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Currently, the academic information system (AIS) at universities processes academic data to facilitate student’s activities. AIS was developed to provide maximum service to students. To optimize the use of information technology and to ensure the appropriateness of the provided AIS services, it is necessary to examine the level of service provided to improve quality. This study aimed to analyze the level of AIS service quality based on user perceptions and expectations. Dissemination of online questionnaires using Google Forms with a total of 100 students as respondents. This study used the modified Webqual 4.0 method as an indicator in the preparation of the questionnaire and the importance-performance analysis (IPA) method as an analysis method. The results of data were classified based on the percentage of user’s satisfaction with AIS services with three classifications, namely good, moderate, and poor. The results of the IPA analysis showed that the AIS had good quality. The results obtained from the analysis of the quality of the AIS system had a conformity level of 90.90%, where respondents perceived close to satisfaction with AIS services. The gap level was -0.3281 which was the result of the perception/performance of the AIS that was not in line with the expectations of the user. The results of this study contribute to Universitas Muhammadiyah Gombong as reference material and evaluation of AIS system services in the future.

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.

Abstract It is important to understand connections between society and the natural environment for anticipating hazards and anthropogenic effects on the Earth system. In this study, we conduct a detailed exploration of interactions between oceanic thunderstorms and maritime traffic. Shipping traffic produces aerosols that perturb the otherwise “clean” ocean environment. Prior work proposed these aerosol effects as the cause of increased lightning over certain shipping lanes. However, introducing tall grounded objects into a high electric field environment might also facilitate lightning discharges, as we see with upward lightning over land. We consider both possibilities. Our analyses of the thunderstorms responsible for enhanced lightning activity over the shipping lane with the clearest anthropogenic signal indicate that the enhancement results from an increased frequency of lightning‐producing storms. Observed variations in thunderstorm microphysics between the shipping lane and nearby oceans are small compared to natural factors such as the Indian monsoon, and are on the same scale as the local variability in the data. By contrast, matching lightning stroke data with ship transponder events in oceanic regions where public data are available reveals a strong signal from direct ship interactions. Lightning is 15× (66×) more likely to occur at a ship location compared to 2 km (25 km) away. These results highlight the central role of direct ship interactions in explaining lightning enhancements over shipping lanes. We also document the frequency of these direct lightning interactions across various categories of vessels and on individual ships present in the public data.

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. [ABSTRACT FROM AUTHOR]

The use of tracking devices, such as vessel monitoring systems or automatic identification system, enabled us to expand our knowledge on the distribution and quantification of fishing activities. However, methods and models based on vessel tracking data are mostly devised to be applied to towed gears, whereas applications to multi-gear and passive fisheries have been underrepresented. Here, we propose a methodology to deal with geospatial data to map and quantify the fishing effort, as soak time, of passive fishing gears used by a multi-gear fishing fleet. This approach can be adapted to other passive multi-or single-gear fisheries, since it requires only three variables that can be extracted from a pre-classified dataset, to identify the beginning (gear deployment) and the end (hauling) of passive fishing events. As far as we are aware, this is the first time a methodology that allows quantifying the soak time of static passive fishing events, within a polyvalent fishery context, is presented. We argue that the information that can be extracted from such approaches could contribute to improved management of multi-gear and static-gear fisheries and the ecosystem-based approach. [ABSTRACT FROM AUTHOR]

Many clinical studies have indicated an association between biomechanical factors and the incidence and pathological progression of adolescent idiopathic scoliosis (AIS). However, at present, the research on AIS is mainly focused on the etiology, and there are few studies reporting the causes of progressive aggravation of AIS. In the present study, we aim to investigate the role of Piezo1 in compressive stress‐induced mouse spinal vertebral growth plate chondrocytes apoptosis. First, a scoliosis mouse model was established, and the expression of Piezo1 as well as the degree of apoptosis were investigated. We found that the expression of Piezo1 and the degree of apoptosis were significantly higher on the concave sides than that on the convex sides of the vertebral growth plate in mice with scoliosis. Spinal vertebral growth plate chondrocytes were further isolated and treated with Yoda1 to mimic Piezo1 overload. Excess Piezo1 significantly promoted apoptosis of spinal vertebral growth plate chondrocytes. Moreover, static gas compressive stress was used to simulate the increased concave compressive stress in the process of scoliosis with or without GsMTx4, a Piezo inhibitor. It was observed that with the increase of static compressive stress, the expression of Piezo1 increased, and the chondrocytes of vertebral growth plate treated with Piezo1 inhibitor GsMTx4 weakened the above phenomena. In conclusion, our results indicated that compressive stress is strongly associated with the different degrees of apoptosis on both sides on the convex and concave sides of the vertebral growth plate in scoliosis via inducing different expressions of Piezo1. Reducing the expression of Piezo1 in the concave side of the vertebral growth plate and inhibiting the apoptosis of chondrocytes in the bilateral vertebral growth plate caused by asymmetric stress on both sides of the concave vertebral body may be a promising treatment strategy for AIS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Globally, there are more than six million deaths due to cerebrovascular disease, which is the second leading cause of death. Although the imaging findings of magnetic resonance imaging (MRI) are more accurate than computed tomography for acute ischemic stroke (AIS), it is uncommon in recombinant tissue plasminogen activator (rTPA) treatment. Alteplase is not only strongly recommended treatment for acute ischemic stroke within 4.5 hours, but also decreases the disability and mortality rate. Besides, low-dose rTPA was associated with significant reductions in symptomatic intracerebral hemorrhage (sICH), compared with standard one. However, the benefits of low-dose rTPA for the treatment of AIS without large vessel occlusion (LVO) have not been fully demonstrated. We evaluated whether the low-dose rTPA in AIS without LVO could improve prognosis in patients three months post-treatment. PATIENTS AND METHODS: This was a cross-sectional study on patients with AIS treated within 4.5 hours of symptom onset admitted to Can Tho S.I.S General Hospital between February 2019 and July 2021. The eligibility criteria were patients aged > 18 years treated with lowdose rTPA (0.6 mg/kg) and screened by 3T MRI. Patients with a pre-hospital modified Rankin score (mRS) ≥ 2 points, intracranial hemorrhage, LVO, or ≥ 3 microbleeds on brain MRI were excluded. The primary outcomes were the favorable outcome rate at three months and safety, which were evaluated by the rates of intracranial hemorrhage and mortality at three months. RESULTS: This study enrolled 92 eligible patients between February 2019 and July 2021. Their National Institute of Health Stroke Scale (NIHSS) scores were 7.5 ± 3.7 at admission, 3.3 ± 3.5 at discharge or seven days after discharge, and 2.2 ± 2.8 at three months. Their mRS were 2.9 ± 0.8 at admission, 1.4 ± 1.3 at discharge or seven days after discharge, and 1.1 ± 1.1 at three months. Elevated cardiac enzymes, age ≥ 75 years, and body mass index ≥ 25 were associated with increased poor outcomes at three months. While AIS was more common in men than women, a similar number of men (33.3%) and women had poor mRS. Three patients had complications associated with low-dose rTPA treatment: one (1.1%) had intracranial hemorrhage, one (1.1%) had new infarcts, and one (1.1%) had gastrointestinal bleeding. No deaths occurred within three months. CONCLUSIONS: Our study indicates the efficacy and safety of low-dose rTPA treatment for AIS without LVO within 4.5 hours. Patient selection for rTPA by 3T MRI decreased complications and mortality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background:The aim of the study was to evaluate the quality of life of a patient afterendarterectomy (CEA) of the Internal Carotid Artery (ICA) following an Acute ischemic stroke (AIS) during COVID-19. Case study: A right-handed, 51-year-old patient, a visual artist, single, in good health and no chronic illnesses to date, became infected with SARS-CoV-2 and contracted COVID-19. The presence of SARS-CoV-2 virus was confirmed by a RT PCR antigen test. The patient was hospitalized, and required mechanical ventilation at an Intensive Care Unit (ICU) before an acute ischemic stroke (AIS) onset. Except for untreated hypertension, her medical history was unremarkable. Her blood pressure was 180/100 mm Hg; her pulse was 76 beats per minute and was regular. AIS from the left middle cerebral artery (MCA) has resulted primarily in damage to the left hemisphere, and secondary effects on the right side resulting in body weakness and mild anomic apha - sia. Magnetic resonance imaging (MRI) confirmed stroke and detected brain tissue damaged by an AIS. It revealed hyperintense foci in the T2 and FLAIR sequences, 21 mm in size in the left hemisphere of the brain. In search of the cause of AIS, CT angiography was performed. It revealed a large (90%) ICA occlusion. The patient was admitted to the emergency room at the Vascular Surgery Clinic with an Endovascular Subunit. The revascularization procedure (CEA) was perform - ed under general endotracheal anesthesia with the use of the protocol and techniques (elaborated at the Department of Vascular Surgery and Endovascular Procedures, The John Paul II Hospital in Krakow). The CEA procedure improved her general health: she regained the ability to name objects and her HRQOL also improved in her perception. The improvement achieved was statistically significant. She returned to painting and functions well in society.Conclusions: The patient’s perception of HRQoL measured by the SF-36 domains was better after the CEA: a significant improvement in self-reported overall health has occured. The HRQoL outcome measures may be valuable in future clinical trials of comparing different methods of treatment offered after AIS. [ABSTRACT FROM AUTHOR]

Maritime and coastal areas are the lifeblood of many countries. They are vital to the prosperity of countries because they provide trade routes, regulate the climate, are a source of organic and inorganic resources and energy, and provide a place for the public to live and relax. Increasingly, however, there are divergences and barriers to the use of marine areas. On the one hand, available and developed technology and knowledge enable increasingly improved use of the sea. On the other hand, the combined effect of these activities leads to conflicts of interest and the deterioration of the marine environment. The purpose of this study is to analyze the feasibility of using automatic identification system (AIS) maritime traffic data, in terms of its suitability, to correctly assess the utilization of the potential of a specific fleet within the offshore shipping industry. In addition, the authors undertake the task of determining to what extent activities relating to the GPS position of the ship, ship type (i.e., cargo or passenger), ship status (i.e., aground, anchored, moored, not under command, restricted maneuverability, underway sailing, or underway using its engine), ship draught, service speed, total engine power, and deadweight constitute areas and methods for optimizing the use of the offshore fleet under all the conditions previously described that limit this optimization. Given the stated goal, this paper uses both a literature review procedure and statistical methods to conduct a comparative analysis. [ABSTRACT FROM AUTHOR]

We present an open-source system that can optimize compressed trajectory representations for large fleets of vessels. We take into account the type of each vessel in order to choose a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. We employ a genetic algorithm that converges to a fine-tuned configuration per vessel type without any hyper-parameter tuning. These configurations can provide synopses that retain less than 10% of the original points with less than 20m approximation error in a real world dataset; in another dataset with 90% less samples than the previous one, the synopses retain 20% of the points and achieve less than 80m error. Additionally the level of compression can be chosen by the user, by setting the desired approximation error. Our system also supports incremental optimization by training in data batches, and therefore continuously improves performance. Furthermore, we employ a composite event recognition engine to efficiently detect complex maritime activities, such as ship-to-ship transfer and loitering; thanks to the synopses generated by the genetic algorithm instead of the raw trajectories, we make the recognition process faster while also maintaining the same level of recognition accuracy. Our extensive empirical study demonstrates the effectiveness of our system over large, real-world datasets. [ABSTRACT FROM AUTHOR]

Achieving maritime security is challenging due to the vastness and complexity of the domain. Monitoring all vessels that use this medium is humanly impossible but is needed for law enforcement. This paper proposes a machine learning solution based on HDBSCAN+ to classify the movements of vessels into 'normal' or 'abnormal'. This classification reduces the number of vessels that have to be monitored by law enforcement agencies to a manageable size. To date, AIS is the primary source of information that can represent vessel movements and enable the detection of maritime anomalies. The proposed model uses latitude, longitude, type of vessel, course and speed as features of the AIS data for analysis. The performance of the proposed model is validated against the marine incidents reported by Information Fusion Centre-Indian Ocean Region (IFC-IOR). The proposed model has successfully detected the incidents reported by IFC-IOR. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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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]

Recent climate change has caused declines in ice coverage which have lengthened the open water season in the Arctic and increased access to resources and shipping routes. These changes have resulted in more vessel activity in seasonally ice-covered regions. While traffic is increasing in the ice-free season, the amount of vessel activity in the marginal ice zone (ice concentration 15–80%) or in pack ice (>80% concentration) remains unclear. Understanding patterns of vessel activities in ice is important given increased safety challenges and environmental impacts. Here, we couple high-resolution ship tracking information with sea ice thickness and concentration data to quantify vessel activity in ice-covered areas of the Pacific Arctic (northern Bering, Chukchi, and western Beaufort Seas). This region is a geo-strategically critical area that contains globally important commercial fisheries and serves as a corridor for Arctic access for wildlife and vessels. We find that vessel traffic in the marginal ice zone is widely distributed across the study area while vessel traffic in pack ice is concentrated along known shipping routes and in areas of natural resource development. Of the statistically significant relationships between vessel traffic and both sea ice concentration and thickness, over 99% are negative, indicating that increasing sea ice is associated with decreasing vessel traffic on a monthly time scale. Furthermore, there is substantial vessel traffic in areas of high concentration for bowhead whales (Balaena mysticetus), and traffic in these areas increased four-fold during the study period. Fishing vessels dominate vessel traffic at low ice concentrations, but vessels categorized as Other, likely icebreakers, are the most common vessel type in pack ice. These findings indicate that vessel traffic in areas of ice coverage is influenced by distant policy and resource development decisions which should be taken into consideration when trying to predict future vessel-ice interactions in a changing climate. [ABSTRACT FROM AUTHOR]

QAnon is a set of conspiracy theories that originated online in 2017 and achieved a sizable following in the U.S. and around the world. QAnon's viral disinformation contributed to violent crimes committed by QAnon followers based on their conspiratorial beliefs, as well as to participation of some QAnon followers in the January 6th attack on Capitol Hill in Washington, DC, and to widespread COVID vaccine hesitancy and rejection among believers. This paper presents a to-date unexplored additional cost of conspiracy beliefs: the emotional, social and political toll a relationship with QAnon followers can have on their loved ones. In an Internet survey of 288 U.S.-based participants, a majority (80%) reported having a QAnon-following family member or friend. These relationships have suffered as a result of the loved one's conspiratorial beliefs, leading to self-reported emotional distress and reevaluation of participants' views of the government and society. Participants with a QAnon loved one reported higher anxiety and PTSD than those without such connection. On average, participants viewed QAnon as significantly more radical (willing to engage in illegal or violent acts) than themselves. Perceived QAnon radicalization was predicted by own anxiety and by relationship deterioration with QAnon loved one. The discussion focuses on the importance of tracking these secondhand effects of conspiracy theories as drivers of political polarization. [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]

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]

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]

In multipolar vertebrate neurons, action potentials (APs) initiate close to the soma, at the axonal initial segment. Invertebrate neurons are typically unipolar with dendrites integrating directly into the axon. Where APs are initiated in the axons of invertebrate neurons is unclear. Voltage-gated sodium (NaV) channels are a functional hallmark of the axonal initial segment in vertebrates. We used an intronic Minos-Mediated Integration Cassette to determine the endogenous gene expression and subcellular localization of the sole NaV channel in both male and female Drosophila, para Despite being the only NaV channel in the fly, we show that only 23 ± 1% of neurons in the embryonic and larval CNS express para, while in the adult CNS para is broadly expressed. We generated a single-cell transcriptomic atlas of the whole third instar larval brain to identify para expressing neurons and show that it positively correlates with markers of differentiated, actively firing neurons. Therefore, only 23 ± 1% of larval neurons may be capable of firing NaV-dependent APs. We then show that Para is enriched in an axonal segment, distal to the site of dendritic integration into the axon, which we named the distal axonal segment (DAS). The DAS is present in multiple neuron classes in both the third instar larval and adult CNS. Whole cell patch clamp electrophysiological recordings of adult CNS fly neurons are consistent with the interpretation that Nav-dependent APs originate in the DAS. Identification of the distal NaV localization in fly neurons will enable more accurate interpretation of electrophysiological recordings in invertebrates.SIGNIFICANCE STATEMENT The site of action potential (AP) initiation in invertebrates is unknown. We tagged the sole voltage-gated sodium (NaV) channel in the fly, para, and identified that Para is enriched at a distal axonal segment. The distal axonal segment is located distal to where dendrites impinge on axons and is the likely site of AP initiation. Understanding where APs are initiated improves our ability to model neuronal activity and our interpretation of electrophysiological data. Additionally, para is only expressed in 23 ± 1% of third instar larval neurons but is broadly expressed in adults. Single-cell RNA sequencing of the third instar larval brain shows that para expression correlates with the expression of active, differentiated neuronal markers. Therefore, only 23 ± 1% of third instar larval neurons may be able to actively fire NaV-dependent APs.

To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.

ICT products like Academic Information Systems (AIS) and LMS are commonly utilized in Indonesian universities. These two ICT tools will be of tremendous use to higher education institutions in managing their academic business procedures. The two systems are still not connected in many higher institutions, nevertheless, for example when it comes to handling academic data. If the management and integration process is carried out conventionally, it will be challenging and inefficient due to the massive volume of data and complexity of these two systems. Observing these issues, the researcher plans to study the two REST-based Web Service systems' connection and make use of the External Database functionality, which will be utilized to synchronize data in real-time from AIS to E-Learning. It is hoped that the integration model resulting from research can be a solution for integration between AIS and LMS and can be implemented in many higher education institutions.

Background: The aim of the study was to compare awake supine manual traction radiograph with standing side bending radiograph in adolescent idiopathic scoliosis (AIS) patients and to analyze, which one among them is the best predictor of spinal flexibility index in varying degrees of AIS curves with minimal to severe Cobb angle. Materials and Methods: AIS patients, who underwent surgery during the period from 2015 to 2018, were retrospectively analyzed. Supine manual traction anteroposterior radiograph were taken with the patient in the supine position, with traction applied by a person with the head halter at the chin and counter traction applied with pelvic belt, leg straps. Patients were divided into three groups A, B, and C based on Cobb angle of the major structural curve. Patients with Cobb angles between 40° to 65°, 65° to 90°, and more than 90° were included in Groups A, B, and C, respectively. Flexibility indices obtained from both traction and bending radiographs were correlated with percent change in Cobb angle correction post-surgery. Statistical analysis was done to find Pearson correlation coefficient (r) between variables and results were tabulated. Results: There were 36, 16, and 16 patients in Group A, B, and C, respectively. The mean age of the cohort was 13.38 years. Group A had a positive correlation to traction radiographs (r = + 0.188, P = 0.455). Group B had a statistically significant positive correlation to bending radiographs (r = + 0.729, P = 0.04). A statistically significant positive correlation with traction radiographs (r = + 0.769, P = 0.016) was seen in Group C. Conclusion: Awake supine manual traction radiographs reveal better flexibility index in severe stiff (Cobb more than 90°) curves. In moderately stiffer curves (Cobb 65°–90°) standing side bending radiographs provide a better flexibility index than traction radiographs. Supine manual traction radiographs should always accompany bending radiographs in evaluating AIS patients, especially with severe stiff curves.

In response to the limitations of the DQN algorithm in adaptability, which result in a low success rate in ship path planning, this paper introduces an improved algorithm based on Deep Q-learning (DQN) to enhance path planning. The proposed algorithm aims to plan a reasonable and cost-effective route to the destination based on all historical track, regardless of the current location of ship within the environment. Firstly, the k-means clustering algorithm is employed to cluster the historical ship locations. The value of k is progressively increased to include more locations, allowing the model to interact with the environment of increasing complexity. This approach enhances the generalization capability of the model by enabling it to autonomously devise a route from any starting point to the destination port. On the other hand, for the ship path problem, the DQN algorithm is enhanced through the optimization of the reward function. This improvement targets the challenges of convergence difficulty and low learning efficiency, which greatly improves the rate of convergence for the model. Finally, the effectiveness of the method is verified by comparing it experimentally in terms of the effectiveness of path planning and model convergence trend. The results demonstrate that the improved DQN algorithm achieves a convergence speed improvement of over 25%. Additionally, with the same training time, the success rate of path planning from any position to destination within the environment is enhanced by 44%. It has better effect on the path planning of ships.

The study of sea area accessibility is often overlooked despite the fact that vessel routes’ density at sea can reflect human activity patterns. Using AIS point data for 2014 and 2018, this paper constructed global daily vessel routes and applied the kernel density estimation method to obtain the probability distribution function of route density for over 700,000 sea grids under a global 0.25° regular grid. An accessibility model for sea areas was then proposed and applied to the grid, resulting in the accessibility assessment of global sea areas in 2014 and 2018. The results indicated that the average global maritime accessibility value increased from 5.2% in 2014 to 14.4% in 2018. Accessibility to various international trade routes, such as those through the Cape of Good Hope and polar regions, significantly increased. However, accessibility to sea areas such as the Mediterranean and sea areas near the Pacific Islands decreased. This study explored the temporal and spatial variation of maritime traffic accessibility, which could enhance our understanding of global vessel activity characteristics and guide maritime rescue resource allocation, marine species protection, and marine spatial planning.

In the maritime environment, the Automatic Identification System (AIS) contains information related to vessel trajectories that can be used to detect unusual maritime occurrences and maritime traffic patterns. To detect such occurrences with supervised learning methods the AIS messages must be manually annotated, which can be a demanding process. Therefore, unsupervised methods are used to identify anomalous traffic patterns based on vessel trajectories. Typically, dense regions of maritime activity are studied to capture common traffic patterns which help identify trajectories that do not follow the norm. However, these approaches cannot detect anomalous behaviors along common pathways or incorporate time-related events into the analysis. Such challenges motivate the approach taken in this work by using auto-regressive techniques to model vessel trajectories and clustering analyses to explore behavior patterns of vessels. Results confirm that the Auto-regressive Integrated Moving Average (ARIMA) and Ornstein-Uhlenbeck (OU) processes are able to model the trajectories and can be used with density-based spatial clustering of applications with noise (DBSCAN), hierarchical clustering (HC), and spectral clustering (SC) to identify different behavioral patterns.