Encounters are celebrated experiences between persons with connectedness in human situations as expectation. While being in a human dynamic and rhythmic interaction, nursing encounters are dyadic relationships illumin...Encounters are celebrated experiences between persons with connectedness in human situations as expectation. While being in a human dynamic and rhythmic interaction, nursing encounters are dyadic relationships illuminated as patterns of an interconnected relationship moving between the nurse and the nursed, and reflecting person-and-otherness events. The purpose of this paper is to describe the theory of Encountering Nursing in a Nurse-Nursed Dyadic Relationship (ThENNDyR) and to illuminate the four nursing practice processes on which the theory is founded: <em>Knowing as appreciating relational moments</em>;<em>Reflecting as engaging moments</em>;<em>Realizing as patterns of living moments</em>;and <em>Transcending as celebrating moments</em>. Nursing practice occurs in moments in which dyadic relationships transpire as nursing encounters. As fleeting as moments are, the four processes of nursing simultaneously take place as understanding conditions that the <em>who</em> and <em>what</em> of the person warrants persons. “Encountering nursing” is a momentary co-existence of persons in a person-and-otherness situation communicating connectedness-interconnectedness in distinct patterning. Interactions in nursing exist as persons remain wholes and complete in the moment.展开更多
为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of a...为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。展开更多
文摘Encounters are celebrated experiences between persons with connectedness in human situations as expectation. While being in a human dynamic and rhythmic interaction, nursing encounters are dyadic relationships illuminated as patterns of an interconnected relationship moving between the nurse and the nursed, and reflecting person-and-otherness events. The purpose of this paper is to describe the theory of Encountering Nursing in a Nurse-Nursed Dyadic Relationship (ThENNDyR) and to illuminate the four nursing practice processes on which the theory is founded: <em>Knowing as appreciating relational moments</em>;<em>Reflecting as engaging moments</em>;<em>Realizing as patterns of living moments</em>;and <em>Transcending as celebrating moments</em>. Nursing practice occurs in moments in which dyadic relationships transpire as nursing encounters. As fleeting as moments are, the four processes of nursing simultaneously take place as understanding conditions that the <em>who</em> and <em>what</em> of the person warrants persons. “Encountering nursing” is a momentary co-existence of persons in a person-and-otherness situation communicating connectedness-interconnectedness in distinct patterning. Interactions in nursing exist as persons remain wholes and complete in the moment.
文摘为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。