A practical framework for the generation and selection of strategic plans of action, The relevance of timeframe considerations and a systems thinking approach

The paper proposes a methodology especially focused on the generation of strategic plans of action, emphasizing the relevance of having a structured timeframe classification for the actions. The methodology explicitly recognizes the relevance of long-term goals as strategic drivers, which must insure that the complex system is capable to effectively respond to changes in the environment. In addition, the methodology employs engineering systems techniques in order to understand the inner working of the system and to build up alternative plans of action. Due to these different aspects, the proposed approach features higher flexibility compared to traditional methods. The validity and effectiveness of the methodology has been demonstrated by analyzing an airline company composed by 5 subsystems with the aim of defining a plan of action for the next 5 years, which can either: improve efficiency, redefine mission or increase revenues.

Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.