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The remote control ship is considered to be the most likely implementation of maritime autonomous surface ships (MASS) in the near-term future. With collaborative control from onboard controllers and operators ashore, ships may operate in three navigation control modes (NCMs), manual, autonomous, and remote control, based on different levels of control authority. The scientific selection of the appropriate NCM for MASS under multiple driving modes is crucial for ensuring ship navigation safety and holds significant importance for operators and regulatory authorities overseeing maritime traffic within specific areas. To aid in selecting the proper NCM, this study introduces a risk-based comparison method for determining optimal control modes in specific scenarios. Firstly, safety control paths and processes for MASS under different NCMs are constructed and analyzed using system-theoretic process analysis (STPA). By analyzing unsafe system control actions, key Risk Influencing Factors (RIFs) and their interrelationships are identified. Secondly, a Hidden Markov Model (HMM) process risk assessment model is developed to infer risk performance (hidden state) through measuring RIF states. Cloud modeling with expert judgments is utilized to parameterize the HMM while addressing inherent uncertainty. Lastly, the applicability of the proposed framework was verified through simulation case studies. Typical navigation scenarios of conventional ships in coastal waters were chosen, and real-time data collected by relevant sensors during navigation were used as simulation inputs. Results suggest that in the same scenario, process risks differ among the analyzed NCMs. Traffic complexity, traffic density, and current become the primary factors influencing navigation risks, and it is necessary to select the appropriate NCM based on their real-time changes. [ABSTRACT FROM AUTHOR]

Collision risk in ship pilotage process has complex characteristics that are dynamic, uncertain, and emergent. To reveal collision risk resonance during ship pilotage process, a hybrid probabilistic risk analysis approach is proposed, which integrates the Functional Resonance Analysis Method (FRAM), Dempster–Shafer (D–S) evidence theory, and Monte Carlo (MC) simulation. First, FRAM is used to qualitatively describe the coupling relationship and operation mechanism among the functions of the pilotage operation system. Then, the D–S evidence theory is used to determine the probability distribution of the function output in the specified pilotage scenario after quantitatively expressing the function variability, coupling effect, and the influence of operation conditions through rating scales. Finally, MC simulation is used to calculate the aggregated coupling variability between functions, and the critical couplings and risk resonance paths under different scenarios are identified by setting the threshold and confidence level. The results show that ship collision risk transmission is caused by function resonance in the pilotage system, and the function resonance paths vary with pilotage scenarios. The critical coupling 'F2-F7(I)' emerges as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. The hybrid probabilistic risk analytical approach to ship pilotage risk resonance with FRAM can be a useful method for analysing the causative mechanism of ship operational risk. [ABSTRACT FROM AUTHOR]

Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation. [ABSTRACT FROM AUTHOR]

Gut microbiota plays an important role in the pathophysiology of obesity. Fungal polysaccharide can improve obesity, but the potential mechanism needs further study. This experiment studied the potential mechanism of polysaccharides from Sporisorium reilianum (SRP) to improve obesity in male Sprague Dawley (SD) rats fed with a high-fat diet (HFD) using metagenomics and untargeted metabolomics. After 8 weeks of SRP (100, 200, and 400 mg/kg/day) intervention, we analyzed the related index of obesity, gut microbiota, and untargeted metabolomics of rats. The obesity and serum lipid levels of rats treated with SRP were reduced, and lipid accumulation in the liver and adipocyte hypertrophy was improved, especially in rats treated with a high dose of SRP. SRP improved the composition and function of gut microbiota in rats fed with a high-fat diet, and decreased the ratio of Firmicutes to Bacteroides at the phylum level. At the genus level, the abundance of Lactobacillus increased and that of Bacteroides decreased. At the species level, the abundance of Lactobacillus crispatus, Lactobacillus helveticus, and Lactobacillus acidophilus increased, while the abundance of Lactobacillus reuteri and Staphylococcus xylosus decreased. The function of gut microbiota mainly regulated lipid metabolism and amino acid metabolism. The untargeted metabolomics indicated that 36 metabolites were related to the anti-obesity effect of SRP. Furthermore, linoleic acid metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, and the phenylalanine metabolism pathway played a role in improving obesity in those treated with SRP. The study results suggest that SRP significantly alleviated obesity via gut-microbiota-related metabolic pathways, and SRP could be used for the prevention and treatment of obesity. [ABSTRACT FROM AUTHOR]

This study presents the analogical assessment of the train-induced vibration and radiated noise in a proposed theater. The theater is to be constructed in a region with crowded metro lines, and the assessment is implemented in an analogical building with comparable structural type and metro condition. Prior to the assessment, the comparability of the analogical building with the theater is validated using the train-induced ground vibration. With the same horizontal distance from the metro line, the train-induced vibration level in the analogical building is 9 dB higher than that in the construction site of the theater. Such results indicate that the lack of soil layers may lead to a dramatic increase in train-induced vibration in the building. In the staircase of the analogical building, the train-induced radiated noise reached 55 dB (A), which is 10 dB (A) higher than the daytime allowable level. As the most important indicator, the noise rating number in the cinema of the analogical building is NR-43, which put forward an enormous challenge on the construction of the theater with a denoise demand of 23 dB. The analogical method applied in this study provides an effective and practical way for the assessment of train-induced vibration and radiated noise in proposed vibration-sensitive buildings. The assessment results that provide necessary reference and support for the anti-vibration design will help guarantee the stage effect of the theater. [ABSTRACT FROM AUTHOR]

In the past 20 years, many studies have been performed on ballast layer inspection and condition evaluation with ground penetrating radar (GPR). GPR is a non-destructive means that can reflect the ballast layer condition (fouling, moisture) by analysing the received signal variation. Even though GPR detection/inspection for ballast layers has become mature, some challenges still need to be stressed and solved, e.g., GPR indicator (for reflecting fouling level) development, quantitative evaluation for ballast fouling levels under diverse field conditions, rapid GPR inspection, and combining analysis of GPR results with other data (e.g., track stiffness, rail acceleration, etc.). Therefore, this paper summarised earlier studies on GPR application for ballast layer condition evaluation. How the GPR was used in the earlier studies was classified and discussed. In addition, how to correlate GPR results with ballast fouling level was also examined. Based on the summary, future developments can be seen, which is helpful for supplementing standards of ballast layer evaluation and maintenance. [ABSTRACT FROM AUTHOR]

The rubber-protected ballast (RPB) is made from natural ballast particles and crumb rubber particles. The crumb rubber is shredded waste tires. RPB was chosen to replace the ballast as it has higher resistance to breakage and abrasion. However, the static and dynamic performance of the RPB has not been confirmed yet. Towards this end, experimental tests and numerical simulations were utilized to study the feasibility of RPB application. Direct shear tests (DSTs) were performed and a DST model and three-sleeper track model with the discrete element method (DEM) were built. The shear strength, settlement, displacement, and acceleration of the RPB were studied. The results show that the RPB has the advantage of increasing the force (stress) distribution and that the smaller crumb rubber size was more suitable for replacing the ballast particles. [ABSTRACT FROM AUTHOR]

In this paper, we studied the alteration of viscoelastic properties of a neat poly(methyl methacrylate) (PMMA), induced by an applied external electric field. The rheological properties of PMMA are measured using a rotational rheometer at elevated temperatures. The electric field effect on the shear resistance of the polymer was studied by examining rheological responses under difference experimental scenarios. We find that the external electric field can remarkably enhance shear resistance and prevent flow of PMMA melt, enabling it to behave more predictably at high temperatures. Dynamic rheological analysis illustrates that the external electric field speeds up the recovery of mechanical properties of the PMMA melt after large deformations, whereas the PMMA melt exhibits thixotropic behaviors. The recovery velocity is influenced by the strength of the electric field, specifically, and is found to be proportional to the electric field strength. Our experimental characterization may provide new evidence on the tuning mechanical properties of polymer melts via controlling segmental polarization. [ABSTRACT FROM AUTHOR]

Ultrasensitive room temperature real-time NO₂ sensors are highly desirable due to potential threats on environmental security and personal respiratory. Traditional NO₂ gas sensors with highly operated temperatures (200-600 °C) and limited reversibility are mainly constructed from semiconducting oxide-deposited ceramic tubes or inter-finger probes. Herein, we report the functionalized graphene network film sensors assembled on an electrospun three-dimensional (3D) nanonetwork skeleton for ultrasensitive NO₂ sensing. The functional 3D scaffold was prepared by electrospinning interconnected polyacrylonitrile (PAN) nanofibers onto a nylon window screen to provide a 3D nanonetwork skeleton. Then, the sulfophenyl-functionalized reduced graphene oxide (SFRGO) was assembled on the electrospun 3D nanonetwork skeleton to form SFRGO network films. The assembled functionalized graphene network film sensors exhibit excellent NO₂ sensing performance (10 ppb to 20 ppm) at room temperature, reliable reversibility, good selectivity, and better sensing cycle stability. These improvements can be ascribed to the functionalization of graphene with electron-withdrawing sulfophenyl groups, the high surface-to-volume ratio, and the effective sensing channels from SFRGO wrapping onto the interconnected 3D scaffold. The SFRGO network-sensing film has the advantages of simple preparation, low cost, good processability, and ultrasensitive NO₂ sensing, all advantages that can be utilized for potential integration into smart windows and wearable electronic devices for real-time household gas sensors., Competing Interests: The authors declare no conflict of interest.