Supporting leadership development by exposure to role models: two case studies

The idiomatic expression “In Rome be a Roman” can be applied to leadership training and development as well. Leaders who can act as role models inspire other future leaders in their behaviour, attitudes and ways of thinking. Based on two examples of current leaders in the fields of Politics and Public Administration, I support the idea that exposure to role models during their training was decisive for their career paths and current activities as prominent characters in their profession. Issues such as how students should be prepared for community or national leadership as well as cross-cultural engagement are raised here. The hypothesis of transculturalism and cross-cultural commitment as a factor of leadership is presented. Based on current literature on Leadership as well as the presented case studies, I expect to raise a debate focusing on strategies for improving leaders’ training in their cross-cultural awareness.

Music and drama in primary schools in the Madeira Island - Narratives of ownership and leadership

Este artigo foi uma das publicações resultantes do projeto financiado pela FCT "Música e Drama no 1º ciclo do Ensino básico – o caso da Região Autónoma da Madeira" (PTDC/CED/72112/2006).

Flexibility in a Stackelberg leadership with differentiated goods

We study the effects of product differentiation in a Stackelberg model with demand uncertainty for the first mover. We do an ex-ante and ex-post analysis of the profits of the leader and of the follower firms in terms of product differentiation and of the demand uncertainty. We show that even with small uncertainty about the demand, the follower firm can achieve greater profits than the leader, if their products are sufficiently differentiated. We also compute the probability of the second firm having higher profit than the leading firm, subsequently showing the advantages and disadvantages of being either the leader or the follower firm.

Maintenance and destrcution of R&D leadership

In the standard Schumpeterian-growth models only follower firms invest in R&D activities and larger economies grow faster. Since these results are counterfactual, this paper reveals that leader firms often support R&D activities and economic growth can be independent of the market size. In particular, the maintenance of R&D leadership increases with: (i) the technological-knowledge gap between leader and followers, since a firm-specific learning effect of accumulated technological knowledge from past R&D is considered, (ii) the leaders’ strategies that delay the next successful R&D supported by some follower firm, (iii) the market size, and (iv) the up-grade of each innovation.

Simultaneous decisions or leadership in an international competition

In this paper, we study an international market with demand uncertainty. The model has two stages. In the first stage, the home government chooses an import tariff to maximize the revenue. Then, the firms engage in a Cournot or in a Stackelberg competition. The uncertainty is resolved between the decisions made by the home government and by the firms. We compare the results obtained in the three different ways of moving on the decision make of the firms.

Linking transformational leadership and organisation commitment

The main goal of this paper is to analyse the impacts of transformational leadership on organisational commitment. To this effect we developed a case study following a quantitative methodological approach. The research was conducted at the Serralves Foundation (Porto, Portugal) to empirically test the proposed research model and its hypothesis. The empirical results confirm that transformational leadership are not significantly influenced by commitment. As the main limitation of this study we highlight the fact that it does not consider the leaders’ perspective on their subordinates’ behaviour.

Stackelberg leadership with demand uncertainty in a differentiated duopoly

On a symmetric differentiated Stackelberg duopoly model in which there is asymmetric demand information owned by leading and follower firms, we show that the leading firm does not necessarily have advantage over the following one. The reason for this is that the second mover can adjust its output level after observing the realized demand, while the first mover chooses its output level only with the knowledge of demand distribution.

TURTLE: Systems and technologies for deep ocean long term presence

This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

Flexibility and leadership advantages in a model with uncertain demand

We consider a differentiated Stackelberg model with demand uncertainty only for the first mover. We study the advantages of flexibility over leadership as the degree of the differentiation of the goods changes.

Flexibility in Stackelberg leadership

We consider a Stackelberg model with demand uncertainty, only for the first mover. We study the advantages of leadership and flexibility with the variation of the demand uncertainty. Liu proved for demand uncertainty parameter greater than three that the follower firm can have an advantage with respect to the leading firm for some realizations of the demand intercept. Here, we prove that for demand uncertainty parameter less than three the leading firm is always in advantage.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.