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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Study Design.: Retrospective matched cohort study. Objective.: The aim of this study was to determine whether females with idiopathic scoliosis (IS), both with and without spine fusion, experience different rates of cesarean section (CS) and epidural anesthesia (EA) than females without scoliosis. Summary of Background Data.: IS is a common spine condition with a higher prevalence in females. It is unclear whether females with scoliosis, treated nonoperatively or operatively, have different rates of cesarean delivery or EA. Materials and Methods.: Patients with IS who delivered in our integrated health care system during a 6-year period were identified (N = 1810). They were matched with a group without scoliosis who delivered during the same period (N = 1810). Rates and relative risk (RR) of CS and EA between cohorts and subgroups were calculated. Results.: The scoliosis cohort had significantly higher rates and RR of EA (P = 0.002 and P = 0.004, respectively). Scoliosis patients treated nonoperatively had an 8% greater RR of EA (P = 0.004) and had a significantly lower rate of CS (23.2% vs. 26%, P = 0.048) compared with the control group. Among only scoliosis patients, those treated with spine fusion had a 38% decreased RR of EA (P < 0.001). Distal fusion level did not seem to influence the RR of EA or CS. Conclusions.: Females with scoliosis were significantly more likely to receive EA at delivery compared with females without scoliosis. Rates and RR of cesarean delivery were not significantly lower among women with scoliosis, but females treated nonoperatively for scoliosis had a significantly lower CS rate than those without scoliosis. Females treated with spine fusion for scoliosis were far less likely to receive EA than both females without scoliosis and females with scoliosis treated nonoperatively. [ABSTRACT FROM AUTHOR]

Introduction: Multiple studies have analyzed the outcome after thoracic Vertebral Body Tethering (VBT). The results seem reproducible with most studies reporting coronal correction rates around 50% and a tether breakage rate near 20% at two years follow-up. There is a paucity of data on lumbar VBT, and no study has yet analyzed the radiographic outcome after lumbar VBT in a double tether technique at two years follow-up, which was the aim of this study. Methods: This is a retrospective, single surgeons' data analysis of all consecutive immature patients who have had VBT of the lumbar spine (to L3 or L4) between January 2019 and September 2020. Primary interest focused on coronal curve correction at two years post-operatively. Suspected tether breakages were analyzed separately and defined as an angular change of more than 5° between two adjacent screws. Results: Forty-one patients were eligible for this study and 35 (85%) had complete two-year follow-up data. Average age at surgery was 14.3 years. All patients had a Sanders stage of 7 or below. Average curve correction for thoracolumbar/lumbar curves at two years follow-up was 50%. 90% of patients had at least one level with a suspected tether breakage. No patient required a revision surgery within two years from surgery but two patients were surgically revised after two years. Conclusion: VBT in the lumbar spine resulted in 50% coronal curve correction two years post-operatively despite a tether breakage in 90% of patients. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. We reveal the inflammation-induced selective and reversible structural plasticity of axon initial segment in rat's superficial spinal cord inhibitory neurons, which underlies inflammatory pain. Physiological or pathology-mediated changes in neuronal activity trigger structural plasticity of the action potential generation site—the axon initial segment (AIS). These changes affect intrinsic neuronal excitability, thus tuning neuronal and overall network output. Using behavioral, immunohistochemical, electrophysiological, and computational approaches, we characterized inflammation-related AIS plasticity in rat's superficial (lamina II) spinal cord dorsal horn (SDH) neurons and established how AIS plasticity regulates the activity of SDH neurons, thus contributing to pain hypersensitivity. We show that in naive conditions, AIS in SDH inhibitory neurons is located closer to the soma than in excitatory neurons. Shortly after inducing inflammation, when the inflammatory hyperalgesia is at its peak, AIS in inhibitory neurons is shifted distally away from the soma. The shift in AIS location is accompanied by the decrease in excitability of SDH inhibitory neurons. These AIS location and excitability changes are selective for inhibitory neurons and reversible. We show that AIS shift back close to the soma, and SDH inhibitory neurons' excitability increases to baseline levels following recovery from inflammatory hyperalgesia. The computational model of SDH inhibitory neurons predicts that the distal shift of AIS is sufficient to decrease the intrinsic excitability of these neurons. Our results provide evidence of inflammatory pain-mediated AIS plasticity in the central nervous system, which differentially affects the excitability of inhibitory SDH neurons and contributes to inflammatory hyperalgesia. [ABSTRACT FROM AUTHOR]

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.

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.

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.

Abstract Background Adolescent idiopathic scoliosis (AIS) is the most prevalent type of scoliosis affecting children between the ages of 10–16 years. However, risk factors for AIS, particularly the modifiable ones, are still largely unknown. This study aims to investigate the associations of lifestyle and social environment factors with AIS in Chinese schoolchildren. Methods This is a matched case–control study based on survey data collected from school-based scoliosis screening program. We used conditional logistic regression models to describe the relative risk of AIS incidence for each variable in the analyses. To examine the independent effect of each factor on developing AIS, a multivariate conditional logistic regression was conducted and odds ratios (ORs) were adjusted for age and other significant variables. Results Overall, 2538 participants from 49 schools were included in this study, comprising 1269 AIS cases and 1269 controls. Mean age of the study population was 13.4 years ± 1.06 (range 10–18). One thousand five hundred and fifty (61.1%) of the study subjects were girls. After adjusting for other significant factors, inappropriate desk heights, either too low (OR = 1.40, 95% CI 1.04–1.90) or too high (OR = 1.61, 95% CI 1.09–2.38), standing with anterior pelvic tilt (OR = 2.73, 95% CI 1.41–5.28), and sleeping on the right side (OR = 1.38, 95% CI 1.00–1.91), remained associated with elevated AIS risks. In contrast, sitting normally and classroom sitting positions change regularly were associated with lower odds of AIS. The adjusted ORs were 0.69 (95% CI 0.50–0.96) for sitting normally, and 0.72 (95% CI 0.53–0.98) for sitting positions change. Conclusions This is the first study to address the associations between desk heights and AIS and showed inappropriate desk heights were related to increased AIS risks. To protect school children from developing AIS, stakeholders are advised to consider introducing height-adjustable desks in the class, changing students’ sitting positions in the classroom on a regular basis, and implementing educational programs to help students maintain correct sitting postures.

This study's goal is to look at the impact of accounting information systems on the performance of Lebanese Banks. This research examines the relevance of using the Accounting Information System (AIS) obtained by Accounting Software (AS) to achieve business success for the owners and management of the organization. Accounting software features such as efficiency; accuracy; dependability; data quality; and simplicity of use are used in this study as a predictor of bank performance. The data for this study was gathered by a survey that included 100 responses from employees of 35 Lebanese commercial banks, which were then studied. The Statistical Package for Social Sciences (SPSS, version 20) was used to perform correlation and multilinear regression analyses on the data. Efficiencies, dependability, and simplicity of use all correlated positively with bank performance, but data quality and accuracy negatively related to bank performance.

The Argo observation network is made up of approximately 4,000 drifting floats, which provide valuable information about the ocean and its role in the climate system. Each one of these floats work in continuous cycles, until their batteries run out. Due to its importance in operational forecasting and climate research, the Argo community continually assesses the status of the sensors mounted on each of the floats. Recovering floats would offer a great opportunity to gain insight into sensor performance and stability, although the economic and environmental costs of dedicating a ship exclusively to recover Argo floats make it unsustainable. In this work, the potential of world shipping traffic as float retrievers has been evaluated through an analysis of encounters based on the Automatic Identification System (AIS) of ships and the location of Argo floats in the years 2019 and 2020. About 18,500 and 28,500 encounters happened for both years, respectively. The Mediterranean Sea hosted the most encounters, and fishing ships were the most suitable type of ship aimed for potential recoveries. A total of 298 and 373 floats interacted with the world shipping traffic in favorable weather conditions in 2019 and 2020, respectively, a figure equivalent to 25% of the annual replacement rate of the Argo network. The same approach was applied to 677 floats affected by abrupt salinity drift (ASD), an issue that has recently come to the attention of the Argo community. It turned out that 59 and 103 ASD-affected floats interacted with ships of opportunity in both years.

IntroductionInfertility has been diagnosed in millions of people around the world and is described as a complex medical, mental, and social problem that affects many aspects of life. The aim of the study was to extract the determining factors and the level of self-esteem and the degree of acceptance of the disease in infertile patients and to find differences between women and men in this aspect.MethodsA total 456 patients (235 women and 221 men) from infertile couples participated in a cross-sectional study. To collect data a Personal Information Form (PIF), Rosenberg Self-Esteem Scale (SES), and Acceptance of Illness Scale (AIS) were used.ResultsThe overall self-esteem score for the whole sample was 30.50 (15 ± 30) points and for acceptance of the disease 32.4 (8 ± 40) points. In the study group, men obtained a slightly higher level of self-esteem than women (31.00 vs. 30.04 points). Additionally, men had a higher level of acceptance of the disease (33.12 vs. 31.80) than women. Socio-demographic factors such as age and level of education had impact on scores SES and AIS. Clinical factors did not determine the results of SES and AIS, both in the overall sample and in the female and male groups.ConclusionSelf-esteem in patients from infertile couples increases with age and level of education. There are also significant differences between women and men, i.e., positive correlations between the level of education and self-esteem in men and the degree of acceptance of the disease in women.

ABSTRACTThis paper is introducing analyses of new design of the automatic identification system (AIS) ships antenna that will serve for both VHF-band radio and satellite tracking and identification system for maritime applications. The current AIS network is an automatic tracking system used by ships of vessel traffic service for identification and location of vessels by electronically exchanging data between nearby ships and on-shore base station. In the AIS system for the electronic data exchange, the very high frequency (VHF) range is used. Utilizing of the VHF range makes the AIS as short-range communication, identification and collision avoidance system between AIS equipped ships and base station is possible in the antennas line-of-sight . In order to widen the range of the AIS from short range up to long or global range, AIS communication should be used via a satellite. In order to provide communication via satellite as well should be designed multifunctional antenna for both radio AIS (R-AIS) and satellite AIS (S-AIS) communication antenna. The analyses and design of a new multifunctional antenna, calculation and development, including implementation and testing of helical VHF antenna for R-AIS and S-AIS equipment are discussed.

IntroductionIn recent years, the adverse effects of escalating maritime trade and international shipping– particularly in regard to increased greenhouse gas emissions and their impact on human health– have come to the fore. These issues have thus instigated a surge in pressure to enhance the regulation of shipborne carbon emissions.MethodsThe study utilized the automatic identification system (AIS) data, Lloyd’s register data, and pollutant emission parameters to calculate the carbon emissions from the main engine, auxiliary engine, and boiler of vessels under varying sailing conditions, utilizing the dynamic method of ships. In relation to geographic information and ship trajectory, a comprehensive inventory of ship carbon emissions was developed, revealing pronounced spatiotemporal characteristics. To assure the accuracy of the substantial AIS dataset, procedures including data cleaning, trajectory integration, data fusion, and completion were executed. Such processes are indispensable, given the potential for transmission and storage errors associated with AIS data. To forecast CO2 emissions over diverse time intervals, a temporal fusion transformer model equipped with attention mechanisms was employed.ResultThe paper furnishes a case study on Tianjin Port, wherein a high-resolution carbon emissions inventory was devised based on AIS data acquired from vessels. This inventory was subsequently employed to generate multi-feature predictions of future carbon emissions. Given the optimal parameter configuration, the proposed method attained P50 and P90 values of 0.244 and 0.118 respectively, thereby demonstrating its efficacy.DiscussionRecognizing the sources of ship carbon emissions in this region and forecasting such emissions in the future substantiates that this method accurately portrays the laws of ship carbon emissions. Our study provides a scientific basis for decision-making in port and pollution management, enabling the creation of targeted emission reduction policies for ships.

When fisheries managers cannot see who is fishing or where fishing occurs, their scope for management interventions is limited. This lack of transparency in spatial fishing activity is considered a key enabler of illegal fishing and overfishing and hinders managers and consumers who aim to achieve sustainable fisheries. Increasing transparency in vessel location tracking is gaining momentum as a promising way to improve management of global fisheries. However, the mechanisms through which transparency in vessel activity can improve management have not been carefully studied. This paper provides a conceptual overview of the potential for greater transparency, both in quantity of vessels tracked and availability of data, from vessel tracking to help achieve sustainable fisheries goals. We identify four pathways through which these data can improve fisheries management and the conditions that enable transparency to be an effective governance tool. We qualitatively examine the costs and benefits of alternative models of transparency, including a hypothetical, fully transparent system. We highlight how potential costs and benefits of greater transparency depend on both governance context and management goals, and identify opportunities for future research to address key information gaps. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Digital technology (DT) has been broadly studied in industry and in scholarly research. This study aimed to examine the use of digital technology in the attainment of strategic performance and considered the mediating role of the accounting information systems (AISs) between them. Furthermore, this study explored the moderation of digital innovation in the linkage between DT and strategic performance. For data collection, 326 questionnaires were collected, and quantitative methods and random sampling techniques were used. The results showed that digital technology is directly associated with strategic performance. The findings also confirm that AISs play a mediating role, and digital innovation plays a moderating role in the association between digital technology and strategic performance. The results of this research encourage businesses to utilize opportunities for growing advanced technologies and developments in the industry to take up novel digital technologies, to advance their digital abilities to grow to be innovation leaders, and to boost the strategic performance of their firms. This study is one of the first pieces of research to provide information on how the latest technologies could have an influence in making innovative products/services and, afterward, boost firms' strategic performance. This study also fills a gap in the literature regarding the driving factors of strategic performance by defining the mediating role of AISs and the moderating role of digital innovation in the association between dynamic factors and performance. The outcomes of this research demonstrate that AISs are extremely helpful and have an influence on the strategic performance of electronics firms. [ABSTRACT FROM AUTHOR]

Chêneau-brace (C-Brace) is a potential tool for the treatment of adolescent idiopathic scoliosis (AIS) with a Cobb angle between 20° and 45° for the primary curve. The aim of the present study was (1) to estimate study cohorts with C-brace therapy success and therapy failure and (2) to analyze possible factors that influence the therapy outcome. Seventy-eight patients with AIS were assessed before the initiation of C-brace treatment. Each patient underwent radiography examinations before the brace, in-brace, and at the therapy end. Cobb angle was considered as increased when the value at the end of therapy was increased more than 5° (Δ > 5°), unchanged—when the value was unchanged within ± 5° and decreased- when the value was decreased more than 5° (Δ < −5°). The study cohort was stratified due to curve topography in the thoracic, thoracolumbar, and lumbar scoliosis groups. Global analysis revealed no statistically significant modification of the Cobb angle (Cobb angle pre-brace vs. Cobb angle post-brace: 30.8° ± 8.2 vs. 29.3° ± 15.2, p = 0.26). However, at the end of C-brace therapy, the primary Cobb angle was decreased by more than 5° in 27 patients (35%), unchanged (Δ within the range of ±5°) in 36 patients (46%), and increased more than 5° in 15 patients (19%). Sub-group analysis due to curve topography and skeletal maturity has shown higher rates of brace therapy failure in thoracic curves and in younger patients (Risser grade 0). Patients with higher Cobb angle correction with C-brace had lower rates of therapy failure. The C-brace can be useful for the prevention of scoliotic curve progression in patients with AIS. However, many factors influence the therapy effect. [ABSTRACT FROM AUTHOR]

Purpose: Preoperative curve assessment is important in adolescent idiopathic scoliosis (AIS). Our objective is to clarify the role of side-bending radiographs (SBR) and fulcrum-bending radiographs (FBR) in predicting postoperative Cobb angle in nonstructural and structural curves. Methods: Twenty-five consecutive patients with AIS who underwent correction surgery were included. The Cobb angles of structural and nonstructural curves were determined. Cobb angles were measured based on pre- and postoperative standing anteroposterior radiographs of the whole spine. The Cobb angles of SBR and FBR were measured preoperatively. The difference between the Cobb angle at each bending and the preoperative Cobb angle was defined as the predicted correction angle, whereas the difference between the preoperative Cobb angle and postoperative Cobb angle was defined as the surgical correction angle. The correction index was calculated by dividing the surgical correction angle by the predicted correction angle. The difference between the predicted correction angle and surgical correction angle was defined as the prediction error. We compared SBR and FBR for both structural and nonstructural curves in these terms. Results: For both curves, the predicted correction angle of FBR was significantly higher than that of SBR, and the correction index of FBR was significantly lower than that of SBR. Patients with a correction index close to 1 and small prediction error had undergone FBR in the structural curve and SBR in the nonstructural curve. Conclusion: FBR is predictive of postoperative correction angle of the structural curve, whereas SBR is predictive of postoperative correction angle of the nonstructural curve. [ABSTRACT FROM AUTHOR]

Background and purpose: The aim was to evaluate the temporal trends, characteristics and in‐hospital outcomes of patients hospitalized with acute ischaemic stroke (AIS) between those with and without current or historical malignancies. Methods: Adult hospitalizations with a primary diagnosis of AIS were identified from the National Inpatient Sample database 2007–2017. Logistic regression was used to compare the differences in the utilization of AIS interventions and in‐hospital outcomes. For further analysis, subgroup analyses were performed stratified by cancer subtypes. Results: There were 892,862 hospitalizations due to AIS, of which 108,357 (12.14%) had a concurrent diagnosis of current cancer (3.41%) or historical cancer (8.72%). After adjustment for confounders, patients with current malignancy were more likely to have worse clinical outcomes. The presence of historical cancers was not associated with an increase in poor clinical outcomes. Additionally, AIS patients with current malignancy were less likely to receive intravenous thrombolysis (adjusted odds ratio 0.66, 95% confidence interval 0.63–0.71). Amongst the subgroups of AIS patients treated with intravenous thrombolysis or mechanical thrombectomy, outcomes varied by cancer types. Notably, despite these acute stroke interventions, outcome remains poor in AIS patients with lung cancer. Conclusions: Although AIS patients with malignancy generally have worse in‐hospital outcomes versus those without, there were considerable variations in these outcomes according to different cancer types and the use of AIS interventions. Finally, treatment of these AIS patients with a current or historical cancer diagnosis should be individualized. [ABSTRACT FROM AUTHOR]

Background and Purpose: Futile recanalization (FRC) is common among large artery occlusion (LAO) patients after endovascular therapy (EVT). We developed nomogram models to identify LAO patients at a high risk of FRC pre- and post-EVT to help neurologists select the optimal candidates for EVT. Methods: From April 2020 to July 2022, EVT and mTICI score ≥ 2b LAO patients were recruited. Nomogram models was developed by two-step approach for predicting the outcomes of LAO patients. First, the least absolute shrinkage and selection operator (LASSO) regression analysis was to optimize variable selection. Then, a multivariable analysis was to construct an estimation model with significant indicators from the LASSO. The accuracy of the model was verified using receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA), along with validation cohort (VC). Results: Using LASSO, age, sex, hypertension history, baseline NIHSS, ASPECTS and baseline SBP upon admission were identified from the pre-EVT variables. Model 1 (pre-EVT) showed good predictive performance, with an area under the ROC curve (AUC) of 0.815 in the training cohort (TrC) and 0.904 in VC. Under the DCA, the generated nomogram was clinically applicable where risk cut-off was between 15%– 85% in the TrC and 5%– 100% in the VC. Moreover, age, ASPECTS upon admission, onset duration, puncture-to-recanalization (PTR) duration, and lymphocyte-to-monocyte ratio (LMR) were screened by LASSO. Model 2 (post-EVT) also demonstrated good predictive performance with AUCs of 0.888 and 0.814 for TrC and VC, respectively. Under the DCA, the generated nomogram was clinically applicable if the risk cut-off was between 13– 100% in the TrC and 22– 85% of VC. Conclusion: In this study, two nomogram models were generated that showed good discriminative performance, improved calibration, and clinical benefits. These nomograms can potentially accurately predict the risk of FRC in LAO patients pre- and post-EVT and help to select appropriate candidates for EVT. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Purpose: Patient blood management has been recently emphasized to avoid perioperative blood transfusion in AIS surgery. Hydroxyapatite charged collagen sponge (HCS) is a bone substitute material made of collagen and ceramized hydroxyapatite, with associated haemostatic properties. The goal of this study was to assess the impact of HCS in the perioperative blood loss in AIS surgery. Methods: After IRB approval, all AIS patients undergoing primary correction were prospectively included over a 15-month period. Patients receiving HCS at the end of the procedure were compared to a control group (matched for age, gender, and fusion levels) without any haemostatic agent or bone substitute. The same perioperative blood saving strategies were used in both groups. Two subfascial drains were used for 48 h in all patients. Perioperative blood loss and transfusion rates were analysed. Results: A total of 34 patients were included in each group. No difference in drainage volume was observed at day 1, but the reduction was statistically different at day 3 (1135 mL [800–1640] versus 930 [480–1510], p = 0.028, 0.63 ml/Kg/h [0.4–0.92] versus 0.46 [0.29–0.7], p = 0.042). Multivariate analysis found that the use of HCS was associated with a decrease in the postoperative blood loss (OR = 1.17 [1.10–1.25]). The transfusion rate was lower in the HCS group [0 (0% vs. 3(8.8%), p = 0.076)]. No infection occurred, and no complication was reported. Conclusion: With 27% reduction in drain volume, hydroxyapatite charged collagen sponge can be considered as a blood salving strategy in AIS surgery. The role of the biomaterial in fusion rate still needs to be further assessed. [ABSTRACT FROM AUTHOR]

The study of cybersecurity issues plays a fundamental role in accounting information systems (AIS) research. However, as the importance of cybersecurity has continued to grow in other disciplines, such as computer science and management information systems, it has become less clear what is distinct about AIS-based cybersecurity research, what unique insights AIS research has contributed to the study of cybersecurity, and what promising directions for AIS research into cybersecurity remain untapped. In order to answer these questions, we perform a literature review covering 56 articles published in 11 AIS-oriented journals. We find four distinct, yet related, categories of research inquiry: cybersecurity risks and threats, cybersecurity controls, cybersecurity-related assurance, and cybersecurity breaches. In highlighting the key insights uncovered from these four areas, we frame "what we know," as well as "what remains to be learned," by outlining a detailed proposal of future research opportunities for AIS researchers. [ABSTRACT FROM AUTHOR]

Vertebral body tethering (VBT) has been originally developed as a growth modulation technique for the surgical management of skeletally immature patients with adolescent idiopathic scoliosis (AIS). Given the positive results obtained in this setting, the use of VBT is gradually expanding to other patient categories, such as those with no or limited remaining growth or with non-idiopathic scoliosis. Aim of this manuscript is to offer an overview over the current applications of VBT, along with imaging and comments derived from the clinical experience. The work was based on a literature search conducted in January 2023 on Pubmed, Scopus and Web of Science databases. Following keywords were used for the search: vertebral body tethering, adolescent idiopathic scoliosis, early onset scoliosis, neuromuscular scoliosis, syndromic scoliosis. Three patient categories in which VBT has been applied have been highlighted: VBT for growth modulation in AIS, VBT as anterior scoliosis correction in AIS and VBT for non-idiopathic curves or early-onset scoliosis. While growth modulation in AIS still represents the most widespread use of VBT, the use of this technique has yielded positive results in different settings as well, such as scoliosis correction in AIS or temporary or definitive curve management in non-AIS curves. While long-term results are lacking, patient selection seems to play a central role to reduce the complication rate and ensure predictable and stable results. [ABSTRACT FROM AUTHOR]

Summary: VDES R‐Mode aims at providing a contingency maritime positioning and navigation system when the operation of Global Navigation Satellite Systems (GNSS) is disrupted. However, VDES R‐Mode, similarly to GNSS, can itself also be subject to different types of attacks, such as jamming or spoofing. In this paper, we evaluate the vulnerabilities of VDES R‐Mode and discuss the effectiveness and cost of different types of countermeasures. The outcome of this cost‐benefit analysis is a recommendation to introduce authentication for the navigation messages of R‐Mode using the Timed Efficient Stream Loss‐Tolerant Authentication (TESLA) protocol. [ABSTRACT FROM AUTHOR]

This work studies air quality by analysing NOx emissions in the inland waters of the Canary Islands, with particular emphasis on determining how maritime transport activity contributes to the emission of NO2 in the environment of the two main islands, Tenerife and Gran Canaria. We explored the capabilities of tropospheric NO2 density derived from the TROPOMI sensor onboard ESA's Sentinel 5P Satellite to be used as an air quality monitoring tool at the regional scale of the Canary Islands. The studied mesoscale emission scenarios allowed us to identify the main sources, associated with urban areas, heavy roads, power plants, ports, and to a lesser extent, shipping routes. Mean values for the metropolitan area of Santa Cruz de Tenerife were 1.38 × 1015 molec cm−2. Similarly, in port areas, mean values of 1.22 × 1015 molec cm−2 were found. These levels can confidently be associated with anthropogenic activities. These were clearly distinguishable from background (noise) values of 7.08 × 1014 molec cm−2 obtained in maritime areas away from the influence of the islands. To investigate the maritime contribution to the NO2 emissions, ship tracks were obtained from an Automated Identification System (AIS) receiving station that covered the channel between the Tenerife and Gran Canaria islands. Multitemporal, and hence accumulative, NO2 scenarios were compared with the ship traffic density within a given temporal window before satellite overpass. We found good spatial agreement between NO2 signal and frequent ship routes between the major islands at several time scales, particularly in weekly averaged scenarios. Enhancements up to 2.0 × 1015 molecules cm−2 relative to surrounding waters were identified in the middle of the main shipping routes between the main islands. Thus, multitemporal NO2 scenarios derived from TROPOMI can lead to an estimate of the ship traffic contribution to NOx emissions in complex environments, such as this one, influenced by land emissions. [ABSTRACT FROM AUTHOR]

Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine deformity governed of the spine. A child’s Risser stage of skeletal maturity must be carefully considered for AIS evaluation and treatment. However, there are intra-observer and inter-observer inaccuracies in the Risser stage manual assessment. A multi-task learning approach is proposed to address the low precision issue of manual assessment. With our developed multi-task learning approach, the iliac area is extracted and forwarded to the improved Swin Transformer for Risser stage assessment. The spatial and channel reconstruction convolutional Swin block is adapted to each stage of the Swin Transformer to achieve better performance. The Risser stage assessment based on iliac region extraction had an overall accuracy of 81.53%. The accuracy increased in comparison to ResNet50, ResNet101, Uni-former, Next-ViT, ConvNeXt, and the original Swin Transformer by 5.85%, 4.6%, 3.6%, 2.7%, 2.25%, and 1.8%, respectively. The Grad-CAM visualization is used to understand the interpretability of our proposed model. The results show that the proposed multi-task learning strategy performs well on the Risser stage assessment. Our proposed automatic Risser stage assessment method benefits the clinical evaluation of AIS. Project address: https://github.com/xyz911015/Risser-stage-assessmentGraphical Abstract: Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine deformity governed of the spine. A child’s Risser stage of skeletal maturity must be carefully considered for AIS evaluation and treatment. However, there are intra-observer and inter-observer inaccuracies in the Risser stage manual assessment. A multi-task learning approach is proposed to address the low precision issue of manual assessment. With our developed multi-task learning approach, the iliac area is extracted and forwarded to the improved Swin Transformer for Risser stage assessment. The spatial and channel reconstruction convolutional Swin block is adapted to each stage of the Swin Transformer to achieve better performance. The Risser stage assessment based on iliac region extraction had an overall accuracy of 81.53%. The accuracy increased in comparison to ResNet50, ResNet101, Uni-former, Next-ViT, ConvNeXt, and the original Swin Transformer by 5.85%, 4.6%, 3.6%, 2.7%, 2.25%, and 1.8%, respectively. The Grad-CAM visualization is used to understand the interpretability of our proposed model. The results show that the proposed multi-task learning strategy performs well on the Risser stage assessment. Our proposed automatic Risser stage assessment method benefits the clinical evaluation of AIS. Project address: https://github.com/xyz911015/Risser-stage-assessment [ABSTRACT FROM AUTHOR]

Activating mutations in the epidermal growth factor receptor (EGFR) promote ligand-independent signaling; however, the mechanisms involved are poorly defined, and it is unknown whether this generates specific vulnerabilities. We previously observed robust expression of protein kinase Cα (PKCα) in lung adenocarcinoma (LUAD) with mutant EGFR (mEGFR), which, unlike the activation of PKCα, is independent of mEGFR activity. Here, we identify a critical role for PKCα in anchorage-independent growth and survival of lung cancer cells with mEGFR. Mechanistically, signaling pathways initiated by mEGFR show a high preference for ligand-independent phosphorylation on Y992, resulting in biased activation and dependence on phospholipase-Cγ and PKCα. Moreover, through bioinformatic approaches, we find that mEGFR LUAD demonstrates a transcriptomic profile most similar to lung basal cells, which exhibit elevated levels of PKCα, suggesting that mEGFR tumors arise in cell types with high intrinsic levels of PKCα. Taken together, these findings explain the dependence of mEGFR on PKCα. [Display omitted] • mEGFR exhibits selective propensity toward PLC activation, leading to robust PKCα signaling • PKCα promotes anchorage-independent growth and survival of mEGFR LUAD • mEGFR LUAD has transcriptomic profiles most similar to basal cells, which express high PKCα Sadeghi et al. show that signaling pathways initiated by mutant EGFR, unlike the ligand-activated wild-type EGFR, are associated with defective autophosphorylation of the receptor. The distinctive autophosphorylation patterns on mutant EGFR lead to selective activation of PKCα, which promotes anchorage-independent growth and survival in an mTORC1-dependent manner. [ABSTRACT FROM AUTHOR]

Global scatter diagrams are used to estimate possible ship motions in a specified route and are required for comprehensive cargo-loading considerations of merchant ships. This study aims to update global wave scatter diagrams using the latest wave hindcast and Automatic Identification System (AIS) data. The AIS data for over 7500 merchant ships were collected between 2015 and 2022. The wave hindcast data ECMWF Reanalysis v.5 (ERA5), preferred for its recent coverage, were adjusted based on CCI satellite data. The duration of the AIS data is much shorter than the design lifetime of merchant ships, which is generally 25 years. This study found almost linear relationships between the maximum significant wave heights in storms and the generalized extreme value (GEV) parameters representing the distribution of encountered significant wave heights, with adjustments made for seasonality and tropical cyclones. By simulating the parameters based on these relationships and the wave hindcast data over 25 years, the encountered significant wave height distributions over 25 years were obtained. The results indicated a reduction in extremely significant wave heights compared with the BMT-GWS, in agreement with previous studies. This study also found that the difference in the wave period distribution between natural and encountered sea states was small in most sea areas for all significant wave height levels. Taking advantage of the abundant data volume of ERA5, this study adopted an empirical distribution of the wave period conditioned by the significant wave height. The novelty of this study is that we obtained wave scatter diagrams globally by month to consider the seasonal effect in detail and evaluated the confidence interval of the encountered wave height in its probability tail. In addition, this study quantifies the uncertainty of the wave height distribution by Monte Carlo simulation, which is not within the scope of previous studies on wave scatter diagrams based on the significant wave heights encountered by merchant ships around the world. • Seasonal Global Wave Scatter Diagrams were established. • AIS data for 7600 commercial ships were collected between 2015 and 2022. • ERA5 wave hindcast data was used with some modifications. • This study proposed a novel statistical model relating encountered waves and the maximum wave height of storms. • The results indicated a reduction in extreme significant wave heights compared with BMT-GWS. [ABSTRACT FROM AUTHOR]

Sea surface currents are of significant importance in various scientific and maritime applications. There are several measurement techniques available to study surface currents, however, they have limitations in spatial coverage and resolution. This study presents a proof-of-concept for a new measurement principle that relies on the difference between a ship's speed relative to water and land. The approach involves estimating the ship speed vector relative to water from optical satellite imagery of Kelvin wakes. This ship speed vector is subtracted from the ship speed over ground, which is determined from Automatic Identification System (AIS) data, to estimate the surface current. A case study in the Strait of Gibraltar was performed using two months of Sentinel-2 imagery, which yielded 81 visible Kelvin wakes over 25 images. Surface currents were estimated in directions parallel and perpendicular to the ship's sailing line for each Kelvin wake. The estimated currents were validated with respect to surface currents derived from High-Frequency Radars (HFRs) and modelled currents from the Copernicus Marine Environmental Monitoring Service (CMEMS). The uncertainty in the two surface current components was estimated using triple collocation. After removing 12 data points with large ship course variability, standard deviations of 0.14 and 0.16 m s−1 were estimated for the surface currents along and across the sailing line, respectively. Despite limitations in measurement frequency due to satellite revisit times, cloud cover and Kelvin wake visibility, this new method can provide accurate estimates of sea surface currents in regions with high vessel density. [Display omitted] • New method to estimate sea surface currents. • Estimated currents show excellent agreement with High-Frequency radar observations. • Well suited to coastal regions with dense shipping traffic. [ABSTRACT FROM AUTHOR]

Patterns of beach litter accumulation in the European Arctic was investigated by sampling beaches on the Norwegian mainland in the southern Barents Sea, on northern Novaya Zemlya (Russia), and the southern shore of the Svalbard archipelago. The coast of Finnmark county on the Norwegian mainland was considerably more polluted than the other regions. More than half the surveyed beaches there were more polluted than the "very clean" designation from the Clean Coast Index, compared to <20 % in the other regions. The mean litter density was 8 items m−1, compared to 0.6–1.2 items m−1 in the other regions. Litter from maritime sources was common, and regional patterns in beach litter densities mirror regional patterns in nearshore (<50 km) fishing vessel activities as indicated by analyses of AIS tracks. A review of previously conducted beach litter Deep Dive analyses also indicate prevalent Norwegian and Russian packaging, further supporting a predominance of local litter sources. • Beach litter sampled in the European Arctic. • The southern Barents Sea (Norwegian mainland) was the most polluted. • Evidence of long-range transport was limited. • Nearshore fishing activities appear an important source of marine litter. [ABSTRACT FROM AUTHOR]