Oil Pollution Effects on Nematodes in Mangrove Sediment: A Microcosm Study

The effects of oil pollution on meiobenthic nematode assemblages in a mangrove sediment were investigated. Microcosms comprised 350 ml plastic jars that were filled with 200 g mangrove sediment and subjected to oiling, with or without addition of fertiliser. In the oiled treatments, 15 ml of Bunker fuel oil 180 and 5 ml/L fertiliser (N: P: K: 3: 2: 5) were added to the soil. After four weeks, nematodes were extracted and identified. In the unfertilised oiled treatment, nematode abundance and species richness were significantly reduced by 87% and 53%, respectively, compared to the control. In the fertilised oiled treatment, nematode abundance and species richness increased by 56% and 30% respectively. The eight taxa present in the control but absent in the oiled treatments (Monhystera, Prodesmodora, Plectus, Rhabditis, Koerneria, Rotylenchus, Tobrilus, and Fictor) were characterised as oil-intolerant. The seven taxa present in the oiled treatments (Monhystera, Ethmolaimus, Panagrolaimus, Camacolaimus, Hemicycliophora typica, and H. ripa and a species of the family Xyalidae) were characterised as oil-tolerant and resilient. In all treatments, the dominant species was Ethmolaimus. Taxa such as Rhabditis, Koerneria and Rotylenchus survived oiling, due to the addition of fertiliser. Fertilizer amendment favoured survival of Rhabditis, Koerneria and Rotylenchus and increased reproduction in Camacolaimus.

Nonlinear model of a dynamic system for forecasting of fuel consumption of a ship using a neural network

В современных условиях коммерческого судоходства, большое внимания уделяется вопросам оптимизации расхода топлива на судах. Наиболее критическим моментом, определяющим эффективность рейса, является количество бункерного топлива, использованного на морском переходе судном. В данной статье предложен подход к прогнозированию расхода топлива на предстоящем переходе судна с помощью использования нейронной сети, обученной с помощью алгоритма Левенберга-Марквардта, а также рассмотрено преимущество данного метода в сравнении с методами других исследователей. Статистическая выборка для машинного обучения составлена на основе эксплуатационных данных с танкера класса Афрамакс . Элементом новизны в данной работе является формирование данных для обучающего множества, а также возможность нелинейного прогнозирования посуточного приращения скорости. Данный метод имеет высокую точность и может применятся как фрахтователем, так и судоводителем для того, чтобы оценить экономическую эффективность предстающего рейса или выбрать оптимальный маршрут по параметру расхода топлива. Ещё одной задачей прогнозирования параметров судна на переходе с помощью нейронной сети является расчёт ожидаемых приращений скорости судна, что таблица расходов бункерного топлива может быть применена только при условиях не более 4-5 баллом во шкале Бофорта. In modern conditions of commercial shipping, much attention is paid to the optimization of fuel consumption on the sea. The most critical moment determining the voyages efficiency is the amount of bunker fuel used by the ship at the sea passage. This article proposes an approach to forecasting fuel consumption at the upcoming passage of a vessel using a neural network taught-in by the Levenberg-Marquardt algorithm, and also considers the advantage of this method in comparison with methods of other researchers. The statistical sample for machine learning is based on operational data from an Aframax class tanker. The novelty element in this work is the formation of data for the training set, as well as the possibility of nonlinear forecasting of the daily increment of speed. This method is highly accurate and can be used by both the charterer and the navigator in order to evaluate the economic efficiency of the upcoming voyage or to choose the optimal route according to the fuel consumption parameter. Another task of predicting the parameters of a vessel at a passage using a neural network is to calculate the expected increments of the vessels speed, with that the table of bunker fuel consumption can be applied only under conditions of no more than 4-5 points on the Beaufort scale.