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Anatoly Oleksiyenko Pilar Mendoza Fredy Esteban Cárdenas Riaño Om Prakash Dwivedi Arif H. Kabir Aliya Kuzhabekova Muweesi Charles Vutha Ros Ielyzaveta Shchepetylnykova 《Higher Education Quarterly》2023,77(2):356-374
Campus crisis management remains an understudied topic in the context of COVID-affected higher education. In this paper, we contrasted the ability to tame the wicked problems brought by the pandemic of COVID-19 in private and public universities in Bangladesh, Cambodia, Colombia, India, Kazakhstan, Uganda, and Ukraine. The cross-country analysis and diversity of institutional types allowed us to consider a wide range of challenges faced by academic leaders and their institutions during the global pandemic. By drawing on institutional policy reviews and interviews with university administrators, we have examined tensions between the human and institutional agencies on these crisis-stricken campuses given differing institutional coupling, sizes, resources, and missions. The focus on agential co-dependencies and institutional coupling lays the ground for conceptualizing campus crisis management as a culturally specific construct in the context of higher education affected by the global pandemic. 相似文献
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Kabir H. Biswas 《Resonance》2017,22(1):37-50
Allostery is a mechanism by which the activity of a large number of proteins is regulated. It is manifested as a change in the activity, either ligand binding or catalysis of one site of a protein due to a ligand binding to another distinct site of the protein. The allosteric effect is transduced by a change in the structural properties of the protein. It has been traditionally understood using either the concerted MWC (Monod, Wyman and Changeux) model, or the sequential KNF (Koshland, Nemethy and Filmer) model of structural changes. However, allostery is fundamentally a thermodynamic process and requires an alteration in the enthalpy or entropy associated with the process. 相似文献
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Teacher-directed learning in view-independent face recognition with mixture of experts using single-view eigenspaces 总被引：1，自引：0，他引：1
We propose a new model for view-independent face recognition by multiview approach. We use the so-called “mixture of experts”, ME, in which, the problem space is divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our model, instead of leaving the ME to partition the face space automatically, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. To force an expert towards a specific view of face, in the representation layer, we provide each expert with its own eigenspace computed from the faces in the corresponding view. Furthermore, we use teacher-directed learning, TDL, in a way that according to the pose of the input training sample, only the weights of the corresponding expert are updated. The experimental results support our claim that directing the experts to a predetermined partitioning of face space improves the performance of the conventional ME for view-independent face recognition. In particular, for 1200 images of unseen intermediate views of faces from 20 subjects, the ME with single-view eigenspaces yields the average recognition rate of 80.51% in 10 trials, which is noticeably increased to 90.29% by applying the TDL method. 相似文献
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