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Abstract This paper proposes an underwater semi-active composite anechoic coating with periodic subwavelength piezoelectric arrays to broaden the low-frequency absorption bandwidth and improve the absorption coefficient. Based on the effective medium method, the dual equivalent properties for the volume density and Young's modulus of the equivalent thin-plate interlayer are computed. Then, a complete two-dimensional theoretical model for oblique incidence is established by combining the shunt damping technique and wave propagation theory in layered media. On the premise of strictly satisfying the two preconditions of subwavelength assumption and stratified convergence criteria, the correctness and effectiveness of the present model are confirmed by successively according to the finite element simulation results, experimental data, and theoretical predictions. At last, change regulations of seven critical parameters on the sound absorption characteristics of the present coating are investigated to reveal the multiple energy dissipation mechanisms of the overburden in the whole research frequency band. [ABSTRACT FROM AUTHOR]

Inappropriate cruising speed, such as speeding, is one of the major contributors to the road safety, which increases both the quantitative number and severity of traffic accidents. Previous studies have indicated that traffic congestion is one of the primary causes of drivers’ frustration and aggression, which may lead to inappropriate speed choice. In this study, the large taxi floating car data (FCD) was used to empirically evaluate how traffic congestion-related negative moods, defined as state aggressiveness, affected drivers’ speed choice. The indirect effect of traffic delay on the cruising speed adjustment through the state aggressiveness was assessed through the mediation analysis. Furthermore, the moderated mediation analysis was performed to explore the effect of driver type, value of time, and working duration on the mediation role of state aggressiveness. The results proved that the state aggressiveness was the mediator of the relationship between travel delays and driving speed adjustment, and the mediation role was different across various driver types. As compared to the aggressive drivers, the normal drivers and the steady drivers tended to behave more aggressively after experiencing non-recurrent congestion during the early stage of the trips. When the value of time was high, steady drivers were more likely to adjust their speed choice although the effect was not statistically significant for other driver types. The validation results indicated that the speed model incorporating state aggressiveness could better predict the travel time than the traditional speed model that only considering the specific expected speed distribution. The prediction results for the manifest indicators of state aggressiveness, such as the maximum speed and the speed deviation, also demonstrated a reasonable reflection of the field data. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to estimate bicycle accessibility for various trip purposes based on a massive dockless bike-sharing dataset in Shanghai, China. Specifically, a Dirichlet multinomial regression topic model (DMR model) is applied to identify bicycle trajectories' trip purposes, simultaneously considering arrival time and drop-off location. Based on obtained trip purposes, we estimate impedance functions using a negative exponential function. Finally, based on estimated impedance functions, two cases of bicycle accessibility for two different purposes - restaurant and hospital - are presented in Shanghais central area. The results show that almost 90% of bicycle trips are less than 30 min or 5 km. Although the difference between the impedance functions between various trip purposes is not significant, we find that trip purposes of "Work and School" have the highest travel impedance for bicyclists. Cyclists in Shanghai accept longer bicycle travel times for leisure (e.g., shopping) than for commuting (e.g., work or school). [ABSTRACT FROM AUTHOR]

[Display omitted] • The enantioselective Michael addition of malonates with unsaturated ketones. • Catalyzed by samarium amide Sm[N(SiMe 3) 2 ] 3 together with the chiral phenoxy-functionalized amino alcohol proligands. • The products with good group tolerance, mostly in high to excellent yields (85–98%) and good to excellent ee values (50– >99%). • A possible mechanism was proposed. A combination of rare-earth metal amides RE[ N (SiMe 3) 2 ] 3 and chiral phenoxy-functionalized amino alcohol proligands was developed to realize the enantioselective Michael addition of malonates with unsaturated ketones. The reactions catalyzed by samarium amide Sm[ N (SiMe 3) 2 ] 3 were performed best together with the chiral proligand H 3 L1 (H 3 L1 = 2,4-di- tert -butyl-6-((((1 S ,2 R)-2-hydroxy-1,2-diphenylethyl) amino)methyl)phenol) in DCE with good group tolerance, mostly in high to excellent yields (85–98 %) and good to excellent ee values (50− >99 %). The possible mechanism was also proposed. [ABSTRACT FROM AUTHOR]

Aiming at the problem of automatic depth control for Remotely Operated Vehicle (ROV) under complex disturbed environment, this paper designs a control scheme used the improved Linear Active Disturbance Rejection Controller (LADRC) based on nutcracker optimization algorithm (NOA). Firstly, in order to solve the disturbance problem in the depth control process of ROV under complex disturbed environment, the LADRC is applied to the depth control of ROV to improve the disturbance rejection capability of the system. After that, due to the difficulty and uncertainty of LADRC parameter tuning, the global search capability of nutcracker optimization algorithm is used to optimize the LADRC controller. It realizes the automatic tuning of LADRC parameters, thus realizes the fast and high-accuracy depth control. Finally, the simulation and experimental verification are completed. The overshooting amount of the NOA-LADRC control scheme in the depth control of ROV is almost zero, the control accuracy and disturbance rejection capability are improved by about 50%. • The disturbance rejection capability and control accuracy of depth control for ROV are improved by NOA-LADRC scheme. • Automatic tuning of controller parameters is realized by improving nutcracker optimization algorithm. • The effectiveness and superiority of the proposed control scheme are verified by simulation and experiments. [ABSTRACT FROM AUTHOR]

How to efficiently and fast remove the serious perniciousness of the residual antibiotics in water environment is crucial for protecting ecosystem. Herein, we reported the Fenton-like system based on layered double hydroxides (LDHs) nanosheets for direct peroxymonosulfate (PMS) activation efficient for fast removing the typical five fluoroquinolones antibiotics ciprofloxacin (CIP), norfloxacin (NOR), levofloxacin (LEV), enrofloxacin (ENR) and ofloxacin (OFL). A series of CoFe-LDHs nanosheets was one-step fabricated by integrating coprecipitation and in-situ exfoliation method. The as-obtained CoFe-LDHs nanosheets catalysts exhibit thin nanosheets (~3 nm) with high redox properties, abundant oxygen vacancies, appreciable adsorption capacity, remarkable catalytic performance and favorable stability. The variable valent state, cheap, easy-to-obtain and low-toxic Co and Fe elements were used to construct stable LDHs for efficient activation of PMS to degrade quinolone antibiotics. Among them, Co 1 Fe 1 -LDHs nanosheets exhibited the best CIP degradation efficiency of 86.9% within 12 min, which is superior to most of previous candidates. The impacts of different reaction parameters, catalyst stability, main reactive oxygen species, the main CIP degradation mechanism and rational degradation pathways were systematically studied. This work offers a feasible strategy to tune the performance of LDHs-based materials in Fenton-like system degradation of antibiotics. [Display omitted] • A series of CoFe-LDHs nanosheets were used to activate PMS to degrade quinolones. • The structure-activity relationship of CoFe-LDHs was studied. • The Co 1 Fe 1 -LDHs/PMS/CIP system could degrade 86.9% CIP within 12 min. • Degradation mechanism and pathway of ciprofloxacin was proposed. [ABSTRACT FROM AUTHOR]

• A new NAH method for low measuring sampling rate was proposed. • Feature extraction and pattern learning capability of deep learning technologies was utilized. • The frequency dimension information of the signal was fully utilized. • A data processing method for the proposed NAH was given. • The proposed NAH method was verified more accurate than the existing methods. Near-field acoustical holography (NAH) is an efficient noise diagnosis method with the deficiencies including wraparound error, which will increase when the spatial sampling rate is reduced below the minimum specified by Shannon-Nyquist theorem. Based on 3D convolutional neural network (3D-CNN) and stacked autoencoder (SAE), a method called CSA-NAH is proposed to reduce the wraparound error under sparse measuring. Subsequently, numerical calculations are carried out to illustrate the feasibility and performance of CSA-NAH. The results show that when holographic measurement point number is 64, average reconstruction error of CSA-NAH on an aluminum plate for sound pressure within 2000 Hz is 4.32%, while the latest existing methods is greater than 10%. For error in 1200 Hz~2000 Hz, the error is reduced from more than 15% of the existing methods to 5.5%. Therefore, the application of the proposed CSA-NAH can cut down the measuring cost by reducing the number of microphones without wraparound error. [ABSTRACT FROM AUTHOR]

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R 2 = 0.774) than the OLS regression model (Adjusted R 2 = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency. [ABSTRACT FROM AUTHOR]