Jacobs (Canada)

ABSTRACT The growth of Arctic shipping requires effective tools to assess the appropriateness of existing practices on safety and resilience levels, especially in the enforcement of the Polar Code. Understanding such, in this study, we develop a resilience index using the Fuzzy Analytical Hierarchy Process (FAHP). Data collection for the index’s implementation started during a research voyage along the Northwest Passage in Canada. It resulted in 61 valid responses, upon which the relative importance and the level of satisfaction of resilience builders were investigated. The findings indicate that factors that rank high on importance are, in many cases, not that high on satisfaction. This presents dilemmas regarding the effectiveness of the current practices in promoting safe and resilient Arctic shipping management practices. This study fills an important gap in the monitoring of safety and resilience practices in Arctic shipping. In turn, this affects the future actions and research of researchers, stakeholders, and right-holders in the Arctic area.

Long-term insight into maritime traffic is critical for port authorities, logistics companies, and port operators to proactively formulate suitable policies, develop strategic plans, allocate budget, and preserve and improve competitiveness. Forecasting freight rate is a spotlight in port traffic literature, but relatively little research has been directed at forecasting long-term vessel traffic trends. Based on forecast long-term freight rate input provided by the recent 10-year strategic planning of the port of Rajaee, the largest port of Iran, the paper implements seasonal autoregressive integrated moving average (SARIMA) and neural network (NN) models to forecast its container vessel traffic between 2020 and 2025. A database consisting of monthly container traffic data for this port from 1999 to 2019 is utilized. The comparison between the two forecasting models is fulfilled by benchmarking the naïve method. The results reveal the superiority of the NN model over SARIMA in this practice. Considering NN model outputs, the port should expect a significant increase in Panamax and Over-Panamax vessels in the future, and, if not timely addressed, this would result in a systemic queue in the port of Rajaee. That said, the approach can be implemented in port planning and design to avoid under- or over-estimations in such capital-intensive projects.

Low productivity while at work (presenteeism) has been reported to produce significant cost excesses for organizations and economies. However, many of these reports have been based on estimates drawn from self-report instruments that are not supported by evidence showing their efficacy. Thus, the aim of this study was to assess associations between responses to leading self-report tests of presenteeism and self-recorded on-the-job productivity. Health care worker self-ratings of productivity were taken from a questionnaire that contained the key item from each presenteeism instrument. Productivity levels were drawn from employee reported daily work activity logs. Test-based productivity estimates did not show strong associations with daily recordings of work activity. Associations were too low to recommend any test as a proxy measure for reported productivity. It is suggested that objective measures of work output be explored.

In recent years, computer vision techniques have made large advances in image recognition and been applied to aid radiological diagnosis. Computational pathology aims to develop similar tools for aiding pathologists in diagnosing digitized histopathological slides, which would improve diagnostic accuracy and productivity amidst increasing workloads. However, there is a lack of publicly-available databases of (1) localized patch-level images annotated with (2) a large range of Histological Tissue Type (HTT). As a result, computational pathology research is constrained to diagnosing specific diseases or classifying tissues from specific organs, and cannot be readily generalized to handle unexpected diseases and organs. In this paper, we propose a new digital pathology database, the ``Atlas of Digital Pathology'' (or ADP), which comprises of 17,668 patch images extracted from 100 slides annotated with up to 57 hierarchical HTTs. Our data is generalized to different tissue types across different organs and aims to provide training data for supervised multi-label learning of patch-level HTT in a digitized whole slide image. We demonstrate the quality of our image labels through pathologist consultation and by training three state-of-the-art neural networks on tissue type classification. Quantitative results support the visually consistency of our data and we demonstrate a tissue type-based visual attention aid as a sample tool that could be developed from our database.

Grounding strategy of a DC microgrid affects the stray current level, the common-mode voltage, the energy supply reliability, personnel/equipment safety and protection system design. Therefore, a comprehensive knowledge of the available grounding strategies and their effects is essential for design and operation of the DC microgrid components and especially its protection. This paper investigates and compares characteristics of different DC microgrid grounding strategies that involve the choice of grounding configurations and grounding devices. The study includes the grounding strategy impacts on line-to-ground (LG) fault detection and protection, transient LG fault current magnitude, stray current level, common mode voltage, personnel/equipment safety, system reliability, service continuity, and insulation requirements. Finally, the strengths and weaknesses of the various grounding strategies are discussed.

The high demolition rate (∼60%) of reinforced concrete (RC) buildings that generally performed well in Christchurch, New Zealand, has been one of the most important lessons from the Canterbury Earthquakes. In an effort to understand such an outcome, various factors influencing the post‐earthquake decisions on buildings (demolition or repair) are explored, focusing on multi‐story RC buildings in Christchurch Central Business District (CBD). Using empirical data, logistic regression analysis was conducted to explain the likelihood of building demolition. Several explanatory factors were found to be statistically significant: assessed damage, occupancy type, heritage status, number of floors, and construction year. From in‐person interviews conducted in New Zealand, contextual factors such as insurance policy and changes in legislation were also found to play a significant role in the post‐earthquake decisions on buildings.