Building a rich and informative model from raw data is a hard but valuable process with many applications. Ship routing and scheduling are two essential operations in the maritime industry that can ...save a lot of resources if they are optimally designed, but still, need a lot of information to be successful. Past and recent works in the field assume the availability of information such as the birth time-windows, cargo volumes, and container handling productivity at ports and cruising speed. They employ navigation maps that contain information about the major sailing paths and have knowledge about bigger or smaller ports and offshore platforms. In this work, we present a methodology for extracting information about the navigation network for an area, using data from the trajectories of multiple vessels, which are collected using the Automatic Identification System (AIS). We introduce a method for identifying the points of major interest to the trajectory of a vessel and two clustering techniques for identifying: i) key areas in the monitored region such as ports, platforms or areas where vessels change their course (e.g., capes); and ii) the speed and course patterns of ships of a particular type when they follow a typical route. The resulting information is modeled using a network abstraction where nodes correspond to the areas identified by the first clustering technique. After, edges are enriched with information about the groups extracted using the second clustering technique. The first analysis on a real dataset in the area of the eastern Mediterranean sea demonstrates the capabilities of the proposed model and the information it can provide. The use of the model in an outlier behavior detection task also shows interesting results.
Trajectory mining aims to provide fundamental insights into decision-making tasks related to moving objects. A fundamental pre-processing step for trajectory mining is trajectory segmentation, where ...a raw trajectory is divided into several meaningful consecutive sub-sequences. In this work, we propose an unsupervised trajectory segmentation algorithm, Sliding Window Segmentation (SWS), that processes an error signal generated by calculating the deviation of the middle point of an octal window from its imaginary interpolated version. This algorithm is flexible and can be applied to different domains by selecting an appropriate interpolation kernel. We examined our algorithm on three datasets of three different domains such as meteorology, fishing, and people moving in a big city. We also compared SWS with three other trajectory segmentation algorithms, namely GRASP-UTS, CB-SMoT, and SPD. Our experiments show that the proposed algorithm achieves the highest harmonic mean of purity and coverage for all datasets and explored algorithms with statistically significant differences.
Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) has profound effects on disease progression and patients' quality of life. Emerging evidence suggests an association between ...alterations in the respiratory microbiome flora species and airway inflammation in patients with AECOPD. The present study aimed to describe the inflammatory cells and bacterial microbiome distributions in respiratory tract in Egyptian patients with AECOPD.
The present cross-sectional study included 208 patients with AECOPD. Sputum and broncho-alveolar lavage samples from the studied patients were submitted to microbial cultures using appropriate media. Total and differential leukocytic counts and were done via automated cell counter.
The present study included 208 AECOPD patients. They comprised 167 males (80.3%) and 41 females (19.7%) with an age of 57.9 ± 4.9 years. AECOPD was categorized as mild, moderate and severe in 30.8%, 43.3% and 26%, respectively. Sputum samples had significantly higher TLC, neutrophil percent and eosinophil percent when compared with BAL samples. In contrast, lymphocyte percent was significantly higher in BAL samples. Sputum specimens had significantly lower frequency of positive growths (70.2% versus 86.5%, p = 0.001). Among the identified organisms, sputum specimens had significantly lower frequency of
(14.4% versus 30.3%, p = 0.001),
(19.7% versus 31.7%, p = 0.024),
(12.5% versus 26.9%, p = 0.011),
(2.9% versus 10%, p = 0.019) and
. (1.9% versus 7.2%, p = 0.012) growths when compared with BAL samples.
The present study could identify a distinctive pattern of inflammatory cell distribution in sputum and BAL samples of AECOPD patients. The most commonly isolated organisms were
and
.
Ateniese et al. proposed the Provable Data Possession (PDP) model in 2007. Following that, Erway et al. adapted the model for dynamically updatable data and called it the Dynamic Provable Data ...Possession (DPDP) model. The idea is that a client outsources her files to a cloud server and later challenges the server to obtain a proof of the integrity of her data. Many schemes have later been proposed for this purpose, all following a similar framework.
We analyze dynamic data outsourcing schemes for the cloud regarding security and efficiency and show a general framework for constructing such schemes that encompasses existing DPDP-like schemes as different instantiations. This generalization shows that a dynamic outsourced data integrity verification scheme can be constructed given black-box access to an implicitly-ordered authenticated data structure. Moreover, for blockless verification efficiency, a homomorphic verifiable tag scheme is also needed. We investigate the requirements and conditions these building blocks should satisfy, using which one may easily check the applicability of a given building block for dynamic data outsourcing. Our framework serves as a guideline/tutorial/survey and enables us to provide a comparison among different building blocks that existing schemes employ.
Predicting vessel trajectories is crucial for enhancing situational awareness and preventing collisions at sea. However, achieving accurate and efficient predictions is challenging due to the ...heterogeneity in vessel movement patterns and changes in vessel mobility modes during voyages. To address this, we propose a new approach that uses historical AIS data to cluster route patterns for each vessel type, thereby improving prediction accuracy. By training machine learning algorithms to focus only on similar vessel types, this approach can better predict individual vessel mobility patterns. This approach offers computational advantages by using a relatively small set of trajectories from the nearest cluster of a selected vessel. Both spatial and course attributes are considered to determine the nearest cluster, while engineered features capture changes in vessel mobility modes. Using an AIS dataset from UTM Zone 10N (US West Coast), we achieved distance errors of 370m, 742m, and 1.2km for horizons 10, 20, and 30 min, respectively, using the Random Forest algorithm for short-term trajectory prediction (≤30 min) with the last 1-hour trajectory of selected vessels as input.
•Proposed framework for predicting the short-term future trajectory of vessels.•Enhanced prediction accuracy by addressing heterogeneity in mobility patterns.•Engineered features are incorporated to consider changes in mobility modes.•Spatial and course attributes are used for trajectory classification.•Evaluation shows high prediction accuracy with low computational cost.
In an outsourced database scheme, the data owner delegates the data management tasks to a remote service provider who is supposed to answer owner's queries on the database. The essential requirements ...are ensuring the data integrity and authenticity with efficient mechanisms. Current approaches employ authenticated data structures to store security information, generated by the client and used by the server, to compute proofs that show the query answers are authentic. The existing solutions have shortcomings with multi-clause queries and duplicate values in a column.
We propose a hierarchical authenticated data structure for storing security information, which alleviates the mentioned problems. Our solution handles many different types of queries, including multi-clause selection and join queries, in a dynamic database. We provide a unified formal definition of a secure outsourced database scheme, and prove that our proposed scheme is secure according to this definition, which captures previously separate properties: correctness, completeness, and freshness. The performance evaluation based on our prototype implementation confirms the efficiency of our proposed scheme, showing ∼3x smaller proofs and ∼5x improvement in proof generation time compared to previous works (Devanbu et al. 2002; Pang et al. 2005; Li et al. 2010; Palazzi et al. 2010).
Recent studies on maritime traffic model the interplay between vessels and ports as a graph, which is often built using automatic identification system (AIS) data. However, only a few works ...explicitly study the evolution of such graphs and, when they do, generally consider coarse-grained time intervals. Our goal is to fill this gap by providing a conceptual framework for the fine-grained systematic study of maritime graphs evolution. To this end, this paper presents the month-by-month analysis of world-wide graphs built using a 3-years AIS dataset. The analysis focuses on the evolution of several topological graph features, as well as their stationarity and statistical correlation. Results have revealed some interesting seasonal and trending patterns that can provide insights in the world-wide maritime context and be used as building blocks toward the prediction of graphs topology.
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