article