Can environmental heterogeneity explain individual foraging variation in wild bottlenose dolphins (<Emphasis Type="Italic">Tursiops</Emphasis> sp.)?

Because behavioral variation within and among populations may result from ecological, social, genetic and phenotypic differences, identifying the mechanism(s) responsible is challenging. Observational studies typically examine social learning by excluding ecological and genetic factors, but this approach is insufficient for many complex behaviors associated with substantial environmental variation. Indian Ocean bottlenose dolphins (Tursiops sp.) in Shark Bay, Western Australia show individual differences in foraging tactics, including possible tool use with marine sponges and social learning may be responsible for this diversity. However, the contributions of ecological factors to the development of these foraging tactics were not previously investigated. Here, we determined the relationship between ecological variables and foraging tactics and assessed whether differences in habitat use could explain individual differences in foraging tactics. We monitored 14 survey zones to identify how foraging tactics were spatially distributed and matched behavioral data to the ecological variables within each zone. Three of four foraging tactics were significantly correlated with ecological characteristics such as seagrass biomass, water depth, presence of marine sponges and season. Further, individual differences in habitat use were associated with some tactics. However, several tactics overlapped spatially and previous findings suggest demographic and social factors also contribute to the individual variation in this population. This study illustrates the importance of environmental heterogeneity in shaping foraging diversity and shows that investigating social learning by ruling out alternative mechanisms may often be too simplistic, highlighting the need for methods incorporating the relative contributions of multiple factors.     

Application of machine learning methods to palaeoecological data

A palaeoecological study was conducted to investigate past environmental conditions and vegetation dynamics around the southwestern Ljubljana Moor. In order to find potential regularities and/or dependencies among co-existent plant species through time, different machine learning methods were applied to pollen records from the cores taken at Bistra and Ho?evarica. The data comprised relative pollen frequencies of the most common plant genera/families at particular core depths that correspond to particular ages in the Early and Mid Holocene periods. The applied methods include equation discovery and hierarchical clustering. Both methods have found plausible and explainable relationships among identified plant genera/families.     

发展我国摆辗技术必须促进摆辗机国产化

介绍了发展我国摆辗技术,首先必须繁荣摆辗工艺,促进摆辗机国产化。     

Thriving on challenge stressors? Exploring time pressure and learning demands as antecedents of thriving at work

In the conceptualization of thriving at work, it is emphasized that employees' learning and vitality are two equally important components of thriving and that thriving is facilitated by contextual features and available resources. In this study, we examined the effects of two challenge stressors (time pressure and learning demands) on thriving at work. Based on the literature on challenge and hindrance stressors, we proposed that challenge stressors positively affect learning and negatively affect vitality. To uncover underlying mechanisms, we measured challenge appraisal and hindrance appraisal of work situations in a diary study. A sample of 124 knowledge workers responded to three daily surveys (before the lunch break, during the afternoon, and at the end of the workday) for a period of five workdays. Results indicate that the indirect effects of learning demands and time pressure on learning are mediated by challenge appraisal, whereas indirect effects of learning demands on vitality are mediated by hindrance appraisal. Overall, our study shows that challenge stressors have a positive total effect on learning but no total effect on vitality. These differential relationships call for a finer distinction between the two components of thriving at work in future research. Copyright © 2016 The Authors Journal of Organizational Behavior Published by John Wiley & Sons Ltd.     

基于ARIMA与LS-SVM组合模型的飞行事故征候预测

应用差分自回归移动平均模型(ARIMA)和最小二乘支持向量机(LS-SVM)的组合模型,对某航空公司的月度事故征候万时率进行了预测分析。对2008—2016年某航空公司的事故征候、飞行小时、航空器数量等历史数据建立ARIMA模型,应用SPSS软件进行模型拟合,获得事故征候万时率的线性部分;随后利用LS-SVM分析ARIMA模型的残差,获取非线性部分,最终通过二者之和获得ARIMA+LSSVM组合模型。对2017年1—3月的月度事故征候万时率进行了预测,并用实际数据验证。结果表明:ARIMA(1,1,1)(1,1,1)12模型较好地拟合了事故征候万时率的历史序列,LS-SVM模型对残差的拟合获得了较好的精度;组合模型的短期(3个月)预测值与航空公司事故征候万时率的趋势完全一致,且预测精确度可接受。     

Streamflow Forecasting Using Singular Value Decomposition and Support Vector Machine for the Upper Rio Grande River Basin

The current study improves streamflow forecast lead‐time by coupling climate information in a data‐driven modeling framework. The spatial–temporal correlation between streamflow and oceanic–atmospheric variability represented by sea surface temperature (SST), 500‐mbar geopotential height (Z₅₀₀), 500‐mbar specific humidity (SH₅₀₀), and 500‐mbar east–west wind (U₅₀₀) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). SVD significant regions are weighted using a nonparametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed model for the period of 1965–2014. The April–August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1–13 months. SVD results showed the streamflow variability was better explained by SST and U₅₀₀ as compared to Z₅₀₀ and SH₅₀₀. The SVM model showed satisfactory forecasting ability with best results achieved using a one‐month lead to forecast the following four‐month period. Overall, the SVM results showed excellent predictive ability with average correlation coefficient of 0.89 and Nash–Sutcliffe efficiency of 0.79. This study contributes toward identifying new SVD significant regions and improving streamflow forecast lead‐time of the URGRB.     

大气腐蚀数据降维最优维度研究

目的确定金属大气腐蚀数据降维的最优维度。方法分别采用PCA、MDS、Isomap和LLE四种方法对大气腐蚀数据进行降维处理,并采用集成学习算法建立预测模型。针对不同的降维方法和近邻点个数计算,使用MAPE(Mean Absolute Percentage Error,相对百分误差绝对值的平均值)对预测结果进行评价,将最佳预测速率所对应的维度作为最优维度。结果不同的降维方法和近邻参数作用下,最优维度从2维到7维不等。流形学习方法对大气腐蚀数据进行降维的MAPE均小于线性降维方法。结论适合每种降维方法的最优维度可能是不同的,通过对MAPE进行对比,得到大气腐蚀数据在各种降维方法的最优维度。     

基于小波分解和SVM的大气污染物浓度预测模型研究

针对大气污染物浓度的精准预测问题,运用小波分解将污染物浓度一维序列分解为高维信息,结合气象及污染物浓度数据,构建了基于小波分解的支持向量机预测模型.最后将模型应用于长沙市2018年PM_2.5和O_{3-8 h}的浓度预测.结果表明：①在其他参数不变的条件下,该模型在平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方根误差（RMSE）、一致性水平（IA）和相关系数（R）指标上均优于未经小波分解的预测模型;②在考虑其他污染物对PM_2.5浓度的影响后,预测模型评价指标MAE、MAPE和RMSE分别减少了5.57%、9.91%和3.44%,有着更小的误差;③在考虑气象因素对O_{3-8 h}浓度的影响后,预测模型评价指标MAE、MAPE和RMSE分别减少了1.59%、3.54%和0.82%,同样也有更小的误差.由此可以看出,本文所提模型能够有效预测大气污染物浓度,为相关研究提供了方法参考.     

造纸废水处理过程的高斯过程回归软测量建模

针对造纸废水处理系统的时变性、非线性和复杂性等特点,本文提出一种基于高斯过程回归的软测量模型.基于平方指数协方差、线性协方差和周期性协方差函数组合构建了7种高斯过程回归模型,分别对出水化学需氧量和出水悬浮固形物浓度进行回归预测.此外,还对比了多元线性回归模型、主成分回归模型、偏最小二乘模型、人工神经网络模型和高斯回归模型的预测效果.对比计算结果表明无论是对输出变量的训练拟合还是预测,高斯过程回归模型的拟合效果均优于非高斯过程回归模型.高斯过程回归模型的预测结果表明：对于出水化学需氧量,线性协方差函数与周期性协方差函数的组合模型可以取得最好的预测结果;对于出水悬浮固形物,平方指数协方差函数与线性协方差函数组合模型可以取得最好的预测结果.     

Thriving at work: A meta‐analysis

Thriving at work refers to a positive psychological state characterized by a joint sense of vitality and learning. On the basis of Spreitzer and colleagues' model, we present a comprehensive meta‐analysis of antecedents and outcomes of thriving at work (K = 73 independent samples, N = 21,739 employees). Results showed that thriving at work is associated with individual characteristics, such as psychological capital (r_c = .47), proactive personality (r_c = .58), positive affect (r_c = .52), and work engagement (r_c = .64). Positive associations were also found between thriving at work and relational characteristics, including supportive coworker behavior (r_c = .42), supportive leadership behavior (r_c = .44), and perceived organizational support (r_c = .63). Moreover, thriving at work is related to important employee outcomes, including health‐related outcomes such as burnout (r_c = ?.53), attitudinal outcomes such as commitment (r_c = .65), and performance‐related outcomes such as task performance (r_c = .35). The results of relative weights analyses suggest that thriving exhibits small, albeit incremental predictive validity above and beyond positive affect and work engagement, for task performance, job satisfaction, subjective health, and burnout. Overall, the findings of this meta‐analysis support Spreitzer and colleagues' model and underscore the importance of thriving in the work context.