Methodology for Maritime Risk Assessment in Ports due to Meteo-oceanographic Factors: The Case of the port of Suape, Brazil.

Exploring the effects of meteo-oceanographic (MO) events on ships' maneuverability and safety has great potential, since most maritime accidents occur in confined waters, where the speed of ships is low, and the forces of wind and current on ships have particular importance. Therefore, we put forward a methodology that will be used to qualify and classify the risks caused by MO factors to how ships maneuver, dock or undock in a port. The objective is to generate important information for managing risk. The methodology is validated and illustrated step-by-step by applying it in Suape, one of the most important ports in Brazil, where the docking of larger tankers (e.g., Suezmax) was not allowed until recently when dredging was done to fit the specifications of such ships, thereby expanding the port's operations. MO data on Suape were collected and recorded from September 2016 to November 2017 and used for the application. Based on expert opinion and discussion with a Suape pilot, 36 accidental scenarios (ASs) were identified and categorized using preliminary hazard analysis. From these, the seven most severe ASs were selected so as to assess in more detail the frequency and consequences of accidents on human health, the environment, and property, for which the MO statistics for the likelihood of an accident and/or dispersal of an oil spill were used. The results show that the methodology is viable to assess risks caused by bad weather and to communicate these to pilots and competent authorities, thus improving the safety of operations.

Unveiling nuances in data analysis to illuminate marine pilot strain.

Maritime studies, encompassing a range of disciplines, increasingly rely on advanced data analytics, particularly in the context of navigation. As technology advances, the statistical averaging of large datasets has become a critical component of these analyses. However, recent studies have highlighted discrepancies between statistical predictions and observable realities, especially in high-stress environments like port approach procedures conducted by marine pilots. This study analyzed physiological responses recorded during simulation exercises involving experienced marine pilots. The focus was not on the specific outcomes of the simulations but on the potential faults arising from conventional statistical signal processing, particularly mean-centered approaches. A large dataset of signals was generated, including one signal with a dominant characteristic intentionally designed to introduce imbalance, mimicking the uneven distribution of real-world data. Initial analysis suggested that the average physiological response of the pilots followed an S-shaped curve, indicative of a psycho-physiological reaction to stress. However, further post hoc analysis revealed that this pattern was primarily influenced by a single participant's data. This finding raises concerns about the generalizability of the S-curve as a typical stress response in maritime pilots. The results underscore the limitations of relying solely on conventional statistical methods, such as mean-centered approaches, in interpreting complex datasets. The study calls into question the validity of standardizing data interpretations based on dominant characteristic curves, particularly in environments as intricate as maritime navigation. The research highlights the need for a re-evaluation of these methods to ensure more reliable and nuanced conclusions in maritime studies. This study contributes to the ongoing discourse on data interpretation in maritime research, emphasizing the critical need to re-assess conventional statistical signal processing techniques. By recognizing the potential pitfalls in data generalization, the study advocates for more robust analytical approaches to better capture the complexities of real-world maritime challenges.

EEG-based analysis for pilots' at-risk cognitive competency identification using RF-CNN algorithm.

Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots' SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/ß (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/ß (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots' cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

[OLGA, digital platform for the management of national health research networks: An effective model for complex organizations?] / OLGA, plateforme numérique d'aide au pilotage de réseaux nationaux de recherche en santé : un modèle pour les organisations complexes ?

OLGA (which stands for "Activity Management Tool" in French) is a digital management platform developed by F-CRIN, the French Clinical Research Infrastructure Network, which was set up in 2012 to improve the performance and attractiveness of clinical research in France. F-CRIN currently represents a community made up of 21 different components - thematic research, investigation and research networks with a national scope - bringing together the equivalent of 1,500 clinical researchers and 400 research centres all around the country and belonging to many different organizations. Faced with the difficulty of gathering uniform collective data that meet the requirements of F-CRIN's supervisory authorities, in 2015 the F-CRIN community decided to develop a specific monitoring and management tool. Designed with input from its future users, OLGA currently has two modules: one for the components' research activity and the other for their financial operations, resources and expenses. These are able to take account of the activity of the components in all their diversity and produce various sets of indicators. Other features, including a space for sharing documents, are currently being developed. Today, OLGA is a reference tool for managing large-scale, complex organisations.

Measuring mental workload and physiological reactions in marine pilots: Building bridges towards redlines of performance.

This paper investigates the effects of shiphandling manoeuvres on mental workload and physiological reactions in ten marine pilots. Each pilot performed four berthings in a ship simulator. Those berthings were differentiated by two factors, level of difficulty and familiarity with the port. Each berthing could also be divided into five phases, three during the execution and two resting periods, one before and one after the execution (dedicated to baseline physiological data collection). Mental workload was measured through two self assessment scales: the NASA TLX and a Likert scale. Power spectral densities on Beta bands 1 and 2 were obtained from EEG. Heart rate and heart rate variability were obtained from ECG. Pupil dilation was obtained from eye tracking. Workload levels were higher as berthings increased in difficulty level and/or the pilots completed the berthings in unfamiliar ports. Responses differed across specific phases of the berthings. Physiological responses could indirectly monitor levels of mental workload, and could be adopted in future applications to evaluate training improvements and performance. This study provides an example of an applied methodology aiming to define an upper redline of task demands in the context of marine pilotage.