The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move to autonomous maritime operations, this research applies a human-centered approach to developing decision support technology, specifically in the context of ice management operations. New technologies, such as training simulators and onboard decision support systems, present opportunities to close the gaps in competence and proficiency. Training simulators, for example, are useful platforms as human behaviour laboratories to capture expert knowledge and test training interventions. The information gathered from simulators can be integrated into a decision support system to provide seafarers with onboard guidance in real time. The purpose of this research is two-fold: (1) to capture knowledge held by expert seafarers, and (2) transform this expert knowledge into a database for the development of a decision support technology. This paper demonstrates the use of semi-structured interviews and bridge simulator exercises as a means to capture seafarer experience and best operating practices for offshore ice management. A case-based reasoning (CBR) model is used to translate the results of the knowledge capture exercises into an early-stage ice management decision support system. This paper will describe the methods used and insights gained from translating the interview data and expert performance from the bridge simulator into a case base that can be referenced by the CBR model.

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Traffic sign recognition is an important problem in today's applications. In this paper, by combining ensemble and active learning methods, a novel fusion mixture of active experts algorithm is proposed for this problem. The active learning algorithm is a popular method for reducing the number of samples. The primary goal of active learning is diminishing complexity, increasing the convergence rate, speeding up training process, and decreasing the cost of samples labeling. The active learning, hence, chooses informative samples to train. In addition, ensemble methods are a combination of simple classifiers for improving accuracy. Each classifier tries to learn a region of dataset better than other regions that all opinions are considered on ensemble methods as an ultimate decision. The mixture of experts is one of the most modern hybrid methods in which the training process takes a relatively long time, and it is a problem for large datasets. Our proposed Mixture of Active Experts tries to solve this problem. It decreases the training time process and increases the speed of convergence for finding optimal weights by selecting only informative samples in active learning phase. It is also applicable for online situations, in which the model should be trained continuously. The results of different experiments on German Traffic Sign Recognition Benchmark dataset demonstrate that the proposed method shows 96.69% accuracy and achieved the 6th rank among all the state of the art algorithms using smaller number (only 60%) of training samples.

Angiomyolipoma (AML) is the most common benign renal mesenchymal neoplasm. This is a report of a 36-year-old female patient with AML with the involvement of the inferior vena cava (IVC) who was admitted to our hospital. The patient complained of mild right flank pain. CT scan results showed a hypo-dense mass with 47×72 mm dimensions at the right kidney›s lower pole suggesting renal AML. In MRI with contrast, venous thrombosis was detected in the right renal vein and IVC. Right radical nephrectomy and IVC thrombectomy were successfully conducted. Renal AML was confirmed by pathological findings, and the presence of tumor thrombosis was approved in the right renal vein and IVC. Although AML is generally benign and vascular invasion is a rare finding in this condition, imaging studies (including CT scans) should always be considered to determine the extent of vascular involvement and choose an appropriate therapeutic plan, including nephrectomy and thrombectomy in case of vascular involvement. Despite its benign nature, it should be considered that AML can invade venous structures in the kidneys. Early imaging studies and therapeutic interventions are necessary for obtaining the best outcome.