It is often argued that the core of organizational success is efficient collaboration.Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual...It is often argued that the core of organizational success is efficient collaboration.Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual skills or expertise.However,the lack of efficient models to manage collaboration properly is a major constraint for organizations to profit from internal and external collaborative initiatives.Currently,much of the collaboration in organizations occurs through virtual network channels,such as e-mail,Yammer,Jabber,Microsoft Teams,Skype,and Zoom.These are even more important in situations where different time zones and even threats of a pandemic constrain face-to-face human interactions.This work introduces a multidisciplinary heuristic model developed based on project risk management and social network analysis centrality metrics graph-theory to quantitatively measure dynamic organizational collaboration in the project environment.A case study illustrates the proposed model’s implementation and application in a real virtual project organizational context.The major benefit of applying this proposed model is that it enables organizations to quantitatively measure different collaborative,organizational,and dynamic behavioral patterns,which can later correlate with organizational outcomes.The model analyzes three collaborative project dimensions:network collaboration cohesion evolution,network collaboration degree evolution,and network team set variability evolution.This provides organizations an innovative approach to understand and manage possible collaborative project risks that may emerge as projects are delivered.Organizations can use the proposed model to identify projects’critical success factors by comparing successful and unsuccessful delivered projects’dynamic behaviors if a substantial number of both project types are analyzed.The proposed model also enables organizations to make decisions with more information regarding the support for changes in observed collaborative patterns as demonstrated by statistical models in general,and linear regressions in particular.Further,the proposed model provides organizations with a completely bias-free data-collection process that eliminates organizational downtime.Finally,applying the proposed model in organizations will reduce or eliminate the risks associated with virtual collaborative dynamics,leading to the optimized use of resources;this will transform organizations to become more lean-oriented and significantly contribute to economic,social,and environmental global sustainability.展开更多
文摘It is often argued that the core of organizational success is efficient collaboration.Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual skills or expertise.However,the lack of efficient models to manage collaboration properly is a major constraint for organizations to profit from internal and external collaborative initiatives.Currently,much of the collaboration in organizations occurs through virtual network channels,such as e-mail,Yammer,Jabber,Microsoft Teams,Skype,and Zoom.These are even more important in situations where different time zones and even threats of a pandemic constrain face-to-face human interactions.This work introduces a multidisciplinary heuristic model developed based on project risk management and social network analysis centrality metrics graph-theory to quantitatively measure dynamic organizational collaboration in the project environment.A case study illustrates the proposed model’s implementation and application in a real virtual project organizational context.The major benefit of applying this proposed model is that it enables organizations to quantitatively measure different collaborative,organizational,and dynamic behavioral patterns,which can later correlate with organizational outcomes.The model analyzes three collaborative project dimensions:network collaboration cohesion evolution,network collaboration degree evolution,and network team set variability evolution.This provides organizations an innovative approach to understand and manage possible collaborative project risks that may emerge as projects are delivered.Organizations can use the proposed model to identify projects’critical success factors by comparing successful and unsuccessful delivered projects’dynamic behaviors if a substantial number of both project types are analyzed.The proposed model also enables organizations to make decisions with more information regarding the support for changes in observed collaborative patterns as demonstrated by statistical models in general,and linear regressions in particular.Further,the proposed model provides organizations with a completely bias-free data-collection process that eliminates organizational downtime.Finally,applying the proposed model in organizations will reduce or eliminate the risks associated with virtual collaborative dynamics,leading to the optimized use of resources;this will transform organizations to become more lean-oriented and significantly contribute to economic,social,and environmental global sustainability.
文摘牛奶中的蛋白质含量会影响牛奶的品质,利用高光谱图像的光谱特征信息研究对牛奶蛋白质含量预测的可行性。本文提出一种基于竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)和连续投影算法(successive projections algorithm, SPA)结合多层前馈神经网络(back propagation, BP)的预测建模方法,实验以含有不同浓度蛋白质的牛奶为对象,利用可见光/近红外高光谱成像系统共采集到5种牛奶共计250组高光谱数据,通过实验对比选择采用标准化方法对获取到的吸收光谱预处理,然后采用CARS结合SPA筛选特征波长,得到18个特征波长,建立CARS-SPA-BP模型,经过试验,CARS-SPA-BP模型的训练集决定系数和测试集决定系数R;和R;分别达到0.971和0.968,训练集均方根误差(root mean square error of calibration,RMSEC)和测试集均方根误差(root mean square error of prediction,RMSEP)达到了0.033和0.034。研究发现,采用CARS结合SPA筛选的牛奶特征波长建立的多层前馈神经网络模型,其模型预测结果与全波长建模相比并没有明显降低,因此将CARS结合SPA用于波长筛选并且结合BP神经网络基本可以完成对牛奶蛋白质含量的预测。为验证CARS-SPA-BP模型的预测能力,在相同数据环境下,使用较为传统的偏最小二乘回归(partial least squares regression, PLSR)进行建模,实验结果表明,CARS-SPA-BP相较于PLSR,R;和RMSEP均有明显提升。研究表明,CARS-SPA-BP可充分利用牛奶光谱特征信息实现较高精度的牛奶蛋白质含量检测。
