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61.
Using a sample of 588 employees in 59 work teams, we tested a model that situates personal learning within the context of teams, viewing it as a joint function of teams' leadership climate (i.e., transformational leadership) and task characteristics (i.e., task routineness and task interdependence). Consistent with our hypotheses, we found that the positive relationships between transformational leadership climate and the two dimensions of personal learning (relational job learning and personal skill development) were moderated by the nature of the teams' tasks. Specifically, transformational leadership climate was more strongly associated with personal learning for members of teams working on tasks that were less routine, rather than more routine. However, no significant moderation was found for leadership climate and task interdependence. Our findings underscore the importance of taking into account the contextual conditions within which leadership influence occurs while also demonstrating the potential role that leaders can play in promoting employees' personal learning. Overall, our study bolsters theories that conceptualize adult learning as a transaction between people and their social environments and points to a practical need to match leadership styles with team task characteristics to unleash transformational leadership effects. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
62.
Monika Suškevičs Thomas Hahn Romina Rodela Biljana Macura Claudia Pahl-Wostl 《Journal of Environmental Planning and Management》2018,61(7):1085-1112
Learning is considered as a promising mechanism to cope with rapid environmental change. The implications of learning for natural resource management (NRM) have not been explored in-depth and the evidence on the topic is scattered across multiple sources. We provide a qualitative review of types of learning outcomes and consider their manifestations in NRM across selected empirical literature. We conducted a systematic search of the peer-reviewed literature (N = 1,223) and a qualitative meta-synthesis of included articles, with an explicit focus on learning outcomes and NRM changes (N = 53). Besides social learning, we found several learning concepts used, including policy and transformative learning, and multiple links between learning and NRM reported. We observe that the development of skills, together with a system approach involving multi-level capacities, is decisive for implications of learning for NRM. Future reviews could systematically compare how primary research applies different learning concepts and discusses links between learning and NRM changes. 相似文献
63.
湖泊水质富营养化评价的模糊神经网络方法 总被引:40,自引:0,他引:40
为了探索人工神经网络用于湖泊营养化评价的可能性,提出了基于多准则学习的模糊神经网络湖泊水质营养化评价模型。该模型应用于我国五大主要湖泊水质营养化的评价结果表明,模糊神经网络用于湖泊水质营养化评价,具有简便、实用、客观性和广泛的通用性。 相似文献
64.
为提高腐蚀管道失效压力的预测精度并简化其计算过程,提出基于粗糙集(RS)和粒子群算法(PSO)融合极限学习机(ELM)的腐蚀管道失效压力预测模型。通过属性约简提取影响失效压力的关键因素,选用PSO优化ELM的输入权值和隐含层偏差,将归一化的核心指标数据代入计算。结果表明:该模型预测结果与实际值基本一致,与单一ELM模型相比,预测结果的均方差(MSE)降至0.255;与其他蚀管道失效压力评价模型相比,该模型预测结果的绝对误差平均值降至0.32。 相似文献
65.
Kathryn A. Powlen Jonathan Salerno Kelly W. Jones Michael C. Gavin 《Conservation biology》2023,37(4):e14058
Protected areas (PAs) are a commonly used strategy to confront forest conversion and biodiversity loss. Although determining drivers of forest loss is central to conservation success, understanding of them is limited by conventional modeling assumptions. We used random forest regression to evaluate potential drivers of deforestation in PAs in Mexico, while accounting for nonlinear relationships and higher order interactions underlying deforestation processes. Socioeconomic drivers (e.g., road density, human population density) and underlying biophysical conditions (e.g., precipitation, distance to water, elevation, slope) were stronger predictors of forest loss than PA characteristics, such as age, type, and management effectiveness. Within PA characteristics, variables reflecting collaborative and equitable management and PA size were the strongest predictors of forest loss, albeit with less explanatory power than socioeconomic and biophysical variables. In contrast to previously used methods, which typically have been based on the assumption of linear relationships, we found that the associations between most predictors and forest loss are nonlinear. Our results can inform decisions on the allocation of PA resources by strengthening management in PAs with the highest risk of deforestation and help preemptively protect key biodiversity areas that may be vulnerable to deforestation in the future. 相似文献
66.
Kunsen Lin Youcai Zhao Lina Wang Wenjie Shi Feifei Cui Tao Zhou 《Frontiers of Environmental Science & Engineering》2023,17(6):77
67.
Zhongyao Liang Yaoyang Xu Gang Zhao Wentao Lu Zhenghui Fu Shuhang Wang Tyler Wagner 《Frontiers of Environmental Science & Engineering》2023,17(6):76
68.
Tienan Ju Mei Lei Guanghui Guo Jinglun Xi Yang Zhang Yuan Xu Qijia Lou 《Frontiers of Environmental Science & Engineering》2023,17(1):8
69.
为研究城市地铁沿线老旧房屋普遍存在结构安全问题,基于机器学习模型,选取房屋年份、楼层、面积等11个属性构建预警指标体系,采用SMOTE过采样、独热编码等方法解决样本离散化、不均衡的问题;利用KNN、Bayes、Logistic、SVM 4种机器学习模型对房屋结构安全数据学习并测试,综合应用Accuracy、F1、AP、... 相似文献
70.
为了更好地反映环境污染变化趋势,为环境管理决策提供及时、全面的环境质量信息,预防严重污染事件发生,开展城市空气质量预报研究是十分必要的.本文针对环境大数据时代下的城市空气质量预报,提出了一种基于深度学习的新方法.该方法通过模拟人类大脑的神经连接结构,将数据在原空间的特征表示转换到具有语义特征的新特征空间,自动地学习得到层次化的特征表示,从而提高预报性能.得益于这种方式,新方法与传统方法相比,不仅可以利用空气质量监测、气象监测及预报等环境大数据,充分考虑污染物的时空变化、空间分布,得到语义性的污染物变化规律,还可以基于其他空气污染预测方法的结果(如数值预报模式),自动分析其适用范围、优势劣势.因此,新方法通过模拟人脑思考过程实现更充分的大数据集成,一定程度上克服了现有方法的缺陷,应用上更加具有灵活性和可操作性.最后,通过实验证明新方法可以提高空气污染预报性能. 相似文献