Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification

To improve the feasibility of system identification in the prediction of ship manoeuvrability, several measures are presented to deal with the parameter identifiability in the parametric modeling of ship manoeuvring motion based on system identification. Drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the multicollinearity in a complicated manoeuvring model, difference method and additional signal method are employed to reconstruct the samples. Moreover, the structure of manoeuvring model is simplified based on correlation analysis. Manoeuvring simulation is performed to demonstrate the validity of the measures proposed

Анализ работоспособности экстракционной колонны

Исследованы причины отклонений работы экстракционного оборудования от регламентных норм, проанализированы дефекты в процессе эксплуатации исследуемого оборудования. Определены характеристики часто повторяющихся дефектов при работе комбинированной экстракционной колонны оригинальной конструкции. Классифицированы отклонения, определены коэффициенты конкордации, установлены основные факторы, влияющие на процесс экстракции. Создан алгоритм диагностики работы оборудования. Рассчитан уровень дефектности оборудования за исследуемый период времени. Предложена методика статистической обработки данных способом анкетирования по выборке факторов, влияющих на процесс экстракции. Разработан метод диагностики работы экстракторов

Assessment of Subgrid-Scale Model Effects on Large Eddy Simulation of a Back-Step Combustor

Much progress has been made in large-eddy simulation (LES) of turbulent combustion in the last two decades, but a robust and cost-effective LES formulation is still lacking for turbulent combustion in practical configurations. In this paper, we present an assessment of different sgs models and the no sgs approach within the context of LES of a backward step combustor. Overall, the dynamic one equation eddy model behaves better than the WALE and one equation eddy models, in both reproducing main features and statistical quantities of the non-reactive and reactive flow fields. Increasing grid resolution does not necessarily improve the predictions. The results are largely dependent on whether the local flow is turbulence or combustion dominated. This implies that along with an adaptive grid refinement, an adaptive combustion model strategy is needed. In combustion simulation, applying only the first term in the series model is insufficient to well predict the dominating features and statistical quantities of the reacting flows. Thus, we suggest as future work the introduction of additional adaptive terms that will control the variance

Heat shock protein 90 in neurodegenerative diseases

Hsp90 is a molecular chaperone with important roles in regulating pathogenic transformation. In addition to its well-characterized functions in malignancy, recent evidence from several laboratories suggests a role for Hsp90 in maintaining the functional stability of neuronal proteins of aberrant capacity, whether mutated or over-activated, allowing and sustaining the accumulation of toxic aggregates. In addition, Hsp90 regulates the activity of the transcription factor heat shock factor-1 (HSF-1), the master regulator of the heat shock response, mechanism that cells use for protection when exposed to conditions of stress. These biological functions therefore propose Hsp90 inhibition as a dual therapeutic modality in neurodegenerative diseases. First, by suppressing aberrant neuronal activity, Hsp90 inhibitors may ameliorate protein aggregation and its associated toxicity. Second, by activation of HSF-1 and the subsequent induction of heat shock proteins, such as Hsp70, Hsp90 inhibitors may redirect neuronal aggregate formation, and protect against protein toxicity. This mini-review will summarize our current knowledge on Hsp90 in neurodegeneration and will focus on the potential beneficial application of Hsp90 inhibitors in neurodegenerative diseases

Towards Omni-supervised Referring Expression Segmentation

Referring Expression Segmentation (RES) is an emerging task in computer vision, which segments the target instances in images based on text descriptions. However, its development is plagued by the expensive segmentation labels. To address this issue, we propose a new learning task for RES called Omni-supervised Referring Expression Segmentation (Omni-RES), which aims to make full use of unlabeled, fully labeled and weakly labeled data, e.g., referring points or grounding boxes, for efficient RES training. To accomplish this task, we also propose a novel yet strong baseline method for Omni-RES based on the recently popular teacher-student learning, where the weak labels are not directly transformed into supervision signals but used as a yardstick to select and refine high-quality pseudo-masks for teacher-student learning. To validate the proposed Omni-RES method, we apply it to a set of state-of-the-art RES models and conduct extensive experiments on a bunch of RES datasets. The experimental results yield the obvious merits of Omni-RES than the fully-supervised and semi-supervised training schemes. For instance, with only 10% fully labeled data, Omni-RES can help the base model achieve 100% fully supervised performance, and it also outperform the semi-supervised alternative by a large margin, e.g., +14.93% on RefCOCO and +14.95% on RefCOCO+, respectively. More importantly, Omni-RES also enable the use of large-scale vision-langauges like Visual Genome to facilitate low-cost RES training, and achieve new SOTA performance of RES, e.g., 80.66 on RefCOCO