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We demonstrate a three-axis fiber-optic acoustic-velocity hydrophone, which is based on three orthogonal planar fiber-laser cantilevers packaged in a pressure-sealed spherical shell full of castor oil. The viscous drag force of the castor oil changes the dominant damping mechanism of the cantilever. The proposed hydrophone responds to the acoustic velocity rather than the acoustic acceleration. We theoretically analyze the correspondence of the fiber laser cantilever to the acoustic motion when it is immerged in the air, water, and castor oil, respectively. We fabricated a three-axis fiber-optic hydrophone and the calibration results show that the proposed hydrophone presents operation bandwidth from 5 to 100 Hz. Its pressure sensitivity of the x-, y-, and z-axis is $2.95\times 10^{{4}}$ , $2.4\times 10^{{4}}$ , and $2.31\times 10^{{4}}$ Hz/Pa at 40 Hz, respectively. The directivity index of the three axes is 27.89, 32.87, and 36.04 dB, respectively. It resists 12.5 MPa hydrostatic pressures due to the design of the spherical housing. This fiber-optic acoustic-velocity hydrophone can find many applications in deep-sea low-frequency acoustic field measurement, such as the buoy of deep water for low-frequency signal detection.

AbstractEnvironmental protection PPP projects (EP-PPP) are subject to uncertainty during the project cycle, setting flexible project contract terms in their contracts can help establish and maintain an effective partnership. This article aims to reveal the contract flexibility mechanism of EP-PPP projects by studying the relationship between flexible contract term setting methods and their effects. Questionnaires, case studies and structural equation modeling (SEM) are applied to conduct quantitative analysis. The research shows that although some flexible terms have been adopted in current EP-PPP contracts, there is still much room for improvement. In order to deal with unforeseen risks and challenges, it is necessary to consider: (1) List uncertainties as much as possible and set up response methods accordingly; (2) Guarantee a certain degree of flexibility in uncertainty response methods; (3) Give the project itself a certain degree of autonomous response power; (4) Leave plenty space for changes that may be needed in the future. The study results can help project practitioners enhance their understanding of flexible contract terms and help to promote contract flexibility in EP-PPP projects in a context-specific manner, thereby facilitating project success.

To achieve automatic, fast, efficient and high-precision pavement distress classification and detection, road surface distress image classification and detection models based on deep learning are trained. First, a pavement distress image dataset is built, including 9017 pictures with distress, and 9620 pictures without distress. These pictures were captured from 4 asphalt highways of 3 provinces in China. In each pavement distress image, there exists one or more types of distress, including alligator crack, longitudinal crack, block crack, transverse crack, pothole and patch. The distresses are labeled by a rectangle bounding box on the pictures. Then ResNet networks and VGG networks are used respectively as binary classification models for distressed and non-distressed imagines classification, and as multi-label classification models for six types of distress classification. Training techniques, such as data augmentation, batch normalization, dropout, momentum, weight decay, transfer learning, and discriminative learning rate are used in training the model. Among the 4 CNNs considered in this study, namely ResNet 34 and 50, and VGG 16 and 19, for the binary classification, ResNet 50 has the highest Accuracy of 96.243%, Precision of 95.183%, and ResNet 34 has the highest Recall of 97.824%, and F2 score of 97.052%. For multi-label classification, ResNet 50 has the best performance, with the highest Accuracy of 90.257%, higher than 90% required by the Chinese standard (JTG H20-2018) for road distresses detection, F2 score −82.231%, and Precision −76.509%, and ResNet 34 has the highest Recall of 87.32%. To locate and quantify the distress areas in the images, the single shot multibox detector (SSD) model is developed, in which the ResNet 50 is used as the base network to extract features. When the intersection over union (IoU) is set to 0, 0.25, 0.50, 0.75, the mean average precision (mAP) of the model are found to be 74.881%, 50.511%, 28.432%, 3.969%, respectively.

Study of membrane performance and permeation model is of great significance for the development of solvent-resistant nanofiltration membranes. In this work, filtration experiments were carried out for ten solutes in methanol and ethanol solvents with two polymeric nanofiltration membranes. The effects of solvent, solute and membrane on the separation performance were investigated. The Donnan-steric-pore nanofiltration transport model (DSPM) based on aqueous nanofiltration was modified for the non-aqueous nanofiltration. It was found that the separation performance of the membrane was dominated by membrane structure parameters, physical properties of solvent and features of solute. The modeling results revealed that the solvent had influences on the average pore radius of the membrane; solute charge, solute-solvent-membrane interactions existed in non-aqueous nanofiltration; and the modified model was suitable to predict rejections of neutral solutes rather than charged solutes in the non-aqueous system. These findings provide a novel modification strategy that could be utilized as a convenient and powerful tool for predicting membrane performance and understanding the separation disciplines of non-aqueous nanofiltration.

Aiming at the new fuzzy neural network guidance law, combined with embedded system and virtual reality technology, a new guidance law simulation platform is designed. This guidance law is designed and calculated by the embedded system platform, and the 3D visual simulation is designed on the PC platform. The mathematical model is solved by numerical analysis first, and then displayed on the screen by computer system, which will increase the sense of reality. Finally, the platform is used to simulate the terminal guidance law of the variable structure with a falling angle as the constraint. The simulation results show that the design and calculation of the guidance law in the platform are closer to the engineering practice, and the virtual reality simulation effect is closer to the real battlefield environment. In fact, the study of guidance law is more like an applied study of mathematics. Most of the research results are presented in the form of data or charts, which makes the abstract theoretical results even more difficult to understand.

To the best of our knowledge, we present what we believe is a novel approach for fading noise reduction in distributed acoustic sensing (DAS). Rayleigh signals in multiple optical modes are collected by a phase-sensitive optical time-domain reflectometry system with a diversity detection scheme. An optimal weighted average algorithm is proposed to synthesize the phase demodulated from the Rayleigh signals, leading to the aggregate phase that presents acoustic signals with optimal fading noise reduction. The validity of the proposed approach is experimentally confirmed by comparison of the fading noise along a 12.8 km sensing fiber in a conventional DAS system and a DAS system that employs the proposed approach.

ABSTRACTOsteoporosis (OP) is a systemic bone metabolic disease. Promotion of osteoblast proliferation and inhibition of cell apoptosis may be helpful for the prevention and clinical treatment of OP. In the current study, we focused on the expression changes and clinical values of lncRNA ROR and miR-145-5p in OP clinical serum samples, and investigated the interactive modulation effect of ROR/miR-145-5p on osteoblast function. Serum samples were obtained from 82 OP patients and 79 healthy individuals. MC3T3-E1 was applied for the cell experiments. Levels of lncRNA ROR and miR-145-5p were detected using qRT-PCR. Transient transfection was performed to regulate gene levels in cells, and cell proliferation and apoptosis were detected. A reciprocal correlation between lncRNA ROR and miR-145-5p was explored. LncRNA ROR was downregulated, and miR-145-5p was overexpressed in OP patients. The combined diagnosis of ROR and miR-145-5p showed good diagnostic value for OP. ROR knockdown promoted the MC3T3-E1 cell apoptosis and inhibited cell proliferation. Luciferase reporting assay verified the target relationship between ROR and miR-145-5p. MiR-145-5p downregulation reversed ROR silence mediated effect on MC3T3-E1 cell proliferation and apoptosis. LncRNA ROR is downregulated and miR-145-5p is highly expressed in OP patients. ROR knockdown may inhibit osteoblast proliferation via targeting miR-145-5p. It may provide a theoretical basis and experimental basis for ROR to be a potential target for the treatment of OP.

A method is proposed to suppress spontaneous modulation instability (MI) and phase noise with a coherent seed in the interferometric fiber sensing systems. By generating coherent seeds with phase modulation, induced MI is excited in an optical fiber. The preferential gain of the coherent seeds compared to the amplified spontaneous emission noise suppresses spontaneous MI. As a result, the induced MI dominates in the optical fiber. The coherence of the output light is enhanced, and the phase noise is suppressed significantly. The maximum input power can be increased significantly, and the sensing range can be extended.

The dynamic characteristics of the cavity mirror support structure strongly influence the quality of the output beam. However, the contradiction between excellent dynamic performance and light weight make the design process challenging. To cope with the problems encountered in the original design of a chemical oxygen iodine laser system, this paper presents a two-dimensional adjustable support structure based on spherical constraints with large specific stiffness in the initial design phase. Subsequently, a two-level optimization strategy containing a macro design and a detailed design is adopted to optimize the initial structure. At the macro design stage, a two-step topology optimization procedure is introduced, in which the scale of the optimization model is dramatically reduced using the independent continuous mapping algorithm to improve the calculation speed in the first step, and the gray elements are eliminated using the bi-directional evolutionary structural optimization method to clearly obtain the optimal topology in the second step. This method is verified to overcome the defect of low efficiency, while still eliminating gray elements. At the detailed design stage, an adaptive surrogate model and the multi-objective design optimization method are employed to seek the best compromise between the lower weight and higher dynamic performance. The results from the application to the example of the cavity mirror support structure show that the mass is reduced by 41.8%, and the dynamic performance requirement is fulfilled.

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future. In practice, emergency applications often require less training time. However, there is a little work on how to obtain good prediction performance with less training time. In this paper, we propose a simplified deep residual network for our problem. By using the simplified deep residual network, we can obtain not only less training time but also competitive prediction performance compared with the existing similar method. Moreover, we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost. Based on the real datasets, we construct a series of experiments compared with the existing methods. The experimental results confirm the efficiency of our proposed methods.