Navigation Patterns Research Articles

Research on Anomaly Detection of Ship Behavior Based on Data Mining

With the increase in the number of ships and the intensifying maritime traffic, maritime accidents such as ship collisions and grounding occur frequently, posing serious threats to people's lives and property. Therefore, timely detection and correction of abnormal ship behaviors are of great significance for improving navigation safety. The Automatic Identification System (AIS) can provide real-time information on ships' positions, speeds, headings, and more, offering a rich data resource for data mining. Through the analysis of these data, it is possible to deeply explore the navigation patterns and characteristics of abnormal behaviors of ships. This paper applies data mining technology to the detection of abnormal ship behaviors, which not only improves the accuracy and efficiency of detection but also provides maritime regulatory authorities with richer decision-making support.

Ips typographus vision system: a comprehensive study.

Aggressive bark beetle species such as the Eurasian spruce bark beetle Ips typographus play a fundamental role in forest ecosystems but can also lead to extensive forest mortality and massive economic damage during outbreaks. Currently I. typographus' eyes, visual perception of the world and recognition of specific targets like host plants are understudied topics. Studying its visual sense can open the way to novel efficient monitoring and management methods, particularly important in avoiding the switch from an endemic to an epidemic condition. In addition, the integration of visual cues in trapping systems may offer new opportunities for surveillance. Vision in I. typographus was investigated by means of morphological analysis, electroretinography (ERG), molecular analysis of opsin genes and behavioural tests. ERG has revealed that the compound eyes contain two classes of photoreceptors, maximally sensitive to UV and green at 370 and 530nm, respectively. The result was further supported by the identification of two relevant opsin genes. Finally, the innate wavelength sensitivity was tested in a Y-maze. Ips typographus consistently preferred UV over non-UV (VIS) light, irrespective of their intensity ratios, but preferred high over low intensity VIS light, consistent with a UV-VIS dichromatic visual system. Overall, the results may open the way to better understand the navigation pattern in tree canopies and the host selection processes of this ecologically and economically important beetle species.

Quantitative modeling of the emergence of macroscopic grid-like representations.

When subjects navigate through spatial environments, grid cells exhibit firing fields that are arranged in a triangular grid pattern. Direct recordings of grid cells from the human brain are rare. Hence, functional magnetic resonance imaging (fMRI) studies proposed an indirect measure of entorhinal grid-cell activity, quantified as hexadirectional modulation of fMRI activity as a function of the subject's movement direction. However, it remains unclear how the activity of a population of grid cells may exhibit hexadirectional modulation. Here, we use numerical simulations and analytical calculations to suggest that this hexadirectional modulation is best explained by head-direction tuning aligned to the grid axes, whereas it is not clearly supported by a bias of grid cells toward a particular phase offset. Firing-rate adaptation can result in hexadirectional modulation, but the available cellular data is insufficient to clearly support or refute this option. The magnitude of hexadirectional modulation furthermore depends considerably on the subject's navigation pattern, indicating that future fMRI studies could be designed to test which hypothesis most likely accounts for the fMRI measure of grid cells. Our findings also underline the importance of quantifying the properties of human grid cells to further elucidate how hexadirectional modulations of fMRI activity may emerge.

From ports to routes: Extracting multi-scale shipping networks using massive AIS data

Maritime transportation is a critical component of global trade and commerce. To ensure maritime safety, fixed shipping routing has been established in many complex waters. However, there is currently a lack of comprehensive digital shipping networks in wide-range maritime areas. To better understand the navigational patterns, this paper proposes a data-driven extraction framework for multi-scale shipping networks, including port-, node-, and route-level shipping networks. It is essentially a hierarchical approach, which progresses from port to route. In particular, for the extraction of port-level shipping networks, the clustering in quest (CLIQUE) and alpha-shapes algorithms are employed to accurately extract the boundaries and spatial extents of individual ports. For the node-level shipping network extraction, an adaptive Douglas-Peucker algorithm is developed to identify crucial feature points, and CLIQUE clustering is further exploited to extract the network waypoints. A novel slice-based traffic flow fitting algorithm is finally introduced to extract the route-level shipping network. To verify the performance of shipping network extraction methods, comprehensive experiments are conducted using the massive Automatic Identification System (AIS) data in different water areas. The experimental results have demonstrated that our method was capable of extracting multi-scale shipping networks, revealing traffic characteristics and vessel behaviours. Overall, the method proposed herein is useful for shipping logistic analysis and provides a foundation for several potential maritime applications, including route planning, trajectory prediction, and others.

Who Am I Here? Care Consumers’ Identity Processes and Family Caregiver Interventions in the Elderscape

Abstract With increasing longevity, the need for institutional elderly care has become commonplace. This study explores the experiences of elderly care consumers in institutional care settings, which we define as the “elderscape”—a heterotopic place shaped by the marketization of care. Drawing from 24 in-depth interviews with elderly care consumers, their family caregivers, and professional caregivers, we present a model of elderly care consumers' navigation patterns and identity processes in the elderscape. The transition to the elderscape often compels elderly consumers to strive to preserve their identities. Boundaries defined by market logic and professional care logic require elderly care consumers to navigate these constraints, sometimes adapting their identities. As a result, distinct navigation patterns emerge: rebuilding personal connections, revaluating possessions, reconsidering activities, and reclaiming space. Furthermore, the identity preservation efforts of elderly care consumers are complicated by the interventions of family caregivers. The findings highlight the dual nature of family caregivers' impact on elderly consumers' identity processes. Depending on their motivations—such as care, obligation, or nostalgia—family caregivers engage in patterned actions that either support or destabilize the elderly consumers' identity processes. This research provides valuable insights for care institutions, family caregivers, and care consumers alike.

The comparison of interactive video learning experiences in branched vs. straight scenarios with the focus of content type

ABSTRACT Interactive videos have the potential to boost content-student interactions. This study is an attempt to observe the logs of interactions in two different learning scenarios (straight and branched scenario) by two different content types (declarative and procedural). The study aims to understand if learning experiences vary across scenario-content combinations. It is built upon experimental design methodology. The experimental group (N = 20) interacted with the branched scenario, while the control group (N = 20) engaged in the straight scenario. Each group engaged in both content types with varying scenarios. Logs, screen recordings, and interviews were the primary data sources. The results indicated that for the declarative content type, time spent in the straight scenario was significantly higher than that of the branched scenario. Still, the collected points of the branched scenario were substantially higher than that of the straight one. These effects were not observed in procedural content. General content and concepts were mentioned frequently in declarative content with a higher amount in branched-declarative than in straight-declarative combination. Retrieval for details was observed frequently in the straight-procedural combination. The navigational patterns varied across combinations, but pausing, forwarding, rewinding, and skipping were commonly observed in declarative content regardless of the scenario type.

The synergistic and dynamic relationship between learning design and learning analytics

The synergistic relationship between learning design and learning analytics has the potential for improving learning and teaching in near real-time. The potential for integrating the newly available and dynamic information from ongoing analysis into learning design requires new perspectives on learning and teaching data processing and analysis as well as advanced theories, methods, and tools for supporting dynamic learning design processes. Three perspectives of learning analytics design provide summative, real-time, and predictive insights. In a case study with 3,550 users, the navigation sequence and network graph analysis demonstrate the potential of learning analytics design. The study aims to demonstrate how the analysis of navigation patterns and network graph analysis could inform the learning design of self-guided digital learning experiences. Even with open-ended freedom, only 608 sequences were evidenced by learners out of a potential number of hundreds of millions of sequences. Advancements of learning analytics design have the potential for mapping the cognitive, social and even physical states of the learner and optimise their learning environment on the fly.

A Ship Trajectory Prediction Method Based on an Optuna–BILSTM Model

In the field of maritime traffic management, overcoming the challenges of low prediction accuracy and computational inefficiency in ship trajectory prediction is crucial for collision avoidance. This paper presents an advanced solution using a deep bidirectional long- and short-term memory network (BILSTM) and the Optuna hyperparameter automatic optimized framework. Utilizing automatic identification system (AIS) data to analyze ship navigation patterns, the study applies Optuna to fine-tune the hyperparameters of the BILSTM network to improve prediction accuracy and efficiency. The developed Optuna–BILSTM model shows a remarkable 7% increase in prediction accuracy over traditional back propagation (BP) neural networks and standard BILSTM models. These results not only improve ship navigation and safety but also have significant implications for the development of autonomous ship collision avoidance systems, marking a significant step toward safer and more efficient maritime traffic management.

Development of Representative Sailing Mode Construction Methodology Using Markov Chain

The strengthening of regulations such as EEXI, EEDI, and CII on ship emissions is underway. Despite their application, objective comparisons of ships are hindered by diverse navigation patterns and varying velocity regulations in different seas and ports. Additionally, a lack of basic data impedes comparisons of the optimal design and objective energy efficiency for ships. To address these issues, representative sailing modes, similar to those in the automobile industry, are needed. However, there is no reference for marine applications. This study introduces a methodology for representative sailing modes using the Markov chain. A hundred candidate sailing modes were created, and representative modes were identified through an evaluation equation. All chi-square values for representative sailing modes are within 1%, indicating significant results. This study’s findings can aid in designing optimized systems for new vessels and computing authorized fuel efficiency for vessels with diverse sailing patterns.

Tracking e-reading behavior: uncovering the effects of task context, electronic experience, and motivation

Although electronic reading of fiction has become mainstream, little is known about how electronic texts are read, and whether this behavior is connected to readers’ motivation or experience reading electronically. To address this gap, sixty undergraduate students’ reading behavior was tracked while reading a 15-page short story. A novel method was used to study participants’ frequency of task-switching, their reading speed, and navigational patterns unobtrusively, outside of the lab. Reading behavior was analyzed by two multilevel models to assess (1) whether variance in behavior could be predicted by the task context, such as location in text or timing of reading sessions, and (2) whether behavior was connected to participants’ situational motivation to read the short story, their contextual motivation toward reading as an activity, or their task-relevant electronic experience. Our results showed that highly experienced and avid readers reacted to text difficulty more adaptively, indicating that motivation and electronic experience may have a key role in supporting comprehension. In contrast, situational motivation was not associated with reading behavior, contrary to our expectations. These findings provide a significant contribution to our understanding of e-reading, which can be used as a foundation to support recreational reading engagement on digital devices.

The Pattern of Cultural Navigation in Iran's Cultural Domains

The Pattern of Cultural Navigation in Iran's Cultural Domains

Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

Spatial navigation patterns in indoor space usage can reveal important cues about the cognitive health of participants. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth low energy (BLE) beacons for tracking indoor movements in a large, 1700 m2 facility used to carry out therapeutic activities for participants with mild cognitive impairment (MCI). The facility is instrumented with 39 edge computing systems, along with an on-premise fog server. The participants carry a BLE beacon, in which BLE signals are received and analyzed by the edge computing systems. Edge computing systems are sparsely distributed in the wide, complex indoor space, challenging the standard trilateration technique for localizing subjects, which assumes a dense installation of BLE beacons. We propose a graph trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle the inconsistent coverage of edge devices. This proposed method helps us tackle the varying signal strength, which leads to intermittent detection of beacons. The proposed method can pinpoint the positions of multiple participants with an average error of 4.4 m and over 85% accuracy in region-level localization across the entire study area. Our experimental results, evaluated in a clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of individuals’ movements, which may reflect health status or response to treatment.

Recognizing Instantaneous Group Patterns in Vessel Trajectory Data: A Snapshot Perspective

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

Speedy bearings to slacked steering: Mapping the navigation patterns and motions of Viking voyages.

Viking sailors ruled the North Atlantic Ocean for about three hundred years. Their main sailing route was the 60° 21' 55'' latitude between Norway and Greenland. Although they did not have a magnetic compass, in sunshine they used a sun-compass to determine the geographical north (solar Viking navigation: SVN). It has been hypothesized that when the Sun was invisible, Viking navigators determined the direction of polarization of skylight with sunstones (dichroic/birefringent crystals), and then estimated the geographical north using the sun-compass (sky-polarimetric Viking navigation: SPVN). Many details of the hypothetical SPVN have been thoroughly revealed in psychophysical laboratory and planetarium experiments. Combining these results with measured celestial polarization patterns, the success of SPVN was obtained as functions of sailing, meteorological and navigation parameters (sunstone type, sailing date, navigation periodicity, night sailing, cloudiness conditions). What was so far lacking in this experimental and computational archeological approach is the study of the success of SVN and a combined navigation using solar cues in sunshine (SVN) and sky polarization at invisible Sun (SPVN), the latter being the most realistic method. In this work we determine the success of the sole SVN and the combined SVN-SPVN relative to the mere SPVN for three navigator types (determining the intended sailing direction with large, medium or small frequencies) at spring equinox and summer solstice, with and without night sailing. We found that to maximize the sailing success, navigators had to choose different navigation methods depending on the navigation frequency. Using sky polarization with very frequent navigation, resulted in the highest chance to survive a three-week voyage from Norway to Greenland.

Learning to Predict Navigational Patterns From Partial Observations

Learning to Predict Navigational Patterns From Partial Observations

Characterizing spatio-temporal patterns of coastal navigation to inform mitigation strategies for conflict between ocean users and the protected humpback whale (Megaptera novaeangliae) in Bahía de Banderas, Mexico

Characterizing specific pressures on natural resources is a key component of developing sustainable management strategies. Here we explored the spatiotemporal distribution of marine vessels in a potentially high conflict area between humpback whales and tourism activities, namely Bahía de Banderas, Mexico, from 2017 to 2023. Using high-resolution satellite imagery, we identified high-use areas and their relationship to the presence of urban areas, vessel departure points, and the coastline before, during, and after restrictions put in place during the peak of the COVID-19 pandemic. High-resolution satellite imagery (3m/px - 5m/px) retrieved through the Planet Application Program was used to perform manual detection and annotation of vessels and spatial patterns of marine navigation were characterized through point pattern analyses The spatial distribution of all marine vessel observations in the study area was not random; four high-use coastal areas closer to urban areas were identified, three of which overlap the current marine protected area established to protect whale nursing grounds. Furthermore, as the COVID-19 pandemic began, coastal navigation decreased due to lockdown before picking up again at a higher level than the pre-COVID baseline due to social distancing restrictions. While these changes were not statistically significant, they potentially indicate a behavioral change caused by COVID-19 social distancing recommendations during 2021; the amplified demand for marine vessels appears to have increased the absolute number of vessel chartering and decreased the number of passengers onboard commercial vessels. Our preliminary work provides a reproducible remote sensing methodology that can support future mitigation strategies.

Cognitive Flexibility Is Selectively Impaired by Radiation and Is Associated with Differential Recruitment of Adult-Born Neurons.

Exposure to elevated doses of ionizing radiation, such as those in therapeutic procedures, catastrophic accidents, or space exploration, increases the risk of cognitive dysfunction. The full range of radiation-induced cognitive deficits is unknown, partly because commonly used tests may be insufficiently sensitive or may not be adequately tuned for assessing the fine behavioral features affected by radiation. Here, we asked whether γ-radiation might affect learning, memory, and the overall ability to adapt behavior to cope with a challenging environment (cognitive/behavioral flexibility). We developed a new behavioral assay, the context discrimination Morris water maze (cdMWM) task, which is hippocampus-dependent and requires the integration of various contextual cues and the adjustment of search strategies. We exposed male mice to 1 or 5 Gy of γ rays and, at different time points after irradiation, trained them consecutively in spatial MWM, reversal MWM, and cdMWM tasks, and assessed their learning, navigational search strategies, and memory. Mice exposed to 5 Gy performed successfully in the spatial and reversal MWM tasks; however, in the cdMWM task 6 or 8 weeks (but not 3 weeks) after irradiation, they demonstrated transient learning deficit, decreased use of efficient spatially precise search strategies during learning, and, 6 weeks after irradiation, memory deficit. We also observed impaired neurogenesis after irradiation and selective activation of 12-week-old newborn neurons by specific components of cdMWM training paradigm. Thus, our new behavioral paradigm reveals the effects of γ-radiation on cognitive flexibility and indicates an extended timeframe for the functional maturation of new hippocampal neurons.SIGNIFICANCE STATEMENT Exposure to radiation can affect cognitive performance and cognitive flexibility - the ability to adapt to changed circumstances and demands. The full range of consequences of irradiation on cognitive flexibility is unknown, partly because of a lack of suitable models. Here, we developed a new behavioral task requiring mice to combine various types of cues and strategies to find a correct solution. We show that animals exposed to γ-radiation, despite being able to successfully solve standard problems, show delayed learning, deficient memory, and diminished use of efficient navigation patterns in circumstances requiring adjustments of previously used search strategies. This new task could be applied in other settings for assessing the cognitive changes induced by aging, trauma, or disease.

Navigation pattern extraction from AIS trajectory big data via topic model

AbstractThis paper introduces a novel approach for extracting vessel navigation patterns from very large automatic identification system (AIS) trajectory big data. AIS trajectory data records are first converted to a series of code documents using vector quantisation, such as k-means and PQk-means algorithms, whose performance is evaluated in terms of precision and computational time. Therefore, a topic model is applied to these code documents from which vessels’ navigation patterns are extracted and identified. The potential of the proposed approach is illustrated by several experiments conducted with a practical AIS dataset in a region of North West France. These experimental results show that the proposed approach is highly appropriate for mining AIS trajectory big data and outperforms common DBSCAN algorithms and Gaussian mixture models.

Website Traffic Patterns and User Behavior: A Comprehensive Study of Visitor Interactions and Engagement Metrics

Websites play a crucial role in disseminating information, promoting communication, and engaging various stakeholders. In the context of universities, websites serve as central online hubs for academic programs, campus life, faculty profiles, resources, news, and more. This study focuses on the website of the University of Antique (UA), a state university in the Philippines, to analyze visitor behavior and engagement. The research objectives include collecting and analyzing data on website visits, page views, bounce rates, time spent on pages, and navigation patterns. The study aims to identify frequently accessed pages, assess the impact of marketing campaigns, capture visitor demographics, and identify areas for website improvement. Data collection took place over a 60-day period using Google Analytics tools, including acquisition, page statistics, and search tools. The study follows a systems approach, where visitor data is automatically recorded in the Google Analytics database. The research methodology involves measuring acquisition metrics, page and screen metrics, device and browser metrics, and geographic metrics. The results indicate that organic search traffic performs well in attracting new users, with the home page showing the highest engagement. Mobile devices are dominant in accessing the website, while the majority of users are from the Philippines. The findings provide insights for optimizing website design, content relevance, and user experience to enhance engagement and achieve conversion goals.

An adaptive threshold fast DBSCAN algorithm with preserved trajectory feature points for vessel trajectory clustering

Vessel navigation pattern recognition plays an important role in the research of intelligent transportation on water. Clustering using the data stored in The Automatic Identification System (AIS) is a current research hotspot. However, there are three problems in the past clustering analysis. First, the traditional Douglas-Peucker (DP) Compression Algorithm exists feature point loss and trajectory distortion when compressing trajectories. Second, Dynamic Time Warping (DTW) and the density-based spatial clustering of applications with noise (DBSCAN) algorithm require high time cost. Finally, most of the studies ignore the interaction between parameters when choosing the parameters of DBSCAN. These problems seriously affect the efficiency and accuracy of clustering. To solve these problems, this paper improves the existing methods by (1) Adaptive selection of compression thresholds and trajectory feature points for each trajectory when using the DP algorithm ensures the realism of the compressed trajectory; (2) using the Fast-DTW algorithm to improve the computation speed and ensure the accuracy of trajectory similarity; (3) Self-selection of parameter combinations based on Silhouette Coefficient (SC) scores was achieved using the similarity distribution of the trajectories in combination with an improved K-Adaptive Nearest Neighbors (KANN). The experiments show that the proposed method can greatly reduce the time cost of clustering compared to the original method and significantly outperforms the three compared algorithms in terms of clustering effect images.