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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.

PurposeThe purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.Design/methodology/approachThis paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.FindingsThe experiment results show that the proposed method outperforms the baseline methods.Originality/valueThis method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

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.

Optical fiber sensor network has attracted considerable research interests for geoscience applications. However, the sensor capacity and ultra-low frequency noise limits the sensing performance for geoscience data acquisition. To achieve a high-resolution and lager sensing capacity, a strain sensor network is proposed based on phase-sensitive optical time domain reflectometer (φ-OTDR) technology and special packaged fiber with scatter enhanced points (SEPs) array. Specifically, an extra identical fiber with SEPs array which is free of strain is used as the reference fiber, for compensating the ultra-low frequency noise in the φ-OTDR system induced by laser source frequency shift and environment temperature change. Moreover, a hysteresis operator based least square support vector machine (LS-SVM) model is introduced to reduce the compensation residual error generated from the thermal hysteresis nonlinearity between the sensing fiber and reference fiber. In the experiment, the strain sensor network possesses a sensing capacity with 55 sensor elements. The phase bias drift with frequency below 0.1 Hz is effectively compensated by LS-SVM based hysteresis model, and the signal to noise ratio (SNR) of a strain vibration at 0.01 Hz greatly increases by 24 dB compared to that of the sensing fiber for direct compensation. The proposed strain sensor network proves a high dynamic resolution of 10.5 pε·Hz-1/2 above 10 Hz, and ultra-low frequency sensing resolution of 166 pε at 0.001 Hz. It is the first reported a large sensing capacity strain sensor network with sub-nε sensing resolution in mHz frequency range, to the best of our knowledge.

PurposeStock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.Design/methodology/approachThis study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.FindingsThe experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.Originality/valueIn this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

An undersampling free-space optical (FSO) communication system based on compressive sensing (CS) and in-fiber grating is proposed. Our proposal drastically mitigates the data scale unfavorable issue and reduces the great amount of required data. Also, our proposal employs low-bandwidth and low-cost photodetectors (PDs) and analog-to-digital converters (ADCs) and decreases the cost of data detection. Besides, the data transmitted in the free space is encrypted, which enhances the security of the system. The in-fiber grating, which has the advantage of highly efficient thanks to its inherent compatibility with optical fiber links, plays the roles of light emitter, in-fiber polarizer, and light diffraction device, simultaneously. A sinusoidal radio frequency (RF) signal with a frequency of 1 GHz is utilized for a 2.4 m data transmission. And each PD with a bandwidth of 300 MHz is employed for detecting the transmitted signal on each channel. The proposal obtains data compression ratios of 25%, 15%, and 5% both in the temporal domain and frequency domain. Besides, a two-tone signal and a random RF signal are applied in the proposed FSO transmission for data transmission. Our proposal not only has the merits of secure, compact, stable, and highly efficient, but also has great potential in extremely secure situations, such as secured indoor and under water data communication.

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.

Abstract Background Newly identified lncRNA zinc finger protein, FOG family member 2 antisense RNA 1 (ZFPM2-AS1) is identified as an oncogenic gene. However, the role of ZFPM2-AS1 in small cell lung cancer (SCLC) is poorly comprehended. Methods The expression of genes in SCLC tissues and cells was measured by qRT-PCR. Colony formation, EdU, CCK-8, transwell and wound healing as well as in vivo assays revealed the function of ZFPM2-AS1 in SCLC. ChIP, luciferase reporter, RIP and RNA pull down assays demonstrated the binding relation among genes. Results ZFPM2-AS1 was significantly upregulated in SCLC tissues and cells. ZFPM2-AS1 deficiency attenuated SCLC cell proliferation, invasion and migration. In addition, ZFPM2-AS1 was transcriptionally activated by Yin Yang 1 (YY1) factor. Further, miR-3612 was confirmed as downstream miRNA of ZFPM2-AS1. Moreover, TNF receptor associated factor 4 (TRAF4) was the target gene of miR-3612 in SCLC. ZFPM2-AS1, miR-3612 and TRAF4 jointly constituted a competing endogenous RNA (ceRNA) network in SCLC. Finally, TRAF4 could countervail ZFPM2-AS1 downregulation-mediated function on SCLC cell proliferation and invasion in vitro and tumor growth in vivo. Conclusion Our study elucidated the oncogenic effect of ZFPM2-AS1 in SCLC progression, indicating it may be a therapeutic target for SCLC.

We demonstrate a multi-wavelength mode-locked Yb-doped fiber laser by incorporating a pair of polarization maintaining 45° tilted fiber gratings (PM-45°TFG) based Lyot filter. Thanks to the functions of the polarizer and the comb filtering introduced by the Lyot filter, dissipative soliton (DS) pulses centered at 1035.26 nm, 1044.93 nm, 1055.62 nm, 1066.11 nm and 1076.63 nm can be generated respectively by finely tuning the intracavity polarization controllers (PCs). Moreover, the laser also can operate in a multi-wavelength regime via appropriately adjusting the pump power and polarization orientation. The high nonlinearity induced by the long cavity length leads to the generation of h-shaped mode-locked pulse with a repetition rate of 566.27 kHz. In the absence of any disturbance, the laser can operate steadily, that can potentially be used in various fields including wavelength division multiplexing systems etc.

A data processing method for correcting the frequency sweeping nonlinearity of a tunable laser in an optical frequency domain reflectometry system is presented. The proposed method calibrates the positions of all the data points by the time-scale factor determined by the zero-crossing points of the auxiliary interferometer. Then, the signal of the main interferometer is interpolated with the calibrated results. In experiments, a spatial resolution of 0.17 mm over a measurable range of 155 m has been obtained even when the optical path difference of the auxiliary interferometer is shorter than that of the main interferometer. This method could resolve the problem that measurable range is limited by the length of delay fiber in the auxiliary interferometer and significantly decrease the requirement of sampling rate.

Distributed fiber optic sensor for multi-parameter measurements plays a crucial role in various applications. In this work, distributed temperature/vibration fiber optic sensor with high precision and wide bandwidth is proposed and experimentally demonstrated. To improve the signal-to-noise ratio (SNR) and sensitivity of the system, the backscattering enhanced optical fiber (BEOF) is employed to provide strong and stable sensing signal. With the assistance of the coherent phase detection, a high precision measurement of phase can be achieved. In addition, the wavelet packet decomposition algorithm is put forward to effectively discriminate the quasi-static temperature and dynamic vibration signals. In the experiment, wide bandwidth vibration signal from 0.01 Hz to 20 kHz can be successfully detected. Owing to the high sensitivity of the system, the local temperature change within the range of only 10 cm can be captured with high precision of 0.095 °C. Besides, the system has a good capability of fast response to the external temperature variation where the response time is around 0.3 seconds, which is meaningful for the early warning of centimeters-level sized fire source in the special oil and gas pipelines monitoring applications.

Many hospitals are suffering from ineffective use of big data analytics with electronic health records (EHRs) to generate high quality insights for their clinical practices. Organizational learning has been a key role in improving the use of big data analytics with EHRs. Drawing on the knowledge-based view and big data lifecycle, we investigate how the three modes of knowledge can achieve meaningful use of big data analytics with EHRs. To test the associations in the proposed research model, we surveyed 580 nurses of a large hospital in China in 2019. Structural equation modelling was used to examine relationships between knowledge mode of EHRs and meaningful use of EHRs. The results reveal that know-what about EHRs utilization, know-how EHRs storage and utilization, and know-why storage and utilization can improve nurses' meaningful use of big data analytics with EHRs. This study contributes to the existing digital health and big data literature by exploring the proper adaptation of analytical tools to EHRs from the different knowledge mode in order to shape meaningful use of big data analytics with EHRs.

Via using an L-band optimized in-fiber polarizing grating device, a GHz L-band femtosecond passively harmonic mode-locked Er-doped fiber laser based on nonlinear polarization rotation is first demonstrated. In total, 4.22 GHz pulses with the duration of 810 fs and super-mode suppression ratio (SMSR) of 32 dB are obtained under the pump power of 712 mW corresponding to 215th harmonic order. The central wavelength of 4.22 GHz pulses is 1581.7 nm with 10.1 nm 3 dB bandwidth. Furthermore, under this fixed pump power, higher harmonic orders can also be attained by rotating the polarization controllers properly. The highest repetition rate we obtained is 7.41 GHz with the SMSR of 20.7 dB.

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.

We propose and demonstrate a quasi-distributed fiber sensor based on cascaded microfiber Fabry-Perot interferometers (MFPIs) for simultaneous refractive index (RI) and temperature measurement. By employing MFPI that is fabricated by taper-drawing the center of a uniform fiber Bragg grating on standard fiber into a section of microfiber, dual-parameters including RI and temperature can be detected through demodulating envelop and resonant wavelength of the reflection spectrum of the MFPI. Then, wavelength-division-multiplexing is applied to realize quasi-distributed dual-parameter sensing by using cascaded MFPIs with different Bragg wavelengths. A prototype sensor system with five cascaded MFPIs is constructed to experimentally demonstrate the sensing performance. The quasi-distributed dual-parameter sensing system has great significance on monitoring gradient parameter-variations in chemical and biological sensing applications.

We demonstrate a fiber Bragg grating (FBG)-based oscillator-accelerometer in which the acceleration sensitivity can be tuned by controlling the location of the mass oscillator. We theoretically and experimentally investigated the performance of the proposed accelerometer. Theoretical analysis showed that both the mass and location of the oscillator affect the sensitivity and resonant frequency of the accelerometer. To simplify the analysis, a nondimensional parameter, P, was introduced to tune the sensitivity of the FBG-based oscillator-accelerometer, which is related to the location of the mass oscillator. Numerical analysis showed that the accelerometer sensitivity is linearly proportional to the P parameter. In the experiment, six FBG-based oscillator-accelerometers with different P parameters (0.125, 0.25, 0.375, 0.5, 0.625, 0.75) were fabricated and tested. The experimental results agree very well with the numerical analysis, in which the sensitivity of the proposed accelerometer linearly increased with the increase in parameter P (7.6 pm/g, 15.8 pm/g, 19.3 pm/g, 25.4 pm/g, 30.6 pm/g, 35.7 pm/g). The resonance frequency is quadratically proportional to parameter P, and the resonance frequency reaches the minimum of 440 Hz when P is equal to 0.5. The proposed oscillator-accelerometer showed very good orthogonal vibration isolation.

Purpose – The purpose of this paper is to develop a new method to extract medical temporal information from online health communities. Design/methodology/approach – The authors trained a conditional random-filed model for the extraction of temporal expressions. The temporal relation identification is considered as a classification task and several support vector machine classifiers are built in the proposed method. For the model training, the authors extracted some high-level semantic features including co-reference relationship of medical concepts and the semantic similarity among words. Findings – For the extraction of TIMEX, the authors find that well-formatted expressions are easy to recognize, and the main challenge is the relative TIMEX such as “three days after onset”. It also shows the same difficulty for normalization of absolute date or well-formatted duration, whereas frequency is easier to be normalized. For the identification of DocTimeRel, the result is fairly well, and the relation is difficult to identify when it involves a relative TIMEX or a hypothetical concept. Originality/value – The authors proposed a new method to extract temporal information from the online clinical data and evaluated the usefulness of different level of syntactic features in this task.

The determination of hemoglobin (Hb) level is indispensable in the pathological study of many blood diseases. Graphene oxide (GO), with its excellent optical properties and great biocompatibility, has attracted significant attention and been widely utilized in biochemical detection. Here, we report an ultrasensitive Hb sensor based on a graphene oxide (GO)-coated microfiber. The GO was utilized as a linking layer deposited on the microfiber surface, which can provide an enhanced local evanescent light field and abundant bonding sites for Hb molecules. The optical microfiber with a compact structure and a strong evanescent light field served as the platform for biosensing. The surface morphology characterized by optical microscope, scanning electron microscope, and Raman spectroscopy offers detailed evidence for the success of GO deposition. The dynamic bonding between GO and target Hb molecules was monitored in real-time through an optical spectrum analyzer. An ultrahigh sensitivity of 6.02 nm/(mg/mL) with a detection limit of 0.17 μg/mL was achieved by tracking the resonant wavelength shift of spectra. It is important to highlight that the detection limit of GO-coated microfiber is 1–2 orders of magnitude lower than other reported fiber optic Hb sensors. Benefiting from high sensitivity, low cost, small size, and fast response, the proposed sensing microfiber coated with GO could be a competitive alternative in the diagnosis of blood diseases and a subject of further research in the medical field.

We characterized a cascaded long-period gratings (LPGs)-based sensor that was operating at the phase-matching turning point (PMTP). The cascaded LPGs constructed an in-fiber Mach–Zehnder interferometer (MZI), which exhibited a series of high-quality-factor (Q) narrow-bandwidth resonance peaks. As the LPG operated at the PMTP, the proposed sensor showed an ultrahigh refractive index (RI) and temperature sensitivity, and high measurement precision. In this study, we took an in-depth look at the effects of grating separation on Q-factor and sensitivity. The results showed that the sensitivity to the surrounding refractive index (SRI) reached 4741.5 nm/RIU at 1.4255 and 2138 nm/RIU, over the range of 1.335–1.373. In addition, the temperature sensitivity was around 4.84 nm/°C. With a 0.02 nm wavelength resolution, the RI and temperature sensing limits were 9.3 × 10−6 RIU and 5.5 × 10−3 °C.

A distributed fiber sensing system based on ultraweak FBGs (UWFBGs) assisted polarization optical time-domain reflectometry (POTDR) is proposed for load and vibration sensing with improved signal-to-noise ratio (SNR) and sensitivity. UWFBGs with reflectivity higher than Rayleigh scattering coefficient per pulse are induced into a POTDR system to increase the intensity of the back signal. The performance improvement of the system has been studied. The numerical analysis has shown that the SNR and sensitivity of the system can be effectively improved by integrating UWFBGs along the whole sensing fiber, which has been clearly proven by the experiment. The experimental results have shown that by using UWFBGs with 1.1 × 10-5 reflectivity and 10-m interval distance, the SNR is improved by 11 dB, and the load and vibration sensitivities of the POTDR are improved by about 10.7 and 9 dB, respectively.

In social networks, individuals have relationships with their neighbor nodes (acquaintance contacts) and also randomly contact other nodes without direct links (stranger contacts). However, these two types of contact patterns are rarely considered together. In this paper, we propose a modified SIS (Susceptible-Infected-Susceptible) model in which a node not only contacts neighbor nodes but also randomly contacts other nodes in the network. We implement the model on a scale-free network and study the influence of different types of contact patterns on epidemic dynamics as well as three possible strategies people adopt when disease outbreaks. The results show that a greater preference for acquaintance contacts makes a disease outbreak less likely. Moreover, the best protective strategy to control the disease is to adjust both the contact number and the contact pattern. In addition, the epidemic is more likely to be controlled when individuals take more information into consideration.

The experiments were performed in a lab-scale countercurrent spraying reactor to study the NOx removal from simulated gas stream by cyclic scrubbing using NaClO solution. The effects of NaClO concentration, initial solution pH, coexisting gases (5% CO2 and 13% O2), NOx concentration, SO2 concentration, and absorbent temperature on NOx removal efficiency were investigated in regard to marine exhaust gas. When NaClO concentration was higher than 0.05 M and initial solution pH was below 8, NOx removal efficiency was relatively stable and it was higher than 60%. The coexisting CO2 (5%) had little effect on NOx removal efficiency, but the outlet CO2 concentration decreased slowly with the initial pH increasing from 6 to 8. A complete removal of SO2 and NO could be achieved simultaneously at 293 K, initial pH of 6, and NaClO concentration of 0.05 M, while the outlet NO2 concentration increased slightly with the increase of inlet SO2 concentration. NOx removal efficiency increased slightly with the increase of absorbent temperature. The relevant reaction mechanisms for the oxidation and absorption of NO with NaClO were also discussed. The results indicated that it was of great potential for NOx removal from marine exhaust gas by wet scrubbing using NaClO solution.

In order to enhance the mass transfer efficiency between gas–liquid interfaces, NaClO2 mist generated by an ultrasonic humidifier was used to remove NO from simulated flue gas. The effects of some key parameters (the gas flow rate, the NaClO2 concentration in the solution, the inlet NO concentration, the NaClO2 solution pH) on NO removal efficiency were investigated preliminarily. The results showed that NaClO2 mist could oxidize NO with a much higher efficiency compared with other mists containing either NaClO or H2O2 as oxidants. With an increase in the gas flow rate from 1.5 to 3.0 L·min−1, the atomizing rate of the NaClO2 solution increased almost linearly from 0.38 to 0.85 mL·min−1. When the gas flow rate was 2.0 L·min−1, a complete removal of NO had been reached. NO removal efficiency increased obviously with an increase in the NaClO2 concentration in the solution. With an increase in the inlet NO concentration, the ratio of NO in the flue gas and NaClO2 in the mist increased almost linearly. Furthermore, the NaClO2 mist exhibited a relatively stable and high NOx removal efficiency in a wide pH range (4–11) of NaClO2 solutions. The reason for the high NO removal efficiency was mainly ascribed to both the strong oxidative ability of NaClO2 and the improved mass transfer at the gas-liquid interface.

Background: More than 80 percent of the China’s population is located in the rural areas, 95 percent of which use coal, wood etc for cooking and heating. Limited by data availability, the association between household solid fuels and cardiovascular diseases (CVDs) in China’s rural areas is ignored in prior studies. Methods: This cross sectional study was conducted from 2010-2012 and carried out on rural population aging 20-80 yr, comprised of 13877 participants from eighteen villages. Self-report questionnaire data were collected. Each outcome represents whether the participant has a kind of CVDs or not and it is reported in participants’ questionnaire. Then the collected data is analyzed by logistic regression models with odds ratios (OR) and 95 percent confidence interval. Results: After adjusting for potential confounders, the use of household solid fuels was significantly associated with an increased risk for hypertension (OR 1.751), CHD (OR 2.251), stroke (OR 1.642), diabetes (OR 1.975) and dyslipidemia (OR 1.185). Residents with the highest tertile of the duration of household solid fuel exposure had an increased odd of hypertension (OR 1.651), stroke (OR 1.812), diabetes (OR 2.891) and dyslipidemia (OR 1.756) compared with those in the lowest tertile of the duration of solid fuel exposure. Conclusion: Indoor pollution exposure from household solid fuels combustion may be a positive risk factor for CVDs in the perspectives of China’s rural population. Our findings should be corroborated in longitudinal studies.