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While solving the shunting scheduling of EMU first-level maintenance (SSEFM), most existing literature assumed a single maintenance route for all trains and considered only a through depot. It neglects the problem-specific characteristics in terms of varied maintenance routes and a stub-end depot, causing the infeasibility of the generated schedule in such particular circumstances. Therefore, the SSEFM problem with flexible maintenance routes in a stub-end depot with a transversal yard configuration is considered in this work. First, a multi-objective mixed-integer linear programming (MILP) model is formulated to maximize the reservation time in the storage area, and minimize the overstay time in the cleaning and inspecting areas. The relationship between constraints including flexible maintenance routes, train shunting conflicts, track occupation conflicts, and train arrival/departure times, are coordinated. Subsequently, a heuristic-based enhanced particle swarm optimization algorithm (EPSO) with two improvements is proposed to tackle this NP-hard problem. Specifically, three heuristic rules about the depth-first operation track allocation, the conflict-free bottleneck track allocation, and the right-shift track occupancy repair are designed to ensure the feasibility of the shunting schedule. Accordingly, a three-level decoding mechanism is designed to achieve a near-optimal shunting schedule with great train and route sequences. Two improvements on crossover and mutation operators are developed to enhance the exploration and exploitation ability. Finally, a real-world instance in China is solved to verify the effectiveness and efficiency of the proposed model and algorithm. Experimental results show that EPSO is relatively more effective than all the compared algorithms. [ABSTRACT FROM AUTHOR]

This study quantifies the absolute sea level (ASL) change along the coast of China from 1993 to 2019 using tide gauge (TG) and Global Navigation Satellite System (GNSS) data. The sea level change at 12 collocated TG and GNSS stations along the Chinese coast are first analyzed by TG + GNSS data. Then the ASL rise and its spatial variation along the coast of China and that in surrounding oceans are studied using TG + GNSS data and satellite altimetry products. The results show that the relative sea level rise along the Chinese coast varied from 2.61 ± 0.76 to 5.56 ± 0.70 mm/yr, while the effect of vertical land motion (VLM) ranged from −1.46 ± 0.30 to 1.07 ± 0.38 mm/yr. After correcting the effect of the VLM, the ASL rise ranged from 2.51 ± 0.86 to 5.03 ± 0.60 mm/yr, with a mean rate of 3.94 ± 0.88 mm/yr, which was 0.7 mm/yr higher than the global average. It is found that ASL rise rate is significantly higher in Chinese seas and neighboring oceans, with a meridional distribution in the East China Sea and a zonal distribution in the South China Sea. This may be related to factors such as sea surface temperature rise, atmospheric pressure decrease, freshwater runoff, zonal wind stress anomaly, and alongshore currents in the area. Plain Language Summary: As a consequence of climate change, the global mean sea level from tide gauges and satellite altimetry observations increased from 1.4 mm/yr over the period 1901–1990 to 3.2 mm/yr over the period 1993–2015 to 3.6 mm/yr over the period 2006–2015. An improved understanding of sea level rise is essential for the safety of society, the economy and the environment. This study quantifies the absolute sea level (ASL) change along the coast of China from 1993 to 2019 using tide gauge data with the vertical land motion corrected by Global Navigation Satellite System data. The derived ASL rise rate ranged from 2.51 ± 0.86 to 5.03 ± 0.60 mm/yr, with a mean rate of 3.94 ± 0.88 mm/yr, which was 0.7 mm/yr higher than the global average. It is also found that the ASL rise rate in Chinese seas and neighboring oceans is significantly higher than the global average, with a meridional distribution in the East China Sea and a zonal distribution in the South China Sea. The local influencing factors of sea level rise along the coast of China were analyzed, including the sea surface temperature rise, atmospheric pressure decrease, freshwater runoff, zonal wind stress anomaly and alongshore currents in the area. Key Points: Tide gauge and collocated Global Navigation Satellite System were first used to analyze the absolute sea level (ASL) changes along the coast of ChinaThe high ASL rise rate along the Chinese coast was a natural manifestation in all Chinese seas and neighboring oceansThe local influencing factors of sea level rise along the coast of China were preliminarily analyzed [ABSTRACT FROM AUTHOR]

When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (AdaBoost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines AdaBoost with the particle swarm optimization (PSO). The PSO-BP-AdaBoost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an AdaBoost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the AdaBoost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-AdaBoost algorithm. The PSO-BP-AdaBoost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-AdaBoost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision. [ABSTRACT FROM AUTHOR]

The atmospheric water vapour is an important indicator of the climate state and evolution, and also the key parameter affecting the hydrological cycle, atmospheric convection, and the weather. This study validated the accuracy of surface pressure and temperature data interpolated from European Centre for Medium-Range Weather Forecasts (ECMWF) Interim ReAnalysis (ERA-Interim) and ECMWF 5th ReAnalysis (ERA5) datasets, and evaluated the performance of the interpolation meteorological data for precipitable water vapour (PWV) retrieval using the measured meteorological data and Global Positioning System (GPS) data of the year 2014 obtained from 25 stations of the Chinese Coastal GPS Observation Network. We then analysed the temporal and spatial distribution of water vapour in the coastal regions of China using a 3-year GPS PWV time series calculated with the interpolated reanalysis meteorological data at 25 stations from 2014 to 2016. Evaluation of the reanalysis meteorological data results showed that the accuracies of the interpolated pressure and temperature data from ERA5 were slightly superior than those from ERA-Interim. The root-mean-square errors (RMSEs) of the surface temperature and pressure interpolated from two reanalysis datasets were less than 2.4 K and 1.6 hPa, respectively. Data based on GPS PWV products that used interpolated parameters were very close to those of meteorological observations, with biases within ±0.4 mm and RMSEs below 0.5 mm in most areas; these data also strongly agreed with radiosonde observations. However, the interpolated meteorological data from reanalysis datasets could not reflect the true change during typhoon events, which could not be used in GPS PWV retrieval. The analysis of the temporal and spatial distribution showed that the distribution of water vapour was mainly affected by latitude, land-sea distribution, and water vapour advection. The variations in water vapour were mainly seasonal with the highest PWV occurring in the summer and the lowest always occurring in the winter. Significant diurnal variations of water vapour varied with season and latitude, with an amplitude of about 0.9–3.5 mm; the peak value occurred in the afternoon or early morning, and was affected by surface evaporation, large-scale land–sea breeze circulation, and valley-wind circulation. [ABSTRACT FROM AUTHOR]

This paper investigated the quality of site-specific surface temperature and surface pressure data in the coastal regions of China, which were interpolated from the European Centre for Medium-Range Weather Forecast Interim reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and the National Centers for Environmental Prediction Final (NCEP FNL) reanalysis surface meteorological datasets as well as from the new Global Pressure and Temperature (GPT2) model data. The measured temperature and pressure along with the collocated GPS data from 2014 were collected from 25 observation stations evenly located in the region. Compared with the actual meteorological observations, the performances of the interpolated data from three reanalysis datasets differ marginally, with the root mean square errors (RMSEs) of the interpolated surface temperature and pressure less than 2.4 K and 1.6 hPa, respectively; however, the RMSEs of the surface temperature and pressure interpolated from the GPT2 model were 3.0 K and 4.2 hPa, respectively. Data based on GPS PWV products that used the meteorological parameters interpolated from three reanalysis data were very close to those of meteorological observations, with biases within ± 0.4 mm and RMSEs below 0.5 mm in most areas, and the RMSE of PWV using the GPT2 model interpolation data was superior by 2 mm. The measurement of GPS PWV using the interpolated reanalysis meteorological data also compared well with radiosonde observations, with RMSE between them tending to increase with a decrease of the GPS station's latitude. However, the GPS PWV based on the interpolated data could not reflect the true change in water vapor during typhoon events. [ABSTRACT FROM AUTHOR]

Frequently occurring and unavoidable perturbations in railway systems cause a reduction in performance and even the infeasibility of the original schedule. In addition, existing related studies assume that the maintenance routes are fixed in a deterministic situation. Therefore, it is necessary to focus on robust scheduling of electric multiple units first-level maintenance with flexible maintenance routes to defend against stochastic uncertainties. Firstly, a mixed-integer linear programming model with uncertain parameters in objective functions and constraints is built for the first time. Multiple constraints are formulated to consider flexible maintenance routes, train shunting conflicts, and track occupation conflicts in a stub-end depot. A robust optimization approach is adopted to obtain a deterministic robust counterpart model with uncertainties in processing/arrival/departure times. In this model, uncertainty and reliability levels are introduced to describe and quantify the disturbance degree of processing/arrival/departure times and the allowable violation degree of resource constraints respectively. Moreover, an adaptive iterative local search is proposed by embedding heuristic rules for the initialization. The proposed algorithm includes problem-specific neighborhood structures to improve the evolutionary ability, a variable neighborhood descent method to guide and drive the search toward promising areas of the search space, and an adaptive perturbation mechanism to enable the algorithm to jump out of local optimum. Numerical results from China's railway system validate the proposed model and quantitatively demonstrate the merit of the proposed algorithm. Further analysis shows that our approach can achieve an appropriate trade-off between robustness and efficiency for various uncertainty and reliability levels. • The SSEFM problem under stochastic uncertainties is addressed. • A multi-objective MILP model with deterministic robust equivalent counterpart is defined. • An adaptive iterative local search is proposed to solve this problem. • The algorithm is enhanced with a variable neighborhood descent and adaptive perturbation. [ABSTRACT FROM AUTHOR]

Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks. [ABSTRACT FROM AUTHOR]

High resolution multibeam bathymetry revealed the occurrence of numerous craterlike depressions, so-called pockmarks, in the sea floor of the North Yellow Sea Basin (NYSB). To investigate whether these pockmarks are related to any type of fluid flow or gas seepage from subsurface stratum, acoustic survey and box samplings were conducted in a selected area containing several typical pockmarks. This allowed for an integrated analysis of the morphology, distribution, and subsurface strata as well as a direct comparison of seafloor characteristics among different areas inside and outside of a pockmark. Additionally, surface sediments were analyzed for particle structure and grain size. Based on the combination of multibeam bathymetry and backscatter intensity data, 282 seafloor depressions were accurately identified. Moreover, the formation of pockmark can be attributed to existence of gas chimneys and other shallow gases revealed by shallow seismic profile records in the subsurface strata. Sediments inside and outside the pockmark showed remarkable discrepancies in terms of grain size, and benthic fauna. Additionally, strong internal reflection area was observed inside the depressions where benthic habitats are abundant and sediments contain strawberry pyrite and carbonate cementation. Side scan sonar also recorded sediment plumes in the water column, which indicates that the pockmarks are still active. Several "pearl-string-like" accumulations of pockmark were found in the study area, which well agree with the distribution of buried channels and lakes. These results thus provide new insights in interpreting shallow water fluid seepage, with implications for our current knowledge of overall sedimentation process in offshore continental shelves. • Active fluid seepage in shallow water continental shelf environment • Shallow-gas related acoustic features recorded on sub-bottom acoustic data • Benthic fauna anomalies, and strawberry pyrite associated with seafloor seeps • The large but shallow pockmarks can be ascribed to low fluid pressure. • String like distribution of pockmarks controlled by subsurface buried channels. [ABSTRACT FROM AUTHOR]