Automated Functions: Their Potential for Impact Upon Maritime Sociotechnical Systems

The shipping industry is evolving towards an unknown and unpredictable future. There is speculation that in the next two decades the maritime industry will witness changes far exceeding those experienced over the past 100 years. The rapid development of artificial intelligence (AI), big data, automation and their impacts upon fully autonomous ships have the potential to transform the maritime industry. While change is inevitable in the maritime domain, automated solutions do not guarantee navigational safety, efficiency or improved seaway traffic management. Such dramatic change also calls for a more systematic approach to designing, evaluating and adopting new solutions into a system. Although intended to support operator decision-making needs and reduce operator workload, the outcomes might create unforeseen changes throughout other aspects of the maritime sociotechnical system. In the maritime industry, the human is seldom put first in technology design which paradoxically introduces human-automation challenges related to technology acceptance, use, trust, reliance and risk. The co-existence and challenges of humans and automation, as it pertains to navigation and navigational assistance, is explored throughout this licentiate.\ua0This thesis considers the Sea Traffic Management (STM) Validation Project \ua0as the context to examine low-level automation functions intended to enhance operator (both Navigators and Vessel Traffic Service Operators) navigational safety and efficiency. The STM functions are designed to improve information sharing between ships and from ship to shore such as: route sharing, enhanced monitoring, and route crosschecking. The licentiate is built on two different data collection efforts during 2017-2018 within the STM Validation project. The functions were tested on two user groups: Bridge Officers and Vessel Traffic Service Operators. All testing was completed in high-fidelity bridge simulators using traffic scenarios developed by subject matter experts.The aim of this licentiate is to study the impact of low levels of automation on operator behavior, and to explore the broader impact upon the maritime sociotechnical system. A mixed-method approach was selected to address these questions and included the following: observations, questionnaires, numerical assessment of ship behavior, and post-simulation debrief group sessions. To analyze and discuss the data, grounded theory, subject matter expert consultation, and descriptive statistics were used. The results point towards a disruption in current working practices for both ship and shore operators, and an uncertainty about the overall impact of low-level automation on operator behaviour. Using a sociotechnical systems approach, gaps have been identified related to new technology testing and implementation. These gaps relate to the overall preparedness of the shipping industry to manage the evolution towards smarter ships. The findings discussed in this licentiate aim to promote further discussions about a quickly evolving industry concerning automation integration in shipping and the potential impact on human performance in safety critical operations

A proposed approach to monitor private-sector policies and practices related to food environments, obesity and non-communicable disease prevention

Private-sector organizations play a critical role in shaping the food environments of individuals and populations. However, there is currently very limited independent monitoring of private-sector actions related to food environments. This paper reviews previous efforts to monitor the private sector in this area, and outlines a proposed approach to monitor private-sector policies and practices related to food environments, and their influence on obesity and non-communicable disease (NCD) prevention. A step-wise approach to data collection is recommended, in which the first (‘minimal’) step is the collation of publicly available food and nutrition-related policies of selected private-sector organizations. The second (‘expanded’) step assesses the nutritional composition of each organization’s products, their promotions to children, their labelling practices, and the accessibility, availability and affordability of their products. The third (‘optimal’) step includes data on other commercial activities that may influence food environments, such as political lobbying and corporate philanthropy. The proposed approach will be further developed and piloted in countries of varying size and income levels. There is potential for this approach to enable national and international benchmarking of private-sector policies and practices, and to inform efforts to hold the private sector to account for their role in obesity and NCD prevention

Weathering the Nest: Privacy Implications of Home Monitoring for the Aging American Population

The research in this paper will seek to ascertain the extent of personal data entry and collection required to enjoy at least the minimal promised benefits of distributed intelligence and monitoring in the home. Particular attention will be given to the abilities and sensitivities of the population most likely to need these devices, notably the elderly and disabled. The paper will then evaluate whether existing legal limitations on the collection, maintenance, and use of such data are applicable to devices currently in use in the home environment and whether such regulations effectively protect privacy. Finally, given appropriate policy parameters, the paper will offer proposals to effectuate reasonable and practical privacy-protective solutions for developers and consumers

A Smart Modular Wireless System for Condition Monitoring Data Acquisition

Smart sensors, big data, the cloud and distributed data processing are some of the most interning changes in the way we collect, manage and treat data in recent years. These changes have not significantly influenced the common practices in condition monitoring for shipping. In part this is due to the reduced trust in data security, data ownership issues, lack of technological integration and obscurity of direct benefit. This paper presents a method of incorporating smart sensor techniques and distributed processing in data acquisition for condition monitoring to assist decision support for maintenance actions addressing these inhibitors