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This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges.

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.

To address the complexity of ship trajectory prediction, this study explored the efficacy of the Mamba model, a relatively new deep-learning framework. In order to evaluate the performance of the Mamba model relative to traditional models, which often struggle to cope with the dynamic and nonlinear nature of maritime navigation data, we analyzed a dataset consisting of intricate ship trajectory data. The prediction accuracy and inference speed of the model were evaluated using metrics such as the mean absolute error (MAE) and root mean square error (RMSE). The Mamba model not only excelled in terms of the computational efficiency, with inference times of 0.1759 s per batch—approximately 7.84 times faster than the widely used Transformer model—it also processed 3.9052 samples per second, which is higher than the Transformer model’s 0.7246 samples per second. Additionally, it demonstrated high prediction accuracy and the lowest loss among the evaluated models. The Mamba model provides a new tool for ship trajectory prediction, which represents an advancement in addressing the challenges of maritime trajectory analysis when compared to existing deep-learning methods.

This paper aims to analyze the North Brazil Current (NBC) rings during the initial 5 months of 2020 using surface currents derived from Automatic Identification System (AIS) data in comparison with altimetry-based Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) current fields. The region of NBC rings is characterized by relatively high marine traffic, facilitating an accurate current estimation. Our investigation primarily focused on a brief period coinciding with intensive in situ measurements (EUREC4A-OA experiment). The Angular Momentum Eddy Detection and tracking Algorithm (AMEDA) detection algorithm was then employed to detect and track eddies in both fields. Subsequently, a particular NBC ring present in the region in January and February 2020 was examined. The comparison demonstrated that AIS data exhibited the precision and resolution necessary to effectively identify the NBC rings and smaller surrounding eddies, aligning well with other datasets such as in situ measurements, sea surface temperature (SST), and sea surface salinity (SSS) data. Moreover, we established that AIS data yielded accurate regional velocity fields, as evidenced by an analysis of energy spectra. Furthermore, our analysis revealed that AIS data captured aspects of eddy–eddy interactions which were not adequately depicted in AVISO fields.

Acute ischemic stroke (AIS) is a cerebrovascular disease that seriously affects the physical and mental health and quality of life of patients. However, there is a lack of reliable prognostic prediction methods. The main objective of this study was to investigate the prognostic value of long non-coding RNA (lncRNA) H19 in lymphocytes of patients with AIS, and to construct a prognostic prediction model for AIS including lncRNA H19 in lymphocytes, which would provide new ideas for the prognostic evaluation of AIS. Poor prognosis was defined when the patient’s modified Rankin scale (mRS) score at 3 months after AIS onset was greater than 2. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the level of lncRNA H19 in lymphocytes. Spearman correlation analysis revealed a positive correlation between lncRNA H19 and mRS score at 3 months after AIS onset (r = 0.1977, p = 0.0032), while lncRNA H19 was negatively correlated with white blood cells counts, lymphocytes counts, and neutrophils counts. Logistic regression analysis identified lncRNA H19 as an independent predictor of poor prognosis (OR = 3.062 [1.69–5.548], p < 0.001). Moreover, a nomogram prediction model incorporating lncRNA H19 in lymphocytes demonstrated effective discrimination, calibration, and clinical applicability in predicting AIS outcomes. The findings suggest that lncRNA H19 in lymphocytes could be a valuable prognostic indicator and a potential pharmacological target for AIS patients, and might be a novel pathway for enhanced prognostic evaluation and targeted therapeutic strategies.

Background: The Risk of Paradoxical Embolism (RoPE) score was developed to identify stroke-related patent foramen ovale (PFO) in patients with cryptogenic stroke. Methods: We conducted a retrospective analysis of the 2016 to 2020 National Inpatient Sample to determine the performance of the modified RoPE score in identifying the presence of a PFO in patients with acute ischemic stroke (AIS). Results: A total of 3,338,805 hospital admissions for AIS were analysed and 3.0% had PFO. Patients with PFO were younger compared to those without a PFO (median 63 years vs. 71 years, p < 0.001) and fewer were female (46.1% vs. 49.7%, p < 0.001). The patients with PFO had greater mean modified RoPE scores (4.0 vs. 3.3, p < 0.001). The area under the curve for the RoPE score in predicting PFOs was 0.625 (95%CI 0.620–0.629). The best diagnostic power of the RoPE score was achieved with a cut-off point of ≥4 where the sensitivity was 55% and the specificity was 64.2%. A cut-off point of ≥5 increased the specificity (83.1%) at the expense of sensitivity (35.8%). The strongest predictor of PFOs was deep vein thrombosis (OR 3.97, 95%CI 3.76–4.20). Conclusions: The modified RoPE score had modest predictive value in identifying patients with PFO among patients admitted with AIS.

This study presents a method to support ship trajectory prediction at waterway confluences using historical Automatic Identification System (AIS) data. The method is meant to improve the recognition accuracy of ship behavior trajectory, assist in the proactive avoidance of collisions, and clarify ship collision responsibility, to ensure the safety of waterway transportation systems in the event of ship encounters induced by waterway confluence or channel limitation. In this study, the ship trajectory based on AIS data is considered from five aspects: time, location, heading, speed, and trajectory by using the piecewise cubic Hermite interpolation method and then quickly clustered by regional navigation rules. Then, an improved K-Nearest Neighbor Algorithm considering the sensitivity of data characteristics (SKNN) is proposed to predict the trajectory of ships, which considers the influence weights of various parameters on ship trajectory prediction. The method is trained and verified using the AIS data of the Yangtze River and Han River intersection in Wuhan. The results show that the accuracy of SKNN is better than that of conventional KNN and Naive Bayes (NB) in the same test case. The accuracy of the ship trajectory prediction method is above 99% and the performance metrics of the SKNN surpass those of both the conventional KNN and NB classifiers, which is helpful for early warning of collision encounters to ensure avoidance.

In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted trajectories. Through quantitative analysis of different datasets, it was observed that GL-STGCNN outperforms previous prediction models with a 31.8% improvement in the average displacement error metric and a 16.8% improvement in the final displacement error metric. Furthermore, trajectory correction through model predictive control shows a performance boost of 44.5% based on the initial predicted trajectory distribution. While GL-STGCNN excels in multi-ship interaction trajectory prediction by reasonably modeling ship interaction adjacency matrices and employing trajectory correction, its performance may vary in different datasets and ship motion patterns. Future work could focus on adapting the model’s ship interaction adjacency matrix modeling to diverse environmental scenarios for enhanced performance.

This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.

Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite observations between 2015 and 2020, these matters are delved into this study. A discernible overall growth trend in Arctic traffic occupancy occurs from 2015 to 2020 during summer and autumn. Excluding passenger ships, the traffic occupancy trend for each ship type closely parallels that for all ships. Variations in traffic occupancy along the Northeast Passage dominate that in the entire Arctic. As sea ice diminishes, both Arctic traffic occupancy and its variability noticeably increase. Further examination of the relationship between shipping activities and ice conditions reveals that increased traffic occupancy corresponds significantly to diminishing sea ice extent, and the constraint imposed by sea ice on Arctic traffic occupancy weakens, while the 6-year AIS data could lead to uncertainties. In summary, as the Arctic sea ice declines continuously, not only sea ice but also additional social, military, and environmental factors constraining marine activities should be considered in the future operation of Arctic shipping.

Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing small ship detection performance, which combines three different characteristics of polarization: newVH, enhanced VH, and enhanced VV. Employing the Radar Cross Section (RCS) value in three stages, the newVH was utilized to detect Automatic Identification System (AIS) -ships and small ships. In the first step, the adaptive threshold (AT) method was applied to newVH with a high RCS condition (>−10.36 (dB)) for detecting AIS-ships. Secondly, the first small ship target was detected with the maximum suppression of false alarms by using the AT with a middle RCS condition (>−16.98 (dB)). In the third step, a candidate group was identified by applying a condition to the RCS values (>−23.01 (dB)), where both small ships and speckle noise were present simultaneously. Subsequently, the enhanced VH and VV polarizations were employed, and an optimized threshold value was selected for each polarization to detect the second small ship while eliminating noise pixels. Finally, the results were evaluated using the AIS and small fishing vessel tracking system (V-Pass) based on the detected ship positions and ship lengths. The average matching results from 26 scenes in 2022 indicated a matching rate of over 86.67% for AIS-ships. Regarding small ships, the detection performance of PCSD was 42.27%, which was over twice as accurate as the previous Constant False Alarm Rate (CFAR) ship detection model. As a result, PCSD enhanced the detection rate of small ships while maintaining the capacity for detecting AIS-equipped ships.

Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability.

Vessel motion simulation as well as model-based accurate trajectory prediction of vessels require accurate models with respect to related dynamic properties. The ability to predict vessel’s trajectory behaviors will become relevant in the case of future autonomous navigation of vessels to predict the behavior of others. The definition of models or parameters can be realized via first principles or by using experimental modeling methods leading to a time invariant or variant model. Existing hydrodynamical modeling approaches are based on mathematical approaches, which use parameters like mass, hydrodynamic forces, wind velocity, depth under the keel, loading parameters, etc. So, determining a dynamic vessel’s model is a complex task, since the model is vessel-specific. For collision avoidance of autonomous or assisted vessels, the trajectory prediction of encountering other vessels is especially required. It is not possible to use complex hydrodynamical models of encountering vessels online due to missing required information/measurements. Even existing deep learning approaches provide better predictions, but are still insufficient for collision avoidance in the case of strong dynamical changes, since the considered input sequences are long. Due to long input sequences, the model does not adapt to strong dynamical changes. In this work, a simple parameter-based approach is developed to predict the intended behavior using the last seconds of the measured position variables. The idea is to globally identify the model parameters of the vessel, which remains constant for the situation, and additionally two parameters for local adaptation, which adapt at every updated input sequence. Typically parameters like rudder angle, wind velocities, and water current affect the behavior of vessels. The introduced approach works with a sliding window approach for which, after identification of the global system, local values are identified based on the last 80 measurements of the vessels. A trajectory prediction (assuming no additional rudder-based maneuvering) is realized for the prediction horizon of 180 s. To confirm the robustness of the new approach, real AIS/GPS-based measurements from a German research inland vessel for different scenarios and sailing conditions including ‘loaded’ and ‘empty’ sailing cases are used. Furthermore, additional results are shown for position data information of different sample rates.

Visualising the fishing behaviour of vessels and quantifying the spatial distribution of fishing effort is the scientific basis for assessing and managing fisheries resources. The information on the dynamics of fishing vessel voyages provided by the automatic identification system (AIS) of vessels serves as high-precision fishery data and provides a means of quantifying fishing effort with high spatial and temporal resolution in the tuna longline fishery. Based on the AIS data of five tuna longliners operating in the waters near the Marshall Islands from 2020 to 2021, this study used three methods, namely the threshold screening method, the construction of a BP neural network and the support vector machine (SVM) to identify the fishing and non-fishing status of the tuna longliners, respectively. This study investigates the status identification and fishing effort estimation of the tuna longliner (VESSEL A) in 2021 based on the constructed optimal model, and spatial correlation analyses are performed between the fishing effort estimated in hours based on AIS data and in hooks based on fishing logbook data, by month. The results showed (1) the recognition accuracy of the threshold screening method is 89.9%, the recognition accuracy of the BP neural network classification model is 95.11%, the kappa coefficient is 0.51, the recognition accuracy of the SVM classification model is 95.74% and the kappa coefficient is 0.52; (2) in comparison, the SVM classification model performs better than the other two status identification methods for tuna longliners; and (3) the correlation coefficients between the two types of effort of VESSEL A were greater than 0.79 on all fishing months, indicating that there was no significant difference in the spatial and temporal distribution between the two types of effort. This study suggests that the SVM model can be used to identify the status and estimate the fishing effort of longliners.

Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and optimal recommendations for alternate routes when the weather conditions deem it necessary can lead to lower costs, reduced emissions, and an increase in the overall efficiency of the industry. To this end, a methodology that yields optimal route recommendations for vessels is presented and evaluated in comparison with real-world vessel trajectories. The proposed approach utilizes historical vessel tracking data to extract maritime traffic patterns and implements an A* search algorithm on top of these patterns. The experimental results demonstrate that the proposed approach can lead to shorter vessel routes compared to another state-of-the-art routing methodology, resulting in cost savings for the maritime industry. This research not only enhances maritime routing but also demonstrates the broader applicability of trajectory mining, offering insights and solutions for diverse industries reliant on trajectory data.

The purpose of the present paper is to assess if Ponte osteotomies (POs) allow for a better correction in adolescent idiopathic scoliosis (AIS) surgery and to investigate their safety profile. A systematic search of electronic databases was conducted. Inclusion criteria: comparative studies that reported the outcomes of AIS patients who underwent surgical correction through posterior-only approach with and without POs. Clinical and radiographic outcomes were extracted and summarized. Meta-analyses were performed to estimate the differences between patients treated with and without POs. p < 0.05 was considered significant. In total, 9 studies were included. No significant difference in thoracic kyphosis (TK) change between patients treated with and without POs was found (+3.8°; p = 0.06). Considering only hypokyphotic patients, a significant difference in TK change resulted in POs patients (+6.6°; p < 0.01), while a non-significant TK change resulted in normokyphotic patients (+0.2°; p = 0.96). No significant difference in coronal correction (2.5°; p = 0.10) was recorded. Significant estimated blood loss (EBL) (142.5 mL; p = 0.04) and surgical time (21.5 min; p = 0.04) differences were found with POs. Regarding complications rate, the meta-analysis showed a non-significant log odds ratio of 1.1 (p = 0.08) with POs. In conclusion, POs allow for the restoration of TK in hypokyphotic AIS, without a significantly greater TK change in normokyphotic patients, nor a significantly better coronal correction. Considering the significantly greater EBL and the trend toward a higher complications rate, the correct indication for POs is crucial.

Intensive shipping activity in port areas is considered one of the leading problems in the maritime sector, which has a negative effect on climate change and local air quality. The compilation of detailed inventories of combustion gases released by ships should therefore provide a more accurate overview of emission levels, which can serve as a basis for analysing impacts on the port community and lead to the establishment of better environmental measures. Thus, the aim of this study was to develop an adaptable and relevant analytical model capable of integrating a comprehensive methodology with large databases of ship movements and technical details to provide clear ship-related emission estimates in large port areas. Considering the lack of research in Croatia that includes the mentioned approach and the insufficient monitoring of air pollutants in ports, the model was used to produce an initial overall emissions inventory for the Port of Split, the busiest passenger port in Croatia. In the model, bottom-up logic with an energy-based method was applied to detailed technical and near-real-time shipping data from AIS, creating the first high-density spatial and temporal overview of shipping emissions in the City port basin. The results showed strong seasonal fluctuations and large discrepancies in the quantities emitted between different ship types and operating modes. The analysis therefore raised the question of the need for the future development and implementation of a scalable system that would provide a more transparent and efficient overview of the important characteristics of air pollution from ships and port areas.

Adolescent idiopathic scoliosis (AIS) is the most prevalent types of scoliosis, affecting up to 3% of children around the world. The progression of AIS can cause alteration in psychological components such as self-perceived body image and self-identity, which negatively affect the teenager quality of life (QoL). The mainly aim of this cross-sectional study is to investigate how mild AIS impacts self-perceived body image in young people. Fifteen participants (mean age = 14.47 ± 2.825) of both sexes (male = 5; female = 10) with a curve magnitude from 10° up to 25° completed the Scoliosis Research Society Patient Questionnaire (SRS-22), the Trunk Appearance Perception Scale (TAPS) and were subject to spinal analysis and photogrammetry. Results display statistical differences between self-perceived body image and other SRS-22 domains (Hotelling t2= 70.29; F(3,12) = 20.08; p < 0.001). Additionally, the regression model, which better explained the self-perceived variability, was fit by function/activity, pain, and mental health domains (F(4,10) = 4.39; p = 0.029; R2 = 0.545). Although AIS was not severe, it negatively affected participants self-perceived body image. More attention in AIS qualify of life is needed, and early treatments could be necessary to prevent psychological impairments self-perception related.

Recognizing vessel navigation patterns plays a vital role in understanding maritime traffic behaviors, managing and planning vessel activities, spotting outliers, and predicting traffic. However, the growth in trajectory data and the complexity of maritime traffic in recent years presents formidable challenges to this endeavor. Existing approaches predominantly adopt a ‘trajectory perspective’, where the instantaneous behaviors of vessel groups (e.g., the homing of fishing vessels) that occurred at certain times are concealed in the massive trajectories. To bridge this gap and to reveal collective patterns and behaviors, we look at vessel patterns and their dynamics at only individual points in time (snapshots). In particular, we propose a recognition framework from the snapshot perspective, mixing ingredients from group dynamics, computational geometry, graph theory, and visual perception theory. This framework encompasses algorithms for detecting basic types of patterns (e.g., collinear, curvilinear, and flow) and strategies to combine the results. Case studies were carried out using vessel trajectory (AIS) data around the Suez Canal and other areas. We show that the proposed methodology outperformed DBSCAN and clustering by measuring local direction centrality (CDC) in recognizing fine-grained vessel groups that exhibit more cohesive behaviors. Our results find interesting collective behaviors such as convoy, turning, avoidance, mooring (in open water), and berthing (in the dock), and also reveal abnormal behaviors. Such results can be used to better monitor, manage, understand, and predict maritime traffic and/or conditions.

The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising all expected traffic in a Region of Interest. We propose a model for the detection of abnormal maritime behaviors that provides the closest behaviors as context to the predictions. The normalcy model relies on two-step clustering, which is first computed based on the positions of the vessels and then refined based on their kinematics. We design for each step a similarity measure, which combined are able to distinguish boats cruising shipping lanes in different directions, but also vessels with more freedom, such as pilot boats. Our proposed abnormality detection model achieved, on a large annotated dataset extracted from AIS logs that we publish, an ROC-AUC of 0.79, which is on a par with State-of-the-Art deep neural networks, while being more computationally efficient and more interpretable, thanks to the contextualization offered by our two-step clustering.

Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters.

Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. The field of radiological imaging has experienced growth in recent years, which could be boosted by the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology and has shown promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years, and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorize how radiomics may change the paradigm for these patients in the coming years.

This study proposes a solution to the problem of inaccurate and time-consuming ship trajectory prediction caused by frequent ship maneuvering in complex waterways. The proposed solution is a ship trajectory prediction model that uses a difference long short-term memory neural network (D-LSTM). To improve prediction performance and reduce time dependence, the model combines the other variables of dynamic time features in the ship’s Automatic Identification System (AIS) data with nonlinear elements in the sequence data. The effectiveness of this method is demonstrated by comparing its accuracy to other commonly used time series modeling techniques. The results show that the proposed model significantly reduces training time and improves prediction accuracy.

The automatic identification system (AIS) facilitates the monitoring of ship movements and provides essential input parameters for traffic safety. Previous studies have employed AIS data to detect behavioral anomalies and classify vessel types using supervised and unsupervised algorithms, including deep learning techniques. The approach proposed in this work focuses on the recognition of vessel types through the “Take One Class at a Time” (TOCAT) classification strategy. This approach pivots on a collection of adaptive models rather than a single intricate algorithm. Using radar data, these models are trained by taking into account aspects such as identifiers, position, velocity, and heading. However, it purposefully excludes positional data to counteract the inconsistencies stemming from route variations and irregular sampling frequencies. Using the given data, we achieved a mean accuracy of 83% on a 6-class classification task.

Automatic identification systems (AIS) can record a large amount of navigation information about ships, including abnormal or illegal ship movement information, which plays an important role in ship supervision. To distinguish the trajectories of ships and analyze the behavior of ships, this paper adopts the method of supervised learning to classify the trajectories of ships. First, the AIS data for the ships were marked and divided into five types of ship tracks. The Tsfresh module was then used to extract various ship trajectory features, and a new ensemble classifier based on traditional classification using a machine learning algorithm was proposed for modeling and learning. Moreover, ten-fold cross validation was used to compare the ship trajectory classification results. The classification performance of the ensemble classifier was better than that of the other single classifiers. The average F1 score was 0.817. The results show that the newly proposed method and the new ensemble classifier have good classification effects on ship trajectories.

Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments.

This research aims to analyze the impact of the Earth-Space link on the Automatic Identification System (AIS) signals of ships. To achieve this, we established a simulation system that measures the receiving power of AIS signals via satellite platforms. We validated the system by utilizing observation data from Tiantuo-5. Through this simulation, we quantitatively analyzed the effects of ionospheric TEC (Total Electron Content) and space loss on the received power. During the processing of observation data, we construct a geometric propagation model utilizing the measured positions of both the satellite and the ship. We then calculate the antenna gain and remove any system errors. Additionally, we eliminate the deviation of elevation and azimuth angles caused by satellite motion. This allows us to determine the actual power of different ships reaching the receiving platform. Upon comparing the measured power data with the simulated power, it was noted that both exhibited an increasing trend as the elevation angle increased. This led to an RMSE (Root Mean Square Error) result of approximately one, indicating the accuracy of the simulation system. These findings hold significant implications for analyzing interference factors in satellite-ground links.

The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.

Nowadays, many businesses recognize the importance of digitization as a strategic advantage to maintain competitiveness. As a result, current research aims to investigate three significant factors that influence digital transformation: digital innovation (DI), digital orientation (DO), and the accounting information system (AIS). These factors are supposed to impact the financial success and performance of firms. The objective of this study is to examine the positive association between digital orientation and financial performance (FP) and to explore how the AIS mediates this relationship. Additionally, this paper investigates the moderating role of digital innovation in the relationship between digital orientation and financial performance. To collect data, a quantitative method was employed using a random sampling technique. Data were collected via a questionnaire survey from small and medium-sized enterprises (SMEs) in China, with a sample size of 550. Structural equation modeling (SEM) and a bootstrapping technique with 5000 iterations were used to test the study hypotheses. The findings of this study indicate a positive link between digital orientation and the financial performance of organizations. Furthermore, the results confirm that the AIS acts as a mediator between digital orientation and financial performance. The outcomes also demonstrate that digital innovation plays a moderating role in the relationship between DO and financial performance. This paper contributes to the existing literature on digital transformation by providing a deeper understanding of the aforementioned antecedents of the digital revolution (DO, AIS, and financial performance) and how they should be integrated within digitalization settings to achieve financial performance.

In some geographically challenging areas (such as deserts, seas, and forests) where direct connectivity to a terrestrial network is difficult, space communication is the only option. In these remote locations, Internet of Space Things (IoST) applications can also be used successfully. In this paper, the proposed payload for IoST applications demonstrates how an Automatic Identification System (AIS) and a fire detection system can be used effectively. A space mission based on efficient and low-cost communication can use a constellation of nanosatellites to better meet this need. These two applications, which use a constellation of nanosatellites, can provide relevant university-level data in several countries as an effective policy for the transfer of space technology in an educational initiative project. To enhance educational participation and interest in space technology, this paper shares the lessons learned from the project feasibility study based on an in-depth design of a nanosatellite with several analyses (data budget, link budget, power budget, and lifetime estimation). Lastly, this paper highlights by experiments the development and application of a cost-effective sensor node for fire detection and the use of GPS to enable AIS capabilities in the IoST framework.

The establishment of ship trajectory prediction is critical in analyzing trajectory data. It serves as a critical reference point for identifying abnormal behavior and potential collision risks for ships. Accurate and real-time ship trajectory prediction is essential during navigation. Since the timing of automatic identification system (AIS) data is irregular, traditional methods usually use time calibration to simulate the data of uniform sequencing before analysis. Inevitably, this increases the chances of error and time delays. To address this issue, we propose a time-aware LSTM (T-LSTM) single-ship trajectory model combined with the generative adversarial network (GAN) to predict multiple ship trajectories. These analysis methods are capable of directly analyzing AIS data and have demonstrated better performance in both single-ship and multi-ship trajectories. Our experimental results show that the proposed method achieves high accuracy and can meet the practical navigation requirements of ships.

As AIS data play an increasingly important role in intelligent shipping and shipping regulation, research on AIS trajectories has attracted more attention. Effective measurement is a critical issue in AIS trajectory research. It directly impacts downstream research areas such as anomaly detection, trajectory clustering, and trajectory prediction. However, the extremely time-consuming and labor-intensive traditional pairwise methods for calculating different types of distances between trajectories hinders the large-scale application and further analysis of AIS data. To tackle these issues, we introduce AISim—a metric learning framework that utilizes heterogeneous graph neural networks. This framework includes a spatial pre-training graph and a hierarchical heterogeneous graph, which incorporate spatial and sequential dependency to extract latent features from vessel trajectories. This approach enhances the model’s ability to capture a more accurate representation of the trajectories and approximate various similarity measurements. Extensive experiments on multiple real trajectory datasets have verified the effectiveness and generality of the proposed framework. AISim outperforms advanced learning-based models by 5% to 66% on the HR10 metric in top-k search tasks. The experimental results demonstrate that the proposed framework facilitates research on AIS trajectory similarity learning, thereby promoting the development of AIS trajectory analysis.

The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by 1.8% and 1.2%, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.

In the context of the rapid development of deep learning theory, predicting future motion states based on time series sequence data of ship trajectories can significantly improve the safety of the traffic environment. Considering the spatiotemporal correlation of AIS data, a trajectory time window panning and smoothing filtering method is proposed for the abnormal values existing in the trajectory data. The application of this method can effectively deal with the jump values and outliers in the trajectory data, make the trajectory smooth and continuous, and ensure the temporal order and integrity of the trajectory data. In this paper, for the features of spatiotemporal data of trajectories, the LSTM structure is integrated on the basis of the deep learning Transformer algorithm framework, abbreviated as TRFM-LS. The LSTM module can learn the temporal features of spatiotemporal data in the process of computing the target sequence, while the self-attention mechanism in Transformer can solve the drawback of applying LSTM to capture the sequence information weakly at a distance. The advantage of complementarity of the fusion model in the training process of trajectory sequences with respect to the long-range dependence of temporal and spatial features is realized. Finally, in the comparative analysis section of the error metrics, by comparing with current state-of-the-art methods, the algorithm in this paper is shown to have higher accuracy in predicting time series trajectory data. The research in this paper provides an early warning information reference for autonomous navigation and autonomous collision avoidance of ships in practice.

Vessel trajectory prediction supports navigation services and collision detection. To maintain safety and efficiency in maritime transportation, vessel trajectory prediction is always an important topic. By using automatic identification system (AIS) data and deep learning methods, the task of vessel trajectory prediction has made significant progress. However, this task is still full of challenges due to the complexity of historical information dependencies and the strong influence of spatial correlations. In this paper, we introduce a novel deep learning model, PESO, based on the structure of Seq2Seq, consisting of Parallel Encoders and a Ship-Oriented Decoder. The Parallel Encoders, including the Location Encoder and the Sailing Status Encoder are designed to integrate more information into feature representation. The Ship-Oriented Decoder is targeted to utilize the Semantic Location Vector (SLV) to guide the prediction, which better represents the spatial correlation of historical track points. In order to verify the efficiency and efficacy of PESO, we conducted comparative experiments with several baseline models. The experimental results demonstrate that PESO is superior to them both quantitatively and qualitatively.

Hydrocharis laevigata (Humb. & Bonpl. ex Willd.) Byng & Christenh. [= Limnobium laevigatum (Humb. & Bonpl. ex Willd.) Heine], Hydrocharitaceae, is a floating-leaf aquatic plant that is native to inland South America. It is an invasive species in several parts of the world. Reports of its presence in Europe have been recently published: naturalised populations occur in three locations on the Iberian Peninsula. The literature also contains records of the species in Hungary and Poland. In addition, it has been observed in Sweden, Belgium, and the Netherlands. H. laevigata is highly adaptable and can profoundly transform habitat conditions in its invasive range, causing major issues for ecosystem conservation and human activities. Until recently, H. laevigata was not to be found in natural environments in Europe. Factors explaining its spread include its use as an ornamental plant, the eutrophication of inland waters, and the effects of global warming. With a focus on Europe, this short communication provides information on the species’ distribution, taxonomy, biology, habitat, and negative impacts.

Background: The Risk of Paradoxical Embolism (RoPE) score was developed to identify stroke-related patent foramen ovale (PFO) in patients with cryptogenic stroke., Methods: We conducted a retrospective analysis of the 2016 to 2020 National Inpatient Sample to determine the performance of the modified RoPE score in identifying the presence of a PFO in patients with acute ischemic stroke (AIS)., Results: A total of 3,338,805 hospital admissions for AIS were analysed and 3.0% had PFO. Patients with PFO were younger compared to those without a PFO (median 63 years vs. 71 years, p < 0.001) and fewer were female (46.1% vs. 49.7%, p < 0.001). The patients with PFO had greater mean modified RoPE scores (4.0 vs. 3.3, p < 0.001). The area under the curve for the RoPE score in predicting PFOs was 0.625 (95%CI 0.620-0.629). The best diagnostic power of the RoPE score was achieved with a cut-off point of ≥4 where the sensitivity was 55% and the specificity was 64.2%. A cut-off point of ≥5 increased the specificity (83.1%) at the expense of sensitivity (35.8%). The strongest predictor of PFOs was deep vein thrombosis (OR 3.97, 95%CI 3.76-4.20)., Conclusions: The modified RoPE score had modest predictive value in identifying patients with PFO among patients admitted with AIS.

Recognition and understanding of ship motion patterns have excellent application value for ship navigation and maritime supervision, i.e., route planning and maritime risk assessment. This paper proposes a semantic recognition method for ship motion patterns entering and leavingport based on a probabilistic topic model. The method enables the discovery of ship motion patterns from a large amount of trajectory data in an unsupervised manner and makes the results more interpretable. The method includes three modules: trajectory preprocessing, semantic process, and knowledge discovery. Firstly, based on the activity types and characteristics of ships in the harbor waters, we propose a multi-criteria ship motion state recognition and voyage division algorithm (McSMSRVD), and ship trajectory is divided into three sub-trajectories: hoteling, maneuvering, and normal-speed sailing. Secondly, considering the influence of port traffic rules on ship motion, the semantic transformation and enrichment of port traffic rules and ship location, course, and speed are combined to construct the trajectory text document. Ship motion patterns hidden in the trajectory document set are recognized using the Latent Dirichlet allocation (LDA) topic model. Meanwhile, topic coherence and topic correlation metrics are introduced to optimize the number of topics. Thirdly, a visualization platform based on ArcGIS and Electronic Navigational Charts (ENCs) is designed to analyze the knowledge of ship motion patterns. Finally, the Tianjin port in northern China is used as the experimental object, and the results show that the method is able to identify 17 representative inbound and outbound motion patterns from AIS data and discover the ship motion details in each pattern.

An aircraft go-around is a costly yet safety critical procedure. While there are many reasons to decide that a go-around is necessary, at Hamburg Finkenwerder airport (EDHI) there is a rather peculiar one: vessel traffic crossing the approach path. As both vessels and aircraft transmit their position at regular intervals through the Automatic Identification System (AIS) and Automatic Dependent Surveillance Broadcast (ADS-B) protocols it is possible to identify vessels that can cause problems to the aircraft’s approach. In this work we identified a 10 time higher than average go-around incidence at Finkenwerder airport and were able to find evidence of its relation to large passing vessels. As vessel traffic has a mostly stable course and speed, we found it is possible to predict the passing vessels well ahead of time in order to determine the best approach and reduce the number of go-arounds, allowing to save both fuel and emissions.

Background : Scoliosis, the most prevalent spinal deformity in children, is often associated with adolescent idiopathic scoliosis (AIS). Curves surpassing 50 degrees can deteriorate, especially thoracic curves, leading to pulmonary limitations. Surgical intervention is usually advised for curvatures exceeding 50 degrees to halt further progression. Severe AIS is notably more frequent in females, yet knowledge regarding subsequent pregnancies and associated risks is scarce. Female patients often inquire about conception, pregnancy, labor, and future back pain (BP) concerns. Reports on the long-term outcomes after pediatric AIS treatment and pregnancy consequences remain limited. Uncertainty looms over the likelihood of increased back pain (BP), cesarean sections (CSs), or other pregnancy-related issues following surgical AIS management. In this investigation, an attempt was made to scrutinize the existing research on individuals afflicted with scoliosis who received either conservative or surgical treatment, with the aim of delineating crucial and pragmatic findings that can serve as a compass for spine surgeons when counseling young patients with adolescent idiopathic scoliosis regarding the ailment, its repercussions, the available treatment modalities, and the associated outcomes. A comprehensive analysis pinpointed the optimal data at hand. Consequently, the primary objective of this investigation was to assess the patient-reported and clinical consequences in scoliosis patients who have undergone segmental posterior fusion and subsequently conceived. Conclusions: While the majority of individuals with AIS are capable of conceiving and bearing children, they may encounter greater challenges in fertility than healthy women unaffected by AIS. Pregnant women with a prior history of spinal fusion for AIS can undergo pregnancy and childbirth with no significant escalation in perinatal complications. Women who have undergone surgical procedures for AIS have been observed to exhibit a prevalence of back pain comparable with that of healthy pregnant women; however, a higher incidence of low back pain is evident when spinal fusion is extended to the L3 or L4 vertebra. Although back pain is a common occurrence in pregnant women with AIS, the spinal alterations induced by pregnancy are typically minor and transient. In comparison with healthy women, individuals with a history of spinal fusion necessitate cesarean sections more frequently. The degree of correction loss during pregnancy is lower in previous reports involving pedicle screw instrumentation than in previous reports involving Harrington or hybrid segmental instrumentation.

The operation of any vessel includes risks, such as mechanical failure, collision, property loss, cargo loss, or damage. For modern container ships, safe navigation is challenging as the rate of innovation regarding design, speed profiles, and carrying capacity has experienced exponential growth over the past few years. Prevention of cargo loss in container ship liners is of high importance for the Maritime industry and the waterborne sector as it can lead to potentially disastrous, harmful, or even life-threatening outcomes for the crew, the shipping company, the marine environment, and aqua-culture. With the installment of onboard decision support system(s) (DSS) that will provide the required operational guidance to the vessel’s master, we aim to prevent and overcome such events. This paper explores cargo losses in container ships by employing a novel weather routing optimization DS framework that aims to identify excessive motions and accelerations caused by bad weather at specific times and locations; it also suggests alternative routes and, thus, ultimately prevents cargo loss and damage.

Data-driven technologies and automated identification systems (AISs) provide unprecedented opportunities for maritime surveillance. As part of enhancing maritime situational awareness and safety, in this paper, we address the issue of predicting a ship’s future trajectory using historical AIS observations. The objective is to use past data in the training phase to learn the predictive distribution of marine traffic patterns and then use that information to forecast future trajectories. To achieve this, we investigate an encoder–decoder architecture-based sequence-to-sequence prediction model and CNN model. This architecture includes a long short-term memory (LSTM) RNN that encodes sequential AIS data from the past and generates future trajectory samples. The effectiveness of sequence-to-sequence neural networks (RNNs) for forecasting future vessel trajectories is demonstrated through an experimental assessment using an AIS dataset.

In order to detect multi-ship encounter situations and improve the safety of navigation, this paper proposed a model which was able to mine multi-ship encounter situations from Automatic identification system (AIS) data and analyze the encounter spatial-temporal process and make collision avoidance decisions. Pairwise encounters identification results and ship motion index were combined into a ship encounter graph network which can use the complex network theory to describe the encounter spatial-temporal process. Network average degree, network average distance and network average clustering coefficient were selected. Based on the recognition results of pairwise encounter identification results, a discrete multi-ship encounter network is constructed. The process of multi-ship encounters from simple to complex to simple is mined based on the process of average network degree from 0 to 0 to obtain a continuous spatial-temporal process. The results can be used for multi-ship encounter situation awareness, multi-ship collision avoidance decision-making and channel navigation evaluation, and also provide data for machine learning. Quaternary dynamic ship domain, fuzzy logic and the weighted PageRank algorithm were used to rank the whole network risk, which is critical to “key ship collision avoidance.” This method overcame the problem that the traditional collision risk evaluation method is only applicable to the difference between two ships and ship perception. The risk rank combined with the artificial potential field method was used. Compared with the traditional artificial potential field method, this method has fewer turns and a smoother trajectory.

Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically.

This article examines the impact of automatic identification system (AIS) data thinning on ship emissions inventory results. AIS data thinning is theoretically proven to lead to a smaller result for a ship’s air pollutant emissions inventory. The AIS dynamic data of six sampled ships for 1 day and for 1 year were thinned at 1 min, 3 min, 10 min, 30 min, and 1 h time intervals, and then CO2, NOX, CH, PM, SO2, and other air pollutant emissions were estimated both with and without AIS data thinning in the different time intervals. The results show that AIS data thinning affects the air pollutant emissions inventory results of the ships, and the impact is greater as the thinning interval increases. When the thinning interval is less than 10 min, the impact is less than 10%, but the impact increases to about 10–15% at a 30 min interval and about 15–20% at a 60 min interval. The impacts of thinning on the emissions of ships with acutely fluctuating speeds are more significant because the constantly changing speed is the main reason why data thinning affects the ship emissions inventory. Therefore, these data suggest that the AIS data can be thinned at intervals of 5 or 10 min when establishing a coastal or national ship air pollutant emissions inventory, the AIS data should be thinned at intervals of less than 3 min when establishing the air pollutant emissions inventory of inland river ships, and data thinning is not recommended when establishing a port or smaller-scale ship air pollutant emissions inventory.

Adolescent idiopathic scoliosis (AIS) is characterized by the radiographic presence of a frontal plane curve, with a magnitude greater than 10° (Cobb technique). Diffusion MRI can be employed to assess the cerebral white matter. The aim of this study was to analyze, by means of MRI, the presence of any alteration in the connectivity of cerebral white matter in AIS patients. In this study, 22 patients with AIS participated. The imaging protocol consisted in T1 and diffusion-weighted acquisitions. Based on the information from one of the diffusion acquisitions, a whole brain tractography was performed with the MRtrix tool. Tractography is a method to deduce the trajectory of fiber bundles through the white matter based on the diffusion MRI data. By combining cortical segmentation with tractography, a connectivity matrix of size 84 × 84 was constructed using FA (fractional anisotropy), and the number of streamlines as connectomics metrics. The results obtained support the hypothesis that alterations in cerebral white matter connectivity in patients with adolescent idiopathic scoliosis (AIS) exist. We consider that the application of diffusion MRI, together with transcranial magnetic stimulation neurophysiologically, is useful to search the etiology of AIS.

Atmospheric duct parameters inversion is an important aspect of microwave-band radar and communication system performance evaluation. AIS (Automatic Identification System) is one of the signal sources used for atmospheric duct parameters inversion. Before the inversion of atmospheric duct parameters, determining the type of atmospheric duct plays an important role in the inversion results, but the current inversion methods ignore this point. We outlined a classifying-inversion method of atmospheric duct parameters using AIS signals combined with artificial intelligence. The method consists of an atmospheric duct classification model and a parameter inversion model. The classification model judges the type of atmospheric duct, and the inversion model inverts the atmospheric duct parameters according to the type of atmospheric duct. Our findings demonstrated that the accuracy of the atmospheric duct classification model based on deep neural network (DNN) even exceeds 97%, and the atmospheric duct parameters inversion model has better inversion accuracy than that of the traditional method, thereby illustrating the effectiveness and accuracy of this novel method.

(1) Background: Robotics coupled with navigation (RAN) is a modern surgical platform shown to increase screw placement accuracy during pediatric scoliosis surgery. Our institution uses a technique which combines the RAN platform for apical pedicle screw placement and the freehand (FH) technique for terminal pedicle screw placement during scoliosis surgery (termed hybrid technique). We question if the complementary use of the RAN technology affects intraoperative outcomes, relative to the FH-only approach. (2) Methods: 60 adolescent idiopathic scoliosis (AIS) patients, ages 11–19 at surgery, who were operated on from 2019 through 2020 by a single surgeon, were retrospectively reviewed. Patients were separated by surgery type (hybrid RAN or FH), matched on demographic and surgical factors, and their intraoperative outcomes were compared statistically. (3) Results: Hybrid RAN patients had more screws placed (p = 0.01) and were of a higher BMI percentile (p = 0.005). Controlling for the number of screws placed, BMI%, and initial curve magnitude, there were no statistical differences in estimated blood loss per screw (p = 0.51), curve correction (p = 0.69), complications (p = 0.52), or fluoroscopy time (p = 0.88), between groups. However, operative time was two minutes longer per screw for hybrid RAN patients (p < 0.001). (4) Conclusions: Hybrid RAN surgeries took longer than FH, but yielded comparable effectiveness and safety as the FH technique during the initial RAN adoption phase.

Due to the high error frequency of the existing methods in identifying a ship’s navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this paper, we propose an algorithm based on the fusion of image sequence and radar information to identify the navigation intention of ships at intersections. Some existing algorithms generally use the Automatic Identification System (AIS) to identify ship intentions but ignore the problems of AIS delay and data loss, resulting in unsatisfactory effectiveness and accuracy of intention recognition. Firstly, to obtain the relationship between radar and image, a cooperative target composed of a group of concentric circles and a central positioning radar angle reflector is designed. Secondly, the corresponding relationship of radar and image characteristic matrix is obtained after employing the RANSAC method to fit radar and image detection information; then, the homographic matrix is solved to realize radar and image data matching. Thirdly, the YOLOv5 detector is used to track the ship motion in the image sequence. The visual measurement model based on continuous object tracking is established to extract the ship motion parameters. Finally, the motion intention of the ship is predicted by integrating the extracted ship motion features with the position information of the shallow layer using a Bayesian framework. Many experiments on real data sets show that our proposed method is superior to the most advanced method for ship intention identification at intersections.

Autonomous Underwater Vehicles (AUVs) are becoming increasingly popular for large number of civil and military applications, such as environmental monitoring, oceanography, archaeology, or mine warfare. Operational safety issue still prevents the exploitation of the full potential of AUVs. Operations of AUVs are limited by constraints including the need to guarantee no collision with manned surface vehicles. To avoid collisions, a solution is to relay surface vessel positions to the AUVs by an underwater communication link. The communication must be optimized to establish a robust and reliable link at various range and depth. Commercial underwater acoustic modems are often dedicated for specific distances and bit rates with performances strongly dependent on the environmental conditions. For the considered application, the modem must be adaptive at the time and frequency selectivity of the channel which is varying according to the operation context. In this work, we propose an adaptive underwater acoustic waveform optimized to exploit at the best the time and frequency diversity the channel. To show the potential gain obtained by using diversity, the performance of the designed modem is evaluated using theoretical and realistic underwater channels. In addition, we propose a method for adapting the waveform based only on the knowledge of the transmission geometry. Finally, the proposed modem is tested to relay Automatic Identification Systems (AIS) picture of an area to AUVs.