Search Results (993)

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article

The growing demand for traceable, high-precision attenuation measurements in electromagnetic compatibility (EMC) testing and low-frequency wireless communication systems has driven the development of a primary attenuation standard covering 1 kHz to 10 MHz. The system employs a dual channel null-detection method using an inductive voltage divider (IVD) as a reference, ensuring the highest accuracy and traceability while eliminating sensitivity to detector nonlinearity. Attenuation at 1 kHz, 9 kHz, and 10 kHz is measured directly against the IVD ratio, while higher-frequency measurements (100 kHz–10 MHz) are performed via heterodyne detection, down-converting signals to 1 kHz for comparison. To ensure comparable accuracy at higher attenuation levels, a double-step method is applied at 9 kHz and 10 kHz to mitigate the increased IVD uncertainty above 1 kHz. Linearity is ensured by suppressing common-mode currents with toroidal ferrite chokes and minimizing inter-channel coupling. Type B (non-statistical) measurement uncertainties are evaluated, with major contributions from the IVD reference, system errors, and mismatch. The expanded uncertainties are 2.2 × 10⁻³ dB at 20 dB, 3.0 × 10⁻³ dB at 40 dB, and 4.0 × 10⁻³ dB at 60 dB attenuation. To facilitate wider dissemination and extend the calibration range, a resistive step attenuator with 10 dB pads is evaluated as a practical transfer standard, providing a simple and robust solution for traceable attenuation calibration in this frequency range. Full article

Traditional Hopfield neural networks (HNNs) suffer from limitations in generating controllable chaotic dynamics, which are essential for applications in neuromorphic computing and secure communications. Memristors, with their memory-dependent nonlinear characteristics, provide a promising approach to regulate neuronal activities, yet systematic studies on attractor offset behaviors remain scarce. In this study, we propose a fully memristive electromagnetic radiation neural network by incorporating three distinct memristors as external electromagnetic stimuli into an HNN. The parameters of the memristors were tuned to modulate chaotic oscillations, while variations in initial conditions were employed to explore multistability through bifurcation and basin stability analyses. The results demonstrate that the system enables large-scale amplitude control of chaotic signals via memristor parameter adjustments, allowing arbitrary scaling of attractor amplitudes. Various offset behaviors emerge, including parameter-driven symmetric double-scroll relocations in phase space and initial-condition-induced offset boosting that leads to extreme multistability. These dynamics were experimentally validated using an STM32-based electronic circuit, confirming precise amplitude and offset control. Furthermore, a multi-channel pseudo-random number generator (PRNG) was implemented, leveraging the initial-boosted offset to enhance security entropy. This offers a hardware-efficient chaotic solution for encrypted communication systems, demonstrating strong application potential. Full article

Crude oil and grain, as two pivotal global commodities, exhibit significant price co-movement that profoundly affects national economic stability and food security. From the perspective of systems theory, the energy and grain markets do not exist in isolation but rather form a highly coupled complex system, characterized by nonlinear feedback, cross-market risk contagion, and cascading effects. This study systematically investigates the transmission mechanisms from international crude oil prices to the domestic prices of Chinese four major grains, employing the DY spillover index, Vector Error Correction Model (VECM), and a mediation effect framework. The empirical findings reveal three key insights. First, rising international crude oil prices significantly strengthen the pass-through of global grain prices to domestic markets, while simultaneously weakening the effectiveness of domestic price stabilization policies. Second, higher crude oil prices amplify international-to-domestic price spillovers by increasing maritime freight costs, a key channel in global grain trade logistics. Third, elevated oil prices stimulate demand for renewable biofuels, including biodiesel and ethanol, thereby boosting international demand for corn and soybeans and intensifying the transmission of price fluctuations in these commodities to the domestic market. These findings reveal the key pathways through which shocks in the energy market affect food security and highlight the necessity of studying the “energy–food” coupling mechanism within a systems framework, enabling a more comprehensive understanding of cross-market risk transmission. Full article

This study investigates the transverse loads acting on high-speed train bogie frames under actual service conditions. To enable direct identification, the locating arms were instrumented as bending sensors and calibrated under realistic lateral-stop constraints, ensuring robustness of the measurement channels. Field tests were conducted on a CR400BF high-speed EMU over a 226 km route at six speed levels (260–390 km/h), with gyroscope and GPS signals employed to recognize typical operating conditions, including straights, curves, and switches (straight movement and diverging movements). The results show that the proposed recognition method achieves high accuracy, enabling rapid and effective identification and localization of typical operating conditions. Under switch conditions, the bogie frame transverse loads are characterized by low-frequency, large-amplitude fluctuations, with overall RMS levels being higher in diverging switches and straight-through depot switches. Curve parameters and speed levels exert significant influence on the amplitude of the transverse-load trend component. On curves with identical parameters, the trend-component amplitude exhibits a quadratic nonlinear relationship with train speed, decreasing first and then increasing in the opposite direction as speed rises. In mainline curves and straight sections, the RMS values of transverse loads on Axles 1 and 2 scale proportionally with speed level, with the leading axle in the direction of travel consistently producing higher transverse loads than the trailing axle. When load samples are balanced across both running directions, the transverse load spectra of Axles 1 and 2 at the same speed level show negligible differences, while the spectrum shape index increases proportionally with speed level. Full article

Silicon carbide (SiC) is a promising semiconductor material for electronics and photonics. Ultrafast laser processing of SiC enables three-dimensional nanostructuring, enriching and expanding the functionalities of SiC devices. However, challenges arise in delivering uniform, high-aspect-ratio (length-to-width) nanostructures due to difficulties in confining light energy at the nanoscale while simultaneously regulating intense photo modifications. In this study, we report the controllable growth of long-distance, high-straightness, and high-parallelism multifilament structures in SiC using ultrafast laser processing. The mechanism is the formation of femtosecond multifilaments through the nonlinear effects of clamping equilibrium, which allow highly confined light to propagate without diffraction in parallel channels, further inducing high-aspect-ratio nanostripe-like photomodifications. By employing an elliptical Gaussian beam—rather than a circular one—and optimizing pulse durations to stabilize multifilaments with regular positional distributions, the induced multifilament structures can reach a length of approximately 90 μm with a minimum linewidth of only 28 nm, resulting in an aspect ratio of over 3200:1. Raman tests indicate that the photomodified regions consist of amorphous SiC, amorphous silicon, and amorphous carbon, and photoluminescence tests reveal that silicon vacancy color centers could be induced in areas with lower light power density. By leveraging femtosecond multifilaments for diffraction-less light confinement, this work proposes an effective method for manufacturing deep-subwavelength, high-aspect-ratio nanostructures in SiC. Full article

Flow dynamics were characterized and stable zones in diversions were quantified using physical modeling, in situ experiments, and 3D numerical simulations. ADV (1 cm spatial resolution) and water-level probes (0.01 cm spatial resolution) were used in the physical experiments in a rectangular channel. ADCP (resolution of 50 cm) was employed for in situ validation at a northern China hub. Numerical simulations using ANSYS 2022R2 Fluent software with RNG k-ε and VOF showed little error (<15%) compared to the experiments. The results quantified the diversion zone into four sub-regions: acceleration (length 0.8–1.2 h); stabilization (1.2–3.5 h); diffusion deceleration (3.5–5.0 h); and stagnation (localized eddies, diameter 0.3–0.8 d). The stable zone length was dominantly controlled by the nonlinear coupling of geometric (B_s/B_m, 42%) and hydraulic (Fr, 28%) parameters. Upstream and downstream stable zone empirical models showed high accuracy (R² = 0.83 and 0.76, p < 0.01), with an average relative error <15%. Based on the proposed zoning principles and flow characteristics, measurement facilities in the irrigation area are presented. These tools enhance irrigation diversion design and management for improved water efficiency. Full article

As global climate change intensifies the frequency of extreme weather events, urban flood control and disaster reduction efforts face unprecedented challenges. With the limitations of traditional, top-down emergency management becoming increasingly apparent, many countries are actively incorporating community-based participation into flood risk governance. While research in this area is expanding, the specific impact of urban flood inundation severity on residents’ participation in relief efforts remains significantly underexplored. To address this research gap, this study employs the Community Capitals Framework (CCF) and a Gradient Boosting Decision Tree (GBDT) model to empirically analyze 1322 survey responses from Zhengzhou, China, exploring the non-linear relationship between flood severity and public participation. Our findings are threefold: (1) As the most direct source of residents’ risk perception, flood inundation severity has a significant association with their participation level. (2) This relationship is distinctly non-linear. For instance, inundation severity within a 200 m radius of a resident’s home shows a predominantly negative relation with participation level, with the negative effect lessening at extreme levels of inundation. The distance from inundated areas, conversely, exhibits an “S-shaped” curve. (3) Flood severity exhibits a significant reinforcement interaction with both communication technology levels and government organizational mobilization. This indicates that, during public crises like flash floods, robust information channels and effective organizational support are positively related to residents’ transition from passive to active participation. This study reveals the complex, non-linear associations between flood severity and civic engagement, providing theoretical support and practical insights for optimizing disaster policies and enhancing community resilience within the broader context of urban land management and sustainable development. Full article

Driven by China’s “dual carbon” strategy, concerns about channel fairness and green investment have become key frontier issues in supply chain management. This study focuses on a two-tier supply chain under a low-carbon background and innovatively incorporates both fairness concerns and green investment perspectives. It systematically explores the impact mechanisms of fairness concern coefficients and green investment levels on channel pricing and profit distribution across four scenarios: information symmetry vs. asymmetry and the presence vs. absence of channel encroachment. The simulation results reveal the following: (1) Under information symmetry and without channel encroachment, an increase in the retailer’s fairness concern significantly enhances its bargaining power and profit margin, while the supplier actively adjusts the wholesale price to maintain cooperation stability. (2) Channel encroachment and changes in information structure intensify the nonlinearity and complexity of profit distribution. The marginal benefit of green investment for supply chain members shows a diminishing return, indicating the existence of an optimal investment range. (3) The green premium is predominantly captured by the supplier, while the retailer’s profit margin tends to be compressed, and order quantity exhibits rigidity in response to green investment. (4) The synergy between fairness concerns and green investment drives dynamic adjustments in channel strategies and the overall profit structure of the supply chain. This study not only reveals new equilibrium patterns under the interaction of multidimensional behavioral factors but also provides theoretical support for achieving both economic efficiency and sustainable development goals in supply chains. Based on these findings, it is recommended that managers optimize fairness incentives and green benefit-sharing mechanisms, improve information-sharing platforms, and promote collaborative upgrading of green supply chains to better integrate social responsibility with business performance. Full article

Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals to classify the success or failure of weaning in mechanically ventilated patients. Electromyographic signals of 40 subjects were recorded using 5-channel surface electrodes placed around the diaphragm muscle, along with an ECG recording through a 3-lead Holter system during extubation. EMG and ECG signals were recorded from mechanically ventilated patients undergoing weaning trials. Linear and nonlinear signal analysis techniques were used to assess the interaction between diaphragm muscle activity and cardiac activity. Supervised machine learning algorithms were then used to classify the weaning outcomes. The study revealed clear differences in diaphragmatic and cardiac patterns between patients who succeeded and failed in the weaning trials. Successful weaning was characterised by a higher ECG-derived respiration amplitude, whereas failed weaning was characterised by an elevated EMG amplitude. Furthermore, successful weaning exhibited greater oscillations in diaphragmatic muscle activity. Spectral analysis and parameter extraction identified 320 parameters, of which 43 were significant predictors of weaning outcomes. Using seven of these parameters, the Naive Bayes classifier demonstrated high accuracy in classifying weaning outcomes. Surface electromyographic and electrocardiographic signal analyses can predict weaning outcomes in mechanically ventilated patients. This approach could facilitate the early identification of patients at risk of weaning failure, allowing for improved clinical management. Full article

This paper examines the impact of import competition on skill premium and the moderating effect of labor market flexibility on it, using panel data from 30 provinces in China from 2010 to 2019. A dynamic panel threshold model with instrumental variables is employed to address the endogeneity problem and to identify the nonlinear moderating effect of labor market flexibility. The results show the following: (1) Import competition has a promoting effect on skill premium, and this effect declines from eastern to western regions in China. (2) The import competition increases the skill premium through the channels of enhancing regional innovation capacity and promoting industrial upgrading and rationalization. (3) There exists a significant threshold effect in the moderating effect of labor market flexibility. When labor market flexibility surpasses the threshold value of 1.330, the enhancing effect of import competition on the skill premium is alleviated, facilitating labor reallocation and wage adjustment. The integration of labor market flexibility into the globalization–inequality debate extends the existing literature for providing a new understanding of the mechanisms behind the skill premium. The policy implications are that targeted labor market reforms are essential for mitigating wage differentials between skilled and unskilled workers arising from intensified import competition. Full article

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM) to cope with the diverse operational conditions of the power amplifiers (PAs) in different user equipment (UEs). CP-OFDM leads to higher throughput when the PAs are operating in their linear region, which is mostly the case for cell-interior users, whereas DFT-S-OFDM is more appealing when PAs are exhibiting non-linear behavior, which is associated with cell-edge users. Therefore, existing waveform selection solutions rely on predefined signal-to-noise ratio (SNR) thresholds that are computed offline. However, the varying user and channel dynamics, as well as their interactions with power control, require an adaptable threshold selection mechanism. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL) that learns optimal switching thresholds for the current operational conditions. In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users’ service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. In addition, the solution accounts for the switching cost, which is related to the interruption of the communication after every switch due to implementation issues, which has not been considered in existing solutions. Results show that our proposed scheme achieves remarkable gains in terms of throughput for cell-edge users without degrading the average throughput. Full article

This paper presents a compact dynamic optical frequency domain reflectometry (D-OFDR) platform enabling millimeter-scale, distributed strain sensing for real-time structural health monitoring (SHM) of reinforced concrete subjected to seismic loading. The proposed D-OFDR interrogator employs a dual-interferometer architecture: a main interferometer for strain sensing and an auxiliary interferometer for nonlinear frequency sweep compensation. Both signals are detected by photodetectors and digitized via a dual-channel FPGA-based DAQ board, enabling high-speed embedded signal processing. A dual-edge triggering scheme exploits both the up-chirp and down-chirp of a 50 Hz bidirectional sweep to achieve a 100 Hz interrogation rate without increasing the sweep speed. Laboratory validation tests on stainless steel cantilever beams showed sub-hertz frequency fidelity (an error of 0.09 Hz) relative to conventional strain gauges. Shake-table tests on a 2 m RC column under incremental seismic excitations (scaled 10–130%, peak acceleration 0.864 g) revealed distinct damage regimes. Distributed strain data and frequency-domain analysis revealed a clear frequency reduction from approximately 3.82 Hz to 1.48 Hz, signifying progressive stiffness degradation and structural yielding prior to visible cracking. These findings demonstrate that the bidirectional sweep-triggered D-OFDR method offers enhanced real-time monitoring capabilities, substantially outperforming traditional point sensors in the early and precise detection of seismic-induced structural damage. Full article

The digital economy plays an important role in promoting green economic growth. This study evaluates the degree of green economic development generated by green innovation and green sharing based on data from 30 provinces in China from 2011 to 2022. An empirical analysis of the digital economy’s influence on the growth of the green economy and its transmission mechanisms is performed. The analysis results demonstrate that the digital economy can significantly promote green economic development, encompassing improvements in both green innovation and green sharing, and exhibits a nonlinear “increasing marginal effect”. The analysis of transmission channels reveals that, on one hand, the digital economy can promote green economic development by optimizing the allocation of data elements, while on the other, its impact is also influenced by the intensity of environmental regulations, exhibiting a threshold effect. Further heterogeneity analysis suggests that the promotional effect of the digital economy on green economic development is more pronounced in regions with high levels of economic development, a robust infrastructure, and strong policy support. Full article