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Online medical platforms have emerged as a popular means for patients to access high-quality medical services efficiently. These platforms offer a variety of services, including paid consultations and free consultations. Given that doctors can increase their revenue through these platforms, researchers should investigate how to improve patients’ willingness to pay for these services. Drawing upon social exchange theory, stimulus-organism-response theory, and the information systems (IS) success model, this study proposes a model and five hypotheses to examine the influence of free medical consultations on patients’ willingness to buy paid services. To test these hypotheses, a questionnaire survey was conducted, and the collected data were analyzed using the structural equation model. The results indicate that the quality of information and services provided by doctors during free consultations positively affects patients’ willingness to pay. By introducing information quality and service quality into the IS success model in the context of free medical consultations, this study contributes to the literature on online medical platforms and expands our understanding of patients’ behavior. The findings of this study can be useful for online medical platforms and doctors to design effective platform functions and individual behavioral strategies.

Abstract In this paper, the tribological characteristics of an oil-soluble magnetic fluid additive under mixed lubrication are studied by experiments and numerical simulation. [bmim][FeCl4] is dissolved in CF10W-40 lubricating oil as a magnetic liquid additive, and its friction coefficient is tested by a point contact friction tester at different temperatures, rotational speeds and magnetic field intensities. The transition condition of lubrication state is obtained through analyzing the Stribeck curves based on the experiments, and the strength model of boundary film is established accordingly. A mixed lubrication model is established by substituting the boundary film strength model and the surface roughness model into the hydrodynamic lubrication model based on Reynolds equation. The results show that the magnetic solution as an additive can obviously reduce friction and wear, and the effect is more obvious under the condition of magnetic field. The boundary film strength model can accurately reflect the transition characteristics of lubrication state in the presence of boundary film, and the mixed lubrication model based on boundary film strength model can more precisely reflect the tribological characteristics of friction pairs, so this study provides a new theoretical method for the related research on the influence of boundary film on lubrication characteristics.

Infrared detection, known for its robust anti-interference capabilities, performs well in all weather conditions and various environments. Its applications include precision guidance, surveillance, and early warning systems. However, detecting infrared dim and small targets presents challenges, such as weak target features, blurred targets with small area percentages, missed detections, and false alarms. To address the issue of insufficient target feature information, this paper proposes a high-precision method for detecting dim and small infrared targets based on the YOLOv7 network model, which integrates both local and non-local bidirectional features. Additionally, a local feature extraction branch is introduced to enhance target information by applying local magnification at the feature extraction layer allowing for the capture of more detailed features. To address the challenge of target and background blending, we propose a strategy involving multi-scale fusion of the local branch and global feature extraction. Additionally, the use of a 1 × 1 convolution structure and concat operation reduces model computation. Compared to the baseline, our method shows a 2.9% improvement in mAP50 on a real infrared dataset, with the detection rate reaching 93.84%. These experimental results underscore the effectiveness of our method in extracting relevant features while suppressing background interference in infrared dim and small target detection (IDSTD), making it more robust.

In this paper, we demonstrated a distributed feedback fiber-laser- (DFB-FL) based ultrasound detection system with a high signal-to-noise ratio (SNR), high sensitivity and wide frequency response range. The DFB-FL was fabricated by UV-inscribing a π phase-shifted grating in the Erbium–Ytterbium co-doped fiber using the phase mask method. The theory of DFB-FL and the sensing principle has been discussed and analyzed. The sensing signal was demodulated via an unbalanced Mach–Zehnder interferometer (MZI) system. The experimental results showed that the sensitivity of the sensor reached 75.18 mV/kPa and the flat frequency response range covered over 30 MHz, which covered the ultrasonic detection frequency range of abnormal discharge in the electric power system. The SNR of this DFB-FL was 42.9 dB, and the corresponding noise-equivalent pressure was calculated as 0.12 kPa.

With the proposal of a “smart battery,” real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical applications. However, many traditional sensing technologies suffer from low sensitivity, large size, and electromagnetic interference problems, rendering them unusable in the harsh and complicated electrochemical environments of batteries. The optical sensor is an alternative approach to realize multiple-parameter, multiple-point measurements simultaneously. Thus, it has garnered significant attention. Through analyzing these measured parameters, the state of interest can be decoded to monitor a battery's health. This review summarizes current progress in optical sensing techniques for batteries with respect to various sensing parameters, discussing the current limitations of optical fiber sensors as well as directions for their future development.

This study introduces a low-friction composite based on PEEK to improve its friction and wear properties. The composite incorporates PTFE as a solid lubricant and utilizes PPTA as a reinforcing material within the PEEK matrix. These components were prepared utilizing a compression molding method, followed by a series of exploratory experiments to identify the optimal preparation conditions for PEEK. This research assesses how the PTFE/PPTA/PEEK composites perform in terms of friction and wear under dry and oil-lubricated conditions. By examining wear tracks using scanning electron microscopy and white light interference microscopy, this study aims to uncover the wear mechanisms of PEEK and its composites under different lubrication scenarios. Results show that the main wear mechanisms for the PTFE/PPTA/PEEK composites and bearing steel are ploughing and adhesive wear. The presence of PPTA helps reduce wear by leveraging its strong fibers and thermal stability, while the coefficient of friction decreases as PTFE creates a smooth, solid lubricating film on the surface. Notably, PEEK composites containing 25 wt% PTFE and 6 wt% PPTA demonstrate the lowest wear rates and reduced coefficient of friction in both dry and oil-lubricated conditions.

This study investigates the effect of the elastic surface micro-texture on the cavitation and lubrication characteristics of the friction pairs through theoretical and experimental research. Through numerical simulations and experiments, the influences of the elastic modulus and sliding speed on the lubrication performance of the friction pair are studied. The results show that under certain speed and load conditions, the friction coefficient of the elastic texture is smaller, and the lubrication performance is better than that of the rigid texture. Increasing the sliding speed and texture spacing properly can improve the lubrication performance of elastic friction pairs. In addition, as the elastic modulus decreases, the elastic deformation and oil film thickness increase, and the cavitation phenomenon becomes more significant. Thus, the lubrication performance of the friction pair is improved.

The freezing strength of the contact interface between foundation soil and infrastructure has been a key and difficult problem in the study of frost jacking of piles in cold regions; previous studies have focused on the influence law of experimental factors on freezing strength, and there are few studies on the significance of the effects of factors and their interactions on the freezing strength in different phase transition zones. In this paper, based on the existing test data, the orthogonal experimental design method considering the interactions, the principles of range analysis and variance analysis were used to investigate the significance of the effects of factors influencing the freezing strength in different phase transition zones and their interactions. Finally, a new method for establishing the prediction model of freezing strength based on the multiple linear regression model was proposed. The research shows that there are significant differences in the effects of the experimental factors and their interactions on the freezing strength in different phase transition zones. The freezing strength prediction model established by the new method can better reflect the variation law of freezing strength with factors in different phase transition zones and the influence degree of the interactions between factors.

Forest stock volume is the main factor to evaluate forest carbon sink level. At present, the combination of multi-source remote sensing and non-parametric models has been widely used in FSV estimation. However, the biodiversity of natural forests is complex, and the response of the spatial information of remote sensing images to FSV is significantly reduced, which seriously affects the accuracy of FSV estimation. To address this challenge, this paper takes China’s Baishanzu Forest Park with representative characteristics of natural forests as the research object, integrates the forest survey data, SRTM data, and Landsat 8 images of Baishanzu Forest Park, constructs a time series dataset based on survey time, and establishes an FSV estimation model based on the CNN-LSTM-Attention algorithm. The model uses the convolutional neural network to extract the spatial features of remote sensing images, uses the LSTM to capture the time-varying characteristics of FSV, captures the feature variables with a high response to FSV through the attention mechanism, and finally completes the prediction of FSV. The experimental results show that some features (e.g., texture, elevation, etc.) of the dataset based on multi-source data feature variables are more effective in FSV estimation than spectral features. Compared with the existing models such as MLR and RF, the proposed model achieved higher accuracy in the study area (R2 = 0.8463, rMSE = 26.73 m3/ha, MAE = 16.47 m3/ha).

Abstract In this paper, the surface texture parameters and distribution patterns are studied by numerical simulation and experiment. First, a three-dimensional micro-textured CFD fluid lubrication model with cavitation effect is established, and different texture arrays are designed to study the influence of different distribution modes on bearing capacity, friction coefficient and pressure distribution of the oil film. Then, the simulation results are further analyzed and verified by the visualized plane slider experimental platform, and the formation rules of cavitation bubbles in the micro-textured array, as well as the influences of the surface shape and different distribution modes of the micro-textured array on the cavitation bubbles are discussed. The results show that the existence of cavitation is one of the main reasons for the microtexture to increase the bearing capacity of the oil film, which cannot be ignored in the simulation study. The texture array with single symmetric orientation is the best to improve the oil film bearing capacity, and the bearing performance is the best when the texture inclination angle is 26.6°.The friction coefficient of the asymmetrically oriented textured array is 29.4% lower than that of the non-textured sample.The results in the experiment are consistent with the simulation.

In the realm of non-cooperative space security and on-orbit service, a significant challenge is accurately determining the pose of abandoned satellites using imaging sensors. Traditional methods for estimating the position of the target encounter problems with stray light interference in space, leading to inaccurate results. Conversely, deep learning techniques require a substantial amount of training data, which is especially difficult to obtain for on-orbit satellites. To address these issues, this paper introduces an innovative binocular pose estimation model based on a Self-supervised Transformer Network (STN) to achieve precise pose estimation for targets even under poor imaging conditions. The proposed method generated simulated training samples considering various imaging conditions. Then, by combining the concepts of convolutional neural networks (CNN) and SIFT features for each sample, the proposed method minimized the disruptive effects of stray light. Furthermore, the feedforward network in the Transformer employed in the proposed method was replaced with a global average pooling layer. This integration of CNN’s bias capabilities compensates for the limitations of the Transformer in scenarios with limited data. Comparative analysis against existing pose estimation methods highlights the superior robustness of the proposed method against variations caused by noisy sample sets. The effectiveness of the algorithm is demonstrated through simulated data, enhancing the current landscape of binocular pose estimation technology for non-cooperative targets in space.

A microfiber with large evanescent field encapsulated in PDMS is proposed and demonstrated for ultrasound sensing. The compact size and large evanescent field of microfiber provide an excellent platform for the interaction between optical signal and ultrasound wave, exhibiting a high sensitivity of 3.5 mV/kPa, which is approximately 10 times higher than the single-mode fiber sensor. Meanwhile, a phase feedback stabilization module is introduced into the coherent demodulation system for long-term stable measurement. In addition, a photoacoustic tomography experiment with the microfiber ultrasound sensor is implemented to verify the excellent performance on imaging, with the depth of 12 mm, the highest lateral resolution of 65 μm and axial resolution of 250 μm, respectively. The highly sensitive microfiber ultrasound sensor provides a competitive alternative for various applications, such as industrial non-destructive testing, biomedical ultrasound and photoacoustic imaging.

Abstract With zero excess lithium, anode‐free lithium metal batteries (AFLMBs) can deliver much higher energy density than that of traditional lithium metal batteries. However, AFLMBs are prone to suffer from rapid capacity loss and short life. Monitoring and analyzing the capacity decay of AFLMBs are of great importance for their future applications. It is known that the capacity fade mainly comes from the formation of solid electrolyte interphase species and dead lithium, which leads to irreversible volume expansion. Therefore, monitoring and distinguishing the irreversible volume expansion or reversible volume expansion are the key points to analyze the capacity fade of AFLMBs. Herein, an applicable technique based on optical fiber sensors to characterize and quantize the volume change of AFLMBs is developed. By attaching fiber Bragg grating (FBG) sensors onto the surface of the multilayered anode‐free pouch cells, the strain evolution of the cells is successfully monitored and correlated with their electrochemical properties. It is found that the decline of surface strain fluctuation amplitude caused by the loss of active lithium is the leading indicator of battery failure. The proposed sensing technique has excellent multiplexing capability that can be considered as an elementary unit for capacity fade analysis in next‐generation battery management system.

For the influence of boundary film on the lubrication state of sliding friction pairs, a boundary film strength model was proposed that can comprehensively reflect the influences of film thickness, pressure, shear stress and temperature. The model parameters were obtained through fitting the test results. Then, a mixed lubrication model considering boundary film strength was established by coupling the boundary film strength model with the hydrodynamic lubrication model and the asperity contact model. The calculation program was developed using the Fortran language, which can effectively capture the tribological characteristics and action ratios of the fluid, boundary film and dry friction components. Simultaneously, the mixed lubrication model was applied to the journal bearing. A parametric analysis was performed to investigate the influences of different working conditions on lubrication performance. Under current operating conditions, the results show that: when the speed is above 200 r/min or the viscosity is higher than 0.09 Pa·s, the boundary film breakdown rate is almost 0 and the friction coefficient is lower than 0.02; when the roughness is reduced from 1.8 μm to 0.8 μm, the ultimate load of the journal bearing rises from 27 MPa to 36 MPa, an increase of about 33%; when the load exceeds 36 MPa or the temperature is higher than 100 °C, more than 25% of the boundary film breaks and the dry friction component accounts for more than 60% of the total friction, which leads to a sudden increase in the friction coefficient. Hence, the study of mixed lubrication considering boundary film strength provides theoretical guidance for accurately reflecting the actual lubrication state and improving the mechanical energy efficiency of friction pairs.

When passing through the ionosphere, the high-frequency (HF) pulse signal of the Mars Exploration Radar is affected by the dispersion effect error, which results in signal attenuation and time delay and brings about a phase advance in such a way that the echo cannot be matched and filtered. In this paper, a high-order phase model is built to overcome the above problems and enable echo matching and filtering. Most studies on the dispersion effect approximate the additional phase after the effect, assuming that the ionosphere is a thin-layer structure. In this paper, an effective model for the HF waveband is constructed to analyze the change of signal propagation paths in the ionosphere. The additional phase is expanded in a Taylor series and retained these expansions as high-order terms to calculate the cumulative additional phase along the path. We show the range-offset variables of signal frequency, bandwidth, and electron density, simulate the effects of the ionosphere under different conditions, and conclude that the model can effectively estimate Mars without considering the effects of magnetic fields and anomalous solar activity and the effect of the ionosphere on synthetic aperture radar (SAR) echoes. The results obtained using ray tracing calculations are different from those obtained by simplifying assumptions, and we can simulate the Martian ionospheric effects by the former.

Specialty fibers have introduced new levels of flexibility and variability in distributed fiber sensing applications. In particular, distributed acoustic sensing (DAS) systems utilized] the unique functions of specialty fibers to achieve performance enhancements in various distributed sensing applications. This paper provides an overview of recent preparations and developments of specialty-fiber-based DAS systems and their sensing applications. The specialty-fiber-based DAS systems are categorized and reviewed based on the differences in scattering enhancement and methods of preparation. The prospects of using specialty fibers for DAS systems are also discussed.

Online social networks are increasingly being used to conduct commercial activities, and many online social networking platforms allow users to sell products to their online connections. Although extensive research has been conducted on the interactions among buyers within a social network, interactions among sellers have rarely been explored. Using seller data from a company that sells on a major online social networking platform in China, we empirically examine how sellers' efforts and sales performance are affected by the efforts and sales performance of other sellers they are connected to (i.e., their inviters and invitees) and the commissions they themselves have received. We find evidence for social influence and competition effects in the "inviter-to-invitee" direction and sellers' free-riding behavior driven by the commissions they receive from their invitees' sales. These results extend the social network literature that has largely focused on connected buyers (or users) to connected sellers and offer implications for social networking platforms to promote seller participation. [ABSTRACT FROM AUTHOR]