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Abstract Background Assisted reproduction technology (ART) has advanced significantly, raising concerns regarding its impact on the secondary sex ratio (SSR), which is the sex ratio at birth in offspring. This study aimed to explore factors affecting SSR in singletons, singletons from twin gestation, and twins from twin gestation within the context of ART. Methods A retrospective analysis was conducted on data from 8335 births involving 6,223 couples undergoing ART. Binary logistic regression assessed relationships between parental and embryonic factors and SSR in singletons and singletons from twin gestation. Multinomial logistic regression models were utilized to identify factors influencing SSR in twins from twin gestation. Results Secondary infertility (OR = 1.164, 95% CI: 1.009–1.342), advanced paternal age (OR = 1.261, 95% CI: 1.038–1.534), and blastocyst embryo transfer (OR = 1.339, 95% CI: 1.030–1.742) were associated with an increased SSR, while frozen embryo transfer (FET) showed a negative association with SSR (OR = 0.738, 95% CI: 0.597–0.912) in singletons. A longer duration of gonadotropin (Gn) usage reduced SSR in singletons (OR = 0.961, 95% CI: 0.932–0.990) and singletons from twin gestation (OR = 0.906, 95% CI: 0.838–0.980). In singletons from twin gestation, male-induced infertility (OR = 2.208, 95% CI: 1.120–4.348) and higher Gn dosage (OR = 1.250, 95% CI: 1.010–1.548) were significantly associated with an increased SSR. Women aged > 35 years and intracytoplasmic sperm injection (ICSI) were associated with lower SSR (OR = 0.539, 95% CI: 0.293–0.990 and OR = 0.331, 95% CI: 0.158–0.690, respectively). In twins from twin gestation, paternal age exceeded maternal age (OR = 0.682, 95% CI: 0.492–0.945) and higher Gn dosage (OR = 0.837, 95% CI: 0.715–0.980) were associated with a higher proportion of male twins. Cleavage stage transfer (OR = 1.754, 95% CI: 1.133–2.716) resulted in a higher percentage of boy-girl twins compared to blastocyst transfer. Conclusion This study demonstrates the complex interplay of various factors in determining the SSR in ART, highlighting the importance of considering infertility type, paternal age, fertilization method, embryo transfer stage, and Gn use duration when assessing SSR. Nevertheless, further research with a large sample size is necessary to confirm and expand upon the findings of this study.

Recent studies on self-supervised monocular depth estimation have achieved promising results, which are mainly based on the joint optimization of depth and pose estimation via high-level photometric loss. However, how to learn the latent and beneficial task-specific geometry representation from videos is still far from being explored. To tackle this issue, we propose two novel schemes to learn more effective representation from monocular videos: (i) an Inter-task Attention Model (IAM) to learn the geometric correlation representation between the depth and pose learning networks to make structure and motion information mutually beneficial; (ii) a Spatial-Temporal Memory Module (STMM) to exploit long-range geometric context representation among consecutive frames both spatially and temporally. Systematic ablation studies are conducted to demonstrate the effectiveness of each component. Evaluations on KITTI show that our method outperforms current state-of-the-art techniques.

When the unmanned aerial vehicle (UAV) is applied to three-dimensional (3D) reconstruction of the offshore ship, it faces two problems: the battery capacity limitation of the UAV and the disturbance of the wind in the environment. Wind disturbance is generally not considered in the path planning process of the existing UAV 3D reconstruction path planning research. Therefore, the planned path is only suitable for no-wind or light-wind scenarios. For the 3D reconstruction of ship targets, we propose a UAV path planning method that can satisfy both reconstruction efficiency and wind disturbance resistance requirements. Firstly, the concept of model surface complexity is proposed to generate a more efficient candidate view set. Secondly, the Min–Max strategy and a new viewpoint construction method are used to generate the initial path. Thirdly, combined with the wind field model, a method for generating a stable path against wind disturbance based on the idea of interval optimization is proposed. Experimental results demonstrate that our method can adaptively determine the number of sample points and viewpoints according to ship’s geometric characteristics and further reduce the number of viewpoints without significantly affecting the reconstruction quality; the path planned by our method is also stable against wind disturbance.

ObjectivesIn order to solve the problems of safety and smoothness in the path planning of an unmanned surface vehicle (USV), a path planning method with a controllable distance from obstacles is proposed.MethodsFirst, the raster environment information is generated in combination with the radar image, and the Voronoi field algorithm (VFA) is used to add the danger potential field to each grid and establish the navigation boundary; second, the risk function associated with the navigation boundary is established to improve the evaluation function of the A-star algorithm, and the improved A-star algorithm is used for path planning; finally, for the problem of the large course altering of the navigation path, the gradient descent method (GDM) is used to plot a continuous smooth navigation path that satisfies the actual navigation requirements of the USV.ResultsThe simulation results show that the proposed path planning method can control the distance between the path and obstacles by setting different navigation boundaries, and the smoothness meets the navigation requirements. ConclusionsThe method proposed herein is reasonable and effective in the path planning process of USVs, and can provide references for USV autonomous obstacle avoidance decision-making.

Purpose – Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles. Design/methodology/approach – First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A* algorithm and uses the improved A* algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained. Findings – The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’ autonomous obstacle avoidance decision-making. Originality/value – This study establishes navigation area boundary for the environment based on the VFA and uses the improved A* algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.

Abnormalities of navigation buoys include tilting, rusting, breaking, etc. Realizing automatic extraction and evaluation of rust on buoys is of great significance for maritime supervision. Severe rust may cause damage to the buoy itself. Therefore, a lightweight method based on machine vision is proposed for extracting and evaluating the rust of the buoy. The method integrates image segmentation and processing. Firstly, image segmentation technology is used to extract the metal part of the buoy based on an improved U-Net. Secondly, the RGB image is converted into an HSV image by preprocessing, and the transformation law of HSV channel color value is analyzed to obtain the best segmentation threshold and then the pixels of the rusted and the metal parts can be extracted. Finally, the rust ratio of the buoy is calculated to evaluate the rust level of the buoy. Results show that both the segmentation precision and recall are above 0.95, and the accuracy is nearly 1.00. Compared with the rust evaluation algorithm directly using the image processing method, the accuracy and processing speed of rust grade evaluation are greatly improved.

Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic monitoring systems and ship-detection algorithms for autonomous ship navigation, affecting maritime safety. The paper proposes an approach to resolve the problem by visually removing rain streaks and fog from images, achieving an integrated framework for accurate ship detection. Firstly, the paper employs an attention generation network within an adversarial neural network to focus on the distorted regions of the degraded images. The paper also utilizes a contextual encoder to infer contextual information within the distorted regions, enhancing the credibility of image restoration. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to achieve rapid multi-scale feature fusion, enhancing the accuracy of maritime ship detection. The proposed GYB framework was validated using the SeaShip dataset. The experimental results show that the proposed framework achieves an average accuracy of 96.3%, a recall of 95.35%, and a harmonic mean of 95.85% in detecting maritime traffic ships under rain-streak and foggy-weather conditions. Moreover, the framework outperforms state-of-the-art ship detection methods in such challenging weather scenarios.

Research on antenna reconfiguration is important in antenna applications. We present design of a multi-polarized plasma antenna with 40.68 MHz radio frequency (RF) power supplies and several discharge tubes filled with argon (neon) and mercury. Furthermore, self-made regulating circuits among the vibrators are introduced for the antenna array system. It can not only make the length of the dipole controllable but also improve the antenna gain and circular polarization (CP) performance under different lengths. Antenna arrays can be classified into linear and circular polarization by quickly adjusting the phase difference between vibrators. Both the experimental and simulation results indicate that the plasma antenna can realize impedance, pattern, and polarization reconstruction expediently. In addition, the linear polarization (LP) and CP can be transformed into each other quickly by adjusting the discharge conditions and regulating circuits. Moreover, compared with a symmetrical cross vibrator plasma antenna and a metal antenna of the same size, the proposed antenna array shows a certain gain and good reconfigurable performance within the frequency band of 150–250 MHz.

Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship’s displacement in the images is calculated based on the ship’s trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship’s speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation.

Accurate and real-time monitoring of the shoreline through cameras is an invaluable guarantee for the safety of near-shore navigation and berthing of unmanned surface vehicles; existing shoreline detection methods cannot meet both these requirements. Therefore, we propose an improved shoreline detection method to detect shorelines accurately and in real time. We define shoreline detection as the combination of water surface area segmentation and edge detection, the key to which is segmentation. To detect shorelines accurately and in real time, we propose an improved U-Net for water segmentation. This network is based on U-Net, using ResNet-34 as the backbone to enhance the feature extraction capability, with a concise decoder integrated attention mechanism to improve the processing speed while ensuring the accuracy of water surface segmentation. We also introduce transfer learning to improve training efficiency and solve the problem of insufficient data. When obtaining the segmentation result, the Laplace edge detection algorithm is applied to detect the shoreline. Experiments show that our network achieves 97.05% MIoU and 40 FPS with the fewest parameters, which is better than mainstream segmentation networks, and also demonstrate that our shoreline detection method can effectively detect shorelines in real time in various environments.

In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the consideration of USV motion characteristics and COLREGs (International Convention on Regulations for Collision Avoidance at Sea) requirements. First, the basis of collision avoidance decisions based on the dynamic window method is introduced. Second, the knowledge of local collision avoidance theory is used to study the local path planning of USV, and finally, simulation experiments are carried out in different situations and environments containing unknown obstacles. The local path planning experiments with unknown obstacles can prove that the local path planning algorithm proposed in this study has good results and can ensure that the USV makes collision avoidance decisions based on COLREGs when it meets with a ship.

A Yagi plasma antenna model was established by HFSS according to the relationship between plasma dielectric constant and electron density. The patterns were simulated by changing plasma parameters and the number of director dipoles. Results show that when the passive vibrators were switched off, the antenna is omnidirectional antenna. The directionality increases with the increase of the number of passive dipole and the main lobe of which narrows down. Then the plasma Yagi antenna model is established by plasma tube, the gain changed by changing the number of passive dipoles, so the plasma Yagi antenna has a very good reconfigurability. Results prove that the feasibility of the plasma Yagi antenna can be used on AIS base station of Ships’ Routeing waters. It can promote the communication and capability of maritime supervision in Ships’ Routeing waters.

Ship tracking provides crucial on-site microscopic kinematic traffic information which benefits maritime traffic flow analysis, ship safety enhancement, traffic control, etc., and thus has attracted considerable research attentions in the maritime surveillance community. Conventional ship tracking methods yield satisfied results by exploring distinct visual ship features in maritime images, which may fail when the target ship is partially or fully sheltered by obstacles (e.g., ships, waves, etc.) in maritime videos. To overcome the difficulty, we propose an augmented ship tracking framework via the kernelized correlation filter (KCF) and curve fitting algorithm. First, the KCF model is introduced to track ships in the consecutive maritime images and obtain raw ship trajectory dataset. Second, the data anomaly detection and rectification procedure are implemented to rectify the contaminated ship positions. For the purpose of performance evaluation, we implement the proposed framework and another three popular ship tracking models on the four typical ship occlusion videos. The experimental results show that our proposed framework successfully tracks ships in maritime video clips with high accuracy (i.e., the average root mean square error (RMSE), root mean square percentage error (RMSPE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE) are less than 10), which significantly outperforms the other popular ship trackers.

In the water transportation, ship speed estimation has become a key subject of intelligent shipping research. Traditionally, Automatic Identification System (AIS) is used to extract the ship speed information. However, transportation environment is gradually becoming complex, especially in the busy water, leading to the loss of some AIS data and resulting in a variety of maritime accidents. To make up for this deficiency, this paper proposes a vessel speed extraction framework, based on Unmanned Aerial Vehicle (UAV) airborne video. Firstly, YOLO v4 is employed to detect the ship targets in UAV image precisely. Secondly, a simple online and real time tracking method with a Deep association metric (Deep SORT) is applied to track ship targets with high quality. Finally, the ship motion pixel is computed based on the bounding box information of the ship trajectories, at the same time, the ship speed is estimated according to the mapping relationship between image space and the real space. Exhaustive experiments are conducted on the various scenarios. Results verify that the proposed framework has an excellent performance with average speed measurement accuracy is above 93% in complex waters. This paper also paves a way to further predict ship traffic flow in water transportation.

Automatically recognizing and tracking dynamic targets on the sea is an important task for intelligent navigation, which is the prerequisite and foundation of the realization of autonomous ships. Nowadays, the radar is a typical perception system which is used to detect targets, but the radar echo cannot depict the target’s shape and appearance, which affects the decision-making ability of the ship collision avoidance. Therefore, visual perception system based on camera video is very useful for further supporting the autonomous ship navigational system. However, ship’s recognition and tracking has been a challenge task in the navigational application field due to the long distance detection and the ship itself motion. An effective and stable approach is required to resolve this problem. In this paper, a novel ship recognition and tracking system is proposed by using the deep learning framework. In this framework, the deep residual network and cross-layer jump connection policy are employed to extract the advanced ship features which help enhance the classification accuracy, thus improves the performance of the object recognition. Experimentally, the superiority of the proposed ship recognition and tracking system was confirmed by comparing it with state of-the-art algorithms on a large number of ship video datasets.

Previous studies established traffic demand equilibrium with an assumption that all the traffic information on road is easily accessed by the in-need traffic participants, which is not true in the real applications (due to data collections difficulty). The newly emerging smart portable devices (e.g., smart phones) generate massive on-site traffic data (speed, density, etc.), which stimulates us re-consider designing the traffic demand equilibrium. For the purpose of analyzing mobile internet service influence on traffic demand, we build up a Probit-based model with consideration of multiple traffic constraints (i.e., traveler type, actual travelling time, perceiving travelling time). The Monte Carlo method is introduced to simulating initial route selection probability distributions, and the Method of Successive Averages (MSA) is developed to help the Monte Carlo algorithm converge at optimal solution. We have implemented our model on typical traffic travelling scenario with very complex traffic network demands. The experimental results suggested that larger mobility service coverage can significantly reduce the overall traffic time in the free flow state, and the mobility service coverage rate ranging from 0.6 to 0.8 is supposed to provide minimum travelling time for the overall traffic network, while larger coverage rate at congested state may reduce the traffic network efficiency.

Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.

Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.

The experiments described in this work were performed with the aim of introducing a new plasma antenna that was excited by a 5–20 kHz alternating current (AC) power supply, where the antenna was transformed into a U-shape. The results show that the impedance, voltage standing-wave ratio (VSWR), radiation pattern and gain characteristics of the antenna can be controlled rapidly by varying not only the discharge power, but also by varying the discharge frequency in the range from 5 to 20 kHz. When the discharge frequency is adjusted from 10 to 12 kHz, the gain is higher within a relatively broad frequency band and the switch-on time is less than 1 ms when the discharge power is less than 5 W, meaning that the plasma antenna can be turned on and off rapidly.

Supplier selection is a complex multiple criteria decision-making (MCDM) problem, which considers a number of alternative suppliers as well as conflicting and noncommensurable criteria. Considering the fact that it is difficult to precisely determine criteria weights and the ratings of alternatives on each criterion in real-life situations, the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method has been modified for intuitionistic fuzzy data in this study for supplier evaluation and selection. Further, we take into account both subjective and objective weights of criteria in the decision-making process, as most supplier selection approaches consider only subjective criteria weights. Finally, two supplier selection examples are provided to illustrate the proposed intuitionistic fuzzy hybrid VIKOR (IFH-VIKOR) method, and its merits are highlighted by comparing it with other relevant approaches.