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In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.

Performance-Based Learning is an advanced teaching approach that emphasizes what students can do as a result of instruction. In other words, teachers cultivate and assess students’ competencies by requiring them to solve a problem or create something in real-life or simulated scenarios using their mathematical knowledge. Hangzhou Yungu School’s math teachers have designed three types of performance tasks: 1) daily-class performance tasks, which are small assignments used in one class, 2) unit performance tasks, which are used during a whole unit of instruction, and 3) multiple-unit performance tasks, which are long-term tasks that last among several related units. Performance tasks focus on competencies acquired in the learning process, assess how well students learn, and guide students to what they can do. Completing performance tasks in mathematics is an excellent deep learning process that meets the expectations of mathematical education. This article presents some cases of the above three performance tasks and provides several suggestions for future research directions.

In our work, we will focus our attention on what we define as 'personal learning' process, trying to answer the following question: how do we build a coherent meaning from our experience? Through the studies of Francisco Varela on the fundamental role played by the sensory-motor coordination in cognition, we propose a model called the 'Magic Eight', which we use to show recurring patterns in the learning process of the person, focusing on interdependent relationships among perception, emotion and action, which define a self-organizing system that allows the emergence of coherent meanings for the person. These relationships are based on the activity of the entire body, allowing the emergence of both the 'inner' world of the person and what she considers her 'outer' world, in a process of generating interrelated and consistent meanings.