Encounter Situations Research Articles

Path planning and dynamic collision avoidance algorithm under COLREGs via deep reinforcement learning

As one of the core technologies of the automatic control system for unmanned surface vehicles (USVs), autonomous collision avoidance algorithm is the key to ensure the safe navigation of USVs. In this paper, path planning and dynamic collision avoidance (PPDC) algorithm which obeys COLREGs is proposed for USVs. In order to avoid unnecessary collision avoidance actions, the risk assessment model is developed, which is used to determine the switching time of path planning and dynamic collision avoidance. In order to train the algorithm which complies with the COLREGs, the encounter situation is divided quantitatively, which is regarded as the input state of the system, so that the high-dimensional input is successfully avoided. The state space of the USV is defined by relative parameters to improve the generalization ability of the algorithm, meanwhile, a network structure based on DDPG is designed to achieve the continuous output of thrust and rudder angle. Combined with path planning, collision avoidance, compliance with COLREGs and smooth arrival task, four kinds of reward functions are designed. In order to solve the problem of low training efficiency of experience replay mechanism in DDPG, cumulative priority sampling mechanism is proposed. Through the simulation and verification in a variety of scenarios, it is proved that PPDC algorithm has the function of path planning and dynamic collision avoidance in compliance with COLREGs, which has good real-time performance and security.

Joint probability of drought encounter among three major grain production zones of China under nonstationary climate

Better understanding for the likelihood of drought occur in the main grain production zones of China is key to minimize the risk for food security, especially in a changing climate. In an attempt to encompass the climate variability in the assessment of drought, a nonstationary standardized precipitation index (NSPI) was established for three major grain production zones (Songnen Plain (SN), North China Plain (NC), and Sichuan Basin (SC), hereinafter referred to GPZs) by means of using the climate indices as covariates. Then three commonly used Archimedean copulas (i.e., Clayton, Frank, and Gumbel-Hougaard) were adopted to determine the joint drought probability model. Using the joint probability distribution, the risk of drought in different degree occurring among the three GPZs, conditional joint probability, and conditional return period of different drought combinations of GPZs were analyzed. The results show that NSPI reflected the drought conditions well in three GPZs, and the copula-based joint probability distributions of different drought situations among the three GPZs were reasonable. For the situation of droughts encounter in two GPZs, synchronous droughts in different degree are more prone to encounter in NC & SC, and the conditional joint drought probability (return period) is also higher (lower) in the combination of SC and NC. For the situation of droughts encounter in three GPZs, the probability of synchronous droughts in the three GPZs is decreasing with increase of drought degree. If a specifically drought (NSPI ≤ -1) occurred in two GPZs, the conditional joint drought probability is highest in NC, then followed by SC, and SN. The findings of this study could provide important implications of decision-making for food security.

Dead or alive? Pitfall of survival analysis with TCGA datasets

ABSTRACT We often encounter situations in which data from the TCGA that have been analyzed in papers we read or reviewed cannot be reproduced, even when TCGA datasets are used, especially in survival analyses. Therefore, we attempted to confirm the data source for TCGA survival analysis and found that several websites used to analyze the survival data of TCGA datasets inappropriately handle the survival data, causing differences in statistical analyses. This causes the misinterpretation of results because figures of survival analysis results in several papers are sometimes exactly as generated by these sites, and the results depend on only the tools provided by these sites. We would like to make this situation widely known and raise the problem for scientific soundness.

A semi-supervised deep learning model for ship encounter situation classification

Maritime safety is an important issue for global shipping industries. Currently, most of collision accidents at sea are caused by the misjudgement of the ship’s operators. The deployment of maritime autonomous surface ships (MASS) can greatly reduce ships’ reliance on human operators by using an automated intelligent collision avoidance system to replace human decision-making. To successfully develop such a system, the capability of autonomously identifying other ships and evaluating their associated encountering situation is of paramount importance. In this paper, we aim to identify ships’ encounter situation modes using deep learning methods based upon the Automatic Identification System (AIS) data. First, a segmentation process is developed to divide each ship’s AIS data into different segments that contain only one encounter situation mode. This is different to the majority of studies that have proposed encounter situation mode classification using hand-crafted features, which may not reflect the actual ship’s movement states. Furthermore, a number of present classification tasks are conducted using substantial labelled AIS data followed by a supervised training paradigm, which is not applicable to our dataset as it contains a large number of unlabelled AIS data. Therefore, a method called Semi-Supervised Convolutional Encoder–Decoder Network (SCEDN) for ship encounter situation classification based on AIS data is proposed. The structure of the network is not only able to automatically extract features from AIS segments but also share training parameters for the unlabelled data. The SCEDN uses an encoder–decoder convolutional structure with four channels for each segment (distance, speed, Time to the Closed Point of Approach (TCPA) and Distance to the Closed Point of Approach (DCPA)) been developed. The performance of the SCEDN model are evaluated by comparing to several baselines with the experimental results demonstrating a higher accuracy can be achieved by our proposed model.

Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data

According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.

Determinants of the intention to speak up about medical error in primary healthcare settings in Chile.

Medical error frequently occurs in ambulatory care, and healthcare professionals may encounter situations in which they need to speak up to ensure better practice. This study aims to investigate the factors that influenced the intention to speak up about medical errors among healthcare professionals in primary care settings. Data were generated through a national cross-sectional survey of primary healthcare centres in the Republic of Chile. A research instrument was designed using the constructs of the theory of planned behaviour and was analysed using the structural equation model technique. In total, 203 healthcare professionals were recruited between March and May 2020. The model showed that the intention to speak up was directly and positively influenced by attitudes towards speaking up and perceived control (standard deviation [SD]=0.284 and 0.576, respectively). Subjective norms indirectly and negatively influenced the intention to speak up through attitudes towards speaking up and perceived control (total effect SD=-0.303). The exploratory construct of willingness to change self-behaviour positively influenced the attitude towards behaviour. The intention to speak up strongly influenced the speaking up behaviour (total effect SD=0.631). The proposed model explained 40% of the variance in behaviour. Based on this model, it was concluded that the intention to speak up strongly influenced the speaking up behaviour and predicted it by 40%. Factors that modify the intention to speak up are expected to influence the occurrence of this behaviour. This knowledge will inform strategies to enhance communication among healthcare professionals, improve speaking up behaviour and improve patient care.

Multi-ship encounter situation adaptive understanding by individual navigation intention inference

The multi-ship encounter situation at sea is characterized by high complexity and uncertainty, which is a big challenge for both traditional ships and the new autonomous ships. In order to make reasonable navigation decisions and perform well under multi-ship encounter situation, it is necessary to grasp the current scenario correctly and intelligently. Therefore, in this paper, an adaptive understanding model for multi-ship encounter situation is proposed. The core function of this model is to infer the navigation intention of other target ships under the same situation. This model is mainly composed of two sub-models. One is the ship encounter situation analysis model, which realizes the cognition of the whole encounter scenario from the global perspective by maintaining the “double matrix”. The second is the ship navigation intention inference model, the key part of the model is a set of well-designed fuzzy inference system. The output of the encounter situation analysis model is the input of the intention inference model, and these two models are closely linked to form a unified whole. This model is verified by both simulation-based and real scenario-based experiments, the results show that this model can perform well under the complex multi-ship encounter situation. Moreover, some necessary discussion and analysis for this inference model are also stated at the end of this paper, in the future, we expect that this model can be applied in real situations.

COLREGs: Compliant Dynamic Obstacle Avoidance of USVs Based on the Dynamic Navigation Ship Domain

Dynamic obstacle avoidance is essential for unmanned surface vehicles (USVs) to achieve autonomous sailing. This paper presents a dynamic navigation ship domain (DNSD)-based dynamic obstacle avoidance approach for USVs in compliance with COLREGs. Based on the detected obstacle information, the approach can not only infer the collision risk, but also plan the local avoidance path trajectory to make appropriate avoidance maneuvers. Firstly, the analytical DNSD model is established taking into account the ship parameters, maneuverability, sailing speed, and encounter situations regarding COLREGs. Thus, the DNSDs of the own and target ships are utilized to trigger the obstacle avoidance mode and determine whether and when the USV should make avoidance maneuvers. Then, the local avoidance path planner generates the new avoidance waypoints and plans the avoidance trajectory. Simulations were implemented for a single obstacle under different encounter situations and multiple dynamic obstacles. The results demonstrated the effectiveness and superiority of the proposed DNSD-based obstacle avoidance algorithm.

Fear of death and its relationship to resilience in nursing students: A longitudinal study

AimTaking a corpus of nursing students enrolled in the 2017−2021 nursing degree, we aim to analyse how students' levels of resilience and fear of death evolve in the first three years of the degree and whether there are differences between students based on age and gender. In addition, we aim to describe the relationship between resilience and fear of death. BackgroundThroughout their training, nursing students will encounter situations of grief and death that may generate reactions of rejection, anxiety and emotional imbalance. Recognising and controlling these emotions through specialised training in end-of-life care and fostering resilience is essential. DesignComparative, correlational and longitudinal study. MethodsOur corpus comprised 100 nursing students who were followed through the first three years of their training. Data were collected using a sociodemographic questionnaire, the Collet-Lester Fear of Death Scale and the Resilience Scale. ResultsThe corpus reported medium-high levels of fear of death overall. Patterns were similar for all academic years: fear of other people's death was invariably the highest scoring subscale; and while individual dimensions of the Collet-Lester scale correlated directly and significantly with each other they were found to be inversely related to student age. High levels of resilience were recorded and were highest in the final year. ConclusionsIn this sample of student nurses, fear of death and resilience do not appear to be related to each other. Nursing education must include training specifically targeted at increasing levels of resilience and decreasing fear of death to improve end-of-life care.

Unpacking How Employees Navigate Unfulfilled, Unreasonable, and Unexpected Occupational Ideals

Occupations have become stabilizing fixtures of modern-day employment, providing employees with a set of established cultural tools, norms, values, and beliefs that offer meaning to employees and guide their workplace interpretations and action. Yet, as employees perform their occupations “on the ground”, they frequently encounter situations that challenge these occupational meaning systems. This symposium showcases four papers that develop novel theory about how individuals respond to incongruities between situational demands, environmental constraints, and audiences’ expectations, on the one hand, and occupational norms, values, and beliefs, on the other hand. More specifically, we examine (1) how workers counter harmful occupational cultures, (2) how women in male-dominated occupations transcend their status as female “tokens”; (3) how employees respond to labels (e.g., “hero”) cast upon their occupation by external audiences; and (4) how employees manage frequent exposure to tasks that conflict with their occupational ideologies extolling the nature and necessity of the work. Taken together, our papers and discussion endeavor to contribute to and generate future research on contemporary occupational dynamics, as well as deliver actionable insights for managers. Meaning Detachment and Deviation from Invincibility Cultural Scripts among Stunt Performers Presenter: Winnie Jiang; INSEAD Presenter: Karim Ginena; U. of Virginia Darden School of Business Transcending Tokenism: How Women in Male-Dominated Occupations Can Challenge Gendered Work Norms Presenter: Julia DiBenigno; Yale School of Management Heroes from Above and from Below: Workers’ Reactions to the Moralizing of their Work Presenter: Lindsey Cameron; The Wharton School, U. of Pennsylvania Presenter: Michel Anteby; Boston U. Questrom School of Business The Music Between the Notes: How Employees Manage Unforeseen Work Realities Presenter: Pascale Fricke; U. of British Columbia Presenter: Natalya Alonso; Haskayne School of Business, U. of Calgary Presenter: Patrick Reilly; U. of British Columbia

Construal Level in Organizational Research

Every day people encounter situations at work with varying demands. How do people navigate changing demands across different situations? Construal level theory argues that people use abstract construals to address distant demands and concrete construals to respond to immediate demands. Decades of psychology research have shown the value of construal level theory in explaining and predicting people’s attitudes and behaviors. More recent research has applied construal level to studying organizational-relevant phenomena. This symposium presents five lines of research that use diverse methodologies and samples to explore the antecedents and consequences of communication abstraction and cognitive construal, including gender, audience engagement, performance, and information processing and trust in groups. This symposium aims to provide an opportunity for knowledge sharing and discussion among researchers who are interested in construal level in organizational research. Gender and Emoji Usage Presenter: Gil Appel; George Washington U. Presenter: Cheryl Wakslak; U. of Southern California Presenter: Elinor Amit; Tel Aviv U. Inviting People In: Does Abstract Language Increase Engagement with Ideas? Presenter: Jean-Nicolas Reyt; McGill U. Presenter: Patricia Staats; Kenan-Flagler Business School, U. of North Carolina at Chapel Hill Presenter: Naomi Beth Rothman; Lehigh U. Construal of Everyday Tasks Presenter: Yidan Yin; U. of California, San Diego Presenter: Pamela K. Smith; U. of California, San Diego Presenter: Batia Mishan Wiesenfeld; New York U. Developing Measures for Abstract Construal and Concrete Construal Presenter: Robert Barrett; U. of Iowa Presenter: Yidan Yin; U. of California, San Diego Presenter: Michele Williams; U. of Iowa Presenter: Batia Mishan Wiesenfeld; New York U. Preventing Groupthink through a Concrete Construal Intervention Presenter: Ashli Carter; NYU Stern School of Business

Takeover Safety Analysis with Driver Monitoring Systems and Driver–Vehicle Interfaces in Highly Automated Vehicles

According to SAE J3016, autonomous driving can be divided into six levels, and partially automated driving is possible from level three up. A partially or highly automated vehicle can encounter situations involving total system failure. Here, we studied a strategy for safe takeover in such situations. A human-in-the-loop simulator, driver–vehicle interface, and driver monitoring system were developed, and takeover experiments were performed using various driving scenarios and realistic autonomous driving situations. The experiments allowed us to draw the following conclusions. The visual–auditory–haptic complex alarm effectively delivered warnings and had a clear correlation with the user’s subjective preferences. There were scenario types in which the system had to immediately enter minimum risk maneuvers or emergency maneuvers without requesting takeover. Lastly, the risk of accidents can be reduced by the driver monitoring system that prevents the driver from being completely immersed in non-driving-related tasks. We proposed a safe takeover strategy from these results, which provides meaningful guidance for the development of autonomous vehicles. Considering the subjective questionnaire evaluations of users, it is expected to improve the acceptance of autonomous vehicles and increase the adoption of autonomous vehicles.

Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels

In this investigation, a smart collision avoidance control design, which integrates a collision avoidance navigation and a nonlinear optimal control method, is developed for unmanned surface vessels (USVs) under randomly incoming ships and fixed obstacle encounter situations. For achieving collision avoidance navigation, a fuzzy collision risk indicator and a fuzzy collision avoidance acting timing indicator are developed. These two risk indicators can offer effective pre-alarms for making the controlled USVs to perform dodge actions in time when obstacles appear. As to nonlinear optimal control law, it provides a precise trajectory tracking ability for the controlled USVs to follow a collision avoidance trajectory, which is generated via a smart collision avoidance trajectory generator. Finally, a power allocation method is used to transform the desired control law into available actuator outputs to guide the USVs to follow a desired collision avoidance trajectory. From simulation results, the proposed collision avoidance strategy reveals a promising collision avoidance performance and an accurate trajectory tracking ability with respect to fixed objects and randomly moving ships under the effect of environmental ocean disturbances.

Yöneticilerin YönetselBecerilerinin Çalışanların Yaşam Doyumları Üzerine Etkisi: İlköğretim Öğretmenleri Üzerinde Bir İnceleme (The Effect ofAdministrativeSkills of Managers on Life Satisfaction of Employees: A Studyon Primary Education Teachers)

Purpose – The aim of this study is; To investigate whether the management skills of school administrators in educational institutions affect the life satisfaction of school staff. With the research, the management skills of school administrators; It is aimed to propose scientific solutions to the problems encountered by analyzing how they affect the life satisfaction of the employees, to what extent, what these factors are, the relationship structure between demographic characteristics and life satisfaction levels, what may be the problems that prevent life satisfaction. Design/methodology/approach – In this study, the relational screening model was used. Relationships between two or more variables are generally revealed in relational survey studies. In this theoretical study, the inductive method was taken as basis and the relationships between the hypotheses determined within the framework of assumptions were analyzed. The study was carried out in order to investigate the effects of management skills of school administrators on the life satisfact ion of employees in Private Primary Education institutions operating in Beylikduzu, Istanbul. 1360 teachers working in 34 primary schools in Beylikduzu constitute the universe of the research. 528 of these teachers were chosen by random sampling method. In order to analyze the data obtained from the surveys used for the research; Reliability, factor, data, correlation and regression analysis were applied. Results – As a result of the regression analysis for this study; taking responsibility, other skills and qualification skills do not affect life satisfaction; In addition, management skills and technical skills were found to affect satisfaction with life. Discussion – Teachers who are in contact with students all day in terms of their life satisfaction and who strive for their upbringing may experience intense stress and pressure. Teachers; They cannot improve themselves in terms of knowledge, professional competence and skills, and they may encounter situations that prevent them from increasing their life satisfaction. Therefore, it can be stated that there is a positive or negative relationship that affects the management skills of school administrators and the life satisfaction of teachers working in educational institutions. The good situation of teachers in terms of economic status, social status and management skills of school administrators can positively affect their life satisfaction. Other studies on the subject of this study are also discussed in Chapter 5; Teachers working in schools, staff; It should be taken into consideration that conducting scientific researches with different school employees in different regions, at different times, with different variables, in order to improve and develop their social and economic status, and to increase their satisfaction and success from the job can provide significant benefits to students, their parents, teachers and staff.

An application-orientated anti-collision path planning algorithm for unmanned surface vehicles

This article presents a reasonable anti-collision path planning algorithm with the properties of strong practicability and high degree of automation for unmanned surface vehicles (USV) or Marine Autonomous Surface Ships (MASS) in dynamic environment situation. Based on the requirements of the International Regulations for Preventing Collisions at Sea (COLREGs), the qualitative provision about the apparent intention of anti-collision maneuver is quantified in the algorithm. Meanwhile, the Boolean expression technology is adopted to help determine the type of encounter situation and action manner automatically, which provides theoretical basis for the application of path-planning algorithm. In addition, the modified velocity obstacle approach and artificial potential fields are combined to improve the practicality and rationality of the optimization solution. Finally, simulation examples consisting different traffic scenarios are discussed to demonstrate the effectiveness and practicality of the proposed scheme. •In this article, the apparent and substantial action is quantified by the parameter relative heading angle rather than heading angle, which is closer to navigation practice. •The Boolean expression is adopted to realize the automatic logical identification of encounter type and action manner, which makes the anti-collision decision scheme more complete. •The concepts of safety course region and feasible course region in VO approach are proposed to help determine the alteration angle for modified APF algorithm. •The concept of presupposed number of steps is proposed to eliminate the course vibration for the optimization results based APF algorithm, which could greatly improve the practicality of the proposed scheme.

Autonomous Collision Avoidance of Unmanned Surface Vehicles Based on Improved A Star And Minimum Course Alteration Algorithms

The ability to avoid shorelines, reefs and moving ships or obstacles is the premise of automatic navigation. For achieving the goal, conventional A star algorithm is improved to plan route globally. The safety distance is set between planning route and obstacles. Additionally, the planning route is further optimized to remove unnecessary waypoints, and only vital waypoints are retained for conforming to navigation practice and track keeping control. Besides, minimum course alteration algorithm (MCA) is proposed to avoid moving ships or obstacles constrained by COLREGs. The proposed algorithms are validated in simulation. The results show that the algorithms are credible in two and multi ships encounter situations, even the targets ships take unexpected course alteration and the own ship could well avoid collision with shorelines, reefs and moving ships or obstacles under external disturbance.

Ethical Issues in the Care of Emerging Adults in Pediatric Intensive Care Units.

Ethical Issues in the Care of Emerging Adults in Pediatric Intensive Care Units.

Ethical Reasoning and the Five Roles of the Assessment Practitioner

Responsibilities of an assessment practitioner are many and diverse (Jankowski & Slotnick, 2015). As with any profession, the responsibilities are not devoid of moral situations in which practitioners must choose between two or more possibilities (i.e., moral or ethical dilemmas). We present an ethical dilemma concerning student equity and assessment reporting and demonstrate that based upon which of the five roles a practitioner takes, s/he must take multiple perspectives and consider decision-making impacts. Assessment practitioners support various persons with differing needs and expectations; this may mean that assessment professionals encounter situations that require ethical reasoning. The Eight Key Questions (8KQ) strategy provides a framework for the practitioner in the form of questions that should be investigated. The process is designed to elicit diverse perspectives on an issue without a pre-determined answer, making sure that important ethical considerations are included in the decision-making process.

Unanswerable Question Correction in Question Answering over Personal Knowledge Base

People often encounter situations where they need to recall past experiences from their daily life. In this paper, we aim to construct a question answering system that enables human to query their past experiences over personal knowledge base. Previous works on knowledge base question answering focus on finding answers for answerable questions. In the real world applications, however, people often muddle up facts and ask those questions that cannot be answered with knowledge base. This work presents a novel system consisting of question answering model and question generation model. It not only answers answerable questions, but also corrects unanswerable questions if necessary. Our question answering model recognizes the question that is inconsistent with the state of the personal knowledge base and suggests facts that can form a feasible question. Then, the facts are converted to an answerable question by the question generation model. For refining question, we propose a question generation model based on the reinforcement learning (RL) with question editing mechanism. Experimental results show that our proposed system is effective for correcting unanswerable questions in personal knowledge base question answering.